DRAFT 

DO NOT CITE OR QUOTE January 2010 

Agency/Interagency Review Draft 

EPA’s Response to “Health Risks from 

Dioxin and Related Compounds Evaluation 

of the EPA Reassessment” Published by the 

National Research Council of the National 

Academies 

NOTICE 

THIS DOCUMENT IS AN AGENCY/INTERAGENCY REVIEW DRAFT. It has not been 

formally released by the U.S. Environmental Protection Agency and should not at this stage be 

construed to represent Agency policy. It is being circulated for comment on its technical 

accuracy and policy implications. 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

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

1/15/10 ii DRAFT—DO NOT CITE OR QUOTE 

DISCLAIMER 

 This document is distributed solely for the purpose of pre-dissemination peer review 

under applicable information quality guidelines. It has not been formally disseminated by EPA. 

It does not represent and should not be construed to represent Agency determination or policy. 

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

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

1/15/10 iii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS 

LIST OF TABLES.....................................................................................................................xviii 

LIST OF FIGURES ..................................................................................................................... xxi 

LIST OF ABBREVIATIONS AND ACRONYMS ................................................................... xxv 

PREFACE.................................................................................................................................. xxix 

AUTHORS, CONTRIBUTORS, AND REVIEWERS .............................................................. xxx 

EXECUTIVE SUMMARY .....................................................................................................xxxiii 

1. INTRODUCTION................................................................................................................1-1 

1.1. SUMMARY OF KEY NAS (2006a) COMMENTS ON DOSE-RESPONSE 

MODELING IN THE 2003 REASSESSMENT........................................................... 1-2 

1.2. EPA’S SCIENCE PLAN .............................................................................................. 1-4 

1.3. OVERVIEW OF EPA’S RESPONSE TO NAS (2006a) “HEALTH RISKS 

FROM DIOXIN AND RELATED COMPOUNDS: EVALUATION OF EPA’s 

2003 REASSESSMENT” ............................................................................................. 1-5 

1.3.1. TCDD Literature Update ..................................................................................... 1-7 

1.3.2. EPA’s 2009 Workshop on TCDD Dose Response.............................................. 1-7 

1.3.3. Overall Organization of EPA’s Response to NAS Recommendations................ 1-9 

2. TRANSPARENCY AND CLARITY IN THE SELECTION OF KEY DATA SETS 

FOR DOSE-RESPONSE ANALYSIS................................................................................. 2-1 

2.1. SUMMARY OF NAS COMMENTS ON TRANSPARENCY AND CLARITY 

IN THE SELECTION OF KEY DATA SETS FOR DOSE-RESPONSE 

ANALYSIS................................................................................................................... 2-1 

2.2. EPA’s RESPONSE TO NAS COMMENTS ON TRANSPARENCY AND 

CLARITY IN THE SELECTION OF KEY DATA SETS FOR DOSERESPONSE ANALYSIS.............................................................................................. 2-2 

2.3. STUDY INCLUSION CRITERIA FOR TCDD DOSE-RESPONSE ANALYSIS..... 2-4 

2.3.1. Study Inclusion Criteria for TCDD Epidemiologic Studies ................................ 2-6 

2.3.2. Study Inclusion Criteria for TCDD In Vivo Mammalian Bioassays................... 2-8 

2.4. EVALUATION OF KEY STUDIES FOR TCDD DOSE RESPONSE..................... 2-10 

2.4.1. Evaluation of Epidemiological Cohorts for Dose-Response Assessment ......... 2-10 

2.4.1.1. Cancer ....................................................................................................... 2-10 

2.4.1.1.1. Cancer cohorts................................................................................... 2-11 

2.4.1.1.1.1. The NIOSH cohort..................................................................... 2-11 

2.4.1.1.1.1.1. Fingerhut et al., 1991.......................................................... 2-12 

2.4.1.1.1.1.1.1. Study summary. .......................................................... 2-12 

2.4.1.1.1.1.1.2. Study evaluation..........................................................2-13 

2.4.1.1.1.1.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-16 

2.4.1.1.1.1.2. Steenland et al., 1999. ........................................................2-17 

2.4.1.1.1.1.2.1. Study summary. .......................................................... 2-17 

2.4.1.1.1.1.2.2. Study evaluation..........................................................2-20 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 iv DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.1.1.1.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-21 

2.4.1.1.1.1.3. Steenland et al., 2001. ........................................................2-22 

2.4.1.1.1.1.3.1. Study summary. .......................................................... 2-22 

2.4.1.1.1.1.3.2. Study evaluation..........................................................2-24 

2.4.1.1.1.1.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-25 

2.4.1.1.1.1.4. Cheng et al., 2006...............................................................2-26 

2.4.1.1.1.1.4.1. Study summary. .......................................................... 2-26 

2.4.1.1.1.1.4.2. Study evaluation..........................................................2-29 

2.4.1.1.1.1.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-29 

2.4.1.1.1.1.5. Collins et al., 2009..............................................................2-30 

2.4.1.1.1.1.5.1. Study summary. .......................................................... 2-30 

2.4.1.1.1.1.5.2. Study evaluation.......................................................... 2-31 

2.4.1.1.1.1.5.3. Suitability of data for dose-response modeling. .........2-32 

2.4.1.1.1.2. The BASF cohort. ...................................................................... 2-33 

2.4.1.1.1.2.1. Thiess and Frentzel-Beyme, 1977 and Thiess et al., 

1982. ................................................................................... 2-33 

2.4.1.1.1.2.1.1. Study summary. .......................................................... 2-33 

2.4.1.1.1.2.1.2. Study evaluation..........................................................2-34 

2.4.1.1.1.2.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-34 

2.4.1.1.1.2.2. Zober et al., 1990................................................................2-34 

2.4.1.1.1.2.2.1. Study summary. .......................................................... 2-34 

2.4.1.1.1.2.2.2. Study evaluation..........................................................2-36 

2.4.1.1.1.2.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-36 

2.4.1.1.1.2.3. Ott and Zober, 1996............................................................2-36 

2.4.1.1.1.2.3.1. Study summary. .......................................................... 2-36 

2.4.1.1.1.2.3.2. Study evaluation..........................................................2-39 

2.4.1.1.1.2.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-40 

2.4.1.1.1.3. The Hamburg cohort.................................................................. 2-40 

2.4.1.1.1.3.1. Manz et al., 1991. ............................................................... 2-40 

2.4.1.1.1.3.1.1. Study summary. .......................................................... 2-40 

2.4.1.1.1.3.1.2. Study evaluation..........................................................2-42 

2.4.1.1.1.3.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-43 

2.4.1.1.1.3.2. Flesch-Janys et al., 1995.....................................................2-44 

2.4.1.1.1.3.2.1. Study summary. .......................................................... 2-44 

2.4.1.1.1.3.2.2. Study evaluation..........................................................2-45 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 v DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.1.1.3.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-46 

2.4.1.1.1.3.3. Flesch-Janys et al., 1998.....................................................2-46 

2.4.1.1.1.3.3.1. Study summary. .......................................................... 2-46 

2.4.1.1.1.3.3.2. Study evaluation..........................................................2-48 

2.4.1.1.1.3.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-48 

2.4.1.1.1.3.4. Becher et al., 1998.............................................................. 2-48 

2.4.1.1.1.3.4.1. Study summary. .......................................................... 2-48 

2.4.1.1.1.3.4.2. Study evaluation..........................................................2-50 

2.4.1.1.1.3.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-50 

2.4.1.1.1.4. The Seveso cohort...................................................................... 2-51 

2.4.1.1.1.4.1. Bertazzi et al., 2001............................................................ 2-52 

2.4.1.1.1.4.1.1. Study summary. .......................................................... 2-52 

2.4.1.1.1.4.1.2. Study evaluation..........................................................2-53 

2.4.1.1.1.4.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-54 

2.4.1.1.1.4.2. Pesatori et al., 2003. ........................................................... 2-54 

2.4.1.1.1.4.2.1. Study summary. .......................................................... 2-54 

2.4.1.1.1.4.2.2. Study evaluation..........................................................2-55 

2.4.1.1.1.4.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-55 

2.4.1.1.1.4.3. Consonni et al., 2008.......................................................... 2-55 

2.4.1.1.1.4.3.1. Study summary. .......................................................... 2-55 

2.4.1.1.1.4.3.2. Study evaluation..........................................................2-56 

2.4.1.1.1.4.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-56 

2.4.1.1.1.4.4. Baccarelli et al., 2006.........................................................2-56 

2.4.1.1.1.4.4.1. Study summary. .......................................................... 2-56 

2.4.1.1.1.4.4.2. Study evaluation..........................................................2-57 

2.4.1.1.1.4.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-57 

2.4.1.1.1.4.5. Warner et al., 2002. ............................................................2-57 

2.4.1.1.1.4.5.1. Study summary. .......................................................... 2-57 

2.4.1.1.1.4.5.2. Study evaluation..........................................................2-58 

2.4.1.1.1.4.5.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-59 

2.4.1.1.1.5. Chapaevsk study. ....................................................................... 2-60 

2.4.1.1.1.5.1. Revich et al., 2001..............................................................2-60 

2.4.1.1.1.5.1.1. Study summary. .......................................................... 2-60 

2.4.1.1.1.5.1.2. Study evaluation.......................................................... 2-61 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 vi DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.1.1.5.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-61 

2.4.1.1.1.6. The Air Force Health (“Ranch Hands” cohort) study................ 2-61 

2.4.1.1.1.6.1. Akhtar et al., 2004. ............................................................. 2-62 

2.4.1.1.1.6.1.1. Study summary. .......................................................... 2-62 

2.4.1.1.1.6.1.2. Study evaluation.......................................................... 2-64 

2.4.1.1.1.6.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-65 

2.4.1.1.1.6.2. Michalek and Pavuk, 2008. ................................................2-65 

2.4.1.1.1.6.2.1. Study summary. .......................................................... 2-65 

2.4.1.1.1.6.2.2. Study evaluation..........................................................2-66 

2.4.1.1.1.6.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-67 

2.4.1.1.1.7. Other studies of potential relevance to dose-response 

modeling. ................................................................................... 2-67 

2.4.1.1.1.7.1. t’ Mannetje et al., 2005—New Zealand herbicide 

sprayers............................................................................... 2-67 

2.4.1.1.1.7.1.1. Study summary. .......................................................... 2-67 

2.4.1.1.1.7.1.2. Study evaluation..........................................................2-69 

2.4.1.1.1.7.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-69 

2.4.1.1.1.7.2. McBride et al., 2009b—New Zealand herbicide 

sprayers............................................................................... 2-70 

2.4.1.1.1.7.2.1. Study summary. .......................................................... 2-70 

2.4.1.1.1.7.2.2. Study evaluation..........................................................2-71 

2.4.1.1.1.7.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-71 

2.4.1.1.1.7.3. McBride et al., 2009a—New Zealand herbicide 

sprayers............................................................................... 2-71 

2.4.1.1.1.7.3.1. Study summary. .......................................................... 2-71 

2.4.1.1.1.7.3.2. Study evaluation..........................................................2-74 

2.4.1.1.1.7.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-74 

2.4.1.1.1.7.4. Hooiveld et al., 1998—Netherlands workers. .................... 2-74 

2.4.1.1.1.7.4.1. Study summary. .......................................................... 2-74 

2.4.1.1.1.7.4.2. Study evaluation.......................................................... 2-76 

2.4.1.1.1.7.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-76 

2.4.1.1.2. Key characteristics of epidemiologic cancer studies......................... 2-77 

2.4.1.1.3. Feasibility of TCDD cancer dose-response modeling—summary 

discussion by cohort. ......................................................................... 2-77 

2.4.1.1.3.1. Using the NIOSH cohort in dose-response modeling................ 2-77 

2.4.1.1.3.2. Using the BASF cohort in dose-response modeling.................. 2-79 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 vii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.1.3.3. Using the Hamburg cohort in dose-response modeling............. 2-80 

2.4.1.1.3.4. Using the Seveso cohort in dose-response modeling................. 2-81 

2.4.1.1.3.5. Using the Chapaevsk related data in dose-response 

modeling. ................................................................................... 2-82 

2.4.1.1.3.6. Using the Ranch Hands cohort in dose-response modeling....... 2-82 

2.4.1.1.4. Discussion of general issues related to dose-response modeling ...... 2-82 

2.4.1.1.4.1. Ascertainment of exposures....................................................... 2-82 

2.4.1.1.4.2. Latency intervals........................................................................ 2-83 

2.4.1.1.4.3. Use of the SMR metric. ............................................................. 2-83 

2.4.1.1.4.4. All cancers versus site-specific.................................................. 2-85 

2.4.1.1.4.5. Summary of epidemiologic cancer study evaluations for 

dose-response modeling............................................................. 2-86 

2.4.1.2. Noncancer ................................................................................................. 2-86 

2.4.1.2.1. Noncancer cohorts............................................................................. 2-87 

2.4.1.2.1.1. The NIOSH cohort..................................................................... 2-87 

2.4.1.2.1.1.1. Steenland et al., 1999. ........................................................2-87 

2.4.1.2.1.1.1.1. Study summary. .......................................................... 2-87 

2.4.1.2.1.1.1.2. Study evaluation..........................................................2-87 

2.4.1.2.1.1.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-88 

2.4.1.2.1.1.2. Collins et al., 2009..............................................................2-88 

2.4.1.2.1.1.2.1. Study summary. .......................................................... 2-88 

2.4.1.2.1.1.2.2. Study evaluation.......................................................... 2-89 

2.4.1.2.1.1.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-89 

2.4.1.2.1.2. The BASF cohort. ...................................................................... 2-89 

2.4.1.2.1.2.1. Ott and Zober, 1996............................................................2-89 

2.4.1.2.1.2.1.1. Study summary. .......................................................... 2-89 

2.4.1.2.1.2.1.2. Study evaluation..........................................................2-90 

2.4.1.2.1.2.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-90 

2.4.1.2.1.3. The Hamburg cohort.................................................................. 2-91 

2.4.1.2.1.3.1. Flesch-Janys et al., 1995.....................................................2-91 

2.4.1.2.1.3.1.1. Study summary. .......................................................... 2-91 

2.4.1.2.1.3.1.2. Study evaluation..........................................................2-91 

2.4.1.2.1.3.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-92 

2.4.1.2.1.4. The Seveso Women’s Health Study (SWHS)............................ 2-92 

2.4.1.2.1.4.1. Eskenazi et al., 2002a—Menstrual cycle characteristics....2-93 

2.4.1.2.1.4.1.1. Study summary. .......................................................... 2-93 

2.4.1.2.1.4.1.2. Study evaluation.......................................................... 2-94 

2.4.1.2.1.4.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-95 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 viii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.2.1.4.2. Eskenazi et al., 2002b—Endometriosis..............................2-95 

2.4.1.2.1.4.2.1. Study summary. .......................................................... 2-95 

2.4.1.2.1.4.2.2. Study evaluation..........................................................2-96 

2.4.1.2.1.4.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-96 

2.4.1.2.1.4.3. Eskenazi et al., 2003—Adverse birth outcomes................. 2-96 

2.4.1.2.1.4.3.1. Study summary. .......................................................... 2-96 

2.4.1.2.1.4.3.2. Study evaluation..........................................................2-97 

2.4.1.2.1.4.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-98 

2.4.1.2.1.4.4. Warner et al., 2004—Age at menarche. .............................2-98 

2.4.1.2.1.4.4.1. Study summary. .......................................................... 2-98 

2.4.1.2.1.4.4.2. Study evaluation.......................................................... 2-99 

2.4.1.2.1.4.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................... 2-99 

2.4.1.2.1.4.5. Eskenazi et al., 2005—Age at menopause. ........................2-99 

2.4.1.2.1.4.5.1. Study summary. .......................................................... 2-99 

2.4.1.2.1.4.5.2. Study evaluation........................................................2-101 

2.4.1.2.1.4.5.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-101 

2.4.1.2.1.4.6. Warner et al., 2007—Ovarian function. ...........................2-101 

2.4.1.2.1.4.6.1. Study summary. ........................................................2-101 

2.4.1.2.1.4.6.2. Study evaluation........................................................2-102 

2.4.1.2.1.4.6.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-102 

2.4.1.2.1.4.7. Eskenazi et al., 2007—Uterine leiomyoma......................2-103 

2.4.1.2.1.4.7.1. Study summary. ........................................................2-103 

2.4.1.2.1.4.7.2. Study evaluation........................................................ 2-103 

2.4.1.2.1.4.7.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-103 

2.4.1.2.1.5. Other Seveso noncancer studies............................................... 2-104 

2.4.1.2.1.5.1. Mocarelli et al., 2008—Semen quality.............................2-104 

2.4.1.2.1.5.1.1. Study summary. ........................................................2-104 

2.4.1.2.1.5.1.2. Study evaluation........................................................2-105 

2.4.1.2.1.5.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-105 

2.4.1.2.1.5.2. Mocarelli et al., 1996, 2000—Sex ratio. ..........................2-105 

2.4.1.2.1.5.2.1. Study summary. ........................................................2-105 

2.4.1.2.1.5.2.2. Study evaluation........................................................2-107 

2.4.1.2.1.5.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-108 

2.4.1.2.1.5.3. Baccarelli et al., 2008—Neonatal thyroid function..........2-108 

2.4.1.2.1.5.3.1. Study summary. ........................................................2-108 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 ix DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.2.1.5.3.2. Study evaluation........................................................2-110 

2.4.1.2.1.5.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-110 

2.4.1.2.1.5.4. Alaluusua et al., 2004—Oral hygiene. .............................2-111 

2.4.1.2.1.5.4.1. Study summary. ........................................................2-111 

2.4.1.2.1.5.4.2. Study evaluation........................................................2-112 

2.4.1.2.1.5.4.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-112 

2.4.1.2.1.5.5. Bertazzi et al., 1989; Consonni et al., 2008—Mortality 

outcomes...........................................................................2-113 

2.4.1.2.1.5.5.1. Study summary. ........................................................2-113 

2.4.1.2.1.5.5.2. Study evaluation........................................................2-114 

2.4.1.2.1.5.5.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-115 

2.4.1.2.1.5.6. Baccarelli et al., 2005—Chloracne...................................2-115 

2.4.1.2.1.5.6.1. Study summary. ........................................................2-115 

2.4.1.2.1.5.6.2. Study evaluation........................................................2-115 

2.4.1.2.1.5.6.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-116 

2.4.1.2.1.5.7. Baccarelli et al., 2002, 2004—Immunologic effects........2-116 

2.4.1.2.1.5.7.1. Study summary. ........................................................2-116 

2.4.1.2.1.5.7.2. Study evaluation........................................................2-117 

2.4.1.2.1.5.7.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-117 

2.4.1.2.1.6. The Chapaevsk study. .............................................................. 2-118 

2.4.1.2.1.6.1. Revich et al. (2001)—Mortality and reproductive 

health. ...............................................................................2-118 

2.4.1.2.1.6.1.1. Study summary. ........................................................2-118 

2.4.1.2.1.6.1.2. Study evaluation........................................................2-119 

2.4.1.2.1.6.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-119 

2.4.1.2.1.7. The Air Force Health (“Ranch Hands” cohort) study.............. 2-119 

2.4.1.2.1.7.1. Michalek and Pavuk (2008)—Diabetes. ..........................2-119 

2.4.1.2.1.7.1.1. Study summary. ........................................................2-119 

2.4.1.2.1.7.1.2. Study evaluation........................................................ 2-120 

2.4.1.2.1.7.1.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-120 

2.4.1.2.1.8. Other noncancer studies of TCDD........................................... 2-120 

2.4.1.2.1.8.1. McBride et al., 2009a—Noncancer mortality. ................. 2-120 

2.4.1.2.1.8.1.1. Study summary. ........................................................2-120 

2.4.1.2.1.8.1.2. Study evaluation........................................................2-121 

2.4.1.2.1.8.1.3. Suitability of data for dose-response analysis...........2-122 

2.4.1.2.1.8.2. McBride et al., 2009b—Noncancer mortality. ................. 2-122 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 x DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.1.2.1.8.2.1. Study summary. ........................................................2-122 

2.4.1.2.1.8.2.2. Study evaluation........................................................2-123 

2.4.1.2.1.8.2.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-123 

2.4.1.2.1.8.3. Ryan et al., 2002—Sex ratio. ...........................................2-123 

2.4.1.2.1.8.3.1. Study summary. ........................................................2-123 

2.4.1.2.1.8.3.2. Study evaluation........................................................2-124 

2.4.1.2.1.8.3.3. Suitability of data for TCDD dose-response 

modeling. .................................................................. 2-125 

2.4.1.2.2. Feasibility of dose-response modeling for noncancer..................... 2-125 

2.4.1.2.3. Summary of epidemiologic noncancer study evaluations for 

dose-response modeling. ................................................................. 2-126 

2.4.2. Summary of Animal Bioassay Studies ............................................................ 2-126 

2.4.2.1. Reproductive Studies .............................................................................. 2-127 

2.4.2.1.1. Bowman et al., 1989a, b (and related Schantz and Bowman, 

1989; Schantz et al., 1986). ............................................................. 2-127 

2.4.2.1.2. Hochstein et al., 2001. ..................................................................... 2-128 

2.4.2.1.3. Ikeda et al., 2005. ............................................................................ 2-130 

2.4.2.1.4. Ishihara et al., 2007. ........................................................................ 2-131 

2.4.2.1.5. Latchoumycandane and Mathur, 2002 (and related: 

Latchoumycandane et al., 2002a, b, 2003)...................................... 2-132 

2.4.2.1.6. Murray et al, 1979. .......................................................................... 2-133 

2.4.2.1.7. Rier et al., 1993, 1995. .................................................................... 2-134 

2.4.2.1.8. Shi et al., 2007................................................................................. 2-136 

2.4.2.1.9. Yang et al., 2000.............................................................................. 2-136 

2.4.2.2. Developmental Studies ........................................................................... 2-138 

2.4.2.2.1. Amin et al., 2000. ............................................................................ 2-138 

2.4.2.2.2. Bell et al., 2007a.............................................................................. 2-139 

2.4.2.2.3. Franczak et al., 2006........................................................................ 2-141 

2.4.2.2.4. Hojo et al., 2002 (and related: Zareba et al., 2002)......................... 2-142 

2.4.2.2.5. Kattainen et al., 2001....................................................................... 2-143 

2.4.2.2.6. Keller et al., 2007, 2008a, b. ........................................................... 2-144 

2.4.2.2.7. Kuchiiwa et al., 2002....................................................................... 2-148 

2.4.2.2.8. Li et al., 2006................................................................................... 2-149 

2.4.2.2.9. Markowski et al., 2001.................................................................... 2-150 

2.4.2.2.10. Miettinen et al., 2006....................................................................... 2-150 

2.4.2.2.11. Nohara et al., 2000. ......................................................................... 2-151 

2.4.2.2.12. Ohsako et al., 2001. ......................................................................... 2-152 

2.4.2.2.13. Schantz et al., 1996.......................................................................... 2-153 

2.4.2.2.14. Seo et al., 1995. ............................................................................... 2-154 

2.4.2.2.15. Simanainen et al, 2004. ................................................................... 2-155 

2.4.2.2.16. Sugita-Konishi et al., 2003. ............................................................. 2-156 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xi DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.2.3. Acute Studies .......................................................................................... 2-157 

2.4.2.3.1. Burleson et al., 1996........................................................................ 2-157 

2.4.2.3.2. Crofton et al., 2005.......................................................................... 2-158 

2.4.2.3.3. Kitchin and Woods, 1979................................................................ 2-159 

2.4.2.3.4. Li et al., 1997................................................................................... 2-160 

2.4.2.3.5. Lucier et al., 1986............................................................................ 2-160 

2.4.2.3.6. Nohara et al., 2002. ......................................................................... 2-161 

2.4.2.3.7. Simanainen et al., 2003. .................................................................. 2-161 

2.4.2.3.8. Simanainen et al., 2002. .................................................................. 2-162 

2.4.2.3.9. Smialowicz et al., 2004. .................................................................. 2-163 

2.4.2.3.10. Vanden Heuvel et al., 1994. ............................................................ 2-163 

2.4.2.4. Subchronic Studies.................................................................................. 2-165 

2.4.2.4.1. Chu et al., 2001................................................................................ 2-165 

2.4.2.4.2. Chu et al., 2007................................................................................ 2-165 

2.4.2.4.3. DeCaprio et al., 1986....................................................................... 2-167 

2.4.2.4.4. Devito et al., 1994. .......................................................................... 2-168 

2.4.2.4.5. Fattore et al., 2000........................................................................... 2-168 

2.4.2.4.6. Fox et al., 1993. ............................................................................... 2-170 

2.4.2.4.7. Hassoun et al., 1998. ....................................................................... 2-170 

2.4.2.4.8. Hassoun et al., 2000. ....................................................................... 2-171 

2.4.2.4.9. Hassoun et al., 2003. ....................................................................... 2-172 

2.4.2.4.10. Kociba et al., 1976........................................................................... 2-173 

2.4.2.4.11. Mally and Chipman, 2002. .............................................................. 2-174 

2.4.2.4.12. Slezak et al., 2000............................................................................ 2-175 

2.4.2.4.13. Smialowicz et al., 2008. .................................................................. 2-176 

2.4.2.4.14. Van Birgelen et al. 1995a, b. ........................................................... 2-177 

2.4.2.4.15. Vos et al., 1973................................................................................ 2-178 

2.4.2.4.16. White et al., 1986. ........................................................................... 2-179 

2.4.2.5. Chronic Studies (Noncancer Endpoints)................................................. 2-180 

2.4.2.5.1. Cantoni et al., 1981.......................................................................... 2-180 

2.4.2.5.2. Croutch et al., 2005. ........................................................................ 2-180 

2.4.2.5.3. Hassoun et al., 2002. ....................................................................... 2-182 

2.4.2.5.4. Kociba et al., 1978........................................................................... 2-183 

2.4.2.5.5. Maronpot et al., 1993. ..................................................................... 2-184 

2.4.2.5.6. National Toxicology Program, 1982. .............................................. 2-185 

2.4.2.5.7. National Toxicology Program, 2006. .............................................. 2-185 

2.4.2.5.8. Rier et al., 2001a, b.......................................................................... 2-187 

2.4.2.5.9. Sewall et al., 1993. .......................................................................... 2-189 

2.4.2.5.10. Sewall et al., 1995. .......................................................................... 2-190 

2.4.2.5.11. Toth et al., 1979............................................................................... 2-192 

2.4.2.6. Chronic Studies (Cancer Endpoints)....................................................... 2-193 

2.4.2.6.1. Kociba et al., 1978........................................................................... 2-193 

2.4.2.6.2. Toth et al., 1979............................................................................... 2-195 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

2.4.2.6.3. NTP, 1982. ...................................................................................... 2-195 

2.4.2.6.4. NTP, 2006. ...................................................................................... 2-196 

2.4.3. Summary of Key Data Set Selection for TCDD Dose-Response Modeling ... 2-198 

3. THE USE OF TOXICOKINETICS IN THE DOSE-RESPONSE MODELING FOR 

CANCER AND NONCANCER ENDPOINTS ................................................................... 3-1 

3.1. SUMMARY OF NAS COMMENTS ON THE USE OF TOXICOKINETICS IN 

DOSE-RESPONSE MODELING APPROACHES FOR TCDD................................. 3-1 

3.2. OVERVIEW OF EPA’S RESPONSE TO THE NAS COMMENTS ON THE USE 

OF TOXICOKENTICS IN DOSE-RESPONSE MODELING APPROACHES 

FOR TCDD................................................................................................................... 3-3 

3.3. PHARMACOKINETICS (PK) AND PK MODELING............................................... 3-4 

3.3.1. PK Data and Models in TCDD Dose-Response Modeling: Overview and 

Scope.................................................................................................................... 3-4 

3.3.2. PK of TCDD in Animals and Humans ................................................................ 3-6 

3.3.2.1. Absorption and Bioavailability................................................................... 3-6 

3.3.2.2. Distribution ................................................................................................. 3-6 

3.3.2.3. Metabolism and Protein Binding ................................................................ 3-9 

3.3.2.4. Elimination................................................................................................ 3-11 

3.3.2.5. Interspecies Differences and Similarities.................................................. 3-11 

3.3.3. PK of TCDD in Humans: Interindividual Variability........................................ 3-12 

3.3.3.1. Life Stage and Gender .............................................................................. 3-12 

3.3.3.1.1. Prenatal period................................................................................... 3-14 

3.3.3.1.2. Infancy and childhood. ...................................................................... 3-15 

3.3.3.1.3. Adulthood and old age. ..................................................................... 3-16 

3.3.3.2. Physiological States: Pregnancy and Lactation ........................................ 3-16 

3.3.3.3. Lifestyle and Habits .................................................................................. 3-17 

3.3.3.4. Genetic Traits and Polymorphism ............................................................ 3-17 

3.3.4. Dose Metrics and Pharmacokinetic Models for TCDD..................................... 3-18 

3.3.4.1. Alternative Dose Metrics for Dose-Response Modeling.......................... 3-18 

3.3.4.2. First-Order Kinetic Modeling ................................................................... 3-21 

3.3.4.3. Biologically-Based Kinetic Models.......................................................... 3-25 

3.3.4.3.1. CADM model.................................................................................... 3-26 

3.3.4.3.1.1. Model structure. ......................................................................... 3-26 

3.3.4.3.1.2. Mathematical representation...................................................... 3-27 

3.3.4.3.1.3. Parameter estimation.................................................................. 3-28 

3.3.4.3.1.4. Model performance and degree of evaluation. .......................... 3-29 

3.3.4.3.1.5. Confidence in CADM model predictions of dose metrics......... 3-30 

3.3.4.3.2. PBPK model...................................................................................... 3-31 

3.3.4.3.2.1. Model structure. ......................................................................... 3-31 

3.3.4.3.2.2. Mathematical representation...................................................... 3-32 

3.3.4.3.2.3. Parameter estimation.................................................................. 3-36 

3.3.4.3.2.4. Model performance and degree of evaluation. .......................... 3-37 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xiii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

3.3.4.3.2.5. Confidence in PBPK model predictions of dose metrics........... 3-40 

3.3.4.4. Applicability of PK Models to Derive Dose Metrics for Dose-Response 

Modeling of TCDD: Confidence and Limitations.................................... 3-42 

3.3.4.5. Recommended Dose Metrics for Key Studies.......................................... 3-44 

3.3.5. Uncertainty in Dose Estimates........................................................................... 3-46 

3.3.5.1. Sources of Uncertainty in Dose Metric Predictions.................................. 3-46 

3.3.5.1.1. Limitations of available PK data. ...................................................... 3-46 

3.3.5.1.1.1. Animal data................................................................................ 3-46 

3.3.5.1.1.2. Human data. ............................................................................... 3-46 

3.3.5.1.2. Uncertainties associated with model specification............................ 3-46 

3.3.5.1.3. Impact of human interindividual variability...................................... 3-48 

3.3.5.2. Potential Magnitude and Sources of Uncertainty in Dose Metrics........... 3-48 

3.3.5.2.1. Magnitude of uncertainty. ................................................................. 3-48 

3.3.6. Use of the Emond PBPK Models for Dose Extrapolation from Rodents to 

Humans.............................................................................................................. 3-50 

4. CHRONIC ORAL REFERENCE DOSE.............................................................................4-1 

4.1. NAS COMMENTS AND EPA’S RESPONSE ON IDENTIFYING 

NONCANCER EFFECTS OBSERVED AT LOWEST DOSES................................. 4-1 

4.2. NONCANCER DOSE-RESPONSE ASSESSMENT OF TCDD ................................ 4-5 

4.2.1.1. Determination of Toxicologically Relevant Endpoints .............................. 4-6 

4.2.2. Use of Toxicokinetic Modeling for TCDD Dose-Response Assessment............ 4-7 

4.2.3. Noncancer Dose-Response Assessment of Epidemiological Data ...................... 4-8 

4.2.3.1. Baccarelli et al. (2008)................................................................................ 4-9 

4.2.3.2. Mocarelli et al. (2008)................................................................................. 4-9 

4.2.3.3. Alaluusua et al. (2004).............................................................................. 4-10 

4.2.3.4. Eskenazi et al. (2002)................................................................................ 4-11 

4.2.4. Noncancer Dose-Response Assessment of Animal Bioassay Data ................... 4-12 

4.2.4.1. Use of Kinetic Modeling for Animal Bioassay Data................................ 4-13 

4.2.4.2. Benchmark Dose Modeling of the Animal Bioassay Data ....................... 4-14 

4.2.4.3. POD Candidates from Animal Bioassays Based on HED........................ 4-15 

4.3. RfD DERIVATION.................................................................................................... 4-16 

4.3.1. Toxicological Endpoints .................................................................................... 4-18 

4.3.2. Exposure Protocols of Candidate PODs ............................................................ 4-19 

4.3.3. Uncertainty Factors (UFs).................................................................................. 4-20 

4.3.4. Human Studies................................................................................................... 4-20 

4.3.5. Derivation of the RfD ........................................................................................ 4-23 

4.4. UNCERTAINTY IN THE RfD .................................................................................. 4-23 

5. CANCER ASSESSMENT ...................................................................................................5-1 

5.1. QUALITATIVE WEIGHT-OF-EVIDENCE CARCINOGEN 

CLASSIFICATION FOR 2,3,7,8-TETRACHLORODIBENZO-p-DIOXIN 

(TCDD)......................................................................................................................... 5-1 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xiv DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

5.1.1. Summary of National Academy of Sciences (NAS) Comments on the 

Qualitative Weight-of-Evidence Carcinogen Classification for 

2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD).................................................... 5-1 

5.1.2. EPA’s Response to the NAS Comments on the Qualitative Weight-ofEvidence Carcinogen Classification for TCDD................................................... 5-2 

5.1.2.1. Summary Evaluation of Epidemiologic Evidence of TCDD and Cancer... 5-3 

5.1.2.1.1. Evidence for causality. ........................................................................ 5-5 

5.1.2.2. Summary of Evidence for TCDD Carcinogenicity in Experimental 

Animals....................................................................................................... 5-9 

5.1.2.3. TCDD Mode of Action ............................................................................. 5-10 

5.1.2.3.1. The aryl hydrocarbon receptor (AhR)............................................... 5-10 

5.1.2.3.1.1. Other AhR considerations.......................................................... 5-12 

5.1.2.3.2. TCDD as a tumor promoter............................................................... 5-14 

5.1.2.3.3. Hypothesized modes of action of TCDD in rodents. ........................ 5-15 

5.1.2.3.3.1. Liver tumors............................................................................... 5-15 

5.1.2.3.3.2. Lung tumors............................................................................... 5-16 

5.1.2.3.3.3. Thyroid tumors........................................................................... 5-17 

5.1.2.3.4. Summary of TCDD mode of action in rodents. ................................ 5-18 

5.1.3. Summary of the Qualitative Weight of Evidence Classification for TCDD ..... 5-19 

5.2. QUANTITATIVE CANCER ASSESSMENT........................................................... 5-19 

5.2.1. Summary of NAS Comments on Cancer Dose-Response Modeling................. 5-19 

5.2.1.1. Choice of Response Level and Characterization of the Statistical 

Confidence Around Low Dose Model Predictions................................... 5-19 

5.2.1.2. Model Forms for Predicting Cancer Risks Below the Point of 

Departure (POD)....................................................................................... 5-20 

5.2.2. Overview of EPA Response to NAS Comments on Cancer DoseResponse Modeling............................................................................................ 5-22 

5.2.3. Updated Cancer Dose-Response Modeling for Derivation of Oral Slope 

Factor ................................................................................................................. 5-22 

5.2.3.1. Dose-Response Modeling Based on Epidemiologic Cohort Data ............ 5-23 

5.2.3.1.1. Evaluation of Epidemiologic Studies in the 2003 Reassessment 

for OSF Derivation............................................................................ 5-23 

5.2.3.1.1.1. Steenland et al. (2001). .............................................................. 5-24 

5.2.3.1.1.2. Becher et al. (1998).................................................................... 5-25 

5.2.3.1.1.3. Ott and Zober (1996). ................................................................ 5-26 

5.2.3.1.2. Evaluation of Epidemiologic Studies Published Since the 2003 

Reassessment for OSF Derivation..................................................... 5-27 

5.2.3.1.2.1. Cheng et al. (2006)..................................................................... 5-27 

5.2.3.1.2.2. Warner et al. (2002). .................................................................. 5-30 

5.2.3.2. Dose-Response Modeling Based on Animal Bioassay Data .................... 5-31 

5.2.3.2.1. Selection of key data sets. ................................................................. 5-32 

5.2.3.2.2. Dose adjustment and extrapolation methods for selected data sets... 5-33 

5.2.3.2.2.1. Dose metric estimation for dose-response modeling................. 5-33 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xv DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

5.2.3.2.2.2. Calculation of human equivalent doses (HEDs)........................ 5-34 

5.2.3.2.3. Dose-response modeling approaches for rodent bioassays. .............. 5-35 

5.2.3.2.3.1. Modeling of individual tumors. ................................................. 5-35 

5.2.3.2.3.2. Multiple tumor (Bayesian) models. ........................................... 5-35 

5.2.3.2.4. Results of dose-response modeling for rodent bioassays.................. 5-38 

5.2.3.2.4.1. Individual tumor models............................................................ 5-38 

5.2.3.2.4.2. Multiple tumor (Bayesian) models. ........................................... 5-39 

5.2.3.2.5. Summary evaluation of slope factor estimates from rodent 

bioassays............................................................................................ 5-39 

5.2.3.2.6. Qualitative uncertainties in slope factor estimates from rodent 

bioassays............................................................................................ 5-40 

5.2.3.2.6.1. Quality of studies relied upon for determining POD................. 5-40 

5.2.3.2.6.2. Interpretation of results from studies relied upon for 

determining POD. ...................................................................... 5-41 

5.2.3.2.6.3. Consistency of results across chronic rodent bioassays............. 5-41 

5.2.3.2.6.4. Human relevance of rodent tumor data...................................... 5-42 

5.2.3.2.6.5. Relevance of rodent exposure scenario...................................... 5-42 

5.2.3.2.6.6. Impact of background TCDD exposures. .................................. 5-42 

5.2.3.2.6.7. Choice of endpoint for POD derivation..................................... 5-43 

5.2.3.2.6.8. Choice of animal-to-human extrapolation method. ................... 5-43 

5.2.3.2.6.9. Choice of model for POD and model uncertainty for POD 

derivation. .................................................................................. 5-43 

5.2.3.2.6.10. Statistical uncertainty in model fits............................................ 5-44 

5.2.3.2.6.11. Choice of risk level for POD derivation. ................................... 5-44 

5.2.3.3. EPA’s Response to the NAS Comments on Choice of Response Level 

and Characterization of the Statistical Confidence Around Low Dose 

Model Predictions ..................................................................................... 5-45 

5.2.3.4. EPA’s Response to the NAS Comments on Model Forms for Predicting 

Cancer Risks Below the POD................................................................... 5-46 

5.2.3.4.1. Choice of extrapolation approach...................................................... 5-46 

5.2.3.4.1.1. TCDD and receptor theory......................................................... 5-46 

5.2.3.4.1.2. Low-dose extrapolation: threshold or no threshold? ................. 5-48 

5.2.3.4.1.3. Extrapolation method................................................................. 5-55 

5.2.3.4.1.4. Consideration of nonlinear methods. ......................................... 5-57 

5.2.3.4.1.4.1. Illustrative RfDs based on tumorigenesis in experimental 

animals................................................................................5-58 

5.2.3.4.1.4.2. Illustrative RfDs based on hypothesized key events in 

TCDD’s MOAs for liver and lung tumors. ........................5-59 

5.2.3.4.1.4.2.1. Liver tumors................................................................ 5-60 

5.2.3.4.1.4.2.2. Lung tumors................................................................ 5-61 

5.2.3.4.1.4.2.3. Limitations of illustrative RfDs based on 

hypothesized key events in TCDD’s MOAs for 

liver and lung tumors. .................................................5-62 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xvi DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

5.2.3.4.1.4.3. Effect of dose metric on linearity of response....................5-64 

5.3. DERIVATION OF THE TCDD ORAL SLOPE FACTOR AND CANCER RISK 

ESTIMATES............................................................................................................... 5-66 

5.3.1. Uncertainty in Estimation of Oral Slope Factors From Human Studies............ 5-68 

5.3.1.1. Uncertainty in Exposure Estimation ......................................................... 5-68 

5.3.1.2. Uncertainty in Shape of the Dose-Response Curve.................................. 5-72 

5.3.1.3. Uncertainty in Defining the Reference Population................................... 5-73 

5.3.1.4. Uncertainty in Cancer Risk Estimates ...................................................... 5-73 

5.3.2. Other Sources of Uncertainty in Risk Estimates From the Epidemiological 

Studies................................................................................................................ 5-74 

5.3.3. Approaches to Combining Estimates From Different Epidemiologic Studies.. 5-76 

5.3.3.1. The Crump et al. (2003) Meta-analysis .................................................... 5-76 

5.3.3.2. EPA’s Decision Not to Conduct a Meta-analysis..................................... 5-78 

6. FEASIBILITY OF QUANTITATIVE UNCERTAINTY ANALYSIS FROM NAS 

EVALUATION OF THE 2003 REASSESSMENT ............................................................ 6-1 

6.1. INTRODUCTION ........................................................................................................ 6-1 

6.1.1. Historical Context for Quantitative Uncertainty Analysis................................... 6-1 

6.1.2. Definition of Terms.............................................................................................. 6-3 

6.1.3. Basic Requirements of a Quantitative Uncertainty Analysis............................... 6-5 

6.1.3.1. Quantitative Model ..................................................................................... 6-6 

6.1.3.2. Marginal Distributions over Model Parameter ........................................... 6-6 

6.1.3.3. Dependence Between Parameter Uncertainties: Aleatoric and Epistemic 

(Uncertainty and Variability)...................................................................... 6-6 

6.1.3.4. Model Uncertainty ...................................................................................... 6-7 

6.1.3.5. Sampling Method........................................................................................ 6-8 

6.1.3.6. Method for Extracting and Communicating Results .................................. 6-8 

6.2. EPA APPROACHES FOR ORAL CANCER AND NONCANCER 

ASSESSMENT............................................................................................................. 6-9 

6.3. HIGHLIGHTS OF NAS REVIEW COMMENTS ON UNCERTAINTY 

QUANTIFICATION FOR THE 2003 REASSESSMENT ........................................ 6-11 

6.4. FEASIBILITY OF CONDUCTING A QUANTITATIVE UNCERTAINTY 

ANALYSIS FOR TCDD ............................................................................................ 6-14 

6.4.1. Feasibility of Conducting a Quantitative Uncertainty Analysis under the 

RfD Methodology .............................................................................................. 6-14 

6.4.1.1. Feasibility of Conducting a Quantitative Uncertainty Analysis for the 

Point of Departure..................................................................................... 6-15 

6.4.1.2. Feasibility of Conducting a Quantitative Uncertainty Analysis with 

Uncertainty Factors................................................................................... 6-18 

6.4.1.3. Conclusion on Feasibility of Quantitative Uncertainty Analysis with 

the RfD Approach..................................................................................... 6-20 

6.4.2. Feasibility of Conducting a Quantitative Uncertainty Analysis for TCDD 

Under the Dose-Response Methodology ........................................................... 6-20 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xvii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

6.4.2.1. Feasibility of Quantitatively Characterizing the Uncertainties 

Encountered when Determining Appropriate Types of Studies 

(Epidemiological, Animal, Both, and Other)............................................ 6-22 

6.4.2.2. Uncertainty in TCDD Exposure/Dose in Epidemiological Studies.......... 6-23 

6.4.2.3. Uncertainty in Toxicity Equivalence (TEQ) Exposures in 

Epidemiological Studies ........................................................................... 6-26 

6.4.2.4. Uncertainty in Background Feed Exposures in Bioassays ....................... 6-27 

6.4.2.5. Feasibility of Quantifying the Uncertainties Encountered When 

Choosing Specific Studies and Subsets of Data ....................................... 6-28 

6.4.2.6. Feasibility of Quantifying the Uncertainties Encountered when 

Choosing Specific Endpoints for Dose-Response Modeling.................... 6-29 

6.4.2.7. Feasibility of Quantifying the Uncertainties Encountered when 

Choosing a Specific Dose Metric (Trade-Off Between Confidence in 

Estimated Dose and Relevance of MOA)................................................. 6-30 

6.4.2.8. Feasibility of Quantifying the Uncertainties Encountered When 

Choosing Model Type and Form .............................................................. 6-31 

6.4.2.9. Threshold MOA for Cancer...................................................................... 6-33 

6.4.2.10. Feasibility of Quantifying the Uncertainties Encountered when 

Selecting the BMR.................................................................................... 6-34 

6.5. CONCLUSIONS......................................................................................................... 6-34 

6.5.1. Summary of NAS Suggestions and Responses.................................................. 6-34 

6.5.2. How Forward? Beyond RfDs and Cancer Slope Factors to Development 

of Predictive Human Dose-Response Functions................................................ 6-38 

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

APPENDIX A: DIOXIN WORKSHOP ..................................................................................... A-1 

APPENDIX B: EVALUATION OF CANCER AND NONCANCER 

EPIDEMIOLOGICAL STUDIES FOR INCLUSION IN 

TCDD DOSE-RESPONSE ASSESSMENT......................................................B-1 

APPENDIX C: KINETIC MODELING......................................................................................C-1 

APPENDIX D: EPIDEMIOLOGICAL KINETIC MODELING............................................... D-1 

APPENDIX E: NONCANCER BENCHMARK DOSE MODELING.......................................E-1 

APPENDIX F: CANCER BENCHMARK DOSE MODELING................................................F-1 

APPENDIX G: ENDPOINTS EXCLUDED FROM REFERENCE DOSE 

DERIVATION BASED ON TOXICOLOGICAL RELEVANCE ................... G-1 

APPENDIX H: CANCER PRECURSOR BENCHMARK DOSE MODELING...................... H-1 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xviii DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES 

2-1. Summary of epidemiologic cancer studies (key characteristics).................................2-199 

2-2. Epidemiology cancer study selection considerations and criteria ...............................2-201 

2-3. Epidemiology noncancer study selection considerations and criteria ......................... 2-205 

2-4. Epidemiology studies selected for TCDD cancer dose-response modeling ................2-209 

2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling ..........2-213 

2-6. Animal bioassays selected for cancer dose-response modeling .................................. 2-217 

2-7. Animal bioassay studies selected for noncancer dose-response modeling..................2-218 

3-1. Partition coefficients, tissue volumes, and volume of distribution for TCDD in 

humans ........................................................................................................................... 3-55 

3-2. Blood flows, permeability factors and resulting half lives (t1⁄2) for perfusion losses 

for humans as represented by the TCDD PBPK model of Emond et al. (2005, 

2006) ..............................................................................................................................3-55 

3-3. Toxicokinetic conversion factors for calculating human equivalent doses from 

rodent bioassays............................................................................................................. 3-56 

3-4. Equations used in the CADM PBPK model* ................................................................3-57 

3-5. Parameters of the CADM model.................................................................................... 3-58 

3-6. Confidence in the CADM model simulations of TCDD dose metrics ..........................3-59 

3-7. Equations used in the TCDD PBPK model of Emond et al. (2006)..............................3-60 

3-8. Parameters of the PBPK model for TCDD....................................................................3-62 

3-9. Regression analysis results for the relationship between log10 serum TCDD at the 

midpoint of observations and the log10 of the rate constant for decline of TCDD 

levels using Ranch Hand data........................................................................................3-65 

3-10. Confidence in the PBPK model simulations of TCDD dose metrics ............................3-65 

3-11. Overall confidence associated with alternative dose metrics for cancer and 

noncancer dose-response modeling for TCDD using rat PBPK model.........................3-66 

3-12. Overall confidence associated with alternative dose metrics for cancer and 

noncancer dose-response modeling for TCDD using mouse PBPK model...................3-66 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xix DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

3-13. Contributors to the overall uncertainty in the selection and use of dose metrics in 

the dose-response modeling of TCDD based on rat and human PBPK models ............3-66 

3-14. Contributors to the overall uncertainty in the selection and use of dose metrics in 

the dose-response modeling of TCDD based on mouse and human PBPK models......3-67 

3-15. Impact of toxicokinetic modeling on the extrapolation of administered dose to 

HED, comparing the Emond PBPK and first-order body burden models .....................3-67 

4-1. POD candidates for epidemiologic studies of TCDD....................................................4-28 

4-2. Models run for each study/endpoint combination in the animal bioassay 

benchmark dose modeling ............................................................................................. 4-28 

4-3. Summary of key animal study NOAELs, LOAELs, and BMDLs for different 

dose metrics (ng/kg-day)................................................................................................4-29 

4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as 

LASC)............................................................................................................................4-32 

4-5. Candidate points of departure for the TCDD RfD using blood-concentration-based 

human equivalent doses.................................................................................................4-40 

4-6. Qualitative analysis of the strengths and limitations/uncertainties associated with 

animal bioassays possessing candidate points-of-departure for the TCDD RfD...........4-43 

4-7. Basis and derivation of the TCDD reference dose.........................................................4-47 

5-1. Cancer slope factors calculated from Becher et al. (1998), Steenland et al. (2001) 

and Ott and Zober (1996) from 2003 Reassessment Table 5-4 .....................................5-79 

5-2. Cox regression coefficient estimate and incremental risk for NIOSH cohort data 

as presented in Cheng et al. (2006) compared with Steenland et al. (2001)..................5-80 

5-3. Comparison of lipid-adjusted serum concentrations, fat concentrations, risk 

specific dose estimates and equivalent oral slope factors based on upper 95th 

percentile estimate of regression coefficient of all fatal cancers reported by 

Cheng et al. (2006) for risk levels of 1 × 10−3

, 1 × 10−4, 1 × 10−5, 1 × 10−6, 

and 1 × 10−7....................................................................................................................5-81 

5-4. Comparison of lipid-adjusted serum concentrations, fat concentrations, risk 

specific dose estimates and equivalent oral slope factors based on best estimate 

of regression coefficienta

 of all fatal cancers reported by Cheng et al. (2006) for 

risk levels of 1 × 10−3

, 1 × 10−4, 1 × 10−5, 1 × 10−6, and 1 × 10−7..................................5-81 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xx DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

5-5. Kociba et al. (1978) male rat tumor incidence dataa

 and blood concentrations for 

dose-response modeling................................................................................................. 5-82 

5-6. Kociba et al. (1978) female rat tumor incidence dataa

 and blood concentrations 

for dose-response modeling ...........................................................................................5-82 

5-7. NTP (1982) female rat tumor incidence dataa and blood concentrations for doseresponse modeling .........................................................................................................5-83 

5-8. NTP (1982) male rat tumor incidence dataa and blood concentrations for doseresponse modeling .........................................................................................................5-83 

5-9. NTP (1982) female mouse tumor incidence dataa and blood concentrations for 

dose-response modeling................................................................................................. 5-84 

5-10. NTP (1982) male mouse tumor incidence dataa

 and blood concentrations for 

dose-response modeling................................................................................................. 5-84 

5-11. NTP (2006) female rat tumor incidence dataa and blood concentrations for doseresponse modelingb ........................................................................................................5-85 

5-12. Toth et al. (1979) male mouse tumor incidence dataa and blood concentrations for 

dose-response modeling................................................................................................. 5-85 

5-13. Comparison of multi-stage modeling results across cancer bioassays using blood 

concentrations ................................................................................................................5-86 

5-14. Individual tumor points of departure and slope factors using blood concentrations .....5-88 

5-15. Multiple tumor points of departure and slope factors using blood concentrations........5-89 

5-16. Illustrative RfDs based on tumorigenesis in experimental animals............................... 5-90 

5-17. Illustrative RfDs based on hypothesized key events in TCDD’s MOAs for liver 

and lung tumors..............................................................................................................5-91 

5-18. Dichotomous Hill model fits to combined adenoma and carcinoma data from 

Kociba et al. (1987) as re-evaluated by Goodman and Sauer (1992)a

...........................5-92 

5-19. Comparison of principal epidemiological studies.......................................................... 5-93 

6-1. Key sources of uncertainty............................................................................................. 6-40 

6-2. PODs and amenability for uncertainty quantification ...................................................6-41 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxi DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES 

2-1. EPA’s process to select and identify in vivo mammalian and epidemiologic studies 

for use in the dose-response analysis of TCDD........................................................... 2-232 

2-2. EPA’s process to evaluate available epidemiologic studies using study inclusion 

criteria for use in the dose-response analysis of TCDD .............................................. 2-233 

2-3. EPA’s process to evaluate available animal bioassay studies using study inclusion 

criteria for use in the dose-response analysis of TCDD .............................................. 2-234 

3-1. Liver/fat concentration ratios in relation to TCDD dose at various times after oral 

administration of TCDD to mice ...................................................................................3-68 

3-2. First-order elimination rate fits to 36 sets of serial TCDD sampling data from 

Seveso patients as function of initial serum lipid TCDD .............................................. 3-69 

3-3. Observed relationship of fecal 2,3,7,8-TCDD clearance and estimated percent 

body fat ..........................................................................................................................3-70 

3-4. Unweighted empirical relationship between percent body fat estimated from body 

mass index and TCDD elimination half-life—combined Ranch Hand and Seveso 

observation.....................................................................................................................3-71 

3-5. Relevance of candidate dose metrics for dose-response modeling, based on mode 

of action and target organ toxicity of TCDD................................................................. 3-72 

3-6. Process of estimating a human-equivalent TCDD lifetime average daily oral 

exposure (dH) from an experimental animal average daily oral exposure (dA) 

based on the body-burden dose metric...........................................................................3-73 

3-7. Human body burden time profiles for achieving a target body burden for different 

exposure duration scenarios...........................................................................................3-74 

3-8. Schematic of the CADM structure.................................................................................3-75 

3-9. Comparison of observed and simulated fractions of the body burden contained in 

the liver and adipose tissues in rats................................................................................ 3-76 

3-10. Conceptual representation of PBPK model for rat exposed to TCDD .......................... 3-77 

3-11. Conceptual representation of PBPK model for rat developmental exposure to 

TCDD.............................................................................................................................3-78 

3-12. TCDD distribution in the liver tissue............................................................................. 3-79 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxii DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

3-13. Growth rates for physiological changes occurring during gestation. (a) Placental 

growth during gestation .................................................................................................3-80 

3-14. Comparisons of model predictions to experimental data using a fixed elimination 

rate model with hepatic sequestration (A) and an inducible elimination rate model 

with (B) and without (C) hepatic sequestration ............................................................. 3-81 

3-15. PBPK model simulation of hepatic TCDD concentration (ppb) during chronic 

exposure to TCDD at 50, 150, 500, 1,750 ng TCDD/BW using the inducible 

elimination rate model compared with the experimental data measured at the 

end of exposure.............................................................................................................. 3-82 

3-16. Model predictions of TCDD blood concentration in 10 veterans (A-J) from Ranch 

Hand Cohort...................................................................................................................3-83 

3-17. Time course of TCDD in blood (pg/g lipid adjusted) for two highly exposed 

Austrian women.............................................................................................................3-84 

3-18. Observed vs. Emond et al. (2005) model simulated serum TCDD concentrations 

(pg/g lipid) over time (ln = natural log) in two Austrian women ..................................3-85 

3-19. Comparison of the dose dependency of TCDD elimination in the Emond model 

vs. observations of nine Ranch Hand veterans and two highly exposed Austrian 

patients........................................................................................................................... 3-86 

3-20. Sensitivity analysis was performed on the inducible elimination rate...........................3-87 

3-21. Experimental data (symbols) and model simulations (solid lines) of (A) blood, 

(B) liver and (C) adipose tissue concentrations of TCDD after oral exposure to 

150 ng/kg-day, 5 days/week for 17 weeks in mice........................................................3-88 

3-22. Comparison of PBPK model simulations with experimental data on liver 

concentrations in mice administered a single oral dose of 0.001−300 μg TCDD/kg ....3-89 

3-23. Comparison of model simulations (solid lines) with experimental data (symbols) 

on the effect of dose on blood (cb), liver (cli) and fat (cf) concentrations following 

repetitive exposure to 0.1−450 ng TCDD/kg, 5 days/week for 13 weeks in mice ........3-90 

3-24. Comparison of experimental data (symbols) and model predictions (solid lines) of 

(A) blood, (B) liver and (C) adipose tissue concentrations of TCDD after oral 

exposure to 1.5 ng/kg-day, 5 days/week for 17 weeks in mice ..................................... 3-91 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxiii DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

3-25. Comparison of experimental data (symbols) and model predictions (solid lines) of 

(A) blood concentration, (B) liver concentration, (C) adipose tissue concentration 

(D) feces excretion (% dose) and (E) urinary elimination (% dose) of TCDD after 

oral exposure to 1.5 ng/kg-day, 5 days/week for 13 weeks in mice ..............................3-92 

3-26. Comparison of experimental data (symbols) and model predictions (solid lines) of 

(A) blood concentration, (B) liver concentration, (C) adipose tissue concentration 

(D) feces excretion (% dose) and (E) urinary elimination (% dose) of TCDD after 

oral exposure to 150 ng/kg-day, 5 days/week for 13 weeks in mice .............................3-93 

3-27. PBPK model simulations (solid lines) vs. experimental data (symbols) on the 

distribution of TCDD after a single acute oral exposure to A−B) 0.1, C−D) 1.0 

and E−F) 10 μg of TCDD/kg of body weight in mice................................................... 3-94 

3-28. PBPK model simulation (solid lines) vs. experimental data (symbols) on the 

distribution of TCDD after a single dose of 24 μg/kgBW on GD 12 in mice ...............3-95 

3-29. Comparison of the near-steady-state body burden simulated with CADM and 

Emond models for a daily dose ranging from 1 to 10,000 ng/kg-day in rats and 

humans ...........................................................................................................................3-96 

3-30. TCDD serum concentration-time profile for lifetime, less-than-lifetime and 

gestational exposure scenarios, with target concentrations shown for each; profiles 

generated with Emond human PBPK model .................................................................3-97 

3-31. TCDD serum concentration-time profile for lifetime, less-than-lifetime and 

gestational exposure scenarios, showing continuous intake levels to fixed target 

concentration; profiles generated with Emond human PBPK model ............................3-98 

4-1. EPA’s process to select and identify candidate PODs from key epidemiologic 

studies for use in the noncancer risk assessment of TCDD........................................... 4-48 

4-2. EPA’s process to select and identify candidate PODs from key animal bioassays 

for use in noncancer dose-response analysis of TCDD .................................................4-49 

4-3. Exposure-response array for ingestion exposures to TCDD.......................................... 4-50 

4-4. Candidate RfD array ......................................................................................................4-51 

5-1. Mechanism of altered gene expression by AhR ............................................................ 5-96 

5-2. TCDD’s hypothesized modes of action in site-specific carcinogenesis ........................5-97 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxiv DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

5-3. EPA’s process to select and identify candidate OSFs from key animal bioassays 

for use in the cancer risk assessment of TCDD ............................................................. 5-98 

5-4. Representative endpoints for each of the hypothesized key events following 

AhR activation for TCDD-induced liver tumors ...........................................................5-99 

5-5. Representative endpoints for two hypothesized key events following AhR 

activation for TCDD-induced lung tumors..................................................................5-100 

5-6. Multistage model fit to cholangiocarcinoma response data (NTP, 2006) with 

comparison to linear model fit .....................................................................................5-101 

5-7. Weibull model fits to Kociba/G&S liver tumor response data with alternative 

dose metrics .................................................................................................................5-101 

6-1. Back-casted vs. predicted TCDD serum levels for a worker subset.............................. 6-42 

6-2. Distribution of in vivo unweighted REP values in the 2004 database........................... 6-43 

6-3. Plot of observed rat cholagiocarcinoma incidence and central estimate of Hill 

Model fit to the data vs. human equivalent dose ........................................................... 6-44 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxv DRAFT—DO NOT CITE OR QUOTE 

LIST OF ABBREVIATIONS AND ACRONYMS 

2,4,5-T 2,4,5-trichlorophenoxyacetic acid 

2,4-D 2,4-dichlorophenoxyacetic acid 

AA ascorbic acid 

ACOH acetanilide-4-hydroxylase 

AHH aryl hydrocarbon hydroxylase 

AhR aryl hydrocarbon receptor 

AhR-/- AhR-deficient 

AIC Akaike Information Criterion 

ANL Argonne National Laboratory 

ANOVA analysis of variance 

APE airborne particulate extract 

ASAT aspartate aminotransferase 

AUC area under the curve 

bHLH-PAS basic helix-loop-helix, Per-Arnt-Sim 

Bmax equilibrium maximum binding capacity 

BMD benchmark dose 

BMDL benchmark dose lower confidence bound 

BMDS Benchmark dose software 

BMI body mass index 

BMR benchmark response 

BPS balanopreputial separation 

BROD benzyloxy resoufin-O-deethylase 

b-TSH blood thyroid-stimulating hormone 

BW body weight 

C cerebellum 

CADM concentration- and age-dependent elimination model 

Cc cerebral cortex 

CI confidence interval 

CSAF chemical-specific adjustment factor 

CSLC cumulative serum lipid concentration 

Cx connexin 

CYP cytochrome P450 

Da:HED ratio of administered dose to HED 

DEN diethylnitrosamine 

df degrees of freedom 

DLC dioxin-like compound 

DRE/XRE dioxin/xenobiotic response elements 

DRL differential reinforcement of low rate 

DSA delayed spatial alteration 

E2 17β-estradiol 

EDx effective dose eliciting x percent response 

EGFR epidermal growth factor receptor 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxvi DRAFT—DO NOT CITE OR QUOTE 

LIST OF ABBREVIATIONS AND ACRONYMS (continued) 

EPA Environmental Protection Agency 

ER estrogen receptor 

EROD 7-ethoxyresorufin-O-deethylase 

ERα estrogen receptor alpha 

EU European Union 

FFA free fatty acid 

FR fixed-ratio 

FSH follicle stimulating hormone 

FT4 free thyroxine 

GD gestation day 

GSH glutathione stimulating hormone 

GSH-Px glutathione stimulating hormone peroxidase 

GST glutathione-S-transferase 

H hippocampus 

HCH hexachlorocyclohexane 

HED human equivalent dose 

HQ hazard quotient 

HR hazard ratio 

Hsp90 heat shock protein 90 

IARC International Agency for Research on Cancer 

IGF insulin-like growth factor 

IL interleukin 

ILSI International Life Sciences Institute 

i.p. intraperitoneal 

IRIS Integrated Risk Information System 

KABS oral absorption parameters 

LASC lipid-adjusted serum concentration 

LD50 lethal dose eliciting x percent response 

LED lower confidence effective dose 

LEDx lower bound of the 95% confidence interval on the dose that yields an x% effect 

LH luteinizing hormone 

LOAEL lowest-observed-adverse-effect level 

LOAELHED HED estimate based on LOAELs 

LOEL lowest-observed-adverse level 

MCH mean corpuscular hemoglobin 

MCMC Markov Chain Monte Carlo 

MCV mean corpuscular volume 

MOA mode of action 

MOE margin of exposure 

MROD 7-methoxyresorufin-O-deethylase 

MTD maximum tolerated dose 

NAS National Academy of Sciences 

NIOSH National Institute for Occupational Safety and Health 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxvii DRAFT—DO NOT CITE OR QUOTE 

LIST OF ABBREVIATIONS AND ACRONYMS (continued) 

NOAEL no-observed-adverse-effect level 

NOEL no-observed-effect level 

NRC National Research Council 

NTP National Toxicology Program 

OR odds ratio 

OSF oral slope factor 

PA permeability x area 

PAI2 plasminogen activator inhibitor 2 

PBMC peripheral blood mononuclear cells 

PBPK physiologically based pharmacokinetic 

PCB polychlorinated biphenyl 

PCDD polychlorinated dibenzo-p-dioxin 

PCDF polychlorinated dibenzofuran 

PEPCK phosphoenolpyruvate carboxykinase 

PF adipose tissue:blood partition coefficient 

PHAH polyhalogenated aromatic hydrocarbons 

PK pharmacokinetic 

PND postnatal day 

POD point of departure 

pp phosphotyrosyl protein 

PRA probabilistic risk assessment 

PRE body:blood partition coefficient 

PROD 7-pentoxyresorufin-O-deethylase 

RAR retinoic acid receptor 

REP relative potency 

RfC reference concentration 

RfD reference dose 

RL reversal learning 

RL risk level 

RR rate ratios 

RR relative risk 

RT-PCR reverse transcription polymerase chain reaction 

RXR retinoid X receptor 

S saline 

SA superoxide anion 

SAhRM SRM for AhRs 

S-D Sprague-Dawley 

SD standard deviation 

SIR standardized incidence ratio 

SMR standardized mortality ratio 

SOD superoxide dismutase 

SRBC sheep red blood cell 

SSB single-strand break 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxviii DRAFT—DO NOT CITE OR QUOTE 

LIST OF ABBREVIATIONS AND ACRONYMS (continued) 

SWHS Seveso Women’s Health Study 

T4 thyroxine 

TBARS thiobarbituric acid-reactive substances 

TCB 3,3’,4,4’-tetrachlorobiphenyl 

TCDD 2,3,7,8-Tetrachlorodibenzo-p-dioxin 

TCP 2,4,5-trichlorophenol 

TEF toxicity equivalence factor 

TEQ toxicity equivalence 

TGFα transforming growth factor α

TK toxicokinetic 

TNF-α tumor necrosis factor alpha 

TOTTEQ total toxicity equivalence 

TSH thyroid stimulating hormone 

TT4 total thyroxine 

TWA time-weighted average 

U.S. NRC U.S. Nuclear Regulatory Commission 

UDP uridine diphosphate 

UDPGT UDP-glucoronosyl transferase 

UED upper confidence bound for the effective dose 

UF uncertainty factor 

UFA interspecies extrapolation factor

UFD database factor

UFH human interindividual variability

UFL LOAEL-to-NOAEL UF

UFS subchronic-to-chronic UF

UGT UDP-glucuronosyltransferase 

UGT1 uridine diphosphate glucuronosyltransferase I 

Vd volume of distribution 

WHO World Health Organization 

ZS@Z zero slope at zero dose 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxix DRAFT—DO NOT CITE OR QUOTE 

PREFACE 

This document contains the U.S. Environmental Protection Agency’s (EPA), Office of 

Research and Development, National Center for Environmental Assessment’s response to the 

National Academy of Sciences (NAS, 2006) review of Exposure and Human Health 

Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds

(U.S. EPA, 2003). Given that the key recommendations of the NAS refer to issues related to 

TCDD dose-response assessment and quantitative uncertainty analysis, EPA’s response focuses 

on understanding human dose response for TCDD (both cancer and noncancer endpoints), and 

the feasibility of conducting quantitative uncertainty analysis in TCDD dose-response 

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

1/15/10 xxx DRAFT—DO NOT CITE OR QUOTE 

AUTHORS, CONTRIBUTORS, AND REVIEWERS 

PRIMARY AUTHORS 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Cincinnati, OH 

Belinda Hawkins 

Janet Hess-Wilson 

Glenn Rice 

Jeff Swartout 

Linda K. Teuschler 

CONTRIBUTING AUTHORS 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Cincinnati, OH 

Scott Wesselkamper 

Michael Wright 

Bette Zwayer 

National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Hisham El-Masri 

Argonne National Laboratory, Argonne, IL 

Margaret MacDonell 

Emory University, Atlanta, GA 

Kyle Steenland 

Resources for the Future, Washington, DC 

Roger M. Cooke 

University of Montreal, Montreal, Canada 

Kannan Krishnan 

CONTRIBUTORS 

National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Washington, DC 

Karen Hogan 

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

1/15/10 xxxi DRAFT—DO NOT CITE OR QUOTE 

AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued) 

CONTRIBUTORS continued 

Argonne National Laboratory, Argonne, IL 

Maryka H. Bhattacharyya 

Andrew Davidson 

Mary E. Finster 

David P. Peterson 

Clark University, Worcester, MA 

Dale Hattis 

Colorado State University, Fort Collins, CO 

Raymond Yang 

Bruce Allen Consulting, Chapel Hill, NC 

Bruce C. Allen 

ICF International, Durham, NC 

Robyn Blain 

Rebecca Boyles 

Patty Chuang 

Cara Henning 

Baxter Jones 

Penelope Kellar 

Mark Lee 

Nikki Maples-Reynolds 

Amalia Marenberg 

Garrett Martin 

Margaret McVey 

Chandrika Moudgal 

Bill Mendez 

Ami Parekh 

Andrew Shapiro 

Audrey Turley 

National Toxicology Program, Research Triangle Park, NC 

Michael Devito 

Penn State University, University Park, PA 

Jack P. Vanden Heuvel 

Risk Sciences International, Ottawa, Ontario 

Jessica Dennis 

Dan Krewski 

Greg Paoli 

Salomon Sand 

Natalia Shilnikova 

Paul Villenueve 

University of California-Berkeley, Berkeley, CA 

Brenda Eskenazi 

University of California-Irvine, Irvine, CA 

Scott Bartell 

University of Montreal; BioSimulation Consulting, Newark, DE 

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

1/15/10 xxxii DRAFT—DO NOT CITE OR QUOTE 

AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued) 

INTERNAL REVIEWERS 

National Center for Environmental Assessment, U.S. Environmental Protection Agency 

Glinda Cooper, Washington, DC 

Ila Cote, Research Triangle Park, NC 

Lynn Flowers, Washington, DC 

Martin Gehlhaus, Washington, DC 

Kate Guyton, Washington, DC 

Samantha Jones, Washington, DC 

Matthew Lorber, Washington, DC 

Eva McLanahan, Research Triangle Park, NC 

Susan Rieth, Washington, DC 

Reeder Sams, Research Triangle Park, NC 

Paul Schlosser, Research Triangle Park, NC 

Jamie Strong, Washington, DC 

John Vandenberg, Research Triangle Park, NC 

Paul White, Washington, DC 

ACKNOWLEDGMENTS 

National Center for Environmental Assessment, U.S. Environmental Protection Agency 

Jeff Frithsen, Washington, DC 

Annette Gatchett, Cincinnati, OH 

Andrew Gillespie, Cincinnati, OH 

Marie Nichols-Johnson, Cincinnati, OH 

Maureen Johnson, Washington, DC 

Peter Preuss, Washington, DC 

Linda Tuxen, Washington, DC 

Colorado State University, Fort Collins, CO 

William H. Farland 

ECFlex, Inc., Fairborn, OH 

Dan Heing 

Heidi Glick 

Amy Prues 

Lana Wood 

IntelliTech Systems, Inc., Fairborn, OH 

Cris Broyles 

Luella Kessler 

Debbie Kleiser 

Stacey Lewis 

Linda Tackett 

National Institute of Environmental Health Sciences, Research Triangle Park, NC 

Linda S. Birnbaum 

Christopher J. Portier 

National Toxicology Program, Research Triangle Park, NC 

Nigel Walker 

2009 Dioxin Workshop Participants 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxxiii DRAFT—DO NOT CITE OR QUOTE 

1 EXECUTIVE SUMMARY 

 2 

 3 

4 OVERVIEW 

5 The U.S. Environmental Protection Agency (EPA) is committed to the development of 

6 risk assessment information of the highest scientific integrity for use in protecting human health 

7 and the environment. Scientific peer review is an integral component of the process EPA uses to 

8 generate high quality toxicity and exposure assessments of environmental contaminants. To this 

9 end, EPA asked the National Academy of Sciences (NAS) to review its comprehensive human 

10 health risk assessment external review draft entitled, Exposure and Human Health Reassessment 

11 of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin (TCDD) and Related Compounds (U.S. EPA, 2003; 

12 “2003 Reassessment”). This current document, EPA’s Response to “Health Risks from Dioxin 

13 and Related Compounds: Evaluation of the EPA Reassessment” Published by the National 

14 Research Council of the National Academies, directly and technically responds to key comments 

15 and recommendations pertaining to TCDD dose-response assessment published by the NAS in 

16 their review (NAS, 2006a). This document only addresses issues pertaining to TCDD dose17 response assessment. 

18 In response to the recommendations presented in the 2006 NAS review, EPA 

19 Administrator Jackson released EPA’s “Science Plan for Activities Related to Dioxins in the 

Environment” (“Science Plan”) on May 26, 2009.1 20 There are five key components of the 

21 Science Plan that pertain to EPA’s response to the NAS comments on TCDD dose-response 

22 assessment: 

23 

24 1. EPA will release a draft report that responds to the recommendations and comments 

25 included in the NAS review of EPA’s 2003 Reassessment. 

26 • EPA’s National Center for Environment Assessment (NCEA), in the Office of 

27 Research and Development (ORD), will prepare a limited response to key comments 

28 and recommendations in the NAS report (draft response to comments report). 

29 • The draft response will focus on dose-response issues raised by the NAS and will 

30 include an analysis of relevant new key studies. 

31 • The draft response will be provided for public review and comment and independent 

32 external peer review. 

 

1

Available online at http://www.epa.gov/dioxin/scienceplan. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xxxiv DRAFT—DO NOT CITE OR QUOTE 

1 • The draft response will also include an evaluation of some of the significant 

2 recommendations that are difficult for EPA to address given the current state of 

3 science, and a detailed rationale for these conclusions. 

4 • The peer review will be conducted by EPA’s Science Advisory Board (SAB), an 

5 independent review body chartered under the Federal Advisory Committee Act. 

6 • The draft response to comments report will be completed and released for public and 

7 peer review by December 31, 2009. 

8 2. EPA will provide the draft response to comments report for internal and external review. 

9 3. The SAB will review the science content of the response to comments report. 

10 4. EPA will review impacts of the draft response to comments report on its 2003 

11 Reassessment. 

12 5. EPA will release the final response to comments report and focus on completion of the 

13 2003 Reassessment. 

14 

15 This document responds to and addresses key NAS comments relating to TCDD dose16 response assessment. Three separate EPA activities address additional NAS comments 

17 pertaining to toxicity equivalence factors (TEFs) and background exposure levels. Information 

18 on the application of the dioxin TEFs is published elsewhere by EPA for both ecological (U.S. 

19 EPA, 2008a) and human health (U.S, EPA, 2009a) risk assessment. As a consequence, EPA 

20 does not directly address TEFs herein, but makes use of the concept of toxicity equivalence 

(TEQ)2 21 as applicable to the analysis of exposure dose in epidemiologic studies. Furthermore, 

22 addressing the NAS recommendations pertaining to the assessment of human exposures to 

23 TCDD and other dioxins, information on updated background levels of dioxin in the U.S. 

24 population has been recently reported by EPA (Lorber et al., 2009). 

25 The NAS identified three key recommendations requiring substantial improvement to 

26 support a scientifically robust characterization of human responses to exposures to TCDD. 

27 These three key areas are (1) improved transparency and clarity in the selection of key data sets 

28 for dose-response analysis, (2) further justification of approaches to dose-response modeling for 

29 cancer and noncancer endpoints, and (3) improved transparency, thoroughness, and clarity in 

30 quantitative uncertainty analysis. NAS also encouraged EPA to calculate a Reference Dose 

31 (RfD), and provided numerous specific comments on various aspects of EPA’s 2003 

 

2

Toxicity equivalence (TEQ) is the product of the concentration of an individual dioxin like compound in an 

environmental mixture and the corresponding TCDD TEF for that compound. These products are summed to yield 

the TEQ of the mixture. 
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1/15/10 xxxv DRAFT—DO NOT CITE OR QUOTE 

1 Reassessment. The three key recommendations specifically pertain to dose-response assessment 

2 and uncertainty analysis. Therefore, EPA’s response to the NAS in this document is focused on 

3 these issues. EPA thoroughly considered the recommendations of the NAS and responds with 

4 scientific and technical evaluation of TCDD dose–response data via: 

5 

6 • an updated literature search that identified new TCDD dose-response studies (see 

7 Section 2); 

8 • a kickoff workshop that included the participation of external experts in TCDD health 

9 effects, toxicokinetics, dose-response assessment and quantitative uncertainty analysis; 

10 these experts discussed potential approaches to TCDD dose-response assessment and 

11 considerations for EPA’s response to NAS (see Appendix A); 

12 • detailed study inclusion criteria and processes for the selection of key studies (see 

13 Section 2.3) and epidemiologic and animal bioassay data for TCDD dose-response 

14 assessment (see Section 2.4.1/Appendix B and Section 2.4.2, respectively); 

15 • kinetic modeling to quantify appropriate dose metrics for use in TCDD dose-response 

16 assessment (see Section 3); 

17 • dose-response modeling for all appropriate noncancer and cancer data sets (see 

18 Section 4.2/Appendix E and Section 5.2.3/Appendix F, respectively); 

19 • thorough and transparent evaluation of the selected TCDD data for use in the derivation 

20 of an RfD and an oral slope factor (OSF) (see Sections 4.2 and 5.2.3, respectively); 

21 • the development of an RfD (see Section 4.3); 

22 • the development of a revised OSF (see Section 5.3) with an updated cancer weight of 

23 evidence determination for TCDD based on EPA’s 2005 Cancer Guidelines (U.S. EPA, 

24 2005) (see Section 5.1.2); 

25 • consideration of nonlinear dose-response approaches for cancer, including illustrative 

26 RfDs for cancer precursor events and tumors (see Section 5.2.3.4) ; and 

27 • discussion of the feasibility and utility of quantitative uncertainty analysis for TCDD 

28 dose-response assessment (see Section 6). 

29 

30 Each of the activities listed above is briefly described in this Executive Summary, and is 

31 described in detail in the related sections of this document. 

32 

33 

34 
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1/15/10 xxxvi DRAFT—DO NOT CITE OR QUOTE 

1 PRELIMINARY ACTIVITIES UNDERTAKEN BY EPA TO ENSURE THAT THIS 

2 TECHNICAL RESPONSE REFLECTS THE CURRENT STATE-OF-THE-SCIENCE 

3 As part of the development of this document, EPA undertook two activities that included 

4 public involvement: an updated literature search and a scientific expert workshop. The adverse 

5 health effects associated with TCDD exposures are documented extensively in epidemiologic 

6 and toxicologic studies. As such, the database of relevant information pertaining to the dose7 response assessment of TCDD is vast and constantly expanding. Responding directly to the 

8 NAS recommendation to use the most current and up-to-date scientific information related to 

9 TCDD, EPA, in collaboration with Argonne National Laboratory (ANL), developed an updated 

10 literature database of peer-reviewed studies on TCDD toxicity, including in vivo mammalian 

11 dose-response studies and epidemiologic studies. An initial literature search for studies 

12 published since the 2003 Reassessment was conducted to identify studies published between the 

13 year 2000 and October 31, 2008. EPA published the initial literature search results in the Federal 

14 Register in November 2008 and invited the public to review the list and submit additional peer15 reviewed relevant studies. Additional studies identified by the public and through continued 

16 work on this response have been incorporated into the final set of studies for TCDD dose17 response assessment (updated through October 2009). EPA believes that the implementation of 

18 this rigorous search strategy ensures that the most current and relevant studies were considered 

19 for the technical response to NAS and TCDD dose-response assessment included herein. 

20 To assist in responding to the NAS, EPA, in collaboration with ANL, convened a 

21 scientific expert workshop (“Dioxin Workshop”) in February 2009 that was open to the public. 

22 The primary goals of the Dioxin Workshop were to identify and address issues related to the 

23 dose-response assessment of TCDD and to ensure that EPA’s response to the NAS focused on 

24 the key issues while reflecting the most meaningful science. EPA and ANL assembled expert 

25 scientists and asked them to identify and discuss the technical challenges involved in addressing 

26 the NAS comments, discuss approaches for addressing these key recommendations, and to assist 

27 in the identification of important published and peer-reviewed literature on TCDD. The 

28 workshop was structured into seven scientific topic sessions as follows: (1) quantitative dose29 response modeling issues, (2) immunotoxicity, (3) neurotoxicity and nonreproductive endocrine 

30 effects, (4) cardiovascular toxicity and hepatotoxicity, (5) cancer, (6) reproductive and 

31 developmental toxicity, and (7) quantitative uncertainty analysis of dose-response. External 
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1 co-chairs (i.e., scientists who were not members of EPA or ANL) were asked to facilitate the 

2 sessions and then prepare summaries of discussions occurring in each session. The session 

3 summaries formed the basis of a final workshop report (U.S. EPA, 2009c, Appendix A of this 

4 document). Some of the key outcomes from the workshop include the following 

5 recommendations: 

6 

7 • to further develop study selection criteria for evaluating the suitability of developing 

8 dose-response models based on animal bioassays and human epidemiologic studies; 

9 • to use kinetic modeling to identify relevant dose metrics and dose conversions between 

10 test animal species and humans, and between human internal dose measures and human 

11 intakes; 

12 • to consider newer human or animal (e.g., NTP, 2006) publications when evaluating 

13 quantitative dose-response models for cancer; 

14 • to consider both linear and nonlinear modeling in the cancer dose-response analysis. 

15 

16 The discussions held during the Dioxin Workshop helped inform, guide, and focus EPA’s 

17 response to NAS. 

18 

19 EPA’S APPROACH TO CONSIDERING TRANSPARENCY AND CLARITY IN THE 

20 SELECTION OF KEY STUDIES AND DATA SETS FOR DOSE-RESPONSE 

21 MODELING 

22 One of the key NAS recommendations to EPA was to utilize a clear and transparent 

23 process for the selection of key studies and data sets for dose-response assessment. EPA agrees 

24 with the NAS and believes that clear delineation of the study selection process and decisions 

25 regarding key studies and data sets will facilitate communication of critical decisions made in the 

26 TCDD dose-response assessment. EPA developed detailed processes and TCDD-specific 

27 criteria for the selection of key dose-response studies. These criteria are based on common 

28 practices and current guidance for point of departure (POD) identification and RfD and OSF 

29 derivation while also considering issues specifically related to TCDD. Following the selection of 

30 key studies, EPA employed additional processes to further select and identify cancer and 

31 noncancer datasets from these key studies for use in dose-response analysis of TCDD. 

32 Figure ES-1 presents EPA’s study evaluation process for the epidemiologic studies 

33 considered for TCDD dose-response assessment, including specific study inclusion criteria (see 
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1 Section 2.3). EPA applied TCDD-specific epidemiologic study inclusion criteria to all 

2 epidemiologic studies published on TCDD and dioxin-like compounds (DLCs) that had been 

3 identified in the TCDD literature database (see Section 2.4.1, Appendix B). The studies were 

4 initially evaluated using five considerations (see Figure ES-1) that provide the most relevant 

5 kinds of information needed for quantitative human health risk analyses. Additionally, EPA 

6 examined whether the human exposures were primarily to TCDD and if the TCDD exposures 

7 could be quantified so that dose-response analyses could be conducted. Finally, EPA required 

8 that the effective dose be estimable: (1) for cancer, information is required on long-term 

9 exposures, and (2) for noncancer, information is required regarding the appropriate time window 

10 of exposure that is relevant for a specific, nonfatal health endpoint. Therefore, the study should 

11 include an appropriate latency period between TCDD exposure and the onset of the effect. Only 

12 studies meeting these criteria were included in EPA’s TCDD dose-response analyses (see 

13 Section 2.4.3). 

14 Figure ES-2 presents EPA’s study evaluation process for mammalian bioassays 

15 considered for TCDD dose-response assessment, including the specific study inclusion criteria 

16 (see Section 2.3). EPA applied TCDD-specific in vivo mammalian bioassay study inclusion 

17 criteria to all of the bioassay studies of TCDD that had been identified in the TCDD literature 

18 database (see Section 2.4.2). After ascertaining that a study had been published in the peer19 reviewed literature, EPA applied dose requirements to the lowest tested average daily doses in 

20 each study, with specific requirements for cancer (≤1 μg/kg-day) and noncancer (≤30 ng/kg-day) 

21 studies to ensure that only low-dose TCDD bioassays would be considered. These dose 

22 requirements were used to eliminate those studies that would not be selected for development of 

23 an RfD or an OSF because the lowest doses tested were too high relative to other TCDD 

24 bioassays. EPA also required that the bioassays exposed the animals via the oral route to TCDD 

25 only and that the purity of the TCDD was specified. Finally, the studies were evaluated using 

26 four considerations (see Figure ES-2) regarded as providing the most relevant information for 

27 development of quantitative human health risk analyses from animal bioassay data. Only the 

28 bioassay studies meeting these criteria and considerations were included in EPA’s TCDD dose29 response analyses (see Section 2.4.3). 

30 Applying the study inclusion criteria for both epidemiologic and mammalian bioassay 

31 datasets resulted in a list of key noncancer and cancer studies that were considered for 
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1 quantitative dose-response analyses of TCDD. Endpoints from these studies that were not 

2 considered to be toxicologically relevant were eliminated from consideration (see 

3 Section 4.2.1.1, Appendix G). The study/endpoint dataset combinations from the remaining 

4 studies were then subjected to dose-response assessment, and PODs for use in developing RfDs 

5 or OSFs were identified. PODs included no-observed-adverse-effect levels (NOAELs), lowest6 observed-adverse-effect levels (LOAELs) or lower bound benchmark dose levels (BMDLs). The 

7 most sensitive PODs were selected as candidates for derivation of the RfD and OSF. 

8 

9 USE OF KINETIC MODELING TO ESTIMATE TCDD DOSES 

10 NAS recommended that EPA utilize state-of-the-science approaches to finalize the 2003 

11 Reassessment. Although NAS concurred with EPA’s use of first-order body burden models in 

12 the 2003 Reassessment, analyses of recent TCDD literature and comments by experts at the 

13 Dioxin Workshop suggested that the understanding of TCDD kinetics had increased significantly 

14 since the release of EPA’s 2003 Reassessment. These advances led to the development of 

15 several pharmacokinetic models for TCDD (e.g., Emond et al., 2004, 2005, 2006; Aylward et al., 

16 2005a) and resulted in EPA’s incorporation of TCDD kinetics in the dose-response assessment 

17 of TCDD. 

18 The evaluation of internal dose in exposed humans and other species is facilitated by an 

19 understanding of pharmacokinetics (i.e., absorption, distribution, metabolism, and excretion). 

20 TCDD pharmacokinetics are influenced by three distinctive features: (1) TCDD is highly 

21 lipophilic, (2) TCDD is slowly metabolized, and (3) TCDD induces binding proteins in the liver. 

22 The overall impact of these factors results in preferential storage of TCDD in adipose tissue, a 

23 long half-life of TCDD in blood due to slow metabolism, and sequestration in liver tissue when 

24 binding induction becomes significant. As these kinetic features control target tissue levels of 

25 dioxin, they become important in relating toxicity in animals to possible effects in humans. 

26 Consideration of pharmacokinetic mechanisms is critical to the selection of the dose 

27 metrics of relevance to dose-response modeling of TCDD. Earlier assessments for TCDD, 

28 including the 2003 Reassessment, used estimates of body burden as the dose metric for 

29 extrapolation between animals and humans. These body burden calculations used a simple one30 compartment kinetic model based on the assumption of a first-order decrease in the levels of 

31 administered dose as a function of time. However, the assumption of a constant half-life value 
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1 for the clearance of TCDD from long-term or chronic exposure is not well-supported 

2 biologically given the dose-dependant elimination observed in rodents and humans. The 

3 dynamic disposition and redistribution of TCDD between blood, fat, and liver as a function of 

4 time and dose is better described using biologically-based models. Additionally, these models 

5 provide estimates for other dose metrics (e.g., serum or tissue levels) that are more biologically 

6 relevant to response than body burden estimated based on an assumption of first-order 

7 elimination over time. 

8 EPA considered the following possible dose metrics for TCDD: administered dose, 

9 absorbed dose, first-order body burden, serum or whole blood concentration, tissue 

10 concentration, and functional-related metrics of relevance to the mode of action (MOA) (e.g., 

11 receptor occupancy) (see Section 3.3.4.1). After careful evaluation of these dose metrics, EPA 

12 chose to use TCDD concentration in whole blood as the dose metric for assessing TCDD dose 

13 response in this document; blood concentration reflects both the body burden and the dose to 

14 target tissues. EPA used the time-weighted average whole-blood concentration over the relevant 

15 exposure periods for all continuous dosing protocols, dividing the area under the time-course 

concentration curve (AUC) by the exposure duration.3 16 

17 Several biologically-based kinetic models for TCDD exist in the literature. The more 

18 recent pharmacokinetic models explicitly characterize the concentration-dependent elimination 

19 of TCDD (Carrier et al., 1995a, b; Emond et al., 2004, 2005, 2006; Aylward et al., 2005a). The 

20 biologically-based pharmacokinetic models describing the concentration-dependent elimination 

21 (i.e., the pharmacokinetic models of Aylward et al. [2005a] and Emond et al. [2005, 2006]) are 

22 relevant for application to simulate the TCDD dose metrics in humans and animals exposed via 

23 the oral route. The rationale for considering the application of the Aylward et al. (2005a) and 

24 Emond et al. (2004, 2005, 2006) models was largely based on the fact that both models reflect 

25 research results from recent peer-reviewed publications, and both models are formulated with 

26 dose-dependent hepatic elimination consistent with the physiological understanding of TCDD 

27 kinetics. 

 

3

For the Seveso cohort, which had a high single exposure followed by low-level background exposures leading to a 

gradual decline in the internal TCDD concentrations, EPA estimated dose as the mean of the peak exposure and the 

average exposure over a defined critical exposure window (see Section 4.2.2). 
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1 Of the two selected models, the pharmacokinetic model developed by Emond et al. 

2 (2004, 2005, 2006) is more physiologically-based, as compared to the Aylward et al. (2005a) 

3 model, and models the blood compartment directly in the rat, mouse, and human; there are also 

4 gestational and life-time nongestational forms of the Emond PBPK model. Therefore, in this 

5 document, EPA chose the Emond rodent physiologically-based pharmacokinetic (PBPK) model 

6 to estimate blood TCDD concentrations based on administered doses (see Section 3.3.4, 

7 Appendix C). EPA assumed that the same blood TCDD levels that led to effects in animals 

8 would also lead to effects in humans; therefore, the Emond human PBPK model was used to 

9 estimate the lifetime average daily oral doses (consistent with the chronic RfD and OSF) that 

10 would correspond to the blood TCDD concentrations estimated to have occurred during the 

11 animal bioassays. EPA used the same Emond human PBPK model to estimate the lifetime 

12 average daily doses that would correspond to the TCDD blood or tissue concentrations reported 

13 in the epidemiological studies. These estimates are the Human Equivalent Doses (HEDs) that 

14 are used to develop candidate RfDs and OSFs for TCDD. 

15 Because TCDD elimination is inducible in the Emond model, ratios of daily averaged 

16 intake to long-term blood concentrations are not linear. OSFs based on internal dose measures 

17 (i.e., blood or fat concentrations) will be linear only with respect to blood or fat TCDD 

18 concentrations; however, ingested TCDD doses are not linear with TCDD fat or blood 

19 concentrations in the Emond PBPK model. Thus, an OSF that is linear with TCDD in the fat is 

20 not linear with the ingested TCDD dose. A consequence, then, of using the Emond PBPK model 

21 is that risk-specific TCDD intake rates corresponding to target risk levels need to be tabulated for 

22 use in human health risk assessment. 

 23 

24 DERIVATION OF AN RfD FOR TCDD 

25 The NAS specifically recommended that EPA derive an RfD for TCDD. Through a 

26 transparent study selection process, EPA identified key studies from both human epidemiologic 

27 and animal bioassay studies. To select candidate PODs for its RfD methodology, EPA applied 

28 additional processes to the key human epidemiologic studies and animal bioassays. Figure ES-3 

29 (exposure-response array) shows all of the candidate PODs graphically in terms of human30 equivalent intake (ng/kg-day). The human study endpoints are shown at the far left of the figure 
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1 and, to the right, the rodent endpoints are arranged by the following study categories: less than 1 

2 year, greater than 1 year, reproductive, and developmental. 

3 For each noncancer epidemiologic study that EPA selected as key, EPA evaluated the 

4 dose-response information developed by the study authors to determine whether the study 

5 provided noncancer effects and TCDD-relevant exposure data for a toxicologically-relevant 

6 endpoint. If such data were available, EPA identified a NOAEL or LOAEL as a candidate POD. 

7 Then, EPA used the Emond human PBPK model to estimate the continuous oral daily intake 

8 (ng/kg-day) that would lead to the relevant blood TCDD concentrations associated with the 

9 candidate POD that could be used in the derivation of an RfD. If all of this information was 

10 available, then the result was included as a candidate POD. 

11 Through this process, EPA identified health effects from the following four 

12 epidemiologic studies to be considered as the basis for the RfD: Eskenazi et al. (2002) 

13 (reproductive—increased length of menstrual cycle), Alaluusua et al. (2004) (developmental—

14 tooth development), Mocarelli et al. (2008) (reproductive—decreased sperm concentrations), and 

15 Baccarelli et al. (2008) (developmental—increased thyroid-stimulating hormone levels in 

16 neonates). All four studies are from the Seveso cohort, whose members were exposed 

17 environmentally to high peak concentrations of TCDD as a consequence of an industrial 

18 accident. This complicated the estimation of average daily doses associated with these specific 

19 endpoints, however EPA was able to calculate candidate PODs for derivation of an RfD from 

20 each of these human studies (see Section 4.2.3). 

21 Figure ES-4 summarizes the strategy employed for identifying and selecting candidate 

22 PODs from the key animal bioassays EPA identified for use in noncancer dose-response analysis 

23 of TCDD (see Section 4.2.4). For each noncancer endpoint, EPA first evaluated the 

24 toxicological relevance of each endpoint, rejecting those judged not to be relevant for RfD 

25 derivation. Next, initial PODs (NOAELs, LOAELs, and BMDLs) based on the first-order body 

26 burden metric, and expressed as continuous human-equivalent oral daily doses (HEDs), were 

27 determined for all relevant endpoints. Because there were very few NOAELs, and BMDL 

28 modeling was largely unsuccessful due to data limitations, the next stage of evaluation was 

29 carried out using LOAELs only. Endpoints not observed at the LOAEL (i.e., reported at higher 

30 doses) with BMDLs greater than the LOAEL were eliminated from further analysis, as they 

31 would not be considered as candidates for the final POD on either a BMDL or NOAEL/LOAEL 
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1 basis (i.e. the POD would be higher than the PODs of other relevant endpoints). In addition, all 

2 endpoints with HEDs for LOAELs (LOAELHEDs) beyond a 100-fold range of the lowest 

3 identified LOAELHED were eliminated from further consideration, as they would not be potential 

4 POD candidates either (i.e. the POD would be higher than the PODs of other relevant endpoints). 

5 For the remaining endpoints, EPA then determined final potential PODs (NOAELs, LOAELs, 

6 and BMDLs) based on TCDD blood concentrations obtained from the Emond rodent PBPK 

7 models. HEDs were then estimated for each of these PODs using the Emond human PBPK 

8 model. From these HEDs, a PODHED was selected for each study as the basis for the candidate 

9 RfD, to which appropriate uncertainty factors were applied following EPA guidelines. The 

10 resulting candidate RfDs were then considered in the final selection process for the RfD. Other 

11 endpoints occurring at slightly higher doses representing additional effects associated with 

12 TCDD exposure (beyond the 100-fold LOAEL range) were evaluated, modeled, and included in 

13 the final candidate RfD array to examine endpoints not evaluated by studies with lower PODs. 

14 In addition, BMD modeling based on administered dose was performed on all endpoints for 

15 comparison purposes. 

16 For selection of the POD to serve as the basis of the RfD, EPA gave the epidemiologic 

17 studies the highest consideration because human data are preferred in the derivation of an RfD, 

18 given that the underlying epidemiologic and animal bioassay data all met EPA’s selection 

19 criteria. This preference for epidemiologic study data also is consistent with reccomendations of 

20 panelists at the Dioxin Workshop. The lower end of the candidate POD distribution is dominated 

21 by mouse studies, with mouse studies comprising six of the first seven animal bioassays yielding 

22 the lowest PODs. EPA has less confidence in the values derived from these mouse bioassays 

23 than the values derived from rat and human studies. The primary reason that the mouse 

24 LOAELHEDs are low is the large toxicokinetic interspecies extrapolation factor applied to mouse 

25 data in the Emond PBPK model. In addition, each of these first seven rodent studies has other 

26 qualitative limitations and uncertainties that make them poor candidates as the basis for the RfD. 

27 Most of the other rodent studies yielding POD values higher than the first seven animal 

28 bioassay studies and lower than the human studies of Mocarelli et al (2008) and Baccarelli et al. 

29 (2008) are of small size, using 10 or fewer animals per dose group and are considered too 

30 uncertain on which to base the final RfD. However, two of the rat bioassays—Bell et al (2007) 

31 and NTP (2006)—were very well designed and conducted, using 30 or more animals per dose 
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1 group (see Section 4.3.4). Bell et al (2007) evaluated several reproductive and developmental 

2 endpoints initiating TCDD exposures well before mating and continuing them through gestation. 

3 NTP (2006) is the most comprehensive evaluation of TCDD chronic toxicity in rodents to date, 

4 evaluating dozens of endpoints at several time points in all major tissues. It is EPA’s judgement 

5 that the toxicokinetic extrapolation of the results of these two studies to humans, however, is still 

6 less certain than the use of human data. Despite the overall strength of the Bell and NTP studies, 

7 EPA considers the human data to be a better basis for the TCDD RfD. 

8 The most relevant human PODs are based on the Baccarelli et al. (2008) and Mocarelli et 

9 al. (2008) studies, which exhibited similar LOAELs of 0.024 and 0.020 ng/kg-day, respectively. 

10 For Baccarelli et al. (2008), EPA defined a LOAEL as the group mean of 39 ppt TCDD in 

11 neonatal plasma which corresponds to thyroid-stimulating hormone (TSH) values above 

12 5 μU/mL. Baccarelli et al. did not estimate the equivalent oral intake associated with TCDD 

13 serum concentrations, rather they provided neonatal serum TCDD concentrations for the groups 

14 above and below 5 μU/mL. EPA estimated the maternal intake at the LOAEL from a maternal 

15 serum-TCDD/TSH regression model presented in Baccarelli et al. by estimating the maternal 

16 TCDD lipid adjusted serum concentration (LASC) at which neonatal TSH exceeded 5 μU/mL. 

17 EPA then used the Emond PBPK model to estimate the continuous daily TCDD intake that 

18 would result in this TCDD LASC. The resulting predicted maternal daily intake rate established 

19 the LOAEL (0.024 ng/kg-day). EPA did not defined a NOAEL because it is not clear what 

20 maternal intake should be assigned to the group below 5 μU/mL. 

21 For Mocarelli et al. (2008), EPA defined a LOAEL as the lowest exposed group mean of 

68 ppt (1st 22 -quartile) corresponding to decreased sperm concentrations, decreased motile sperm 

23 counts, and decreased serum estradiol in men who were 1–9 years old at the time of the Seveso 

24 accident (initial TCDD exposure event). TCDD LASC levels were measured within 

25 approximately one year of the initial exposure event. Because effects were only observed in men 

26 who were under 10 years of age at the time of exposure, EPA assumed a maximum 10-year 

27 critical exposure window for elicitation of these effects. EPA has estimated a continuous daily 

28 oral intake of 0.020 ng/kg-day associated with the designated LOAEL from the lowest exposure 

29 group (68 ppt), (see Section 4.2.3.2). The reference group is not designated as a NOAEL 

30 because there is no clear zero-exposure measurement for any of these endpoints, particularly 

31 considering the contribution of background exposure to DLCs, which futher complicates the 
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1 interpretation of the reference group response as a true “control” response (see discussion in 

2 Section 4.4). However, males less than 10 years old can be designated as a sensitive population 

3 by comparison to older males who were not affected. 

4 The two human studies, Baccarelli et al. (2008) and Mocarelli et al. (2008), have similar 

5 LOAELs of 0.024 and 0.020 ng/kg-day, respectively. Together, these two studies constitute the 

6 best foundation for establishing a POD for the RfD, and are designated as co-principal studies. 

7 Therefore, increased TSH in neonates (Baccarelli et al., 2008) and male reproductive effects 

8 (decreased sperm count and motility, increased estradiol) are designated as cocritical effects. 

9 Although the exposure estimate used in determination of the LOAEL for Mocarelli et al. (2008) 

10 is more uncertain than the Baccarelli et al. (2008) exposure estimate, the slightly lower LOAEL 

11 of 0.020 ng/kg-day from Mocarelli et al. is designated as the POD. 

12 EPA used a composite UF of 30 for both studies. EPA applied a factor of 10 for UFL to 

account for lack of a NOAEL. EPA also applied a factor of 3 (100.5 13 ) for UFH to account for 

14 human interindividual variability because the effects were elicited in sensitive populations. A 

15 further reduction to 1 was not made because the sample sizes in these two epidemiologic studies 

16 were relatively small, which, combined with uncertainty in exposure estimation, may not fully 

17 capture the range of interindividual variability. The resulting RfD for TCDD in standard units is 

7 × 10−10 18 mg/kg-day (Table 4-7 details the RfD derivation). 

19 

20 WEIGHT-OF-EVIDENCE STATEMENT FOR CARCINOGENICITY 

21 The NAS recommended that EPA update its cancer classification for TCDD and the 

22 weight-of-evidence (WOE) statement to reflect the current state of the science and incorporate 

23 the latest EPA Cancer Guidelines (U.S. EPA, 2005). Several notable new studies addressing 

24 TCDD’s carcinogenic potential have been published since the release of EPA’s 2003 

25 Reassessment, including several new studies of the Seveso epidemiologic cohort and an NTP 

26 2-year cancer bioassay in female rats (NTP, 2006). 

27 Under the 2005 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005) TCDD is 

28 characterized as carcinogenic to humans, based on the available data as of 2009 (see 

29 Section 5.1.2). When evaluating the carcinogenic potential of a compound, EPA employs a 

30 WOE approach in which all available information is evaluated and considered. In the case of 

31 TCDD, EPA based the classification on numerous lines of evidence, including: multiple 
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1 occupationally- and accidentally-exposed epidemiologic cohorts showing an association between 

2 TCDD exposure and certain cancers or increased mortality from all cancers; extensive evidence 

3 of carcinogenicity at multiple tumor sites in both sexes of multiple species of experimental 

4 animals; consensus that the mode of TCDD’s carcinogenic action in animals involves aryl 

5 hydrocarbon receptor (AhR)-dependent key precursor events and proceeds through modification 

6 of one or more of a number of cellular processes; the human AhR and rodent AhR are similar in 

7 structure and function, and human and rodent tissue and organ cultures respond to TCDD in a 

8 similar manner and at similar concentrations; and general scientific consensus that AhR 

9 activation is anticipated to occur in humans and to progress to tumors. 

10 

11 DERIVATION OF CANDIDATE OSFs FROM EPIDEMIOLOGIC STUDIES AND 

12 ANIMAL BIOASSAYS

13 There are several well-studied occupationally-exposed epidemiologic cohorts showing an 

14 association between TCDD and increased all-cancer mortality, and several epidemiologic 

15 cohorts exposed to TCDD as a consequence of industrial accidents showing an association 

16 between TCDD and cancer or cancer mortality (see Section 5.2.3.1). The 2003 Reassessment 

17 included cancer dose-response analyses based on the following three occupational cohorts: the 

18 NIOSH cohort, an occupational cohort subject to chronic TCDD exposures (Steenland et al., 

19 2001); the Hamburg cohort, an occupational cohort also subject to chronic TCDD exposures 

20 (Becher et al., 1998); and the BASF cohort, an occupational cohort subject to peak TCDD 

21 exposures through clean-up following an industrial accident (Ott and Zober, 1996). In this 

22 document, EPA determined that each of these studies met the epidemiologic study inclusion 

23 criteria. Thus, after further evaluating the OSFs presented in the 2003 Reassessment for these 

24 three studies, EPA accepted those OSF estimates and retained them as candidate OSFs in this 

25 document. EPA also determined that two additional studies met the epidemiologic study 

26 inclusion criteria: Cheng et al. (2006) (NIOSH cohort) and Warner et al. (2002) (Seveso cohort, 

27 a cohort exposed environmentally as a consequence of an industrial accident). EPA was unable 

28 to derive a credible OSF from the data presented by Warner et al. (2002) but did derive an OSF 

29 from Cheng et al. (2006), as detailed in Text Box ES-1. In Table ES-1, EPA presents estimates 

of OSFs for specific TCDD intake rates based on target risk levels of 1 × 10−3

, 1 × 10−4, 1 × 10−5 30 , 

1 × 10−6

, and 1 × 10−7 31 based on Cheng et al. (2006). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xlvii DRAFT—DO NOT CITE OR QUOTE 

1 

2 

3 

4 

5 

Text Box ES-1 

OSF Calculations Using Cheng et al. (2006) Information 

Following the estimation of dose using the AUC values developed from the kinetic modeling, Cheng 

and colleagues derived dose-response estimates for the NIOSH cohort. For exposures lagged 15 years, the 

regression coefficient of the linear slope derived by Cheng et al. (2006) was 3.3 × 10−6

 per ppt-year lipidadjusted serum TCDD (the standard error of this regression coefficient was 1.4 × 10−6

). The upper 5% of the 

exposure range (individuals ≥252,950 ppt-year lipid adjusted serum TCDD) was excluded in estimating this 

slope. Because this exclusion reduces the upper portion of the response where the slope is shallow, this likely 

better represents the slope in the region of the curve where the fatal cancer risk is increasing with dose, which is 

the equivalent of dropping the highest dose in an animal bioassay. 

To develop an OSF for TCDD, EPA used information from Cheng et al. (2006) in its calculations as 

follows: 

• Upper 95th percentile slope (β95) of 6.0 × 10−6

 per ppt-year lipid adjusted serum TCDD. 

• Background cancer mortality risk estimate (R0) of 0.112. 

• Risk in the exposed group associated with a 1% extra risk of fatal cancer (Rexp ) of 0.12088. 

• Incremental cancer mortality risk in the exposed population based on a 1% extra risk (RD) of 

8.9 × 10−3 using the equation, RD = Rexp − R0

• Cumulative TCDD concentration in the fat compartment for a 1% extra risk (AUC01) using the 

following formula corresponding to equations used in Cheng et al. (2006): 

AUC01 = LN(RD + R0/R0)/β95

Cheng et al. (2006) appear to assume that the TCDD concentration in fat is the same as ppt-yr lipid 

adjusted serum concentration. 

• OSF associated with 1% extra risk [OSF(AUC01)], calculated to be 7.92 × 10−7

 by dividing 0.01 by the 

AUC01. OSF(AUC01) is linear with the TCDD concentration in fat. Ingested TCDD doses, however, 

are not linear with the predicted TCDD fat concentrations in the Emond pharmacokinetic model. 

Thus, the OSF(AUC01) that is linear with TCDD in the fat is not linear with ingested TCDD dose. 

EPA calculated estimates of OSFs for specific TCDD intake rates based on target risk levels (RLs) of 1 × 10−3

, 

1 × 10−4

, 1 × 10−5, 1 × 10−6, and 1 × 10−7, using the following calculations: 

• Area under the TCDD fat concentration curve associated with a target risk level (AUCRL) (ppt-yr) [i.e., 

RL/7.92 × 10-7 (ppt-yr)-1]. 

• Lifetime averaged concentration of TCDD in the fat compartment associated with the target risk level 

(FATRL) (ng/kg). The AUCRL estimates were then further divided by 70 years to identify (FATRL) 

(ng/kg). This step essentially reverses the integration undertaken to calculate AUC01. 

• Continuous daily TCDD intake (DRL)(ng/kg-day) associated with a target risk level over a lifetime. 

Using the Emond pharmacokinetic model, EPA estimated the DRL necessary to achieve the FATRL. 

• Oral slope factor at the target risk level (OSFRL) (per mg/kg-day). At the target risk levels, the 

associated OSFRLs range from 3.7 × 105

 to 1.3 × 106 per mg/kg-day. These are calculated as 

OSFRL = RL/DRL × 106

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

1/15/10 xlviii DRAFT—DO NOT CITE OR QUOTE 

1 EPA also identified candidate OSFs for TCDD from key animal bioassays (see 

2 Section 5.2.3.2). Based on the inclusion criteria, EPA selected four key rodent cancer bioassays 

3 suitable for quantitative dose-response assessment. These included Kociba et al. (1978), NTP 

4 (1982), and Toth et al. (1979) that were evaluated in the 2003 Reassessment, and the new NTP 

5 (2006) rat chronic bioassay. EPA conducted dose-response modeling for each tumor type 

6 separately (individual tumor models) as well as composite tumor incidence dose estimates 

7 (multiple tumor models). The tumor types that EPA analyzed are shown in Table ES-2. 

8 For each in vivo animal cancer study that qualified for TCDD dose-response assessment, 

9 EPA selected the species/sex/tumor dataset combinations characterized as having statistically 

10 significant increases in tumor incidences, then used the Emond rodent PBPK model to estimate 

11 blood concentrations corresponding to each study’s average daily administered dose for use in 

12 dose-response modeling. BMDL01s were then estimated for the blood concentration by two 

13 different methodologies: (1) using the multistage cancer model for each species/sex/tumor 

14 combination within each study, and (2) using a Bayesian Markov Chain Monte Carlo framework 

15 that assumes independence of tumors, modeling all tumors together for each species/sex 

16 combination within each study. The final selected models were subjected to goodness-of-fit tests 

17 and visual inspection of fit to the raw data. Thus, for each sex/species combination within each 

18 study, EPA generated a BMDL01 for each single tumor type and another BMDL01 for the 

19 combined tumors. Using the Emond human PBPK model, BMDLHEDs were then calculated for 

20 each of the BMDL01s, and using a linear extrapolation, OSFs were calculated by 

21 OSF = 0.01/BMDLHED The highest OSF for a species/sex combination for either a single tumor 

22 type or all combined tumors was selected as a candidate OSF. The OSF candidates from the key 

23 animal bioassays are shown in Table ES-2. 

24 

25 DERIVATION OF TCDD ORAL SLOPE FACTOR AND RISK ESTIMATES 

26 EPA was able to derive OSFs for tumor incidence data from four animal cancer 

27 bioassays, as well as cancer mortality data from four epidemiological cohort studies that were 

28 selected for TCDD dose-response modeling using the study inclusion criteria (see Section 5.3). 

29 For the animal data, OSFs based on individual tumors were developed for 25 study/sex/endpoint 

combinations, and the results ranged from 1.8 × 104

 to 5.9 × 106 30 (per mg/kg-day). The OSFs 

31 based on combined tumors were developed for seven study/sex combinations, and the results 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 xlix DRAFT—DO NOT CITE OR QUOTE 

ranged from 3.2 × 105

 to 9.4 × 106 1 (per mg/kg-day). EPA also developed OSFs based on four 

epidemiologic studies from three cohorts, ranging from 3.75 × 105 to 2.5 × 106 2 (per mg/kg-day). 

3 EPA has chosen to use the human data over the animal data as recommended by expert 

4 panelists at EPA’s 2009 Dioxin Workshop (U.S. EPA, 2009c) and in the 2005 Cancer Guidelines 

5 (U.S. EPA, 2005). OSFs derived from the human data are consistent with the animal bioassay 

6 results; human OSFs fall within the same range as the animal bioassay OSFs. 

7 Among the human studies, the occupational TCDD exposures in the NIOSH and 

8 Hamburg cohorts are assumed to be reasonably constant over the duration of occupational 

9 exposure. In contrast, the TCDD exposure pattern for the Seveso and BASF accidents is acute, 

10 high dose, followed by low-level background exposure. Such exposure patterns similar to those 

11 experienced by the BASF and Seveso cohorts have been shown to yield higher estimates of risk 

12 when compared to constant exposure scenarios with similar total exposure magnitudes (Kim et 

13 al. 2003; Murdoch and Krewski, 1988; Murdoch et al, 1992). Thus, EPA has judged that the 

14 NIOSH and Hamburg cohort response data are more relevant than the BASF and Seveso data for 

15 assessing cancer risks from continuous ambient TCDD exposure in the general population. 

16 The NIOSH (Steenland et al. 2001; Cheng et al. 2006) and Hamburg (Becher et al. 1998) 

17 cohort studies report cumulative TCDD levels in the serum for cohort members. The most 

18 significant difference among the Cheng et al. (2006) analysis and those of Steenland et al. (2001) 

19 and Becher et al. (1998) is the method used to back-extrapolate exposure concentrations based 

20 on serum TCDD measurements. Steenland et al. (2001) and Becher et al. (1998) back21 extrapolated exposures and body burdens using a first-order model with a constant half-life. In 

22 contrast, Cheng et al. (2006) back-extrapolated body burdens using a kinetic modeling approach 

23 that incorporated concentration- and age-dependent elimination kinetics. 

24 Although all three of these are high-quality studies, the kinetic modeling used by Cheng 

25 et al. (2006) is judged to better reflect TCDD pharmacokinetics, as currently understood, than the 

26 first-order models used by Steenland et al. (2001) and Becher et al. (1998). EPA believes that 

27 the representation of physiological processes provided by Cheng et al (2006) is more realistic 

28 than the assumption of simple first-order kinetics and this outweighs the attendant modeling 

29 uncertainties. Furthermore, the use of kinetic modeling is consistent with recommendations both 

30 by the NAS and the Dioxin Workshop panel. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 l DRAFT—DO NOT CITE OR QUOTE 

1 Therefore EPA has selected the results from the Cheng et al. (2006) study for derivation 

2 of the TCDD OSF (see Section 5.3). Table ES-1 shows the oral slope factors at specific target 

risk levels (OSFRLs) which range from 3.7 × 105

 to 1.3 × 106 3 per (mg/kg-day). EPA 

recommends the use of an OSF of 1.3 × 106

 per (mg/kg-day) when the target risk range is 10−5 4 to 

10−7. 5 

6 

7 CONSIDERATION OF NONLINEAR DOSE-RESPONSE APPROACHES FOR 

8 CANCER 

9 The NAS focused much of its review on EPA’s derivation of a cancer slope factor, 

10 commenting extensively on the extrapolation of dose-response modeling below the POD. The 

11 NAS questioned EPA’s choice of a linear, nonthreshold model for extrapolating risk associated 

12 with exposure levels below the POD, concluding that the current scientific evidence was 

13 sufficient to justify the use of nonlinear methods when extrapolating below the POD for dioxin 

14 carcinogenicity. 

15 While, based on the 2005 Cancer Guidelines, EPA deemed linear extrapolation to be 

16 most appropriate for TCDD, EPA carefully considered the NAS recommendation to provide risk 

17 estimates using both linear and nonlinear methods. In this document, EPA has evaluated the 

18 information available for identifying a threshold and for estimating the shape of the dose19 response curve below the POD (see Section 5.2.3.4). EPA presents a hypothetical sublinear 

20 dose-response modeling example of rodent carcinogenicity. EPA also presents two illustrative 

21 examples of RfD development (i.e., nonlinear method) for carcinogenic effects of TCDD, using 

22 data derived from animal bioassays. EPA derives illustrative RfDs for cancer based on 

23 combined tumor response and also on hypothesized key events in TCDD’s MOA for female rat 

24 liver and lung tumors. EPA identifies a number of limitations that prevent making strong 

25 conclusions based on the nonlinear dose-response modeling exercises. 

 26 

27 FEASIBILITY OF QUANTITATIVE UNCERTAINTY ANALYSIS 

28 EPA also addresses the third key recommendation of the NAS, specifically, improving 

29 transparency, thoroughness, and clarity in quantitative uncertainty analysis (see Section 6). In 

30 summary, NAS suggested that EPA should 

 31 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 li DRAFT—DO NOT CITE OR QUOTE 

1 • describe and define (quantitatively to the extent possible) the variability and 

2 uncertainty for key assumptions used for each key endpoint-specific risk 

3 assessment (choices of data set, POD, model, and dose metric), 

4 • incorporate probabilistic models to the extent possible to represent the range of 

5 plausible values, 

6 • clearly state it when quantitation is not possible and explain what would be 

7 required to achieve quantitation (NAS, 2006a, p. 9). 

8 

9 Although the NAS summarized the shortfalls in the 2003 Reassessment categorically, the 

10 elaborations within their report often contain the qualification “if possible” and do not take a 

11 position with regard to the feasibility of many suggestions. With appreciation for the extent of 

12 information available for dioxin, EPA’s goal herein was to examine the feasibility of a 

13 data-driven quantitative uncertainty analysis for TCDD dose-response assessment. 

14 In examining feasibility of quantitative uncertainty analysis, EPA recognized that 

15 different kinds of uncertainty require different statistical treatment. Cognitive uncertainty

16 concerns uncertainty that can be expressed as probabilities and may be operationalized using 

17 either frequentist or Bayesian approaches. For example, classical statistical methods yield 

18 distributions on model parameters which reflect sample fluctuations, assuming that the model is 

19 true. This type of uncertainty can be taken into account in the BMDL estimation. Also, for 

20 TCDD epidemiologic data, the dose reconstruction often involves assumptions that may be 

21 amenable to data-driven uncertainty analysis if sufficient data can be retrieved; back22 extrapolated TCDD levels, biological half-life, body fat, and background levels are example 

23 variables that could be included in such an analysis. In addition, a Monte Carlo analysis has 

24 been examined to develop quantitative uncertainty distributions for the RfD (e.g., Swartout et al., 

25 1998). Given a set of animal bioassay data, quantifying dose-response uncertainty may be 

26 approached in different ways. The differences reflect different types of uncertainty that are 

27 captured. A recent evaluation enumerates the following possible methodologies (Bussard et al., 

28 2009): 

 29 

30 Benchmark Dose Modeling (BMD): Choose the ‘best’ model, and 

31 assess uncertainty assuming this model is true. Supplemental results can compare 

32 estimates obtained with different models, and sensitivity analyses can investigate 

33 other modeling issues. 
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1 Probabilistic Inversion with Isotonic Regression (PI-IR): Define 

2 model-independent ‘observational’ uncertainty, and look for a model that captures 

3 this uncertainty by assuming the selected model is true and providing for a 

4 distribution over its parameters. 

5 Non-Parametric Bayes (NPB): Choose a prior mean response (potency) 

6 curve (potentially a “non-informative prior”) and a precision parameter to express 

7 prior uncertainty over all increasing dose-response relations, and update this prior 

8 distribution with the bioassay data. 

9 Bayesian Model Averaging (BMA) (as considered here): Choose an 

10 initial set of models, and then estimate the parameters of each model with 

11 maximum likelihood. Use classical methods to estimate parameter uncertainty, 

12 given the truth of the model. Determine a probability weight for each model 

13 using the Bayes Information Criterion (BIC), and use these weights to average the 

14 model results. 

15 

16 The first of the above methods involves standard classical statistical methods and captures 

17 sampling uncertainty conditional on the truth of the model used. The other methods are “exotic” 

18 in the sense that they attempt to capture uncertainty that is not conditional on the truth of a given 

19 model. In this response document, EPA has not applied such methods, but recognizes that 

20 quantitative uncertainty analysis is possible in these cases. 

21 In contrast to cognitive uncertainty, Volitional uncertainty concerns uncertainty regarding 

22 choices on the best course of action to take; volitional uncertainty cannot be analyzed by 

23 sampling from a probability distribution and, thus, is not amenable to a complete quantitative 

24 uncertainty analysis. Some of the choices made in TCDD dose-response assessment that are 

25 volitional include: choice of occupational cohort data set or bioassay data set; choice of PODs 

26 (e.g., ED01, ED05, and ED10); choice of species, strain, or sex within an animal bioassay; and 

27 choice of dose metric (e.g., administered doses, blood concentrations, lipid-adjusted serum 

28 concentrations). These volitional uncertainties cannot be quantified by sampling an input 

29 distribution. However, EPA believes that NAS was requesting that dose-response modeling 

30 results be shown for specific choices of interest to TCDD assessment. To this end, for the cancer 

31 dose-response modeling, BMDLs are reported for 1, 5, and 10% extra risk levels, and a 

32 comparison is provided of linear and nonlinear dose-response assessments for cancer. For the 

33 noncancer dose-response modeling, different model forms are run and contrasted. Finally, 

34 TCDD kinetic doses from the Emond et al. (2005, 2006) PBPK model that is primarily used in 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 liii DRAFT—DO NOT CITE OR QUOTE 

1 the technical analysis in this document are compared with those predicted by the Aylward et al. 

2 (2005a) model. 

3 Uncertainty quantification is an emerging area in science. There are many examples of 

4 highly vetted and peer-reviewed uncertainty analyses based on structured expert judgment. 

5 Under this process, experts in effect synthesize a wide diversity of information in generating 

6 their subjective probability distributions. Where considerable data exist for an environmental 

7 pollutant, such as for the well-studied TCDD, it is natural to ask whether these extensive data can 

8 be leveraged more directly in uncertainty quantification. This is an area where research could be 

9 focused. Additional research topics relevant to dioxin that could further inform health 

10 assessments include population variability of biokinetic constants and threshold mechanisms for 

11 the mass action model. Further data and improved methodologies in these areas, combined with 

12 developments illustrated elsewhere in this report, will help reduce or better quantify uncertainties 

13 and strengthen EPA’s understanding of potential health implications of environmental TCDD 

14 exposures. 

15 
1 Table ES-1. Comparison of lipid-adjusted serum concentrations, fat 

2 concentrations, risk specific dose estimates and equivalent oral slope factors 

based on upper 95th 3 percentile estimate of regression coefficienta of all fatal 

cancers reported by Cheng et al. (2006) for risk levels of 1 × 10−3

, 1 × 10−4 4 , 

1 × 10−5

, 1 × 10−6, and 1 × 10−7 5 

6 

Risk level 

(RL) 

AUCRL, 

(ppt-yr) 

FATRL

(ng/kg) 

Risk specific dose 

(DRL) (ng/kg-day) 

Equivalent oral slope 

factors (OSFRL) per 

(mg/kg-day) 

1 × 10−3 1.26 × 10−3 1.803 × 101 2.73 × 10−3 3.7 × 105

1 × 10−4 1.26 × 102 1.803 × 10 1.23 × 10−4 8.1 × 105

1 × 10−5 1.26 × 101 1.803 × 10−1 8.57 × 10−6 1.2 × 106

1 × 10−6 1.26 × 10 1.803 × 10−2 7.77 × 10−7 1.3 × 106

1 × 10−7 1.26 × 10−1 1.803 × 10−3 7.62 × 10−8 1.3 × 106

7 a 8 Based on regression coefficient of Cheng et al. (2006; Table III), excluding observations in the upper 5% range 

(≥252,950 ppt-year lipid adjusted serum TCDD) of the exposures; where reported β = 3.3 × 10−6 9 ppt-years and 

standard error = 1.4 × 10−6 10 . Upper 95th percentile estimate of regression coefficient (β95) calculated to be: 

6.04 × 10−6 = (3.3 × 10−6) + 1.96 × (1.4 × 10−6 11 ). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 lv DRAFT—DO NOT CITE OR QUOTE 

1 Table ES-2. Tumor points of departure and oral slope factors using blood 

2 concentrations 

3 

Study Sex/species: tumor sites 

BMDLHEDa

(ng/kg-day) 

OSF 

(per mg/kg-day)

NTP, 1982 Male mice: liver adenoma and carcinoma, 

lung 

1.1E−03 9.4E+6 

Toth et al., 1979 Male mice: liver tumors 1.9E−03 5.2E+6 

NTP, 1982 Female mice: liver adenoma and carcinoma, 

thyroid adenoma, subcutaneous 

fibrosarcoma, all lymphomas 

5.3E−03 1.9E+6 

NTP, 1982 Female rats: liver neoplasitc nodules, liver 

adenoma and carcinoma, adrenal cortex 

adenoma or carcinoma, thyroid follicular cell 

adenoma 

5.7E−03 1.8E+6 

Kociba et al., 

1978 

Female rats: liver adenoma carcinoma, oral 

cavity, lung 

7.3E−03 1.4E+6 

NTP, 1982 Male rats: thyroid follicular cell adenoma, 

adrenal cortex adenoma 

9.6E−03 1.0E+6 

NTP, 2006 Female rats: liver cholangiocarcinoma, 

hepatocellular adenoma, oral mucosa 

squamous cell carcinoma, lung cystic 

keratinizing epithelioma, pancreas adenoma, 

carcinoma 

2.3E−02 4.4E+5 

Kociba et al., 

1978 

Male rats: adrenal cortex adenoma, tongue 

carcinoma, nasal/palate carcinoma 

3.1E−02 3.2E+5 

4 a 5 BMDLHEDs are from the multiple tumor analyses, with the exception of Toth et al. (1979) which is the result of 

6 modeling a single tumor site. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 lvi DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure ES-1. EPA’s process to evaluate available epidemiologic studies using 

3 study inclusion criteria for use in the dose-response analysis of TCDD. EPA 

4 applied its TCDD-specific epidemiologic study inclusion criteria to all studies published 

5 on TCDD and DLCs. The studies were initially evaluated using five considerations 

6 regarded as providing the most relevant kind of information needed for quantitative 

7 human health risk analyses. For each study that satisfied most of these considerations 

8 and was published in the peer-reviewed literature, EPA then examined whether the 

9 exposures were primarily to TCDD and if the TCDD exposures could be quantified so 

10 that dose-response analyses could be conducted. Finally, EPA required that the effective 

11 dose and oral exposure be estimable: (1) for cancer, information is required on long-term 

12 exposures, (2) for noncancer, information is required regarding the appropriate time 

13 window of exposure that is relevant for a specific, nonfatal health endpoint, and (3) for 

14 all endpoints, the latency period between TCDD exposure and the onset of the effect is 

15 needed. Only studies meeting these criteria were included in EPA’s TCDD dose16 response analysis. 

List of available epidemiologic studies on TCDD and DLCs

Evaluate study using five considerations:

• Methods used to ascertain health outcomes are clear and unbiased.

• Confounding exposures or other sources of bias are controlled for.

• Dose-response is apparent between TCDD and adverse health effect(s).

• Exposure methods are described including exposure duration and latency. 

• Statistical precision and power are sufficient.

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Exposure 

windows and 

latency information 

available for RfD 

assessment?

Key study included 

for TCDD noncancer

dose-response 

assessment

Key study included

for TCDD cancer

dose-response

assessment

Long-term 

exposures and 

latency information 

available for cancer 

assessment?

Exposure 

primarily to TCDD 

and quantified?

No No

Most

elements of the five

considerations 

satisfied?

No

Yes

Yes

Yes

List of available epidemiologic studies on TCDD and DLCs

Evaluate study using five considerations:

• Methods used to ascertain health outcomes are clear and unbiased.

• Confounding exposures or other sources of bias are controlled for.

• Dose-response is apparent between TCDD and adverse health effect(s).

• Exposure methods are described including exposure duration and latency. 

• Statistical precision and power are sufficient.

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Exposure 

windows and 

latency information 

available for RfD 

assessment?

Key study included 

for TCDD noncancer

dose-response 

assessment

Key study included

for TCDD cancer

dose-response

assessment

Long-term 

exposures and 

latency information 

available for cancer 

assessment?

Exposure 

primarily to TCDD 

and quantified?

No No

Most

elements of the five

considerations 

satisfied?

No

Yes

Yes

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

1/15/10 lvii DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure ES-2. EPA’s process to evaluate available animal bioassay studies using 

3 study inclusion criteria for use in the dose-response analysis of TCDD. EPA 

4 evaluated all available in vivo mammalian bioassay studies on TCDD. Studies had to be 

5 published in the peer-reviewed literature. Next, to ensure working in the low-dose range 

6 for TCDD dose-response analysis, EPA applied dose requirements to the lowest tested 

7 average daily doses in each study, with specific requirements for cancer (≤1 μg/kg-day), 

8 and noncancer (≤30 ng/kg-day) studies. Third, EPA required that the animals were 

9 exposed via the oral route to only TCDD and that the purity of the TCDD was specified. 

10 Finally, the studies were evaluated using four considerations regarded as providing the 

11 most relevant kind of information needed for quantitative human health risk analyses 

12 from animal bioassay data. Only studies meeting all of these criteria and considerations 

13 were included in EPA’s TCDD dose-response analysis. 

List of available in vivo mammalian bioassay studies on TCDD

Evaluate study further using four considerations:

• Strain, gender, and age of test species is identified.

• Testing protocol, including duration and timing of dosing is clear.

• Study design is consistent with standard toxicological practices.

• Magnitude of animal responses is outside range of normal variability. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Lowest dose 

tested for noncancer 

endpoint <30

ng/kg-day?

Key study included 

for TCDD cancer and/or noncancer

dose-response assessment

Lowest 

dose tested for

cancer endpoint <1

μg/kg-day?

Oral

exposure to TCDD 

only with purity

specified?

No No

Were

elements of the four

considerations 

satisfied?

No

Yes

Yes

Yes

No

List of available in vivo mammalian bioassay studies on TCDD

Evaluate study further using four considerations:

• Strain, gender, and age of test species is identified.

• Testing protocol, including duration and timing of dosing is clear.

• Study design is consistent with standard toxicological practices.

• Magnitude of animal responses is outside range of normal variability. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Lowest dose 

tested for noncancer 

endpoint <30

ng/kg-day?

Key study included 

for TCDD cancer and/or noncancer

dose-response assessment

Lowest 

dose tested for

cancer endpoint <1

μg/kg-day?

Oral

exposure to TCDD 

only with purity

specified?

No No

Were

elements of the four

considerations 

satisfied?

No

Yes

Yes

Yes

No
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 lviii DRAFT: DO NOT CITE OR QUOTE 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02

Alaluusua et al., 2004 (H)

Baccarelli et al., 2008 (H)

Moccarelli et al., 2008 (H)

Li et al., 1997 (R)

Crofton et al., 2005 (R)

White et al., 1986 (M)

Chu et al., 2007 (R)

Fattore et al., 2000 (R)

Seo et al., 1995 (R)

Smialowicz et al., 2008 (M)

Van Birgelen et al., 1995a (R)

Sewall et al., 1995 (R)

Cantoni et al., 1981 (R)

NTP, 1982 (M)

Kociba et al., 1978 (R)

Toth et al., 1979 (M)

NTP, 2006 (R)

Latch. and Mathur, 2002 (R)

Shi et al., 2007 (R)

Murray et al., 1979 (R)

Hojo et al., 2002 (R)

Kattainen et al., 2001 (R)

Keller et al., 2007, 2008a,b (M)

2,3,7,8-TCDD Ingestion Rate

(Human-equivalent dose, ng/kg-d)

Less than 

1 year

Greater than 

Human 1 year D

Reproductive 

1 2 Figure ES-3. Exposure-response array for ingestion exposures to TCDD. 3 
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1/15/10 DRAFT lix —DO NOT CITE OR QUOTE 

1 

2 Figure ES-4. EPA’s process to select and identify candidate PODs from key animal 

3 bioassays for use in noncancer dose-response analysis of TCDD. For each noncancer 

4 endpoint found in the studies that qualified for TCDD dose-response assessment using 

5 the study inclusion criteria, EPA first determined if the endpoint was toxicologically 

6 relevant. If so, EPA determined the NOAEL, LOAEL, and BMDL Human Equivalent 

Dose (HED) based on 1st 7 -order body burdens for each endpoint. These potential PODs 

8 were examined for statistical relevance and included when the endpoint was observed at 

9 the LOAEL. If the BMDL was less than the LOAEL, and if the endpoint was less than 

10 the minimum LOAEL × 100, EPA then calculated NOAELs, LOAELs, or BMDLs based 

11 on blood concentrations from the Emond rodent PBPK model. Then, for all of the 

12 candidate PODs, HEDs were estimated using the Emond human PBPK model. Finally, 

13 the lowest group of the toxicologically relevant candidate PODs was selected for final 

14 use in derivation of an RfD. 

Include NOAEL/LOAEL/BMDL 

as a POD candidate

Exclude endpoint 

as a POD candidate

List of key noncancer animal studies for 

quantitative dose-response analysis of TCDD

No

Determine NOAEL, LOAEL, and BMDL (if possible) human equivalent dose 

(HED) based on 1st-order body burden for each study/endpoint combination

Estimate a Human Equivalent Dose (HED) 

corresponding to each blood concentration NOAEL, LOAEL, or BMDL 

using the Emond human PBPK model

Is the endpoint less 

than the minimum 

LOAEL X 100?

Determine a NOAEL, LOAEL, and BMDL (if possible) for each 

study/endpoint combination, based on blood concentrations from the 

Emond rodent PBPK model

Is the 

endpoint observed

at the LOAEL?

No

Yes

Is the 

endpoint under consideration 

toxicologically

relevant?

Is the BMDL less 

No than the LOAEL?

Yes

Yes

No

Include NOAEL/LOAEL/BMDL 

as a POD candidate

Exclude endpoint 

as a POD candidate

List of key noncancer animal studies for 

quantitative dose-response analysis of TCDD

No

Determine NOAEL, LOAEL, and BMDL (if possible) human equivalent dose 

(HED) based on 1st-order body burden for each study/endpoint combination

Estimate a Human Equivalent Dose (HED) 

corresponding to each blood concentration NOAEL, LOAEL, or BMDL 

using the Emond human PBPK model

Is the endpoint less 

than the minimum 

LOAEL X 100?

Determine a NOAEL, LOAEL, and BMDL (if possible) for each 

study/endpoint combination, based on blood concentrations from the 

Emond rodent PBPK model

Is the 

endpoint observed

at the LOAEL?

No

Yes

Is the 

endpoint under consideration 

toxicologically

relevant?

Is the BMDL less 

No than the LOAEL?

Yes

Yes

No

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

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1 1. INTRODUCTION 

 2 

 3 

4 Dioxins and dioxin-like compounds (DLCs), including polychlorinated dibenzo-dioxins, 

5 polychlorinated dibenzofurans, and polychlorinated biphenyls are structurally and 

toxicologically related halogenated dicyclic aromatic hydrocarbons.4 6 Dioxins and DLCs are 

7 released into the environment from several industrial sources such as chemical manufacturing, 

8 combustion, and metal processing; from individual activities including the burning of household 

9 waste; and from natural processes such as forest fires and volcanic activity. Dioxins and DLCs 

10 are widely distributed throughout the environment and typically occur as chemical mixtures. 

11 Additionally, they do not readily degrade; therefore, levels persist in the environment, build up in 

12 the food chain, and accumulate in the tissues of animals. Human exposure to these compounds 

13 occurs primarily through the ingestion of contaminated foods (Lorber et al., 2009). 

14 The health effects from exposures to dioxins and DLCs have been documented 

15 extensively in epidemiological and toxicological studies. 2,3,7,8-Tetrachlorodibenzo-p-dioxin 

16 (TCDD) is one of the most toxic members of this class of compounds and has a robust 

17 toxicological database. Characterization of TCDD toxicity is critical to the risk assessment of 

18 mixtures of dioxins and DLCs because it has been selected repeatedly as the “index chemical” to 

19 serve as the basis for standardization of the toxicity of components in a mixture of dioxins and 

20 DLCs. The dose-response information for TCDD is used to evaluate risks from exposure to 

21 mixtures of DLCs (Van den Berg et al., 1998, 2006; also see the World Health Organization’s 

Web site for the dioxin toxicity equivalence factors [TEFs]),5 22 therefore, it is imperative to 

23 correctly assess the dose response of TCDD and understand the uncertainties and limitations 

24 therein. 

25 In 2003, the U.S. Environmental Protection Agency (EPA) produced an external review 

26 draft of the multiyear comprehensive reassessment of dioxin exposure and human health effects 

27 entitled, Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin 

28 (TCDD) and Related Compounds (U.S. EPA, 2003). This draft report, herein called the “2003 

29 Reassessment,” consisted of (1) a scientific review of information relating to sources of and 

30 exposures to TCDD, other dioxins, and DLCs in the environment; (2) detailed reviews of 

 

4

For further information on the chemical structures of these compounds, see U.S. EPA (2003, 2008a). 5

Available at http://www.who.int/ipcs/assessment/tef_update/en/. 
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1 scientific information on the health effects of TCDD, other dioxins, and DLCs; and (3) an 

2 integrated risk characterization for TCDD and related compounds. 

3 In 2004, EPA asked the National Research Council of the National Academy of Sciences 

4 (NAS) to review the 2003 Reassessment. The NAS Statement of Task was as follows 

 5 

 6 

7 In 2006, the NAS published its review of EPA’s 2003 Reassessment entitled Health Risks from 

8 Dioxin and Related Compounds: Evaluation of the EPA Reassessment (NAS, 2006a). 

 9 

10 1.1. SUMMARY OF KEY NAS (2006a) COMMENTS ON DOSE-RESPONSE 

11 MODELING IN THE 2003 REASSESSMENT 

12 While recognizing the effort that EPA expended to prepare the 2003 Reassessment, the 

13 NAS committee identified three key areas that they believe require substantial improvement to 

14 support a scientifically robust risk assessment. These three key areas are 

 15 

The National Academies’ National Research Council will convene an expert committee that will 

review EPA’s 2003 draft reassessment of the risks of dioxins and dioxin-like compounds to 

assess whether EPA’s risk estimates are scientifically robust and whether there is a clear 

delineation of all substantial uncertainties and variability. To the extent possible, the review will 

focus on EPA’s modeling assumptions, including those associated with the dose-response curve 

and points of departure; dose ranges and associated likelihood estimates for identified human 

health outcomes; EPA’s quantitative uncertainty analysis; EPA’s selection of studies as a basis 

for its assessments; and gaps in scientific knowledge. The study will also address the following 

aspects of EPA’s 2003 Reassessment: (1) the scientific evidence for classifying dioxin as a 

human carcinogen; and (2) the validity of the nonthreshold linear dose-response model and the 

cancer slope factor calculated by EPA through the use of this model. The committee will also 

provide scientific judgment regarding the usefulness of toxicity equivalence factors (TEFs) in 

the risk assessment of complex mixtures of dioxins and the uncertainties associated with the use 

of TEFs. The committee will also review the uncertainty associated with the 2003 

Reassessment’s approach regarding the analysis of food sampling and human dietary intake 

data, and, therefore, human exposures, taking into consideration the Institute of Medicine’s 

report Dioxin and Dioxin-Like Compounds in the Food Supply: Strategies to Decrease 

Exposure. The committee will focus particularly on the risk characterization section of EPA’s 

2003 Reassessment report and will endeavor to make the uncertainties in such risk assessments 

more fully understood by decision makers. The committee will review the breadth of the 

uncertainty and variability associated with risk assessment decisions and numerical choices, 

including, for example, modeling assumptions, including those associated with the doseresponse curve and points of departure. The committee will also review quantitative uncertainty 

analyses, as feasible and appropriate. The committee will identify gaps in scientific knowledge 

that are critical to understanding dioxin reassessment (NAS, 2006a, p. 43, Box 1-1).
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 • transparency and clarity in selection of key data sets for analysis; 

2 • justification of approaches to dose-response modeling for cancer and noncancer 

3 endpoints; and 

4 • transparency, thoroughness, and clarity in quantitative uncertainty analysis. 

 5 

6 In their Public Summary, the NAS made the following overall recommendations to aid 

7 EPA in addressing their key concerns: 

 8 

9 • EPA should compare cancer risks by using nonlinear models consistent with a receptor 

10 mediated mechanism of action and by using epidemiological data and the new National 

11 Toxicology Program (NTP) animal bioassay data (NTP, 2006). The comparison should 

12 include upper and lower bounds, as well as central estimates of risk. EPA should clearly 

13 communicate this information as part of its risk characterization (NAS, 2006a, p. 9). 

14 • EPA should identify the most important data sets to be used for quantitative risk 

15 assessment for each of the four key end points (cancer, immunotoxicity, reproductive 

16 effects, and developmental effects). EPA should specify inclusion criteria for the studies 

17 (animal and human) used for derivation of the benchmark dose (BMD) for different 

18 noncancer effects and potentially for the development of RfD (reference dose) values and 

19 discuss the strengths and limitations of those key studies; describe and define 

20 (quantitatively to the extent possible) the variability and uncertainty for key assumptions 

21 used for each key end-point-specific risk assessment (choices of data set, POD [point of 

22 departure], model, and dose metric); incorporate probabilistic models to the extent 

23 possible to represent the range of plausible values; and assess goodness-of-fit of 

24 dose-response models for data sets and provide both upper and lower bounds on central 

25 estimates for all statistical estimates. When quantitation is not possible, EPA should 

26 clearly state it and explain what would be required to achieve quantitation (NAS, 2006a, 

27 p. 9). 

28 • When selecting a BMD as a POD, EPA should provide justification for selecting a 

29 response level (e.g., at the 10%, 5%, or 1% level). In either case, the effects of this 

30 choice on the final risk assessment values should be illustrated by comparing point 

31 estimates and lower bounds derived from selected PODs (NAS, 2006a, p. 9). 

32 • EPA should continue to use body burden as the preferred dose metric but should also 

33 consider physiologically based pharmacokinetic modeling as a means to adjust for 

34 differences in body fat composition and for other differences between rodents and 

35 humans (NAS, 2006a, p. 9). 

36 • Although EPA addressed many sources of variability and uncertainty qualitatively, the 

37 committee noted that the 2003 Reassessment would be substantially improved if its risk 

38 characterization included more quantitative approaches. Failure to characterize 

39 variability and uncertainty thoroughly can convey a false sense of precision in the 

40 conclusions of the risk assessment (NAS, 2006a, p. 5). 
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1 Importantly, the NAS encouraged EPA to calculate an RfD as the 2003 Reassessment 

2 does not contain an RfD derivation. The committee suggested that 

3 

4 ...estimating an RfD would provide useful guidance to risk managers to help 

5 them (1) assess potential health risks in that portion of the population with intakes 

6 above the RfD, (2) assess risks to population subgroups, such as those with 

7 occupational exposures, and (3) estimate the contributions to risk from the major 

8 food sources and other environmental sources of TCDD, other dioxins, and DLCs 

9 for those individuals with high intakes (NAS, 2006a, p. 6). 

10 

11 The NAS made many thoughtful and specific recommendations throughout their review; 

12 additional NAS recommendations and comments pertaining to the dose-response assessment of 

13 TCDD will be presented and addressed in various sections throughout this document. 

 14 

15 1.2. EPA’S SCIENCE PLAN 

16 In response to recommendations presented in the 2006 NAS review, EPA Administrator 

17 Jackson released EPA’s “Science Plan for Activities Related to Dioxins in the Environment” 

(“Science Plan”) on May 26, 2009.6 18 There are five key components of the Science Plan that 

19 pertain to EPA’s response to the NAS comments on TCDD dose-response assessment: 

 20 

21 1. EPA will release a draft report that responds to the recommendations and comments 

22 included in the NAS review of EPA’s 2003 Reassessment. 

23 • EPA’s National Center for Environmental Assessment in the Office of Research and 

24 Development, will prepare a limited response to key comments and recommendations 

25 in the NAS report (draft response to comments report). 

26 • The draft response will focus on dose-response issues raised by the NAS and will 

27 include an analysis of relevant new key studies. 

28 • The draft response will be provided for public review and comment and independent 

29 external peer review. 

30 • The draft response will also include an evaluation of some of the significant 

31 recommendations that are difficult for EPA to address given the current state of 

32 science, and a detailed rationale for these conclusions. 

33 • The peer review will be conducted by EPA Science Advisory Board, an independent 

34 review body chartered under the Federal Advisory Committee Act. 

 

6

Available at http://www.epa.gov/dioxin/scienceplan. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 • The draft response to comments report will be completed and released for public and 

2 peer review by December 31, 2009. 

3 2. EPA will provide the draft response to comments report for internal and external review. 

4 3. The Science Advisory Board will review the science content of the response to comments 

5 report. 

6 4. EPA will review impacts of the draft response to comments report on its 2003 

7 Reassessment. 

8 5. EPA will release the final response to comments report and focus on completion of the 

9 2003 Reassessment. 

 10 

11 This document comprises EPA’s report that responds to the recommendations and 

12 comments on TCDD dose-response assessment included in the NAS review of EPA’s 2003 

13 Reassessment. This document focuses on TCDD only. 

14 

15 1.3. OVERVIEW OF EPA’S RESPONSE TO NAS (2006a) “HEALTH RISKS FROM 

16 DIOXIN AND RELATED COMPOUNDS: EVALUATION OF EPA’s 2003 

17 REASSESSMENT” 

18 In their key recommendations, the NAS commented that EPA should thoroughly justify 

19 and communicate approaches to dose-response modeling, increase transparency in the selection 

20 of key data sets, and improve the communication of uncertainty (particularly quantitative 

21 uncertainty). They also encouraged EPA to calculate an RfD. These main areas of improvement 

22 refer to issues specifically related to TCDD dose-response assessment (and uncertainty analysis); 

23 therefore, as noted in the Science Plan, EPA’s response to the NAS is particularly focused on 

24 these issues. 

25 EPA thoroughly considered the recommendations of the NAS and responds with 

26 scientific and technical evaluation of TCDD dose–response data via: 

27 

28 • an updated literature search that identified new TCDD dose-response studies (see Section 

29 2); 

30 • a kickoff workshop that included the participation of external experts in TCDD health 

31 effects, toxicokinetics, dose-response assessment and quantitative uncertainty analysis; 

32 these experts discussed potential approaches to TCDD dose-response assessment and 

33 considerations for EPA’s response to NAS (see Appendix A); 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 • detailed study inclusion criteria and processes for the selection of key studies (see Section 

2 2.3) and epidemiologic and animal bioassay data for TCDD dose-response assessment 

3 (see Section 2.4.1/Appendix B and Section 2.4.2, respectively); 

4 • kinetic modeling to quantify appropriate dose metrics for use in TCDD dose-response 

5 assessment (see Section 3); 

6 • dose-response modeling for all appropriate noncancer and cancer data sets (see 

7 Section 4.2/Appendix E and Section 5.2.3/Appendix F, respectively); 

8 • thorough and transparent evaluation of the selected TCDD data for use in the derivation 

9 of an RfD and an oral slope factor (OSF) (see Sections 4.2 and 5.2.3, respectively); 

10 • the development of an RfD (see Section 4.3); 

11 • the development of a revised OSF (see Section 5.3) with an updated cancer weight of 

12 evidence determination for TCDD based on EPA’s 2005 Cancer Guidelines (U.S. EPA, 

13 2005) (see Section 5.1.2); 

14 • consideration of nonlinear dose-response approaches for cancer, including illustrative 

15 RfDs for cancer precursor events and tumors (see Section 5.2.3.4) ; and 

16 • discussion of the feasibility and utility of quantitative uncertainty analysis for TCDD 

17 dose-response assessment (see Section 6). 

18 

19 Each of these activities is described in detail in subsequent sections of this document. 

20 It should be noted that three separate EPA activities address additional TCDD issues, 

21 specifically related to the application of dioxin TEFs and to TCDD and DLC background 

22 exposure levels. Information on the application of the dioxin TEFs is published elsewhere by 

23 EPA for both ecological (U.S. EPA, 2008a) and human health risk assessment (U.S. EPA, 

24 2009a). As a consequence, EPA does not directly address TEFs herein, but makes use of the 

concept of toxicity equivalence7 25 as applicable to the analysis of exposure dose in 

26 epidemiological studies. Furthermore, this document does not address the NAS 

27 recommendations pertaining to the assessment of human exposures to TCDD and other dioxins. 

28 Information on updated background levels of dioxin in the U.S. population has been recently 

29 reported (Lorber et al., 2009). 

30 

 

7

Toxicity equivalence (TEQ) is the product of the concentration of an individual DLC in an environmental mixture 

and the corresponding TCDD TEF for that compound. These products are summed to yield the TEQ of the mixture. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 1.3.1. TCDD Literature Update 

2 EPA has developed a literature database of peer-reviewed studies on TCDD toxicity, 

3 including in vivo mammalian dose-response studies and epidemiological studies. An initial 

4 literature search for studies published since the 2003 Reassessment was conducted by the U.S. 

5 Department of Energy’s Argonne National Laboratory (ANL) through an Interagency Agreement 

6 with EPA. ANL used the online National Library of Medicine database (PubMed) and identified 

7 studies published between the year 2000 and October 31, 2008. Supporting references published 

8 since the release of the 2003 Reassessment were also identified. Supporting studies were 

9 classified as studies pertaining to TCDD kinetics, TCDD mode-of-action, in vitro TCDD studies, 

10 and TCDD risk assessment approaches. The literature search strategy explicitly excluded studies 

11 addressing (1) analytical/detection data and cellular screening assays; (2) environmental fate, 

12 transport and concentration data; (3) dioxin-like compounds and toxic equivalents; 

13 (4) nonmammalian dose-response data; (5) human exposure analyses only, including body 

14 burden data; and (6) combustor or incinerator or other facility-related assessments absent 

15 primary dose-response data. EPA published the initial literature search results in the Federal 

16 Register on November 24, 2008 (73 FR 70999; November 24, 2008) and invited the public to 

17 review the list and submit additional peer-reviewed in vivo mammalian dose-response studies for 

18 TCDD, including epidemiological studies that were absent from the list (U.S. EPA, 2008b). The 

19 literature search results and subsequent submissions were used during a 2009 scientific 

20 workshop, which was open to the public and featured a panel of experts on TCDD toxicity and 

21 dose-response modeling (discussed below). Additional studies identified during the workshop 

22 and those collected by EPA scientists during the development of this report through October 

23 2009 have been incorporated into the final set of studies for TCDD dose-response assessment. 

24 

25 1.3.2. EPA’s 2009 Workshop on TCDD Dose Response 

26 To assist EPA in responding to the NAS, EPA, and ANL convened a scientific workshop 

27 (the “Dioxin Workshop”) on February 18−20, 2009, in Cincinnati, Ohio. The goals of the 

28 Dioxin Workshop were to identify and address issues related to the dose-response assessment of 

29 TCDD and to ensure that EPA’s response to the NAS focused on the key issues and reflected the 

30 most meaningful science. The Dioxin Workshop included seven scientific sessions: quantitative 

31 dose-response modeling issues, immunotoxicity, neurotoxicity and nonreproductive endocrine 
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1 effects, cardiovascular toxicity and hepatotoxicity, cancer, reproductive and developmental 

2 toxicity, and quantitative uncertainty analysis of dose-response. During each session, EPA asked 

3 a panel of expert scientists to perform the following tasks: 

4 

5 • Identify and discuss the technical challenges involved in addressing the NAS comments 

6 related to the dose-response issues within each specific session topic and the TCDD 

7 quantitative dose-response assessment. 

8 • Discuss approaches for addressing the key NAS recommendations. 

9 • Identify important published, independently peer-reviewed literature―particularly 

10 studies describing epidemiological studies and in vivo mammalian bioassays expected to 

11 be most useful for informing EPA’s response. 

12 

13 The sessions were followed by open comment periods during which members of the 

14 audience were invited to address the expert panels. The session’s Panel Co-chairs were asked to 

15 summarize and present the results of the panel discussions―including the open comment 

16 periods. The summaries incorporated points of agreement as well as minority opinions. Final 

17 session summaries were prepared by the session Panel Co-chairs with input from the panelists, 

18 and they formed the basis of a final workshop report (U.S. EPA, 2009c, Appendix A of this 

19 report). Because the sessions were not designed to achieve consensus among the panelists, the 

20 summaries do not necessarily represent consensus opinions; rather reflect the core of the panel 

21 discussions. Some of the key discussion points from the workshop that influenced EPA’s 

22 development of this document are listed below (see Appendix A for detail): 

23 

24 • In the development of study selection criteria, more relevant exposure-level (i.e., dose) 

25 decision points using tissue concentrations could be defined. 

26 • A linear approach to body-burden estimation, which was utilized in the 2003 

27 Reassessment (U.S. EPA, 2003), does not fully consider key toxicokinetic issues related 

28 to TCDD―e.g., sequestration in the liver and fat, age-dependent elimination, and 

29 changing elimination rates over time. Thus, kinetic/mechanistic modeling could be used 

30 to quantify tissue-based metrics. In considering human data, lipid-adjusted serum levels 

31 may be preferable over body burden, although the assumptions used in the back 

32 calculation of the body burden in epidemiologic cohorts are of concern. In considering 

33 rat bioassay data, lipid-adjusted body-burden estimates may be preferable. 
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1 • New epidemiological studies on noncancer endpoints have been published since the 2003 

2 Reassessment that may need to be considered (e.g., thyroid dysfunction literature from 

3 Wang et al. [2005] and Baccarelli et al. [2008]). 

4 • The 1% of maximal response (ED01) that was utilized in the 2003 Reassessment has not 

5 typically been used in dose-response assessment. Some alternative ideas were as follows: 

6 (1) the POD should depend on the specific endpoint; (2) for continuous measures, the 

7 benchmark response (BMR) could be based on the difference from control and consider 

8 the adversity level; and (3) for incidence data, the BMR should be set to a fixed-risk 

9 level. 

10 • The quantitative dose-response modeling for cancer could be based on human or animal 

11 data. There are new publications in the literature for four epidemiological cohort studies 

12 (Dutch cohort, NIOSH cohort, BASF accident cohort, and Hamburg cohort). The 

13 increase in total cancers could be considered for modeling human cancer data. However, 

14 non-Hodgkin’s lymphoma and lung tumors are the main TCDD-related cancer types seen 

15 from human exposure. In reviewing the rat data, the NTP (2006) data sets are new and 

16 can be modeled. Although the liver and lungs are the main target organs, modeling all 

17 cancers, as well as using tumor incidence in lieu of individual rats as a measure, should 

18 be considered. 

19 • Both linear and nonlinear model functions should be considered in the cancer 

20 dose-response analysis because there are data and rationales to support use of either 

21 below the POD. 

22 • For quantitative uncertainty analysis, consider the impacts of choices among plausible 

23 alternative data sets, dose metrics, models, and other more qualitative choices. Issues to 

24 consider include how much difference these choices make and, also, how much relative 

25 credence should be put toward each alternative as a means to gauge and describe the 

26 landscape of imperfect knowledge with respect to possibilities for the true dose response. 

27 This may be difficult to do quantitatively because the factors are not readily expressed as 

28 statistical distributions. However, the rationale for accepting or questioning each 

29 alternative in terms of the available supporting evidence, contrary evidence, and needed 

30 assumptions, can be delineated. 

31 

32 1.3.3. Overall Organization of EPA’s Response to NAS Recommendations 

33 The remainder of this document is divided into five sections that address the three 

34 primary areas of concern resulting from the NAS (2006a) review. Section 2 describes EPA’s 

35 approach to the recommendation for transparency and clarity during selection of key data 

36 sets―including criteria for the selection of key dose-response studies, evaluations of the 

37 important epidemiologic studies and animal bioassays, and a summary of the key studies used 

38 for subsequent dose-response modeling. Sections 3, 4, and 5 present EPA’s response to the NAS 

39 recommendation to better justify the approaches used in dose-response modeling of TCDD. 
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1 Section 3 discusses the toxicokinetic modeling EPA conducted to support the dose-response 

2 analyses. Section 4 presents EPA’s approach to noncancer data set selection, dose-response 

3 modeling, derivation of an RfD for TCDD, and contains a qualitative discussion of the 

4 uncertainties associated with the RfD. Section 5 presents an updated cancer weight-of-evidence 

5 summary, EPA’s approach to cancer data set selection, dose-response modeling, derivation of an 

6 OSF for TCDD, and a qualitative discussion of the uncertainties associated with the OSF, 

7 including an evaluation of alternative approaches to cancer assessment of TCDD. Finally, 

8 Section 6 discusses the feasibility of conducting a quantitative uncertainty analysis of TCDD 

9 dose response. 
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1 2. TRANSPARENCY AND CLARITY IN THE SELECTION OF KEY DATA SETS 

2 FOR DOSE-RESPONSE ANALYSIS 

 3 

 4 

5 This section focuses on addressing transparency and clarity in the study selection process 

6 and on identifying key data sets for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) dose-response 

7 analysis. Section 2.1 summarizes the National Academy of Sciences (NAS) committee’s 

8 comments specifically regarding this issue. Section 2.2 presents U.S. Environmental Protection 

9 Agency’s (EPA’s) response to those comments and describes EPA’s approach to ensuring 

10 transparency and clarity in selection of studies for subsequent dose-response analyses. 

11 Section 2.3 describes the TCDD-specific study inclusion criteria and evaluation process EPA 

12 used in this document for determining the eligibility of both epidemiologic and experimental 

13 animal studies for TCDD dose-response analysis. Section 2.4.1 summarizes epidemiologic data 

14 and evaluates the suitability of these data for TCDD dose-response analyses. Section 2.4.2 

15 summarizes animal bioassay data that have met the study inclusion criteria for TCDD 

16 dose-response assessment. Finally, Section 2.4.3 identifies key TCDD epidemiologic and animal 

17 bioassay studies that were determined using the study inclusion criteria. Then it previews the 

18 evaluation processes used to further identify study/endpoint combination data sets for developing 

19 TCDD toxicity values for noncancer (see Section 4) and cancer (see Section 5) effects. 

 20 

21 2.1. SUMMARY OF NAS COMMENTS ON TRANSPARENCY AND CLARITY IN 

22 THE SELECTION OF KEY DATA SETS FOR DOSE-RESPONSE ANALYSIS 

23 The NAS committee proposed that EPA develop a clear and readily understandable 

24 methodology for evaluating and including epidemiologic and animal bioassay data sets in 

25 dose-response evaluations. The NAS committee recommended the development and application 

26 of transparent initial criteria to judge whether or not specific epidemiologic or animal bioassay 

27 studies be included in TCDD dose-response analysis. 

28 Specific NAS comments on the topic of study evaluation and inclusion criteria include 

29 

30 EPA should specify inclusion criteria for the studies (animal and human) used for 

31 derivation of the benchmark dose (BMD) for different noncancer effects and 

32 potentially for the development of RfD values and discuss the strengths and 

33 limitations of those key studies (NAS, 2006, p. 27). 
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1 ...in its [EPA’s] evaluation of the epidemiological literature of carcinogenicity, it 

2 did not outline eligibility requirements or otherwise provide the criteria used to 

3 assess the methodological quality of other included studies (NAS, 2006, p. 56.) 

4 With regard to EPA’s review of the animal bioassay data, the committee 

5 recommends that EPA establish clear criteria for the inclusion of different data 

6 sets (NAS, 2006, p. 191). 

7 ...the committee expects that EPA could substantially improve its assessment 

8 process if it more rigorously evaluated the quality of each study in the database 

9 (NAS, 2006, p. 56). 

10 EPA could also substantially improve the clarity and presentation of the risk 

11 assessment process for TCDD...by using a summary table or a simple summary 

12 graphical representation of the key data sets and assumptions...(NAS, 2006, 

13 p. 56). 

14 

15 2.2. EPA’S RESPONSE TO NAS COMMENTS ON TRANSPARENCY AND CLARITY 

16 IN THE SELECTION OF KEY DATA SETS FOR DOSE-RESPONSE ANALYSIS 

17 EPA agrees with the NAS committee regarding the need for a transparent and clear 

18 process for selecting studies and key data sets for TCDD dose-response analyses. The 

19 delineation of the study selection process and decisions regarding key data sets will facilitate 

20 communication regarding critical decisions made in the TCDD dose-response assessment. In 

21 keeping with the NAS committee’s recommendation to use a transparent process and improve 

22 clarity and presentation of the risk assessment process for TCDD, Figure 2-1 overviews the 

23 approach that EPA has used in this document to develop a final list of key cancer and noncancer 

24 studies for quantitative dose-response analysis of TCDD. The steps in Figure 2-1 are further 

25 explained below. 

26 

27 Literature search for in vivo mammalian and epidemiologic TCDD studies 

28 (2000−2008): EPA conducted a literature search to identify peer-reviewed, dose-response 

29 studies for TCDD that have been published since the 2003 Reassessment. This search 

30 included in vivo mammalian and epidemiological studies of TCDD from 2000 to 2008. 

31 Additional details describing the conduct of this literature search are presented in 

32 Section 1.3.1 of this document. 

33 Federal Register Notice—Web publication of literature search for public comment: 

34 In November 2008, EPA published a list of ~500 citations from results of this literature 

35 search (U.S. EPA, 2008b) and invited the public to review this preliminary list of 

36 dose-response citations for use in TCDD dose-response assessment. EPA requested that 

37 interested parties identify and submit peer-reviewed studies for TCDD that were absent 
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1 from this list. All additional studies submitted to EPA were included in the final TCDD 

2 literature database considered by EPA for TCDD dose-response analysis. 

3 Initial study inclusion criteria development for TCDD in vivo mammalian 

4 bioassays: EPA developed an initial set of draft criteria for evaluating the extensive 

5 TCDD database of in vivo mammalian bioassays. These initial inclusion criteria had 

6 three purposes. First, they provided a transparent and rigorous evaluation of the scientific 

7 quality of each study in EPA’s database, a deficiency in the 2003 Reassessment identified 

8 by the NAS committee. Second, given the vast TCDD mammalian bioassay database, 

9 they provided a transparent method for initially screening studies to be considered for 

10 TCDD dose-response analyses. Third, they served as a starting point for discussions of 

11 study inclusion criteria by expert panelists who were convened by EPA for its scientific 

12 workshop on TCDD dose-response analysis (the Dioxin Workshop), described next (also 

13 see the workshop report in Appendix A). 

14 Dioxin Workshop and expert refinement of TCDD in vivo mammalian bioassay 

15 inclusion criteria: In February 2009, EPA convened “A Scientific Workshop to Inform 

16 EPA’s Response to NAS Comments on the Health Effects of Dioxin in EPA’s 2003 

17 Dioxin Reassessment.” The goals of this 3-day public and scientific workshop were to 

18 identify and address issues related to the dose-response assessment of TCDD. Sessions at 

19 the workshop examined toxicities associated with TCDD, issues related to developing 

20 dose-response estimates based on these data and associated uncertainties. At the 

21 workshop, EPA presented the draft set of study inclusion criteria for evaluating the 

22 extensive TCDD in vivo mammalian bioassay literature and asked workshop panelists to 

23 discuss these criteria and make recommendations for their revision. Further details on 

24 this workshop are presented in Section 1.3.2 of this document, and the complete report 

25 from this workshop is available in Appendix A, including detailed summaries of the 

26 panels’ comments on the inclusion criteria in relation to the various toxic endpoints that 

27 were discussed. 

28 Final development of inclusion criteria for TCDD in vivo mammalian studies: Based 

29 on discussions at the Dioxin Workshop, the initial draft inclusion criteria for evaluating 

30 the TCDD mammalian bioassay literature were revised and are presented in Section 2.3.2 

31 (see Figure 2-3). An initial criterion is that studies for consideration must be publically 

32 available and published in a peer-reviewed scientific journal. Because the methodology 

33 EPA uses to develop reference doses (RfDs) and cancer oral slope factors (OSFs) relies 

34 on identification of studies reporting potential adverse effects at low doses (relative to the 

35 overall database), another important criterion shown in Section 2.3.2 identifies a 

36 maximum value for the lowest TCDD dose tested in a bioassay. This maximum value 

37 was used to eliminate those studies that could not be selected for development of an RfD 

38 or an oral slope factor because tested doses were too high relative to other TCDD 

39 bioassays. 

40 Development of inclusion criteria for epidemiologic studies: Following the Dioxin 

41 Workshop, EPA determined that an evaluation process was also needed for inclusion of 

42 epidemiologic studies for TCDD dose-response assessment. These criteria were 

43 developed and are detailed in Section 2.3.1 (see Figure 2-2). Analogous to animal 

44 bioassay data, epidemiologic studies for consideration must also be publically available 
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1 and published in a peer-reviewed scientific journal. In addition to assessing the 

2 methodological considerations relative to epidemiologic cohorts and studies (e.g., 

3 statistical power and precision of estimates, consideration of latency periods), a key 

4 consideration for use of a data set in TCDD dose-response modeling is characterization 

5 of the exposure assessment methodology, and specifically, whether this methodology 

6 allowed assignment of individual-level exposures within a study. 

7 Final literature collection (October 2009): Additional literature was collected as it was 

8 identified by EPA following the Dioxin Workshop through October 2009 to ensure the 

9 consideration of all recently published data for this report. 

10 Studies screened using inclusion criteria: The two sets of TCDD-specific study 

11 inclusion criteria presented in Section 2.3 were used to evaluate all studies included in the 

12 2003 Reassessment, studies identified in the 2000−2008 literature search, studies 

13 identified through public comment and submission, and studies collected in 2009 as 

14 identified by EPA during the development of this document. Section 2.4 presents results 

15 of EPA’s evaluation of epidemiologic and mammalian bioassay literature for both cancer 

16 and noncancer endpoints. 

17 Final list of key cancer and noncancer studies for quantitative dose-response 

18 analysis of TCDD: Application of the study inclusion criteria concludes in Section 2.4 

19 with development of a list of key noncancer and cancer studies that were considered for 

20 quantitative dose-response analyses of TCDD in Sections 4 and 5, respectively. In those 

21 sections, points of departure (PODs) are developed and evaluated for all study/endpoint 

22 combinations from these final key study lists, and key data sets and PODs for the 

23 development of TCDD toxicity values are identified. 

 24 

25 2.3. STUDY INCLUSION CRITERIA FOR TCDD DOSE-RESPONSE ANALYSIS 

26 One of the three major recommendations made by the NAS (2006a) committee was that 

27 EPA should provide greater clarity and transparency on the selection of studies that were used in 

28 the quantitative dose-response modeling of TCDD in the 2003 Reassessment. In this section, 

29 EPA describes TCDD-specific study inclusion criteria that have been developed to evaluate 

30 epidemiologic studies and animal bioassays for TCDD dose-response assessment. These criteria 

31 reflect EPA’s goal of developing an RfD and a cancer OSF for TCDD through a transparent 

32 study selection process; they are intended to be used by EPA for TCDD dose-response 

33 assessment only. These criteria were applied to each of the ~500 studies listed in Preliminary 

34 Literature Search Results and Request for Additional Studies on 2,3,7,8-Tetrachlorodibenzo-p35 Dioxin (TCDD) Dose-Response Studies (U.S. EPA, 2008b); studies identified and submitted by 

36 the public and by participants in the Dioxin Workshop (U.S. EPA, 2009c); studies included in 
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1 the 2003 Reassessment, and other relevant published studies collected by EPA scientists through 

2 October 2009. 

3 EPA has undertaken different approaches for epidemiologic versus in vivo animal 

4 bioassay study evaluation and key data set selection. The significant differences between animal 

5 and human health effects data and their use in EPA risk assessment support development of 

6 separate criteria for study inclusion and different approaches to study evaluation. For the vast 

7 majority of compounds on EPA’s Integrated Risk Information System (IRIS), cancer and 

8 noncancer toxicity values have been derived using animal bioassay data; therefore, approaches to 

9 dose-response modeling and POD selection from in vivo mammalian bioassays have been 

10 standardized and codified (U.S. EPA, 2008c). The study criteria shown below and in Figure 2-3 

11 for animal bioassay data reflect EPA’s preferences for TCDD-specific study inclusion, some of 

12 which are based on common practices and guidance for POD selection and RfD and OSF 

13 derivation. Far fewer IRIS toxicity values have been derived from human data, although some 

14 examples do exist. For example, benzene, beryllium and compounds, chromium IV, and 

15 1,3-butadiene have RfDs, Reference Concentrations, Inhalation Unit Risks and/or OSFs all based 

16 on occupational cohort data and the methyl mercury RfD is based on high fish consuming 

17 cohorts (U.S. EPA, 2009b). The modeling and interpretation of such human data have been 

18 conducted on a case-by-case basis because each cohort is uniquely defined and has its own set of 

19 exposure conditions, significant confounders, and biases that may need to be considered in 

20 dose-response modeling. For TCDD, not all data are from occupational cohorts, but include 

21 cohorts exposed for relatively short time periods to high concentrations as a consequence of 

22 industrial accidents, a scenario that has not commonly been used to establish chronic EPA 

23 toxicity values. 

24 Because of these differences in data characteristics, divergent selection approaches are 

25 used in this document to present and evaluate the epidemiologic studies (see Section 2.3.1) and 

26 the in vivo animal bioassays (see Section 2.3.2). In Section 2.4.1, all of the available 

27 epidemiologic studies on TCDD are summarized and evaluated for suitability for dose-response 

28 modeling using the TCDD-specific study inclusion criteria below and shown in Figure 2-2; only 

29 studies meeting the inclusion criteria are presented as key studies in Section 2.4.3 (see Tables 2-4 

30 and 2-5 for the cancer and noncancer endpoints, respectively). In Section 2.4.2, because 

31 summarizing and showing the evaluation of the thousands of available animal bioassays on 
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1 TCDD was prohibitive, only studies first meeting the in vivo animal bioassays study inclusion 

2 criteria below (and shown in Figure 2-3) are summarized. These studies are also presented as 

3 key studies in Section 2.4.3 (see Tables 2-6 and 2-7 for cancer and noncancer endpoints, 

4 respectively). 

5 

6 2.3.1. Study Inclusion Criteria for TCDD Epidemiologic Studies 

7 This section identifies the process EPA used to select epidemiologic studies for defining 

8 candidate PODs for TCDD dose-response modeling. These criteria are based on EPA’s 

9 approaches for deriving OSFs and RfDs. A discussion of the considerations used in selecting 

10 epidemiologic data for quantitative dose-response modeling is valuable, particularly given EPA’s 

11 preference to use high-quality human studies over animal studies because such human studies are 

12 regarded as providing the most relevant information needed for quantitative human health risk 

13 analyses (U.S. EPA, 2005). As described by Hertz-Picciotto (1995), key components needed for 

14 the use of an epidemiologic study as a basis for quantitative risk assessment include issues 

15 regarding exposure assessment (a well-quantified exposure assessment with exposures linked to 

16 individuals) and study quality (“strong biases,” for example with respect to inclusion criteria for 

17 membership in the cohort and follow-up procedures “ruled out or unlikely” and “confounding 

18 controlled or likely to be limited”). The strength of the association, either within the full study or 

19 within a high exposure subgroup, can also be considered in the evaluation of suitability for 

20 dose-response modeling (Hertz-Picciotto, 1995). Stayner et al. (1999), however, note that even 

21 weak associations could be useful in terms of providing an estimate of a potential upper bound 

22 for a quantitative risk estimate. 

23 EPA’s method for applying the TCDD study inclusion criteria to epidemiologic data is 

24 detailed below and in Figure 2-2. Based on the framework discussed above, EPA evaluated the 

25 available epidemiologic cohorts and studies based on the five following considerations: 

26 

27 1. The methods used to ascertain health outcomes are clearly identified and unbiased, with 

28 high sensitivity and specificity. 

29 2. The risk estimates generated from the study are not susceptible to important biases 

30 arising from an inability to control for potential confounding exposures or other sources 

31 of bias arising from either study design or statistical analysis. 
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1 3. The study demonstrates an association between TCDD and an adverse health effect 

2 (assuming minimal misclassification of exposure and absence of important biases) with 

3 some suggestion of an exposure-response relationship. 

4 4. The exposure assessment methodology is clearly described and can be expected to 

5 provide adequate characterization of exposure, with assignment of individual-level 

6 exposures within a study (e.g., based on biomarker data, or based on a 

7 job-exposure-matrix approach). Limitations and uncertainties in the exposure assessment 

8 are considered. 

9 5. The size and follow-up period of a cohort study are large enough and long enough, 

10 respectively, to yield sufficiently precise estimates for use in development of quantitative 

11 risk estimates and to ensure adequate statistical power to limit the possibility of not 

12 detecting an association that might be present (i.e., to avoid Type II Errors due to failing 

13 to reject the null hypothesis when the null hypothesis is true). Similar considerations 

14 regarding sample size and statistical precision and power apply to case-control studies. 

15 

16 Three specific study inclusion criteria were used to select studies for further evaluation 

17 and potential TCDD quantitative dose-response assessment 

18 

19 • The study is published in the peer-reviewed scientific literature and includes an 

20 appropriate discussion of strengths and limitations. 

21 • The exposure is primarily to TCDD, rather than dioxin-like compounds (DLCs), and is 

22 properly quantified so that dose-response relationships can be assessed. 

23 • The effective dose and oral exposure must be reasonably estimable. The measures of 

24 exposure must be consistent with the current biological understanding of dose. For 

25 TCDD dose-response assessment, it is critical that reported dose is consistent with a dose 

26 that is likely to be toxicologically relevant. The timing of the measurement of effects 

27 (i.e., the response) also must be consistent with current biological understanding of the 

28 effect and its progression. 

29 For cancer endpoints, EPA assumes that cumulative TCDD dose estimates are 

30 toxicologically relevant measures. Thus, cancer studies must provide information 

31 about long-term TCDD exposure levels. Further, EPA reasons that measures of 

32 cancer occurrence or death need to allow for examination of issues of latency 

33 between the end of effective exposure and cancer detection or death. 

34 For noncancer endpoints, exposure estimates and analysis must allow for examination 

35 of issues of latency and other issues regarding the appropriate time window of 

36 exposure relevant for specific endpoints. Also, to be consistent with the RfD 

37 methodology, the response must be to a nonfatal endpoint. 

38 
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1 Those studies that met the aforementioned considerations and inclusion criteria (see 

2 Sections 2.4.1, 2.4.3, and Appendix B) were then subjected to further consideration for 

3 quantitative dose-response analyses. 

 4 

5 2.3.2. Study Inclusion Criteria for TCDD In Vivo Mammalian Bioassays 

6 This section identifies the criteria EPA applied to select nonhuman in vivo mammalian 

7 studies for defining candidate PODs for use in TCDD dose-response modeling. These inclusion 

8 criteria are based on EPA’s approaches for deriving OSFs and RfDs from bioassay data (U.S. 

9 EPA, 2005). EPA agrees with the NAS committee regarding the utility of an oral RfD and the 

10 need for reevaluation of the OSF for TCDD, specifically in light of data that have been published 

11 since the 2003 Reassessment was released. RfDs and OSFs are generally derived using data sets 

12 that demonstrate the occurrence of adverse effects, or their precursors, in low-dose range for that 

13 chemical. RfDs and OSFs are derived from a health protective perspective for chronic 

14 exposures. Thus, when a group of studies is available on a chemical for which a number of 

15 effects are observed at various doses across those studies, the studies using the lowest exposures 

16 that show effects will typically drive the RfD and OSF derivations, all other considerations being 

17 equal. Studies conducted at higher exposures relative to other available studies are used as 

18 supporting evidence for the final RfD or OSF since they were conducted at doses too high to 

19 impact the numeric derivations of toxicity values. EPA expresses RfDs and OSFs in terms of 

20 average daily doses, usually as mg/kg-day and per mg/kg-day, respectively. Thus, the study 

21 inclusion criteria for the animal bioassay data presented in this section include requirements that 

22 average daily exposures in the studies are within a low dose range where, relative to other 

23 studies, they could be considered for development of a toxicity value. These low-dose 

24 requirements do not imply that TCDD studies conducted at higher doses are of poor quality, 

25 simply that they are not quantitatively useful in the development of toxicity values because other 

26 studies with lower exposures will drive the RfD and OSF derivations under current EPA 

27 practice. Because EPA has identified ~2,000 studies on TCDD that may be considered for this 

28 purpose, the development and application of these study inclusion criteria has been critical to 

29 moving the risk assessment process forward. 

30 EPA’s method for applying study inclusion criteria for mammalian bioassays is detailed 

31 below and in Figure 2-3. The first study inclusion criterion is that the study is published in the 
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1 peer-reviewed scientific literature. Then, two specific study inclusion criteria were used to select 

2 studies for further evaluation and potential TCDD quantitative dose-response analyses and 

3 identification of candidate PODs: 

4 

5 1. The lowest dose level tested is ≤1 μg/kg-day for cancer studies and ≤30 ng/kg-day for 

6 noncancer studies. 

7 2. The study design consists of orally administered TCDD-only doses, and specifies the 

8 purity and matrix used to administer the doses. 

9 

10 Then, EPA evaluated the remaining in vivo animal studies based on the following four 

11 considerations. 

12 

13 1. The study tests mammalian species, identifying the strain, gender, and age of the tested 

14 animals. 

15 2. The study clearly documents testing protocol, including dosing frequency, duration, and 

16 timing of dose administration relative to age of the animals. 

17 3. The overall study design is consistent with standard toxicological principles and 

18 practices. The control group or groups are appropriate, given the testing protocol, and are 

19 well characterized. Clinical and pathological examinations conducted during the study 

20 are endpoint-appropriate, particularly for negative findings. 

21 4. The magnitude of animal responses is outside the range of normal variability exhibited by 

22 control animals (e.g., greater than or less than one standard deviation). 

23 

24 Those studies that met the aforementioned considerations and inclusion criteria (see 

25 Sections 2.4.2 and 2.4.3) were then subjected to dose-response analysis. 

26 The criteria for dose requirements, although somewhat arbitrary, are intended to be 

27 reasonable cutoffs that restrict the number of studies that would need to be modeled while 

28 ensuring that all study/data set combinations that could be candidates for the cancer slope factor 

29 or RfD were modeled. Thus, the dose range under consideration allows for liberal ranges of 

30 no-observed-adverse-effect levels (NOAELs), lowest-observed-adverse-effect levels (LOAELs), 

31 and benchmark dose lower confidence bound (BMDLs) for assessment of both cancer and 

32 noncancer effects. 

33 For cancer studies, the dose requirements were selected based on an initial evaluation of 

34 available average daily doses administered in TCDD animal bioassays in which adverse effects 
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1 were observed. For example, in cancer studies, a sample of the relatively low ranges of tested 

2 average daily doses include 1-1000 ng/kg-day (Toth et al., 1979), 1−100 ng/kg-day (Kociba et 

3 al., 1978), 1.43−286 ng/kg-day (NTP, 1982) and 2.14−71.4 ng/kg-day (NTP, 2006) with 

4 statistically significant increases in tumor incidence via pair-wise or trend tests found in all of 

5 these studies. The entire range of each these studies is ≤1 μg/kg-day. The linearized multi-stage 

6 model used by EPA to estimate OSFs is most appropriately applied to studies from which PODs 

7 can be estimated as closely as possible to the experimental data. Thus, given the dose ranges in 

8 these studies that are available for modeling, the restriction to ≤1 μg/kg-day for cancer was 

9 considered to be a reasonable cutoff. 

10 For noncancer studies, dose ranges are more complex and vary according to study 

11 endpoint. Examples of the lowest administered doses (not including developmental studies) that 

12 might be considered as NOAELs or LOAELs in POD determinations for noncancer endpoints 

13 include 1 ng/kg-day (Toth et al., 1979), 1.43 ng/kg-day (Cantoni et al., 1981), 1.07 ng/kg-day 

14 (Smialowicz et al., 2008) 1.43 ng/kg-day (NTP, 1982) and 2.14 ng/kg-day (NTP, 2006). Most of 

15 the lowest tested doses in the TCDD studies have been designated as LOAELs (see Section 4.1). 

16 Given the available database, it is likely that the same composite uncertainty factor (e.g., of 300; 

17 3 for UFA [interspecies], 10 for UFH [intraspecies], and 10 for UFL [LOAEL to NOAEL]) would 

18 be applied to any animal noncancer LOAEL used to derive an RfD for TCDD. This implies that 

19 any study that has a LOAEL of 30 ng/kg-day or more would result in a candidate RfD that is 

20 more than an order of magnitude higher than the example doses of 1−2 ng/kg-day shown here. 

21 BMDLs that might be derived from such data also would not be expected to be lower than these 

22 example doses of 1−2 ng/kg-day. Thus, a tested dose ≤30 ng/kg-day is considered to be a 

23 reasonable cutoff where the lowest tested dose would never be used as a POD to derive an RfD 

24 given that much lower tested doses (associated with adverse effects) are available from other 

25 studies of acceptable quality. 

 26 

27 2.4. EVALUATION OF KEY STUDIES FOR TCDD DOSE RESPONSE 

28 2.4.1. Evaluation of Epidemiological Cohorts for Dose-Response Assessment 

29 2.4.1.1. Cancer 

30 In the 2003 Reassessment, EPA selected three cohort studies from which to conduct a 

31 quantitative dose-response analysis: the National Institute for Occupational Safety and Health 
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1 (NIOSH) cohort (Steenland et al., 2001), the BASF cohort (Ott and Zober, 1996), and the 

2 Hamburg cohort (Becher et al., 1998). Although these studies were deemed suitable for 

3 quantitative dose-response analysis, the criteria EPA used to reach this conclusion were unclear. 

4 In this section, the study selection criteria and methodological considerations presented in 

5 Section 2.3 are systematically applied to evaluate a number of studies to determine their 

6 suitability for inclusion in dose-response modeling. In addition to the three cohorts used in 

7 previous TCDD quantitative risk assessment, considerations are applied to other relevant TCDD 

8 epidemiological data sets that were identified through a literature review for epidemiological 

9 studies of TCDD and cancer. Study summaries and suitability for quantitative dose-response 

10 analysis evaluations are discussed below. 

11 

12 2.4.1.1.1. Cancer cohorts. 

13 2.4.1.1.1.1. The NIOSH cohort. 

14 In 1978, the NIOSH undertook research that identified workers employed by U.S. 

15 chemical companies that made products contaminated with TCDD between 1942 and 1982. 

16 TCDD was generated in the production of 2,4,5-trichlorophenol and subsequent processes. This 

17 chemical was used to make 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), which was a major 

18 component of the widely-used defoliant, Agent Orange. The NIOSH cohort is the largest cohort 

19 of occupational workers studied to date and has been the subject of a series of investigations 

20 spanning more than two decades. It is important to note that this cohort consists mostly of male 

21 workers that were exposed to TCDD via daily occupational exposure, as compared to an acute 

22 accidental exposure scenario seen with other cohorts. The investigations have progressed from a 

23 comparison of the mortality patterns of the cohort to the U.S. general population to 

24 dose-response modeling using serum-derived estimates of TCDD that have been 

25 back-extrapolated several decades. Analyses of cancer data from the NIOSH cohort that are 

26 addressed in this section include Fingerhut et al. (1991), Steenland et al. (1999, 2001), Cheng et 

27 al. (2006), and Collins et al. (2009). 

28 
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1 2.4.1.1.1.1.1. Fingerhut et al., 1991. 

2 2.4.1.1.1.1.1.1. Study summary.

3 The investigation of Fingerhut and her colleagues published nearly two decades ago 

4 attracted widespread attention (Fingerhut et al., 1991). This retrospective study examined 

5 patterns of cancer mortality for 5,172 workers who comprised the NIOSH cohort, which 

6 combined workers from the company-specific cohorts of Dow Chemical (Ott et al., 1987; Cook, 

7 1981) and the Monsanto Company (Zack and Gaffey, 1983; Zack and Suskind, 1980). These 

8 workers were employed at 12 plants producing chemicals contaminated with TCDD. Almost all 

9 workers in the cohort (97%) had production or maintenance jobs with processes involving 

10 TCDD contamination. On average, workers were employed for 2.7 years specifically in 

11 processes that involved TCDD contamination, and overall, were employed for 12.6 years. The 

12 mortality follow-up began in 1940 and extended until the end of 1987. Vital status was 

13 determined using records from the Social Security Administration, the Internal Revenue Service, 

14 or the National Death Index. The ascertainment of vital status in the cohort was nearly complete, 

15 with less than 1% of the cohort not followed up until death or the end of the study period. 

16 Comparisons of mortality were made relative to the U.S. male general population and 

17 expressed using the standardized mortality ratio (SMR) metric and 95% confidence intervals 

18 (CIs). Life-table methods were used to generate person-years of risk accrued by cohort members 

19 at each plant. Person-years and corresponding deaths were tabulated across age, race, and year 

20 of death strata, which permitted the SMRs to be examined for potential confounding from these 

21 three characteristics. No unadjusted SMRs were presented in the paper. Cross-classification of 

22 person-years and deaths was also done across several exposure-related groupings, including 

23 duration of employment, years since first exposure, years since last exposure, and duration of 

24 exposure. Employment duration was categorized as <5, 5− <10, 10− <15, 15− <20, 20− <25, 

25 25− <30, and ≥30 years. The variable “years since first exposure” (<10, 10− <20, and ≥20 years) 

26 was used to evaluate associations in relation to different latency periods. The analysis was 

27 jointly stratified by duration of employment and for varying latency intervals to evaluate whether 

28 cohort members with higher cumulative TCDD levels had higher cancer mortality rates than 

29 those cohort members with lower cumulative levels. 

30 Overall, the cohort of workers had slightly elevated cancer mortality than the general 

31 population (SMR = 1.15, 95% CI = 1.02−1.30). Comparisons to the general population, 
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1 however, yielded no statistically significant excess for any site-specific cancer. Cancer mortality 

2 was examined for the subset of workers that worked for at least one year and had a latency 

3 interval of at least 20 years (n = 1,520). The 1-year cut-point was selected based on analyses of 

4 serum levels in a subset of 253 workers which revealed that every worker employed for at least 

5 one year had a lipid-adjusted serum level that exceeded the mean (7 ppt). Relative to the 

6 U.S. general population, statistically significant excesses in cancer mortality were observed for 

7 all cancers (SMR = 1.46, 95% CI = 1.21−1.76), cancers of the respiratory system (SMR = 1.42, 

8 95% CI = 1.03−1.92), and for soft tissue sarcoma (SMR = 9.22, 95% CI = 1.90−26.95) among 

9 this subset of 1,520 male workers. The elevated SMR for soft tissue sarcoma, however, was 

10 based on only three cases in this subset. 

11 SMRs also were generated across joint categories of duration of exposure and period of 

12 latency for deaths from all cancer sites (combined), and cancer of the trachea, bronchus, and 

13 lung. Increased SMRs were observed in strata defined by longer exposure and latency, but no 

14 statistically significant linear trends were found. 

15 

16 2.4.1.1.1.1.1.2. Study evaluation.

17 This cohort was the largest of four the International Agency for Research on Cancer 

18 (IARC) considered in its 1997 classification of TCDD as a Group 1 human carcinogen (IARC, 

19 1997). Duration of employment in processes that involved TCDD contamination was used as a 

20 surrogate measure of cumulative exposure. In using this exposure metric, Fingerhut et al. (1991) 

21 assumed that TCDD exposures were equivalent at all production plants. Doses for individual 

22 cohort members were not reconstructed for these analyses, although they were in subsequent 

23 analyses of this cohort. 

24 Workers in this cohort also were exposed to other chemicals, which could lead to bias 

25 due to confounding if these exposures were associated with both TCDD exposure and the health 

26 outcomes being examined. At one plant, workers were exposed to 4-aminobiphenyl. Previous 

27 investigators also reported that workers at another plant were exposed to 2,4,5-T and 

28 2,4-dichlorophenoxyacetic acid (2,4-D) (Bond et al., 1988, 1989; Ott et al., 1987). Subsequent 

29 analyses revealed only modest correlations between duration of employment (a surrogate 

30 measure of exposure to other chemicals) and cumulative TCDD exposure. This suggests that 

31 confounding due to other coexposures is unlikely to have biased the results. In addition, 
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1 subsequent analyses of this cohort indicate that after excluding workers exposed to 

2 pentachlorophenols from analyses, positive associations between cumulative TCDD and all 

3 cancer mortality persisted (Steenland et al., 1999). Removal of workers who died from bladder 

4 cancer also did not substantially change the dose-response association between TCDD and 

5 cancer mortality from all other sites combined. This finding suggests that exposures to 

6 4-aminobiphenyl did not confound the association between cancer mortality and TCDD 

7 exposure. Overall, there is little evidence of confounding by these co-exposures among this 

8 cohort, however, exposure to other possible confounders, such as dioxin-like compounds, was 

9 not examined. 

10 The study collected no information on smoking behavior of the workers, and therefore, 

11 the SMRs do not account for any differences in the prevalence of smoking that might have 

12 existed between the workers and the general population. For several reasons, however, the 

13 inability to take into account smoking is unlikely to have been an important source of bias. First, 

14 mortality from other smoking-related causes of death such as nonmalignant respiratory disease 

15 were not more common in the cohort than in the general population (SMR = 0.96, 

16 95% CI = 0.54−1.58). Second, stratified analyses of workers with at least a 20-year latency 

17 (assuming this subset shared similar smoking habits) revealed that excesses were apparent only 

18 among those who were exposed for at least 1 year. Specifically, when compared to the general 

19 population, the SMR among workers exposed for at least 1 year with a latency of 20 years was 

20 1.46, (95% CI = 1.21−1.76) while those exposed for less than 1 year had an SMR of 1.02 

21 (95% CI = 0.76−1.36). Third, for comparisons of cancer mortality between blue-collar workers 

22 and the general population, smoking is unlikely to explain cancer excesses of greater than 

23 10−20% (Siemiatycki et al., 1988). Finally, the investigators found no substantial changes in the 

24 results for lung cancer when risks were adjusted for smoking histories obtained in 1987 from 

25 223 workers employed at two plants. These data were used to adjust for the expected number of 

26 lung cancer deaths expected in the entire cohort (Fingerhut et al., 1991). Following this 

27 adjustment, the expected number of lung cancer deaths resulted in only a small change in the 

28 SMR for lung cancer in the overall cohort from 1.11 (95% CI = 0.89−1.37) to 1.05 

29 (95% CI = 0.85−1.30). Similarly, there was little change in the SMR for lung cancer in higher 

30 exposure subcohort (SMR = 1.39, 95% CI = 0.99−1.89; to SMR = 1.37, 95% CI = 0.98−1.87). 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 The use of death certificate information from the National Death Index is appropriate for 

2 identifying cancer mortality outcomes. For site-specific cancers such as soft tissue sarcoma, 

3 however, the coding of this underlying cause of death is more prone to misclassification (Percy 

4 et al., 1981). Indeed, a review of tissues from four men concluded to have died from soft-tissue 

5 sarcoma determined that two deaths had been misclassified (Fingerhut et al., 1991). A review of 

6 hospital data revealed that two other individuals had soft tissue sarcomas that were not identified 

7 by death certificate information. The use of death certificate information to derive SMRs for 

8 cancer as a whole is likely not subject to significant bias; the same might not hold true, however, 

9 for some site-specific cancers such as soft tissue sarcoma. 

10 Using the SMR metric to compare an occupational cohort with the general population is 

11 subject to what is commonly referred to as the “healthy worker effect” (Li and Sung, 1999; Choi, 

12 1992). The healthy worker effect is a bias that arises because those healthy enough to be 

13 employed have lower morbidity and mortality rates than the general population. The healthy 

14 worker effect is likely to be larger for occupations that are more physically demanding 

15 (Aittomaki et al., 2005; Checkoway et al., 1989), and the healthy worker effect is considered to 

16 be of little or no consequence in the interpretation of cancer mortality (McMichael, 1976; 

17 Monson, 1986). Few cancers are associated with a prolonged period of poor health that would 

18 affect employability long before death. Also recognized is that, as the employed population 

19 ages, the magnitude of the healthy worker effect decreases as the absolute reduction in mortality 

20 becomes relatively smaller in older age groups (McMichael, 1976). The mortality follow-up of 

21 occupational cohorts generally spans several decades, which should minimize the associated 

22 healthy worker effect in such studies. Bias could also be introduced in that workers who are 

23 healthier might be more likely to stay employed and therefore accrue higher levels of exposure. 

24 In the NIOSH cohort, however, mortality was ascertained for those who could have left the 

25 workforce or retired by linking subjects to the National Death Index. Although internal cohort 

26 comparisons can minimize the potential for the healthy worker effect for the reasons presented 

27 above, for cancer outcomes, the SMR statistic is a valuable tool for characterizing whether 

28 occupational cohort are more likely to die of cancer than the general population. Moreover, 

29 stratified analyses across categories of duration of exposure, or latency periods within a cohort 

30 can yield important insights about which workers are at greatest risk. Perhaps most important, 

31 subsequent analyses of the NIOSH cohort that presented risk estimates derived from external 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 comparisons using the SMR were remarkably consistent with rate ratios derived using an internal 

2 referent (Steenland et al., 1999). 

3 

4 2.4.1.1.1.1.1.3. Suitability of data for TCDD dose-response modeling.

5 This cohort meets most of the identified considerations for conducting a quantitative 

6 dose-response analysis for mortality from all cancer sites combined. The NIOSH cohort is the 

7 largest cohort of TCDD-exposed workers, exposure characterization at an individual level is 

8 possible but not available in this particular study, the follow-up period is long enough to evaluate 

9 latent effects, and the study is not subject to important sources of bias that would materially alter 

10 findings. For the purpose of quantitative dose-response modeling, it is important to note that 

11 subsequent studies of this cohort adopted methods that greatly improved the characterization of 

12 TCDD exposure in this cohort and increased the follow-up interval (Steenland et al., 2001; 

13 Cheng et al., 2006). As such, for all practical purposes, due consideration for dose-response 

14 modeling should focus on the more recently developed data sets. 

15 For quantitative dose-response modeling for individual cancer sites, the data are much 

16 more limited. A statistically significant positive association with TCDD was noted only for soft17 tissue sarcoma among those with more than 1 year of exposure and 20 years of latency 

18 (SMR = 9.22, 95% CI = 1.90−26.95). However there were only three deaths from soft tissue 

19 sarcoma among this exposed component of the cohort, and four deaths in total in the overall 

20 cohort. Also, misclassification of outcome for soft-tissue sarcoma through death registries is 

21 well recognized and supported with additional review of tissue from two of the men. 

22 Specifically, tissues from the four men who died of soft-tissue sarcoma revealed that only two of 

23 these cases were coded correctly. 

24 The design of this initial publication of the NIOSH cohort did not allow for exposures to 

25 other possible confounders, such as dioxin-like compounds and other occupational exposures, to 

26 be examined and accounted for in the analyses. TCDD exposures were based on duration of 

27 employment and effective doses could not be estimated based on the TCDD exposure measure 

28 (i.e., duration of employment). Therefore, dose-response modeling was not conducted for this 

29 study. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.1.2. Steenland et al., 1999. 

2 2.4.1.1.1.1.2.1. Study summary.

3 A subsequent analysis of the NIOSH cohort extended the follow-up interval of Fingerhut 

4 et al. (1991) by 6 years (i.e., from 1940−1993) and improved characterization of TCDD exposure 

5 (Steenland et al., 1999). A key distinction from the work of Fingerhut et al. (1991) was the 

6 exclusion of several workers that had been included in the previous mortality analyses. The 

7 authors excluded 40 workers who were either female, had never worked in TCDD-exposed 

8 departments, or had missing date of birth information. An additional 238 workers were excluded 

9 as occupational data for characterizing duration of exposure were lacking, preventing their use in 

10 a subcohort dose-response analysis. This subcohort was further reduced by excluding workers 

11 from four plants (n = 591) because the information on the degree of TCDD contamination in 

12 work histories was limited, preventing the characterization of TCDD levels by job type. 

13 Thirty-eight additional workers were excluded from the eight remaining plants because TCDD 

14 contamination could not be estimated. Finally, 727 workers were excluded because they had 

15 been exposed to pentachlorophenol. In total, exposures were assigned to 3,538 (69%) members 

16 of the overall cohort, a cohort substantially reduced from the 5,172 on which Fingerhut et al. 

17 (1991) reported. Steenland et al. (1999) also evaluated the mortality experience of a subcohort 

18 of 608 workers with chloracne who had no exposure to pentachlorophenol. 

19 For each worker, a quantitative exposure score for each day of work was calculated based 

20 on the concentration of TCDD (μg/g) present in process materials, the fraction of the day 

21 worked, and a qualitative contact level based on estimates of the amount of TCDD exposure via 

22 dermal absorption or inhalation. The authors derived a cumulative measure of TCDD exposure 

23 by summing the exposure scores across the working lifetime history for each worker. The 

24 authors validated this cumulative exposure metric indirectly by comparing values obtained for 

25 workers with and without chloracne. Such a validation is appropriate, given that chloracne is 

26 considered a clinical sign of exposure to dioxin. The median exposure score among those with 

27 chloracne was 11,546 compared with 77 among those without (Steenland and Deddens, 2003). 

28 Cancer mortality was compared using two approaches. As in Fingerhut et al. (1991), 

29 external comparisons were made to the U.S. general population using the SMR statistic. The 

30 authors adjusted the SMR statistics for race, age, and calendar time. They also applied life-table 

31 methods to characterize risks across the subcohort of 3,538 workers with exposure data by 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 categorizing the workers into seven cumulative exposure groups. The cut-points for these 

2 categories were selected so that the number of deaths in each category was nearly equal to 

3 optimize study power. Life-table analyses were extended further to consider a 15-year lag 

4 interval, which in a practical sense means that person-years at risk would not begin to accrue 

5 until 15 years after the first exposure occurred. The person-years and deaths that occurred in the 

6 first 15 years were included in the lowest exposure grouping. The Cox proportional hazards 

7 model was used to characterize risk within the cohort. Cox regression was used to provide an 

8 estimate of the hazard ratios and the 95% CIs for ischemic heart disease, all cancers combined, 

9 lung cancer, smoking related cancers, and all other cancers. The authors also performed Cox 

10 regression analyses using the seven categories of exposure, adjusting the regression coefficients 

11 for year of birth and age. The regression models were run for both unlagged and lagged 

12 (15 years) cumulative exposure scores. 

13 Overall, when compared with the U.S. general population, a slight excess of cancer 

14 mortality (from all sites) was noted in the 5,132 cohort study population (SMR = 1.13, 

15 95% CI = 1.02−1.25). This result did not substantially differ from the earlier finding that 

16 Fingerhut et al. (1991) published (SMR = 1.15, 95% CI = 1.03−1.30). Site-specific analyses 

17 revealed statistically significant excesses relative to the U.S. general population for bladder 

18 cancer (SMR = 1.99, 95% CI = 1.13−3.23) and for cancer of the larynx (SMR = 2.22, 

19 95% CI = 1.06−4.08). In the chloracne subcohort (n = 608), a borderline statistically significant 

20 excess relative to the general population was found for all cancer sites combined (SMR = 1.25, 

21 95% CI = 0.98−1.57) and for lung cancer (SMR = 1.45, 95% CI = 0.98−2.07). The authors also 

22 found statistically significant excesses for connective and soft tissue sarcomas (SMR = 11.32, 

23 95% CI = 2.33−33.10) and for lymphatic and hematopoietic malignancies (SMR = 3.01, 

24 95% CI = 1.43−8.52). 

25 External comparisons made by grouping workers into septiles of cumulative TCDD 

26 exposure and generating an SMR for each septile using the U.S. population as the referent group 

27 suggested a dose-response relationship. For all cancer sites combined, workers in the highest 

28 exposure score category had an SMR of 1.60 (95% CI = 1.15−1.82); increases also were 

29 observed in the sixth (SMR = 1.34) and fifth (SMR = 1.15) septiles. The two-sided p-value 

30 associated with the test for trend for cumulative TCDD exposure was statistically significant 

31 (p = 0.02). A similar approach for lung cancer revealed virtually the same pattern. The 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 incorporation of a 15-year latency for the analyses of all cancer deaths, in general, produced 

2 slightly higher SMRs across the septiles, although a slight attenuation of effect was noted in the 

3 highest septile (SMRunlagged = 1.60 vs. SMRlagged = 1.54). For a 15-year lag, the lung cancer 

4 SMRs were mixed compared to the unlagged results with some septile exposure categories 

5 increasing and others decreasing relative to the lowest exposure group. 

6 For the internal cohort comparisons using Cox regression analyses higher hazard ratios 

7 were found among workers in the higher exposure categories than in the lowest septile. The 

8 linear test for trend, however, was not statistically significant (p = 0.10). The associations across 

9 the septiles for the unlagged exposure for the internal cohort comparisons were not as strong as 

10 for the external cohort comparisons. The opposite was true, however, for cumulative exposures 

11 lagged 15 years. 

12 Relative to the lowest septile, stratified analyses revealed increased hazard ratios in the 

13 upper septiles of the internal cohort comparisons for both smoking- and nonsmoking-related 

14 forms of cancer. The test for linear trend was statistically significant for all other cancers (after 

15 smoking-related cancers were excluded). These analyses suggest that the overall cancer findings 

16 were not limited to an interaction between TCDD and smoking. Additional sensitivity analyses 

17 by the authors indicated the findings for smoking-related cancers were largely unaffected by the 

18 exclusion of bladder cancer cases. This observation suggests that the exposure to 

19 4-aminobiphenyl, which occurred at one plant and might have contributed to an increased 

20 number of bladder cancers, did not substantially bias the dose-response relationship between 

21 TCDD and all cancers combined. 

22 The investigators also evaluated the dose-response relationship with a Cox regression 

23 model separately for each plant using internal cohort comparisons and found some heterogeneity. 

24 This finding is not unexpected particularly given the relatively small number of cancer deaths at 

25 each plant, and given that exposures were quite low for one plant at which no positive 

26 association was found. The variability among plants was taken into account by modeling plant 

27 as a random effect measure in the Cox model, which produced little change in the slope 

28 coefficient (β = 0.0422 vs. β = 0.0453). 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.1.2.2. Study evaluation.

2 This study represents a valuable extension of that by Fingerhut et al. (1991). Internal 

3 comparisons were performed to help minimize potential biases associated with using an external 

4 comparison group (e.g., healthy worker effect, and differences in other risk factors between the 

5 cohort and the general population). That similar dose-response relationships were found for 

6 internal and external comparison populations suggests that the bias due to the health worker 

7 effect in the cohort might be minimal for cancer mortality. More importantly, the construction of 

8 the cumulative exposure scores provides an improved opportunity to evaluate dose-response 

9 relationships compared with the length of exposure and duration of employment metrics that 

10 Fingerhut et al. (1991) used. 

11 A potential limitation of the NIOSH study was the inability to account for cigarette 

12 smoking. If cigarette smoking did contribute to the increased cancer mortality rates in this and 

13 other cohorts, increased cancer mortality from exposure to TCDD would be expected only for 

14 smoking-attributable cancers. This study demonstrates associations with TCDD for both 

15 smoking- and nonsmoking-related cancers, including a stronger association for 

16 nonsmoking-related cancers. Therefore, the data provide evidence that associations between 

17 TCDD and cancer mortality are not likely due to cigarette smoking. 

18 The findings regarding latency should be interpreted cautiously as the statistical power in 

19 the study to compare differences across latency intervals was limited. Caution also should be 

20 heeded, given that latency intervals can vary on an individual basis as they are often 

21 dose-dependent (Guess and Hoel, 1977). The evaluation of whether TCDD acts as either an 

22 initiating or promoting agent (or both) is severely constrained by the reliance on cancer mortality 

23 data rather than incidence data. This constraint is due to the fact that survival time can be quite 

24 lengthy and can vary substantially across individual and cancer subtype. For example, the 5-year 

25 survival among U.S. males for all cancer sites combined ranged between 45 and 60% (Clegg et 

26 al., 2002). When only mortality data are available, evaluating the time between when individuals 

27 are first exposed and when they are diagnosed with cancer is nearly impossible. 

28 Starr (2003) suggested that Steenland et al. (1999) focused too heavily on the exposures 

29 that incorporated a 15-year period of latency and that those who experienced high exposures 

30 would inappropriately contribute person-years to the lowest exposure group “irrespective of how 

31 great the workers’ actual cumulative exposure scores may have been.” Most cancer deaths 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 would, however, typically occur many years postemployment. Given that the follow-up interval 

2 of the cohort was long and the average exposure duration was 2.7 years, at the time of death, 

3 person-years for those with high cumulative exposures would be captured appropriately. The 

4 median 5-year survival for all cancers is approximately 50% (Clegg et al., 2002), so applying a 

5 minimum latency of 5 years when using cancer mortality rather than cancer incidence data is 

6 needed to assure that the exposure metric is capturing exposures that occur before diagnoses. 

7 Increasing this latency period, for example to 10 or 15 years, would eliminate consideration of 

8 exposures that occur in the period between tumor occurrence and tumor detection (diagnosis), 

9 and allows for an appropriate focus on exposures that act either early or late in the pathogenic 

10 process. The IARC recommendation also underscores the relevance of including a period of 

11 latency when estimating cancer risks related to TCDD. Specifically, IARC indicated that if the 

12 association of TCDD with cancer is causal, effects might become apparent only at high 

13 exposures and with adequate latency; they suggested that a latency interval of 15 years could be 

14 too short (IARC, 1997). EPA considers the Steenland et al. (1999) presentation to be balanced in 

15 that they provided the range in lifetime excess risk estimated across the various models used. 

16 The authors’ finding that the models with a 15-year lag provided a statistically significant 

17 improvement in fit based on the chi-square test statistic should not be readily dismissed. 

18 

19 2.4.1.1.1.1.2.3. Suitability of data for TCDD dose-response modeling.

20 This study meets most of the epidemiological considerations for conducting a 

21 quantitative dose-response analysis for mortality from all cancer sites combined. This study 

22 excludes a large number of workers who were exposed to pentachlorophenol, thus eliminating 

23 the potential for bias from this exposure and used an improved methodology for assigning TCDD 

24 exposures to the workers. However, given that exposures to other dioxin-like compounds were 

25 not described, it is unclear if the exposures among this cohort were primarily to TCDD. 

26 Therefore, dose-response modeling was not pursued for this study, but was for the subsequent 

27 NIOSH study by Steenland et al. (2001), which did examine exposure to dioxin-like compounds. 

28 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.1.3. Steenland et al., 2001. 

2 2.4.1.1.1.1.3.1. Study summary.

3 In 2001, Steenland et al. published a risk analysis using the NIOSH cohort that for the 

4 first time incorporated serum measures in the derivation of TCDD exposures for individual 

5 workers. The authors applied the same exclusion criteria to the entire cohort of workers across 

6 the 12 plants in the Steenland et al. (1999) study, which left 3,538 workers for which risk 

7 estimates could be calculated. Cumulative TCDD serum levels were estimated on an individual 

8 basis for all 3,538 workers by developing predictive models that used a subset of 170 workers for 

9 which both serum measures and TCDD exposures scores were available (Steenland et al., 2001). 

10 Unlike previous analyses of the NIOSH cohort that considered several different mortality 

11 outcomes, the analyses presented in Steenland et al. (2001) focused exclusively on mortality 

12 from all cancers sites combined. The authors observed 256 cancer deaths in the cohort during 

13 the follow-up interval that extended from 1942 until the end of 1993. All risks estimated in the 

14 Steenland et al. (2001) study were based on internal cohort comparisons. 

15 Characterization of TCDD exposure levels among the workers was based on serum 

16 measures obtained in 1988 from 199 workers who were employed in one of the eight plants. The 

17 researchers restricted the development of the model to include only those workers whose 

18 measured serum levels were deemed to be greater than the upper range of background levels 

19 (10 ppt), which resulted in 170 workers. 

20 The authors developed a regression model that could estimate the level of TCDD at the 

21 time of last exposure for the 170 workers. The model was developed based on the estimated 

22 half-life of TCDD, the known work history of each worker, a pharmacokinetic model for the 

23 storage and excretion of TCDD, and exposure scores for each job held by each worker over time. 

24 The resulting equation follows 

25 

26 ylast exposure = y1988 exp(λ∆t) (Eq. 2-1) 

27 

28 The first-order elimination rate constant (λ) was based on a half-life of 8.7 years 

29 previously reported for the Ranch Hands cohort (Michalek et al., 1996). The background rate of 

30 TCDD exposure was assumed to be 6.1 parts per trillion (ppt), which was based on the median 

31 level in a sample of 79 unexposed workers in the NIOSH cohort (Piacitelli et al., 1992). This 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 value was subtracted when TCDD values were back-extrapolated, and then added again after the 

2 back-extrapolation was completed. A background level of 5 ppt also was used in some of the 

3 analyses with minimal demonstrable effects on the results. Sensitivity analyses also were 

4 incorporated to consider a 7.1-year half-life estimate that had been developed for the earlier 

5 Ranch Hands study (Pirkle et al., 1989). 

6 After back-extrapolating to obtain TCDD serums levels at the time of last exposure, the 

7 investigators estimated cumulative (or “area under the curve”) TCDD serum levels for every 

8 cohort member. This estimation procedure was the same method Flesch-Janys et al. (1998) 

9 applied to the Hamburg cohort to derive a coefficient for relating serum levels to exposure 

10 scores. The “area under the curve” approach integrates time-specific serum levels over the 

11 employment histories of the individual workers. The slope coefficient was estimated using a 

12 no-intercept linear regression model. This model is based on the assumption that a cumulative 

13 score of zero is associated with no serum levels above background. 

14 Cox regression was also used to model the continuous measures of TCDD. A variety of 

15 exposure metrics were considered that took into account different lags, nonlinear relationships 

16 (e.g., log-transform and cubic spline), as well as threshold and nonthreshold exposure metrics. 

17 Categorical analyses were used to evaluate risks across TCDD exposure groups, while different 

18 shapes of dose-response curves were evaluated through the use of lagged and unlagged 

19 continuous TCDD measures. Categorical analyses of TCDD exposure were conducted using the 

20 Cox regression model to derive estimates of relative risk (RR) as described by hazard ratios and 

21 95% CIs. The reference group in this analysis was those workers in the lowest septile 

22 cumulative exposure grouping (<335 ppt-years). The septiles were chosen based on cumulative 

23 serum levels that considered no lag and also a 15-year lag. 

24 The investigators also conducted dose-response analyses using the toxicity equivalence 

25 (TEQ) approach. The TEQ is calculated as the sum of all exposures to dioxins and furans 

26 weighted by the potency of each specific compound. In this study, TCDD was assumed to be 

27 account for all dioxin exposures in the workplace. For background TEQ levels, the investigators 

28 used a value of 50 ppt in the dose-response modeling. This is based on the assumption that 

29 TCDD accounted for 10% of the toxicity of all dioxins and furans (WHO, 1988), and is 

30 equivalent to using a background level of 5 ppt/yr that was used in the derivation of cumulative 

31 serum TCDD levels. A statistically significant dose-response pattern was observed for all cancer 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 mortality and TCDD exposure based on log of cumulative TEQs with a 15-year lag. A 

2 comparison of the overall model chi-square values indicated that the fit of this model was not as 

3 good as that for TCDD. 

4 The hazard ratios among workers grouped by categories of cumulative TCDD exposure 

5 (lagged 15 years) suggested a dose-response relationship. Steenland et al. (2001) found 

6 statistically significant excesses in the higher exposure categories compared to the lowest septile. 

7 The RR was 1.82, 95% CI = 1.18−2.82 for the sixth septile (7,568−20,455 ppt-years) and 1.62, 

8 95% CI = 1.03−2.56) for the seventh septile (>20,455 ppt-years). Cox regression indicated that 

9 log TCDD serum concentrations (lagged 15 years) was positively associated with cancer 

10 mortality (β = 0.097, standard error (β) = 0.032, p < 0.003). A statistically significant 

11 improvement in fit was observed when a 15-year lag interval was incorporated into the model 

compared to a model with no such lag [Model χ

2 12 with 4 degrees of freedom (df) = 7.5]. Results 

13 were similar when using a half-life of 7.1 years rather than 8.7 years. The excess lifetime risk of 

14 death from cancer at age 75 for TCDD intake (per 1.0-picogram per kilogram [pg/kg] of body 

15 weight (BW) per day) was about 0.05−0.9% above a background lifetime risk of cancer death of 

16 12.4%. The results from the best-fitting models provide lifetime risk estimates within the ranges 

17 derived using data from the Hamburg cohort (Becher et al., 1998). 

18 In both categorical and continuous analyses of TCDD based on a linear exposure metric, 

19 the dose-response pattern tailed off at high exposures suggesting nonlinear effects. This 

20 phenomenon could be due to saturation effects (Stayner et al., 2003) or, alternatively, could have 

21 resulted from increased exposure misclassification of higher exposures (Steenland et al., 2001). 

22 As the authors highlighted, some of the highest exposures might have been poorly estimated as 

23 they occurred in workers exposed to short-term high exposures during the clean-up of a spill. 

24 The choice of a linear model to develop data from a single time point can also result in exposure 

25 misclassification in those individuals that have differences in the length of exposure (Emond et 

26 al., 2005). Misclassification would be less likely at low concentrations where dose-dependent 

27 elimination is minimal. 

28 

29 2.4.1.1.1.1.3.2. Study evaluation.

30 An important consideration in the Steenland et al. (2001) study was the use of a small 

31 subset of workers (n = 170) to infer exposures for the remainder of the cohort. This subset 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 comprised surviving members of the cohort (in 1988), and therefore, their age distribution would 

2 have differed from the rest of the cohort. Furthermore, these workers were employed at a single 

3 plant, at which the work histories were less detailed than at other plants; thus, the development of 

4 the exposure scores differed between this plant and that of the others. Also, many of the workers 

5 at this plant had the same job title and were employed during the same calendar period. The use 

6 of serum data from this subset adds a level of uncertainty that is not readily characterized. 

7 Despite this limitation, the use of these sera data to derive cumulative measures for all cohort 

8 workers has merit given the strong correlation observed between the exposure scores, and TCDD 

9 serum levels estimates at the time of last exposure (Spearman r = 0.90). 

10 The authors performed an extensive series of sensitivity analyses and considered several 

11 alternative exposure metrics to the simple linear model. The lifetime excess risk above 

12 background was nearly twice as high for the log cumulative serum measures with a 15-year lag 

13 when compared to the piecewise linear models with no lag. An important observation was that 

14 the exposure metric based on cumulative serum (lagged 15 years) did not fit the data as well as 

15 the cumulative exposure score used in earlier analyses (Steenland et al., 1999). A priori, one 

16 would expect that a better fit would be obtained with serum-based measures because serum 

17 levels are a better measure of relevant biological dose. As the authors noted, inaccuracies 

18 introduced in estimating the external-based exposure scores could have contributed to a poorer 

19 fit of the data. Alternatively, exposure misclassification error could be introduced if serum 

20 samples based on the 170 workers were not representative of the entire cohort. Although the 

21 serum-based measures did not fit the data as well as the exposures scores, the authors regarded 

22 them as providing a reasonable fit. Moreover, the serum-based measures enabled better 

23 characterization of risk in units (pg/kg-day) that can be used in regulation exposures. 

24 

25 2.4.1.1.1.1.3.3. Suitability of data for TCDD dose-response modeling.

26 This study meets all of the epidemiological considerations for conducting a quantitative 

27 dose-response analysis for mortality from all cancer sites combined. As mentioned previously, 

28 the NIOSH cohort is the largest assembled to date for which TCDD-related risks of cancer 

29 mortality can be estimated. The use of serum-based measures provides an objective measure of 

30 TCDD exposure. Repeated measures in other study populations have provided reasonable 

31 estimates of the half-life of TCDD, which permitted back-extrapolation of exposures. 
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1 The authors have made extensive efforts to evaluate a wide variety of nonlinear and 

2 linear models with varying lengths of latency and log transformations. The model chi-square test 

statistics were fairly similar for the log cumulative serum (15-year lag) (Model χ2

(4df) 3 = 11.3) 

model and the piecewise linear model (no lag) (Model χ2

(5df) 4 = 12.5). These models, however, 

5 produced results with twofold differences in lifetime excess risks. These differences underscore 

6 the importance of characterizing uncertainty in modeling approaches when conducting 

7 dose-response analysis. 

8 The Steenland et al. (2001) study characterizes risk in terms of pg/kg of body weight per 

9 day. Given that tolerable daily intake dioxin levels are typically expressed in pg/kg of body 

10 weight (WHO, 1998), the presentation of risks in terms of these units is an important advance 

11 from the earlier analyses that used exposure scores (Steenland et al., 1999). Many of the 

12 Steenland et al. (2001) findings are consistent with earlier work from this cohort, which is not 

13 surprising given that exposures scores were used to derive serum-based levels for the cohort. 

14 The findings of excess lifetime risks obtained for the best- fitting model are also consistent with 

15 those derived from the Hamburg cohort (Becher et al., 1998). This study meets the 

16 epidemiological considerations noted previously as there is no evidence that the study is subject 

17 to bias from confounding due to cigarette smoking or other occupational exposures. Given the 

18 considerable efforts to measure effective dose to TCDD among the study participants, this study

19 also meets the requisite dose-response modeling criteria and will be used in quantitative 

20 dose-response analyses of cancer mortality. 

21 

22 2.4.1.1.1.1.4. Cheng et al., 2006. 

23 2.4.1.1.1.1.4.1. Study summary.

24 Cheng et al. (2006) undertook a subsequent quantitative risk assessment of 3,538 workers 

25 in the NIOSH cohort using serum-derived estimates of TCDD. This dose-response analysis was 

26 published after the 2003 Reassessment document was released. The goal of this study was to 

27 examine the relationship between TCDD and cancer mortality (all sites combined) using a new 

28 estimate of dose that estimated TCDD as a function of both exposure intensity and age using a 

29 kinetic model. This physiologically based pharmacokinetic model has been termed the 

30 “concentration- and age-dependent elimination model” (CADM) and was developed by Aylward 
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1 et al. (2005b). This model describes the kinetics of TCDD following oral exposure to humans by 

2 accounting for key processes affecting kinetics by simulating the total concentration of TCDD 

3 based on empirical consideration of hepatic processes (see Section 3.3). An important feature of 

4 this kinetic model is that it incorporates concentration- and age-dependent elimination of TCDD 

5 from the body; consequently, the effective half-life of TCDD elimination varies based on 

6 exposure history, body burden, and age of the exposed individuals. The study was motivated by 

7 the reasoning that back-calculations of TCDD using a first-order elimination model and a 

8 constant half-life of 7−9 years underestimated exposures to TCDD among workers. This 

9 underestimate, in turn, would result in overestimates of the carcinogenic potency of TCDD. 

10 As with the earlier Steenland et al. (2001) analyses, the cohort follow-up period was 

11 extended from 1942 until the end of 1993 and work histories were linked to a job exposure 

12 matrix to obtain cumulative TCDD scores. Two cumulative serum lipid exposure metrics (in 

13 ppt-years) were constructed using the data obtained from the sample of 170 workers. The first 

14 replicated the metric used in a previous analysis of the cohort (Steenland et al., 2001) and was 

15 based on a first-order elimination model with an 8.7-year half-life (Michalek et al., 1996). The 

16 second metric was based on CADM and had two first-order elimination processes (Aylward et 

17 al., 2005a). This metric assumes that the elimination of TCDD in humans occurs at a faster rate 

18 when body concentrations are high and at slower rates in older individuals (Aylward et al., 

19 2005a, b). The model was optimized using individuals for which serial measures of serum 

20 TCDD were available. These measures were obtained from 39 adults with initial serum levels 

21 between 130 and 144,000 ppt (Aylward et al., 2005b). This group included 36 individuals who 

22 had been exposed in the Seveso accident and 3 exposed in Vienna, Austria. In practice, for 

23 serum levels greater than 1,000 ppt, the effective half-life would be less than 3 years, and for 

24 serum TCDD levels less than 50 ppt, the effective half-life would be more than 10 years 

25 (Aylward et al., 2005b). Results from the model indicate that men eliminate TCDD faster than 

26 women do as demonstrated previously by Needham et al. (1994). These age- and 

27 concentration-dependent processes were assumed to operate independently on TCDD in hepatic 

28 and adipose tissues, and TCDD levels in liver and adipose tissue were assumed to be a nonlinear 

29 function of body concentration. Cheng et al. (2006) calibrated CADM using a dose of 156 ng 

30 per unit of exposure score and assumed a background exposure rate of 0.01 ng/kg-month. The 

31 average TCDD ppt-years derived from CADM with a 15-year lag was 4.5−5.2 times higher than 
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1 with the first-order elimination model. The two metrics, however, were highly correlated based 

2 on a Pearson correlation coefficient of 0.98 (p < 0.001). Comparisons of fit between the CADM 

and first-order elimination model were made using R2 3 values and presented in Aylward et al. 

4 (2005b). 

5 Cheng et al. (2006) compared the mortality experience of NIOSH workers to the U.S. 

6 general population using the SMR statistic. SMR statistics also were generated separately for 

7 each of the 8 plants and for all plants combined. Cox regression models were used to analyze 

8 internal cohort dose-response. These models used age as the time variable, and penalized 

9 smoothing spline functions of the CADM metric also were considered. The possible 

10 confounding effects of other occupational exposures and other regional population differences 

11 were assessed by repeating analyses after excluding one plant at a time. Lagged and unlagged 

12 TCDD exposures were analyzed separately, and stratified analyses compared risk estimates for 

13 smoking- and nonsmoking-related cancers. Cheng et al. (2006) adjusted the slope estimates 

14 derived from the Cox model for potential confounding effects of race and year of birth. 

15 Overall, a statistically significant excess in all cancer mortality in the cohort occurred 

16 relative to the general population (SMR = 1.17, 95% CI = 1.03−1.32). The plant-specific SMRs 

17 ranged from 0.62−1.87, with a statistically significant excess evident only for plant 10 

18 (SMR = 1.87, 95% CI = 1.35−2.52). For lung cancer mortality, the overall SMR was not 

19 statistically significant (SMR = 1.11, 95% CI = 0.89−1.37). A statistically significant excess for 

20 lung cancer also was found for plant 10 (SMR = 2.35, 95% CI = 1.44−3.64). The SMRs between 

21 smoking- (SMR = 1.22, 95% CI = 1.01−1.45) and nonsmoking-related cancers (SMR = 1.12, 

22 95% CI = 0.94−1.33) were comparable. 

23 For the internal cohort analyses of serum-derived measures, the authors were able to 

24 replicate the one-compartmental model used previously (Steenland et al., 2001). As had been 

25 noted by Steenland et al. (2001), an inverse-dose-response pattern was seen for individuals with 

high exposures (above 95th 26 percentile); this type of pattern is often seen in occupational studies 

27 (Stayner et al., 2003). Excluding these data produced a stronger association between TCDD and 

28 all-cause mortality. In fact, only when the upper 2.5% or 5% of observations was removed did a 

29 statistically significant positive association become evident with the untransformed data. 

30 Similarly, when the model incorporated a lag of 15 years, a statistically significant association 

31 was noted only for the untransformed TCDD ppt-years with the upper 5% of observations 
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1 removed. Stratified analyses revealed little difference between smoking- and 

2 nonsmoking-related cancers, and the removal of one plant at a time from the analyses of TCDD 

3 ppt-years changes did not substantially change the slope. 

4 

5 2.4.1.1.1.1.4.2. Study evaluation.

6 The authors reported that CADM provided an improved fit over the one-compartmental 

7 model, but presented no evidence regarding any formal test of statistical significance. A 

comparison of R2

 values presented in Aylward et al. (2005b), however, does reveal that the R2 8 

9 value increased from 0.27 (first-order compartmental model with an 8.7-year half-life) to 0.40 

10 for CADM. TCDD exposures estimated using CADM were approximately fivefold higher than 

11 the one-compartmental model estimates among cohort members with higher levels of exposure. 

12 Differences in exposure estimates between the two metrics were less striking among individuals 

13 with lower TCDD exposures. The net effect was that CADM produced a 6- to 10-fold decrease 

14 in estimated risks compared to estimates previously reported (Steenland et al., 2001). 

15 Nonetheless, the estimates produced by CADM span more than two orders of magnitude under 

16 various assumptions. Further uncertainties arise from between-worker variability of TCDD 

17 elimination rates, possible residual confounding, and the variability associated with the use of 

18 data obtained from other cohorts. Nevertheless, the use of the CADM model to estimate TCDD 

19 exposure is considered a significant advantage over the previous first-order body burden 

20 calculations. 

21 

22 2.4.1.1.1.1.4.3. Suitability of data for TCDD dose-response modeling.

23 The value of including the NIOSH cohort data has already been established based on 

24 investigations published by Steenland et al. (1999, 2001). The decision to include data from the 

25 quantitative dose-response analysis that Cheng et al. (2006) conducted relates to the added value 

26 that the CADM exposure estimates would provide. The earlier modeling work of Aylward et al. 

27 (2005b) provided some support for a modest improvement of the fit of CADM over the 

28 first-order compartmental model, and they also confirmed previous studies that found that TCDD 

29 elimination rates varied by age and sex. Recent work by Kerger et al. (2006) also demonstrates 

30 that the half-life for TCDD is shorter among Seveso children than the corresponding half-life for 

31 adults, and that body burdens influence the elimination of TCDD in humans. That estimates of 
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1 half-lives among men have been remarkably consistent, with mean estimates ranging between 

2 6.9 and 8.7 years (Flesch-Janys et al., 1996; Pirkle et al., 1989; Michalek et al., 2002; Needham 

3 et al., 2005), however, is noteworthy. Based on the underlying strengths of the NIOSH cohort 

4 data and efforts by Cheng et al. (2006) to improve estimates of effective dose, these data support 

5 further dose-response modeling. 

6 

7 2.4.1.1.1.1.5. Collins et al., 2009. 

8 2.4.1.1.1.1.5.1. Study summary.

9 In a recent study, Collins et al. (2009) investigated the relationship between serum TCDD 

10 levels and mortality rates in a cohort of trichlorophenol workers exposed to TCDD. These 

11 workers were part of the NIOSH cohort having accounted for approximately 45% of the 

12 person-years in an earlier analysis (Bodner et al., 2003). The investigators completed an 

13 extensive dioxin serum evaluation of workers employed by the Dow Chemical plant in Midland, 

14 Michigan, that made 2,4,5-trichlorophenol (TCP) from 1942 to 1979 and 2,4,5-T from 1948 to 

15 1982. Collins et al. (2009) developed historical TCDD exposure estimates for all TCP and 

16 2,4,5-T workers. This study represents the largest group of workers from a single plant ever 

17 studied for the health effects of TCDD. Little information on how vital status was ascertained, 

18 either in this paper or in the Bodner et al. (2003) report of mortality in this cohort. Although the 

19 authors indicate that death certificates were obtained from the states in which the employees 

20 died, whether vital status was ascertained from company records or through record linkage to the 

21 National Death Index is unclear.

22 The follow-up interval for these workers covered the period between 1942 and 2003. 

23 Thus, the study included 10 more years of follow-up than earlier investigations of the entire 

24 NIOSH cohort. Serum samples were obtained from 280 former workers collected during 

25 2004−2005. A simple one-compartment first-order pharmacokinetic model and elimination rates 

26 as estimated from the BASF cohort were used (Flesch-Janys et al., 1996). The “area under the 

27 curve” approach was used to characterize workers’ exposures over the course of their working 

28 careers and provided a cumulative measure of exposure. Analyses were performed with and 

29 without 165 of the 1,615 workers exposed to pentachlorophenol to evaluate the impact of these 

30 exposures. 
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1 External comparisons of cancer mortality rates to the general U.S. population were made 

2 using SMRs. Internal cohort comparisons of exposure-response relationships were made using 

3 the Cox regression model. This model used age as the time variable, and was adjusted for year 

4 of hire and birth year. Only those causes of death for which an excess was found based on the 

5 external comparisons or for which previous studies had identified a positive association were 

6 selected for dose-response analyses. 

7 A total of 177 cancer deaths were observed in the cohort. For the external comparison 

8 with the U.S. general population, overall, no statistically significant differences were observed in 

9 all cancer mortality among all workers (SMR = 1.0, 95% CI = 0.8−1.1). Results obtained after 

10 excluding workers exposed to pentachlorophenol were similar (SMR = 0.9, 95% CI = 0.8−1.1). 

11 Statistically significant excesses in the cohort were found for leukemia (SMR = 1.9, 

12 95% CI = 1.0−3.2) and soft tissue sarcoma (SMR = 4.1, 95% CI = 1.1−10.5). Although not 

13 statistically significant SMRs for other lymphohemopoietic cancers included non-Hodgkin’s 

14 lymphoma SMR = 1.3; 95%CI = 0.6, 2.5) and Hodgkin’s disease (SMR = 2.2; 95% CI = 0.2, 

15 6.4). 

16 Internal cohort comparisons using the Cox regression model were performed for all 

17 cancers combined, lung cancer, prostate cancer, leukemia, non-Hodgkin’s lymphoma, and 

18 soft-tissue sarcoma. Whether the internal comparisons excluded those workers exposed to 

19 pentachlorophenol is not entirely clear from the text or accompanying table, but presumably they 

20 do not. The RR was 1.002 (95% CI = 0.991−1.013) for all cancer mortality per 1 ppb-year 

21 increase in cumulative TCDD exposure was not statistically significant. Except for soft tissue 

22 sarcomas, no statistically significant exposure-response trends were observed for any cancer site. 

23 For soft tissue sarcoma, analyses were based on only four deaths. 

24 

25 2.4.1.1.1.1.5.2. Study evaluation.

26 A key limitation of this study is that SMRs were not derived for different periods of 

27 latency for the external comparison group analysis. The original publication on the NIOSH 

28 cohort found that SMRs increased when a 20-year latency period was incorporated (Fingerhut et 

29 al., 1991), and similar patterns have been observed in other occupational cohorts (Manz et al., 

30 1991; Ott and Zober, 1996) and among Seveso residents (Consonni et al., 2008). Additionally, 

31 dose-response analyses showed marked increases in slopes with a 15-year latency period 
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1 (Steenland and Deddens, 2003; Cheng et al., 2006). In this context, the absence of an elevated 

2 SMR for cancer mortality is consistent with previous findings of the NIOSH cohort. While the 

3 cohort did have sufficient follow-up, no evaluation of possible latent effects was presented and 

4 this is a major limitation of this study. Further, the evaluation of the exposure metrics should be 

5 expanded from what was presented in Collins et al. (2009) due to the previous analyses of the 

6 same workers finding positive associations between cancer mortality and TCDD (Steenland et 

7 al., 2001). 

8 Unfortunately, the Collins et al. (2009) study did not include a categorical analysis of 

9 TCDD exposure and cancer mortality. This categorical analysis would have enabled an 

10 evaluation of whether a nonlinear association exists between TCDD exposure and cancer risk. 

11 The analyses of both Cheng et al. (2006) and Steenland et al. (2001) suggest an attenuation of 

12 effects at higher doses, and several investigations have considered log-transformed associations 

13 as a means to address nonlinearity. Also, the earlier plant-specific dose-response analyses of 

14 Steenland et al. (2001) are not consistent with the findings for the Midland plant that Collins et 

15 al. (2009) presented. These differences could be due to differences in the construction of 

16 exposure metrics, additional follow-up, or lagging of exposures. 

17 

18 2.4.1.1.1.1.5.3. Suitability of data for dose-response modeling.

19 The Collins et al. (2009) study uses serum levels to derive TCDD exposure estimates and 

20 does not appear to be subject to important biases. The reliance on data from one plant offers 

21 some advantages over the multiplant analyses, as heterogeneity in exposure to other occupational 

22 agents would be lower. The number of individuals who provided serum samples (n = 280) is 

23 greater than the 170 individuals used to derive TCDD estimates for the NIOSH cohort. This 

24 study’s main limitation is that it lacks rigor in sensitivity analyses to explore the impact that 

25 other exposure metrics and model assumptions had on the study findings. The data used in 

26 quantitative dose-response modeling should retain flexibility to account for latency effects. The 

27 data, as structured for this study, are inadequate for quantitative dose-response analysis given 

28 that no dose-response effects were detected. 

29 
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1 2.4.1.1.1.2. The BASF cohort. 

2 In 1953, dioxin contamination occurred as a result of an autoclave accident during the 

3 production of trichlorophenol at the BASF plant in Ludwigshafen, Germany. A second dioxin 

4 incident occurred in 1988 that was attributed to the blending of thermoplastic polyesters with 

5 brominated flame retardants. Of the two events, the one on November 13, 1953, was associated 

6 with more severe acute health effects, including chloracne that resulted in immediate 

7 hospitalizations for seven workers. These adverse events were not linked to TCDD until 1957 

8 when TCDD was identified as a byproduct of the production of trichlorophenol and was shown 

9 to induce chloracne (Zober et al., 1994). Zober and colleagues (1998) noted that with the 1988 

10 accident, affected individuals did not exhibit clinical symptoms or chloracne, but rather were 

11 identified through “analytical measures.” In both instances, efforts were made to limit the 

12 potential for exposure to employees. 

13 

14 2.4.1.1.1.2.1. Thiess and Frentzel-Beyme, 1977 and Thiess et al., 1982. 

15 2.4.1.1.1.2.1.1. Study summary.

16 A study of the mortality of workers employed at the BASF plant was first presented in 

17 1977 (Thiess and Frentzel-Beyme, 1977) with subsequent updates in both 1982 (Thiess et al., 

18 1982), and in 1990 (Zober et al., 1990). In the first published paper (Thiess et al., 1982), 

19 74 employees involved in the 1953 accident were traced and their death certificate information 

20 extracted. Of these, 66 suffered chloracne or severe dermatitis. Observed deaths were compared 

21 to the expected number using three external reference groups: the town of Ludwigshafen 

22 (n = 180,000), the district of Rhinehessia-Palatinate (n = 1.8 million), and the Federal Republic 

23 of Germany (n = 60.5 million). Another comparison group was assembled by selecting 

24 age-matched employees taken from other cohorts under study. This additional comparison was 

25 aimed at avoiding potential biases associated with healthy worker effect when using an external 

26 referent.

27 During a follow-up interval of up to 26 years (1953−1979), 21 individuals died. Of 

28 these, seven deaths were from cancer. The expected number of cancer deaths derived for the 

29 three external comparison groups ranged between 4.1 and 4.2, producing an SMR of 1.7 

30 (p-values ranged between 0.12 and 0.14). Excess mortality was found for stomach cancer based 

31 on the external comparisons (p < 0.05); however, this was based on only three cases. No other 
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1 statistically significant excesses were found with the external comparisons made to the other 

2 cohorts of workers. 

3 

4 2.4.1.1.1.2.1.2. Study evaluation.

5 In the Thiess et al. (1982) study, no TCDD exposures were derived for the workers, thus 

6 no dose-reconstruction was performed. The findings from this study are limited by the small size 

7 of the cohort. The 74 workers followed in this cohort represent the smallest number of workers 

8 across the occupational cohorts (Fingerhut et al., 1991; Steenland et al., 2001; Becher et al., 

9 1998; Hooiveld et al., 1998; Michalek and Pavuk, 2008; McBride et al., 2009a, b) that have 

10 investigated TCDD exposures and cancer mortality. Mechanisms of follow-up were excellent as 

11 all individuals were traced, and death certificates were obtained from all deceased workers. 

12 Although the study does compare the mortality experience to other occupational cohorts, 

13 the paper provides insufficient information to adequately interpret the associated findings. For 

14 example, a description of these occupations is lacking making it impossible to determine whether 

15 these cohorts were exposed to other occupational carcinogens that might have confounded the 

16 associations between TCDD exposure and cancer mortality. 

17 

18 2.4.1.1.1.2.1.3. Suitability of data for TCDD dose-response modeling.

19 Subsequent data assembled for the BASF cohort provide more detailed exposure 

20 characterization and also include information for 243 male workers employed at the plant. As 

21 such, this study did not meet the considerations for further dose-response analysis. 

22 

23 2.4.1.1.1.2.2. Zober et al., 1990. 

24 2.4.1.1.1.2.2.1. Study summary.

25 Zober et al. (1990) also examined the mortality patterns of 247 individuals involved in 

26 the 1953 accident at the BASF plant. As detailed in their paper, the size of the original cohort 

27 was expanded by efforts to locate all individuals who were exposed in the accident or during the 

28 clean-up. Three approaches were followed in assembling the cohort. Sixty-nine cohort members 

29 were identified from the company physician’s list of employees exposed as a result of the 

30 accident (Subcohort C1). Sixty-six of these workers were included in the original study 

31 population of workers Thiess et al. (1982) examined. Eighty-four other workers who were 
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1 potentially exposed to TCDD due to their involvement in demolitions or operations were added 

2 to the cohort. This group included 43 firemen, 18 plant workers, 7 bricklayers, 5 whitewashers, 

3 4 mechanics, 2 roofers, and 5 individuals in other occupations (Subcohort C2). The cohort was 

4 further augmented through the Dioxin Investigation Program, which sought to locate those who 

5 were involved in the 1953 accident and were still alive in 1986. Current and former workers 

6 enrolled in the study were asked to identify other current or former coworkers (including 

7 deceased or retired) who might have been exposed from the accident. This third component of 

8 94 workers (Subcohort C3) included 27 plant workers, 16 plumbers, 10 scaffolders, 

9 10 professionals, 7 mechanics, 6 transportation workers, 5 bricklayers, 5 laboratory assistant, 

10 3 insulators, and 5 individuals in other occupations. A medical examination was performed for 

11 those identified through the Dioxin Investigation Program, and blood measures were obtained for 

12 28 of these workers. 

13 External comparisons of the workers’ mortality experience to the general population of 

14 the Federal Republic of West Germany were made using SMRs. Person-years were tabulated 

15 across strata defined by calendar period, sex, and age group. Sixty-nine deaths including 

16 twenty-three from cancer were detected among the workers during the 34-year follow-up period 

17 (November 17, 1953 through December 31, 1987). Cause-specific death rates for these same 

18 strata were available for the Federal Republic of West Germany. Stratified analyses were 

19 conducted to examine variations in the SMRs according to years since first exposure (0−9, 

20 10−19, and ≥20 years) for each of the three subcohorts, as well as 114 workers with chloracne. 

21 Although it was consistent in magnitude with findings from the NIOSH cohort, a 

22 statistically significant SMR for all cancer mortality was not observed (SMR = 1.17, 

23 90% CI = 0.80−1.66). The SMRs for each of the three subcohorts varied substantially. For 

24 Subcohorts C1, C2, and C3, the SMRs were 1.30 (90% CI = 0.68−2.26), 1.71 

25 (90% CI = 0.96−2.83), and 0.48 (90% CI = 0.13−1.23), respectively. The SMRs increased 

26 dramatically when analyses were restricted to those with 20 or more years since first exposure in 

27 Subcohort C1 (SMR = 1.67, 90% CI = 0.78−3.13) and Subcohort C2 (SMR = 2.38, 

28 90% CI = 1.18−4.29). Meanwhile, in a subgroup analysis of those with chloracne, for the period 

29 of 20 or more years after first exposure, a statistically significant excess in cancer mortality was 

30 noted (SMR = 2.01; 90% CI = 1.22−3.15). 

31 
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1 2.4.1.1.1.2.2.2. Study evaluation.

2 An important limitation of the study is the manner in which the cohort was constructed. 

3 Subcohort C3 was constructed by identifying individuals who were alive in 1986. This resulted 

4 in 97 active and retired employees who participated in the program, with 94 included in the 

5 analysis. Although these individuals did identify other workers who might have also retired or 

6 died, inevitably, some individuals who had died were not included in the cohort. This would 

7 serve to underestimate the SMRs that were generated with external comparisons to the German 

8 population. Indeed, cancer mortality rates in this subcohort were about half of what would have 

9 been expected based on general population rates (SMR = 0.48, 90% CI = 0.13−1.23). 

10 Additionally, more than half of Subcohort C2 were firemen (43 of 84), who would likely have 

11 been exposed to other carcinogens as a consequence of their employment. Quantitative analyses 

12 of epidemiologic data for firefighters have demonstrated increased cancer risk for several 

13 different forms of cancer (Youakim, 2006). Therefore, potential confounding from other 

14 occupational exposures of the firefighters could have contributed to the higher SMR in 

15 Subcohort C2 cohort and is a concern. Data on cigarette smoking were not available either. No 

16 excess for nonmalignant respiratory disease was found, however, suggesting this might not be an 

17 important source of bias. 

18 

19 2.4.1.1.1.2.2.3. Suitability of data for TCDD dose-response modeling.

20 As with the Thiess et al. (1982) publication, worker exposure was not estimated. Lack of 

21 exposure estimates precludes a quantitative dose-response analysis using these data. Also, the 

22 study design is not well suited to characterization of risk using the SMR statistic. Mortality is 

23 also likely under-ascertained in the large component of the cohort that was constructed through 

24 the identification of surviving members of the cohort. 

25 

26 2.4.1.1.1.2.3. Ott and Zober, 1996. 

27 2.4.1.1.1.2.3.1. Study summary.

28 Ott and Zober (1996) extended the analyses of the BASF cohort to include estimates of 

29 individual-level measures of TCDD. The researchers also investigated associations with cancer 

30 mortality and identified incident cancer cases. The cohort follow-up period of 39 years extended 

31 until December 31, 1992, adding 5 years to a previous study (Zober et al., 1990). Ott and Zober 
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1 (1996) identified incident cases of cancer using occupational medical records, death certificates, 

2 doctor’s letters, necropsy reports, and information from self-reported surveys sent to all 

3 surviving cohort members. Self-reported cancer diagnoses were confirmed by contacting the 

4 attending physician. 

5 This study characterized exposure by two methods: (1) determining chloracne status of 

6 the cohort members and (2) estimating cumulative TCDD (μg/kg) levels. In 1989, serum 

7 measures were sought for all surviving members of the 1953 accident, and serum TCDD levels 

8 were quantified for 138 individuals. These serum levels were used to estimate cumulative 

9 TCDD concentrations for all 254 members of the accident cohort. Ott et al. (1993) published a 

10 description of the exposure estimation procedure, which was a regression model that accounted 

11 for the circumstances and duration of individual exposure. The average internal half-life of 

12 TCDD was estimated to be 5.8 years based on repeated serum sampling of 29 individuals. The 

13 regression model allowed for this half-life to vary according to the percentage of body fat, and 

14 yielded half-lives of 5.1 and 8.9 years among those with 20% and 30% body fat, respectively. 

15 Previous analyses of this cohort had used a half-life of 7.0 years (Ott et al., 1993). 

16 TCDD half-life has been reported to increase with percentage of body fat in both 

17 laboratory mammals (Geyer et al., 1990) and humans (Zober and Papke, 1993). Ott and Zober 

18 (1996) contend that observed correlations with chloracne severity and cumulative estimates of 

19 TCDD exposure indirectly validated this exposure metric. Specifically, the mean TCDD 

20 concentration for those without chloracne was 38.4 ppt; for those with moderate and severe 

21 forms of chloracne, the mean was 420.8 ppt and 1,008 ppt, respectively. 

22 Unlike for the NIOSH cohort, individual-level data were collected for other cancer risk 

23 factors. These factors included body mass index at time of first exposure, history of 

24 occupational exposure to β-naphthylamine and asbestos, and history of smoking. Smoking data 

25 were available for 86% of the cohort. SMRs were based on the external referent population of 

26 West Germany. For cancer incidence, Ott and Zober (1996) generated standardized incidence 

27 ratios (SIRs) using incidence rates for the state of Saarland (1970−1991) as the external referent. 

28 They calculated SMRs (and SIRs) for three categories of cumulative TCDD levels: <0.1 μg/kg, 

29 0.1−0.99 μg/kg and ≥1 μg/kg. The Cox regression model was used to characterize risk within 

30 the cohort using a continuous measure of TCDD. These analyses considered the potential 

31 confounding influence of age, smoking, and body mass index using a stepwise regression 
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1 modeling approach. The Cox modeling employed a stratified approach using the date of first 

2 exposure to minimize possible confounding between calendar period and exposure. The three 

3 first exposure groups were exposure within the first year of the accident, exposure between 

4 1 year after the accident and before 1960, and exposure after 1959. The Cox regression 

5 estimates were presented in terms of conditional risk ratios (i.e., hazard ratios adjusted for body 

6 mass index, smoking and age). 

7 Although no statistically significant excesses relative to the general population were 

8 detected for all cancer mortality, there was some suggestion of an exposure-response 

9 relationship. In the 0.1−0.99 μg/kg and ≥1 μg/kg exposure groups, the all cancer SMRs were 1.2 

10 (95% CI = 0.5−2.3) and 1.6 (95% CI = 0.9−2.6), respectively. Higher SMRs for cancer (all sites 

11 combined) were also found with an increased interval since exposure first occurred. 

12 Specifically, when observed versus expected counts of cancer were compared in the time interval 

13 20 years after first exposure, the SMR in the highest exposure group (≥1 μg/kg) was 1.97 

14 (95% CI = 1.05−5.36). An excess in lung cancer also was noted with the same lag in this 

15 exposure group (SMR = 3.06, 95% CI = 1.12−6.66). For cancer incidence, a statistically 

16 significant increased SIR for lung cancer was observed in the highest exposure category 

17 (SIR = 2.2, 95% CI = 1.0−4.3), but no other statistically significant associations were detected 

18 for any other cancer site. No cases of soft-tissue sarcoma were found among the cohort members 

19 in this analysis. 

20 Based on internal cohort comparisons, Cox regression models also were used to generate 

21 hazard ratios as measures of relative risk for TCDD exposures following adjustment for 

22 smoking, age and body mass index. A statistically significant association between TCDD dose 

23 (per μg/kg) and cancer mortality was detected (RR = 1.22, 95% CI = 1.00−1.50), but not for 

24 cancer incidence (RR = 1.11, 95% CI = 0.91−1.35). Statistically significant findings were 

25 observed for stomach cancer mortality (RR = 1.46, 95% CI = 1.13−1.89) and incidence 

26 (RR = 1.39, 95% CI = 1.07−1.69). 

27 The Ott and Zober (1996) study also compared the relationship between TCDD exposure 

28 categories and cancer mortality from all sites combined according to smoking status. 

29 Associations were noted between increased exposure to TCDD and mortality from cancer among 

30 smokers, but not among nonsmokers or former smokers. 

31 
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1 2.4.1.1.1.2.3.2. Study evaluation.

2 The Ott and Zober (1996) study characterizes exposure to TCDD at an individual level. 

3 Therefore, unlike in past studies involving this cohort, these data can provide an opportunity for 

4 conducting quantitative dose-response modeling. As with the more recent studies involving the 

5 NIOSH cohort, serum samples were obtained from surviving cohort members and then used to 

6 back-extrapolate TCDD values for all cohort members. In the BASF cohort, however, serum 

7 data were available for a much higher percentage of cohort members (54%) than in the NIOSH 

8 cohort (5%). An additional study strength was the collection of questionnaire data, which 

9 allowed for the potential confounding from cigarette smoking and body mass index to be 

10 examined. 

11 The Ott and Zober (1996) study also evaluates the relationship between TCDD and 

12 cancer incidence. Most cohort studies of TCDD-exposed workers have relied solely on mortality 

13 outcomes. The availability of incidence data better allows for period of latency to be described, 

14 and moreover, to characterize risks associated with cancers that typically have long survival 

15 periods. The authors provide few details on the expected completeness of ascertainment for 

16 incident cancer cases, which makes determining any associated bias difficult. They do, however, 

17 suggest that nonfatal cancers are more likely to have been missed in the earlier part of the 

18 follow-up. The net result of differential case ascertainment over time makes evaluating 

19 differences in risk estimates across different periods of latency impossible. 

20 The small sample size of the cohort (n = 243 men) likely limited the statistical power to 

21 detect small associations for some of the exposure measures. This also effectively limited the 

22 ability to analyze dose-response relationships quantitatively, particularly across strata such as 

23 time since exposure. For site-specific analyses, the cancer site with the most cancer deaths was 

24 the respiratory system (n = 11). Thus, quantitative dose-response analysis using these cohort 

25 data would be limited to the evaluation of all cancer sites combined. 

26 The most important limitation of this study is related to the construction of the third 

27 component of the cohort. As mentioned earlier, this cohort was assembled by actively seeking 

28 out surviving members of the cohort in the mid-1980s. The mortality experience of this cohort is 

29 much lower than that of the general population over the entire follow-up, a result that is expected 

30 given that the individuals were known to be alive as of 1986. The net result is likely an 

31 underestimate of the SMR. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.2.3.3. Suitability of data for TCDD dose-response modeling.

2 This study was included in the quantitative dose-response modeling for the 2003 

3 Reassessment (U.S. EPA, 2003). The characterization of exposure data and availability of other 

4 risk factor data at an individual level are appropriate for use in quantitative dose-response 

5 analyses. 

6 

7 2.4.1.1.1.3. The Hamburg cohort. 

8 The Hamburg cohort has been the subject of several cancer risk assessments. As with the 

9 NIOSH and BASF cohorts, analyses have progressed from basic comparisons of mortality 

10 experience to general population rates to more sophisticated internal cohort analyses involving 

11 the reconstruction of TCDD exposures using serum measures. This cohort consists of 

12 approximately 1,600 workers who were employed in the production of herbicides at a plant in 

13 Hamburg, Germany during 1950−1984 (Flesch-Janys et al., 1995; Becher et al., 1998). The 

14 herbicides produced included 2,4,5-T, β-hexachlorocyclohexane and lindane. The production of 

15 TCP and 2,4,5-T was halted in 1954 following a chloracne outbreak. The plant ceased 

16 operations in 1984. Approximately 20 different working areas were identified, which, in turn, 

17 were grouped into five main areas based on putative TCDD exposure levels. One working area 

18 was deemed to be extremely contaminated, having TCDD exposures at least 20-fold higher than 

19 in other areas. In this section, the studies undertaken in this cohort that have examined cancer 

20 mortality are summarized. 

21 

22 2.4.1.1.1.3.1. Manz et al., 1991. 

23 2.4.1.1.1.3.1.1. Study summary.

24 Manz et al. (1991) investigated patterns of mortality in the Hamburg cohort. The study 

25 population consisted of 1,583 workers (1,184 men, 399 women) who were employed for at least 

26 three months between 1952 and 1989. Casual workers were excluded as they lack sufficient 

27 personal identifying information thereby not allowing for associations with mortality outcomes 

28 to be examined. Vital status was determined using community-based registries of inhabitants 

29 throughout West Germany. Cause of death until the end of 1989 was determined from medical 

30 records for all cancer deaths and classified based on the ninth revision of the International 

31 Classification of Diseases (WHO, 1978). Although Manz et al. (1991) present some data on 
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1 cancer incidence for the cohort, the data are incomplete as information was available on only 

2 12 cases; 93 cancer deaths were observed in the cohort. 

3 In this study, the authors used information on production processes to group workers into 

4 categories of low, medium, or high exposure to TCDD. This information was based on TCDD 

5 concentrations in precursor materials, products, waste, and soil from the plant grounds, measured 

6 after the plant closed in 1984. The distribution of workers into the low, medium, and high 

7 exposure groups was 186, 901, and 496, respectively. The authors examined the validity of the 

8 three exposure categories using a separate group of 48 workers who provided adipose tissue 

9 samples. The median exposure of the 37 volunteers in the high group was 137 and 60 ng/kg in 

10 the remaining 11. Information about chloracne in the cohort was incomplete, and, therefore, was 

11 not used as a marker of TCDD exposure. Other surrogate measures of exposure were considered 

12 in this study, including duration of exposure and year of first employment. For the latter 

13 measure, employment that began after 1954 was assumed to result in much lower exposures 

14 given that production of 2,4,5-T and TCP stopped in 1954. 

15 External comparisons of cancer mortality were made by calculating SMRs using the 

16 general population of West Germany as a referent. Comparisons of mortality in the cohort also 

17 were made to a separate cohort of 3,417 gas supply workers to avoid bias from a healthy worker 

18 effect. Vital status and cause of death in the gas supply workers were determined using the same 

19 methods as used in the Hamburg cohort. SMRs were calculated relative to both referent 

20 populations (West Germany and gas supply workers) across low, medium, and high TCDD 

21 exposure groups. The comparison of mortality to the gas supply workers, however, extended 

22 only until the end of 1985, whereas, comparisons to the general population extended until 1989. 

23 Stratified analyses were undertaken to calculate SMRs for each of the three exposure groups for 

24 categories of duration of employment (<20 versus ≥20 years) and date of entry into the cohort 

25 (≤1954 versus >1954). 

26 When compared to the general population, overall cancer mortality was elevated in male 

27 cohort members (SMR = 1.24, 95% CI = 1.00−1.52) but not in females (SMR = 0.80, 

28 95% CI = 0.60−1.05). A two-fold increase in female breast cancer mortality was noted but was 

29 of borderline statistical significance (SMR = 2.15, 95% CI = 0.98−4.09). The SMR among men 

30 was further increased when analyses were restricted to workers who were employed for at least 

31 20 years (SMR = 1.87, 95% CI = 1.11−2.95). Analyses restricted to those in the highest 
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1 exposure group produced an even higher SMR for those with at least 20 years of employment 

2 (SMR = 2.54, 95% CI = 1.10−5.00). Statistically significant excesses in risk were detected 

3 among those who first worked before 1954, but not afterward. Furthermore, a dose-response 

4 trend was observed across increasing exposure categories in the subset of workers employed 

5 before 1954. The SMRs using the cohort of gas supply workers as the referent group for the low, 

6 medium, and high groups in this subset were 1.41 (95% CI = 0.46−3.28), 1.61 

7 (95% CI = 1.10−2.44), and 2.77 (95% CI = 1.59−4.53), respectively. This finding is consistent 

8 with what was known about TCDD exposures levels at the plant, namely, that TCDD 

9 concentrations were much higher between 1951 and 1954, with subsequent declining levels after 

10 1954. 

11 Generally speaking, patterns of excess mortality were similar when the cohort of gas 

12 workers was used as a reference group. The overall SMR for men was 1.39 

13 (95% CI = 1.10−1.75); and was 1.82 (95% CI = 0.97−3.11) when analyses were restricted to 

14 workers with 20 or more years of employment. A dose-response trend also was observed across 

15 exposure categories when analyses were restricted to those employed for at least 20 years. In 

16 particular, with these analyses, no cancer deaths were observed among those in the lowest 

17 exposure group, while the SMRs in the middle and high exposure groups were 1.36 

18 (95% CI = 0.50−2.96) and 3.07 (95% CI = 1.24−6.33). 

19 SMRs also were generated for several site-specific cancers relative to the West German 

20 general population and the gas worker cohort. No statistically significant excesses were 

21 observed using the general population reference. In contrast, statistically significant excesses 

22 were observed for lung cancer (SMR = 1.67, 95% CI = 1.09−2.44) and hematopoietic system 

23 cancer (SMR = 2.65, 95% CI = 1.21−5.03) relative to the gas workers cohort. 

24 

25 2.4.1.1.1.3.1.2. Study evaluation.

26 The Manz et al. (1991) findings indicate an excess of all cancer mortality among the 

27 workers with the highest exposures, particularly those who worked for at least 20 years and were 

28 employed before 1954. The findings across categories of exposure within the subsets of workers 

29 employed for at least 20 years and before 1954, particularly using the cohort of gas supply 

30 workers, are consistent with a dose-response relationship. These elevated cancer mortality rates 

31 found among those employed before 1954 were likely due to higher TCDD exposures. Other 
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1 carcinogenic coexposures, such as benzene, asbestos, and dimethyl sulfate, could have occurred 

2 among this population. Given that no substantial changes in the production processes at the 

3 Hamburg plant occurred after 1954, comparable levels of these coexposures would be expected 

4 before and after 1954. Therefore, confounding due to these coexposures is unlikely to explain 

5 the dose-response relationship demonstrated between all cancer mortality and TCDD exposures. 

6 No information, however, was presented on potential exposure to other dioxin-like compounds 

7 which may confound the associations that were detected.

8 Detailed information on workers’ smoking behaviors was not collected. Limited 

9 evidence indicated, however, that smoking prevalence between the Hamburg cohort and the gas 

10 supply workers cohort was quite similar. A nonrepresentative sample of 361 workers in the 

11 Hamburg cohort and the sample of 2,860 workers in the gas supply cohort indicated that the 

12 self-reported smoking prevalence was 73% and 76%, respectively. This suggests that the two 

13 cohorts are comprised predominantly of smokers. The similarity in overall smoking prevalence 

14 indicates that comparisons of cancer mortality between the two groups are not unduly influenced 

15 by an inability to adjust for smoking. 

16 

17 2.4.1.1.1.3.1.3. Suitability of data for TCDD dose-response modeling.

18 The data compiled for the Manz et al. (1991) study do satisfy many of the considerations 

19 for conducting quantitative dose-response analysis; health outcomes appear to be ascertained in 

20 an unbiased manner, and exposure was characterized on an individual-level basis. However, as 

21 demonstrated in later studies, there was a large dioxin-like compound component that was not 

22 quantified or assessed in this study. Dose-response associations between TCDD and cancer 

23 mortality were detected, with stronger associations observed with increased periods of latency 

24 and for those who first worked when TCDD was at higher levels. 

25 The size of the cohort, although not as large as the NIOSH cohort, does offer sufficient 

26 statistical power to evaluate TCDD-related risk for cancers from all cancer sites. The data are 

27 limited, however, for characterizing cancer risks among women; only 20 cancer deaths occurred 

28 in the 399 women included in the cohort. It is unlikely that the findings are biased by 

29 confounding due to cigarette smoking since dose-response patterns were strengthened when 

30 comparisons were made to the cohort of gas supply workers rather the general population 

31 referent where smoking rates were likely lower. The inability to account for other occupational 
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1 exposure when TCDD exposures were much higher (pre-1955) could result in confounding if 

2 these other exposures were related to TCDD and the health outcomes under consideration. This 

3 data set would be suitable for quantitative dose-response modeling if the exposure 

4 characterization of the cohort could be improved using biological measures of dose. 

5 

6 2.4.1.1.1.3.2. Flesch-Janys et al., 1995. 

7 2.4.1.1.1.3.2.1. Study summary.

8 In 1995, Flesch-Janys et al. published an analysis of the male employees from the 

9 Hamburg cohort that extended the follow-up to 40 years (1952−1992). Inclusion of these three 

10 additional years of follow-up resulted in a sample size of 1,189 male workers. 

11 The authors estimated a quantitative exposure variable for concentrations of TCDD in 

12 blood at the end of exposure (i.e., when employment in a department ended) and above German 

13 median background TCDD levels. The TCDD exposure assessment defined 14 production 

14 departments according to TCDD levels in various products in the plant, in waste products, and in 

15 various buildings. The time (in years) each worker spent in each department then was 

16 calculated. Concentrations of TCDD were determined in 190 male workers using serum 

17 (n = 142) and adipose tissue samples (n = 48). The authors used a first-order kinetic model to 

18 calculate TCDD levels at the end of exposure for the 190 workers with available polychlorinated 

19 dibenzo-р-dioxin (PCDD) and -furan (PCDF) at various time points. Half-lives were calculated 

20 from an elimination study of 48 workers from this cohort, and the median TCDD background 

21 level was estimated at 3.4 ng/kg blood fat from the German population (Papke et al., 1994; 

22 Flesch-Janys et al., 1994). Using the one-compartment, first-order kinetic model, the half-life of 

23 TCDD was estimated to be 6.9 years (Flesch-Janys, 1997). Increased age and higher body fat 

24 percentage were associated with increased TCDD half-life, while smoking was associated with a 

25 higher decay rate for most of the congeners examined (Flesch-Janys et al., 1996). Cumulative 

26 TCDD exposures were estimated by summing exposures over the time spent in all production 

27 departments and were expressed in terms of ng/kg of blood fat. The authors also applied a 

28 metric of total toxicity equivalence (TOTTEQ) as the weighted sum of all congeners where 

29 weights were TEQs that denoted the toxicity of each congener relative to TCDD. 

30 Similar to previous analyses on this cohort, comparisons were made using an external 

31 referent group of workers from a gas supply company (Manz et al., 1991). In contrast to 
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1 previous analyses where SMR statistics were generated using this “external” reference, however, 

2 Flesch-Janys et al. (1995) used Cox regression. The Cox regression models treated the gas 

3 worker cohort as the referent group, and six exposure groups were defined by serum-derived 

4 cumulative TCDD estimates. The groups were determined by using the first four quintiles with 

5 the upper two exposure categories corresponding to the ninth and tenth deciles of the cumulative 

6 TCDD. Internal cohort comparisons used those workers in the lowest quintile as the referent 

7 group, as opposed to the cohort of gas workers. A similar approach was used to model TEQs. 

8 No known TCDD exposures occurred in the gas workers, so they were assigned exposures based 

9 on the median background levels in the general population. RRs were calculated based on 

10 exposure above background levels; in other words, background levels were assumed to be 

11 equivalent across all workers and also for those employed by the gas supply company. The RRs 

12 derived using the Cox model were adjusted for total duration of employment, age, and year when 

13 employment began. 

14 The Cox regression with the cohort of gas workers as the referent exposure group yielded 

15 a linear dose-response relationship between cumulative TCDD exposure and cancer mortality for 

16 all sites combined (p < 0.01). The RRs for all-cancer mortality were 1.59, 1.29, 1.66, 1.60, 1.70, 

17 and 3.30. For four of the six categories (excluding the referent group), the RRs were statistically 

18 significant (p < 0.05); in the highest TCDD exposure category (344.7−3890.2 ng/kg) the RR was 

19 3.30 (95% CI = 2.05−5.31). Similar findings were evident with TOTTEQ. A dose-response 

20 pattern for all cancer mortality (p < 0.01) based on the internal cohort comparisons was also 

21 detected. 

22 The authors performed an additional analysis to evaluate the potential confounding role 

23 of dimethylsulfate. Although no direct measures of dimethylsulfate were available, the 

24 investigators repeated analyses by excluding 149 workers who were employed in the department 

25 where dimethylsulfate was present. A dose-response pattern persisted for TCDD (p < 0.01), and 

26 those in the highest exposure group (344.7−3,890.2 ng/kg of blood fat) had a RR of 2.28 

27 (95% CI = 1.14−4.59). 

28 

29 2.4.1.1.1.3.2.2. Study evaluation.

30 The Flesch-Janys et al. (1995) study used serum-based measures to determine cumulative 

31 exposure to TCDD at the end of employment for all cohort members. They used the standard 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 one-compartment, first-order kinetic model and samples obtained from 190 male workers. This 

2 quantitative measure of exposure permits an estimation of a dose-response relationship. 

3 Confounding for other occupational exposures is unlikely to have biased the results. A 

4 dose-response relationship persisted after excluding workers exposed to dimethylsulfate. Other 

5 potential exposures of interest included benzene and isomers of hexachlorocyclohexane. 

6 Exposure to these agents, however, was highest in the hexachlorocyclohexane and lindane 

7 department, where TCDD exposures were lower. As outlined earlier, the study findings are 

8 unlikely to be biased for cigarette smoking as cigarette smoking in the cohort was similar to that 

9 in the comparison population. Moreover, more recent analyses of serum-based TCDD exposure 

10 measures found no correlation with smoking status in this cohort (Flesch-Janys et al., 1995)—a 

11 necessary condition for confounding. 

12 The authors used an exposure metric that described cumulative TCDD exposure of 

13 workers at the time they were last exposed. As a result, the authors were unable to characterize 

14 risks associated with this metric for different periods of latency despite a sufficient follow-up 

15 period. Subsequent analyses constructed time-dependent measures of cumulative TCDD and 

16 accounted for excretion of TCDD during follow-up. 

17 In contrast to most risk assessments of TCDD exposure, this study modeled the 

18 relationship between other dioxin-like compounds and the risk of cancer mortality using the 

19 TOTTEQ metric. 

20 

21 2.4.1.1.1.3.2.3. Suitability of data for TCDD dose-response modeling.

22 The data used in this study satisfy most of the considerations developed for performing a 

23 quantitative dose-response analysis. However, latency period was not examined in this study. 

24 Dose-response analyses were, therefore, limited to a subsequent study of this cohort (Becher et 

25 al., 1998), which did examine latency. 

26 

27 2.4.1.1.1.3.3. Flesch-Janys et al., 1998. 

28 2.4.1.1.1.3.3.1. Study summary.

29 Flesch-Janys et al. (1998) undertook another analysis on this cohort that incorporated 

30 additional sera data for 275 workers (39 females and 236 males). The follow-up period was the 

31 same as that used in the 1995 analyses, with mortality follow-up extending until December 31, 
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1 1992. Analyses were based on 1,189 males who were employed for at least 3 months from 

2 January 1, 1952 onward. The authors continued this dose-response analysis to address 

3 limitations in their previous work. One limitation was that the previous method did not account 

4 for the elimination of TCDD while exposures were being accrued during follow-up. A second 

5 limitation was that the amount of time workers spent in different departments was not 

6 considered. In the 1998 study, the “area under the curve” approach was used because it accounts 

7 for variations in concentrations over time and reflects cumulative exposure to TCDD. The 

8 authors used a first-order kinetic model to link blood levels and working histories to derive 

9 department-specific dose rates for TCDD. The TCDD background level of 3.4 ng/kg blood fat 

10 for the German population was used (Papke et al., 1994). The dose rates were applied to 

11 estimate the concentration of TCDD at every point in time for all cohort members. A cumulative 

12 measure expressed as ng/kg blood fat multiplied by years was calculated and used in the SMR 

13 analysis. SMRs were calculated using general population mortality rates for the German 

14 population between 1952 and 1992. No lag period was incorporated into the derivation of the 

15 SMRs. The SMRs were estimated for the entire cohort and for exposure groups based on 

16 quartiles obtained from the area under the curve. Linear trend tests were also performed. The 

17 overall SMR for cancer mortality in the cohort was 1.41 (95% CI = 1.17−1.68). This SMR value 

18 was higher than the SMR of 1.21 reported for this same cohort with 3 fewer years of follow-up 

19 (Manz et al., 1991). In terms of site-specific cancer mortality, excesses were found for 

20 respiratory cancer (SMR = 1.71, 95% CI = 1.24−2.29) and rectal cancer (SMR = 2.30, 

21 95% CI = 1.05−2.47). Increased risk for lymphatic and hematopoietic cancer (SMR = 2.16, 

22 95% CI = 1.11−3.17) were also noted largely attributable (SMR = 3.73, 95% CI = 1.20−8.71) to 

23 lymphosarcoma (i.e., non-Hodgkin’s lymphoma). A dose-response relationship was observed 

24 across quartiles of cumulative TCDD for all-cancer mortality (p < 0.01). The SMRs for these 

25 quartiles were 1.24, 1.34, 1.34, and 1.73. Dose-response relationships were not observed for 

26 lung cancer or hematopoietic cancers using this same metric. Dose-response relationships were 

27 not observed with cumulative TEQ for any of the cancer sites examined (i.e., all cancers, lung 

28 cancer, hematopoietic cancer). 

29 
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1 2.4.1.1.1.3.3.2. Study evaluation.

2 The approach used in the Flesch-Janys et al. (1998) study offers a distinct advantage over 

3 earlier analyses involving the same cohort. Three more years of follow-up were available, and 

4 the characterization of exposure using the “area under the curve” better captures changes in 

5 cumulative exposure using a person-years approach rather than cumulative TCDD at the time of 

6 last exposure. As noted previously, other occupational exposures or cigarette smoking are 

7 unlikely to have biased the study findings. A sufficient length of follow-up had accrued, and 

8 dose-response associations were evident. Dioxin-like compounds were evaluated in this study. 

9 For TCDD, the mean concentration was 101.3 ng/kg at the time of measurement. For other 

10 higher chlorinated congeners, the corresponding mean (without TCDD) was 89.3 ng/kg. 

11 

12 2.4.1.1.1.3.3.3. Suitability of data for TCDD dose-response modeling.

13 The data used in this study satisfy most of the considerations developed for performing a 

14 quantitative dose-response analysis. However, latency was not examined in this study. 

15 Dose-response analyses were, therefore, limited to a subsequent study of this cohort (Becher et 

16 al., 1998) which did examine latency and supersedes the Flesch-Janys et al. (1998) study. 

17 

18 2.4.1.1.1.3.4. Becher et al., 1998. 

19 2.4.1.1.1.3.4.1. Study summary.

20 The Becher et al. (1998) quantitative cancer risk assessment for the Hamburg cohort was 

21 highlighted in the 2003 Reassessment as being appropriate for conducting dose-response 

22 analysis. The integrated TCDD concentration over time, as estimated in the Flesch-Janys et al. 

23 (1998) study, was used as the exposure variable. Estimates of the half-life of TCDD based on 

24 the sample of 48 individuals with repeated measures were incorporated into the model that 

25 back-calculated TCDD exposures to the end of the employment (Flesch-Janys et al., 1996). This 

26 method took into account the age and body fat percentage of the workers. In Becher et al. 

27 (1998), the analysis used the estimate of cumulative dose (integrated dose or area under the 

28 curve) as a time-dependent variable. 

29 Poisson and Cox regression models were used to characterize dose-response 

30 relationships. Both models were applied to internal comparisons where a person-years offset 

31 was used and to an external comparison where an offset of expected number of deaths was used. 
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1 The person-years offset was used to account for varying person-time accrued by workers across 

2 exposure categories. The use of the expected number of deaths as an offset allows risks to be 

3 described in relation to that expected in the general population. Within each classification cell of 

4 deaths and person-years, a continuous value TCDD and TEQ levels based on the geometric mean 

5 were entered into the Poisson model. For the Cox model, accumulated dose was estimated based 

6 on area under the curve for TCDD, TEQ, TEQ without TCDD, and β-hexachlorocyclohexane. 

7 These other coexposure metrics were adjusted for in the Cox regression analyses. Other 

8 covariates considered included in the models were year of entry, year of birth, and age at entry 

9 into the cohort. A background level of 3.4 ng/kg blood fat for the German population was used 

10 (Papke et al., 1994). A variety of latencies was evaluated (0, 5, 10, 15, and 20 years), and 

11 attributable risk and absolute risk were estimated. The unexposed cohort of gas workers was 

12 used for most internal analyses. 

13 Internal and external comparisons using the Poisson model found positive associations 

14 with TCDD exposure and mortality from all cancers combined. The slope associated with the 

15 continuous measure of TCDD (μg/kg blood fat × years) for the internal comparison was 0.027 

16 (p < 0.001), which decreased to 0.0156 (p = 0.07) after adjusting for age and calendar period. 

17 The slope for the external comparison was 0.0163 (p = 0.055); this estimate was not adjusted for 

18 other covariates. For TEQ, the slopes based on the internal comparisons were 0.0274 (p < 0.001) 

19 in the univariate model and 0.0107 (p = 0.175) in the multivariate model after adjusting for age 

20 and calendar period. The external estimate of slope for TEQ was 0.0109 (p = 0.164). Cox 

21 regression of TCDD across six exposure categories, with a lag of 0 years, found a statistically 

22 significant linear trend (p = 0.03) and those in the upper exposure group had a RR of 2.19 

23 (95% CI = 0.76−6.29). These estimates were adjusted for year of entry, age at entry, and 

24 duration of employment. A similar pattern was observed with the Cox regression analysis of 

25 TEQ; the linear test for trend, however, was marginally statistically significant (p = 0.06). 

26 Cox regression models that included both TCDD and TEQ (excluding TCDD) were 

27 applied. In this model, the slope parameter for TCDD was marginally statistically significant 

28 (β = 0.0089, p = 0.058), while the coefficient for TEQ (excluding TCDD) was not statistically 

29 significant (β = -0.024, p = 0.70). This suggests that confounding by other dioxin-like 

30 compounds was unlikely and the increased risk of cancer was due to TCDD exposure. For all 
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1 TEQs combined, the parameter estimate was generated from another Cox regression model and 

2 was borderline statistically significant (β = 0.0078, p = 0.066). 

3 The authors used multiple Cox models to evaluate the effect of latency. The slope 

4 estimates for both TCDD and TEQ increased dramatically with increasing latency. The slope 

5 estimates for TCDD increased from 0.0096 to 0.0160 (p < 0.05) when latency was increased 

6 from 0 to 20 years. Similar changes in the TEQ slopes were noted (0.0093 to 0.0157). 

7 Evaluations of dose-response curves found that the best-fitting curve was concave in shape, 

8 thereby yielding higher risk at low exposure. Differences between the fit of the class of models 

9 considered [i.e., RR(x,β) = exp (β log(kx = 1))], however, were small. 

10 Attributable risks were generated only for TCDD, as the data suggested no effects with 

11 other TEQs. The additional lifetime risk of cancer assuming a daily intake of 1 pg TCDD/kg 

12 body weight/day was estimated to range between 0.001 and 0.01. 

13 

14 2.4.1.1.1.3.4.2. Study evaluation.

15 The Becher et al. (1998) study represent perhaps the most detailed analyses performed on 

16 any cohort to date. The findings were robust, as similar patterns were found with and without 

17 using the gas supply worker cohort as the referent group. Exposures to other potential 

18 confounding coexposures, such as dioxin-like compounds, were taken into account, and workers 

19 with exposure to other carcinogens (e.g., lindane) were excluded. Furthermore, latency was 

20 examined in this study, unlike earlier studies of this cohort. 

21 

22 2.4.1.1.1.3.4.3. Suitability of data for TCDD dose-response modeling.

23 This study was included in the quantitative dose-response modeling for the 2003 

24 Reassessment (U.S. EPA, 2003). The data in the Becher et al. (1998) study are suitable for 

25 conducting quantitative dose-response modeling. The exposure data capture cumulative 

26 exposure to TCDD as well as exposures to other dioxin-like compounds. The length of the 

27 follow-up is sufficient, and the study appears to not be subject to confounding or other types of 

28 biases. Therefore, this study is utilized in quantitative dose-response analysis. 

29 
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1/15/10 DRAFT 2-51 —DO NOT CITE OR QUOTE 

1 2.4.1.1.1.4. The Seveso cohort. 

2 Several studies have evaluated the morbidity and mortality effects of residents exposed to 

3 TCDD following a July 10, 1976, accidental release through an exhaust pipe at a chemical plant 

4 in the town of Meda near Seveso, Italy. The released fluid mixture contained 2,4,5-T, sodium 

5 trichlorophenate, ethylene glycol, and sodium hydroxide. Vegetation in the area showed 

6 immediate signs of damage, and in the days following the accident, residents developed nausea, 

7 headaches, eye irritation, and dermal lesions, particularly children. 

8 This accident transported TCDD up to 6 km from the plant. Soil samples taken near the 

plant revealed average levels of TCDD that ranged from 15.5 μg/m2 to 580.4 μg/m2 9 in the most 

10 contaminated area near the plant (referred to as Zone A) (Bertazzi et al., 2001). Zone A covered 

11 87 hectares and extended 2,200 m south from the plant. Another, more distant contaminated 

12 zone (Zone B) covering 270 hectares also had contaminated soil levels, but the TCDD 

concentration range was much lower (1.7−4.3 μg/m3 13 ). A reference zone (Zone R), which 

surrounded the two contaminated areas, had lower TCDD soil levels (range: 0.9−1.4 μg/m3 14 ) and 

15 included approximately 30,000 residents. Following the accident, most residents in Zone A left 

16 the area. Although residents in Zone B remained, they were under strict regulations to avoid 

17 consuming homegrown products. In total, 736, 4,737, and 31,800 individuals lived in Zones A, 

18 B, and R, respectively. Within days of the accident, 3,300 animals (mostly poultry and rabbits) 

19 were found dead. Emergency slaughtering was undertaken to prevent TCDD from entering the 

20 food chain, and within 2 years more than 80,000 animals had been slaughtered. Mechanisms 

21 were put into place for long-term follow-up of these residents. Unlike the other studies based on 

22 occupational cohorts, the follow-up of this population allows for risks to be characterized for 

23 females. 

24 The mortality studies from Seveso published to date have not incorporated serum TCDD 

25 levels that were measured in individuals. Needham et al. (1997) describe the collection of serum 

26 samples from a sample of the exposed population and control subjects in 1976. In 1988, human 

27 exposure to TCDD was assessed by measuring small volumes of serum remaining from medical 

28 examinations done in 1976. An examination of these data revealed some of the highest serum 

29 TCDD levels ever reported, that the half-life of TCDD in this population was between 7 and 

30 8 years, and that half-life varied between women and men. The half-life of TCDD in serum was 

31 longer in women (~9 years) than in men (~7 years) (Needham et al., 1994). In this report, the 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-52 —DO NOT CITE OR QUOTE 

1 findings of studies that characterized cancer risks in relation to exposure to TCDD from the 1976 

2 accident are highlighted. These studies include comparisons of cancer mortality rates to the 

3 general population based on zone of residence at the time of accident (Bertazzi et al., 2001; 

4 Consonni et al., 2008). More recent work done by Warner et al. (2002) investigated the 

5 relationship between serum-based measures of TCDD and breast cancer among participants in 

6 the Seveso Women’s Health Study (SWHS). 

7 

8 2.4.1.1.1.4.1. Bertazzi et al., 2001. 

9 2.4.1.1.1.4.1.1. Study summary.

10 Several studies have reported on the mortality experience of Seveso residents. The more 

11 recent publications having a longer follow-up of the cohort are evaluated here. In 2001, the 

12 findings from a 20-year mortality study of Seveso residents was published (Bertazzi et al., 2001). 

13 The Bertazzi et al. (2001) study was an extension of the 10- and 15-year follow-ups for mortality 

14 (Bertazzi et al., 1989, 1997; Pesatori et al., 1998) and the 10-year follow-up for cancer incidence 

15 (Bertazzi et al., 1993). 

16 In this cohort, TCDD exposures were assigned to the population using a three-level 

17 categorical variable representative of the individual’s place of residence (Zones A, B, or R) at the 

18 time of the accident or when the person first became a resident of the zone, if that was after 

19 1976. An external comparison to the province of Lombardy was made by generating rate ratios 

20 (RR) using Poisson regression techniques. Person-years of follow-up were tabulated across 

21 strata defined by age, zone of residence, duration of residence, gender, calendar time, and 

22 number of years that had elapsed since the time of exposure. Mortality rates during the 

23 preaccident period also were compared to evaluate potential changes in rates due to the accident 

24 and to evaluate whether patterns were consistent before and after the accident. 

25 No overall excess in mortality rates from all cancer sites combined was observed in 

26 Zones A or B (combined) when compared to the reference population of Lombardy 

27 (n = 9 million residents) (RR = 1.0, 95% CI = 0.9−1.2). Analyses of site-specific cancer 

28 mortality revealed statistically significant excesses among residents in Zones A or B (combined) 

29 for cancer of the rectum (RR = 1.8, 95% CI = 1.0−3.3) and lymphatic and hematopoietic 

30 malignancies (RR = 1.7, 95% CI = 1.2−2.5). Lymphatic and hematopoietic malignancies were 

31 elevated in women (RR = 1.8, 95% CI = 1.1−3.2) and in men (RR = 1.7, 95% CI = 1.0−2.8). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-53 —DO NOT CITE OR QUOTE 

1 Analyses stratified by the number of years since first exposure (i.e., 1976) revealed 

2 higher risk among men with an increased number of years elapsed. Similar to other studies, the 

3 RR for all cancers (combined) was 1.3 (95% CI = 1.0-1.7) among men 15−20 years after first 

4 exposure. No such increase after 15 years postexposure, however, was noted in women 

5 (RR = 0.8, 95% CI = 0.6−1.2). 

6 

7 2.4.1.1.1.4.1.2. Study evaluation.

8 Ascertainment of mortality appears to be excellent. Vital status was established using 

9 similar methods for both the exposed and reference populations. No individual data were 

10 collected and, therefore, the possibility that confounding by individual characteristics such as 

11 cigarette smoking cannot be entirely dismissed. Bertazzi et al. (2001) do note that the 

12 sociodemographic characteristics of residents in the three zones were similar based on 

13 independently conducted surveys, and no differences in chronic respiratory disease were found 

14 across the different zones. If excess mortality was attributable to cigarette smoking, such 

15 excesses would be expected to be evident during the entire study period. Latency analyses 

16 revealed elevated risks 15−20 years postaccident. Finally, no excesses were observed for other 

17 smoking-related cancers of the larynx, esophagus, pancreas, and bladder. The observed excesses 

18 in all cancer mortality do not appear to be attributed to differential smoking rates between the 

19 two populations. 

20 To examine potential for bias due to noncomparability in the two study populations, a 

21 comparison of cancer mortality rates between the Seveso regions and the reference population of 

22 Lombardy was conducted. Elevated rates for brain cancer mortality were noted in Seveso 

23 relative to Lombardy, but the higher rates of leukemia mortality were found in Lombardy 

24 relative to Seveso. That no excess was reported for all cancer sites combined lends credence to 

25 the hypothesis that the exposure to TCDD from the accident increased rates of cancer after a 

26 sufficient period of latency. 

27 Stratified analyses were performed across several categorical variables including gender 

28 and time since exposure. The numbers of cancer site-specific deaths are quite small in many of 

29 the 5-year increments since first exposure. The study, therefore, has limited statistical power to 

30 detect differences in mortality rates among the comparison groups for many cancer sites. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-54 —DO NOT CITE OR QUOTE 

1 Bertazzi et al. (2001) assigned exposures based on zone of residence. Soil sampling 

2 within each zone revealed considerable variability in TCDD soil levels within each zone. 

3 Moreover, some individuals would have left the area shortly after the accident, and determining 

4 the extent to which individuals in Zone B who were subject to the recommendations near the 

5 time of the accident adhered to them is difficult. As a result, exposure misclassification is 

6 possible, and the use of individual measures of TCDD level in serum is preferred over zone of 

7 residence for determining exposure. As noted by the authors, the study is better suited to “hazard 

8 identification” than to quantitative dose-response analysis. 

9 

10 2.4.1.1.1.4.1.3. Suitability of data for TCDD dose-response modeling.

11 Given the variability in soil TCDD levels within each zone and the lack of individual 

12 level, no effective dose can be estimated for quantitative dose-response analyses. Uncertainty in 

13 identifying the critical exposure window for the Seveso cohort is a key limitation. The 

14 evaluation of this study indicates that this study is not suitable for quantitative dose-response 

15 analysis. 

16 

17 2.4.1.1.1.4.2. Pesatori et al., 2003. 

18 2.4.1.1.1.4.2.1. Study summary.

19 Pesatori et al. (2003) published a review of the short- and long-term studies of morbidity 

20 and mortality outcomes in the Seveso cohort in 2003. This paper presented external comparisons 

21 of cancer incidence from 1977 to 1991 for Seveso males and females separately. As in the 

22 Bertazzi et al. (2001) study, RRs were estimated using Poisson regression. For males who lived 

23 in Zones A and B, the only statistically significantly elevated RR was for lymphatic and 

24 hematopoietic cancers (RR = 1.9, 95% CI = 1.1−3.1). This excess was due primarily to 

25 non-Hodgkin’s lymphoma, which accounted for 8 of the 15 incidence cases (RR = 2.6, 

26 95% CI = 1.3−5.3). Among females living in Zones A and B, higher rates were observed for 

27 multiple myeloma (RR = 4.9, 95% CI = 1.5−16.1), cancer of the vagina (RR = 5.5, 

28 95% CI = 1.3−23.8), and cancer of the biliary tract (RR = 3.0, 95% CI = 1.1−8.2). 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-55 —DO NOT CITE OR QUOTE 

1 2.4.1.1.1.4.2.2. Study evaluation.

2 All RRs presented in the Pesatori et al. (2003) study were based on fewer than 

3 five incident cases. 

4 

5 2.4.1.1.1.4.2.3. Suitability of data for TCDD dose-response modeling.

6 As with the studies of mortality among Seveso residents, the Pesatori et al. (2003) study 

7 does not capture TCDD exposure on an individual basis, and soil TCDD levels considerably vary 

8 within each zone. Therefore, the quality of the exposure data is insufficient for estimating the 

9 effective dose needed for quantitative dose-response analysis. 

10 

11 2.4.1.1.1.4.3. Consonni et al., 2008. 

12 2.4.1.1.1.4.3.1. Study summary.

13 Similar analytic methods were applied with 25 years of follow-up of the Seveso cohort 

14 (Consonni et al., 2008). An important addition in this paper was the presentation of RRs for 

15 Zone R, which had the lowest TCDD levels. Poisson regression models were used to calculate 

16 RRs of mortality using Seregno as the reference population. Cancer deaths observed in Zones A 

17 and B were 42 and 244, respectively. 

18 No statistically significant differences in all cancer mortality relative to the reference 

19 population were noted in any of the zones (Zone A: RR = 1.03, 95% CI = 0.76−1.39; Zone B: 

20 RR = 0.92, 95% CI = 0.81−1.05; Zone R: RR = 0.97, 95% CI = 0.92−1.02). Statistically 

21 significant excesses in mortality from non-Hodgkin’s lymphoma (RR = 3.35, 

22 95% CI = 1.07−10.46) and multiple myeloma (RR = 4.34, 95% CI = 1.07−17.52) were observed 

23 in the area with the highest TCDD levels (Zone A). No other statistically significant increases in 

24 cancer mortality relative to the reference population were apparent. The absence of elevated 

25 breast cancer mortality among women in this study was noteworthy, as this finding differs from 

26 the results of a study of Seveso women for which TCDD exposures were estimated using serum 

27 samples (Warner et al., 2002). A more detailed description of this study is provided later in this 

28 section. 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-56 —DO NOT CITE OR QUOTE 

1 2.4.1.1.1.4.3.2. Study evaluation.

2 Although no individual-level data on smoking were available, the potential for 

3 confounding is likely minimal. Independent smoking surveys found that the smoking prevalence 

4 rates in Desio, one of cities affected by the accident, were similar to those in districts just outside 

5 the study area (Cesana et al., 1995). As mentioned earlier, one would expect elevated RRs over 

6 the entire study period if smoking had biased the study results, and not just after 15−20 years 

7 since exposure to TCDD. 

8 

9 2.4.1.1.1.4.3.3. Suitability of data for TCDD dose-response modeling.

10 The lack of individual-level exposure data precludes quantitative dose-response modeling 

11 using these data. 

12 

13 2.4.1.1.1.4.4. Baccarelli et al., 2006. 

14 2.4.1.1.1.4.4.1. Study summary.

15 Given previous findings from Seveso, Baccarelli et al. (2006) examined t(14;18) 

16 translocations in the DNA of circulating lymphocytes of healthy dioxin-exposed individuals. 

17 These translocations are associated with the development of cancer, namely follicular 

18 lymphomas. The study included 211 healthy subjects of the Seveso area, and 101 who had 

19 developed chloracne. The investigators analyzed data from 72 high-TCDD plasma level 

20 individuals (≥10 ppt) and 72 low-TCDD plasma levels (<10 ppt). A three-level categorical 

21 variable was used to evaluate dose-response. This variable was developed by dividing those 

22 with exposures ≥10 ppt into two groups: 10– <50 ppt, and 50−475.0 ppt. Trained interviewers 

23 administered a questionnaire that collected data on demographic characteristics, diet, and 

24 residential and occupational history. 

25 The prevalence of t(14;18) was estimated as those individuals having a t(14;18) positive 

26 blood sample divided by the t(14;18) frequency (number of copies per million lymphocytes). 

27 Baccarelli et al. (2006) found that the frequency of t(14;18) was associated with plasma TCDD 

28 levels, but no association between TCDD and the prevalence of t(14;18) was detected. 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-57 —DO NOT CITE OR QUOTE 

1 2.4.1.1.1.4.4.2. Study evaluation.

2 Whether the frequency of t(14;18) associated with plasma TCDD levels translates into an 

3 increased risk of lymphoma is uncertain as prospective data of TCDD on those who developed 

4 non-Hodgkin’s lymphoma are lacking. Moreover, the t(14;18) translocation could be an 

5 important event in the pre-B stage cell that contributes to tumorigenicity, however subsequent 

6 exposure to carcinogenic agents might be necessary for t(14;18) cells to develop into a 

7 malignancy (Hoglund et al., 2004). 

8 

9 2.4.1.1.1.4.4.3. Suitability of data for TCDD dose-response modeling.

10 Given that current TCDD plasma levels were measured for this study, it is unclear if the 

11 effects of lymphocyte translocations may be due to initial high exposure or are a function of the 

12 cumulative exposure for a longer exposure window. Additionally, whether the frequency of 

13 t(14;18) associated with plasma TCDD levels translates into an increased risk of lymphoma is 

14 unknown. Dose-response analysis for this outcome, therefore, was not conducted. 

15 

16 2.4.1.1.1.4.5. Warner et al., 2002. 

17 2.4.1.1.1.4.5.1. Study summary.

18 To date, Warner et al. (2002) is the only published investigation of the relationship 

19 between serum-based measures of TCDD and cancer in Seveso. Eligible participants from the 

20 SWHS (see Section 2.4.1.2.1.4 for details) were women who, at the time of the accident in 1976, 

21 were 40 years of age or younger, had lived in one of the most highly contaminated zones (A or 

22 B), and had adequate sera collected soon after the explosion. Enrollment in SWHS was begun in 

23 March 1996 and lasted until July 1998. Of the total 1,271 eligible women, 981 agreed to 

24 participate in the study. Cancer cases were identified during interview and confirmed through 

25 review of medical records. Information on other risk factors including reproductive history and 

26 cigarette smoking was obtained through interview.

27 Serum volumes greater than 0.5 mL collected between 1976 and 1981 volume were 

28 analyzed. Most sera were collected in 1976/77 (n = 899); samples were collected in 1978−1981 

29 for 54 women, and in 1996/97 for 28 women. For most samples collected after 1977, serum 

30 TCDD levels were back-extrapolated using a first-order kinetic model with a 9-year half-life 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-58 —DO NOT CITE OR QUOTE 

1 (Pirkle et al. 1989). For 96 women with undetectable values, a serum level that was equal to 

2 one-half the detection level was used. 

3 Analyses were based only on women who provided serum samples; no extrapolation of 

4 values to a larger population was done. Risks were therefore generated using data collected at an 

5 individual level. Serum TCDD was analyzed as both a continuous variable and a categorical 

6 variable. The distribution of serum TCDD levels of the 15 cases of breast cancer was examined 

7 in relation to the distribution of all women in the SWHS. The median exposure was slightly 

8 higher among with the 15 cases of breast cancer (71.8 ppt) compared to those without (55.1 ppt), 

9 and the exposure distribution among breast cancer cases appeared to be shifted to the right (i.e., 

10 the exposures were higher but followed the same distribution); however, no formal test of 

11 significance was conducted. 

12 Warner et al. (2002) used Cox proportional hazards modeling techniques to evaluate risk 

13 of breast cancer in relation to TCDD serum levels while controlling for a variety of potential risk 

14 factors. In all, 21 women had been diagnosed with cancer, and of these, 15 cases were cancer of 

15 the breast. The analysis revealed that for every 10-fold increase in TCDD log-serum levels (e.g., 

16 from 10 to 100 ppt) the risk of breast cancer increased by 2.1 (95% CI = 1.0−4.6). Risk 

17 estimates also were generated across four categories (<20, 20.1−44, 44.1−100, >100 ppt), with 

18 the lowest category used as the reference. The RRs estimated in the third and fourth highest 

19 exposure categories were 4.5 (95% CI = 0.6−36.8) and 3.3 (95% CI = 0.4−28.0). Although 

20 statistical significance was not achieved for either category, likely because of the small number 

21 of cases, the greater than threefold risk evident in both categories is worth noting. Given that the 

22 reference category had only one incident case underscores the limited inferences that can be 

23 drawn from these analyses. The authors adjusted for numerous potential confounders, but 

24 observed no differences between the crude and adjusted results; the authors, therefore, presented 

25 unadjusted risks. 

26 

27 2.4.1.1.1.4.5.2. Study evaluation.

28 The findings from the Warner et al. (2002) study differ from reports in earlier studies in 

29 which mortality outcomes noted the absence of an SMR association. The design of this study is 

30 much stronger than earlier ones, given the improved characterization of exposure, the ability to 

31 compare incidence rates within the cohort, the ability to control for potential confounding 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-59 —DO NOT CITE OR QUOTE 

1 variables at an individual level, and the availability of incident outcomes. The use of incident 

2 cases (versus mortality data) should also help minimize potential bias due to disease survival. 

3 Another important advantage was the ability to measure TCDD near the time of the accident, 

4 thereby reducing the potential for exposure measurement error. 

5 A potentially important limitation of the Warner et al. (2002) study was that information 

6 was collected only from those who were alive as of March 1996. Therefore, TCDD and other 

7 relevant risk factor data could not be collected for those who had previously died of breast 

8 cancer. Thirty-three women could not participate because they were either too ill or had died. 

9 Of these, three died of breast cancer. Given that there were only 15 breast cancer cases, the 

10 exclusion of these 3 cases could have dramatically impacted the findings in either direction. 

11 Another limitation was that, at the time of the follow-up, most women were still 

12 premenopausal and therefore, most of the cohort had not yet attained the age of greater risk of 

13 breast cancer. An ongoing follow-up of the cohort should be completed by 2010, which should 

14 allow for increased number of incident breast cancers to be identified. Given that the current 

15 analyses were based only on 15 incident cases, this will substantially improve the statistical 

16 power of the study. A secondary benefit is that the increased follow-up will allow for an 

17 investigation of possible differential effects according to the age the women were at the time of 

18 exposure. 

19 

20 2.4.1.1.1.4.5.3. Suitability of data for TCDD dose-response modeling.

21 Several aspects of the Warner et al. (2002) study are weaknesses in the consideration of 

22 this study for further dose-response modeling. Only 15 cases of breast cancer were available, 

23 and no increases in risk were found with serum TCDD exposures between 20.1 and 44 ppt 

24 (n = 2) when compared to those with <20 ppt (n = 1). The average age at the time of enrollment 

25 was 40.8 years while the average age at diagnosis among the cases was 45.2 years. As most 

26 women had not yet reached the age when breast cancer cases are typically diagnosed, additional 

27 follow-up of the cohort would improve the quantitative dose-response analysis and strengthen 

28 this study. A key strength of this study, however, is that Warner et al. (2002) includes an 

29 investigation of the relationship between individual serum-based measures of TCDD and cancer 

30 in Seveso. Despite the weaknesses, this study meets the evaluation considerations and criteria 

31 for inclusion and will be analyzed for quantitative dose-response modeling. 
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1 2.4.1.1.1.5. Chapaevsk study. 

2 Industrial contamination of dioxin in the Chapaevsk region of Russia has been the focus 

3 of research on the environmentally-induced cancer and other adverse health effects. The 

4 Chapaevsk region is located in the Samara region of Russia and has a population of 83,000. The 

5 region is home to a chemical plant that produced lindane and its derivatives between 1967 and 

6 1987, which are believed to be responsible for local dioxin contamination. Soil sampling has 

7 demonstrated a strong gradient of increased TCDD concentrations with decreased proximity to 

8 the chemical plant (Revich et al., 2001). 

9 

10 2.4.1.1.1.5.1. Revich et al., 2001. 

11 2.4.1.1.1.5.1.1. Study summary.

12 Revich et al. (2001) used a cross-sectional study to compare mortality rates of Chapaevsk 

13 residents to two external populations of Russia and the region of Samara. Mortality rates for all 

14 cancers combined among males in Chapaevsk were found to be 1.2 times higher when compared 

15 to the Samara region as a whole and 1.3 times higher than Russia. Similar to other studies, 

16 statistically significant excess was noted in men (SMR = 1.8, 95% CI = 1.6−1.9) but not in 

17 women (SMR = 0.9, 95% CI = 0.8−1.1). Among men, the excess was highest for the 

18 smoking-related cancers of the lung (SMR = 3.1, 95% CI = 2.6−3.5) and larynx (SMR = 2.3, 

19 95% CI = 1.2−3.8) and urinary organs (SMR = 2.6, 95% CI = 1.7−3.6). Among females, there 

20 was no increased SMR for all cancer sites combined, but excesses for breast cancer (SMR = 2.1, 

21 95% CI = 1.6−2.7) and cancer of the cervix (SMR = 1.5, 95% CI = 1.0−3.1) were statistically 

22 significant. 

23 Revich et al. (2001) also compared age-standardized cancer incidence rates in Chapaevsk 

24 to those in Samara. Although statistical tests examining these differences were not reported, 

25 higher incidence rates were observed for all cancers combined, cancer of the lip, cancer of the 

26 oral cavity, and lung and bladder cancer among males in Chapaevsk. Considerably lower cancer 

27 incidence rates also were observed for prostate cancer, cancer of the esophagus, and 

28 leukemia/lymphoma among males from Chapaevsk. Among females, incidence rates were 

29 higher in 1998 for all cancers in Chapaevsk when compared to Russia and the Samara region, an 

30 observation that appears somewhat counter to the presented SMR of 0.9 for all cancer mortality 

31 from 1995−1998. Like mortality, rates of breast cancer incidence among women in Chapaevsk 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 were higher than in Russia, as were rates of cervical cancer. Leukemia/lymphoma rates were 

2 higher among women in Chapaevsk than in those who lived in the reference populations of 

3 Samara and Russia. This finding is contrary to the finding for males who had lower rates of 

4 leukemia/lymphoma in Chapaevsk. 

5 

6 2.4.1.1.1.5.1.2. Study evaluation. 

7 Although the Revich et al. (2001) findings suggest TCDD exposures in Chapaevsk are 

8 quite high relative to other parts of the world (Akhmedkhanov et al., 2002), evaluation of health 

9 outcomes to date have been based on ecological data only. This analysis did not adjust for the 

10 influence of other risk factors (e.g., smoking, reproductive characteristics) that could contribute 

11 to increased cancer rates for lung cancer in men and breast cancer in women. Given that both the 

12 SMRs and SIRs for cancer outcomes vary considerably between men and women, this suggests 

13 the possibility that occupational exposures might be a contributing factor in these adverse health 

14 outcomes. 

15 Future research in Chapaevsk includes plans to conduct a breast cancer case-control 

16 study. Women who were born from 1940 onward and who have been diagnosed with breast 

17 cancer before the age of 55 were included in the study, although the plan to characterize TCDD 

18 using serum is uncertain (Revich et al., 2005). 

19 

20 2.4.1.1.1.5.1.3. Suitability of data for TCDD dose-response modeling.

21 This study did not meet the considerations and criteria for inclusion in a quantitative 

22 dose-response assessment. Given the lack of exposure data on an individual basis, no effective 

23 dose can be estimated for this study population. As such, no dose-response modeling was 

24 conducted. 

25 

26 2.4.1.1.1.6. The Air Force Health (“Ranch Hands” cohort) study. 

27 Between 1962 and 1971, the U.S. military sprayed herbicides over Vietnam to destroy 

28 crops that opposition forces depended upon, to clear vegetation from the perimeter of U.S. bases, 

29 and to reduce the ability of opposition forces to hide. These herbicides were predominantly a 

30 mixture of 2,4-D, 2,4,5-T, picloram, and cacodylic acid (Institute of Medicine, 2006). A main 

31 chemical sprayed was Agent Orange, which was a 50% mixture of 2,4-D and 2,4,5-T. TCDD 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 was produced as a contaminant of 2,4,5-T and had levels ranging from 0.05 to 50 ppm (Institute 

2 of Medicine, 1994). A series of studies have investigated cancer outcomes among Vietnam 

3 veterans. A review of military records to characterize exposure to Agent Orange led Stellman 

4 and Stellman (1986) to conclude that assignment of herbicide levels should not be based solely 

5 on self-reports or a crude measure such as military branch or area of service within Vietnam. 

6 Investigations have been performed on the Ranch Hands cohort, which consisted of those who 

7 were involved in the aerial spraying of Agent Orange between 1962 and 1971. More elaborate 

8 methods were used to characterize exposures among these individuals, and these studies are 

9 summarized below. 

10 

11 2.4.1.1.1.6.1. Akhtar et al., 2004. 

12 2.4.1.1.1.6.1.1. Study summary.

13 Akhtar et al. (2004) investigated the incidence of cancer in the Ranch Hand cohort, which 

14 was published after the release of the 2003 Reassessment document (U.S. EPA, 2003). The 

15 Ranch Hand Unit was responsible for aerial spraying of herbicides, including Agent Orange, in 

16 Vietnam from 1962 to 1971. Cancer incidence in the Ranch Hand cohort were compared to a 

17 cohort that included other Air Force personnel who served in Southeast Asia during the same 

18 period but were not involved in the spraying of pesticides. Health outcomes were identified 

19 during the postservice period that extended from the time each veteran left Southeast Asia until 

20 December 31, 1999. In contrast to previous analyses of this cohort, the Akhtar et al. (2004) 

21 study took into account concerns that both the comparison and spraying cohorts had increased 

22 risks of cancer, and addressed the possibility that workers with service in Vietnam or Southeast 

23 Asia might have increased cancer risk. The authors addressed the latter concern by adjusting risk 

24 estimates for the time spent in Southeast Asia and for the proportion of time spent in Vietnam.

25 The Ranch Hand cohort comprised 1,196 individuals, and the comparison cohort had 

26 1,785 individuals. The comparison cohort was selected by matching date of birth, race, and 

27 occupation (i.e., officer pilot, officer navigator, nonflying officer, enlisted flyer, or enlisted 

28 ground personnel). TCDD levels were determined using serum levels collected from veterans 

29 who completed a medical examination in 1987. For those who did not have a serum measure 

30 taken in 1987, but provided one in subsequent years, TCDD levels were back-extrapolated to 

31 1987 using a first-order kinetic model that assumed a half-life of 7.6 years. Those with 
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1 nonquantifiable levels were assigned a value of the limit of detection divided by the square root 

2 of 2. A total of 1,009 and 1,429 individuals in the Ranch Hand and comparison cohorts, 

3 respectively, provided serum measures that were used in the risk assessment. Veterans also were 

4 categorized according to the time their tours ended. This date corresponded to changes in 

5 herbicide use. These categories were before 1962 or after 1972 (no herbicides were used), 

6 1962−1965 (before Agent Orange was used), 1966−1970 (when Agent Orange use was greatest), 

7 and 1971−1972 (after Agent Orange was used). Information on incident cases of cancer in the 

8 cohort was determined from physical examinations and medical records. Some malignancies 

9 were discovered at death and coded from the underlying causes of death as detailed on the death 

10 certificate. A total of 134 and 163 incident cases of cancer were identified in the Ranch Hand 

11 and comparison cohort, respectively. Akhtar et al. (2004) describe case ascertainment verified 

12 by record review as being complete. 

13 External comparisons were made based on the expected cancer experience derived from 

14 U.S. national rates using SIRs and the corresponding 95% confidence interval. Person-years and 

15 events were tabulated by 5-year calendar and age intervals. 

16 When compared to the general population, no statistically significant excesses in all 

17 cancer incidence were observed for either the Ranch Hand (SIR = 1.09, 95% CI = 0.91−1.28) or 

18 the comparison cohort (SIR = 0.94, 95% CI = 0.81−1.10). Statistically significant differences 

19 were found for three site-specific cancers in the Ranch Hands cohort relative to the general 

20 population. Excesses were noted for malignant melanoma (SIR = 2.33, 95% CI = 1.40−3.65) 

21 and prostate cancer (SIR = 1.46, 95% CI = 1.04−2.00). In contrast, a reduced SIR was found for 

22 cancers of the digestive system (SIR = 0.61, 95% CI = 0.36−0.96). The excess in prostate cancer 

23 was also noted in the comparison cohort (SIR = 1.62, 95% CI = 1.23−2.10) relative to the 

24 general population. External comparisons were repeated by restricting the cohorts to the period 

25 when Agent Orange was used (1966−1970). Again, no statistically significant excesses in all 

26 cancer incidence were noted in the Ranch Hand (SIR = 1.14, 95% CI = 0.95−1.37) or 

27 comparison cohort (SIR = 0.94, 95% CI = 0.80−1.11). Statistically significant excesses 

28 continued to be observed for malignant melanoma (SIR = 2.57, 95% CI = 1.52−4.09) and 

29 prostate cancer (SIR = 1.68, 95% CI = 1.19−2.33) in the Ranch Hand component of the cohort. 

30 No other statistically significant differences were found among Ranch Hands personnel. 
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1 For internal cohort analyses, veterans were assigned to one of four exposure categories. 

2 Those in the comparison cohort were assigned to the “comparison category.” Ranch Hand 

3 veterans that had TCDD serum levels <10 ppt were assigned to the “background” category. 

4 Those with a TCDD levels >10 ppt had their TCDD level estimated at the end of their Vietnam 

5 service with a first-order kinetic model that used a half-life of 7.6 years. These 

6 back-extrapolated values that were less than 118.5 ppt were assigned to a “low” exposure group, 

7 while those with values above 118.5 ppt were classified as “high” exposure. Akhtar et al. (2004) 

8 used Cox regression models to describe risks across the exposure groups using the comparison 

9 category as the reference. Risks were adjusted for age at tour, military occupation, smoking 

10 history, skin reaction to sun exposure, and eye color. Internal cohort analyses were restricted to 

11 those who spent no more than 2 years in Southeast Asia and Ranch Hand workers who served 

12 exclusively in Vietnam, and the comparison cohort who served exclusively outside of Vietnam. 

13 Statistically significant excesses of cancer incidence (all sites combined) were observed 

14 in the highest two exposure groups. A statistically significant trend test (p = 0.04) was detected 

15 based on the RRs for the background-, low-, and high- exposure groups: 1.44 

16 (95% CI = 0.82−2.53); 2.23 (95% CI = 1.24−4.00), and 2.02 (95% CI = 1.03−3.95). For 

17 malignant melanoma, the RRs across the three increasing exposure categories were 2.99, 7.42, 

18 and 7.51. The corresponding risk estimates for prostate cancer were 1.50, 2.17, and 6.04. 

19 

20 2.4.1.1.1.6.1.2. Study evaluation.

21 An important strength of this study is the manner in which TCDD exposure was 

22 estimated. Serum data were available for most veterans, and therefore, generalizing exposure 

23 from a small sample of cohort members is not a concern as was the case with the NIOSH and 

24 Hamburg cohorts. Back-extrapolating to derive past exposures was based on a methodology that 

25 has been applied in many of the cohorts, thereby facilitating risk comparisons. An additional 

26 strength of the study is the examination of incidence as a measure of disease occurrence rather 

27 than mortality. 

28 In contrast to the previous analysis (Ketchum et al., 1999) the analysis by Akhtar et al. 

29 (2004) was restricted to individuals who spent no more than 2 years in Southeast Asia. Previous 

30 research had demonstrated that increased time spent in Southeast Asia was associated with an 

31 increased risk of cancer. Confounding might have been introduced given that the comparison 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 cohort spent much more time in Southeast Asia than the Ranch Hands. To illustrate, the median 

2 number of days spent in Southeast Asia was 790 for comparison cohort members, and the 

3 median days for the Ranch Hand cohort in the background, low, and high exposure groups were 

4 426, 457, and 397, respectively. After restricting to those who spent at most 2 years, statistically 

5 significant associations were observed for all cancer sites combined, prostate cancer, and 

6 malignant melanoma using the internal cohort comparisons. 

7 An important issue in the study is the high correlation between 2,4,5-T and 2,4-D, given 

8 that both were used in equal concentrations in Agent Orange. As a result, distinguishing the 

9 effects of each is impossible. This point is relevant, given that 2,4-D has been associated with 

10 prostate cancer in several studies. As a result, the dose-response association with prostate cancer 

11 might be due to 2,4-D exposure and not TCDD. This issue also has implications for the 

12 interpretation of the dose-response pattern for all cancer sites combined, given that incident 

13 prostate cancers accounted for 4 of the 12 incident cases in the high-exposure group. 

14 

15 2.4.1.1.1.6.1.3. Suitability of data for TCDD dose-response modeling.

16 The ascertainment of incident cases and characterization of exposure to TCDD based on 

17 serum measures are strong features of the cohort. Confounding by 2,4-D is a major concern. 

18 Since delineating the independent effects of other Agent Orange contaminants is not possible, 

19 quantitative dose-response analysis was not conducted on this study. 

20 

21 2.4.1.1.1.6.2. Michalek and Pavuk, 2008. 

22 2.4.1.1.1.6.2.1. Study summary.

23 Michalek and Pavuk (2008) recently published an updated analysis of the incidence of 

24 cancer and diabetes in the cohort of Ranch Hand veterans. As with the Akhtar et al. (2004) 

25 analysis, the study included a comparison cohort of other Air Force veterans who served in 

26 Southeast Asia at the same time but were not involved with the spraying of herbicides. This 

27 study extended previous analyses (Henriksen et al., 1997; Ketchum et al., 1999) by addressing 

28 the number of days of herbicide spraying, calendar period of service, and the time spent in 

29 Southeast Asia. Veterans who attended at least one of five examinations were eligible for 

30 inclusion. Incident cancer cases also were identified from medical records. 
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1 The methods used to determine TCDD exposures were as described above in the review 

2 of the Akhtar et al. (2004) study. Blood measures also were taken in 1992, 1997, and 2002 for 

3 subjects with no quantifiable TCDD levels in 1987, those who refused in 1987, and those new to 

4 the study. TCDD dose at the end of service in Vietnam was assigned to Ranch Hands that had 

5 TCDD levels above background using a constant half-life of 7.6 years. Each veteran was then 

6 assigned to one of four dose categories: comparison, background (i.e., Ranch Hands with 1987 

7 TCDD ≤10 ppt), low (Ranch Hands with 1987 levels 10.1−91 ppt), and high (Ranch Hands with 

8 1987 ≥91 ppt). Serum TCDD estimates are available for 1,597 veterans in the comparison 

9 cohort, and 986 veterans in the Ranch Hand cohort. The comparison cohort was selected by 

10 matching on date of birth, race, and occupation of the Ranch Hands. 

11 Michalek and Pavuk (2008) used Cox regression to characterize risks of cancer incidence 

12 across the three upper exposure categories using the comparison category as the referent group. 

13 Risk estimates were adjusted for year of birth, race, smoking, body mass index at the qualifying 

14 tour, military occupation, and skin reaction to sun exposure. Tests for trend for increased risk of 

15 cancer were conducted by testing the continuous covariate log10TCDD.

16 Overall, no association between the TCDD exposure categories and RR of all-site cancer 

17 was observed. Those in the highest exposure group had an RR of 0.9 (95% CI = 0.6−1.4). 

18 Stratified analyses by calendar period of service showed more pronounced risk for those who 

19 served before 1986 (when higher amounts of Agent Orange were used). A statistically 

20 significant dose-response trend (p < 0.01) was observed for cancer risk and log10TCDD 

21 exposure. The RRs for the background, low, and high groups used in these comparisons were 

22 0.7 (95% CI = 0.4−1.3), 1.7 (95% CI = 1.0−2.9), and 1.5 (95% CI = 0.9−2.6). A statistically 

23 significant increase, however, was noted when analyses were restricted to those who had sprayed 

24 for at least 30 days before 1967 and spent time in Southeast Asia (RR = 2.2, 95% CI = 1.1−4.4). 

25 

26 2.4.1.1.1.6.2.2. Study evaluation.

27 Michalek and Pavuk (2008) used the same study population that Akhtar et al. (2004), and 

28 so it has the same strengths and limitations as noted above. The follow-up, however, extends an 

29 additional 5 years (until the end of 2004). The findings for the dose-response analyses were not 

30 as compelling as the earlier Akhtar et al. (2004) findings. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.6.2.3. Suitability of data for TCDD dose-response modeling.

2 The key limitation precluding dose-response analysis for the Michalek and Pavuk (2008) 

3 study is the possible confounding from the inability to control for 2,4-D and other agents used in 

4 Agent Orange. As such, quantitative dose-response analysis was not conducted on this study. 

5 

6 2.4.1.1.1.7. Other studies of potential relevance to dose-response modeling.

7 2.4.1.1.1.7.1. t’ Mannetje et al., 2005—New Zealand herbicide sprayers. 

8 2.4.1.1.1.7.1.1. Study summary.

9 t’Mannetje et al. (2005) described the mortality experience of a cohort of New Zealand 

10 workers who were employed in a plant located in New Plymouth. The plant produced phenoxy 

11 herbicides and pentachlorophenol between 1950 and the mid-1980s. This study population also 

12 was included in the international cohort of producers and sprayers of herbicides that was 

13 analyzed by IARC (Saracci et al., 1991; Kogevinas et al., 1997). In this 2005 study, analyses 

14 were restricted to those who had worked at least 1 month; clerical, kitchen, and field research 

15 staff were excluded. The authors followed up 1,025 herbicide producers and 703 sprayers from 

16 1969 and 1973, respectively, until the end of 2000.

17 The cohort consisted of two components: those involved with the production of 

18 herbicides and those who were sprayers. For the herbicide producers, exposures were 

19 determined by consulting occupational history records; no direct measures of exposure were 

20 available. Each department of employment was assigned to one of 21 codes as in the IARC 

21 international cohort (Saracci et al., 1991). Industrial hygienists and factory personnel with 

22 knowledge of potential exposures in this workforce classified each job according to potential to 

23 be exposed to TCDD, other chlorinated dioxins, and phenoxy herbicides. Exposure was defined 

24 as a dichotomous variable (i.e., exposed and unexposed). Among producers, 813 were classified 

25 as exposed, with the remaining 212 considered unexposed. 

26 The “sprayer” component of the cohort includes those who were registered in the national 

27 registry of applicators at any time from January 1973 until the end of 1984. For the sprayers, 

28 detailed occupational information was lacking. Exposure was, therefore, based on an exposure 

29 history questionnaire completed in a previous study of congenital malformations (Smith et al., 

30 1982). This questionnaire, administered to 548 applicators in 1980 and 232 applicators in 1982, 

31 achieved a high response rate (89%). Participants were asked to provide information about 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2,4,5-T-containing product use on an annual basis from 1969 up to the year the survey was 

2 completed. As the use of 2,4,5-T ceased in the mid-1980s, data on occupational exposure to 

3 TCDD among these workers are fairly complete. Virtually all sprayers (699 of 703) were 

4 exposed to TCDD, higher chlorinated dioxins, and phenoxy herbicides. 

5 Deaths among workers were identified through record linkage to death registrations in the 

6 New Zealand Health Information Service. Electoral rolls, drivers’ licenses, and social security 

7 records also were consulted to confirm identified deaths. External comparisons of mortality 

8 were made to the New Zealand population using the SMR statistic. The mortality follow-up for 

9 the producers began on January 1, 1969 and extended until December 31, 2000. For the 

10 sprayers, the follow-up period extended from January 1, 1973 until December 31, 2000. A total 

11 of 43 cancer deaths occurred in the producer group and 35 cancer deaths occurred in the sprayer 

12 group in the cohort. Where possible, stratified analyses by duration of employment and 

13 department were conducted. The departments examined for producers included synthesis, 

14 formulation and lab, maintenance and waste, packing and transport, other, and unexposed. 

15 SMRs were generated using the New Zealand population as an external referent. A linear test 

16 for trend was applied to evaluate dose-response trends according to categories of duration of 

17 employment. Stratified analyses also were also done for sprayers who started working before 

18 1973, as TCDD levels in 2,4,5-T produced at the New Zealand plant dropped dramatically after 

19 1973. Although an SMR was presented for female producers, given that only one cancer death 

20 was observed, this study can provide no insight on differential risks between the sexes. 

21 Among TCDD-exposed producers, for all cancers combined, no statistically significant 

22 excess mortality was found when compared to the general population (SMR = 1.24, 

23 95% CI = 0.90−1.67). No dose-response trend in the SMRs for all cancers was observed with 

24 duration of employment (p = 0.44). No statistically significant elevated SMR was observed in 

25 any of the duration of employment categories for any of the six specific departments examined. 

26 A statistically significant positive linear trend, however, was noted among synthesis workers 

27 (p = 0.04). There was some suggestion of reduced mortality in the upper exposure levels for 

28 workers in the formulation and lab departments. For sprayers, the SMR for all cancer sites 

29 combined was not elevated relative to the New Zealand general population (SMR = 0.82, 

30 95% CI = 0.57−1.14), nor was a dose-response pattern observed with increasing duration of 

31 employment (p = 0.86). Additionally, no statistically significant excess in cancer mortality for 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 all sites combined was evident in workers who were first employed either before 1973 

2 (SMR = 0.75, 95% CI = 0.50−1.07) or from 1973 on (SMR = 1.81, 95% CI = 0.59−4.22). For 

3 site-specific analyses of cancer mortality, an excess of multiple myeloma was observed among 

4 production workers relative to the general population (SMR = 5.51, 95% CI = 1.14−16.1). This 

5 SMR was based on three deaths. No statistically significant excess (or deficit) of mortality was 

6 found for any other cancer site examined in either the sprayers or the producers. 

7 

8 2.4.1.1.1.7.1.2. Study evaluation.

9 The physical activity demands of spraying contribute to a healthy worker effect that 

10 manifests itself in a lower SMR based on both external comparisons to the general population as 

11 a referent, and the SMR generated for the producers in the cohort. The analyses conducted using 

12 a simple dichotomy of exposure and duration of employment are limited, as nearly all of the 

13 sprayers were unexposed.

14 The dose-response pattern with duration of employment coupled with the observation 

15 that higher levels of exposure to TCDD occurred among workers in the synthesis department is 

16 an important finding. These workers were also exposed to several other contaminants, however, 

17 that include processing chemicals, technical products, intermediates, and byproducts (Kauppinen 

18 et al., 1993). These included phenoxy herbicides and dioxin-like compounds such as chlorinated 

19 dioxins. Since the dichotomous exposure measure was based on exposure to TCDD, chlorinated 

20 dioxins and phenoxy herbicides, the associated dose-response analyses presented in this study 

21 should be interpreted cautiously in light of the inability to either characterize or control for these 

22 potential confounders. As such, these co-exposures might have contributed to the dose-response 

23 pattern observed with increased duration of employment in the synthesis workers. 

24 

25 2.4.1.1.1.7.1.3. Suitability of data for TCDD dose-response modeling.

26 Although the study authors completed a subsequent analysis of this cohort using 

27 serum-derived TCDD (McBride et al., 2009a), the lack of individual-level TCDD exposures 

28 precludes dose-response modeling. 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.1.1.7.2. McBride et al., 2009b—New Zealand herbicide sprayers. 

2 2.4.1.1.1.7.2.1. Study summary.

3 McBride et al. (2009b) published an updated analysis of the mortality of the New 

4 Zealand cohort. The follow-up period was from January 1, 1969 to December 31, 2004 

5 extending the previous study by an additional 4 years. In contrast to the previous study where 

6 the cohort comprised individuals employed for at least 1 month prior to 1982 (or 1984) 

7 (t’Mannetje et al., 2005), the cohort in this study consisted of all those who worked at least one 

8 day between January 1, 1969 and October 1, 2003. This resulted in a cohort of 1,754 workers, of 

9 which 247 died in the follow-up interval. Seventeen percent of the cohort members were lost to 

10 follow-up, which could be a source of selection bias if loss to follow-up was related to both the 

11 exposure metrics and the health outcome of interest. Previous data from this cohort (t’Mannetje 

12 et al., 2005), however, showed fairly comparable loss to follow-up rates among the unexposed 

13 (23%) and the exposed populations (17%). 

14 Comparisons to the New Zealand general population were made using the SMR statistic. 

15 Stratified analyses were conducted by duration of employment (<3 months, ≥3 months), sex, 

16 latency (<15 years, ≥15 years), and period of hire (<1976, ≥1976). The authors defined latency 

17 as the period between the day last worked and the earliest of date of death, date of emigration or 

18 loss to follow-up, or December 31, 2004. 

19 The overall SMR for mortality from all cancer sites combined relative to the New 

20 Zealand population was 1.01 (95% CI = 0.85−1.10). Although not statistically significant there 

21 was suggestion of an increased risk of rectal cancer (SMR = 2.03; 95%CI = 0.88–4.01) among 

22 the employees. SMRs for lymphatic and hematopoietic cancers (overall SMR = 1.21, 

23 95% CI = 0.52–2.39) included 3.12 (95% CI = 0.08−17.37) for Hodgkin’s disease, 1.59 

24 (95% CI = 0.43–4.07) for non-Hodgkin’s lymphoma and 3.73, 95% CI = 1.20−8.71), and 1.66 

25 (95% CI = 0.20–5.99) for multiple myeloma. No statistically significant excess of cancer 

26 mortality was noted among workers employed for <3 months (SMR = 1.19, 

27 95% CI = 0.65−2.00), or for ≥3 months (SMR = 0.98, 95% CI = 0.75−1.26). A statistically 

28 significant excess of digestive cancers was found for those who worked fewer than 3 months 

29 relative to the New Zealand population (SMR = 2.52, 95% CI = 1.15−4.78). No excesses were 

30 observed for any site-specific cancers when analyses were restricted to those who worked for 3 

31 or more months. No statistically significant elevated SMRs were found for all cancers 
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1 (combined) either for a latency period of fewer than 15 years (SMR = 1.14, 95% CI = 0.72−1.71) 

2 or a latency period of ≥15 years (SMR = 0.96, 95% CI = 0.72−1.26). Similarly, no statistically 

3 significant excess in cancer mortality was observed for all cancer sites combined, or any 

4 site-specific cancer when analyses were stratified by date of hire (<1976, ≥1976) or by sex. The 

5 SMR among women who were employed at the site was 0.68 (95% CI = 0.45−1.00). 

6 

7 2.4.1.1.1.7.2.2. Study evaluation.

8 High rates of emigration in New Zealand (9% among workers in the cohort) contributed 

9 to a fairly high loss to follow-up (22% among workers) during the study period. The loss to 

10 follow-up would reduce the overall mortality estimates among the workers, which could 

11 underestimate the SMRs if loss to follow-up (and health status) was not comparable in the 

12 general population. For example, it is unclear if workers and the general population who 

13 emigrated were sicker than those remaining in the cohort. Previous data from the cohort workers 

14 suggests that loss to follow-up rates were slightly higher among the low and unexposed 

15 populations (t’Mannetje et al., 2005; McBride et al., 2009a) worker population, so presumably 

16 the highly exposed workers were not lost to follow-up more so than other workers. 

17 

18 2.4.1.1.1.7.2.3. Suitability of data for TCDD dose-response modeling.

19 This study extended the mortality follow-up and included stratified analyses to 

20 investigate effect modification by period of latency, sex, and date of hire. A key limitation was 

21 the lack of direct measures of exposure for study participants which precluded estimating 

22 effective dose needed for dose-response modeling. This study did not meet the considerations 

23 and criteria for inclusion in quantitative dose-response analysis. 

24 

25 2.4.1.1.1.7.3. McBride et al., 2009a—New Zealand herbicide sprayers. 

26 2.4.1.1.1.7.3.1. Study summary.

27 McBride et al. (2009a) recently published the mortality experience of the New Zealand 

28 cohort in relation to serum estimates of TCDD levels. This study included 1,599 workers who 

29 were employed between 1969 and November 1, 1989, which was the date that 2,4,5-T was last 

30 used. As in their study published earlier in the same year (McBride et al., 2009b), the follow-up 

31 period extended from the first day of employment until December 31, 2004. Vital status was 
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1 ascertained through record linkage to the New Zealand Health Information Service Mortality 

2 Collection and the Registrar General’s Index to Deaths for deaths up to 1990. 

3 All current and former workers who lived within 75 km of the plant were invited to 

4 provide serum samples. A total of 346 of the eligible workers (68%) provided samples, which 

5 represented 22% of the overall study population (346/1599). Based on the serum measures, 70% 

6 (241/346) had been exposed to TCDD. This percentage is similar to the estimated 71% of 

7 workers who were deemed to have been exposed based on a review of occupational records. The 

8 mean serum TCDD value was 9.9 ppt. The highest exposures were observed for those employed 

9 in the trichlorophenol operation (23.4 ppt). Values among unexposed workers averaged 4.9 ppt, 

10 which is close to the background level of 3.9 ppt among individuals of similar age in the New 

11 Zealand general population (Bates et al., 2004). Details on smoking histories of individuals were 

12 also collected for the 346 individuals who provided serum, allowing for an examination of the 

13 potential confounding role that smoking might have on derived risk estimates for TCDD. 

14 Cumulative exposure to TCDD as a time-dependent metric was estimated for each 

15 worker. A detailed description of the methods used to derive TCDD exposure was described in 

16 Aylward et al. (2009). The qualitative TCDD scores available for those with serum measures 

17 were used to estimate the cumulative exposures based on a half-life of approximately 7 years. A 

18 time-dependent estimate of TCDD exposure was derived and the area under the curve was used 

19 to obtain cumulative workplace TCDD exposure above background levels. Model performance 

appears modest as the model explained only 30% of the variance (adjusted R2 20 ) when these 

21 TCDD exposure estimates were compared with actual serum levels (Aylward et al., 2009).

22 As with previous analyses of the cohort (t’ Mannetje et al., 2005; McBride et al., 2009b), 

23 external comparisons to the New Zealand general population were made using the SMR statistic. 

24 The SMR statistic also was used to compare mortality across four exposure groups relative to the 

25 general population, as defined by the serum TCDD estimates: 0−68.3, 68.4−475.0, 

26 475.1−2085.7, and ≥2085.8 ppt-month. The proportional hazards model also was used to 

27 conduct internal cohort comparisons across these same four exposure groups. In these analyses, 

28 age was used as the time variable, and the covariates of date of hire, sex, and birth year were 

29 included in the proportional hazards model. The cut-points for these four exposure categories 

30 were chosen so that approximately equal numbers of deaths were included in each category. 
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1 Consistent with earlier SMR analyses of the same cohort, no increased cancer mortality 

2 was observed among “ever” exposed workers in this cohort when compared to the general 

3 population (SMR = 1.1, 95% CI = 0.9−1.4). No statistically significant excess was noted for any 

4 of the site-specific cancers, although there was some suggestion of increased risk of soft tissue 

5 sarcoma (SMR = 3.4, 95% CI = 0.1–19.5), multiple myeloma (SMR = 2.2, 95% CI = 0.2−8.1), 

6 non-Hodgkin’s lymphoma (SMR = 1.6, 95% CI = 0.3–4.7), and cancer of the rectum 

7 (SMR = 2.0, 95% CI = 0.7−4.4). No statistically significant increases in cancer mortality (all 

8 sites combined) was found in any of the four exposure categories as measured by the SMR 

9 statistic, nor was a dose-response trend noted with increasing exposure categories. No 

10 dose-response trend (based on SMR analyses) was noted for five site-specific cancers examined 

11 (i.e., digestive organs, bronchus, trachea and lung, soft tissue sarcomas, lymphatic and 

12 hematopoietic tissue, and non-Hodgkin’s lymphoma), although SMRs for three of the 

13 four exposure categories exceeded 2.0. 

14 In contrast to the external cohort comparisons, the RRs generated with the proportional 

15 hazards model supported a dose-response trend, as rate ratios increased across increasing TCDD 

16 exposure categories. The RRs and their 95% confidence intervals relative to the lowest of the 

17 four groups were 1.05 (95% CI = 0.48−2.26), 1.38 (95% CI = 0.64−2.97) and 1.58 

18 (95% CI = 0.71−3.52). Neither the linear (p = 0.29) or quadratic (p = 0.82) test for trend, 

19 however, was statistically significant. An increased risk of lung cancer mortality was observed 

20 in the highest TCDD exposure category relative to the lowest (RR = 5.75, 

21 95% CI = 0.76−42.24). The tests for trend for lung cancer, however, also were not statistically 

22 significant. 

23 A smoking survey was administered to a sample of surviving workers of this cohort, and 

24 smoking prevalence was found to be slightly higher among those with higher cumulative 

25 exposure (61%) compared to lower exposures (51−56%). These minor differences in smoking 

26 prevalence unlikely was a strong enough confounder to explain the fivefold increase in risk of 

27 lung cancer found in the highest exposure category. Although the smoking data assessment was 

28 a strength of the study, it was limited to only sample of workers and was not available for those 

29 who died of lung cancer. 

30 
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1 2.4.1.1.1.7.3.2. Study evaluation.

2 Given high rates of emigration, loss to follow-up (22%) was a potential concern in this 

3 study. If comparable emigration rates did occur among the general population then the SMRs 

4 would be underestimated. It is unclear to what extent emigration occurred among the general 

5 population and whether emigration in both the worker and general populations was dependent on 

6 health status. If emigration rates were comparable among these two populations, the associated 

7 bias from the under-ascertainment of mortality in the lost to follow-up group would likely 

8 attenuate a positive association between TCDD and cancer mortality. Among the worker 

9 population, there was not much evidence of differential loss to follow-up with respect to 

10 exposure as average exposures were lower (3.2 ppt) among those loss to follow up compared to 

11 those with complete follow-up (5.7 ppt). Previous studies among this population also found 

12 slightly higher loss to follow-up rates among the unexposed (23%) compared to the exposed 

13 (17%) workers (t’Mannetje et al., 2005). 

14 McBride et al. (2009a) did not present results using a continuous measure of TCDD 

15 exposure (lagged or unlagged) as was done in most other occupational cohorts. Additionally, the 

16 modeling did not consider the use of different periods of latency. 

17 

18 2.4.1.1.1.7.3.3. Suitability of data for TCDD dose-response modeling.

19 There is no evidence that the authors considered exposure metrics that are consistent with 

20 environmental cancer-causing agents such as exposure modeling that takes latency into account. 

21 Given that past occupational cohort studies of TCDD-exposed workers have consistently 

22 demonstrated stronger association with lag interval of 15 years, such an approach should be 

23 applied to this cohort. This precludes this study from consideration for quantitative 

24 dose-response modeling. 

25 

26 2.4.1.1.1.7.4. Hooiveld et al., 1998—Netherlands workers. 

27 2.4.1.1.1.7.4.1. Study summary.

28 Hooiveld et al. (1998) re-analyzed the mortality experience of a cohort of workers 

29 employed in two chemical plants in the Netherlands using 6 additional years of follow-up from 

30 an earlier study (Bueno de Mesquita et al., 1993). The cohort consisted of those employed 

31 between 1955 and June 30, 1985, and vital status was ascertained until December 31, 1991 (i.e., 
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1 36 years of follow-up). These cohort members were involved in the synthesis and formulation of 

2 phenoxy herbicides, of which the main product was 2,4,5-trichlorophenoxyacetic acid and 

3 monochloroacetic acid. This cohort, with a shorter follow-up interval than the original study 

4 (t’Mannetje et al., 2005), was included in the IARC international cohort. The cohort consisted of 

5 1,167 workers, of which 906 were known to be alive at the end of the follow-up. The average 

6 length of follow-up was 22.3 years, and only 10 individuals were lost to follow-up. 

7 The authors used detailed occupational histories to assign exposures. Workers were 

8 classified as exposed to phenoxy herbicides or chlorophenols and contaminants if they worked in 

9 selected departments (i.e., synthesis, finishing, formulation, packing, maintenance/repair, 

10 laboratory, chemical effluent waste, cleaning, shipping-transport, or plant supervision); were 

11 exposed to the accident in 1963; or were exposed by proximity (i.e., if they entered an exposed 

12 department at least once a week). The 1963 accident was the result of an uncontrolled reaction 

13 in the autoclave in which 2,4,5-trichlorophenol was synthesized; an explosion resulted, with 

14 subsequent release of PCDDs that included TCDD. Based on these methods of exposure 

15 assignment, 562 workers were deemed to be exposed to phenoxy herbicides or chlorophenols, 

16 and 567 were unexposed. Due to limited information, 27 workers were classified as having 

17 unknown exposure. 

18 TCDD exposures also were assigned using serum measured on a sample of workers who 

19 were employed for at least 1 year and first started working before 1975. Dioxin-like compounds 

20 including PCDDs were also measured in the serum samples, but not analyzed for this study. Of 

21 the 144 subjects who were invited to provide samples, 94 agreed. TCDD levels were 

22 back-extrapolated to the time of maximum exposure using a one-compartment, first-order kinetic 

23 model that used a half-life estimate of 7.1 years. The mathematical model used was 

24 ln(TCDDmax) = ln(TCDD) + lag × ln(2)/7.1. The lag was defined as the number of years since 

25 last exposure for those exposed by virtue of their normal job duties. For those exposed as a 

26 result of the accident in 1963, the lag was defined as the number of years since the accident 

27 occurred. 

28 The authors made external comparisons of cohort mortality to the Netherlands population 

29 using the SMR statistics. Poisson regression was used to perform internal cohort comparisons 

30 using unexposed workers as the referent. RRs (measured using rate ratios) generated from the 

31 Poisson model also were used to compare mortality based on low, medium, and high TCDD 
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1 serum-derived categories. The Poisson model included the following covariates as adjustment 

2 factors: age, calendar period at end of follow-up, and time since first exposure. 

3 When compared to the general population, workers had an excess mortality from cancer 

4 (SMR = 1.5, 95% CI = 1.1−1.9), based on 51 cancer deaths. Generally, no excesses were 

5 observed for site-specific cancers. The exception included eight deaths from cancers of the 

6 urinary organs (SMR = 3.9, 95% CI = 1.7−7.6). Although not statistically significant, SMRs 

7 comparable in magnitude to other studies were detected for non-Hodgkin’s lymphoma 

8 (SMR = 3.8, 95% CI = 0.8−11.0) and Hodgkin's disease (SMR = 3.2, 95% CI = 0.1−17.6). A 

9 statistically significant excess of cancer mortality (n=20 deaths among occupational workers) 

10 also was also observed relative to the general population when analyses were restricted to those 

11 exposed as a result of the 1963 accident (SMR = 1.7, 95% CI = 1.1−2.7). Three deaths from 

12 prostate cancer were also noted among these workers (SMR = 5.2, 95% CI = 1.1−15.3), but no 

13 excess was observed with any other cancer site. 

14 Internal cohort comparison also demonstrated an increased risk of all cancer mortality 

15 among those exposed to phenoxy herbicides, chlorophenols, and contaminants relative to those 

16 unexposed (RR = 4.1, 95% CI = 1.8−9.0). A statistically significant increased risk was also 

17 noted for respiratory cancer mortality (RR = 7.5, 95% CI = 1.0−56.1). Analyses across 

18 categories of TCDD exposure revealed excesses in cancer mortality for all cancer sites 

19 combined; however, no dose-response trend was apparent. 

20 

21 2.4.1.1.1.7.4.2. Study evaluation.

22 Several other studies that have characterized cohorts by TCDD levels have used the area 

23 under the curve approach and thus have derived an exposure metric that is time dependent. 

24 Hooiveld et al. (1998) instead created an exposure metric to capture the maximum exposure 

25 attained during the worker’s employment. Characterizing risks using this metric assumes that 

26 other TCDD exposures accrued during a workers’ lifetime are not relevant predictors of cancer 

27 risk. 

28 

29 2.4.1.1.1.7.4.3. Suitability of data for TCDD dose-response modeling.

30 One study limitation is that although dioxin-like compounds were measured in the serum 

31 samples, Hooiveld et al. (1998) reported associations with mortality for TCDD only. There is 
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1 some utility to examining dose-response analyses using alternative exposure metrics as those 

2 constructed in this cohort. However, the small number of identified cancer deaths, limitations in 

3 terms of the exposure assignment (based on nonrepresentative sample, and maximum exposure 

4 level) and concern over potential confounding by co-exposures preclude using these data for a 

5 dose-response analysis. 

6 

7 2.4.1.1.2. Key characteristics of epidemiologic cancer studies 

8 See Table 2-1 at the end of the chapter for a comparison of the length of follow-up, 

9 latency period used, the half-life for TCDD used, and the fraction of TEQs accounted for by 

10 TCDD (when applicable) for each study. 

11 

12 2.4.1.1.3. Feasibility of TCDD cancer dose-response modeling—summary discussion by 

13 cohort. 

14 2.4.1.1.3.1. Using the NIOSH cohort in dose-response modeling. 

15 It is important to evaluate the NIOSH cohort in cancer dose-response modeling of TCDD. 

16 This cohort is the largest assembled to date, direct measures of TCDD based on sampling are 

17 available, and the lengthy follow-up interval allows for latent effects to be taken into account. 

18 Further, although this cohort consists mostly of male workers, these workers were occupationally 

19 exposed to TCDD daily, as compared to the acute accidental exposures of other occupational 

20 cohorts. Although the most recent analyses of a subset of the NIOSH cohort showed no 

21 association between serum TCDD levels and cancer mortality, the study authors did not examine 

22 latency effects (Collins et al., 2009). Incorporation of latency intervals is important in light of 

23 the stronger dose-response relationships that consistently have been observed with a 15−20 year 

24 latency interval in previous investigations of the NIOSH and other cohorts (Steenland et al., 

25 2001). 

26 Most published studies of the NIOSH cohort did not evaluate exposures to dioxin-like 

27 compounds. An exception is the analysis by Steenland et al. (2001). Although Steenland et al. 

28 (2001) did not incorporate individual-level data on dioxin-like compounds, based on their 

29 previous work (Piacitelli et al., 1992) they assumed that TEQ occupational exposures occurred as 

30 a result of TCDD alone in this population. TCDD exposures provided a better fit to the data than 

31 the TEQ-based metric, and 15-year latencies improved the fit for both metrics (relative to 
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1 unlagged exposures). The lifetime risk estimates for an increase in 10 TEQs (pg/kg of body 

2 weight/day/sex) ranged from 0.05−0.18%. The value added for this measure is the incorporation 

3 of the contribution of other dioxin-like compounds to the background rates. 

4 Blue collar workers, such as those in the NIOSH cohort, typically have higher rates of 

5 smoking than the general population (Lee et al., 2007; Bang and Kim, 2001). This potential 

6 source of confounding would be expected to produce a higher SMR for lung cancer mortality, 

7 and could contribute to the excess noted in the cohort with longer lag intervals. This bias, 

8 however, likely is not large as no statistically significant excess of nonmalignant respiratory 

9 mortality was found in these workers. Any associated bias from smoking would be expected to 

10 be smaller for comparisons conducted within the cohort, as fellow workers would be expected to 

11 be more homogeneous with respect to their risk factor profile than with an external general 

12 population referent group. Stratified analyses using both internal and external comparison 

13 groups also did not identify important differences in associations with TCDD exposure between 

14 smoking and nonsmoking cancers. Thus, fatal cancer risk estimates reported for workers in the 

15 NIOSH cohort appear to provide a reasonable estimate of the carcinogenic potency of TCDD. 

16 Although the Steenland et al. (2001) study did not directly account for the possible 

17 confounding effects of other occupational exposure, the authors did address this source of 

18 potential bias. No known occupational exposures to carcinogens occurred, with the exception of 

19 4-aminobiphenyl, which occurred at one plant. Two deaths from mesothelioma also occurred in 

20 the cohort, so some exposure to asbestos might also have occurred in the cohort (Fingerhut et al., 

21 1991). The statistical analyses suggested that the inability to control for other occupational 

22 exposures would not have unduly affected risk estimates generated from internal cohort 

23 comparisons. For instance, the removal of one plant at a time from the analysis did not 

24 materially change dose-response estimates generated from the Cox model (Cheng et al., 2006). 

25 Moreover, adding a variable to represent plant in the Cox regression had little impact on the risk 

26 estimates. Given that other occupational exposures varied by plant, a change in risk estimates 

27 would be expected if such exposures were strong confounders. 

28 The Cheng et al. (2006) analysis provides important information about the impact of 

29 applying kinetic models to the data. The CADM TCDD kinetic model resulted in dramatic 

30 decreases in the TCDD cancer mortality risk estimates when compared to the one-stage 

31 compartmental model that had been applied. Although Cheng et al. (2006) suggested that the 
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1 CADM model provides a better fit to the data than the typically used simple one-compartmental 

2 model, statistical comparisons of model fit were not reported. Therefore, there is value in 

3 presenting the range in risk estimates across different models when characterizing dose-response 

4 relationships. 

5 Finally, the half-life of TCDD is generally recognized to vary according to body fat 

6 percentage, data that were not available for the NIOSH workers. The inability to account for 

7 between-worker variability in body fat would introduce exposure measurement error. That body 

8 fat percentage would not be expected to correlate with cumulative exposure to TCDD exposure, 

9 however, would limit the potential for misclassification bias. The effect of any nondifferential 

10 exposure measurement error likely would serve to attenuate the risk estimates of the study. 

11 

12 2.4.1.1.3.2. Using the BASF cohort in dose-response modeling. 

13 The availability of blood lipid data for TCDD allows for characterization of cumulative 

14 TCDD exposures in the BASF cohort. TCDD blood lipid data were collected for 90% of the 

15 surviving members of the cohort (138 of 154) and these serum measures were used to generate 

16 TCDD exposure estimates for all 254 cohort members. Therefore, the potential for 

17 misclassification from extrapolating these exposures to the entire cohort may not be as likely as 

18 for the NIOSH cohort where sera data were available for only a small fraction of workers. These 

19 data were, however, collected long after the accident (36 years) and had to be back-extrapolated 

20 to derive the initial exposures. 

21 The data on this cohort included several risk factors such as cigarette smoking and body 

22 mass index. One advantage is that cumulative TCDD levels by body mass index can be 

23 estimates on an individual-level basis. As expected, the derived cumulative measures appear to 

24 compare well with severity scores of chloracne. The finding that more pronounced risks are 

25 found 15−20 years after first exposure are also consistent with findings from several other 

26 cohorts (Fingerhut et al., 1991; Manz et al., 1991; Bertazzi et al., 2001). 

27 One key limitation of the BASF cohort is its relatively small sample size (n = 243), which 

28 limits the ability to evaluate dose-response relationships for site-specific cancers. Also, the 

29 quality of the ascertainment of cancer incidence cannot be readily evaluated as the geographic 

30 area of the cohort is not covered by a tumor registry. Ott and Zober (1996) state that nonfatal 

31 cancers could have been more likely to be missed in early years, which could partially contribute 
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1 to the larger standardized incidence ratio found for cancer with longer latencies. Commenting on 

2 risk differences derived from incident and decedent cancer outcomes is difficult. Among those 

3 comprising the cohort, the ascertainment of incident outcomes was recognized to be less 

4 complete in early years. Although the ascertainment of mortality outcomes was generally 

5 regarded to be good among the 243 workers, some workers who died or moved likely were 

6 missed when the cohort was constructed. These deaths would have been more likely to have 

7 occurred several years before the second component of the cohort was assembled. 

8 The use of the SMR statistic for this study population is associated with important 

9 sources of uncertainties. Deaths were surely missed, particularly for the third component of the 

10 cohort that accounts for approximately 38% (94/247) of the entire cohort; this factor would serve 

11 to underestimate the overall SMR. As mentioned before, this component of the cohort was 

12 assembled through the recruitment of workers known to be alive in 1986. Despite this limitation, 

13 the characterization of exposure data and availability of other risk factor data at an individual 

14 level allow the development of quantitative dose-response analyses. 

15 

16 2.4.1.1.3.3. Using the Hamburg cohort in dose-response modeling. 

17 The Hamburg cohort lacked data on cigarette smoking, and, therefore, effect estimates 

18 could not be adjusted for this covariate. Additional analyses that excluded lung cancers resulted 

19 in an even stronger dose-response relationship between all cancer mortality and TCDD. Serum 

20 levels of TCDD also were also not associated with smoking status in a subgroup of these workers 

21 (Flesch-Janys et al., 1995) suggesting that smoking is not likely a confounder of the association 

22 between all cancer mortality and TCDD. 

23 An important limitation of the cohort is the reliance on blood and tissue measurements of 

24 190 workers that likely represent a highly selective component of the cohort. This subset of 

25 workers was identified at the end of the observation period, and therefore, excludes workers who 

26 died or could not be traced. There are uncertainties in deriving department- and period-specific 

27 estimates for a period that extends over three decades using this number of workers. 

28 Additionally, the criteria applied to the reference population could have introduced some bias. 

29 Workers were included only in the reference group if they had been employed for at least 

30 10 years in a gas supply industry. The criteria were much different for the workers who were 

31 exposed to TCDD (only 3 months of employment). As a result, the reference group likely would 
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1 be more susceptible to the healthy worker effect. Internal cohort comparisons, which should be 

2 void of such bias, however, generally produced results similar to those based on the external 

3 comparison population. Therefore, the Becher et al. (1998) study meets the criteria and 

4 additional epidemiological considerations for dose-response modeling. 

5 

6 2.4.1.1.3.4. Using the Seveso cohort in dose-response modeling. 

7 Unlike many of the occupational cohorts that were examined, data from the Seveso 

8 cohort are representative of a residential population whose primary exposure was from a single 

9 TCDD release. A notable exception is the BASF cohort where workers were exposed primarily 

10 through two accidents that occurred in the plant. The Seveso data, therefore, might permit 

11 cancer dose-response investigations in women and children. 

12 Uncertainty in identifying the critical exposure window for most of the outcomes related 

13 to the Seveso cohort is a key limitation. An important feature of the Seveso cohort, however, is 

14 that TCDD levels were much lower among those in the highest exposure zones in Seveso 

15 (medians range from 56−136 ng/kg) (Eskenazi et al., 2004) than those in the occupational 

16 cohorts who had TCDD exposures that were sometimes more than 1,000 ng/kg. Given these 

17 dramatic differences in exposures, the standardized mortality ratios (after incorporating a 

18 15−20 year latency period) for all cancer sites combined are remarkably similar between the 

19 Seveso and the occupational cohort analyses. Perhaps more importantly, the data from Seveso 

20 might be more relevant for extrapolating to lower levels, given that exposures to TCDD are 

21 two orders of magnitude higher than background levels (Smith and Lopipero, 2001). 

22 The Warner et al. (2002) study found a positive association between serum levels of 

23 TCDD and breast cancer. As noted previously, ascertainment of incident cases for all cancers 

24 would allow for a dose-response relationship to be evaluated. Moreover, future breast cancer 

25 analyses in this cohort should strengthen the quantitative dose response analyses of this specific 

26 cancer site. The strengths of the Warner et al. (2002) study outlined earlier suggest that this 

27 study should be considered for cancer dose-response modeling. 

28 Earlier Seveso studies likely are unsuitable for conducting quantitative risk assessment. 

29 These previous studies used an indirect measure of TCDD exposure, namely, zone of residence. 

30 Soil concentrations of TCDD varied widely in these three zones (Zone A: 15.5−580.4 ppt; 

31 Zone B: 1.7−4.3 ppt; and Zone R: 0.9−1.4 ppt), which could have resulted in considerable 
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1 exposure misclassification. The Warner et al. (2002) study greatly improved the characterization 

2 of TCDD exposure using serum measures, and also allowed for control of salient risk factors that 

3 may have resulted in bias due to confounding. 

4 At this time it is unclear whether any study has examined the relationship between cancer 

5 and serum estimates of TCDD among Seveso males exposed from the 1976 accident. 

6 

7 2.4.1.1.3.5. Using the Chapaevsk related data in dose-response modeling. 

8 Currently, individual-level exposure data are lacking for residents of this area and there is 

9 no established cohort for which cancer outcomes can be ascertained. These limitations, 

10 therefore, preclude the inclusion of Chapaevsk data in a quantitative dose-response analysis. 

11 

12 2.4.1.1.3.6. Using the Ranch Hands cohort in dose-response modeling. 

13 An important limitation of the Ranch Hands cohort for TCDD and cancer dose-response 

14 modeling is an inability to isolate TCDD effects from the effects of other agents found in the 

15 associated herbicides. Exposure to other dioxin-like compounds was not estimated in this study 

16 and could confound the previously reported associations. As such, dose-response analyses on 

17 this population were not conducted. 

18 

19 2.4.1.1.4. Discussion of general issues related to dose-response modeling 

20 2.4.1.1.4.1. Ascertainment of exposures. 

21 Several series of epidemiological data have used serum measures to estimate TCDD 

22 levels. Serum data offer a distinct advantage in that they provide an objective means to 

23 characterize TCDD exposure at the individual level. The serum measures in the occupational 

24 cohorts, however, are limited in two important ways. First, these samples are generally collected 

25 from small subsets of the larger cohorts; therefore, using these measures to extrapolate to the 

26 remainder of the cohort could introduce bias due to exposure misclassification. The second 

27 limitation is related to estimating the half-life of TCDD. As noted previously, exposures to 

28 TCDD were back-extrapolated several decades from serum samples collected among surviving 

29 members of several cohorts. This approach was used in the NIOSH, Ranch Hands, BASF, New 

30 Zealand, and Hamburg cohorts. The reported half-life of TCDD among these populations was 

31 reported between 7.1 to 9.0 years and shown to vary with several individual characteristics 
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1 including age, body fat composition, and smoking. The derivation of half-lives from a sample of 

2 workers, and application of these estimates to retrospectively characterize exposure can 

3 introduce uncertainty into the lifetime exposure estimates. It is important to note, however, that 

4 sensitivity analyses results in several studies have been fairly consistent when evaluating the 

5 impact of half-life of TCDD (Steenland et al., 2001; Flesch-Janys et al., 1995). 

6 A unique advantage of the Seveso study is that serum measures were taken shortly after 

7 the accident, and therefore characterization of TCDD exposure in this population does not 

8 depend on assumptions needed to back-extrapolate exposures several decades. 

 9 

10 2.4.1.1.4.2. Latency intervals. 

11 Many of the epidemiological studies indicate stronger associations between TCDD and 

12 cancer outcomes once a latency period has been considered. Generally, risks are higher when a 

13 lag period of 15−20 years is included. As noted previously, this observation is consistent with 

14 many other environmental carcinogens such as radon, radiation, and cigarette smoking. That 

15 recent exposures do not contribute to increased cancer risk provides some support that the 

16 initiation and promotion phases might occur many years before death making recent exposures 

17 irrelevant for these analyses. The ability to discriminate between models of varying latency, 

18 however, was limited in many studies. The application of biologically based modeling could 

19 provide additional important insights on which phase(s) of carcinogenesis TCDD exerts an 

20 influence. Such modeling, however, would necessitate having data on an individual-level basis. 

21 Ideally, this modeling would use cancer incident data rather than mortality outcomes, given that 

22 for many cancers, the median survival time exceeds 5 years. 

23 

24 2.4.1.1.4.3. Use of the SMR metric. 

25 The occupational cohorts and the studies in Seveso and Chapaevsk have made inferences 

26 regarding the effects of TCDD on mortality using the SMR. When compared to the general 

27 population, the healthy worker effect may result in a downward bias in the SMR. This often can 

28 manifest as SMRs less than 1 for several causes of mortality. The effect of this bias is, however, 

29 generally lower for cancer outcomes. Cancer outcomes, whether incidence or death, typically 

30 occur later in life and do not generally affect an individual’s ability to work at earlier ages. 
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1 There are several approaches that can be taken to minimize potential biases introduced by 

2 the healthy worker effect, which would account for workers being healthier than the general 

3 population. Comparisons of mortality (or cancer incidence) can be made to other cohorts of 

4 similar workers. If done properly, this can allow for some control of characteristics such as 

5 sociodemographic characteristics and smoking as the two populations can be matched by these 

6 factors. However, it may be the case that other working populations are exposed to other 

7 harmful exposures, thereby making it difficult to estimate risk associated with a specific agent 

8 (such as TCDD) in the cohort of interest. A second and preferred approach to control for the 

9 healthy worker effect, should it prove feasible, is to conduct comparisons of health outcomes in 

10 relation to exposure within the cohort. These comparisons are less likely to be influenced by 

11 other potential confounding variables such as smoking, socioeconomic status, and other 

12 occupational exposures that are generally more homogeneous within the cohort relative to 

13 external populations. Moreover, the mechanisms used to identify health outcomes and follow 

14 individuals over time are generally applied in the same manner to all cohort members. Taken 

15 together, where different comparisons have been made to generate risk estimates, those that have 

16 been conducted using internal cohort comparisons are preferable. 

17 In addition to potential bias from the health worker effect, the comparison of SMRs 

18 between studies is not always straightforward and is not recommended by some (Rothman, 1986; 

19 Myers and Thompson, 1998). The SMR is the ratio of the observed number of deaths to the 

20 expected number of deaths and is often referred to as the method of indirect standardization. The 

21 expected number of deaths is estimated by multiplying the number of person-years tabulated 

22 across individuals in the cohort, stratified by age, by rates from a reference population that are 

23 available for the same strata. Therefore, each population cohort will have an estimated number 

24 of cases derived using a different underlying age structure. As outlined by Rothman (1986), the 

25 mortality rates might not be directly comparable to each other, although the impact of such bias 

26 will be much less if the age-distribution of the cohorts is similar. While it might be reasoned that 

27 the TCDD exposed workers would have similar age distributions this is in fact not the case 

28 (Becher et al., 1998; Ott et al., 1993; Thiess et al., 1982). This may be due to exposure occurring 

29 both chronically, as well as from acute exposures due to accidental releases that happened at 

30 various times at different plants. This is evident with the Hamburg and the BASF cohorts, as 

31 most individuals comprising the BASF cohort were employed at the time of the accident 
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1 (1953/1954), while most of the Hamburg cohort (852/1048) was employed after 1954; the 

2 follow-up of these cohorts ended at approximately the same time. 

3 The method of direct standardization allows for a more meaningful comparison of 

4 mortality rates to be made between cohorts. With this approach, weights (usually based on age 

5 and sex) are drawn from a standard population and are, in turn, applied to disease rates for the 

6 same strata observed in the cohort of interest. A comparison of weighted rates between different 

7 cohorts would then be based on the same population standard. 

8 Despite these limitations in comparing SMRs between studies, Armstrong (1995) argues 

9 that the comparisons are valid if the underlying stratum specific rates in each exposure grouping 

10 are in constant proportion to external rates. Comparisons of the SMRs between studies will be 

11 biased only if there is an interaction between age and TCDD (i.e., the RR of disease due to 

12 exposure differs by age). For cancer outcomes, the finding that associations become stronger 

13 after a period of latency is incorporated into the analyses suggests that this assumption does not 

14 hold true. That is, risk estimates would be lower among young workers. Similarly, for 

15 noncancer outcomes, some of the data from the Seveso cohort suggests differential effects 

16 according to the age at exposure. 

17 The use of the SMR might also be biased in that workers exposed to TCDD could be 

18 subject to more intensive follow-up than the general population, and as a result, differential 

19 coding biases with cause of death might occur. Moreover, some cohorts (e.g., the BASF cohort) 

20 have been assembled, in part, by actively seeking out survivors exposed to accidental releases of 

21 dioxins. As such, they would not include persons who have died or who were lost to follow-up. 

22 This would result in underascertainment of deaths and SMRs developed from these data. The 

23 use of an internal cohort comparison offers distinct advantages to overcome potential sources of 

24 selection bias. Given these uncertainty about comparability across the different studies, 

25 conducting a meta-analysis of cancer outcomes for TCDD using the SMR statistic is not 

26 warranted for this analysis. 

27 

28 2.4.1.1.4.4. All cancers versus site-specific. 

29 An important consideration for quantitative dose-response modeling is the application of 

30 models for all cancers combined, or for site-specific cancers. Consistency is often lacking for 

31 site-specific cancers, which might be due in large part to the relatively small number of cases 
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1 identified for site-specific cancers in the cohorts. Although the risk estimates produced for all 

2 cancer sites have important limitations and uncertainties, the data are far more consistent in 

3 terms of the magnitude of an association and latency intervals. The IARC evaluation has put 

4 forth the possibility of a pleuripotential mode of action between TCDD and the occurrence of 

5 cancer. Despite the criticism of this assertion by some (Cole et al., 2003), the general 

6 consistency of an increased risk for all-cancer mortality across the occupational cohorts when 

7 latency intervals have been incorporated, provides adequate justification for dose-response 

8 quantification of all cancer sites combined. 

9 

10 2.4.1.1.4.5. Summary of epidemiologic cancer study evaluations for dose-response modeling.

11 All epidemiologic cancer studies summarized above were evaluated for suitability of 

12 quantitative dose-response assessment using the TCDD-specific considerations and study 

13 inclusion criteria. The results of this evaluation are summarized in a matrix style array (see 

14 Table 2-2) at the end of this section, and descriptively in Appendix B. Table 2-4 summarizes the 

15 key epidemiologic cancer studies suitable for further TCDD dose-response analyses. 

16 

17 2.4.1.2. Noncancer 

18 In this section, the available epidemiological data that could be used in a dose-response 

19 analysis for noncancer endpoints are evaluated. Because many of the key studies also evaluated 

20 cancer outcomes, the noncancer studies are presented in the same order as presented in 

21 Section 2.4.1.1. Generally, the strengths and limitations of the cancer studies also apply to the 

22 noncancer outcomes. In this section, key features of these studies that have direct relevance to 

23 modeling of noncancer outcomes in particular are highlighted. To reduce redundancy, a detailed 

24 overview of many of these studies is not provided here. Instead, the reader should refer to 

25 Section 2.4.1.1.1. 

26 
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1 2.4.1.2.1. Noncancer cohorts. 

2 2.4.1.2.1.1. The NIOSH cohort. 

3 2.4.1.2.1.1.1. Steenland et al., 1999. 

4 2.4.1.2.1.1.1.1. Study summary.

5 The 1999 published report of NIOSH workers exposed to TCDD also conducted external 

6 cohort comparisons to the U.S. general population using SMRs for mortality outcomes other than 

7 cancer (Steenland et al., 1999). Analyses are based on 3,538 workers employed at 8 plants from 

8 1942 to 1984. SMRs were based on a mortality follow-up that was extended until the end of 

9 1993. Cox regression analyses were used to compare mortality risk in relation to TCDD 

10 exposure within the cohort. 

11 

12 2.4.1.2.1.1.1.2. Study evaluation.

13 Overall, no statistically significant differences in all-cause mortality (SMR = 1.03, 

14 95% CI = 0.97−1.08) were observed. Mortality from ischemic heart disease (SMR = 1.09, 

15 95% CI = 1.00−1.20) and accidents (SMR = 1.25, 95% CI = 1.03−1.50) was slightly elevated. 

16 The dose-response relationship for ischemic heart disease observed with the SMRs calculated 

17 across septile exposure categories, however, was not statistically significant (p = 0.14). Overall, 

18 excess risk was not evident for diabetes, cerebrovascular disease, or nonmalignant respiratory 

19 disease using the external population comparisons. Internal cohort comparisons using the Cox 

20 regression model were performed using 0 and 15-year lag intervals. A dose-response trend was 

21 observed for the derived ratios across the septiles for ischemic heart disease (p = 0.05) and 

22 diabetes (p = 0.02). For ischemic heart disease mortality, those in the upper two septiles had rate 

23 ratios of 1.57 (95% CI = 0.96−2.56) and 1.75 (95% CI = 1.07−2.87), respectively, relative to 

24 those in the lowest septile. In contrast, an inverse dose-response relationship was observed for 

25 diabetes mortality. The inverse association found for diabetes is inconsistent with the positive 

26 association reported in the Ranch Hands study (Michalek and Pavuk, 2008). However, previous 

27 reports have questioned the use of death certificates as the means to ascertain outcome as 

28 diabetes may be under-reported especially among descendents with diabetes who die from cancer 

29 (McEwen et al., 2006). 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.2.1.1.1.3. Suitability of data for TCDD dose-response modeling.

2 The inverse association with diabetes precludes dose-response analysis for this outcome. 

3 Although a dose-response pattern was observed for ischemic heart disease mortality, it was 

4 borderline statistically significant, and this association was not observed in other cohorts. 

5 Furthermore, fatal outcomes are not a suitable basis for development of an RfD. For these 

6 reasons, dose-response analysis for this outcome is precluded. 

7 

8 2.4.1.2.1.1.2. Collins et al., 2009. 

9 2.4.1.2.1.1.2.1. Study summary.

10 Collins et al. (2009) recently described the mortality experience of Dow employees who 

11 worked in Midland, Michigan. This plant produced 2,4,5-trichlorophenol between 1942 and 

12 1979, and 2,4,5-T between 1948 and 1982. The cohort consisted of 1,615 workers exposed to 

13 TCDD from as early as 1942; the follow-up of the cohort extended until 2003. 

14 TCDD exposures were derived using serum samples obtained from 280 surviving 

15 individuals. A simple one-compartment, first-order pharmacokinetic model was used to estimate 

16 time-dependent TCDD measures. The area under the curve approach was then applied to 

17 estimate cumulative TCDD exposure above background. A half-life of 7.2 years for TCDD 

18 based on earlier work was incorporated into the exposure estimation (Flesch-Janys et al., 1996). 

19 Collins et al. (2009) made an external comparison of the mortality rates of the cohort to 

20 the U.S. general population using the SMR statistic. Noncancer causes of death included all 

21 causes, diabetes, cerebrovascular disease, nonmalignant respiratory disease, cirrhosis of the liver, 

22 and accidents. Overall, no statistically significant difference in all-cause mortality of these 

23 workers was detected when compared to the general population (SMR = 0.9, 95% CI = 0.9−1.0). 

24 Except for cirrhosis of the liver (SMR = 0.4, 95% CI = 0.1−0.8), no differences were found for 

25 any of the noncancer causes of death relative to the general population. 

26 Internal cohort analyses based on cumulative measures of TCDD were conducted for 

27 mortality from diabetes, ischemic heart disease, and nonmalignant respiratory disease using the 

28 Cox regression model. These models adjusted for possible confounders such as year of hire and 

29 birth year. No statistically significant association was found between continuous measure of 

30 TCDD and these causes of death. 
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1 2.4.1.2.1.1.2.2. Study evaluation.

2 Given that the external comparisons may result in bias from the healthy worker effect, 

3 results from the internal cohort comparisons using the Cox regression model are preferred. 

4 These analyses were performed for diabetes, ischemic heart disease, and nonmalignant 

5 respiratory disease. TCDD levels for these workers were estimated using a simple 

6 one-compartment pharmacokinetic model (Aylward et al., 2007). The hazard ratios generated 

7 from the Cox regression model were not statistically significant for any of the three noncancer 

8 outcomes modeled. 

9 

10 2.4.1.2.1.1.2.3. Suitability of data for TCDD dose-response modeling.

11 No association of an increased risk for an adverse effect was observed with any of the 

12 noncancer outcomes. Therefore, dose-response modeling based on this population was not 

13 conducted. 

14 

15 2.4.1.2.1.2. The BASF cohort.

16 2.4.1.2.1.2.1. Ott and Zober, 1996. 

17 2.4.1.2.1.2.1.1. Study summary.

18 In 1996, Ott and Zober published a report on the mortality experience of the cohort of 

19 243 BASF male workers who were accidentally exposed to 2,3,7,8-TCDD in 1954 or in the clean 

20 up that followed. The mortality follow-up of this cohort extended until the end of 1992. 

21 External comparisons of mortality were made to the German population using the SMR statistic. 

22 Internal cohort comparisons were also made by estimating cumulative TCDD for the cohort 

23 using serum measures that were obtained from 138 workers. Ott et al. (1993) provided a detailed 

24 account of the methodology to estimate TCDD. Briefly, a cumulative measure of TCDD 

25 expressed in μg/kg was derived, by first estimating the half-life of TCDD using individuals who 

26 had repeated serum measures; the half-life was estimated to be 5.8 years. Individual-level data 

27 on body fat were used to account for the influence of body fat on decay rates. Half-life estimates 

28 of TCDD varied (range: 5.1−8.9 years) and were dependent on body fat composition (20% and 

29 30%, respectively). This approach differed from previous analysis of this cohort that used a 

30 constant 7-year half-life (Ott et al., 1993). TCDD levels at the time of serum sampling were then 

31 estimated as the product of TCDD concentration in blood lipid and the total lipid weight for each 
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1 worker. Nonlinear models then were applied to estimate the contribution of duration of exposure 

2 to TCDD dose extrapolated to the time of exposure. 

3 External comparisons to the German population using the SMR statistic also were 

4 examined across dose categories. The noncancer causes of death examined by Ott and Zober 

5 (1996) included all-cause mortality, diseases of the circulatory system, ischemic heart disease, 

6 diseases of the digestive system, external causes, suicide, and residual causes of death. Overall, 

7 no statistically significant differences in the SMR with the general population for all-causes of 

8 death (SMR = 0.9, 95% CI = 0.7−1.1) were found. No statistically significant differences were 

9 noted for any of the other causes of death examined. 

10 Ott and Zober (1996) performed internal cohort comparisons using the Cox regression 

11 model. These analyses found no dose-response patterns when cause-specific mortality was 

12 examined across increasing cumulative TCDD exposure categories. Although an inverse 

13 association for diseases of the respiratory system (SMR = 0.1, 95% CI = 0.0−0.8) was detected, 

14 it was based only on 1 reported case. Many of these comparisons are limited by small sample 

15 sizes as 92 deaths occurred in the cohort, and of these, 31 were from cancer. Also, the third 

16 component of the cohort was identified primarily from former employees who were alive in 

17 1986. As a result, the SMR based on the general population might be underestimated by the 

18 exclusion of deceased workers. 

19 

20 2.4.1.2.1.2.1.2. Study evaluation.

21 As noted previously, caution should be exercised in the interpretation of SMR values of 

22 noncancer outcomes as they could be influenced by the healthy worker effect. Although the 

23 mechanism of identifying vital status appears to be excellent and unbiased, SMRs might be 

24 underestimated for the cohort due to the manner in which they were constructed. Specifically, a 

25 large component of the cohort was assembled by actively seeking out former workers who were 

26 known to be alive in 1986. 

27 

28 2.4.1.2.1.2.1.3. Suitability of data for TCDD dose-response modeling.

29 No dose-response patterns were observed between TCDD and the noncancer outcomes in 

30 the Ott and Zober (1996) study. Therefore, dose-response modeling was not conducted. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.2.1.3. The Hamburg cohort. 

2 2.4.1.2.1.3.1. Flesch-Janys et al., 1995. 

3 2.4.1.2.1.3.1.1. Study summary.

4 Flesch-Janys et al. (1995) reported on the mortality experience of a cohort of individuals 

5 employed by an herbicide-producing plant in Hamburg, Germany, covering the period 1952 to 

6 1992. As described in more detail in Section 2.4.1.1.1.3, the authors developed a cumulative 

7 measure of TCDD using serum measures from 190 workers. This study also examined the 

8 relationship between total TEQ and mortality. In the study population, the mean TEQ without 

9 TCDD was 155 ng/kg, and for the mean TEQ including TCDD was 296.5 ng/kg. 

10 Risks relative to the unexposed referent group of gas workers were estimated using Cox 

11 regression across six exposed TCDD groups (i.e., the first four quintiles, and the ninth and tenth 

12 deciles). A linear dose-response relationship was found with all causes of mortality and 

13 cardiovascular mortality (p < 0.01). The RR for all cardiovascular deaths in the upper exposure 

14 category was 1.96 (95% CI = 1.15−3.34), although there was no evidence of a linear 

15 dose-response trend (p = 0.27). The dose-response relationship was most marked for ischemic 

16 heart disease, with a RR of 2.48 (95% CI = 1.32−4.66) in the highest exposure group. A 

17 dose-response relationship was also observed across TEQ groupings for all cause mortality, 

18 cardiovascular disease mortality, and ischemic heart disease mortality. The authors did not 

19 perform joint modeling of TEQ (without TCDD) and TCDD, so determining the extent that 

20 dioxin-like compounds contributed to an increased risk of mortality is not possible. 

21 

22 2.4.1.2.1.3.1.2. Study evaluation.

23 The Flesch-Janys et al. (1995) study lacks information on other potential risk factors for 

24 cardiovascular disease, which could result in confounding if those risk factors are also related to 

25 TCDD exposure. Dose-response patterns were strong, however, and persisted across numerous 

26 TCDD (and TEQ) exposure categories based on the use of an external reference group (i.e., gas 

27 workers) or based on the internal comparison. The findings based on the internal comparison are 

28 noteworthy in that these groups should be more homogenous with respect to confounding 

29 factors. As noted previously, the poor correlation between TCDD and smoking among workers 

30 and similar smoking prevalence between the workers and the external gas company workers 

31 suggest that smoking was not likely a confounder of the TCDD and cardiovascular disease 
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1 relationship. No other evaluation of noncancer mortality outcomes has been undertaken in this 

2 cohort since 1995. 

3 A strength of the Flesch-Janys et al. (1995) study was that it included the collection of 

4 blood serum measures, which provided an objective measure of TCDD exposure. Blood serum 

5 data, however, were obtained only for 16% of the cohort. The assumption of the first-order 

6 kinetic elimination model is critical, given that measures were taken at the end of follow-up. The 

7 model also assumed the half-life of TCDD was 6.9 years. If the kinetics are not first order, or if 

8 the half-life estimate is inaccurate, estimates of TCDD levels during exposure would be biased, 

9 particularly for workers having longer periods between exposure and PCDD and PCDF assays. 

10 Sensitivity analyses completed by the authors suggest that such bias is not likely to present 

11 because the results were unaffected when different model assumptions regarding kinetic and 

12 half-lives were examined. The lack of an impact on RR estimates with varying half-life 

13 estimates was similar to findings by Steenland et al. (2001). 

14 

15 2.4.1.2.1.3.1.3. Suitability of data for TCDD dose-response modeling.

16 Despite the aforementioned study strengths, the study focused on fatal outcomes such as 

17 all cause mortality, cardiovascular disease mortality, and ischemic heart disease mortality. As 

18 such, dose-response analysis was not conducted since these outcomes are not suitable for 

19 development of an RfD. 

20 

21 2.4.1.2.1.4. The Seveso Women’s Health Study (SWHS). 

22 Eskenazi et al. (2000) presented an overview of the SWHS. The SWHS is the first 

23 comprehensive epidemiologic study of the reproductive health of a female population exposed to 

24 TCDD. The primary objective of the SWHS is to investigate the relationship of TCDD and 

25 several reproductive endpoints, including endometriosis, menstrual cycle characteristics, birth 

26 outcomes, infertility, and age at menopause. A second phase of follow-up that focuses on 

27 osteoporosis, thyroid hormone, breast cancer, diabetes, and metabolic syndrome is expected to be 

28 completed in 2010. 

29 Women were eligible for participation in the SWHS if they resided in Zones A and B (the 

30 most contaminated areas) at the time of the explosion, were 40 years of age or younger at the 

31 time of the explosion in 1976, and samples of their blood were collected and stored between 
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1 1976 and 1980. The enrollment of women in the SWHS began in March 1996 and continued 

2 until July 1998. Of the 1,271 eligible women, 17 could not be found, 21 had died, and 12 were 

3 too ill to participate. Of the 96% of the remaining women, 80% (n = 981) participated in the 

4 study. Participation in the SWHS included a blood draw and an interview by a trained nurse who 

5 was blind to subjects’ TCDD level and zones of residence at the time of the accident. The 

6 interview included detailed information on potential confounders including occupational, 

7 medical, and reproductive, and pregnancy history. Also, women who were premenopausal were 

8 asked to undergo a vaginal ultrasound and pelvic exam and to complete a daily diary on 

9 menstruation. 

10 Depending on the health outcome under study, TCDD exposures were characterized for 

11 the women at different times. For example, TCDD exposure levels were estimated at the time of 

12 the accident for some studies and at the time of conception for others. The SWHS study 

13 population has been used to investigate associations between maternal TCDD levels and the 

14 following health outcomes: menstrual cycle characteristics (Eskenazi et al., 2002a); 

15 endometriosis (Eskenazi et al., 2002b); birth outcomes (Eskenazi et al., 2003); age at menarche 

16 (Warner et al., 2004); age at menopause (Eskenazi et al., 2005); uterine leiomyomas (Eskenazi et 

17 al., 2007); and ovarian function (Warner et al., 2007). An evaluation of the studies in 

18 chronological order is presented in this section. 

19 

20 2.4.1.2.1.4.1. Eskenazi et al., 2002a—Menstrual cycle characteristics. 

21 2.4.1.2.1.4.1.1. Study summary.

22 Eskenazi et al. (2002a) evaluated serum TCDD exposures in relation to several menstrual 

23 cycle characteristics in the SWHS. A total of 981 women who were 40 years of age or younger 

24 at the time of the accident comprised the SWHS. The following exclusion criteria was applied 

25 44 years of age or older, women with surgical or natural menopause, those with Turner’s 

26 syndrome, and those who in the past year had been pregnant, breastfed, or used an intrauterine 

27 device or oral contraceptives. 

28 A trained interviewer collected data on menstrual cycle characteristics using a 

29 questionnaire. Women were asked to indicate how long their cycles were, whether the cycles 

30 were regular (e.g., irregular cycle defined as length varied by more than 4 days), how many days 

31 the menstrual flow lasted, and whether this flow was “scanty, moderate, or heavy.” Information 
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1 was also collected on obstetric and gynecological conditions. TCDD exposures were derived 

2 from serum samples collected in 1976−1985. The authors selected the earliest available serum 

3 sample, and back-extrapolated to 1976 values using either the Filser model (Kreuzer et al., 1997) 

4 for women aged 16 years or younger in 1976 (n = 20) or the first-order kinetic model (n = 6) 

5 (Pirkle et al., 1989). 

6 Serum TCDD levels were transformed using the log10 scale, and the relationships 

7 between these levels and length of menstrual cycle and days of menstrual flow were examined 

8 using linear regression. The authors applied logistic regression to characterize the risk between 

9 log10TCDD and heaviness of flow or regularity of cycle. In these analyses, moderate or heavy 

10 flow and regular cycle were used as the reference categories. Stratified analysis was performed 

11 by menarcheal status at the time of the accident. 

12 Overall, the association with TCDD exposure (per 10-fold increase) and length of 

13 menstrual cycle was of marginal statistical significance for premenarcheal (β = 0.93, 

14 95% CI = −0.01, 1.86) women, but not postmenarcheal women (β = −0.03, 95% CI = −0.61, 

15 0.54). The corresponding estimates found for days of menstrual flow were β = 0.18 

16 (95% CI = −0.15, 0.51) and β = 0.16 (95% CI = −0.18, 0.50), respectively. Reduced flow was 

17 not associated with TCDD when compared to moderate or heavy flow (odds ratio [OR] = 0.84, 

18 95% CI = 0.44, 1.61); effect modification by menarcheal status, however, was evident (p = 0.03). 

19 Specifically, women exposed to TCDD who were premenarcheal had lower odds of reduced 

20 flow, while those exposed to TCDD who were postmenarcheal did not. These findings counter 

21 the hypothesis that TCDD exposure is related to ovarian dysfunction. Finally, statistically 

22 significant ORs were found between serum TCDD levels (per 10-fold increase) and having an 

23 irregular cycle (OR = 0.46, 95% CI = 0.23, 0.95). This inverse association was evident in both 

24 premenarcheal women (OR = 0.50, 95% CI = 0.18, 1.38) and postmenarcheal women 

25 (OR = 0.41, 95% CI = 0.15, 1.16). 

26 

27 2.4.1.2.1.4.1.2. Study evaluation.

28 Overall, the findings from the Eskenazi et al. (2002a) study suggest that exposures to 

29 TCDD can affect menstrual cycle characteristics among women who were exposed before 

30 menarche. Exposures to TCDD were well characterized using serum samples available on an 

31 individual-level basis, and the design allowed for the influence of other risk factor data to be 
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1 controlled for in regression analyses. Analysis of TCDD levels and the length of menstrual cycle 

2 in premenarcheal women produced associations that were of marginal statistical significance. 

3 However, it is unclear whether the endpoints that were measured constitute adverse health 

4 outcomes as they are not definitive markers of ovarian dysfunction. Another source of 

5 uncertainty is measurement error due to the subjective nature of menstrual flow reporting. Any 

6 resulting misclassification of the outcome should be nondifferential, as the measurement error is 

7 unlikely to be dependent on TCDD exposure. 

8 

9 2.4.1.2.1.4.1.3. Suitability of data for TCDD dose-response modeling.

10 The lack of a clear adverse health outcome related to TCDD exposure is a weakness of 

11 this study. Although it is difficult to define the critical window of exposure for quantitative 

12 exposure calculations, it can be estimated for the women that were premenarcheal at the time of 

13 the accident as 13 years. Therefore, this study is suitable for further consideration for 

14 quantitative dose-response modeling.

15 

16 2.4.1.2.1.4.2. Eskenazi et al., 2002b—Endometriosis. 

17 2.4.1.2.1.4.2.1. Study summary.

18 The SWHS provided the opportunity to investigate the association between serum TCDD 

19 levels and endometriosis (Eskenazi et al., 2002b). The rationale the authors provided for 

20 undertaking this study was the experimental animal studies that suggested an association, the 

21 high prevalence of endometriosis among infertile women where breast milk concentrations of 

22 dioxin are high, and the unknown etiology of endometriosis. The study consisted of 601 women 

23 who were younger than 30 years at the time of the Seveso accident. Stored sera that had been 

24 collected between 1976 and 1980 were also available for these women. 

25 Given that laparoscopy could not be performed on women unless clinically indicated, no 

26 “gold” standard was available for endometriosis diagnosis. Based on the results of a validation 

27 study they conducted in a clinical population, the researchers classified women as having 

28 endometriosis based on symptom report, gynecologic exam results, and vaginal ultrasound. 

29 TCDD was measured in sera in 1976 for 93% of the women. Values for women whose 

30 serum TCDD levels were collected after 1977 and had values exceeding 10 ppt were 

31 back-extrapolated to 1976 using either the Filser model (<16 years of age) (Kreuzer et al., 1997) 
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1 or a first-order kinetic model (≥16 years) (Pirkle et al., 1989). These estimates of TCDD were 

2 then modeled as both continuous (on a log scale) and categorical (≤20, 20.1−100, and >100 ppt) 

3 exposures. 

4 Polytomous logistic regression was applied within the cohort used to generate RRs. In 

5 relation to women in the lowest exposure category, the RR for endometriosis among women in 

6 the middle and upper categories was 1.2 (95% CI = 0.3−4.5) and 2.1 (95% CI = 0.5−8.0), 

7 respectively. The trend tests were not statistically significant for either the categorical (p = 0.25) 

8 and continuous measures of TCDD (p = 0.84). 

9 

10 2.4.1.2.1.4.2.2. Study evaluation.

11 It is important to note that disease misclassification could have led to an underestimate of 

12 the true risk of endometriosis if this misclassification was not differential with respect to TCDD 

13 exposure. Also, younger women were likely to be under-represented as those who had never 

14 been sexually active could not be examined due to cultural reasons. Other dioxin-like 

15 compounds (PCDD, PCDFs, or polychlorinated biphenyls [PCBs]) were not considered because 

16 of small serum volumes, but any potential TEQ exposures occurring in the population were 

17 thought to be mostly attributable to TCDD in the exposed women. 

18 

19 2.4.1.2.1.4.2.3. Suitability of data for TCDD dose-response modeling.

20 Given that no statistically significant dose-response patterns were observed with either 

21 log-transformed or across TCDD exposure categories, and that the elevated risks among those 

22 with higher exposures had very wide confidence intervals (that included unity) quantitative 

23 dose-response analyses were not recommended for this outcome.

24 

25 2.4.1.2.1.4.3. Eskenazi et al., 2003—Adverse birth outcomes. 

26 2.4.1.2.1.4.3.1. Study summary.

27 Eskenazi et al. (2003) examined the relationship between serum TCDD levels and birth 

28 outcome measures. Analyses were based on 745 of the 981 women enrolled in the SWHS who 

29 reported having been pregnant (n = 1,822). Most of these pregnancies (888 pregnancies among 

30 510 women) occurred after the accident. Analysis of spontaneous abortions was restricted to 

31 769 pregnancies among 476 women that did not end in abortion or in ectopic or molar 
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1 pregnancy. Congenital anomalies were evaluated for the 672 pregnancies that did not end in 

2 spontaneous abortion. For the birth outcomes of fetal growth and gestational age, analysis was 

3 performed using 608 singleton births from women without hypertensive pregnancy disorders. 

4 TCDD exposures were based on serum measures, most of which were taken shortly after 

5 the accident. Serum was collected in 1976−1977 for 413 women, between 1978 and 1981 for 

6 12 women, and in 1996 for 19 women. TCDD exposures based on serum samples collected from 

7 1977 onward were back-extrapolated to 1976. 

8 Statistical analyses were performed on pregnancies that ended between 1976 and the time 

9 of interview. A continuous measure of log10TCDD (base 10 scale) was used to investigate 

10 associations with adverse birth outcomes. Logistic regression was used to characterize the 

11 relationship between TCDD exposure spontaneous abortions, small for gestational age, and 

12 preterm birth (<37 weeks gestation). Linear regression was used to describe the relationship 

13 between TCDD and birth weight (in grams) and gestational age (in weeks). 

14 The risk estimates were adjusted for a series of characteristics that included sex of infant, 

15 history of low birthweight child, maternal height, maternal body mass index, maternal education, 

16 maternal smoking during pregnancy, and parity. No association was evident between TCDD 

17 serum levels and spontaneous abortion for pregnancies between 1976 and 1998 (OR = 0.8, 

18 95% CI = 0.6−1.2), or those between 1976 and 1984 (OR = 1.0, 95% CI = 0.6−1.6). No 

19 statistically significant associations were found for birth weight or small gestational age, though 

20 the association with birth weight for pregnancies between 1976 and 1984 associated with a 

21 10-fold increase in TCDD was fairly large and marginally statistically significant (β = −92, 

22 95% CI = −204 to 0). No association was noted for preterm delivery in relation to log10TCCD 

23 levels. 

24 

25 2.4.1.2.1.4.3.2. Study evaluation.

26 This study was well-designed with well characterized exposures. Statistically significant 

27 associations were not evident, although the birth-weight findings should be pursued with further 

28 follow-up of the cohort. As the authors point out, those who were most vulnerable at the time of 

29 the accident (the youngest) had not yet completed their childbearing years. While the study 

30 lacked exposure data for the fathers, the authors indicated that only a small proportion were 

31 believed to have high exposures to TCDD. The key limitation of the study was a reliance on 
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1 self-reported measures of pregnancy history, which may lead to some misclassification of the 

2 birth outcomes. The observation that a large proportion of Seveso women had a voluntary 

3 abortion because of fears of possible birth defects due to exposures from the accident suggest an 

4 awareness bias is possible as a result of differential reporting of birth outcomes according to 

5 exposure status. 

6 

7 2.4.1.2.1.4.3.3. Suitability of data for TCDD dose-response modeling.

8 No statistically significant associations were found in the study; in addition, possible 

9 awareness bias could have influenced the self-reported measures of birth outcomes. Therefore, 

10 quantitative dose-response assessment was not considered for this study. 

11 

12 2.4.1.2.1.4.4. Warner et al., 2004—Age at menarche. 

13 2.4.1.2.1.4.4.1. Study summary.

14 Warner et al. (2004) examined the relationship between TCDD and age at menarche in 

15 the SWHS cohort. As described earlier in this report, the SWHS comprised 981 participants. 

16 This study was restricted only to those who were premenarcheal at the time of the accident 

17 (n = 282). The proportional hazards model was used to model TCDD exposures and age at 

18 menarche. Age at menarche was determined by questionnaire administered by a trained 

19 interviewer. Covariates examined as potential confounders included height, weight, body mass 

20 index, athletic training at the time of interview, smoking, and alcohol consumption. 

21 TCDD exposures were determined using serum samples collected from 257 of these 

22 women between 1976 and 1977. For the remaining women, TCDD levels were quantified from 

23 measures collected between 1978 and 1981 (n = 23) and in 1996 (n = 2). TCDD levels were 

24 back-extrapolated to the time of the explosion in 1976. TCDD was modeled as both a 

25 continuous variable (log10TCDD) and a categorical variable based on quartile values (≤55.9, 

26 56−140.2, 140.3–300, >300 ppt). The lowest group was further subdivided into those with levels 

27 ≤20, and >20 ppt; this cut-point represented background levels found in a sample of women 

28 living in an unexposed area. 

29 No association was found between the continuous measure of TCDD and age at 

30 menarche (hazard ration [HR] = 0.95, 95% CI = 0.83−1.09). Analyses restricted to those who 

31 were younger than 8 in 1976 produced similar results (HR = 1.08, 95% CI = 0.89−1.30). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-99 —DO NOT CITE OR QUOTE 

1 Additionally, no dose-response trend was observed with categorical measures of TCDD among 

2 all women, as well as those under the age of 8. A marginally statistically significant association 

3 with earlier menarche was found when analyses were limited to 84 women under the age of 5 at 

4 the time of the accident (HR = 1.20, 95% CI = 0.98−1.60). 

5 

6 2.4.1.2.1.4.4.2. Study evaluation.

7 An important strength of the Warner et al. (2004) study is the ability to characterize 

8 TCDD exposures using serum samples that were collected shortly after the accident occurred. 

9 The outcome of interest, age at menarche, was determined by asking women “At what age did 

10 you get your first menstrual period?” Recent work suggests that self-reported measures of age at 

11 menarche decades later have modest agreement with responses provided during adolescence with 

12 recall varying by education and by history of an adverse birth outcome (Cooper et al., 2005). In 

13 the Seveso study, bias would be introduced if recall varied according to exposure levels. 

14 

15 2.4.1.2.1.4.4.3. Suitability of data for TCDD dose-response modeling.

16 Although the TCDD exposure characterization of study subjects was based on serum 

17 data, and no major biases were introduced from the study design, the analyses produced largely 

18 null associations. Therefore, quantitative dose-response assessment was not considered for this 

19 study. 

20 

21 2.4.1.2.1.4.5. Eskenazi et al., 2005—Age at menopause. 

22 2.4.1.2.1.4.5.1. Study summary.

23 Eskenazi et al. (2005) evaluated the relationship between age at onset of menopause and 

24 serum levels of TCDD among women in the SWHS. Of the 981 women who agreed to 

25 participate in SWHS, this analysis was restricted to those who had not reached natural 

26 menopause before the time of the accident and who were at least 35 years of age at the time of 

27 the interview. The recruitment and interview of women occurred approximately 20 to 22 years 

28 after the accident (March 1996−July 1998). 

29 The population was divided into quintiles of serum TCDD levels for the categorical 

30 analysis. For most women (n = 564), TCDD levels were estimated from samples provided in 

31 1976−1977. For the remaining women included in these analyses, TCDD levels were estimated 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-100 —DO NOT CITE OR QUOTE 

1 from samples collected between 1978 and 1982 (n = 28) and between 1996 and 1997 (n = 24). 

2 As noted previously, exposure levels for women with post-1977 detectable levels of TCDD were 

3 back-extrapolated to 1976 using either the first-order kinetic model (Pirkle et al., 1989) 

4 (>16 years at time of accident) or the Filser model (<16 years at time of accident) (Kreuzer et al., 

5 1997). Women were classified as premenopausal if they were still menstruating or if they had 

6 amenorrhea as a result of pregnancy or lactation (at the time of interview) with an indication of 

7 subsequent menstruation based on maintained diaries or further examination. Subjects for which 

8 amenorrhea had persisted for at least 1 year with no apparent medical explanation were classified 

9 into a natural menopause category. The category, surgical menopause, pertained to women with 

10 a medically confirmed hysterectomy or an oophorectomy. Finally, impending menopause was 

11 defined for subjects in which menstruation had been absent for 2 months, but who provided 

12 evidence of subsequent menstruation, or had a secretory endometrial lining, or indicated less 

13 predictable cycles in the previous 2−5 years. If participants’ menopausal status could not be 

14 determined, they were grouped into the “other” category. This category included those for 

15 whom status could not be determined due to current use of oral contraceptives, hormone 

16 replacement therapy, or previous cancer chemotherapy. 

17 Statistical analysis was based on both a continuous measure of log-transformed TCDD 

18 exposures and categories based on quintiles (<20.4 ppt; 20.4−34.2 ppt; 34.3−54.1 ppt; 

19 54.2−118.0 ppt; >118.0 ppt). The Cox model was used to generate hazard ratios as estimates of 

20 relative risks and their 95% confidence intervals examining natural menopause as the outcome. 

21 Several covariates previously identified as associated with menopausal status in the literature 

22 were considered as potential confounders. These covariates included body mass index, physical 

23 activity, premenopausal smoking, education, marital status, history of heart disease and other 

24 medical conditions, and other reproductive characteristics. 

25 The RRs were found to increase across the second through fourth quintiles (RRs = 1.1, 

26 1.4, and 1.6, respectively) of serum TCDD categories in relation to those in the lowest category, 

27 but not in the upper quintile (RR = 1.0, 95% CI = 0.6−1.8). A statistically significant test of 

28 trend was detected across the first four quartiles (p = 0.04) but not across all five quintiles 

29 (p = 0.44). A statistically significant association with onset of menopause was not detected 

30 (RR = 1.02, 95% CI = 0.8−1.3) based on the logTCDD continuous measure. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-101 —DO NOT CITE OR QUOTE 

1 2.4.1.2.1.4.5.2. Study evaluation.

2 The categorical exposure results from this study support a non-monotonic 

3 dose-related-association for earlier menopause with increased serum TCDD levels up to 

4 approximately 100-ppt TCDD serum, but not above. Eskenazi et al. (2005) speculated that the 

5 inverse “U” shape of the dose-response relationship is explained by the mimicking of hormones 

6 at lower doses of a chemical, while at higher levels the toxic effect of a chemical does not have 

7 the capacity to either inhibit or stimulate hormonal effects. 

8 A study limitation is the potential for residual confounding due to adjustment based on 

9 current smoking status and not at the time of onset of menopause. It is unclear to what extent 

10 smoking status may differ between these two time periods and whether smoking is related to 

11 TCDD exposures in this cohort. Exposures to other dioxin-like compounds were not considered 

12 in this study because of small serum volumes, but any potential TEQ exposures occurring in the 

13 exposed population were thought to be mostly attributable to TCDD in the exposed women. 

14 

15 2.4.1.2.1.4.5.3. Suitability of data for TCDD dose-response modeling.

16 To date, this study is the only one that has examined the relationship between TCDD 

17 levels and onset of menopause. Although the findings suggest the possibility of a nonlinear 

18 dose-response function, the log10TCDD exposure metric was not statistically significant, nor 

19 were any category-specific hazard ratios statistically significant relative to the lowest category. 

20 Therefore, a quantitative dose-response analysis was not undertaken. 

21 

22 2.4.1.2.1.4.6. Warner et al., 2007—Ovarian function. 

23 2.4.1.2.1.4.6.1. Study summary.

24 Warner et al. (2007) investigated the association between serum TCDD levels and 

25 ovarian function in subjects in the SWHS who were younger than 40 in 1976 and for whom sera 

26 collected after the accident had been stored. These women were recruited from March 1996 until 

27 July 1998. Ovarian function analysis was limited to 363 women between 20 and 40 years of age 

28 and who were not using oral contraceptives. Of these, 310 underwent transvaginal ultrasound 

29 and were included in the functional ovarian cyst analysis. Ninety-six women were in the 

30 preovulatory stage of their menstrual cycles and were included in the follicle analysis. For the 

31 hormone analysis, 126 women who were in the last 2 weeks of their cycle were included. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-102 —DO NOT CITE OR QUOTE 

1 The authors used logistic regression to examine the relationship between TCDD and the 

2 prevalence of ovarian follicles greater than 10 mm. Linear regression models examined the 

3 continuous outcome variables: number of ovarian follicles >10 mm and diameter of dominant 

4 ovarian follicle. Covariates considered for inclusion in the model were age at ultrasound, age at 

5 accident, age at menarche, marital status, parity, gravidity, lactation history, current body mass 

6 index, age at last birth, and smoking history. For the serum hormone analyses, estradiol and 

7 progesterone were measured in blood at the time of interview. Ovulation status was defined as a 

8 dichotomous variable (yes/no) based on a serum progesterone cut-point value of 3 ng/mL. 

9 The adjusted ORs across categories of TCDD exhibited no dose-response trend for the 

10 presence of follicles in relation to TCDD in the follicular phase; also, no statistically significant 

11 differences were noted in any of the upper exposure categories relative to those in the lowest. 

12 The adjusted OR for the continuous measure of log10TCDD was 0.99 (95% CI = 0.4−2.2). A 

13 similar nonstatistically significant finding was found for log10TCDD in relation to ovulation in 

14 both the luteal (OR = 0.99, 95% CI = 0.5−1.9) and mid-luteal phases (OR = 1.03, 

15 95% CI = 0.4−2.7). Analyses of progesterone and estradiol also were not related to serum 

16 TCDD levels for either the luteal or mid-luteal phases (p = 0.51 and p = 0.47). 

17 

18 2.4.1.2.1.4.6.2. Study evaluation.

19 The investigators found no relationship between serum TCDD levels and serum 

20 progesterone and estradiol levels among women who were in the luteal phase at the time of 

21 blood draw. No association with number of ovarian follicles detected from ultrasound. 

22 Although no association was found, the authors suggested that the lack of significant results 

23 could be because the women in SWHS were all exposed postnatally and the relevant and critical 

24 time period for an effect might be in utero (animal studies support relevance of in utero 

25 exposures). 

26 

27 2.4.1.2.1.4.6.3. Suitability of data for TCDD dose-response modeling.

28 One limitation of the study was the lack of examination of confounding by dioxin-like 

29 compounds. The absence of associations between TCDD and adverse health effects in this study 

30 precludes conducting quantitative dose-response analyses. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-103 —DO NOT CITE OR QUOTE 

1 2.4.1.2.1.4.7. Eskenazi et al., 2007—Uterine leiomyoma. 

2 2.4.1.2.1.4.7.1. Study summary.

3 Associations between TCDD exposures and uterine leiomyoma (i.e., fibroids) were also 

4 examined among 956 women in the SWHS (Eskenazi et al., 2007). The sample population was 

5 based on the on the original 981 SWHS participants excluding 25 women diagnosed with 

6 fibroids before the date of the accident (July 10, 1976). Women who previously had fibroids 

7 were identified both through the administered questionnaire and the review of medical records. 

8 Transvaginal ultrasounds were performed for 634 women to determine if they had fibroids at the 

9 time of follow-up. Similar to other SWHS studies, exposure to TCDD was estimated using 

10 serum collected from women shortly after the time of the accident, between 1978 and 1981 and 

11 in 1996. TCDD levels were back-extrapolated to 1976 levels. 

12 The study authors performed statistical analyses using two definitions of fibroids as 

13 outcome measures. The first was fibroids detected before the study, and the second was fibroids 

14 detected via ultrasound. A proportional odds method Dunson and Baird (2001) developed was 

15 used to model the cumulative odds of onset of fibroids. This method combines historical and 

16 current information of diagnoses of fibroids. Continuous and categorical measures of TCDD 

17 were modeled. Regression models were adjusted for known or suspected risk factors of fibroids 

18 including parity, family history of fibroids, age at menarche, body mass index, smoking, alcohol 

19 use, and education. 

20 

21 2.4.1.2.1.4.7.2. Study evaluation.

22 Categorical measures of TCDD suggested an inverse dose-response relationship with the 

23 onset of fibroids. Relative to those with TCDD levels less than 20 ppt, those having TCDD 

24 exposures between 20.1 and 75.0 ppt and greater than 75.0 ppt had RRs of 0.58 

25 (95% CI = 0.41−0.81), and 0.62 (95% CI = 0.44−0.89), respectively. The continuous measure of 

26 log10TCDD produced a hazard ratio of 0.83 (95% CI = 0.65−1.07). 

27 

28 2.4.1.2.1.4.7.3. Suitability of data for TCDD dose-response modeling.

29 The inverse association between TCDD and uterine fibroids supports the possibility of an 

30 anti-estrogenic effect of TCDD. The observed direction of the reported associations precludes 

31 quantitative dose-response modeling. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-104 —DO NOT CITE OR QUOTE 

1 2.4.1.2.1.5. Other Seveso noncancer studies.

2 2.4.1.2.1.5.1. Mocarelli et al., 2008—Semen quality. 

3 2.4.1.2.1.5.1.1. Study summary.

4 Mocarelli et al. (2008) examined the relationship between TCDD and endocrine 

5 disruption and semen quality in a cohort of Seveso men. A total of 397 subjects of the eligible 

6 417 males (<26 years old in 1976) from Zone A and nearby contaminated areas were invited to 

7 participate. Frozen serum samples were used to derive TCCD exposures. Also, 372 healthy 

8 blood donors not living in the TCCD-contaminated area were invited to participate. The 

9 researchers collected a health questionnaire and semen samples from participants. Analyses 

10 were based on 257 individuals in the exposed group and 372 in the comparison group. 

11 Semen samples were collected postmasturbatory at home. Ejaculate volume, sperm 

12 motility, and sperm concentration were measured on these samples. Fasting blood samples also 

13 were collected from the subjects for reproductive hormone analyses, including 17β-estradiol 

14 (E2), follicle stimulating hormone (FSH), inhibin B, luteinizing hormone (LH), and testosterone. 

15 The researchers estimated serum concentrations of TCDD from samples provided in 

16 1976−1977, and also in 1997−1998 for individuals whose earlier samples had TCDD values that 

17 exceeded 15 ppt. Serum concentrations for the comparison group were assumed to be less than 

18 15 ppt in 1976 and 1977 and <6 ppt in 1998/2002 on the basis of serum results for residents in 

19 uncontaminated areas. The exposed and comparison groups were divided into three groups 

20 based on their age in 1976: 1−9, 10−17, and 18−26 years. Mocarelli et al. (2008) applied a 

21 general linear model to the sperm and hormone data and included exposure status, age, smoking 

22 status, body mass index, and occupational exposures as covariates. 

23 Men exposed between the ages of 1 and 9 had reduced semen quality 22 years later. 

24 Reduced sperm quality included decreases in sperm count (p = 0.025), progressive sperm 

25 motility (p = 0.001), and total number of motile sperm (p = 0.01) relative to the comparison 

26 group. The opposite pattern was observed for several indices of semen quality among those aged 

27 10−17 at the time of the accident. For the hormone analyses, those in the exposed group had 

28 lower serum E2 levels, and higher follicle stimulating hormone concentrations. Neither 

29 testosterone levels nor inhibin B concentrations were associated with TCDD exposure. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-105 —DO NOT CITE OR QUOTE 

1 2.4.1.2.1.5.1.2. Study evaluation.

2 The findings of the Mocarelli et al. (2008) study support the hypothesis that exposure to 

3 TCDD in infancy/prepuberty reduces sperm quality and could contribute to reported decrease in 

4 sperm quality in young men in the industrialized world. Although most semen analysis studies 

5 have low compliance rates in general population samples (20−40%) (Jorgenson et al., 2001; 

6 Muller et al., 2004), the compliance rate in this study was much higher (60%). Given that the 

7 compliance rates were similar between the exposed and comparison groups and the strong 

8 differences detected across the two age groups, selection bias appears unlikely in this study. 

9 

10 2.4.1.2.1.5.1.3. Suitability of data for TCDD dose-response modeling.

11 Health outcomes are well defined in the Mocarelli et al. (2008) study, and exposures are 

12 well characterized using serum data. Because the men exposed to elevated TCDD levels 

13 between the ages of 1 and 9 had reduced semen quality 22 years later, it is difficult to identify the 

14 relevant time interval over which TCDD dose should be considered. Specifically, it is difficult 

15 to discern whether this effect is a consequence of the initial high exposure between 1 and 9 years 

16 of age or a function of the cumulative exposure for this entire exposure window beginning at the 

17 early age. However, the differences between these two dose estimates (the initial high exposure 

18 versus the cumulative exposure for the 9 year window) are minimal (i.e. within an order of 

19 magnitude). Despite the uncertainty in estimating the critical window of exposure, 

20 dose-response analysis for this outcome was conducted. 

21 

22 2.4.1.2.1.5.2. Mocarelli et al., 1996, 2000—Sex ratio. 

23 2.4.1.2.1.5.2.1. Study summary.

24 A letter to the editor was the first report of a possible change in the sex ratio from dioxin 

25 among Seveso residents following the July 10, 1976 accident (Mocarelli et al., 1996). The 

26 authors reported that 65% (n = 48) of the 74 total births that had occurred from April 1977 to 

27 December 1984 were females. This male to female ratio of 26:48 (35%) is significantly different 

28 from the worldwide birth ratio of 106 males to 100 females (51%) (James, 1995). Between 1985 

29 and 1994, the Seveso male to female ratio leveled out at 60:64 (48%). The authors suggested 

30 that the finding supported the hypothesis that dioxin might alter the sex ratio through several 

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

1/15/10 DRAFT 2-106 —DO NOT CITE OR QUOTE 

1 Mocarelli et al. (2000) later reported on an investigation between serum-based TCDD 

2 measures in parents and the sex ratio of offspring. In this study, serum samples were collected 

3 from mothers and fathers who lived in the areas at the time of the explosion, were between the 

4 ages of 3 and 45 at the time of the explosion, and produced offspring between April 1, 1977 and 

5 December 31, 1996. The study population included 452 families and 674 offspring, and serum 

6 measures were available for 296 mothers and 239 fathers. An estimate of TCDD at the time of 

7 conception was also examined in relation to male to female birth ratios. TCDD exposure 

8 estimates between the years of 1976 and 1996 were estimated using Filser’s model (Kreuzer et 

9 al., 1997). 

10 Mocarelli et al. (2000) used chi-square test statistics to compare observed sex ratio to an 

11 expected value of 0.51 in this Seveso population. Concentrations of TCDD were modeled as 

12 categorical variables in several ways. First, a dichotomous variable was used whereby 

13 unexposed parents were defined as those who lived outside Zones A, B, and R or had a serum 

14 TCDD concentration of less than 15 ppt; parents with exposures of 15 ppt or higher were 

15 considered exposed. Second, a trichotomous exposure variable was created that consisted of 

16 parents who (1) lived outside Zones A, B, and R or had serum concentrations of less than 15 ppt, 

17 (2) had serum concentrations of 15−80 ppt, and (3) had serum concentrations that exceeded 

18 80 ppt. These cut-points were chosen as they represented tertiles based on the distribution of 

19 TCDD among parents. Analyses were conducted separately for paternal and maternal TCDD 

20 levels. 

21 The overall proportion of 0.49 male births (based on male to female ratio of 328:346) was 

22 not significantly different from the expected proportion of 0.51 (p > 0.05). Statistically 

23 significant differences were found, however, if both parents had TCDD levels >15 ppt (sex 

24 ratio = 0.44) or just the father had serum TCDD levels >15 ppt (sex ratio = 0.44). No 

25 statistically significant differences were found when the fathers had TCDD levels less than 

26 15 ppt, irrespective of the maternal levels. A dose-response pattern in the sex ratio was found 

27 across the paternal exposure categories. That is, the sex ratio decreased with increased paternal 

28 TCDD levels (linear test for trend, p = 0.008). In the unexposed group, the sex ratio (male to 

29 female) was 0.56 (95% CI = 0.49−0.61), while in the highest exposure group 

30 (281.0−26,400.0 ppt) the corresponding sex ratio was 0.38 (95% CI = 0.28−0.49). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-107 —DO NOT CITE OR QUOTE 

1 Stratified analyses by age at paternal exposure revealed that the sex ratio was altered to a 

2 greater degree among fathers who were younger than 19 at the time of the explosion. The male 

3 to female ratio among the unexposed fathers was 0.56 (95% CI = 0.50−0.62), while it was 0.38 

4 (95% CI = 0.30−0.47) for those younger than 19 when exposed and 0.47 (95% CI = 0.41−0.53) 

5 for those exposed after 19. Regardless of the age at the time of exposure, however, fathers who 

6 were exposed had a statistically significantly different birth ratio (they were more likely to father 

7 girls) than those who were unexposed (p < 0.05). 

8 Separate analysis of birth ratios based on paternal TCDD exposure estimated at the time 

9 of conception did not show the same dose-response pattern but did show strong evidence of 

10 consistently decreased male births relative to females. More specifically, the male to female 

11 birth ratios among the four successive quartiles (first through fourth) were 0.41, 0.33, 0.33, 

12 and 0.46. 

13 

14 2.4.1.2.1.5.2.2. Study evaluation.

15 Mocarelli et al. (2000) based the characterization of TCDD exposure on serum samples, 

16 which is an objective method for characterizing dose. Unlike for the occupational cohorts, serum 

17 measures for this study were taken close to the time of the accident, and therefore, 

18 back-extrapolation of TCDD exposures is unnecessary. Exposure received before the age of 19 

19 at the time of the explosion were more strongly associated with a reduced male to female ratio 

20 than those received after the age of 19. The cut off age of 19 seems to be somewhat arbitrary, 

21 resulting in a highly uncertain critical exposure window. TCDD levels at the time of conception 

22 did not demonstrate a dose-response relationship, but paternal exposures resulted in consistently 

23 reduced male to female birth ratios (range: 0.33−0.46). 

24 The study findings are unlikely to be influenced by age at conception as these values 

25 were found, on average, to be similar across calendar years. This suggests that age at conception 

26 was not an important confounder and that the birth ratio findings may be related to paternal 

27 exposures. 

28 The methods used to identify births appear to be appropriate. Even if some 

29 under-ascertainment of births occurred, there is no reason to believe that ascertainment would be 

30 related to TCDD exposure and the sex of the baby. Therefore, no bias is suspected due to 

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

1/15/10 DRAFT 2-108 —DO NOT CITE OR QUOTE 

1 2.4.1.2.1.5.2.3. Suitability of data for TCDD dose-response modeling.

2 TCDD exposures were well-characterized, and internal cohort analyses demonstrate 

3 association between paternal TCDD levels at the time of the accident and birth ratio. However, 

4 the change in sex ratio was only statistically significant when exposure occurred before 19 years 

5 of age. It is impossible to identify the relevant time interval over which TCDD dose should be 

6 considered for dose-response analysis; specifically, it is difficult to discern whether the different 

7 sex ratio is a consequence of the initial peak exposure before 19 years of age or a function of the 

8 average cumulative exposure over this entire exposure window. Assuming the initial high 

9 exposure is the correct exposure window, using the initial exposures in a dose-response model 

10 would yield LOAELs that are too high to be relevant to factor into the RfD calculation. The 

11 differences between the two dose estimates are quite large. Dose-response analysis for this 

12 outcome, therefore, was not conducted. 

13 

14 2.4.1.2.1.5.3. Baccarelli et al., 2008—Neonatal thyroid function. 

15 2.4.1.2.1.5.3.1. Study summary.

16 Baccarelli et al. (2008) investigated the relationship between thyroid function and TCDD 

17 among offspring of women of reproductive age who were exposed in the 1976 accident. This 

18 health endpoint is relevant because thyroid function is important for energy metabolism and 

19 nutrients and for stimulating growth and development of tissues. Neonatal thyroid function at 

20 birth is evaluated through blood thyroid-stimulating hormone (b-TSH). Apart from iodine 

21 deficiency, no other environmental exposure has been associated consistently with reduced 

22 neonatal thyroid functioning. 

23 The study population was drawn from 1,772 women who were identified as having lived 

24 in the highly contaminated areas (Zones A or B) at the time of the accident or between July 10, 

25 1976 and December 31, 1947; were of fertile age (born after 1947); and were alive as of 

26 January 1, 1994. A random sample of 1,772 unexposed women who lived in the reference area 

27 was selected using frequency matching by year of birth to the exposed women, and residency in 

28 the reference area at the time of the accident. The reference area represents the noncontaminated 

29 areas that surround the three zones of decreasing exposure (Zones A, B and R). In total, 

30 55,576 women had lived in the reference area. Population registry offices (n = 472) were 

31 contacted to detect children born to these women. Records could be traced for virtually all 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-109 —DO NOT CITE OR QUOTE 

1 subjects (1761/1772 exposed; 1762/1772 unexposed). Children born outside the Lombardy area 

2 were excluded as b-TSH could not be obtained for them. This accounted for 156 of the 

3 1,170 children identified. The analyses were based on the remaining 56, 425, and 533 singletons 

4 born between January 1, 1994, and June 30, 2005 in Zone A, B, and from the reference area, 

5 respectively. 

6 Thyroid function is tested in all newborns by b-TSH measures in the region of Lombardy 

7 where Seveso is located. These measures are obtained from blood samples taken 72 hours after 

8 birth using a standardized protocol. The b-TSH levels were log transformed to approximate a 

9 normal distribution. Linear regression analysis was used to conduct test for trends in mean 

10 b-TSH levels across different covariates. Logistic regression was used to assess associations 

11 between elevated b-TSH levels defined by the cutpoint of 5 μU/mL and residence in particular 

12 zones of contamination. Generalized estimating equations were used to adjust the standard 

13 errors of the ORs for correlation between siblings. 

14 The mean levels of b-TSH were positively associated with average soil TCDD 

15 concentrations in the three areas (Zone A: 1.66 μU/mL; Zone B: 1.35 μU/mL; and Zone R: 

16 0.98 μU/mL) (p < 0.001). Plasma TCDD levels also were shown to be much higher in a group of 

17 51 newborns that had b-TSH levels >5 μU/mL. Compared to the reference population, adjusted 

18 ORs were elevated for Zone B (OR = 1.90, 95% CI = 0.94−3.86) and Zone A (OR = 6.63, 

19 95% CI = 2.36−18.6). These ORs were adjusted for gender, birth weight, birth order, maternal 

20 age at delivery, hospital, and type of delivery. The adjusted ORs however differed only slightly 

21 from those that were unadjusted (Zone B, OR = 1.79, 95% CI = 0.92−3.50; Zone A OR = 6.60, 

22 95% CI = 2.45−17.8). Of the risk factors considered, both gender and birth weights were 

23 associated with neonatal b-TSH. 

24 The paper also included an analysis of children born to 109 women who were part of the 

25 Seveso Chloracne Study (Baccarelli et al., 2005). A total of 51 children were born to 38 of these 

26 women, of these 12 lived in Zone A, 10 in Zone B, 20 in Zone R, and 9 from the reference 

27 population. Several congeners including TCDD were measured in maternal plasma. TCDD 

28 levels were extrapolated to the date of delivery using a first-order pharmacokinetic model 

29 (Michalek et al., 1996). The elimination rate used was 9.8 years based on the mean half-life 

30 estimate from a previous study of women in the Seveso region (Michalek et al., 2002). TEQs 

31 were calculated for a mixture of dioxin-like compounds by multiplying the concentration of each 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-110 —DO NOT CITE OR QUOTE 

1 congener by its toxicity equivalence factor. The maternal average TEQ was 44.8 ppt 

2 (range: 11.6−330.4) among 51 mothers. The measurement of noncoplanar PCBs occurred only 

3 later in the study (1996) and, therefore, total mean TEQs (i.e., including the sum of PCDDs, 

4 PCDFs, coplanar PCBs, and noncoplanar PCBs) are available only on a subset (n = 37) of the 

5 population. Dioxin-like congeners were examined in this study as several studies suggest 

6 associations between the sum of PCBs, or individual congeners having decreased thyroxine (T4; 

7 Longnecker et al., 2000; Sandau et al., 2002), and increased TSH (Alvarez-Pedrerol et al., 2008; 

8 Chevrier et al., 2007). 

9 The authors used a linear model to examine the association between maternal TCDD 

10 levels and b-TSH. The standardized regression coefficient obtained from this model was 0.47 

11 (p < 0.001). For the evaluation of TEQs, a similar association was noted for PCDDs, PCDFs, 

12 and coplanar PCBs (n = 51, β = 0.45, p = 0.005) but not with non-coplanar PCBs (n = 37, 

13 β = 0.16, p = 0.45). Multivariate regression models that were adjusted for several covariates 

14 (i.e., gender, birth weight, birth order, maternal age at delivery, hospital, and type of delivery) 

15 found statistically significant associations with plasma TCDD, PCDDs, PCDFs, and coplanar 

16 PCBs, but not with non-coplanar PCBs. The sum of all total TEQs from the measured 

17 compounds was not statistically significant (n = 37, β = 0.31, p = 0.14). 

18 

19 2.4.1.2.1.5.3.2. Study evaluation.

20 The Baccarelli et al. (2008) study satisfies the epidemiological considerations and criteria 

21 for determining whether dose-response modeling should be pursued. The outcome is well 

22 defined, and a dose-response pattern was observed. The study also contained a substudy that 

23 characterized TCDD and exposures to other dioxin-like congeners and used serum measures for 

24 a sample of mothers. Results were consistent among the zone of residence analysis and the 

25 substudy based on serum measures. 

26 

27 2.4.1.2.1.5.3.3. Suitability of data for TCDD dose-response modeling.

28 Given the potential for exposure misclassification due to variability in TCDD soil levels 

29 within each zone, modeling should rely on individual-level TCDD exposures derived from the 

30 serum sampling substudy. The study data provide an opportunity for quantitative dose-response 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 analyses as the critical exposure window of 9 months can be used for exposure assessment 

2 purposes. 

3 

4 2.4.1.2.1.5.4. Alaluusua et al., 2004—Oral hygiene. 

5 2.4.1.2.1.5.4.1. Study summary.

6 Alaluusua et al. (2004) examined the relationship between TCDD and dental defects, 

7 dental caries, and periodontal disease among Seveso residents who were children at the time of 

8 the accident. Subjects were randomly selected from those individuals who had previously 

9 provided serum samples in 1976, which was shortly after the accident. A total of 65 subjects 

10 who were less than 9.5 years of age at the time of the accident, and who lived in Zones A, B, or 

11 R were invited to participate. Recruitment was initiated 25 years after the time of the Seveso 

12 accident. An additional 130 subjects from the surrounding area (outside Zones A, B, or R or 

13 “non-ABR zone”) having the same age restriction were recruited. Subjects were frequency 

14 matched for age, sex, and education. Questionnaires were administered to these individuals to 

15 collect detailed information on dental and medical histories, education, and smoking behaviors. 

16 Ten subjects who had completed at least high school were randomly excluded from the non-ABR 

17 zone to create groups with similar educational profiles. Participation rates for the ABR and 

18 non-ABR zones were 74% and 58%, respectively. 

19 One dentist who was blind to the patients’ TCDD exposure levels assessed dental 

20 aberrations. Dental caries was assessed using recommendations of the World Health 

21 Organization. Periodontal status was described following a detailed evaluation of the surfaces of 

22 the teeth. A radiographic examination was done to identify missing teeth, alveolar bone loss, 

23 deformities in the roots, and jaw cysts. 

24 Comparisons of the presence of dental enamel defects according to exposure status were 

25 performed using logistic regression. Chi-square test statistics were applied to compare the 

26 distributions in the prevalence of dental defects across several categorical covariates (i.e., 

27 education, age, and serum TCDD level). For those who were younger than 5 at the time of the 

28 accident, dental defects were more prevalent among patients in zone ABR (42%) than those in 

29 the non-ABR zone (26%) (p = 0.14). Zone ABR is characterized by higher levels of soil TCDD 

30 levels relative to non-ABR. Serum levels permitted an improved characterization of risk as they 

31 were available at an individual level, rather than using a zone of residence. Defect prevalence 
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1 was highest among those in the upper serum TCDD category (700−26,000 ng/kg) with 60% of 

2 subjects having dental defects. The continuous measure of serum TCDD was associated with 

3 developmental defects (p = 0.007) and hypodontia (p = 0.05). 

4 

5 2.4.1.2.1.5.4.2. Study evaluation.

6 Although the subjects with serum measures were selected randomly, no direct measures 

7 of TCDD were made in subjects from the unexposed area (i.e., non-ABR zones). That those who 

8 resided in the non-ABR areas had lower TCDD exposures would be a reasonable assumption. 

9 Alaluusua et al. (2004), however, provide few details about the sampling frame used to identify 

10 these participants. Despite this fact, it is important to note that a dose-response pattern was 

11 observed between TCDD exposure and presence of developmental defects in the ABR 

12 population alone (p = 0.016). This finding is based on 27 subjects with developmental defects. 

13 This positive association provides support for a quantitative dose-response modeling of dental 

14 aberrations. The numbers of such subjects are small, however, with one, five, and nine subjects 

15 having defects in the exposure groups of 31−226, 238−592, and 700−26,000 ng/kg TCDD, 

16 respectively. 

17 TCDD exposures were characterized using serum measures for those who resided in zone 

18 ABR in 1976 (near the time of the accident). The authors could not account for additional 

19 exposure to TCDD across subjects that might have occurred since the time of the accident, so 

20 there is considerable uncertainty in delineating the critical exposure window for the reported 

21 effects. In addition, the lack of exposure data for those in the non-ABR zone, however, makes 

22 interpretation of the findings difficult. This difficulty is particularly evident, given that the 

23 prevalence of dental defects was less among those in the low exposure category of zone ABR 

24 (31−226 ng/kg TCDD) (10%) when compared to those in the non-ABR zone (26%). 

25 

26 2.4.1.2.1.5.4.3. Suitability of data for TCDD dose-response modeling.

27 Most of the considerations for conducting a dose-response analysis have been satisfied 

28 with the study population, although, exposure assessment uncertainties are a limitation of this 

29 study. For example, it is difficult to discern whether these health effects are a consequence of 

30 the initial high exposure during childhood or a function of the cumulative exposure for this entire 

31 exposure window beginning at the early age. If the latter is true, averaging exposure over the 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 critical window would add considerable uncertainty to effective dose estimates given the large 

2 difference between initial TCDD body burden and body burden at the end of the critical 

3 exposure window. Despite the uncertainty in defining the critical window of exposure, 

4 dose-response analysis was conducted for this outcome. 

5 

6 2.4.1.2.1.5.5. Bertazzi et al., 1989; Consonni et al., 2008—Mortality outcomes. 

7 2.4.1.2.1.5.5.1. Study summary.

8 Several studies have evaluated the mortality of Seveso residents exposed to TCDD 

9 following the 1976 accident. The earlier section of this report described the designs of these 

10 studies and discussed their findings as they relate to cancer mortality. In this section, some of 

11 the findings for other causes of death are described. A key feature of these studies is that 

12 patterns of mortality among Seveso residents were investigated according to their zone of 

13 residence at the time of explosion relative to general population rates. 

14 A 10-year mortality follow-up of residents of Seveso was published in 1989 (Bertazzi et 

15 al., 1989). Poisson regression was used to derive RRs for those who had lived in Zone A at the 

16 time of explosion using a referent group consisting of inhabitants who had lived in the 

17 uncontaminated study area. Between 1976 and 1986, no statistically significant difference was 

18 observed in all-cause mortality relative to the general population among those who lived in the 

19 most highly exposed area (Zone A) at the time of the accident. This finding was evident in both 

20 males (RR = 0.86, 95% CI = 0.5−1.4) and females (RR = 1.14, 95% CI = 0.6−2.1). A 

21 statistically significant excess in circulatory disease mortality was found among males relative to 

22 those in the referent population (RR = 1.75, 95% CI = 1.0−3.2); this increased risk was more 

23 pronounced when the follow-up period was restricted to the first 5 years after the accident 

24 (1976−1981) (RR = 2.04, 95% CI = 1.04−4.2). Between 1982 and 1986, the RR decreased 

25 substantially and was not statistically significant (RR = 1.19, 95% CI = 0.4−3.5). Among 

26 females, a risk similar in magnitude was detected for circulatory disease mortality although it 

27 was not statistically significant (RR = 1.89, 95% CI = 0.8−4.2). Contrary to the calendar 

28 period-specific findings for males, the excess of circulatory mortality among females occurred 

29 between 1982 and 1986 (RR = 2.91, 95% CI = 1.1−7.8) and not between 1976 and 1981 

30 (RR = 1.12, 95% CI = 0.3−4.5). The number of deaths in this cohort with the 10 years of 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 follow-up was relatively small; in Zone A, 16 deaths were observed among males and 11 among 

2 females. 

3 The most recently published account of the mortality experience of Seveso residents 

4 provides further information on follow-up of these residents until the end of 2001 (25 years after 

5 the accident) (Consonni et al., 2008). Three exposure groups were considered: Zone A (very 

6 high contamination), Zone B (high contamination), and Zone R (low contamination). The 

7 reference population consisted of those residents who lived in unaffected surrounding areas, as 

8 well as residents of five nearby towns. The authors used Poisson regression to compare 

9 mortality rates for each zone relative to the reference population. 

10 For all causes of death, no excess was found in Zone A, B, or R relative to the reference 

11 population. Statistically significant excesses were noted for those who lived in Zone A relative 

12 to the reference population for chronic rheumatic heart disease (RR = 5.74, 

13 95% CI = 1.83−17.99) and chronic obstructive pulmonary disease (RR = 2.53, 

14 95% CI = 1.20−5.32). These risks, however, were based on only 3 and 7 deaths, respectively. 

15 For those in Zone A, no statistically significant excesses in mortality were noted for diabetes, 

16 accidents, digestive diseases, ischemic heart disease, or stroke. Among Zone A residents, 

17 stratified analysis by time since accident showed increased rates of circulatory disease 5−9 years 

18 since the accident (RR = 1.84, 95% CI = 1.09−3.12). Increased mortality from diabetes relative 

19 to the reference population was noted among females who lived in Zone B (RR = 1.78, 

20 95% CI = 1.14−2.77). 

21 

22 2.4.1.2.1.5.5.2. Study evaluation.

23 The ascertainment of mortality in this cohort is nearly complete. Misclassification of 

24 some health outcomes, such as diabetes, may occur due to use of death certificate data. 

25 The characterization of exposure is based on zone of residence. Soil sampling indicated 

26 considerable variability in TCDD soil levels, and therefore, the generation of risks based on zone 

27 of residence likely does not accurately reflect individual exposure. Exposure misclassification 

28 might also occur because residency in the areas does not necessarily reflect whether the 

29 individual would have been present in the area at the time the accident occurred. Any exposure 

30 misclassification would likely be nondifferential which would tend to bias the risk estimates 

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

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1 Although some excess of circulatory disease mortality was found, the finding was not 

2 consistent between men and women. Moreover, excess circulatory disease mortality was more 

3 pronounced among men within the first 5 years of exposure, while, for women, the excess was 

4 more pronounced in years 5−10. Numerous other risk factors for circulatory disease were not 

5 controlled for in these analyses and may be confounders if related to TCDD exposure. Taken 

6 together, the possibility that TCDD increased circulatory disease mortality based on these data is 

7 tenuous at best. 

8 

9 2.4.1.2.1.5.5.3. Suitability of data for TCDD dose-response modeling.

10 There is considerable uncertainty in these data due to the potential for outcome and 

11 exposure misclassification. The lack of the individual-level TCDD levels and the examination of 

12 fatal outcomes reported in this study are not a suitable basis for development of an RfD. For 

13 these reasons, dose-response analysis for this outcome is not conducted. 

14 

15 2.4.1.2.1.5.6. Baccarelli et al., 2005—Chloracne. 

16 2.4.1.2.1.5.6.1. Study summary.

17 Baccarelli et al. (2005) published findings from a case-control study of 110 chloracne 

18 cases and 211 controls. The authors collected information on pigment characteristics and an 

19 extensive list of diseases. This study was performed to yield information about the health status 

20 of chloracne cases, TCDD-chloracne exposure response, and factors that could modify TCDD 

21 toxicity. TCDD was measured from plasma. Following adjustment for confounding, TCDD was 

22 associated with chloracne (OR = 3.7, 95% CI = 1.5−8.8), and the risk of chloracne was 

23 considerably higher in subjects younger than 8 at the time of the accidents (OR = 7.4, 

24 95% CI = 1.8−30.3). Among individuals with lighter hair, the association between TCDD and 

25 chloracne was stronger than among those with darker hair. 

26 

27 2.4.1.2.1.5.6.2. Study evaluation.

28 Although a dose-response association was observed, chloracne is a rare health outcome 

29 likely only to occur among those highly exposed. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.2.1.5.6.3. Suitability of data for TCDD dose-response modeling.

2 Given the very high TCDD levels needed to cause chloracne, quantitative dose-response 

3 modeling to characterize risks for the general population with much lower TCDD exposures 

4 would be of little value. Therefore, quantitative dose-response assessment for the Baccarelli et 

5 al. (2005) study was not conducted. 

6 

7 2.4.1.2.1.5.7. Baccarelli et al., 2002, 2004—Immunologic effects. 

8 2.4.1.2.1.5.7.1. Study summary.

9 The relationship between TCDD and immunological effects was evaluated in a sample of 

10 Seveso residents (Baccarelli et al., 2002, 2004). Both studies were based on findings from 

11 62 individuals who were randomly selected from Zones A and B. An additional 59 subjects 

12 were chosen from the surrounding noncontaminated areas. Residency was based on where 

13 subjects lived at the time of the accident (July 10, 1976) (Landi et al., 1998). Frequency 

14 matching ensured that the two groups of subjects were similar with respect to age, sex, and 

15 cigarette smoking status. 

16 TCDD levels were determined by mass spectrometric analysis of plasma samples. 

17 TCDD levels at the time of sampling were obtained, and estimates of levels at the time of the 

18 accident also were estimated by assuming an 8.2-year half-life (Landi et al., 1998). The plasma 

19 was also used to characterize levels of the immunoglobulins (Ig) IgG and IgM and the 

20 complement components C3 and C4. One subject was excluded due to lack of an immunological 

21 evaluation. Analyses are, therefore, based on 58 subjects in the noncontaminated areas and 

22 62 individuals from the contaminated areas. 

23 Nonparametric tests were applied to test for differences between the two groups. 

24 Multiple regression also was used to describe the relationship between the variables. Adjustment 

25 was made for several potentially confounding variables that were collected via a questionnaire. 

26 An inverse association was noted with increasing TCDD levels and plasma IgG levels; 

27 this result remained statistically significant after adjusting for other potential confounding 

28 variables in the regression models. Specifically, the slope coefficient and p-value for the 

29 unadjusted model were −0.35 (p = 0.0002) and for the adjusted model the p-value was 0.0004. 

30 The authors did not present the slope coefficient for the adjusted model in either paper but noted 

31 minimal differences between the adjusted and unadjusted results. In the 2004 analysis, the 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 authors present IgG, IgM, IgA, C3, and C4 median and interquartile values across TCDD 

2 exposure quintiles. Decreased levels of IgG were observed in the highest exposure groups. 

3 Specifically, the median values across the five quintiles (for lowest to highest) were 1,526; 

4 1,422; 1,363; 1,302; and 1,163. The Kruskal-Wallis test for differences across the TCDD 

5 categories was statistically significant (p = 0.002), which is consistent with the findings for the 

6 continuous measures of TCDD. This finding persisted after excluding those subjects with 

7 inflammatory diseases and those who used antibiotics or nonsteroidal anti-inflammatory drugs. 

8 For the other plasma measures, no dose-response relationship was apparent based on median 

9 values for IgM, IgA, C3, or C4 across TCDD quintiles. The authors highlight the need for 

10 additional research, particularly given the excess of lymphatic tumors noted in the area. 

11 Exposure to other dioxin-like compounds for both the TCDD and nonexposed areas were 

12 reported to be at background levels. 

13 

14 2.4.1.2.1.5.7.2. Study evaluation.

15 Both TCDD exposure and health outcome measures are well characterized. TCDD 

16 exposures, in particular, are based on current serum measures and, therefore, are not dependent 

17 on assumptions needed to back-extrapolate to earlier time periods of exposure. 

18 A dose-response relationship between TCDD and IgG is well documented for the 

19 unadjusted model, but no details are provided on the change in the slope coefficient when other 

20 covariates were added to the model. 

21 Interpreting the inverse association between TCDD exposure and IgG in terms of clinical 

22 significance is not possible. The IgG values reported are much higher than those subjects with 

23 antibody immunodeficiency disorders. 

24 

25 2.4.1.2.1.5.7.3. Suitability of data for TCDD dose-response modeling.

26 Although the data support an inverse dose-response association between IgG and TCDD, 

27 because the relationship cannot be described in terms of clinical relevance with respect to a 

28 specific adverse health outcome, these data were not suitable for qunatitative dose-response 

29 modeling. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.2.1.6. The Chapaevsk study. 

2 2.4.1.2.1.6.1. Revich et al. (2001)—Mortality and reproductive health. 

3 2.4.1.2.1.6.1.1. Study summary.

4 Revich et al. (2001) describe a series of investigations that have evaluated adverse health 

5 outcomes among residents of Chapaevsk where ecological measures of TCDD have been noted 

6 to be higher than expected. In the earlier cancer section of this report, the cross-sectional 

7 comparisons of mortality that the authors carried out between Chapaevsk residents and a general 

8 population reference were described. Although the general focus of this paper is on cancer, the 

9 authors examined other adverse health outcomes. 

10 For all-cause mortality, rates were found to be higher in Chapaevsk relative to the Samara 

11 region and other nearby towns. The magnitude of this increase, however, was not quantified in 

12 the review by Revich. Cardiovascular mortality accounted for nearly two-thirds of women’s 

13 deaths and almost half of those among men. The rates of cardiovascular mortality among 

14 Chapaevsk men have been reported to be 1.14 times higher than those in Russia. 

15 Revich et al. (2001) also reported on the occurrence of adverse reproductive events. 

16 Although the authors indicated that official medical information was used to make comparisons 

17 between regions, no details were provided about data quality, completeness, or surveillance 

18 differences across areas. The presented rates for reproductive health outcomes should be 

19 interpreted cautiously. A higher rate of spontaneous abortions (24.4 per 100 pregnancies 

20 finished by delivery) was found in Chapaevsk women relative to rates that ranged between 10.6 

21 and 15.2 found in five other areas. The frequency of preeclampsia also was found to be higher in 

22 Chapaevsk women (44.1/100) relative to other towns, as was the proportion of low birth-weight 

23 babies and preterm births. The percentage of newborns with low birth weight was slightly larger 

24 in Chapaevsk (7.1%) when compared to other towns in Samara (5.1−6.2%); observed 

25 differences, however, were not statistically significant. The authors also reported on the sex ratio 

26 of newborns born between 1983 and 1997. These ratios (boys:girls) were highly variable and 

27 ranged between 0.79 and 1.29. Given the annual variability of this ratio on a year-to-year basis, 

28 it is unclear if this is largely due to natural fluctuations and to what extent this may result from 

29 prior TCDD (or other contaminants) exposure TCDD and other contaminants. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.1.2.1.6.1.2. Study evaluation.

2 The review by Revich et al. (2001) highlights analyses that have been undertaken using 

3 largely cross-sectional data. Although soil sampling measures appear to demonstrate decreasing 

4 levels of TCDD in the soil with increasing distance from the plant, at this time, no 

5 individual-level TCDD exposure data are available. Increased rates of mortality relative to the 

6 Samara region in Russia were observed among Chapaevsk men for all cancer sites combined; 

7 this excess risk however, was not observed among women. Although the authors provide 

8 compelling evidence of increased adverse events among residents of Chapaevsk, the study lacks 

9 a discussion about the validity of comparing health data across regions, and suffers from inherent 

10 limitations from ecological studies such as exposure misclassification. 

11 

12 2.4.1.2.1.6.1.3. Suitability of data for TCDD dose-response modeling.

13 As with the cancer outcomes presented in this study, the data for noncancer outcomes are 

14 limited by the absence of TCDD levels on an individual-level basis and information on other 

15 potential confounding variables that could have biased the comparisons. Additional studies are 

16 being undertaken to evaluate the relationship between TCDD and the sexual and physical 

17 development of boys. The cross-sectional nature of the data that were presented does not 

18 provide the necessary level of detail needed to estimate effective dose given the lack of 

19 individual-level exposure data. Therefore, a quantitative dose-response analysis was not 

20 conducted. 

21 

22 2.4.1.2.1.7. The Air Force Health (“Ranch Hands” cohort) study.

23 2.4.1.2.1.7.1. Michalek and Pavuk (2008)—Diabetes. 

24 2.4.1.2.1.7.1.1. Study summary.

25 Michalek and Pavuk (2008) examined both the incidence of cancer and the prevalence of 

26 diabetes in the cohort of Ranch Hand workers exposed to TCDD. As noted previously, these 

27 veterans were responsible for aerial spraying of Agent Orange in Vietnam between 1962 and 

28 1971. Exposure to TCDD was estimated using serum collected from participants in 1987 and 

29 assayed for TCDD. Exposure to TCDD was estimated using a first-order pharmacokinetic model 

30 with a half-life of 7.6 years and provided an estimate of TCDD at the end of the tour of duty in 

31 Vietnam. Veterans were grouped into four categories: comparison, background, low, and high. 
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1 Diabetes was identified from diagnoses during the post-Vietnam era from medical records. 

2 Overall, no differences were shown in the RR of diabetes between the Ranch Hand unit and the 

3 reference group (RR = 1.21, p = 0.16). Stratified analyses by days of spraying (<90 days, 

4 ≥90 days), however, revealed a significant increase in risk of diabetes (RR = 1.32, p = 0.04) 

5 among those who sprayed for at least 90 days. A dose-response relationship was also evident 

6 when log10TCDD was modeled in the combined cohort. Also, stratification by calendar period 

7 showed a dose-response relationship for those whose last year of service was during or before 

1969. 8 

9 

10 2.4.1.2.1.7.1.2. Study evaluation.

11 The Michalek and Pavuk (2008) study provides an opportunity to characterize risks of 

12 diabetes as the study is not subject to some of the potential bias of case ascertainment based on 

13 death certificates (D’Amico et al., 1999). The quality of the TCDD exposure estimates is high, 

14 given that serum data were available at an individual-level basis for all Ranch Hand and 

15 comparison veterans used in the cohort. Although disentangling the effects of 2,4-D and TCDD 

16 is not possible because their concentrations in Agent Orange are equivalent, 2,4-D has not been 

17 associated with diabetes. 

18 

19 2.4.1.2.1.7.1.3. Suitability of data for TCDD dose-response modeling.

20 The reported dose-response relationship between TCDD and diabetes is supported by 

21 study strengths including the use of the individual-level level TCDD serum measures and the 

22 identification of diabetes through medical records are important strengths of the Michalek and 

23 Pavuk (2008) study. Nonetheless, the possible confounding from the inability to control for 

24 2,4-D and other agents used in Agent Orange precludes a quantitative dose-response analysis. 

25 

26 2.4.1.2.1.8. Other noncancer studies of TCDD. 

27 2.4.1.2.1.8.1. McBride et al., 2009a—Noncancer mortality. 

28 2.4.1.2.1.8.1.1. Study summary.

29 The McBride et al. (2009a) mortality study of New Zealand workers employed as 

30 producer or sprayers with potential exposure to TCDD was described earlier in this report. 

31 These individuals were employed at a plant that manufactured 2,4,-dichlorophenoxyacetic acid, 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 and later 2,4,5-T and 4-chloro 2-methyphenoxyacetic acid. In 1987, the plant closed and 2,4,5-T 

2 production ceased in 1988. 

3 The cohort consisted of 1,754 individuals who were employed for at least one day at the 

4 New Plymouth site between January 1, 1969, and October 1, 2003. Vital status was determined 

5 until the end of 2004. Comparisons of mortality were made to the New Zealand general 

6 population using the SMR statistic. Exposure was characterized by duration of employment. 

7 Person-years of follow-up were tabulated across strata defined by age, calendar period, duration 

8 of employment, sex, latency, and period of hire. Analyses were stratified to compare risks by 

9 duration of employment (<3 or ≥3 months), latency (<15 or ≥15 years), and period of hire 

10 (<1976, ≥1976). 

11 Overall, no statistically significant differences in all-cause mortality relative to the 

12 general population were found among those who worked for at least 3 months (SMR = 0.92, 

13 95% CI = 0.80−1.06) or for less than 3 months (SMR = 1.23, 95% CI = 0.91−1.62). No 

14 statistically significant excesses were found for mortality from diabetes, cerebrovascular disease, 

15 heart diseases, or accidents. The incorporation of a latency period of 15 years revealed no 

16 statistically significant excesses for these same causes of death. Similarly, no excesses for any 

17 cause of death were noted among those who were hired either before or after 1976. 

18 In subsequent analyses of the same cohort that used estimated TCDD levels from serum 

19 samples, McBride et al. (2009b) found no excesses for all-cause mortality or mortality from 

20 diabetes or heart disease. 

21 

22 2.4.1.2.1.8.1.2. Study evaluation.

23 For the McBride et al. (2009a) study, the size of the cohort is large enough to characterize 

24 mortality risks relative to the general population for most common causes of deaths. An 

25 important limitation of this study is the loss to follow-up of a substantial percentage of workers 

26 (22%). This would have impacted statistical power by reducing the number of deaths among the 

27 workers. If this incomplete ascertainment of mortality outcomes did not occur in a similar 

28 fashion with the general population then the SMR may also be biased. 

29 For noncancer causes of death, the use of the SMR statistic is more likely to be 

30 influenced by the healthy-worker effect. Therefore, the findings obtained for these outcomes 

31 should be interpreted with caution. Subsequent analyses published by the same authors 
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1 (McBride et al., 2009b) provide improved characterization of TCDD exposure using serum 

2 samples. 

3 

4 2.4.1.2.1.8.1.3. Suitability of data for dose-response analysis.

5 Overall, no associations were evident between surrogate measures of TCDD (duration of 

6 employment, year of hire) and noncancer mortality outcomes. Further, the use of mortality 

7 endpoints is inconsistent with EPA RfD methodology. As such, these data do not support further 

8 use in a quantitative dose-response analysis. 

9 

10 2.4.1.2.1.8.2. McBride et al., 2009b—Noncancer mortality. 

11 2.4.1.2.1.8.2.1. Study summary.

12 McBride et al. (2009b) further analyzed the cohort of New Zealand workers to include 

13 estimates of TCDD exposure based on serum samples. Current and former employees who were 

14 still alive and living within 75 km of the site were asked to provide serum samples. Samples 

15 were collected from 346 workers representing 22% (346/1599) of the entire study population. 

16 These serum measures were used to estimate cumulative TCDD levels for all workers. The 

17 exposure assessment approach by Flesch-Janys et al. (1996) was used to estimate time-dependent 

18 exposures based on area under the curve models. This was based on a one-compartment 

19 first-order kinetic model with a half-life of 7.2 years. 

20 Comparisons of mortality were made to the general population using the SMR statistic. 

21 The Cox proportional hazards model was used to conduct an internal cohort analysis across four 

22 categories of cumulative TCDD levels for diabetes and ischemic heart disease mortality. The 

23 RRs generated from these models were adjusted for sex, hire year, and birth year. No diabetes 

24 deaths were observed among women, and therefore, analysis of this outcome was limited to men. 

25 Relative to the general population, no difference in the all-cause mortality experience was 

26 observed in exposed cohort members (SMR = 1.0, 95% CI = 0.9−1.2). Similarly, no excess in 

27 these workers was observed for heart disease (SMR = 1.1, 95% CI = 0.9−1.5); cerebrovascular 

28 disease (SMR = 1.1, 95% CI = 0.6−1.9); diabetes (SMR = 0.7, 95% CI = 0.2−2.2); or 

29 nonmalignant respiratory disease (SMR = 0.8, 95% CI = 0.4−1.4). For the internal cohort 

30 analysis, the RR associated with cumulative categorical TCDD measure was 1.0 for both 

31 diabetes and ischemic heart disease. 
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1 2.4.1.2.1.8.2.2. Study evaluation.

2 The McBride et al. (2009b) study extends the earlier work the same authors completed in 

3 two ways. First, serum measures were used to estimate cumulative TCDD with methodology 

4 that has been applied to several other cohorts of workers exposed to TCDD. Second, the authors 

5 used regression analyses that examined individual-level TCDD exposures in relation to various 

6 outcomes as part of the internal cohort comparisons. For noncancer outcomes, no dose-response 

7 associations with TCDD were observed with the internal comparisons. Also, as found with 

8 earlier analyses of this same cohort, no excess noncancer mortality relative to the New Zealand 

9 general population was observed. 

10 Associations between TCDD and diabetes have been found previously in TCDD-exposed 

11 populations, most notably in the Ranch Hands cohort (Michalek and Pavuk, 2008). In this 

12 cohort, only five deaths from diabetes were identified, and of these, only three occurred among 

13 those who were exposed to TCDD. The study, therefore, has limited statistical power to 

14 characterize associations between TCDD and mortality from diabetes. Further, the identification 

15 of diabetes deaths is subject to misclassification errors due to under-reporting (McEwen et al., 

16 2006). 

17 

18 2.4.1.2.1.8.2.3. Suitability of data for TCDD dose-response modeling.

19 McBride et al. (2009b) found no statistically significant associations in any of the 

20 noncancer causes of death. Furthermore, the use of mortality endpoints is inconsistent with EPA 

21 RfD methodology. Therefore, the data were not suitable for quantitative dose-response analysis 

22 for these outcomes. 

23 

24 2.4.1.2.1.8.3. Ryan et al., 2002—Sex ratio. 

25 2.4.1.2.1.8.3.1. Study summary.

26 Ryan et al. (2002) conducted an investigation on the sex ratio in offspring of children of 

27 pesticide workers who were involved with the production of trichlorophenol and the herbicide 

28 2,4,5-T in Ufa, Bashkortostan, Russia. Ufa was the site of a state agrochemical plant that has 

29 been in operation since the 1940s. Between 1961 and 1988, the plant employed more than 

30 600 workers, most in their early 20s. Females, however, accounted for about 15% of the 

31 workforce that produced 2,4,5-T and 30% for 2,4,5-trichlorophenol. 
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1 Serum samples previously taken in 1992 among 60 men, women, and children from the 

2 factory and city of Ufa showed TCDD exposures that were approximately 30 times higher than 

3 background levels (Ryan and Schecter, 2000). Blood data were subsequently measured on a 

4 sample of 20 workers between 1997 and 2000, and on 23 2,4,5-trichlorophenol workers between 

5 1997 and 2001. In all, 84 individuals who provided blood samples formed the basis of the 

6 analysis in this study. Of these, 55 were exposed to 2,4,5-T and 29 were exposed to 

7 2,4,5-trichlorophenol. 

8 Ryan et al. (2002) reviewed company records for these workers to determine the number, 

9 sex, and date of birth of any children; birth data were available for 198 workers. Awareness of 

10 the study led other workers who had not provided serum to provide information on births that 

11 occurred 9 months after the time of first employment in the factory. 

12 The authors calculated descriptive statistics for the 198 workers and compared them to 

13 values for the city of Ufa between 1959 and 1996. Tests of statistical significance were made 

14 using the z-test, and the chi-square test. The observed proportion of male births (0.40) among 

15 the factory workers was much lower than that for the city of Ufa (0.51) (p < 0.001). Stratified 

16 analyses revealed that this lower ratio was observed only among those paternally exposed to 

17 TCDD. Specifically, the proportion of male births among exposed fathers was 0.38 and among 

18 exposed mothers was 0.51. This pattern was observed in both the workers exposed to 2,4,5-T 

19 (proportion of male births = 0.40) and 2,4,5-trichlorophenol (proportion of male births = 0.35). 

20 

21 2.4.1.2.1.8.3.2. Study evaluation.

22 The Ryan et al. (2002) findings are consistent with earlier work completed for Seveso 

23 residents (Mocarelli et al., 2000). Although serum measures were available for 84 individuals, 

24 no dose-response of birth ratios was performed using exposure quantified at an individual-level 

25 basis. This approach would have been preferred and consistent with that which Mocarelli et al. 

26 (2000) used. All comparisons were made using an external comparison group, namely the sex 

27 ratio observed in Ufa between 1959 and 1996. 

28 Although serum measures were used to describe TCDD exposure for a sample of the 

29 workers, individual-level dose estimates were not calculated for the study population. 

30 Specifically, exposures were characterized many years after exposure, and no attempt was made 

31 to back-extrapolate to the time of conception. The two groups of workers in the study also 
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1 reportedly had high exposure levels of 1,2,3,7,8-pentachlorodibenzo-p-dioxin. So, the group 

2 level exposure classification (by plant) did not allow consideration of confounding due to other 

3 dioxin-like compounds. Another limitation of the study is that the study population is likely 

4 nonrepresentative of all workers employed at the plant. Participants included only those willing 

5 to provide serum samples and those who volunteered to participate in the study after learning 

6 about it in a public forum. If participation was dependent on TCDD exposures and the 

7 reproductive health of these subjects, then bias may have occurred. 

8 

9 2.4.1.2.1.8.3.3. Suitability of data for TCDD dose-response modeling.

10 The findings are notable in their consistency with those found in Seveso residents by 

11 Mocarelli et al. (2000). For the Ryan et al. (2002) study, serum data were quantified at an 

12 individual-level basis. Risk estimates, however, were not derived in relation to these exposures 

13 but instead in two separate subgroups (2,4,5-T and 2,4,5-trichlorophenol workers). This 

14 important limitation precludes the use of these data for quantitative dose-response modeling. 

15 

16 2.4.1.2.2. Feasibility of dose-response modeling for noncancer. 

17 Relatively few study populations permit quantitative dose-response modeling to be 

18 performed for noncancer outcomes. The serum collected among Seveso men and women 

19 provide an opportunity to characterize risks for several health conditions in relation to TCDD 

20 exposure. The collection of these serum samples, shortly after the accident does not require the 

21 back-extrapolation of TCDD levels as in the occupational cohorts, which should reduce the 

22 exposure assessment uncertainty and minimize the potential for exposure misclassification. 

23 An added feature of the SWHS is the detailed collection of other risk factor data from 

24 trained interviewers. These data allow for risk estimates to be adjusted for potential confounding 

25 variables. For the evaluations of reproductive health outcomes, this adjustment is critical given 

26 there are various documented risk factors for the different outcomes that were examined. For 

27 some health outcomes, continued follow-up of the cohort is needed, given that several of the 

28 Seveso studies suggest that those exposed at a very young age might be more susceptible to 

29 subsequent adverse health effects. 

30 The findings of positive associations and dose-response relationships with serum-based 

31 measures of TCDD suggest several noncancer health outcomes could be associated with TCDD 
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1 exposure. These health outcomes include neonatal thyroid function, sex ratio, diabetes, and 

2 semen quality. Although findings have suggested an association between TCDD and age at 

3 menopause, they were not statistically significant and no dose-response trend was observed. 

4 Weak or nonstatistically significant associations have been noted for endometriosis and 

5 menstrual cycle characteristics and do not support quantitative dose-response analyses. 

6 Associations between TCDD exposure and cardiovascular disease have been noted in 

7 some, but not all, of the occupational cohorts, and also shortly after the accident among Seveso 

8 residents. Findings from the cohort studies based on external comparisons using the SMR 

9 statistic should be interpreted cautiously due to potential bias from the healthy worker effect. 

10 Because the magnitude of the healthy worker bias is recognized to be larger for cardiovascular 

11 diseases than for cancer outcomes, risk estimates in some occupational cohorts might be 

12 underestimated for cardiovascular outcomes. Information on cardiovascular risk factors 

13 generally was not captured in these studies, and sensitivity analyses were generally designed to 

14 examine risk estimates generated for cancer outcomes. 

15 

16 2.4.1.2.3. Summary of epidemiologic noncancer study evaluations for dose-response 

17 modeling. 

18 All epidemiologic noncancer studies summarized above were evaluated for suitability of 

19 quantitative dose-response assessment using the TCDD-specific considerations and study 

20 inclusion criteria. The results of this evaluation are summarized in a matrix style array (see 

21 Table 2-3) at the end of the chapter, and descriptively in Appendix B. The key epidemiologic 

22 noncancer studies suitable for further TCDD dose-response assessment are presented in 

23 Table 2-5. 

 24 

25 2.4.2. Summary of Animal Bioassay Studies 

26 This section summarizes studies that have met the in vivo animal bioassay TCDD study 

27 inclusion criteria (see Section 2.3.2) and are considered in the dose-response modeling conducted 

28 later in this document (see Sections 4 and 5). The sections that follow summarize the 

29 experimental protocol, the results, and the NOAELs and LOAELs identified in reproductive 

30 studies, developmental studies, and general toxicity studies (subdivided by duration). 
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1 To evaluate and discuss studies consistently, doses were converted to nanograms per 

2 kilogram body weight per day (ng/kg-day) and were also adjusted for continuous exposure. 

3 Some doses were adjusted based on daily dietary intake and body weight. For these studies, 

4 EPA uses 10% of an animal’s body weight as the daily feed rate. More commonly, doses were 

5 adjusted from 5 days/week to a 7 days/week standard adjustment, in which case administered 

6 doses were multiplied by 5 and divided by 7 to obtain continuous doses. To adjust for weekly 

7 dosing, the weekly administered doses were multiplied by the administration frequency per week 

8 (in days) and divided by 7 to give continuous doses. 

9 Other exposure protocols used a single loading dose followed by weekly maintenance 

10 doses. To adjust these doses, the loading dose was added to the maintenance doses multiplied by 

11 the administration frequency, and this sum was divided by the exposure duration to give a 

12 continuous dosing rate. The doses administered in single dose studies were not averaged over 

13 the observation period. 

14 

15 2.4.2.1. Reproductive Studies 

16 2.4.2.1.1. Bowman et al., 1989a, b (and related Schantz and Bowman, 1989; Schantz et al., 

17 1986). 

18 Female rhesus monkeys (6 to 10 years old; 8 per treatment) were exposed to 0 or 5 ppt 

19 (for 3.5 years), or 25 ppt (for 4 years) TCDD (purity not specified) (Bowman et al., 1989a, b; 

20 Schantz and Bowman, 1989; Schantz et al., 1986). Female monkeys were mated to unexposed 

21 males after 7 months (Cohort I) and 27 months (Cohort II) of exposure, then again 10 months 

22 post-exposure (Cohort III). The average daily doses to mothers were equivalent to 0, 0.15, and 

23 0.67 ng/kg-day. The 0.67 ng/kg-day dose group had reduced reproductive rates in both Cohorts I 

24 (p < 0.001) and II (p < 0.025; Bowman et al., 1989a). The mean number of days of offspring 

25 survival (p < 0.023) also decreased. No effects on birth weight or growth, or physical evidence 

26 of toxicity (Bowman et al., 1989b) were observed. Behavioral effects were observed in the 

27 offspring (Cohort I: 7, 6, and 0 offspring, respectively; Cohort II: 3, 5, and 0 offspring, 

28 respectively; Cohort III: 6, 7, and 3, respectively). In the 0.67 ng/kg-day dose group, the number 

29 of offspring was insufficient to form a group in either Cohorts I or II. Offspring in the 

30 0.15 ng/kg-day dose group had alterations in social behavior of the mother-infant pairs (mothers 

31 had increased care giving, which appeared to be an effect of the infants and not due to the 
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1 treatment of the mother) and peer group of the offspring after weaning (Cohort I offspring were 

2 more dominant or aggressive and exhibited more self-directed behavior; Bowman et al., 1989b). 

3 The performance of learning tasks was inversely related to the level of TCDD in the body fat. 

4 Schantz and Bowman (1989) examined effects using discrimination-reversal learning (RL) and 

5 delayed spatial alteration (DSA). RL detected effects in the 0.15 ng/kg-day group as measured 

6 by retarded learning of the shape reversal (p < 0.05), but DSA did not. Schantz et al. (1986) 

7 combined the cohorts and looked at 5, 5, and 3 mother-infant pairs in the 0, 0.15, and 

8 0.67 ng/kg-day groups, respectively. They found that TCDD-exposed mother-infant pairs spent 

9 more time in close, social contact compared to the controls (mutual ventral contact, p < 0.025; 

10 nipple contact, p < 0.01) and infants had reduced locomotor activity (p < 0.05), but the 

11 dose-effect was complex. Of note is that the control groups contained fewer males than did the 

12 TCDD-exposed groups. 

13 In a follow-up study, Rier et al. (2001) examined the DLC levels of sera collected from 

14 some monkeys in this study. They reported that animals in this study had elevated serum PCB77 

15 and PCB126 levels and an increased serum TEQ. In fact, the fractional contribution of serum 

16 TCDD levels to total serum TEQ was 30% in treated animals. In this study, it is not possible to 

17 determine the contribution of TCDD alone to the developmental effect due to the background 

18 contamination; thus, EPA has not developed a TCDD LOAEL from the study. 

19 

20 2.4.2.1.2. Hochstein et al., 2001. 

21 Adult female mink (12/treatment group) were administered dietary concentrations of 

22 0.0006 (control), 0.016, 0.053, 0.180, or 1.40 ppb TCDD (purity >99.8%) for 132 days 

23 (Hochstein et al., 2001). This dose is estimated to be equivalent to 0.03 (control), 0.8, 2.65, 9, 

24 and 70 ng/kg-day assuming a food consumption of 5% of body weight per day. Females were 

25 mated with unexposed males beginning on treatment day 35. Females were allowed to mate 

26 every fourth day during a 29-day mating period or until a confirmed mating. Mated females 

27 were presented with a second male either the day after initial mating or 8 days later. In the 

28 70 ng/kg-day group, the treated animals were lethargic after 4 to 5 weeks, with several having 

29 bloody (tarry) stools near the end of the trial. Two animals in the 70 ng/kg-day dose group died 

30 prior to study termination. These animals had lost a large percentage of their body weight 

31 (24−43%), and had pale yellow livers and intestinal hemorrhages. Histopathology from both 
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1 mink indicated marked diffuse hepatocellular vacuolation. The mean body weight decreased in 

2 all treatment groups including the control (losing an average of 3.29% of initial body weight), 

3 compared to a dose-dependent loss of up to 26% in the 70 ng/kg-day group. Mating and 

4 reproduction were considered subnormal in all groups. The number of females that gave birth in 

5 the 0.03 (control), 0.8, 2.65, 9, and 70 ng/kg-day dose groups were 5/12, 0/12, 3/12, 8/12, and 

6 0/11, respectively. The study authors speculated that the subnormal breeding and reproductive 

7 performances in the control females likely were due to the indoor environment in which the mink 

8 were housed. In the three groups that gave birth, there was a dose-dependent decrease in kit 

9 body weight at birth, which was significant (p < 0.05) in the 9 mg/kg-day group compared to the 

10 controls. The body weight in the kits was not significantly different at 3 or 6 weeks after birth. 

11 Three-week survival rates of 71, 47, and 11% were recorded for kits in the 0.03 (control), 2.65, 

12 and 9 ng/kg-day dose groups, respectively. Six-week kit survival rates were 62, 29, and 11% in 

13 the 0.03 (control), 2.65, and 9 ng/kg-day dose groups, respectively. 

14 In the adult females, clinical signs of toxicity were noted in the 70 ng/kg-day group near 

15 the end of the study and included alopecia and notably thickened, deformed, and elongated 

16 toenails. There was a dose-dependent decrease in plasma total solids, total protein, and 

17 osmolality that reached statistical significance (p < 0.05) in the two highest exposure groups. 

18 Anion gap was significantly decreased (p < 0.05) and alanine aminotranferase was significantly 

19 increased in the 70 ng/kg-day group compared to the controls. At terminal sacrifice, there was a 

20 dose-related decrease in body weight. There was a dose-related increase in liver weight that 

21 reached statistical significance (p < 0.05) in the 70 ng/kg-day dose group. The brains of 42% of 

22 the animals in the 70 ng/kg-day dose group had localized accumulation of lymphatic cells within 

23 the meninges with mild extension into the adjacent neuropil and mild gliosis. Of the 10 mink 

24 surviving to study termination in the 70 ng/kg-day group, 3 had periportal hepatocellular 

25 vacuolation. These same brain and liver lesions were not observed in the control mink. 

26 As there were no litters produced in the low-dose group and pregnancy outcomes were 

27 not dose related, the 0.8 ng/kg-day exposure level does not inform the choice of NOAEL or 

28 LOAEL. Thus, the LOAEL for this study is 2.65 ng/kg-day (132-day maternal exposure 

29 duration) based on reduced kit survival (47% of control at 6 weeks). A NOAEL cannot be 

30 determined for this study. 

31 
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1 2.4.2.1.3. Ikeda et al., 2005. 

2 Ikeda et al. (2005) studied the effect of repeated TCDD exposure to F0 dams on the male 

3 gonads of F1 generation and sex ratio in the F2 generation. Twelve female Holtzman rats were 

4 treated with a single dose of 400 ng/kg TCDD (≥98% purity) orally, via gavage, followed by 

5 weekly treatment doses of 80 ng/kg TCDD (16.5 ng/kg-day adjusted for continuous exposure of 

6 10 weeks; specified 2 weeks premating, assumed 1 week for successful mating, 3 weeks of 

7 gestation, and specified 4 weeks to weaning) during mating, pregnancy, and lactational periods 

8 (total exposure duration approximately 10 weeks). Corn oil served as the control in another 

9 group of 12 dams. Four dams were sacrificed on gestation day (GD) 20 to evaluate the in utero 

10 toxicity of TCDD. Litter sizes from the remaining eight dams were examined on postnatal day 

11 (PND) 2, and some of the F1 offspring were sacrificed to estimate TCDD tissue concentrations. 

12 The remaining offspring were weaned on PND 28. Some of the F1 (number not specified) 

13 offspring were mated with untreated females on PND 98, following which, litter size, sex ratio, 

14 weight, and anogenital distance of F2 pups were examined on PND 2. Mated and unmated F1 

15 males were sacrificed and the testes, epididymis, seminal vesicle, and the ventral prostate were 

16 weighed; the cauda epididymis was weighed and examined for sperm count. 

17 All fetuses in the control and TCDD group as a result of in utero exposure in the F0 

18 generation survived. Litter size, sex ratio, and anogenital distance in the F1 generation on 

19 PND 2 were not altered as a result of in utero TCDD exposure. Pup weight was significantly 

20 (p < 0.05) lower in the TCDD-treated group than in controls. TCDD concentration in the 

21 adipose tissue of the F0 dams on GD 20 was significantly (p < 0.05) higher than in the liver. 

22 Adipose TCDD was significantly (p < 0.01) reduced at weaning, however, compared to 

23 concentrations on GD 20. F1 pup liver TCDD concentration increased significantly (p < 0.01) 

24 and was higher on PND 28 than PND2. The liver weight in F1 males increased by 14-fold at 

25 PND 28 compared to PND 2, implying a transfer of approximately 850 pg of TCDD from the 

26 dam to the F1 pup livers during lactation. TCDD also was detected in pup adipose tissue on 

27 PND 28. Body weight of TCDD-exposed F1 males was significantly (p < 0.001) lower than 

28 control males at weaning (PND 28). No significant differences in testis and cauda epididymis 

29 weights were observed between the control and treated groups. Ventral prostate weight in the F1 

30 males exposed to TCDD, however, was approximately 60% lower than controls. No change in 
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1 weight of the body, brain, testes, cauda epididymis, or seminal vesicle was observed at PND 120. 

2 Ventral prostate weight, however, was 16% lower than that of the control group (p < 0.001). 

3 Sperm count in the cauda epididymis of the F1 males was not affected by TCDD exposure. 

4 Examination of F2 generation litters indicated no significant differences in litter size, pup 

5 body weight, and anogenital distance between TCDD-treated or vehicle control groups. The 

6 percentage of male F2 pups born to maternally and lactationally TCDD-exposed males was 

7 significantly (p < 0.05) lower (38%) than those sired by control group males (52%). Every 

8 female mated with maternally TCDD-exposed F1 males delivered more female than male pups. 

9 A LOAEL for TCDD of 16.5 ng/kg-day for an estimated 10 week exposure duration in 

10 F0 rat dams is identified in this study for decreased development of the ventral prostate in the F1 

11 generation (60% lower than controls) and for significantly (p < 0.05) altered sex ratio (decreased 

12 percentage of males) in the F2 generation. A NOAEL cannot be determined for this study. 

13 

14 2.4.2.1.4. Ishihara et al., 2007. 

15 Ishihara et al. (2007) examined the effect of repeated TCDD exposure of F0 males on the 

16 sex ratio of F1 offspring. Seven-week-old male ICR mice (n = 127) were divided into three 

17 groups and treated via gastric intubation with an initial loading dose of either 2 or 2,000 ng 

18 TCDD/kg BW or an equivalent volume of sesame oil (vehicle) as control, followed by a weekly 

19 maintenance doses of 0, 0.4, or 400 ng/kg until the animals were 12 weeks old. One week after 

20 the last exposure, the animals were mated with untreated female mice. On the day a vaginal plug 

21 was identified, F0 male mice were sacrificed and major organs including testes, epididymis, and 

22 liver were removed and weighed. Organ tissues also were examined for histopathological and 

23 immunohistochemical changes. Treatment levels, averaged over the 6 week period from start of 

24 treatment to mating (five maintenance doses), were 0, 0.095, and 950 ng/kg-day for the control, 

25 low dose and high dose groups, respectively. 

26 All TCDD-treated males successfully impregnated untreated females and yielded viable 

27 offspring. Mortality, pup weights, and mating and fertility indices were not affected by TCDD 

28 exposure. There were no significant differences in body weights or in relative weights of testes, 

29 epididymis, or livers in the TCDD-treated F0 males compared to the control group. The livers of 

30 some animals (number not specified) in the high-dose group, however, were larger and heavier 
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1 than in the controls or the low-dose group. Hence, tissues from the high-dose animals were 

2 selected for detailed immunohistochemical examination. 

3 General histopathological findings in the TCDD-treated groups showed no changes in 

4 cell morphology in germ, Sertoli, and Leydig cells of the testes. Arrangement of the germ cells 

5 was normal and there was no difference in the epididymis spermatozoon number in either of the 

6 TCDD-treated groups compared to controls. Livers of some of the animals in the high-dose 

7 group however, showed enlarged and vacuolated areas in the centrilobular area when compared 

8 to the low-dose group and the control group. Immunohistochemical and quantitative 

9 immunohistological findings showed a marked increase in staining intensity for cytochrome 

10 P450 (CYP)1A1 in the cytoplasm of the hepatocytes in the centrilobular area of the high-dose 

11 TCDD group compared to the cells in the low-dose and the control groups. In addition, 

12 proportions of immunoreactive CYP1A1 areas in the liver sections of the high-dose group were 

13 higher than in the low-dose and control groups. The proportions of immunoreactive CYP1A1 

14 also varied across animals (n = 33) in the high-dose group. 

15 In addition to the above findings, there was a dose-related decrease in the male/female 

16 sex ratio. The proportion of male offspring of the high-dose group was significantly lower 

17 (p < 0.05) than that observed in controls (46.2% versus 53.1%, respectively). Hepatic 

18 immunoreactive CYP1A1 staining levels in individual F0 males were strongly correlated with 

19 the sex ratio of their offspring. 

20 A LOAEL for TCDD of 950 ng/kg-day for a 6 week exposure duration of F0 male mice 

21 is identified for significantly (p < 0.05) decreased male/female sex ratio (i.e., higher proportion 

22 of female offspring) in the F1 generation. The NOAEL is 0.095 ng/kg-day. 

23 

24 2.4.2.1.5. Latchoumycandane and Mathur, 2002 (and related: Latchoumycandane et al., 

25 2002a, b, 2003). 

26 Latchoumycandane and Mathur (2002) conducted a study to determine whether treatment 

27 with vitamin E protected rat testes from TCDD-induced oxidative stress. Groups of albino male 

28 Wistar rats (n = 6) were administered an oral dose of 0 (vehicle alone) 1, 10, or 100 ng 

29 TCDD/kg-day for 45 days, while another group of animals (n = 6) was co-administered TCDD at 

30 the same doses, along with vitamin E at a therapeutic dose of 20 mg/kg-day for 45 days. At 

31 study termination, animals were fasted overnight, weighed, and sacrificed. Testis, epididymis, 
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1 seminal vesicles, and ventral prostate were removed, weighed, and preserved for further 

2 examination. The left testis was used to determine daily sperm production, while the right testis 

3 was used for biochemical studies. Superoxide dismutase, catalase, glutathione reductase, and 

4 glutathione peroxidase activity were measured in the testes, along with production of hydrogen 

5 peroxide and lipid peroxidation. 

6 Body weights of TCDD-treated rats did not differ significantly from the control group. 

7 Testis, epididymis, seminal vesicle, and ventral prostate weights in the TCDD-treated groups, 

8 however, decreased significantly (p < 0.05) when compared to controls. None of these changes 

9 were observed in the TCDD-exposed groups receiving vitamin E. There was a dose-related 

10 decrease in daily sperm production (p < 0.05) in all three TCDD-treated groups when compared 

11 to the control group. In contrast, the TCDD treatment groups that also received vitamin E did 

12 not show any significant changes in daily sperm production compared to the controls. The 

13 TCDD-treated groups also showed significantly (p < 0.05) lower activities of the antioxidant 

14 enzymes (superoxide dismutase, catalase, glutathione reductase, and glutathione peroxidase) than 

15 the control group. Levels of hydrogen peroxide and lipid peroxidation increased significantly 

16 (p < 0.05) in the testes of the rats treated with TCDD compared to the corresponding controls. 

17 The TCDD-treated groups that had been co-administered vitamin E show no difference in 

18 antioxidant enzyme activities or in reactive oxygen species production when compared with 

19 controls. 

20 A LOAEL for TCDD of 1.0 ng/kg-day for a 45-day exposure duration in rats is identified 

21 in this study for significantly (p < 0.05) reduced sperm production and significantly (p < 0.05) 

22 decreased reproductive organ weights. A NOAEL cannot be determined for this study. 

23 

24 2.4.2.1.6. Murray et al, 1979. 

25 Male (10−16 per treatment) and female (20−32 per treatment) Sprague-Dawley rats were 

26 administered diets containing TCDD (purity >99%) to achieve daily concentrations of 1, 10, or 

27 100 ng/kg-day through three generations. After 90 days of treatment, F0 rats were mated to 

28 produce F1a offspring. Thirty-three days after weaning of the last F1a litter, the F0 rats were 

29 mated again to produce F1b offspring. Some F0 rats were mated a third time for a cross-mating 

30 study. The F1b and F2 rats were mated at about 130 days of age to produce the F2 and F3 

31 generations. No clinical signs of toxicity or changes in body weight and food consumption were 
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1 observed in F0 rats during the 90 days of treatment before mating. The 100 ng/kg-day group was 

2 discontinued due to the lack of offspring. In the three surviving offspring (all males), no changes 

3 in appearance, body weight, or food consumption occurred. A dose of 10 ng/kg-day caused a 

4 consistent decreased body weight in both sexes of F1 and F2 rats, which was associated with 

5 decreased food consumption. A significant (p < 0.05) decrease in fertility in F1 and F2 rats 

6 occurred, but not in F0 rats, administered 10 ng/kg-day. The number of live pups and gestational 

7 survival index were significantly (p < 0.05) decreased in the 100 ng/kg-day F0 rats and in the 

8 10 ng/kg-day F1 and F2 rats. The gestational survival index also was significantly (p < 0.05) 

9 decreased in F2 rats administered 1 ng/kg-day. Postnatal survival was significantly (p < 0.05) 

10 reduced only in F2 rats administered 10 ng/kg-day. Growth (as measured by body weight) was 

11 affected at 10 ng/kg-day only in the third generation. In the 10 ng/kg-day group, a significant 

12 (p < 0.05) decrease in relative thymus weight and increase in liver weight also occurred in F3 rats 

13 (weights were not measured in F2 rats). Additionally, mating 100 ng/kg-day TCDD-treated 

14 females with untreated males increased the percent of implants resorbed as assessed by uterine 

15 histopathology. 

16 The reproductive LOAEL is 10 ng/kg-day, based on a significant (p < 0.05) decrease in 

17 fertility (33−37% lower than controls); decrease in the number of live pups (18−27% lower than 

18 controls); decrease in gestational survival (10−11% lower than controls); decrease in postnatal 

19 survival (32% lower than controls); and decreased postnatal body weight (14−19% lower than 

20 controls at weaning) in one or more generations. The reproductive NOAEL is 1 ng/kg-day. 

21 

22 2.4.2.1.7. Rier et al., 1993, 1995. 

23 Reir et al. (1993, 1995) examined the impact of chronic TCDD exposure on 

24 endometriosis in monkeys. Female rhesus monkeys (8 animals per treatment group) were 

25 exposed to 0, 5, or 25 ppt TCDD (purity not specified) in feed for 4 years. Previously, Bowman 

26 et al. (1989b) determined that these dietary concentrations were equivalent to 0, 0.15, and 

27 0.67 ng/kg-day, respectively. Ten years after termination of TCDD treatment, the presence of 

28 endometriosis was determined via laparoscopic surgical procedure, and the severity of the 

29 disease was assessed. The study authors reported that three monkeys in the 0.67 ng/kg-day 

30 exposure group died at 7, 9, and 10 years after termination of TCDD treatment. Autopsy results 

31 attributed the deaths to widespread and severe peritoneal endometriosis (all three monkeys) 
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1 along with obstruction of the colon (one monkey) and blockage of the jejunum (one monkey). 

2 Other deaths also occurred in the control group (1 death from birthing complications and another 

3 from an unknown cause); in the 0.15 ng/kg-day dose group (1 death due to natural causes with 

4 no endometriosis), and in the 0.67 ng/kg-day dose group (1 death due to a breeding fight with no 

5 incidence of endometriosis). At study termination, 17 live animals plus the 3 that had previously 

6 died of endometriosis were evaluated (total n = 20). 

7 Incidence of endometriosis was significantly (p < 0.05) higher than in the control group 

8 with 71 and 86 % incidence rates in the 0.15 and 0.67 ng/kg-day dose groups, respectively, 

9 compared to 33% in the control group. Severity of endometriosis was also significantly 

10 (p < 0.001) correlated with TCDD dose. Staging by rAFS indicated that untreated control 

11 animals had either minimal or no incidence of endometriosis. In comparison, endometriosis was 

12 absent in 2 of the 7 monkeys in the 0.15 ng/kg-day dose group, while only 1 of the 7 animals in 

13 the high dose group was disease free. Moderate-to-severe disease was observed in 3 of the 

14 7 animals in the 0.15 ng/kg-day dose group and 5 of the 7 animals in the 0.67 ng/kg-day dose 

15 group. Moderate-to-severe disease was not observed in the control group. The authors also 

16 compared the incidence and severity of endometriosis in TCDD-exposed animals with 

17 304 normal, non-neutered females with no dioxin exposure and reported that the disease was not 

18 present in monkeys that were less than 13 years of age, while the disease rate was 30% among 

19 animals 13 years of age or older. The study authors report that these findings are in agreement 

20 with human and rhesus studies demonstrating that the prevalence of detectable endometriosis can 

21 increase with advanced age. 

22 As noted previously, in a follow-up study, Rier et al. (2001) examined the DLC levels of 

23 sera collected from some monkeys in this study. They reported that animals in this study had 

24 elevated serum PCB77 and PCB126 levels and an increased serum TEQ; the fractional 

25 contribution of serum TCDD levels to total serum TEQ was 30% in treated animals. They also 

26 reported that the severity of the endometriosis corresponded to the serum PCB77 concentrations 

27 rather than total TCDD. In this study, it is not possible to determine the contribution of TCDD 

28 alone to the edometriosis due to the background contamination; thus, EPA has not developed a 

29 TCDD LOAEL from the study. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.2.1.8. Shi et al., 2007. 

2 Pregnant Sprague-Dawley rat dams (3 per treatment group) were administered 0, 1, 5, 50, 

3 or 200 ng/kg TCDD (purity >99%) in corn oil by gavage on GD 14 and GD 21 and on PND 7 

4 and PND 14 for lactational exposure to pups (Shi et al., 2007). Ten female pups per treatment 

5 were selected and administered TCDD weekly at the same dose levels through their reproductive 

6 lifespan (approximately 11 months). The corresponding equivalent daily TCDD doses are 0, 

7 0.14, 0.71, 7.14, and 28.6 ng/kg-day. Vaginal opening was slightly but significantly (p < 0.05) 

8 delayed in 28.6 ng/kg-day females. Vaginal opening was also delayed, but not significantly, in 

9 the 0.14 and 7.14 ng/kg-day groups. Reproductive senescence with normal cyclicity was 

10 significantly (p < 0.05) accelerated beginning at 9 months in 7.14 and 28.6 ng/kg-day females. 

11 Serum estradiol concentrations were decreased at all time points across the estrous cycle in a 

12 dose-dependent manner with a statistically significant decrease (p < 0.05) in all but the lowest 

13 dose group. TCDD exposure, however, did not affect the number or size distribution of ovarian 

14 follicles; responsiveness of the pituitary gland to gonadotropin-releasing hormone, or serum 

15 profiles of FSH, LH, or progesterone. 

16 A LOAEL for TCDD of 0.71 ng/kg-day for an 11-month exposure duration was 

17 identified in this study based on significantly (p < 0.05) decreased estradiol levels in offspring. 

18 The NOAEL for this study is 0.14 ng/kg-day. 

19 

20 2.4.2.1.9. Yang et al., 2000. 

21 Yang et al. (2000) studied the impact of TCDD exposure on the incidence and severity of 

22 endometriosis in female rhesus monkeys. Groups of 7- to 10-year old nulliparous cynomolgus 

23 monkeys were treated with 0 (n = 5), 1, 5, or 25 (n = 6 per group) ng/kg BW TCDD 5 days per 

24 week via gelatin capsules for 12 months. Because the monkeys received one capsule 5 days per 

25 week, the doses adjusted for continuous exposure were 0, 0.71, 3.57, and 17.86 ng/kg-day. Prior 

26 to TCDD administration, all animals had endometriosis induced during days 12–14 of the 

27 menstrual cycle by auto-transplantation of endometrial-strips in multiple abdominal sites. All 

28 TCDD-treated and control groups were laparoscopically examined during months 1, 3, and 6 to 

29 monitor the survival of endometrial implantations and to obtain peritoneal fluid to determine the 

30 concentration and immunotype of endometrial growth regulator cytokines interleukin-6 (IL-6) 

31 and interleukin-6 soluble receptor (IL-6sR). Because insufficient peritoneal fluids were present 
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1 in the treated and control monkeys, however, the study authors collected blood samples at 6 and 

2 12 months during laparoscopy for routine hematology and to assess the circulating levels of IL-6 

3 and IL-6sR. All animals were sacrificed at 12 months, and circulating levels of gonadal steroids 

4 also were measured at the time of necropsy. 

5 No changes were observed among treatment levels in general toxicological endpoints 

6 such as body weight changes, food consumption, hematological endpoints, general activity 

7 levels, and caretaker interaction. In addition, TCDD did not impact circulating levels of gonadal 

8 steroids measured during necropsy. Similarly, there were no differences in the number of 

9 menstrual cycles, the length of the menstrual cycle, and bleeding intervals. Endometrial implants 

10 were found in at least one site in all TCDD-treated and control monkeys during the first 

11 laparoscopic examination. Follow-up laparoscopies revealed that there was a continuous loss of 

12 endometrial implants over time in each dose group. At the 1-, 3-, and 6-month examination, the 

13 number of endometrial losses was not significantly different among different dose groups. At 

14 the 12-month examination, however, a significantly (p < 0.05) higher rate of survival of 

15 endometrial implants was observed in the 3.57 and 17.86 ng/kg-day dose groups compared to the 

16 control group. The highest rate of endometrial implant survival was observed in the ovaries 

17 regardless of the dose group. In contrast, all lesions disappeared from the left broad ligament, 

18 whereas two on the right broad ligament and one on the uterine fundus survived. There was a 

19 dose-dependent divergence in the growth response of endometrial implants following TCDD 

20 exposure. Both the maximum and minimum implant diameters in the 17.86 ng/kg-day dose 

21 group were significantly (p < 0.05) larger compared to controls. In contrast, the maximum and 

22 minimum implant diameters in the 0.71 ng/kg-day dose group were significantly (p < 0.05) 

23 smaller compared to controls. TCDD did not impact implant diameters in the 3.57 ng/kg-day 

24 dose group when compared to controls. Histological examinations revealed that endometrial 

25 glands and stromal cells were present in all surviving implants. Sections examined in the 

26 17.86 ng/kg-day of TCDD possessed cystic endometrial glands that were more frequently 

27 observed in this dose group compared to other groups including controls. In addition, circulating 

28 levels of IL-6 were significantly (p < 0.05) lower in monkeys exposed to 17.86 ng/kg-day TCDD 

29 both at 6 and 12 months compared to the control group. In contrast, circulating levels of IL-6sR 

30 were significantly (p < 0.05) higher in animals treated with 3.57 and 17.86 ng/kg-day TCDD at 

31 6 months, while the levels were higher only in the 17.86 ng/kg-day TCDD group at 12 months. 
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1 A LOAEL for TCDD of 17.86 ng/kg-day for a 1 year exposure duration was identified in 

2 this study for significantly (p < 0.05) increased endometriosis induced by endometrial implant 

3 survival, significantly (p < 0.05) increased maximum and minimum implant diameters, and 

4 growth regulatory cytokine dysregulation (as assessed by significantly decreased IL-6 levels, 

5 p < 0.05). A NOAEL of 3.57 ng/kg-day is identified in this study. 

6 

7 2.4.2.2. Developmental Studies 

8 2.4.2.2.1. Amin et al., 2000. 

9 Amin et al. (2000) studied the impact of in-utero TCDD exposure on the reproductive 

10 behavior in male pups. Groups of pregnant Harlan Sprague-Dawley rats (n = 108 divided into 

11 4 cohorts; number of animals in the TCDD treatment group is ~3 per dose group) were dosed via 

12 gavage with 0, 25, or 100 ng/kg-day TCDD (purity >98%) in corn oil on GDs 10−16. On the 

13 day of birth (PND 0), pups were examined for gross abnormalities and the number of live pups, 

14 their weights, and sex were recorded from each litter. Litters consisting of more than eight pups 

15 were reduced to eight, comprised of four males and four females when possible. Litters 

16 consisting of fewer than five pups were excluded from the study to minimize between-litter 

17 differences in growth rate, maternal behavior, and lactational exposure. After this exclusion, 

18 approximately 10 to 11 litters per exposure group remained. All pups were weaned on day 21 

19 and one male and one female were retained to assess reproductive development, play behavior, 

20 reproductive behavior, and saccharin preference behavior. Both male and female pups were 

21 tested for saccharin preference between 189 and 234 days of age. A saccharin preference test 

22 was conducted for 8 days. For the first 4 days, rats were provided bottles containing tap water, 

23 and on days 5 and 6 the animals were provided a bottle containing water and a bottle containing 

24 0.25% saccharin solution. On days 7 and 8, the animals were provided water and a bottle 

25 containing 0.50% of saccharin solution. A 0.50% saccharin solution was used because previous 

26 studies have reported that male rats exhibited a greater reduction in preference for this saccharin 

27 concentration compared to females, hence the sex difference in preference is more marked at this 

28 saccharine dose. 

29 None of the treated dams exhibited any signs of toxicity as a result of exposure to TCDD. 

30 Gestational body weight, liver weight, litter size and percent live births were all comparable to 

31 the corresponding control group. Birth rate and weaning weight of the pups also were not 
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1 affected by TCDD exposure. Sex-related water consumption, however, was significantly 

2 (p < 0.001) affected during the first 4 days with female pups drinking more water per 100 g of 

3 body weight compared to the respective male counterparts. Saccharin consumption was 

4 significantly (p < 0.001) affected, with females consuming greater amounts of saccharin solution 

5 per 100 g body weight compared to the corresponding males. Additionally, both male and 

6 female pups drank significantly (p < 0.001) more of the 0.25% saccharin solution compared to 

7 the 0.50% saccharin solution. Females of all exposure groups consumed less of both the 0.25 

8 and 0.50% saccharin solution compared to the same-sex control group. Comparisons of each 

9 exposure group to the control group indicated that only the high TCDD exposure group 

10 (100 ng/kg-day) different significantly (p < 0.05) compared to control in the consumption of 

11 0.25% saccharin solution. In contrast, for the 0.50% saccharin solution, both the low and high 

12 TCDD dose groups differed significantly (p < 0.05 and p < 0.01, respectively) compared to the 

13 control group. The saccharin preference of TCDD-exposed male rats did not differ from that of 

14 the male control group. The TCDD-exposed females’ preference for saccharin solution, 

15 however, was significantly reduced in both the 25 (p < 0.05) and the 100 ng/kg-day (p < 0.005) 

16 dose group compared to that of the female controls. The study authors state that the reduction in 

17 saccharin consumption and preference in females could be due to the anti-estrogenic action of 

18 TCDD and that recent research reports suggest that TCDD can decrease the level of estrogen 

19 receptor (ER) mRNA by blocking the ability of ER to transactivate from the estrogen response 

20 element. 

21 A LOAEL for TCDD of 25 ng/kg-day for 7 days of gestational exposure is identified for 

22 significantly (p < 0.05) decreased preference in the consumption of 0.25% saccharin solution. A 

23 NOAEL cannot be determined for this study. 

24 

25 2.4.2.2.2. Bell et al., 2007a. 

26 Bell et al. (2007a) examined the reproductive effects of TCDD in rats exposed during 

27 development. Female CRL:WI (Han) rats were treated with TCDD (99% purity; dissolved in 

28 acetone) in the diet at concentrations of 0 (acetone alone; n = 75), 28, 93, or 530 

29 (n = 65/group) ng TCDD/kg diet, which provided average doses of 0, 2.4, 8, or 46 ng/kg-day, 

30 respectively. Rats were exposed to TCDD 12 weeks prior to mating, during mating, and through 

31 pregnancy. Dams were switched to the control diet after parturition. Litters from pregnant dams 
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1 were reduced to a maximum size of eight on PND 4 and to five males (if possible) on PND 21. 

2 These males were left untreated until sacrificed (25/group, one/litter) on PND 70, while all 

3 remaining animals were sacrificed on PND 120. All sacrificed animals were necropsied and 

4 received a seminology examination. Prior to sacrifice, during weeks 12 and 13, 20 animals from 

5 each dose group were tested for learning ability and motor activity, and were also administered a 

6 functional observation battery. During postnatal week 16, groups of 20 male F1 rats from each 

7 treatment group were paired with untreated virgin females for 7 days, and mated females were 

8 killed on GD 16 and examined for terminal body weights, pregnancy status, number of corpora 

9 lutea, and number of intrauterine implantations. 

10 The study authors found no evidence of direct maternal toxicity from exposure to TCDD. 

11 In the high-dose groups, 8 of 27 dams suffered complete litter loss compared to 3 dams in the 

12 control group, but the difference was not statistically significant. Pup survival at PND 4 was also 

13 lower in the high-dose group, but the difference again was not statistically significant. 

14 A dose-related decrease in mean pup body weight was observed on PND 1, and this trend 

15 continued throughout the lactation period. High-dose male pups had lower body weights when 

16 compared to controls at PND 21, with this trend continuing over the course of the study. 

17 Balanopreputial separation (BPS) was significantly (p < 0.05) delayed compared to controls in 

18 all three treatment groups by 1.8, 1.9, and 4.4 days in the low-, medium-, and high-dose groups, 

19 respectively. The study authors reported that adjustment for lower body weights observed at 

20 PND 21 and PND 42 did not affect the estimate of delay in BPS. No adverse effects from 

21 maternal treatment were observed on learning or in functional observational battery performance. 

22 Offspring in the high-dose group exhibited less activity when compared to controls (p < 0.05) 

23 when they were subjected to a test of motor activity for 30 minutes. 

24 The median precoital time was 2−3 days for all 20 F1 males that were mated during 

25 postnatal week 16. The uterine and implantation data were similar in all dose groups and there 

26 were no significant differences in the proportion of male offspring between groups. Epididymal 

27 sperm counts and sperm motility did not differ significantly between dose groups in animals 

28 sacrificed during postnatal week 10. The mean number of spermatids was significantly lower 

29 (14%; p < 0.05) and the proportion of abnormal sperm was significantly (p < 0.05) higher in the 

30 high-dose group when compared to controls on PND 70. These effects, however, were not seen 

31 in animals sacrificed on PND 120. 
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1 Terminal body weights were significantly (p < 0.05) decreased in the high-dose group 

2 (6.9 %) compared to controls on PND 120, while the depression in body weight in the 

3 medium-dose group (5.5%) was not statistically significant. At PND 70, the relative and 

4 absolute testis weight of the high-dose group was less than the controls (12 and 18%, 

5 respectively). Absolute spleen weight in the high-dose group was significantly higher (8%) on 

6 PND 70, and increased significantly (p < 0.05) by 1−3% on PND 120 in all dose groups 

7 compared to controls. Kidney weight in the low and medium-dose groups was significantly 

8 (p < 0.05) greater than in controls (~2%) at PND 120. In addition to these organs, ventral 

9 prostate (9.4%) and relative liver (~4.5%) weights were significantly (p < 0.05) higher than 

10 controls on PND 120 in the medium- and low- and high-dose groups, respectively. On 

11 PND 120, absolute brain weight was significantly (p < 0.05) less than the control in the 

12 medium-dose group, while relative brain weight was significantly (p < 0.05) higher than the 

13 control in the low- and high-dose group. Histological examination revealed no unusual findings. 

14 A LOAEL for TCDD of 2.4 ng/kg-day following an estimated 17 week exposure duration 

15 of dams was identified in this study for significantly (p < 0.05) delayed BPS. A NOAEL was not 

16 identified in this study. 

17 

18 2.4.2.2.3. Franczak et al., 2006. 

19 Franczak et al. (2006) examined the impact of chronic TCDD exposure on the onset of 

20 reproductive senescence in female rats. Pregnant Sprague-Dawley rats (n = 2–3/dose group) 

21 were fed 50 or 200 ng/kg TCDD (>99% purity) or corn oil vehicle (4 mL/kg) orally on GD 14 

22 and 21 and PND 7 and 14 to provide in utero and lactational exposure to TCDD. On PND 21, 

23 female pups (n = 7/dose group) were weaned and were subsequently given weekly doses of 50 or 

24 200 ng/kg-week TCDD by gavage (7.14 or 28.6 ng/kg-day adjusted for continuous exposure; 

25 administered doses divided by 7) or corn oil vehicle. Exposure continued for up to 8 months, 

26 and animals were observed for changes in estrus cycle at 4, 6, and 8 months. Rats were 

27 sacrificed at 8 months of age when the TCDD-treated animals had entered the transition to 

28 reproductive senescence. Following sacrifice, diestrus concentrations of serum LH, FSH, 

29 progesterone, and estradiol were measured, and the ovaries were collected for examination. 

30 Estrus cycles at 4 months exhibited normal cyclicity in both TCDD-exposed groups and 

31 did not differ significantly from the control group. At 6 months, however, there was a tendency 
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1 (p < 0.1) toward loss of normal estrus cyclicity in animals treated with TCDD. At the 8 month 

2 observation, estrus cyclicity was significantly (p < 0.05) different in both dioxin-exposed groups 

3 compared to controls (cumulative TCDD exposure is reported as 1.7 and 8 μg/kg for the 50 and 

4 200 ng/kg dose groups, respectively). The study authors noted that although the low-dose 

5 animals showed an increased prevalence of prolonged cycles, persistent estrus or diestrus was 

6 observed in only 10% of the rats. Conversely, approximately 50% of the rats exhibited loss of 

7 cyclicity in the high-dose group. There were no changes in the number and size distribution of 

8 ovarian follicles or the number of corpora lutea at either dose. Progesterone levels at 8 months 

9 tended to be higher (p < 0.08) in animals receiving either 7.14 or 28.6 ng/kg-day TCDD 

10 compared to controls, while serum estradiol concentrations were significantly (p < 0.03) lower at 

11 diestrus. Serum LH levels in TCDD-treated animals were comparable to those in the control 

12 group, while FSH levels were elevated in rats receiving 7.14 ng/kg-day TCDD, but not in the 

13 28.6 ng/kg-day dose group. 

14 A LOAEL for TCDD of 7.14 ng/kg-day for an 8-month exposure duration was identified 

15 for significantly (p < 0.03) decreased serum estradiol levels. A NOAEL cannot be determined 

16 for this study. 

17 

18 2.4.2.2.4. Hojo et al., 2002 (and related: Zareba et al., 2002). 

19 Hojo et al. (2002) studied the impact of prenatal exposure to TCDD on sexually 

20 dimorphic behavior in rats. Thirty-six pregnant Sprague-Dawley rats were assigned according to 

21 a randomized block design to groups receiving 0, 20, 60, or 180 ng/kg TCDD (98% purity) on 

22 GD 8. Litters from pregnant dams were culled to 5 females and 5 males on PND 4 and allowed 

23 to wean normally, at which time 5, 5, 6, and 5 litters from the 0, 20, 60, and 180 ng/kg TCDD 

24 treatment groups, respectively, were maintained for examination of behavioral response. 

25 Offspring were exposed to TCDD (from a single maternal exposure) for about 35 days through 

26 gestation and lactation. After weaning at PND 21, offspring were fed ad libitum until PND 80, at 

27 which time a fixed amount of food was supplied daily to maintain constant body weights. At 

28 90 days old, the rats in these treatment groups were trained to press a lever to obtain food pellets 

29 using two operant behavior procedures. Initially, each lever press was reinforced. The 

30 fixed-ratio (FR) requirement was then increased every fourth session from the initial setting of 1 

31 to values between 6 and 71. The responses for 30 days were studied under a multiple schedule 
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1 combining FR 11 and another schedule requiring a pause of at least 10 sec between responses 

2 (differential reinforcement of low rate, or DRL 10-sec) 

3 Pup and dam body weights were not affected by TCDD exposure, and all pups were 

4 successfully trained in the lever-press response within 3–4 days. Analyses of the FR procedure 

5 data indicated that the male pups responded at a lower rate at all TCDD doses when compared to 

6 the control group. In case of female pups, all TCDD-treated groups responded at a higher rate 

7 than controls. None of these results were, by themselves, however, statistically significant. 

8 Examination of the FR 11 and DRL 10-second data indicated that when considering the FR 

9 component of this multiple procedure, males from all three treatment groups responded at lower 

10 rates when compared to the controls. Conversely, all female pups responded at a higher rate than 

11 controls. In addition, the treatment-by-sex interaction was significant (p = 0.036), with the 

12 60 ng/kg female pups responding at a higher rate than the 60-ng/kg male pups. Examination of 

13 the delayed response component in the multiple FR 11 and DRL 10-sec procedures indicated that 

14 almost all TCDD treatment groups were affected. Like the FR component, male pups at all 

15 TCDD dose groups responded at a lower rate compared to controls, while female pups at all dose 

16 groups responded at a higher rate than controls. There was also a significant (p = 0.001) 

17 sex-by-treatment interaction for the DRL 10-sec similar to the FR component. Following 

18 behavioral testing, the animals were sacrificed and cortical depth measurements were taken in 

19 selected right and left brain regions. Reduced cortical thickness and altered brain morphometry 

20 were observed in both male and female offspring in the 180-ng/kg exposure group when 

21 compared to controls (reported in a separate article; Zareba et al., 2002). 

22 A nominal LOAEL for TCDD of 20 ng/kg for a single exposure on GD 8 is established 

23 for this study based on abrogation of sexually dimorphic neurobehavioral responses. A NOAEL 

24 cannot be derived for this study. 

25 

26 2.4.2.2.5. Kattainen et al., 2001. 

27 Pregnant Line A, B, and C rats derived from Han/Wistar and Long-Evans rats 

28 (4−8 pregnant dams/strain/treatment group) were administered a single gavage dose of 0, 30, 

29 100, 300, or 1,000 ng/kg TCDD (purity >99%) in corn oil on GD 15 (Kattainen et al., 2001). On 

30 PND 1, the litters were culled to three males and three females. Offspring were weaned on 

31 PND 28. Female pups were sacrificed on PND 35 and male pups were sacrificed on PND 70. 
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1 TCDD treatment did not affect body weight or cause clinical signs of toxicity in the dams. In 

2 Line B offspring, body weights in the 1,000 ng/kg group were slightly decreased during 

3 PND 1−7, while Line C offspring had slightly decreased body weights throughout the study 

4 period (data were not provided). The development of the third molar was affected the most in 

5 Line C offspring. In 5 of 10 Line C females and 6 of 10 Line C males treated with 1,000 ng/kg 

6 TCDD, the lower third molar did not develop. In comparison, 1 of 19 Line A females and 1 of 

7 18 Line B females administered 1,000 ng/kg TCDD lacked the third molar at sacrifice. Third 

8 molars were present in all the controls and all male Line A and B offspring administered 

9 1,000 ng/kg. Due to the lack of eruption of the third molar in the majority of Line B and C 

10 control females (only 30% erupted), however, the effects of TCDD on third molar eruption could 

11 only be evaluated in Line A female offspring (with 94% eruption). There was a dose-dependent 

12 decrease in the eruption of the lower third molar in Line A female offspring with a significant 

13 (p < 0.05) decrease observed in the 300 and 1,000 ng/kg dose groups. In the male offspring, any 

14 third molar that developed erupted by PND 70. The mesiodistal length of the existing lower 

15 third molar was reduced in a dose-dependent manner in both genders of all three rat lines. In 

16 Line A and C females, the decrease was significant (p < 0.05) at all doses. The size of the 

17 second molars was also significantly decreased with 1,000 ng/kg (p < 0.05) in all but Line C 

18 males. 

19 A developmental LOAEL for TCDD of 30 ng/kg for maternal exposure on GD 15 is 

20 established for this study, based on impaired tooth development (significantly reduced 

21 mesiodistal length of the lower third molar by approximately 12% to 38% [p < 0.05]). A 

22 NOAEL could not be determined. 

23 

24 2.4.2.2.6. Keller et al., 2007, 2008a, b. 

25 Keller et al. (2007, 2008a, b) conducted three separate experiments to assess the impact 

26 of TCDD on molar tooth development using different mouse strains. In Experiment 1, Keller et 

27 al. (2007) used six inbred mouse strains (C57BL/6J, BALB/cByJ, A/J, CBA/J, C3H/HeJ, and 

28 C57BL/10J) known to possess high affinity ligand-binding aryl hydrocarbon receptor alleles (b), 

29 two with b1 alleles (C57BL/6J and CBA/J), and four with b2 alleles (BALB/cByJ, A/J, 

30 C3H/HeJ, and CBA/J). Females (number not specified) from each strain were mated with males 

31 of the same strain. On GD 13, each pregnant female was assigned to one of the four dose groups 
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1 and treated with 0, 10, 100, or 1,000 ng TCDD/kg BW via oral gavage. The control group 

2 received corn oil. GD 13 was chosen for dosing because the first morphological signs of tooth 

3 development occur on GD 11. The first visible signs of the M1 (molar) occur on GDs 13–14 

4 followed by final cuspal morphology, which is determined on GD 15. The F1 offspring of 

5 females from each strain were weaned and separated by sex at PND 28 and were euthanized at 

PND 70. Each F1 mouse was examined for the presence or absence of both maxillary (M3 6 ) and 

7 mandibular third molars (M3) on both the left and right sides. In addition, all mice were scored 

8 as either normal or variant in M1 morphology for both molar rows. 

9 In Experiment 2 (Keller et al., 2008a), dams from six inbred mouse strains (C57BL/6J, 

10 BALB/cByJ, A/J, CBA/J, C3H/HeJ, and C57BL/10J) were orally dosed on GD 13 with 0, 10, 

11 100, or 1,000 ng TCDD/kg BW in corn oil. GD 13 was used as the dosing day because it 

12 coincided with the formation of Meckel’s cartilage (a major signal center) in the mouse mandible 

13 that is followed shortly by intramembranous bone formation on GD 15. The A/J mouse strain 

14 was abandoned because the authors had difficulty rearing the offspring from this strain. All 

15 offspring (n = 4 or 5 per treatment group) from the remaining strains were euthanized at 70 days 

16 of age. Mandible size and shape from all selected offspring were examined using geometric 

17 morphometric methods to assess the impact of TCDD exposure. 

18 In Experiment 3 (Keller et al., 2008b), dams from six inbred mouse strains (C57BL/6J, 

19 BALB/cByJ, A/J, C3H/HeJ, CBA/J, and C57BL/10J) were treated with a single oral dose of 0, 

20 10, 100, or 1,000 ng TCDD/kg-BW in corn oil. GD 13 was chosen as the dosing day because the 

21 first visible signs of the first molar (M1) occurs on GDs 13–14 and the final cuspal morphology 

22 (the pattern of projections on the chewing surface of the tooth) is not determined until after 

23 GD 15. Similar to Experiment 2, the A/J mouse strain was abandoned due to difficulty in rearing 

24 offspring. All offspring (n = 107–110 in each of the five strains for all treatment groups) were 

25 euthanized at 70 days of age and their molar size, shape, and asymmetry traits were examined 

26 using geometric morphometric methods. 

27 In Experiment 1, all four M3s were present in all dose groups in mice from C57BL/6J, 

28 BALB/cByJ, and C57BL/10J strains. A similar response was observed in the A/J strain mice 

29 with only 3 of 51 F1 mice exhibiting missing third molars. Approximately one-third of the mice 

from the CBA/J and C3H/HeJ strains, however, were missing at least one M3 30 or M3 molar. The 

numbers of CBA/J mice missing one or both M3 or M3 31 molars were 0/29, 2/21, 6/29, and 30/30 
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1 in the 0, 10, 100, and 1,000 ng/kg groups, respectively. In the C3H/Hej animals, the numbers 

2 missing one or both molars were 1/24, 3/28, 1/26, and 30/36, respectively. 

3 Maternal TCDD exposure was also found to affect the frequency of M1 variants, but only 

4 in the C57BL/10J strain, and the dose-response relationship was nonmonotonic. The proportions 

5 of variants observed in the 0, 10, 100, and 1,000 ng/kg dose groups were 33, 68, 59, and 58%, 

6 respectively. 

7 A LOAEL for TCDD of 10 ng/kg maternal exposure on GD 13 is identified for this study 

8 for increased incidence (33%) of the M1 variant in the C57BL/10J mouse strain. A NOAEL 

9 cannot be determined in this study. 

10 In Experiment 2 TCDD exposure of dams did not affect offspring survival or 10-week 

11 body weight in any of the inbred mouse strains used. Analysis of variance (ANOVA) indicated 

12 that although mandible size in both male and female offspring varied significantly (p < 0.0001) 

13 among strains, it was not affected by TCDD exposure. In contrast, analysis of covariance 

14 indicated that TCDD exposure significantly (p = 0.0033) decreased the mandible size in male 

15 offspring in the C3H/HeJ strain at all treatment groups. The mean mandible size was similar 

16 across all treatment groups in both sexes in all strains with male offspring exhibiting larger 

17 mandibles compared to females. Males in the C3H/HeJ strain exhibited a significant (level not 

18 reported) downward trend in mandible size throughout all treatment groups. Females in the C3H 

19 strain also showed a similar trend in mandible size, but the trend was not significant. ANOVA 

20 on mandible shape indicated that males had significantly (p < 0.0001) different mandible shape 

21 in strain × treatment groups. In contrast, in female offspring, although the mandible shape was 

22 significantly (p < 0.0001) different due to strains, treatment groups, and litter, the strain 

23 × treatment interaction was not significant. Male offspring from the C3H/HeJ and C57BL/6J 

24 mouse strains appear to be more sensitive to TCDD than BALB/cByJ or CBA/J mice, with the 

25 C57BL/10J strain exhibiting intermediate sensitivity. In addition to these analyses, Procrustes 

26 distance analysis also indicated that C3H/HeJ mice had the greatest response to the highest dose 

27 of TCDD, followed by the C57BL/6J strain. Female offspring in the C3H/HeJ and C57BL/6J 

28 strains also exhibited the largest change in Procrustes distance with TCDD exposure. This trend, 

29 however, was not statistically significant (p = 0.29). 
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1 A LOAEL for TCDD of 10 ng/kg maternal exposure on GD 13 was identified for this 

2 study for significantly (p = 0.0033) decreased mandible shape and size in male C3H/HeJ mice. 

3 A NOAEL cannot be determined in this study. 

4 In Experiment 3, effect of TCDD exposure on offspring survival or body weight was not 

5 reported. Three-way ANOVA results showed significant (p < 0.0001) differences in molar size 

6 among strains, sexes, and litters, but not between treatment groups. Molar size difference in sex 

7 × strain interaction was significant (p = 0.03), whereas differences in sex × treatment and sex 

8 × strain × treatment were not significant. Additionally, molar size in treatment × strain 

9 interaction also was not statistically significant. Based on these results, the authors reported that 

10 molar size varied significantly (p < 0.0001) among all five strains tested, with all strains 

11 exhibiting similar trends in all four treatment groups. Strain differences in molar size were more 

12 apparent in male offspring. A hormesis-like trend in molar size was observed in all strains 

13 (except in BALBc/ByJ) and sexes with an increase at the 100 ng/kg dose and a decrease in the 

14 1,000 ng/kg dose. In addition to lack of difference in molar size for all treatment groups in all 

15 strains, fluctuating asymmetry in molar size also did not increase with increasing doses of 

16 TCDD. 

17 In contrast to these results on molar size, the Procrustes ANOVA indicated that molar 

18 shape was significantly (p < 0.0001) affected by strain, sex, treatment, and litter size. Molar 

19 shape in sex × strain and sex × strain × treatment interactions was also highly significant 

20 (p < 0.0001). Based on these results, the authors concluded that differences between males and 

21 females varied based on the strain, and that the effect of TCDD exposure on each strain also 

22 differed for male and female offspring. Because molar shape in treatment × strain interaction 

23 was significant (p < 0.0001), differences in molar shape between the three treatment groups and 

24 the control group were analyzed for each strain using nonorthogonal contrasts. In male 

25 offspring, contrasts between the control group and 1,000 ng/kg were statistically significant only 

26 in the C3H/HeJ (p < 0.0001) and CBA/J (p < 0.03) strains. These results suggest that these two 

27 strains are most susceptible to TCDD effect on molar shape, and similar results were observed in 

28 female offspring of these two strains. The contrast in molar shape between the control and the 

29 100 ng/kg treatment group for the female C57BL/6J mice also was statistically significant 

30 (p = 0.0096). On the whole, when considering Procrustes distance results for molar shape, the 

31 C3H/HeJ male offspring had the largest response at the low and high doses, while the female 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 offspring had the largest response at low and mid doses. This observation in male C3H/HeJ 

2 mice is consistent with that of TCDD-induced changes in mandible size from Keller et al. 

3 (2008a). 

4 A LOAEL for TCDD of 10 ng/kg maternal exposure on GD 13 is identified for this study 

5 for significant (p < 0.0001) differences in molar shape in male C3H/HeJ mice. A NOAEL 

6 cannot be determined in this study. 

7 

8 2.4.2.2.7. Kuchiiwa et al., 2002. 

9 Kuchiiwa et al. (2002) studied the impact of in utero and lactational TCDD exposure on 

10 serotonin-immunoreactive neurons in raphae nuclei on F1 male mouse offspring. Twenty-one 

11 adult female ddY mice (seven per treatment group) were administered TCDD (99.1% purity) by 

12 oral gavage once a week for 8 weeks at doses of 0, 4.9, or 490 ng/kg (0, 0.7, or 70 ng/kg-day 

13 average daily dose; administered doses divided by 7) or an equivalent volume of olive oil vehicle 

14 (6.7 mL/kg) by gavage. Immediately following the final treatment, the mice were housed with 

15 untreated male mice for mating. At approximately 20–21 days after mating, 3 female mice from 

16 each dose group, including the control group gave birth to 10–12 offspring. One day after birth, 

17 each litter was culled to 10 offspring to accommodate similar lactational TCDD exposure. On 

18 PND 28, the offspring were weaned, and three offspring from each TCDD exposed group and 

19 the control group were selected for an immunocytochemical examination at 42 days of age. 

20 Following sacrifice of these offspring, the brain of each animal was removed and every second 

21 serial section of the brain was processed for immunocytochemistry. In addition to the serial 

22 sections of the brain, cells from 18 offspring (6 males per treatment group) were used to assess 

23 the number of cells in the dorsal and median raphe nucleus, the supralemniscal area, and the 

24 Nucleus raphe magnus. 

25 Examination of external morphology, birth, and postnatal body weights indicated that 

26 there were no differences between the male TCDD-exposed offspring and the control male 

27 offspring. TCDD-exposed males, however, were aggressive toward other normal mice and were 

28 also hypersensitive to soft touch. 

29 Serotonin-immunoreactive neurons were found to be distributed throughout the entire 

30 brainstem in 42-day-old males, and the general pattern in the TCDD-exposed animals was 

31 consistent with those observed in control male offspring. Serotonergic neurons were identified 
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1 and counted in the caudal linear nucleus, the median and dorsal raphe nucleus, Nucleus raphe 

2 pontis, interpeduncular nucleus, supralemniscal area, pedunculopontine segmental nuclei, deep 

3 mensencephalic nucleus, Nucleus raphe magnus, pallidus, and obscurus, dorsal and medial to the 

4 facial nucleus and the ventrolateral medulla. Results from computerized cell counts (n = 6) 

5 showed an average of 1,573.3 immunoreactive neurons in the raphe nuclei from the control 

6 group versus 716.3 and 419.8 neurons in the low- and high-dose offspring, respectively. The 

7 numbers of immunoreactive neurons in the individual raphe nuclei (dorsalis, medianus, magnus, 

8 and B9) from the TCDD-exposed offspring were significantly (p < 0.01) lower than control 

9 values, with the degree of reduction being dose-related. 

10 In the absence of other relevant neurotoxicity endpoints, reduced serotonin is not an 

11 adverse endpoint of toxicological significance in and of itself, thus, neither a NOAEL nor a 

12 LOAEL can be established for this study. A lowest-observed-effect level (LOEL) of 

13 0.7 ng/kg-day for an 8-week exposure duration is identified in this study for a significantly 

14 (p < 0.01) lower number of serotonin-immunoreactive neurons in the raphe nuclei of male 

15 offspring. A no-observed-effect level (NOEL) cannot be determined for this study. 

16 

17 2.4.2.2.8. Li et al., 2006. 

18 Pregnant and pseudopregnant (obtained by mating normal estrous female mice with 

19 vasectomized male mice) NIH mice (10 per treatment group) were exposed to 0, 2, 50, or 

20 100 ng/kg-day of TCDD (purity 99%) during early gestation (GDs 1–8), preimplantation 

21 (GDs 1−3), or peri-implantation to postimplantation (GDs 4–8) (Li et al., 2006). On GD 9, 

22 animals were evaluated. The two highest TCDD doses (50 and 100 ng/kg-day) caused 

23 significant (p < 0.05) early embryo loss independent of gestational exposure time. At 

24 100 ng/kg-day, however, the embryo loss was greater when administered during GDs 1–8 or 

25 GDs 1–3 compared to GDs 4–8 (p < 0.01). Uterine weight was significantly decreased in the 

26 pseudopregnant mice when administered 50 or 100 ng/kg-day TCDD during GDs 1–8 

27 (p < 0.001) or 1–3 (p < 0.01), but was only decreased at 100- ng/kg-day in pseudopregnant mice 

28 when administered during GDs 4–8 (p < 0.01). Estradiol levels were increased at all TCDD 

29 treatment levels (100% at the lowest dose), but statistical significance was not indicated. All 

30 doses at all treatment times resulted in a significant reduction (p < 0.01) in serum progesterone 

31 levels, with a 45% decrease at the lowest dose. Because the hormone effects were observed 
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1 following 4 days of treatment, the nominal doses were averaged over the entire test period of 

2 8 days prior to measurement. The resulting average daily doses of TCDD were 0, 1, 25, and 

3 50 ng/kg-day. 

4 A LOAEL of 2 ng/kg-day administered for 4 to 8 days is established in this study for a 

5 significant (p < 0.01) decrease in progesterone (45% above control) and an approximate 2-fold 

6 increase in estradiol levels (significance not indicated). A NOAEL cannot be determined. 

7 

8 2.4.2.2.9. Markowski et al., 2001. 

9 Pregnant Holtzman rats (4–7 per treatment group) were administered a single gavage 

10 dose of 0, 20, 60, or 180 ng/kg TCDD (purity not specified) in olive oil on GD 18 (Markowski et 

11 al., 2001). One female rat from each liter (4–7 per treatment group) was assigned to training on 

12 a wheel apparatus to respond on a lever for brief opportunities to run. Once animals responded 

13 to an FR1 schedule of reinforcement, the requirement for lever pressing was increased to FR2, 

14 FR5, FR10, FR20, and FR30 schedules. After each training session, the estrous cycle stage was 

15 determined. Maternal body weight, length of gestation, number of pups per litter, and sex 

16 distribution within litters were unaffected by treatment. For each of the FR schedules, there was 

17 a significant dose-related (p = 0.0001) decrease in the number of earned run opportunities, lever 

18 response rate, and total number of revolutions in the wheel in the adult female offspring. There 

19 was no correlation between estrous cycle and responding for access to wheel running. 

20 The developmental LOAEL for this study is a single dose of 20 ng/kg administered on 

21 GD 18 for neurobehavioral effects. A NOAEL cannot be determined for this study. 

22 

23 2.4.2.2.10. Miettinen et al., 2006 

24 Miettinen et al. (2006) administered a single oral dose of 0, 30, 100, 300, or 1,000 ng/kg 

25 TCDD (purity >99%) in corn oil on GD 15 to pregnant Line C rats. The offspring (24–32 per 

26 treatment group) were assigned to a sugar-rich cariogenic diet (via feed and drinking water) and 

27 were orally inoculated three separate times with fresh cultures of Streptococcus mutans. Three 

28 control groups varied with regard to TCDD exposure and administration of a cariogenic diet. 

29 Two of the control groups received no TCDD, and the offspring were either maintained on a 

30 normal diet without inoculation with S. mutans (C1; n = 48) or were given the cariogenic diet 

31 with S. mutans inoculation (C2; n = 42). The final control group was maternally exposed to 
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1 1,000 ng/kg TCDD with offspring fed a normal diet without S. mutans inoculation (C3; n = 12). 

2 TCDD did not affect the maternal or offspring body weight. Survival of the offspring was 

3 reduced in the 1,000 ng/kg dose group (50–58% survival compared to 83–95% in C1 and C2, 

4 respectively). All offspring administered 1,000 ng/kg were missing all lower third molars. 

5 Two animals (8%) in the 100 ng/kg group were missing one of their lower third molars. All 

6 doses, except the 100 ng/kg dose, caused a significant (p < 0.05) increase in the number of caries 

7 lesions compared to group C2 (60, 79, 76, 83, and 91% in the C2, 30, 100, 300, and 1,000 ng/kg 

8 groups, respectively). Group C3 (1,000 ng/kg TCDD exposure, normal diet) animals also had 

9 increased caries lesions compared to C1 (8% versus 0%, respectively). There were no changes 

10 in tooth mineral composition that could explain the increase in caries susceptibility. 

11 The developmental LOAEL from this study is a single dose of 30 ng/kg administered on 

12 GD 15 based on the significant (p < 0.05) increase in dental caries in pups (30% above control). 

13 A NOAEL cannot be determined from this study. 

14 

15 2.4.2.2.11. Nohara et al., 2000. 

16 Pregnant Holtzman rats were administered 0, 12.5, 50, 200, or 800 ng/kg TCDD in corn 

17 oil by gavage on GD 15 (Nohara et al., 2000). On PND 2, five males were randomly selected 

18 from each litter and dose group. TCDD was detected in the thymus, spleen, and bone marrow of 

19 the male pups on PND 21 and PND 49. TCDD was still detected in the thymus and spleen on 

20 PND 120 but the levels decreased over time. The TCDD concentration was highest in the 

21 thymus at all time points. There were no changes in the body, thymus, or spleen weights of the 

22 male offspring on PND 5, PND 21, PND 49, or PND 120. On PND 5, there was a 200-fold 

23 increase in CYP1A1 in the thymus of the high-dose male pups. CYP1A1 was only slightly 

24 increased in the spleen. This induction decreased through PND 49. There was a slight (not 

25 statistically significant) dose-dependent decrease in thymus cellularity in the male offspring at 

26 PND 120. Spleen cellularity at PND 49 decreased in a dose-dependent manner (15–50% of the 

27 control), with a statistically significant (p < 0.05) decrease observed in the high-dose group. A 

28 slight but not significant reduction in spleen cellularity was noted in the high-dose group at 

29 PND 21. The same effect was not observed at PND 120, nor was there any change in the percent 

30 of B or T cells in the spleen. No changes in cytokine levels were observed in the 800-ng/kg 

31 group. 
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1 Although a change in spleen cellularity on PND 49 (puberty) was observed, this effect 

2 was transient and there were no coexisting changes in the percentage of splenic lymphocytes, 

3 spleen weight, and cytokine levels. Therefore, a developmental NOAEL of a single dose of 

4 800 ng/kg administered on GD 15 is identified for this study. A LOAEL is not established. 

5 

6 2.4.2.2.12. Ohsako et al., 2001. 

7 Pregnant Holtzman rats (6 per treatment group) were administered 0, 12.5, 50, 200, or 

8 800 ng/kg TCDD (purity >99.5%) in corn oil by gavage on GD 15 (Ohsako et al., 2001). On 

9 PND 2, five males were randomly selected from each litter. Two male offspring from each litter 

10 were sacrificed on PND 49 and PND 120. Neither maternal nor male offspring body weight was 

11 affected by TCDD treatment. TCDD was detected in both fat and testes at all dose levels 

12 (including controls) with highest levels found in fat. There were no apparent treatment-related 

13 effects on testicular weight, epididymal weight, daily sperm production, cauda epididymal sperm 

14 reserves, luteinizing hormone, follicle stimulating hormone, or testosterone levels. There was, 

15 however, a clear dose-dependent decrease in urogenital complex weight and ventral prostate 

16 weight at both PND 49 and PND 120. For male offspring, statistically-significant (p < 0.05) 

17 decreases were noted in urogenital complex weight at PND 120 in the 200 and 800 ng/kg groups, 

18 in ventral prostate weight at PND 49 in 800 ng/kg group, and at PND 120 in the 200 and 

19 800 ng/kg groups. There was also a dose-dependent decrease in anogenital distance (the length 

20 between the base of the genital tubercle and the anterior edge of the anus); the decrease was not 

21 statistically significant at PND 49. At PND 120, however, male offspring in all but the lowest 

22 dose group had significantly (p < 0.05) reduced anogenital distance compared to the control 

23 animals. There was also a dose-dependent increase in 5αR-II mRNA expression in the ventral 

24 prostate on PND 49 with significant increases (p < 0.05) in the 200 and 800 ng/kg animals. 

25 There was a significant (p < 0.01) decrease in the androgen receptor mRNA in the ventral 

26 prostate on PND 49 at all doses tested. Similar effects were not observed on PND 120 or in the 

27 caput epididymis on PND 49. 

28 The developmental LOAEL for this study is a single dose of 50 ng/kg administered on 

29 GD 15 for significantly (p < 0.01) reduced anogenital distance in male offspring (approximately 

30 14%). The NOAEL for this study is 12.5 ng/kg. 

31 
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1 2.4.2.2.13. Schantz et al., 1996. 

2 Schantz et al. (1996) studied the impact of in utero TCDD exposure on spatial learning in 

3 male and female pups. Groups of pregnant Harlan Sprague-Dawley rats (n = 108, divided into 

4 4 cohorts; number of animals in each TCDD group approximately 4 per treatment group) were 

5 dosed via gavage with 0, 25, or 100 ng/kg-day TCDD (purity >98%) in corn oil on GDs 10−16. 

6 On the day of birth (post natal day [PND] 0), the pups were examined for gross abnormalities 

7 and the number of live pups, weight, and sex were recorded for each litter. On PND 2, litters 

8 were culled to eight animals and were balanced to include four males and four females whenever 

9 possible. To minimize litter-size effects, litters with fewer than five pups were excluded from 

10 the study. The exclusion of these litters resulted in 10−11 litters per treatment group. Pups were 

11 weaned on PND 21 and one male and one female pup from each litter were maintained for the 

12 learning tests. Pups were tested 5 days per week for spatial learning and memory in a radial arm 

13 maze and a T-maze. A radial arm maze working memory test and a T-maze DSA task were used 

14 a part of the testing process. 

15 TCDD treatment did not affect dam gestational weight gain, dam liver weight, gestation 

16 length, litter size, percentage of live births, birth weight, or postnatal growth of the pups 

17 observed during the course of the study. Exposed pups, however, exhibited some signs of 

18 toxicity in all exposure groups. Thymus weight was decreased and liver weight was increased in 

19 the 100 ng/kg-day TCDD dose group. Also, liver microsomal 7-ethoxyresorufin-O-deethylase 

20 (EROD) activity was markedly induced in pups from both the 25 and 100 ng/kg-day dose 

21 groups. In the radial maze test, rats from all TCDD exposure groups displayed a significant 

22 (p < 0.01) learning behavior as shown by progressively fewer errors from the first block of 

23 sessions through the fourth session. The treatment by sex and treatment by session block 

24 interactions were not significant. Comparisons between the average number of errors per session 

25 block in the TCDD-exposed and control group indicated that both the 25 and the 100 ng/kg-day 

26 dose groups made significantly (p < 0.05 and p < 0.001, respectively) fewer errors compared to 

27 the control group. TCDD did not significantly affect adjacent arm selection behavior as 

28 measured by C statistic; hence the reduction in errors observed did not appear to be accounted 

29 for by an increased tendency to run into adjacent arms. Female pups had a significant (p < 0.05) 

30 shorter radial arm maze latency, however, compared to the male pups. In the T-maze test, 

31 TCDD did not significantly affect the percent of correct performance. All exposure groups 
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1 performed best at the shortest delay, which showed a decline as the length of the inter-trial delay 

2 interval was increased. Additionally, all treated groups improved their performance over a 

3 three-block session period. This finding indicated that animals in all groups could learn the task. 

4 These observations were confirmed by a highly significant main effect of delay (p < 0.001) and 

5 highly significant main effect of session blocks (p < 0.001). At the shortest 15-second delay, 

6 average percent correct performance increased from 75 to 92%, while at the longest 40-second 

7 delay, the average percent correct performance increased from 62 to 82%. A significant 

8 (p < 0.05) main effect of exposure was evident in latency to respond in the T-maze. 

9 Comparisons of the exposed group to control group, however, indicated that none of the 

10 individual exposure groups differed significantly from the controls. Because no clear pattern 

11 was observed in the various exposure groups, differences in latency to respond had no impact on 

12 learning of the task. 

13 Based on these results, the study authors state that the fact TCDD seems to have a 

14 facilitatory effect on radial arm maze learning in rats should be interpreted with caution and 

15 needs further evaluation using different and more varied learning tasks. No toxicologically 

16 adverse endpoints were concurrently examined. Thus, a LOAEL and a NOAEL cannot be 

17 determined for this study. 

18 

19 2.4.2.2.14. Seo et al., 1995. 

20 To study developmental effects of TCDD on thyroid hormone levels, time-mated female 

21 Sprague-Dawley rat dams (n = 10−14/treatment group) were administered 25 or 100 ng/kg-day 

22 of TCDD (>98% pure) in corn oil via gavage from GDs 10–16. Vehicle controls received 

23 equivalent amounts of corn oil. The study also investigated PCB treatment outcomes. At birth, 

24 pups were weighed and grossly examined for abnormalities. At 2 days of age, litters with fewer 

25 than 5 pups were excluded from the analysis and the remaining litters were culled to 4 males and 

26 4 females. Each treatment group contained 10 or 11 litters. Pups remained with the dams until 

27 weaning. At weaning, 4−6 pups were retained for neurobehavioral tests (which were not 

28 reported as part of this study). The remaining offspring were sacrificed, which provided 

29 5−9 litters per treatment group. Data were collected from one male and one female where 

30 possible. No signs of toxicity were evident in the dams; measurements on dams included 

31 gestational weight gain, liver weight, litter size, and live births. Pup birth weight and weaning 
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1 weight were unaffected by treatment. In pups sacrificed at weaning (21 days old), a significant 

2 (p < 0.05) decrease occurred in thymus weight for the high-dose group, but not in thyroid, liver, 

3 or brain weight. A significant (p < 0.05) decrease (20.4%) was observed in T4 in high-dose 

4 females. Thyroid stimulating hormone and T3 were unaffected by treatment. Uridine 

5 diphosphate (UDP)-glucuronosyl transferase activity towards 4-nitrophenol significantly 

6 (p < 0.05) increased in both treatment groups over control values, and the increase in the 

7 high-dose group was significantly (p < 0.05) greater than in the low-dose group. Liver 

8 microsomal EROD activity was significantly (p < 0.05) increased in both treatment groups, but 

9 is considered to be an adaptive response and not adverse. 

10 A LOAEL of 100 ng/kg-day for decreased thymus weights and decreased thyroxine is 

11 identified for this study. A NOAEL of 25 ng/kg-day is established. 

12 

13 2.4.2.2.15. Simanainen et al, 2004. 

14 Simanainen et al. (2004) studied the impact of in utero and lactational TCDD exposure 

15 on the male reproductive system in three rat lines that are differentially sensitive to TCDD. 

16 Groups of 5 to 8 pregnant Line A, B, and C C57BL/6N CYP1A2 dams were given a single dose 

17 of 0, 30, 100, 300, or 1,000 ng/kg of TCDD (purity >99%) in corn oil on GD 15 via oral gavage. 

18 Control animals were similarly dosed with a corn oil vehicle. One day after birth, litters were 

19 randomly culled to include three males and three females to allow uniform postnatal exposure. 

20 Offspring were weaned on PND 28. Dam and pup viabilities were monitored throughout the 

21 study. Pup body weights were determined on PNDs 1, 4, 7, 14, and 28. Anogenital distance and 

22 crown-rump length were measured on PNDs 1 and 4. On day 70, pups were sacrificed and trunk 

23 blood was collected. Serum was collected for testosterone analysis. The testes, cauda of the 

24 right epididymis, ventral prostrate, seminal vesicles, and thymus was dissected and weighed. 

25 Absolute and relative organ weights were determined, and cauda epididymis and testes were also 

26 preserved for sperm count analysis. 

27 TCDD caused no mortality or overt signs of toxicity to the dams. Pup survival from 

28 implantation to the day after birth also was not affected by TCDD exposure. Survival from the 

29 day of implantation to the day after birth, however, was uncharacteristically lower in control 

30 Line B rats (41%), resulting in a significant difference compared with the two lowest doses (30 

31 and 100 ng/mg TCDD). The average survival percentage in the controls for Line A, B, and C 
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1 rats was 85% (range 80−86%); 64% (41−86%); and 74% (63−85%); respectively. Percentage of 

2 male pup survival in each line between PND 1 and PND 28 was 99% except for Line B males 

3 exposed to 30 ng/kg TCDD and Line C males exposed to 30 or 100 ng/kg, where male survival 

4 rate averaged 81% (range 81−83%). On PND 70, a significant (p < 0.05) reduction in body 

5 weight was observed only in Line B and C rats at 1,000 ng/kg. In pups exposed to 1,000 ng/kg 

6 TCDD, both absolute and relative weight of the ventral, anterior, and dorsolateral prostrate 

7 decreased in all three lines at most postnatal time points measured. The change was most 

8 consistent and significant (p < 0.05) in the ventral lobe. Animals exposed to 1,000 ng/kg TCDD 

9 had an average decrease in absolute weight of the anterior prostrate of 37, 32, and 34% in 

10 Lines A, B and C, respectively. Additionally, the average dorsolateral prostrate weight was also 

11 decreased by 34, 28, and 39% in Lines A, B, and C, respectively. The effect on the ventral 

12 prostrate was reversible with the only significant (p < 0.05) decrease in weight observed in 

13 Line B rats at PND 70 in the 1,000 ng/kg TCDD dose group. The authors reported that TCDD 

14 had no consistent effects on the weight of seminal vesicles. The absolute weights of the testis 

15 and epididymis showed a significant (p < 0.05) increase on PNDs 28−49, but the relative testis, 

16 epididymis, and cauda epididymis weights remained unchanged. In pups exposed to 1,000 ng/kg 

17 TCDD, severe malformation, including small caput and cauda and degeneration of corpus 

18 epididymis, was observed. Malformations in the epididymis were observed in 6 of 44 Line C 

19 male rat offspring and 3 of 47 Line A male rat offspring. In Line A, B, and C rats at PND 70in 

20 the 1,000 ng/kg TCDD dose group, daily sperm production was reduced by 9, 25, and 36% and 

21 cauda epididymal sperm reserves were reduced by 18, 42, and 49%, respectively. Daily sperm 

22 reduction (17%) was significant (p < 0.05) in Line C rats at a TCDD dose of 300 ng/kg and in 

23 Line B and C rats at 1,000 ng/kg. A reduction in cauda epididymal sperm reserves (25%) was 

24 significant (p < 0.05) in Line C rats at 300 and 1,000 ng/kg TCDD. 

25 A LOAEL for TCDD of 300 ng/kg is identified for reduction in daily sperm production 

26 and cauda epididymal sperm reserves in Line C rats. A NOAEL of 100 ng/kg is identified for 

27 this study. 

28 

29 2.4.2.2.16. Sugita-Konishi et al., 2003. 

30 Sugita-Konishi et al. (2003) examined the immunotoxic effects of lactational exposure to 

31 TCDD in newborn mice. Eight pregnant female C57BL/6NCji mice were administered 0, 1.8, or 
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1 18 ng/L of TCDD via drinking water from parturition to weaning of the offspring (for a total of 

2 17 days). Based on an average water intake of 14–16 mL/day, the average daily intake of TCDD 

3 for the dams was 1.14 and 11.3 ng/kg-day in the low- and high-dose groups, respectively. In 

4 male offspring sacrificed at weaning (21 days after birth), there was a statistically-significant 

5 (p < 0.05) decrease in relative spleen weight and a statistically-significant (p < 0.005) increase in 

6 thymic CD4+ cells in the high-dose group. The changes in relative spleen weight and thymic 

7 CD4+ cells were dose related, but effects in the low-dose group did not achieve statistical 

8 significance. Changes in spleen weight and CD4+ cell numbers were not observed in the female 

9 offspring. In a separate experiment, offspring infected with Listeria monocytogenes following 

10 lactational TCDD exposure exhibited a statistically significant increase in serum tumor necrosis 

11 factor alpha (TNF-α) 2 days after infection in both sexes in the low- (p < 0.05) and high-dose 

12 (p < 0.005) groups. There was also a statistically significant increase in serum interferon gamma 

13 in Listeria-infected high-dose females (p < 0.05). The number of bacteria in the spleen was also 

14 significantly increased (p < 0.05) 2 days after infection in the high-dose females compared to the 

15 controls, but not in males. Listeria levels in the spleen returned to control levels by 4 days after 

16 infection in both sexes. 

17 Based on these results, a LOAEL for TCDD of 11.3 ng/kg-day following a 17 day 

18 exposure to dams was identified for significantly (p < 0.05) decreased spleen weight (in male 

19 pups), a significant (p < 0.005) increase in thymic CD4+ cells (in male pups), and for increased 

20 susceptibility to Listeria monocytogenes (in male and female pups). The NOAEL for this study 

21 is 1.14 ng/kg-day. 

22 

23 2.4.2.3. Acute Studies 

24 2.4.2.3.1. Burleson et al., 1996. 

25 Burleson et al. (1996) studied the impact of TCDD exposure on mice that were 

26 challenged with the influenza virus 7 days after treatment with TCDD. Groups of 8-week-old 

27 female B6C3F1 mice (n = 20, 2 replicate groups) were treated one time with 0, 1, 5, 10, 50, 100, 

28 or 6,000 ng/kg TCDD (purity >99%, dissolved in corn oil) via oral gavage. In addition to the 

29 treated groups, randomly selected animals were assigned as a sentinel group and screened for 

30 numerous pathogens. Results of all tests performed on this sentinel group were negative. Seven 

31 days after TCDD treatment, all animals were lightly anesthetized and infected intranasally with a 
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1 highly lethal influenza A/Hong Kong/8/68 virus (H3N1; passage 14). The animals were infected 

2 with sufficient H3N1 virus to achieve a 30% mortality rate in the control animals. Animals were 

3 observed for mortality and morbidity for 21 days following viral infection. Six mice from each 

4 treatment group were sacrificed on days 3, 9, and 12 post-infection, and body, thymus, and wet 

5 lung weights were recorded. Influenza viral titers were examined by sacrificing eight mice each 

6 at 2 hours and at 1, 4, 6, 7, 8, 9, 10, and 11 days post infection. 

7 Exposure to TCDD resulted in significantly (p < 0.05) increased mortality in the 10, 50, 

8 and 100 ng/kg dose groups. No statistically significant difference in the percentage alive was 

9 observed between these dose groups. TCDD doses of 1 and 5 ng/kg did not alter mortality in 

10 influenza infected animals. A time-related increase in the wet weights of the lungs in infected 

11 mice as a result of increased edema also was reflected in an increase in the lung weight-to-body 

12 weight ratio. The study authors stated that this ratio was not altered as a result of TCDD 

13 exposure. TCDD-only exposures at 1, 10, or 100 ng/kg did not affect thymus weight. Similarly, 

14 animals infected with the influenza virus following TCDD exposure also showed no loss in 

15 thymic weight. Enhanced mortality in TCDD-treated animals was not correlated with an 

16 increase in influenza virus titers. Additionally, animals treated with 1, 10, 100, or 1,000 ng/kg 

17 did not affect pulmonary viral titer assays on days 6, 7, and 8 post-infection. The authors also 

18 concluded that TCDD did not alter Hong Kong virus replication or clearance. 

19 Although these results support immunotoxic effects induced by TCDD, the findings were 

20 not reproduced by Nohara et al. (2002) using the identical study design, and the translation of 

21 these findings to humans is dubious. Thus, no LOAEL/NOAEL was established. A LOEL for 

22 TCDD of 10 ng/kg for a single exposure is identified for significantly (p < 0.05) increased 

23 mortality in mice infected 7 days later with the influenza virus. The NOEL for this study is 

24 5 ng/kg. 

25 

26 2.4.2.3.2. Crofton et al., 2005. 

27 Crofton et al. (2005) studied the impact of TCDD exposure in addition to the impact of 

28 mixtures of thyroid disrupting chemicals and PCBs on serum total thyroxine (TT4) 

29 concentration. Groups of female Long-Evans rats were dosed via oral gavage with 0, 0.1, 3, 10, 

30 30, 100, 300, 1,000, 3,000, or 10,000 ng/kg-day TCDD (purity >99%) in corn oil (n = 14, 6, 12, 

31 6, 6, 6, 6, 6, 6, and 4, respectively) for 4 consecutive days. On the day following the last dose, 
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1 animals were sacrificed, trunk blood was collected, and serum obtained via centrifugation was 

2 assayed for TT4 concentration using standard radioimmunoassay methods. 

3 No visible signs of toxicity or changes in animal body weight as a result of TCDD 

4 exposure were observed. Serum T4 levels showed a dose-dependent decrease, with the levels 

5 dropping sharply beginning at 100 ng/kg-day dose. Percent serum T4 levels were 96.3, 98.6, 

6 99.8, 93.3, 70.9, 62.5, 52.7, 54.7, and 49.1% in the 0.1, 3, 10, 30, 100, 300, 1,000, 3,000, and 

7 10,000 ng /kg-day groups, respectively. 

8 A LOAEL for TCDD of 100 ng/kg-day for 4 consecutive days of exposure is identified in 

9 this study for a reduction in serum T4 levels (70.9% compared to 100% in controls). The 

10 NOAEL for this study is 30 ng/kg-day. 

11 

12 2.4.2.3.3. Kitchin and Woods, 1979. 

13 Female Sprague-Dawley rats (nine per control and four per treatment group) were 

14 administered a single dose of 0, 0.6, 2, 4, 20, 60, 200, 600, 2,000, 5,000, or 20,000 ng/kg TCDD 

15 (purity >99%) in corn oil. Animals were sacrificed 3 days after treatment and CYP level and 

16 benzo(a)pyrene hydroxylase activity in the liver were measured. A significant (p < 0.05) 

17 increase in cytochrome P450 levels occurred with doses of 600 ng/kg or greater and in 

18 benzo(a)pyrene hydroxylase activity with doses of 2 ng/kg or greater. Cytochrome P450 was 

19 significantly (p < 0.05) higher 1 month after a single exposure of 2,000 ng/kg (the only dose 

20 measured), but not after 3 or 6 months. Aryl hydrocarbon hydralase (AHH; p < 0.05) and EROD 

21 (p < 0.01) were both significantly increased through 3 months after treatment, and although 

22 elevated at 6 months, the results were not significant. 

23 CYP induction alone is not considered a significant toxicologically adverse effect given 

24 that CYPs are induced as a means of hepatic processing of xenobiotic agents. Thus, no LOAEL 

25 or NOAEL was established for this study because adverse endpoints (e.g., indicators of 

26 hepatotoxicity) were not measured. The acute LOEL, however, is 2 ng/kg based on a significant 

27 (p < 0.05) increase in benzo(a)pyrene hydroxylase activity (37% above control). The NOEL is 

28 0.6 ng/kg. 

29 
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1 2.4.2.3.4. Li et al., 1997. 

2 Female Sprague-Dawley rats (22 days old; 10 per treatment) were administered a single 

3 oral dose of TCDD (>98% pure) in corn oil via gavage at doses of 3, 10, 30, 100, 300, 1,000, 

4 3,000, 10,000, or 30,000 ng/kg. Vehicle controls received equivalent amounts of corn oil, while 

5 naïve controls were sham-treated only. In a preliminary time-course study, animals received a 

6 single dose of 10,000 ng/kg and were sacrificed at 1, 2, 4, 8, 16, 24, 48, and 72 hours. The 

7 time-course study showed two peaks in LH and FSH levels at 1 hour and 24 hours, with a 

8 decrease to control values by 48 hours. Thus, in the dose-response study, animals were 

9 sacrificed at 1 or 24 hours after treatment, blood was collected, and serum FSH and LH were 

10 measured. The dose-response study demonstrated that the peak at 1 hour was related to the 

11 vehicle as the peak also occurred in the vehicle controls, but did not occur in the naïve controls. 

12 At 24 hours, FSH was increased at 10 ng/kg and higher (>4-fold increase at 10 ng/kg). Doses of 

13 10 to 1,000 ng/kg showed similar increases (not all reached statistical significance; p < 0.05). A 

14 dose-dependent increase occurred for doses ≥3000 (p < 0.05) with a maximum increase of 

15 20-fold over the vehicle control. At 24 hours, the LH response significantly (p < 0.05) increased 

16 only for doses ≥300 ng/kg with a maximum increase of 15-fold above the vehicle control. The 

17 study authors calculated an ED50 of 500 ng/kg for gonadotropin increase. The dose-dependent 

18 release of LH was confirmed in in vitro studies, but did not occur with the same magnitude. The 

19 increase did not occur in calcium-free medium and was unrelated to gonadotropin releasing 

20 hormone. 

21 Based on the increase in serum FSH, the LOAEL was 10 ng/kg and the NOAEL was 

22 3 ng/kg. 

23 

24 2.4.2.3.5. Lucier et al., 1986. 

25 Adult female Sprague-Dawley rats (six per treatment) were administered a single gavage 

26 dose of TCDD (purity not specified) in either corn oil or contaminated soil at doses of 15, 40, 

27 100, 200, 500, 1,000, 2,000, 5,000 (corn oil), or 5,500 (contaminated soil) ng/kg. Animals were 

28 sacrificed 6 days later and livers were removed for analysis. No clinical signs of acute toxicity 

29 or changes in body weight were observed at any dose. AHH increased in a dose-dependent 

30 manner with significant (p < 0.05) increases observed at 15 ng/kg or greater in corn oil or 

31 40 ng/kg or greater in contaminated soil. Cytochrome P450 was significantly (p < 0.05) 
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1 increased with doses of 1,000 ng/kg or greater in corn oil or 500 ng/kg or greater in contaminated 

2 soil. A dose-dependent increase was observed for UDP glucoronyltransferase (significance of 

3 individual doses not reported), with the results twice as high with corn oil than with 

4 contaminated soil. The authors state that the results indicate bioavailability from soils is 50%. 

5 Because the association between AHH activity and TCDD-mediated hepatotoxicity is 

6 unknown and no adverse endpoints were measured, a LOAEL or NOAEL was not determined 

7 for this study. The acute LOEL for this study is 15 ng/kg, based on the significant (p < 0.05) 

8 increase (80% above control) in AHH. No NOEL is established. 

9 

10 2.4.2.3.6. Nohara et al., 2002. 

11 Male and female B6C3F1 (C57BL/6 x C3H), BALB/c, C57BL/6N, and DBA2 mice 

12 (10−40 per treatment group) were administered a single dose of 0, 5, 20, 100, or 500 ng/kg 

13 TCDD in corn oil via gavage. Seven days following TCDD treatment, mice were infected with a 

14 mouse-adapted strain of influenza (A/PR/34/8; H1N1) at a plaque forming unit dose designed to 

15 target approximately 30% mortality in each strain. TCDD did not affect the body weight or 

16 survival in any of the infected mouse strains at any dose. 

17 Therefore, no LOAEL is established in this study. The NOAEL is 500 ng/kg. 

18 

19 2.4.2.3.7. Simanainen et al., 2003. 

20 Simanainen et al. (2003) studied the short-term effects of TCDD exposure to determine 

21 the efficacy and potency relationships among three differentially susceptible rat lines. The three 

22 rat lines used were A, B, and C, which were selectively bred from TCDD-resistant Han/Wistar 

23 and TCDD-sensitive Long-Evans rats. The study authors reported that Line A rats were most 

24 resistant to TCDD acute lethality followed by Line B and C. Groups of five or six randomly 

25 selected rats (sex not specified) were treated with a single oral dose of TCDD (purity >99%) in 

26 corn oil by oral gavage. The dose of TCDD was reported to range between 30 ng/kg and 

27 3,000 μg/kg for Line A, 30 ng/kg and 1,000 μg/kg in Line B, and 30 ng/kg and 100 μg/kg for 

28 Line C. Control animals were similarly dosed with a corn oil vehicle. Rats were sacrificed on 

29 day 8 post-exposure, and trunk blood was collected and serum separated. Liver and thymus were 

30 removed and weighed, and liver samples were collected and preserved. Liver EROD activity, 
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1 serum aspartate aminotransferase (ASAT) activity, free fatty acid (FFA) concentration, and total 

2 bilirubin concentration were determined. Teeth were also examined. 

3 Relative thymus weights were reduced 25% at 300 ng/kg relative to controls in Line B 

4 rats. Liver enzyme (CYP1A1) induction, as measured by EROD activity, was evident at all 

5 exposure levels; CYP induction is considered to be an adaptive effect and not adverse in itself. 

6 No other endpoints were affected below 1 μg/kg in any of the three rat lines. 

7 A LOAEL for TCDD of 300 ng/kg is identified for decreased relative thymus weight in 

8 Line B rats. A NOAEL of 100 ng/kg is identified for this study. 

9 

10 2.4.2.3.8. Simanainen et al., 2002. 

11 To study the short-term effects of TCDD on hormone levels, adult female Long-Evans 

12 (TCDD-sensitive) and Han/Wistar (TCDD-resistant) rats (n = 9−11/treatment) were administered 

13 a single dose of TCDD (>99% pure) in corn oil via gavage at doses ranging from 30 ng/kg to 

14 100 μg/kg. Vehicle controls received an equivalent amount of corn oil. The study also 

15 examined other polychlorinated dibenzo-p-dioxins outcomes. Rats were sacrificed on day 8 

16 post-exposure, and trunk blood was collected and serum separated. Liver and thymus were 

17 removed and weighed, and liver samples were collected and preserved. Liver EROD activity, 

18 serum ASAT activity, FFA concentration, and total bilirubin concentration were determined. 

19 Teeth were also examined. 

20 Neither FFA or ASAT levels in Han/Wistar rats showed a dose-response relationship. In 

21 Long-Evans rats, however, a significant (p < 0.05) dose-dependent increase in FFA occurred at 

22 300 ng/kg TCDD. Serum ASAT sharply increased in Long-Evans rats between 3,000 and 

23 10,000 ng/kg. Body weight change and relative thymus weights were significantly decreased 

24 (p < 0.05) in Han/Wistar rats with doses ≥10,000 ng/kg and in Long-Evans rats with doses 

25 ≥1,000 ng/kg. Liver EROD activity was significantly (p < 0.05) increased with all doses in both 

26 strains. Serum T4 was significantly (p < 0.05) decreased in Long-Evans rats at concentrations 

27 ≥300 ng/kg, but were not significantly affected in Han/Wistar rats. Serum bilirubin was 

28 significantly (p < 0.05) increased with doses ≥10,000 ng/kg in Long-Evans rats and 

29 ≥30,000 ng/kg in Hans/Wistar rats. Both strains of rat showed a dose-dependent increase in 

30 mean severity of incisor tooth defects. The results indicate that TCDD was the most potent 

31 congener tested in both rat strains. 
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1 A LOAEL of 300 ng/kg for decreased T4 in the Long-Evans rat is identified for this 

2 study. A NOAEL of 100 ng/kg is is established. 

3 

4 2.4.2.3.9. Smialowicz et al., 2004. 

5 Smialowicz et al. (2004) examined the impact of TCDD exposure on immunosuppression 

6 in mice. Groups of female (number not specified) C57BL/6N CYP1A2 (+/+) wild-type mice 

7 were administered a single dose of 0, 30, 100, 300, 1,000, 3,000, or 10,000 ng/kg TCDD (purity 

8 >99%) in corn oil via oral gavage. Control animals were similarly dosed with a corn oil vehicle. 

9 To assess immune function, 7 days after TCDD administration, all mice were immunized with 

10 sheep red blood cells (SRBCs) via injection into the lateral tail vein. Five days after 

11 immunization, mice were sacrificed, blood was collected, and enzyme-linked immunosorbant 

12 assays were performed. Additionally, spleen, thymus, and liver weights also were measured. 

13 Body and spleen weights of the wild-type mice were unaffected by the TCDD exposure. 

14 A decrease in thymus weights of the mice appeared to be dose related. Only mice treated with 

15 10,000 ng/kg TCDD, however, showed a statistically significant (p < 0.05) decrease in thymus 

16 weights compared to corresponding controls. Liver weights also showed a dose-related increase 

17 with only animals treated with 3,000 and 10,000 ng/kg TCDD showing statistical significance 

18 (p < 0.05) compared to the control group. The antibody response to SRBCs indicated a 

19 dose-related suppression in the wild-type mice, with animals treated with 1,000, 3,000, and 

20 10,000 ng/kg TCDD showing statistically significant (p < 0.05) suppression compared to the 

21 controls. 

22 A LOAEL for TCDD of 1,000 ng/kg is identified in female C57BL/6N CYP1A2 (+/+) 

23 wild-type mice for significant (p < 0.05) suppression of SRBCs. The NOAEL for this study is 

24 300 ng/kg. 

25 

26 2.4.2.3.10. Vanden Heuvel et al., 1994. 

27 Vanden Heuvel et al. (1994) examined the dose-response relationship between TCDD 

28 exposure and induction of hepatic mRNA. Groups of 10-week-old female Sprague-Dawley rats 

29 were administered TCDD (purity ~99%) in corn oil once at 0, 0.1, 0.05, 1, 10, 100, 1,000, or 

30 10,000 ng/kg-BW. Four days after TCDD treatment, animals were sacrificed and livers were 

31 excised and preserved. Total hepatic RNA was extracted using guanidine thiocyanate and DNA 
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1 was removed using standard phenol-chloroform-isoamyl alcohol partitioning procedures. 

2 Quantitative competitive RNA-PCR method was used to analyze CYP1A1, 

3 UDP-glucuronosyltransferase I (UGT1), plasminogen activator inhibitor 2 (PAI2), β-actin, and 

4 transforming growth factor α (TGFα). In addition to hepatic mRNA levels, microsomal protein 

5 was assayed for EROD activity and livers were tested for TCDD concentration. 

6 CYP1A1 mRNA induction levels in the TCDD-treated groups were low in the low-dose 

7 region and sharply increased to plateaus at higher doses. The lowest dose that showed a 

8 statistically significant (p < 0.05) difference compared to controls was the 1 ng/kg dose, which 

9 showed a three-fold increase in CYP1A1 mRNA levels. In contrast, a 130-fold increase 

10 occurred at 100 ng/kg and a 4,000- and 7,000-fold increase occurred at 1,000 and 10,000 ng/kg, 

11 respectively. A slight increase in the CYP1A1/β-actin levels was observed in the 0.1 ng/kg 

12 group, but this increase was not significant. EROD activity exhibited a pattern similar to 

13 CYP1A1 activity. EROD activity, however, was approximately 100-fold less sensitive 

14 compared to mRNA levels in TCDD-treated groups. Statistical significance (p-value not 

15 provided) in CYP1A1 level was observed at the 100 ng/kg dose compared to the 1 ng/kg dose. 

16 The study authors reported that, despite this difference in CYP1A1 and EROD activity, the 

17 correlation between CYP1A1 enzyme activity and mRNA levels was good. Dose-response 

18 relationships for the induction of UGT1, PAI2, and TGFα mRNA differed from what had been 

19 observed for CYP1A1 mRNA. UGT1 mRNA was induced, but at the much higher dose of 

20 1,000 ng/kg. Additionally, the five-fold maximum induction of UGT1 mRNA was much less 

21 than the 7,000-fold induction observed for CYP1A1 mRNA at the 10,000 ng /kg dose. The 

22 authors state that this could be a result of the constitutive level of UGT1, which is much higher 

23 than CYP1A1, which makes detecting induction of UGT1 in the low dose regions more difficult. 

24 PAI2 and TGFα mRNA were not affected by TCDD in rat liver in the dose range tested. These 

25 results indicate that dioxin-inducible genes have a quite dissimilar dose-response relationship. 

26 Induction of CYP1A1 expression is not considered an adverse effect, as the role of 

27 CYP1A1 in TCDD-mediated hepatotoxicity is unsettled. Therefore, in the absence of other 

28 indicators of hepatotoxicity, a NOAEL/LOAEL cannot be determined for this study. A LOEL 

29 for TCDD of 1 ng/kg for a single exposure was identified for statistically significant (p < 0.05) 

30 increase in CYP1A1 mRNA levels. The NOEL for this study is 0.1 ng/kg. 

 31 
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1 2.4.2.4. Subchronic Studies 

2 2.4.2.4.1. Chu et al., 2001. 

3 Adult female Sprague-Dawley rats (five per treatment group) were administered TCDD 

4 (purity >99%) in corn oil by gavage at doses of 0, 2.5, 25, 250, or 1,000 ng/kg-day for 28 days 

5 (Chu et al., 2001). The 1,000 ng/kg-day dose of TCDD caused a significant (p ≤ 0.05) decrease 

6 in body weight gain (36% lower than the control), increase in relative liver weight (40% greater 

7 than the control), and decrease in relative thymus weight (50% lower than the control). There 

8 was a significant (p ≤ 0.05) increase in EROD activity, methoxy resoufin-O-deethylase (MROD) 

9 activity, and UDP-glucuronosyl transferase (UDPGT) activity in the liver of female rats 

10 receiving 250 or 1,000 ng/kg-day TCDD. In addition, significant (p ≤ 0.05) increases in serum 

11 cholesterol were observed in the 250 and 1,000 ng/kg-day dose groups, and liver ascorbic acid 

12 (AA) also was significantly increased in the 1,000 ng/kg-day dose group. There was ~1.5-fold 

13 increase in liver glutathione-S-transferase (GST), which was not statistically significant. Other 

14 significant (p ≤ 0.05) findings for the 1,000 ng/kg-day group included a decrease in liver 

15 vitamin A (51% lower than the control), an increase in kidney vitamin A (15.5-fold increase 

16 above the control), an increase in liver benzyloxy resoufin-O-deethylase (BROD, 30-fold 

17 increase above control), a decrease in liver pentoxyresoufin-O-deethylase (PROD, 37% lower 

18 than the control), increase in serum albumin (18% above the control), and a decrease in mean 

19 corpuscular hemoglobin (MCH, 7% below the control) and mean corpuscular volume (MCV, 7% 

20 below the control). 

21 Based on the numerous significant (p ≤ 0.05) liver-related biochemical changes and 

22 significant (p ≤ 0.05) increased relative liver weight, as well as significantly decreased body 

23 weight and relative thymus weight, the LOAEL for 28 days of exposure in this study is 

24 1,000 ng/kg-day and the NOAEL is 250 ng/kg-day. 

25 

26 2.4.2.4.2. Chu et al., 2007. 

27 Chu et al. (2007) examined the potential impact of TCDD on various organs and the 

28 toxicological impacts as a result of interactions between TCDD and PCBs in rats. Groups of 

29 female Sprague-Dawley rats (n = 5 per treatment group) were treated daily for 28 days via 

30 gavage with 0, 2.5, 25, 250, or 1,000 ng /kg-day TCDD (purity not specified) dissolved in corn 

31 oil. Body weights were determined three times per week, and clinical observations were made 
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1 daily. At study termination, all animals were sacrificed and blood was analyzed for various 

2 biochemical and hematological parameters. Liver, spleen, heart, thymus, brain, and kidneys 

3 were removed and weighed. A small portion of the liver was homogenized and assayed for 

4 BROD; EROD; MROD; and PROD. UDPGT, GST, and ascorbic acid levels also were 

5 measured. Vitamin A levels in the liver, kidney, and lungs were analyzed as free retinol 

6 (vitamin A), and histopathological analysis was conducted on various tissues. 

7 Growth rate and thymic weights in rats treated with 1,000 ng/kg-day TCDD were 

8 significantly (p ≤ 0.05) inhibited compared to the control group. Enzyme analysis indicated that 

9 measured levels of TCDD in the liver correlated with hepatic microsomal enzyme activity. The 

10 authors reported that liver microsomal EROD and MROD activities were significantly (p < 0.05 

11 for EROD activity, significance level for MROD not reported) increased in the 250 and 

12 1,000 ng/kg-day TCDD dose groups compared to the control group. UDPGT levels were 

13 significantly (significance level not reported) increased in the 250 and 1,000 ng/kg-day TCDD 

14 dose groups compared to the controls. Serum albumin levels were significantly (p < 0.05) 

15 increased in the 1,000 ng/kg-day TCDD dose group compared to the control group. Serum 

16 cholesterol levels were significantly (level not reported) increased compared to the control group 

17 at 250 ng/kg-day TCDD dose, while liver ascorbic acid concentrations were significantly (level 

18 not reported) increased in the 1,000 ng/kg-day dose group. Hematological analysis indicated that 

19 hemoglobin, packed cell volume, MCH, MCV, and platelet values were decreased in the 

20 1,000 ng/kg-day TCDD dose group. Significant (p ≤ 0.05) differences were observed only in 

21 MCH and MCV levels compared to the control. Vitamin A levels in the liver and kidney were 

22 significantly (p < 0.05) lower in the 1,000 ng/kg-day TCDD group compared to the control 

23 group. Histopathological evaluation of various tissues indicated that liver, thyroid, and thymus 

24 were the target organs. No TCDD-related affects were found in other tissues. A dose-dependent 

25 alteration in the thymus consisted of reduced thymic cortex and increased medullar volume with 

26 more animals exhibiting these changes at the 250 and 1,000 ng/kg-day dose level compared to 

27 the control group. Alterations in thyroid included reduced follicles, reduced colloid density, and 

28 increased epithelial height. A dose-dependent change in the thyroid was observed, with the 

29 highest impact evident in reduced follicles and reduced colloid density beginning at a dose of 

30 25 ng/kg-day TCDD. Changes in liver were characterized by accentuated hepatic zones, 

31 anisokaryosis of hepatocytes, increased cytoplasmic density, and vacuolation. These changes 
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1 were also dose dependent, with more animals exhibiting these histopathological changes with 

2 increasing TCDD dose. Based on these results, the study authors concluded that exposure to 

3 TCDD resulted in a wide range of adverse effects with the thyroid proving to be most sensitive. 

4 A LOAEL for TCDD of 25 ng/kg for a 28-day exposure is identified for alterations in 

5 thyroid, thymus, and liver histopathology. The NOAEL for this study is 2.5 ng/kg-day. 

6 

7 2.4.2.4.3. DeCaprio et al., 1986. 

8 Hartley guinea pigs (10 per sex per dose) were administered TCDD (purity not specified) 

9 in the diet for 90 days at concentrations of 0, 2, 10, 76, or 430 ppt (equivalent to 0, 0.12, 0.61, 

10 4.9, and 26 ng/kg-day in males and 0, 0.12, 0.68, 4.86, and 31 ng/kg-day in females calculated by 

11 the study authors using food consumption and body weights). Other animals were administered 

12 the high-dose diet (i.e., 430 ppt) for 11, 21, or 35 days and then administered the control diet 

13 (i.e., no exposure) for the remainder of the 90 days for recovery analysis. Four high-dose males 

14 died and two were sacrificed moribund by day 45; the remaining four animals were sacrificed on 

15 day 46 for necropsy. Four high-dose females also died and two were sacrificed moribund by day 

16 55 with the remaining females sacrificed on day 60 for necropsy. Animals in the 76- and 

17 430-ppt groups had significantly (p < 0.05) reduced body weights. Organ weights were not 

18 obtained in the 430-ppt group due to the early sacrifice, but in the 76-ppt group a significant 

19 decrease in relative thymus weight (p < 0.05) was observed, and relative liver (p < 0.01) and 

20 brain (p < 0.05) weights in males increased. Although a similar trend occurred in the females, 

21 the results were not statistically significant. Males administered 76 ppt in the diet also had a 

22 53% increase in triglycerides (p < 0.05). The same increase was observed in females, but was 

23 not statistically significant. In the recovery groups, mortality during the recovery period after 11 

24 or 21 days of treatment was 10% and after 35 days of treatment was 70%. Animals lost weight 

25 during the treatment period. Although the body weight increased during the recovery period, the 

26 body weight remained low compared to the control for the study duration. 

27 The LOAEL from this study is 4.9 ng/kg-day for 90 days of exposure, based on 

28 decreased body weight (12−15%; p < 0.05) and changes in organ weights (10−30%, significant 

29 only in the males). The NOAEL is 0.61 ng/kg-day. 

30 
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1 2.4.2.4.4. Devito et al., 1994. 

2 Female B6C3F1 mice (5 per treatment) were administered 0, 1.5, 4.5, 15, 45, or 

3 150 ng/kg TCDD (98% pure) in corn oil via gavage, 5 days a week for 13 weeks. This dose is 

4 equivalent to 0, 1.07, 3.21, 10.7, 32.1, 107 ng/kg-day (adjusted for continuous exposure, 

5 administered dose multiplied by 5 and divided by 7). Body weight was recorded weekly and 

6 animals were sacrificed 3 days after the last treatment. Examinations were performed on the 

7 lung, skin, uterus, and liver. No differences were observed in the liver or uterus weights or in the 

8 estrogen receptor levels in these two tissues. A dose-dependent increase in EROD activity (an 

9 indicator of CYP1A1 [CYP] induction) in the lung, skin, and liver was observed, with significant 

10 (p < 0.05) increases even at the lowest dose. The TCDD doses used did not achieve maximal 

11 EROD induction. A significant (p < 0.05) increase in liver acetanilide-4-hydroxylase (ACOH; 

12 an indicator of CYP1A2 induction) also was observed with all doses. A maximum induction of 

13 ACOH occurred with doses of 3.21 ng/kg-day and greater. A dose-dependent increase in 

14 specific phosphotyrosyl protein (pp) levels also was observed. Levels of pp34 and pp38 were 

15 significantly (p < 0.05) increased even at the lowest dose, while pp32 reached statistical 

16 significance (p < 0.05) with doses of 4.5 ng/kg-day and above. 

17 The role of CYPs and phosphorylated pp32, pp34, and pp38 in TCDD-mediated toxicity 

18 is unknown, and changes in the activity or function of these proteins are not considered adverse 

19 Therefore, no LOAEL or NOAEL is established. The 13-week LOEL is 1.07 ng/kg-day, based 

20 on a significant (p < 0.05) increase in EROD, ACOH, pp34, and pp38 levels (all increased by at 

21 least 2-fold). No NOEL is established for this study. 

22 

23 2.4.2.4.5. Fattore et al., 2000. 

24 Fattore et al. (2000) examined TCDD-induced reduction of hepatic vitamin A levels in a 

25 subchronic rat bioassay on Sprague-Dawley rats. Four experiments were conducted; 

26 Experiments 1, 2, and 3 were conducted in both male and female rats, while Experiment 4 was 

27 conducted only in female rats. The dosing regimens for each experiment were as follows 

28 

29 Experiment 1: Groups of six Iva:SIV 50 rats (male and female) were maintained on a diet 

30 consisting of 0, 200, 2,000, or 20,000 ng TCDD/kg diet and 3-μg vitamin A/kg diet for 

31 13 weeks. Assuming food consumption of 10% of body weight per day, the average daily 

32 doses are 0, 20, 200, and 2,000 ng/kg-day TCDD. 
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1 Experiment 2: Groups of six male and female rats were treated with 0 or 200 ng 

2 TCDD/kg-day and 3 μg vitamin A/kg diet for 13 weeks. 

3 Experiment 3: Groups of six male and female rats were fed 0, 200, or 1,000 ng 

4 TCDD/kg-day and 3 μg vitamin A/kg diet for 13 weeks. 

5 Experiment 4: Groups of female rats (number not specified; IVA;SIV 50 Sprague-Dawley 

6 strain) were treated with TCDD for 26 and 39 weeks in addition to a 13-week dietary 

7 treatment with 0 or 100 ng TCDD/kg-day and 3 μg vitamin A/kg diet for 13 weeks. 

 8 

9 For a 13-week exposure duration employed in all four experiments, male and female rats 

10 were treated at 0, 20, 100 (females only), 200, 1,000, or 2,000 ng/kg-day. In all four 

11 experiments, liver from control and treated animals was analyzed at termination for free 

12 retinol content to determine hepatic vitamin A levels. 

13 

14 Results: 

15 Experiment 1: Liver and body weights in both treated males and females were significantly 

16 affected at all but the lowest dose tested (20 ng/kg-day). Liver injury was severe, particularly 

17 in female rats treated with 2,000 ng TCDD/kg-day. Dietary intake of vitamin A in male rats 

18 was comparable to intake in controls, except in the 2,000 ng/kg-day group, which showed a 

19 reduction of 16% in the dietary intake of vitamin A compared to controls. There was no 

20 effect of TCDD on vitamin A intake in female rats. Hepatic vitamin A levels showed a 

21 dose-dependent reduction with levels dropping sharply in the 200 and 2,000 ng/kg-day dose 

22 groups, particularly in treated females. The reduction was significant at 200 ng/kg-day 

23 (p < 0.05) and 2,000 ng/kg-day (p < 0.01) in males, and at 200 ng/kg-day (p < 0.5) and 

24 2,000 ng/kg-day (p < 0.001) in females. The reductions ranged from 68–99% in males and 

25 72–99% in females when compared to corresponding controls. 

26 Experiment 2: Changes in liver and body weights were not reported. Hepatic vitamin A 

27 level in males and females were reduced by 70% and 99%, respectively, compared to 

28 controls, in rats receiving 20 ng/kg-day (significance level in females: p < 0.01). 

29 Experiment 3: Similar to the results of Experiments 1 and 2, a dose-related trend of 

30 significantly (p < 0.001) reduced hepatic vitamin A level was observed in both males and 

31 females, with males exhibiting a particularly sharp drop at the 1,000 ng/kg-day dose 

32 compared to controls. 

33 Experiment 4: Females treated with 100 ng/kg-day showed significant reductions in hepatic 

34 vitamin A levels (p < 0.05–0.001) at all three treatment durations (13, 26, and 39 weeks). 

35 

36 A LOAEL for TCDD of 20 ng/kg-day for a 13-week subchronic exposure was identified 

37 in this study for decreased hepatic vitamin A levels (27 and 24 % lower than the corresponding 
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1 control in female and male rats, respectively). This LOAEL is determined using data from 

2 Experiment 1. A NOAEL was not identified in this study. 

3 

4 2.4.2.4.6. Fox et al., 1993. 

5 Sprague-Dawley rats (6 per sex per dose) were gavaged with TCDD (purity not 

6 specified) in corn oil using a dose-loading regime to achieve and maintain steady-state levels of 

7 0.03, 30, or 150 ng/g in the liver. The regime consisted of an initial loading dose of 5, 2,500, or 

8 12,000 ng/kg followed every 4 days with a maintenance dose of 0.9, 600, or 3,500 ng/kg. 

9 Averaging the doses over the 14 days provides average daily doses of 0.55, 307, and 

10 1,607 ng/kg-day (e.g., 5 ng/kg-day on day 1 and 0.9 ng/kg-day on days 5, 9, and 13 is 5 + 0.9 

11 + 0.9 + 0.9/14 = 0.55 ng/kg-day). Body weight, liver weight, and liver gene expression were 

12 measured at 7 and 14 days. A significant (p < 0.05) decrease in body weight occurred in 

13 high-dose males (at 14 weeks only) and females (at 7 and 14 days). A significant (p < 0.05) 

14 increase in absolute and relative liver weights was observed in mid- and high-dose males and 

15 females at both 7 and 14 days. Although the liver of treated animals indicated moderate 

16 vacuolization and swelling, there was no indication of necrosis. An increase in gene expression 

17 (clone 1, CYP1A1, CYP1A2, and albumin) was observed in the mid- and high-dose groups. A 

18 significant (p < 0.05) decrease in labeling index (indication of cell proliferation) occurred in both 

19 females (all doses) and males (high-dose only) during week 1, but not during week 2. 

20 The 14-day LOAEL is 307 ng/kg-day for significant (p < 0.05) increases in absolute and 

21 relative liver weights (25─34%). The NOAEL is 0.55 ng/kg-day. 

22 

23 2.4.2.4.7. Hassoun et al., 1998. 

24 Female B6C3F1 mice (number not specified) received TCDD (>98% pure) in corn oil 

25 5 days per week for 13 weeks via gavage at doses of 0, 0.45, 1.5, 15, or 150 ng/kg (equivalent to 

26 0, 0.321, 1.07, 10.7, and 107 ng/kg-day adjusted for continuous exposure; administered dose 

27 multiplied by 5 and divided by 7). Three days after the final dose, animals were sacrificed and 

28 brains were removed for oxidative stress testing. Biomarkers for oxidative stress included 

29 production of superoxide anion, lipid peroxidation, and DNA single-strand breaks. A significant 

30 (p < 0.05) increase was observed in superoxide anion production, lipid peroxidation as measured 
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1 by thiobarbituric acid-reactive substances (TBARS), and DNA single-strand breaks with all 

2 doses tested. 

3 No other indicators of brain pathology were assessed, and it is unfeasible to link the 

4 markers of oxidative stress to a TCDD-induced toxicological outcome in the brain. Thus, no 

5 LOAEL/NOAEL was established. The subchronic (13-week) LOEL is 0.32 ng/kg-day, based on 

6 significant (p < 0.05) increases in superoxide anion production (80% above control); lipid 

7 peroxide production (25% above the control); and DNA single-strand breaks (2-fold over the 

8 control). No NOEL is established. 

9 

10 2.4.2.4.8. Hassoun et al., 2000. 

11 Hassoun et al. (2000) examined the effect of subchronic TCDD exposure on oxidative 

12 stress in hepatic and brain tissues. Groups of 8-week-old female Harlan Sprague-Dawley rats 

13 (6 rats/group) were administered TCDD (98% purity, dissolved in 1% acetone in corn oil) via 

14 gavage at 0, 3, 10, 22, 46, or 100 ng/kg-day, 5 days/week for 13 weeks (0, 2.14, 7.14, 15.7, 32.9, 

15 or 71.4 ng/kg-day adjusted for continuous exposure; administered doses were multiplied by 5 

16 and divided by 7 days/week). Animals were sacrificed at the end of the study period, and brain 

17 and liver tissues were collected and used to determine the production of reactive oxygen species, 

18 lipid peroxidation, and DNA single-strand breaks (SSBs). 

19 A dose-dependent effect was observed in both the liver and brain tissue as a result of 

20 TCDD treatment. Based on the maximal induction of superoxide anion by various doses, more 

21 production of superoxide anion was observed in the liver tissue when compared to the brain 

22 tissue with an observed increase of 3.1- and 2.2-fold respectively, when compared to the control 

23 group. A similar dose-dependent effect was observed in the induction of lipid peroxidation in 

24 TCDD-treated animals with an approximately 1.8-fold increase in lipid peroxidation in both 

25 tissues relative to the corresponding controls. A dose-dependent relationship was also observed 

26 for DNA SSBs in both the hepatic and brain tissues at all TCDD-treated doses compared to 

27 controls. Increases were statistically significant (p ≤ 0.05) beginning at the lowest administered 

28 dose. 

29 Similar to the statement above, because no adverse endpoints were measured, no 

30 LOAEL/NOAEL was established. However, a LOEL for TCDD of 2.14 ng/kg-day for a 

31 13-week exposure duration was identified in this study for significant increases (p ≤ 0.05) in 
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1 superoxide anion, lipid peroxidation, and DNA SSBs in the liver and brain tissues. A NOEL 

2 cannot be determined for this study. 

3 

4 2.4.2.4.9. Hassoun et al., 2003. 

5 Hassoun et al. (2003) examined the role of antioxidant enzymes in TCDD-induced 

6 oxidative stress in various regions of the rat brain after subchronic exposure. Groups of 

7 8-week-old female Harlan Sprague-Dawley rats (12 rats/group) were administered TCDD (98% 

8 purity, dissolved in 1% acetone in corn oil) via gavage at 0, 10, 22, or 46 ng/kg-day (0, 7.14, 

9 15.7, or 32.9 ng/kg-day adjusted for continuous exposure; administered doses were multiplied by 

10 5 and divided by 7) daily for 13 weeks. Animals were sacrificed at the end of the study period 

11 and the brain was immediately removed and dissected to the following regions: cerebral cortex 

12 (Cc), hippocampus (H), cerebellum (C), and brain stem including midbrain, pons, and medulla. 

13 Four pooled samples from each region per dose (i.e., 3 animals/pooled sample) were used in the 

14 study. Dissected regions were subsequently assayed for lipid peroxidation (thiobarbituric acid 

15 reactive substances, or TBARS), superoxide dismutase, catalase, and glutathione peroxidase. 

16 Because the cytochrome c reduction method was used to determine superoxide anion (SA) 

17 production in brain tissues, superoxide dismutase (SOD) was added to some of the brain tissue 

18 samples that had the highest SA production (tissue homogenates from Cc and H from rats treated 

19 with 46 ng/kg-day TCDD). 

20 A dose-dependent increase in the production of SA was observed in the Cc and H, but 

21 significant changes in SA production were not observed in either the C or the mid-brain, pons, or 

22 medulla brain stem cells. Similar to SA production, there was a dose-dependent increase in the 

23 production of TBARS in the Cc and H regions of the brain, but no significant changes were 

24 observed in either the C or the B sections of the brain. The study authors also measured the 

25 activities of various enzymes as a result of TCDD treatment and reported a dose-dependent 

26 increase in SOD activity in the C and B sections, while there was dose-dependent suppression in 

27 SOD activity in Cc and H. In contrast, catalase activity was significantly (p < 0.05) increased in 

28 H and Cc at the 10 ng/kg-day TCDD dose level compared to controls and the mid- and high-dose 

29 animals. Catalase activity also was increased in a dose-dependent manner in the C section, but 

30 no significant changes in the activity of this enzyme were observed in the B section at any of the 

31 three TCDD tested doses. The effects of subchronic exposure to different doses of TCDD on 
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1 glutathione stimulating hormone peroxidase (GSH-Px) showed a different response compared to 

2 other enzymes. There was a dose-dependent increase in the activity of this enzyme in the C and 

3 B regions of the brain, while a significant increase in the activity of GSH-Px occurred in Cc and 

4 H only at the 10 ng/kg-day TCDD dose. In addition, the activity of this enzyme was suppressed 

5 in a dose-dependent manner in the Cc and H at 22 and 46 ng/kg-day TCDD doses. Based on 

6 these results, the study authors concluded that induction of oxidative stress by TCDD in the rat 

7 brain occurs mainly in the Cc and H regions. 

8 Similar to the statement above, because no adverse endpoints were measured, no 

9 LOAEL/NOAEL was established. However, a LOEL for TCDD of 7.14 ng/kg-day for a 

10 13-week exposure duration was identified for this study for increases in superoxide anion and 

11 lipid peroxidation production, as well as increased activity in SOD, catalase, and GSH-Px. 

12 

13 2.4.2.4.10. Kociba et al., 1976. 

14 Adult Sprague-Dawley rats (12 per sex per treatment group) were administered TCDD 

15 (purity not reported) in corn oil via gavage 5 days per week at doses of 0, 1, 10, 100, or 

16 1,000 ng/kg-day (equivalent to 0, 0.71, 7.14, 71.4, or 714 ng/kg-day averaged over 7 days; 5/7 of 

17 dose). Five animals per group were sacrificed at the end of treatment, and the remaining animals 

18 were observed over 13 weeks post treatment (only initial results for the post-treatment period 

19 were provided in the report). Body weights and food consumption were measured semi-weekly. 

20 Hematology and clinical chemistry were measured after 36−37 or 85−86 days of treatment and 

21 59−60 days after termination of treatment. Forty-eight hour urine samples were collected from 

22 select rats from 85−89 days of treatment and 52−56 days after cessation of treatment. Gross and 

23 histopathological exams were conducted on the tissues. 

24 Four high-dose females died during treatment. Two high-dose females and two 

25 high-dose males died during the post-treatment period. Animals treated with 714 ng/kg-day 

26 were less active during the treatment period, which became less evident during the 

27 post-treatment period. Yellow discoloration of the external pinnae also was noted in this group, 

28 both during treatment and during the post-treatment period. A significant (p < 0.05) reduction in 

29 body weight and food consumption was observed in the 71.4 and 714 ng/kg-day groups. The 

30 following significant (p < 0.05) hematology changes were observed in the high-dose 

31 (714 ng/kg-day) males at all measured time points: decreased packed cell volume, decreased red 
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1 blood cells, decreased hemoglobin, increased reticulocytes, and decreased thrombocytes. 

2 Significant (p < 0.05) changes also occurred in the high-dose females, but the only consistent 

3 observation was a decrease in thrombocytes and increased leukocytes. Significant changes in 

4 clinical chemistry (p < 0.05) and urinalysis (p < 0.05) were more consistent between the sexes in 

5 the high-dose group and included increases in total and direct serum bilirubin; increase in serum 

6 alkaline phosphatase; decreased urinary creatinine; and increased urinary coproporphyrin, 

7 uroporphyrin, and delta-amino-levulinic. The following significant (p < 0.05) changes were 

8 observed in the 71.4 ng/kg-day group: decreased packed cell volume (4−9%) in males; decreased 

9 red blood cells (2−10%) in males; decreased hemoglobin (2−13%) in males; increased urinary 

10 coproporyphin (2.2-fold increase during treatment) in females; increased urinary 

11 delta-amino-levulinic (47% increase during treatment) in females; increased total and direct 

12 serum bilirubin (48−61%) in females; and increased serum alkaline phosphatase (2-fold) in 

13 females. The following significant (p < 0.05) changes in relative organ weights were observed 

14 increased brain weight in 714 ng/kg-day males and females; increased liver weight in males 

15 (71.4 and 714 ng/kg-day) and females (7.14, 71.4, and 714 ng/kg-day); increased spleen weight 

16 in 714-ng/kg-day males and females; decreased thymus weight in 71.4 and 714 ng/kg males and 

17 females; and increased testes weight in 714 ng/kg-day males. Microscopic changes were 

18 observed in the thymus, and in other lymphoid tissues, and in the liver in rats treated with 

19 71.4 ng/kg-day or greater. 

20 The subchronic (13-week) LOAEL is 71.4 ng/kg-day, based on the numerous changes 

21 noted in body weight, hematology, clinical chemistry, urinalysis, and histopathology. The 

22 NOAEL is 7.14 ng/kg-day. 

23 

24 2.4.2.4.11. Mally and Chipman, 2002. 

25 Female F344 rats (3 per treatment group) were administered TCDD at concentrations of 

26 0, 2.5, 25, or 250 ng/kg in corn oil via gavage for either 3 consecutive days or 2 days per week 

27 for 28 days (Mally and Chipman, 2002). The average daily doses for the 28-day study when 

28 adjusted for 7 days a week were 0, 0.71, 7.1, and 71 ng/kg-day (i.e., 2/7 of administered dose). 

29 No clinical signs of toxicity were observed. Histological examination of the liver revealed no 

30 abnormalities. All doses of TCDD reduced the number of connexin (Cx) 32 plaques and Cx32 

31 plaque area in the liver, which was considered the target tissue. The reductions were not 
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1 statistically significant after the 3-day treatment, but were significant after the 28-day treatment 

2 (p < 0.05). TCDD also caused a reduction in the Cx32 plaque number and area in the thyroid 

3 after 28 days, but the results were not statistically significant. Although the reduction in Cx32 

4 plaque number and plaque area in the liver and thyroid occurred at all dose levels, there was no 

5 relation to dose. TCDD did not induce hepatocyte proliferation. 

6 In the absence of additional indicators of hepatotoxicity, changes in Cx32 plaques are not 

7 clearly linked to TCDD-mediated hepatotoxicity, nor are they considered an adverse effect. 

8 Additionally, no toxicologically-relevant endpoints were examined. Therefore, a NOAEL or 

9 LOAEL cannot be determined. A 28-day LOEL at the lowest dose of 0.71 ng/kg-day for 

10 significantly (p < 0.05) decreased Cx32 plaque area is evident (approximately 70% of the 

11 controls). 

12 

13 2.4.2.4.12. Slezak et al., 2000. 

14 Slezak et al. (2000) studied the impact of subchronic TCDD exposure on oxidative stress 

15 in various organs of B6C3F1 female mice. Groups of 8- to 10-week-old female B6C3F1 mice 

16 (number not specified) were administered TCDD (purity >98%, dissolved in corn oil) via gavage 

17 at 0, 0.15, 0.45, 1.5, 15, or 150 ng/kg-day (0, 0.11, 0.32, 1.07, 10.7, or 107.14 ng/kg-day adjusted 

18 for continuous exposure) 5 days per week for 13 weeks. Three days after the last treatment, the 

19 animals were sacrificed and organs were removed for the measurement of oxidative stress 

20 indicators including SA, lipid peroxidation (TBARS), and GSH-Px. Tissue TCDD 

21 concentrations also were measured. 

22 The study authors reported that TCDD dose range resulted in overlapping tissue 

23 concentrations for liver, lung, kidney and spleen. Liver had the highest TCDD concentration, 

24 with each tissue demonstrating a dose-dependent increase in TCDD concentration. Compared to 

25 controls, SA production was significantly (p < 0.05) lower at the 0.15 ng/kg-day TCDD dose, 

26 while it was significantly (p < 0.05) higher at 15 and 150 ng/kg-day. A dose-dependent increase 

27 in hepatic TBARS production was observed, although the rate of production was significant 

28 (p < 0.05) only at the highest TCDD administered dose (150 ng/kg-day) compared to controls. 

29 AA also followed the same pattern observed for SA and TBARS with AA production 

30 significantly (p < 0.05) increased at the 15 and 150 ng/kg-day TCDD doses. Contrary to the SA, 

31 TBARS, and AA responses, GSH levels were decreased at 0.15 ng/kg-day, were increased at 
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1 0.45 and 150 ng/kg-day, and did not change at 1.5 or 15 ng/kg-day when compared to the control 

2 group. Unlike the liver, there was no significant increase in SA production in the lung at any of 

3 the TCDD tested doses; a dose dependent reduction, however, was observed at 0.45, 15, and 

4 150 ng/kg-day compared to controls. GSH and AA production was decreased at 0.15 ng/kg-day, 

5 while AA production was significantly (p < 0.05) increased at 15 and 150 ng/kg-day. Kidney 

6 SA production showed a statistically significant (p < 0.05) increase only at the 15 and 

7 150 ng/kg-day doses. GSH, like the liver and the lung, exhibited a decrease in production 

8 following treatment at 0.15 ng/kg-day with this trend continuing at 0.45 and 1.5 ng/kg-day. AA 

9 levels were significantly (p < 0.05) lower at all subchronic doses, except at 1.5 ng/kg-day dose. 

10 SA levels in the spleen differed little from the control group at any of the TCDD doses. Total 

11 GSH was higher only at the 150 ng/kg-day dose level, while the AA levels were significantly 

12 (p < 0.05) decreased at 0.15, 1.5, and 150 ng/kg-day. 

13 Similar to the statements regarding the Hassoun et al. studies above, because no adverse 

14 endpoints were measured, no LOAEL/NOAEL was established. Therefore, a NOAEL or 

15 LOAEL cannot be determined. However, a NOEL and LOEL of 1.07 and 10.7 ng/kg-day, 

16 respectively, are identified in this study for increases in superoxide anion in the liver. 

17 

18 2.4.2.4.13. Smialowicz et al., 2008. 

19 Female B6C3F1 mice (8–15 per treatment group) were administered TCDD (purity 

20 >98%) in corn oil by gavage at doses of 0, 1.5, 15, 150, or 450 ng/kg-day, 5 days a week for 

21 13 weeks (1.07, 10.7, 107, or 321 ng/kg-day, adjusted for continuous exposure; i.e., 5/7 of the 

22 dose) (Smialowicz et al., 2008). Mice were immunized 3 days after the final TCDD exposure 

with an intravenous injection of an optimal concentration of 4 × 107 23 SRBCs and sacrificed 4 days 

24 later. No TCDD-related effects on body weight were observed. There was a dose-related 

25 decrease in relative spleen weight (9–19% lower than control values) with statistically significant 

26 (p < 0.05) decreases at all but the lowest dose. Additionally, there was a statistically significant 

27 (p < 0.05) increase in relative liver weight (5–21%) in all treatment groups compared to controls. 

28 Statistically significant dose-dependent decreases were observed in the antibody response to 

29 SRBCs (24–89% lower than control values), as measured by both the number of plaque forming 

cells per 106 30 cells and plaque forming cells per spleen. 
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1 The 13-week LOAEL for this study is 1.07 ng/kg-day based on a significant (p < 0.05) 

2 increase in relative liver weight (10%) and a significant (p < 0.05) decrease in antibody response 

3 to SRBCs (24%). A NOAEL cannot be determined for this study. 

4 

5 2.4.2.4.14. Van Birgelen et al. 1995a, b. 

6 Van Birgelen et al. (1995) studied the impact of TCDD exposure on various biochemical 

7 endpoints in rats. Groups of 7-week-old female Sprague-Dawley rats (n = 8 per treatment group) 

8 were treated with 0, 200, 400, 700, 5,000, or 20,000 ng/kg TCDD (purity >99%) in diet for 

9 13 weeks. Daily TCDD intake based on food consumption, diet level, and mean weight was 

10 estimated to be 0, 14, 26, 47, 320, or 1,024 ng/kg-day. Blood samples were collected from 

11 treated animals and assayed for retinol (vitamin A), triiodothyronine, and total (TT4) and free 

12 (FT4) thyroxine. At study termination, the animals were sacrificed and the liver, thymus, spleen, 

13 and kidneys were removed and weighed. Parts of the liver were homogenized and assayed to 

14 determine EROD; CYP1A1; CYP1A2; and UDPGT activity. Liver samples also were analyzed 

15 for retinol content. 

16 TCDD-treated animals showed a dose-related decrease in food consumption. Animals 

17 treated with 1,024 ng/kg-day TCDD consumed 32% less food compared to controls. Similarly, a 

18 dose-related decrease in body weight gain was observed in all animals treated with TCDD. 

19 Animals treated with ≥47 ng/kg-day of TCDD showed a statistically significant (p < 0.05) 

20 decrease in body weight gain. Relative liver weights were significantly (p < 0.05) increased in 

21 the 320 and 1,024 ng/kg-day TCDD dose groups compared to the controls. Absolute and relative 

22 thymus weights were significantly (p < 0.05) decreased at all TCDD dose groups compared to 

23 the control group. Relative kidney and spleen weights were significantly (p < 0.05) higher in 

24 animals dosed with ≥47 ng/kg-day of TCDD compared to the control group, with the greatest 

25 increase occurring in animals treated with 1,024 ng/kg-day TCDD (121 and 173% higher than 

26 controls for kidney and spleen, respectively). Cytochrome P450 enzymes, including EROD, 

27 CYP1A2, CYP1A1, and UDPGT, exhibited statistically significant (p < 0.05) increases in 

28 activity at all TCDD dose groups compared to the control group. TT4 and FT4 thyroid hormone 

29 concentrations were statistically significantly (p < 0.05) decreased only at TCDD doses 

30 ≥47 ng/kg-day. A dose-dependent increase was observed in the plasma retinol concentrations 

31 with significant (p < 0.05) increases occurring at ≥47 ng/kg-day TCDD after a 13-week 
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1/15/10 DRAFT 2-178 —DO NOT CITE OR QUOTE 

1 exposure. A dose-dependent reduction in liver retinoid levels also was observed after 13 weeks 

2 of TCDD exposure with the levels dropping significantly (p < 0.05) at all TCDD-treated doses 

3 compared to the control group. 

4 A LOAEL for TCDD of 14 ng/kg for a 13-week exposure is identified for significantly 

5 (p < 0.05) decreased absolute and relative thymus weights and significantly (p < 0.05) decreased 

6 liver retinoid levels. A NOAEL cannot be determined for this study. 

7 

8 2.4.2.4.15. Vos et al., 1973. 

9 Vos et al. (1973) conducted a study to examine the immune response in laboratory 

10 animals treated with TCDD. In one experiment, 10 female Hartley strain guinea pigs were orally 

11 treated with 8 weekly doses of 0, 8, 40, 200, and 1,000 ng/kg TCDD in corn oil (purity of TCDD 

12 not specified) (0, 1.14, 5.71, 28.6, and 143 ng/kg-day adjusted for continuous exposure; 

13 administered dose divided by 7). At study termination, the animals were sacrificed, and heart 

14 blood was used to determine total leukocyte and differential leukocyte counts. In another 

15 experiment, the effect of TCDD on humoral immunity was determined by injecting 0.1 mL of 

16 tetanus toxoid into the right hind-foot pad on day 28 (1 left foot tetanus toxoid, aluminum 

17 phosphate-adsorbed) and again on day 42 (1 left foot tetanus toxoid, unadsorbed). Blood was 

18 collected (n = 10) on days 35 and 49, and the serum tetanus-antitoxin concentrations were 

19 determined using a modified single radial immunodiffusion technique. 

20 All guinea pigs receiving 1,000 ng/kg-day TCDD either died or were killed when 

21 moribund between 24 and 32 days. These animals showed severe weight loss, lymphopenia, and 

22 depletion of the lymphoid organs, especially the thymus. Microscopic observations revealed 

23 severe atrophy of the thymic cortex with substantial destruction of lymphocytes, with the nuclear 

24 debris being engulfed by macrophages. Large cystic Hassall bodies, filled with 

25 polymorphonuclear leukocytes were observed in the medulla. All animals treated with 0, 8, 40, 

26 or 200 ng/kg-day TCDD survived until study termination. Body weight gain was significantly 

27 (p < 0.01) lower in the 200 ng/kg-day group. Absolute thymus weight was significantly reduced 

28 in the 40 and 200 ng/kg-day treatment groups (p < 0.01 and p < 0.05, respectively). In contrast, 

29 relative thymus weight was significantly (p < 0.01) reduced only in the 200 ng/kg-day dose 

30 group. The absolute weight of the superficial cervical lymph nodes was significantly (p < 0.05) 

31 decreased in the 200 ng/kg-day group, while the relative adrenal weight was significantly 
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1 (p < 0.05) increased in the 200 ng/kg-day dose group. Total leukocyte count was significantly 

2 (p < 0.05) decreased in the 40 ng/kg-day dose group and total lymphocyte count was 

3 significantly decreased at 8, 40, and 200 ng/kg-day (p < 0.01, p < 0.05, and p < 0.05, 

4 respectively). A significant (p-values not provided) monotonic dose-response relationship was 

5 determined for body weight (decrease), relative thymus weight (decrease), relative adrenal 

6 weight (increase), and total leukocyte and lymphocyte count (decrease). Microscopic 

7 examination of the lymphoid organs and adrenals showed no effects, while slight cortical atrophy 

8 of the thymus was observed at the 200 ng/kg-day dose. 

9 Animals receiving the tetanus toxoid injection showed a small but significant increase in 

10 serum tetanus antitoxin concentrations at the 8 and 40 ng/kg-day dose (p < 0.05 and p < 0.01, 

11 respectively). Measurement at days 49 and 56 indicated that serum antitoxin levels had 

12 decreased sharply and the significant (p < 0.05 on day 49 and p < 0.01 on day 56) effect was 

13 seen only at the 200 ng/kg-day dose level. 

14 A LOAEL for TCDD of 5.71 ng/kg-day for an 8-week exposure is identified in this study 

15 for significantly (p < 0.01) reduced absolute thymus weight, significantly (p < 0.05) reduced 

16 leukocyte and lymphocyte count, and significantly (p < 0.01) increased serum tetanus antitoxin 

17 concentration. The NOAEL for this study is 1.14 ng/kg-day. 

18 

19 2.4.2.4.16. White et al., 1986. 

20 White et al. (1986) studied the impact of TCDD exposure on serum complement levels. 

21 Groups of female (C57BL/6 × C3H)F1(B6C3F1) mice were treated for 14 consecutive days with 

22 TCDD in corn oil (purity of TCDD not specified) at doses of 0, 10, 50, 100, 500, 1,000 or 

23 2,000 ng/kg-day via gastric intubation (n = 6−8). At study termination, blood was collected from 

24 anesthetized animals and assayed for serum complement activity and complement component C3 

25 levels. 

26 Serum complement activity between the 10 and 100 ng/kg-day doses was between 69 and 

27 59% compared to the vehicle control group, with all treatment groups being significantly 

28 (p < 0.05) low compared to the vehicle control. In contrast, C3 levels were comparable to the 

29 vehicle control with levels ranging between 98 and 94% of the control group. The higher doses 

30 of 500, 1,000, and 2,000 ng/kg-day, however, produced a marked decrease of the component 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 2-180 —DO NOT CITE OR QUOTE 

1 hemolytic activity (45, 35, and 19% of the vehicle control) and of C3 levels (91, 81, and 74 % of 

2 the vehicle control, respectively; significance level at p < 0.05). 

3 A LOAEL for TCDD of 10 ng/kg-day for a 14-day exposure is identified in this study for 

4 significantly (p < 0.05) lower serum complement activity. A NOAEL cannot be determined for 

5 this study. 

 6 

7 2.4.2.5. Chronic Studies (Noncancer Endpoints) 

8 2.4.2.5.1. Cantoni et al., 1981. 

9 CD-COBS rats (4 per treatment) were orally administered TCDD (purity not specified) 

10 dissolved in acetone:corn oil (1:6) at doses of 0 (vehicle alone), 10, 100, or 1,000 ng/kg per week 

11 (equivalent to 1.43, 14.3, and 143 ng/kg-day adjusted for continuous exposure, administered 

12 dose by dividing the dose by 7) for 45 weeks. Urine was collected several times during 

13 treatment and tested for porphyrin excretion. Twenty-four hours after the final dose, animals 

14 were sacrificed and their livers, spleens, and kidneys were removed for analysis of total 

15 porphyrins. All treatment groups had a significant (p < 0.05) increase in coproporphyrin 

16 excretion beginning at 6, 3, or 2 months, respectively. Uroporphyrin excretion was significantly 

17 (p < 0.05) increased in the 14.3 ng/kg-day group at 10 months and in the 143 ng/kg-day group 

18 beginning at 6 months. The high-dose group also had a significant (p < 0.05) increase in 

19 excretion of heptacarboxylic methyl ester beginning at 6 months. The high-dose group had a 

20 marked porphyric state beginning at 8 months as indicated by a 70-fold increase above controls 

21 in total urinary porphyrin excretion. This group also had a significant (p < 0.05) increase in total 

22 porphyrins in the liver, kidneys, and spleen. 

23 The 45-week LOAEL for this study is 1.43 ng/kg-day, based on a 2- to 3-fold increase in 

24 urinary coproporphyrin excretion. No NOAEL was established for this study. 

25 

26 2.4.2.5.2. Croutch et al., 2005. 

27 Croutch et al. (2005) examined the impact of TCDD exposure on body weight via 

28 insulin-like growth factor (IGF) signaling. Female Sprague-Dawley rats were randomly assigned 

29 in groups of five to initial loading doses of TCDD (purity >98.5%, dissolved in corn oil) at 0, 

30 12.5, 50, 200, 800, or 3,200 ng/kg-day, followed by treatment with maintenance doses equivalent 

31 to 10% of the initial loading dose every third day to maintain a pharmacokinetic steady state 
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1 throughout the entire study (equivalent to: 14-day average = 0, 1.25, 5, 20, 80, or 320 ng/kg-day; 

2 28-day average = 0, 0.85, 3.4, 13.6, 54.3, or 217 ng/kg-day; 63-day average = 0, 0.60, 2.4, 9.5, 

3 38, or 152 ng/kg-day; and 128-day average dose = 0, 0.51, 2.0, 8.1, 32.5, or 130 ng/kg-day). 

4 Following 2, 4, 8, 16, 32, 64, or 128 days of initial dosing, the animals were sacrificed, livers 

5 were removed and weighed, and trunk blood was collected to analyze glucose content. Rat liver 

6 phosphoenolpyruvate carboxykinase (PEPCK) mRNA and protein levels also were analyzed, and 

7 PEPCK activity was measured. 

8 Body weights of TCDD-treated animals decreased after the second week of the 

9 3,200 ng/kg-day TCDD loading dose, with significant differences beginning at week 9. There 

10 was also a statistically significant (p ≤ 0.05) difference in body weights at weeks 10, 11, 13, 18, 

11 and 19 at the highest loading dose (3,200 ng/kg-day). PEPCK activity in the liver was also 

12 decreased in a dose-dependent manner following TCDD administration at approximately 

13 16 days. PEPCK inhibition was statistically significant (p ≤ 0.05) on day 4 in rats treated with 

14 either 800 or 3,200 ng/kg-day TCDD when compared to animals treated with a loading dose of 

15 200 ng/kg-day. A similar statistically significant change was observed in animals treated with 

16 3,200 ng/kg-day on day 16 when compared to the 200 ng/kg-day treatment group. In contrast, 

17 differences in PEPCK activity at other doses or time points were not statistically significant. In 

18 TCDD-treated animals, there was also a dose-dependent decrease in PEPCK mRNA expression 

19 along with a decrease in PEPCK protein levels in the liver. In addition to body weight and 

20 PEPCK activity changes, animals treated with 3,200 ng/kg-day TCDD showed a sharp decline in 

21 circulating IGF-I levels on day 8 compared to the control group (corn oil) and TCDD-treated 

22 animals at lower doses. In the highest dose animals, IGF-I levels continued to decline to 42% of 

23 the control group by day 16 of the study. The IGF-I levels at the highest dose plateaued at an 

24 average decrease of 66% through day 128 when compared to controls. Beginning at day 8, the 

25 decrease in IGF-I was statistically significant at every time point through day 128 compared to 

26 the control group, as well as groups treated with either 12.5 or 50 ng/kg-day TCDD. Similar 

27 statistically significant decreases also were observed for the 800 ng/kg-day TCDD-treated groups 

28 with an initial decrease of 37% on day 16 followed by a further decline to approximately 45% 

29 thereafter compared to controls and the 12.5, 50, and 200 ng/kg-day dose groups. In contrast to 

30 these results, circulating levels of insulin and glucose were unaffected by TCDD treatment, while 
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1/15/10 DRAFT 2-182 —DO NOT CITE OR QUOTE 

1 the active or phosphorylated form of AMPK-α protein increased with dose as a result of TCDD 

2 treatment. 

3 A LOAEL for TCDD of 217 ng/kg-day for a 28-day exposure duration (because this 

4 represented the most sensitive time for elicitation of effects) was identified in this study for 

5 decreased body weight, significant (p ≤ 0.05) inhibition of PEPCK activity, and reduced IGF-I 

6 levels (42% lower than the control group). A NOAEL of 54.3 ng/kg-day was identified in this 

7 study. 

8 

9 2.4.2.5.3. Hassoun et al., 2002. 

10 Hassoun et al. (2002) examined the potential of TCDD and other dioxin-like chemicals to 

11 induce oxidative stress in a chronic rat bioassay. Groups of six Harlan Sprague-Dawley female 

12 rats were treated with 0, 3, 10, 22, 46, or 100 ng/kg-day TCDD (98% purity), 5 days a week via 

13 gavage for 30 weeks. The administered doses adjusted for continuous exposure were 0, 2.14, 

14 7.14, 15.7, 32.9, and 71.4 ng/kg-day, respectively (administered doses were multiplied by 5 and 

15 divided by 7). At study termination, hepatic and brain tissues from all treated rats were divided 

16 into two portions and examined for the production of reactive oxygen species and SSBs in DNA. 

17 When compared to controls, there was a dose-dependent increase in the production of 

18 superoxide anion in TCDD-treated animals ranging from 21–998% and 66–257% in hepatic and 

19 brain tissues, respectively. Hepatic tissues had statistically significant (p < 0.05) increases in 

20 superoxide anion production at doses ≥7.14 ng/kg-day, while the brain tissue had a statistically 

21 significant (p < 0.05) increase over controls at all doses. Similarly, increases in lipid 

22 peroxidation were observed in hepatic and brain tissues with a 481% increase (p < 0.05) at 

23 71.4 ng/kg-day in the hepatic tissue when compared to controls. The increase in lipid oxidation 

24 in brain tissue ranged from 33–188% (p < 0.05) in the 2.14–71.4 ng/kg-day dose groups. DNA 

25 SSBs were also observed in both hepatic and brain tissue in all treated groups. When compared 

26 to the control group, there was a dose-dependent statistically significant (p < 0.05) increase in 

27 DNA SSBs ranging from 58–322% and 29–137% in hepatic and brain tissues, respectively. 

28 Nonmonotonic dose-response relationships were observed for superoxide production and lipid 

29 peroxidation in liver tissues, with greater-than-linear increases in effect between the two highest 

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

1/15/10 DRAFT 2-183 —DO NOT CITE OR QUOTE 

1 As stated above, because no adverse endpoints were measured, no LOAEL/NOAEL was 

2 established. However, a LOEL for TCDD of 2.14 ng/kg-day for a 30-week exposure duration is 

3 identified in this study for significant (p < 0.05) increases in superoxide anion, lipid peroxidation 

4 production, and DNA SSBs in the liver and brain tissues. A NOEL cannot be determined for this 

5 study. 

6 

7 2.4.2.5.4. Kociba et al., 1978. 

8 Sprague-Dawley rats (50 per sex per treatment group) were administered TCDD (purity 

9 >99%) in the diet at doses of 0, 1, 10, or 100 ng/kg-day for 2 years. Body weights and food 

10 consumption were routinely measured. Hematology, clinical chemistry, and urinalysis were 

11 measured after 3, 12, or 23 months of treatment. Animals were routinely palpitated for tumors. 

12 Gross and histopathological exams were conducted on the tissues of dead or dying animals or at 

13 terminal sacrifice. Specific organs also were weighed. 

14 The high-dose females had a statistically significant (p < 0.05) increase in mortality 

15 compared to the controls during the second half of the study. Mortality changes in males were 

16 variable and of questionable toxicological significance. A significant (p < 0.05) reduction in 

17 body weight occurred in the 100 ng/kg-day males and females beginning at 6 months. Mid-dose 

18 females also had reduced body weight, but to a lesser degree during the same time frame. There 

19 were no consistent changes in food consumption. The following significant (p < 0.05) 

20 hematology changes were observed in the high-dose animals: decreased packed cell volume in 

21 males after 3 months and in females after 1 year, decreased red blood cells in females after 

22 1 year and in males at terminal sacrifice, decreased hemoglobin in males after 3 months and in 

23 females after 1 year, and decreased total white blood cell count in females after 1 year. Changes 

24 in clinical chemistry (p < 0.05) occurred only in high-dose females and consisted of an increase 

25 in serum alkaline phosphatase and gamma glutamyl transferase. Significant changes in 

26 urinalysis occurred only in females and included increased urinary coproporphyrin in the mid27 and high-dose groups, increased urinary uroporphyrin in the mid- and high-dose groups, and 

28 increased urinary delta-amino-levulinic acid in the high-dose group. Significant (p < 0.05) 

29 changes in relative organ weights were observed, including increased liver weight in mid- and 

30 high-dose females and decreased thymus weight in high-dose females. Mid- and high-dose rats 

31 showed hepatocellular degeneration and inflammatory and necrotic changes in the liver. Thymic 
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1 and splenic atrophy were noted in high-dose females. An increase in non-neoplastic lung lesions 

2 was noted in mid-dose females and high-dose males and females. High-dose females had an 

3 increase in uterine changes. High-dose males had a significant (p < 0.05) increase in the 

4 incidence of stratified squamous cell carcinomas of the tongue. High-dose males and females 

5 had a significant (p < 0.05) increase in the incidence of squamous cell carcinomas of the hard 

6 palate/turbinates. 

7 The chronic (2-year) LOAEL is 10 ng/kg-day, based on the numerous significant 

8 (p < 0.05) changes noted in coproporphyrin excretion (67% increase above control) and an 

9 increase in liver and lung lesions in female rats. The NOAEL is 1 ng/kg-day. 

10 

11 2.4.2.5.5. Maronpot et al., 1993. 

12 An initiation-promotion study was performed in female Sprague-Dawley rats (8−10 rats 

13 per group). Rats were initiated with saline or diethylnitrosamine (DEN), followed 2 weeks later 

14 by promotion with biweekly administration of TCDD (purity not specified) in corn oil via 

15 gavage for 30 weeks. The doses were stated to be equivalent to 3.5, 10.7, 35.7, or 

16 125 ng/kg-day. Rats were sacrificed 7 days after the final treatment. A significant (p < 0.05) 

17 decrease in body weight occurred in the 125 ng/kg-day group. A significant (p < 0.05) increase 

18 in relative liver weight occurred in the 35.7 and 125 ng/kg-day groups. There was a significant 

19 (p < 0.05) increase in the labeling index in the 125 ng/kg-day group, but only with DEN 

20 initiation. In the TCDD-alone group, a 2-fold increase in labeling index occurred in the 

21 125 ng/kg-day group that did not reach statistical significance. A significant (p < 0.05) trend for 

22 increased alkaline phosphatase levels was observed in TCDD-treated animals, but despite a 50% 

23 increase in the highest dose group the increase was not statistically significant. Total cholesterol 

24 and triglycerides were significantly (p < 0.05) higher in the 125 ng/kg-day TCDD-alone group. 

25 A significant (p < 0.05) increase in 5’-nucleotidase occurred in the 35.7 and 125 ng/kg-day 

26 TCDD-alone groups. A dose-dependent increase in the incidence and severity of liver toxicity as 

27 measured by microscopic lesions was observed. 

28 The 30-week LOAEL is 35.7 ng/kg-day, based on a significant (p < 0.05) increase in 

29 relative liver weight (12%, accompanied by increases in incidence and severity of liver lesions). 

30 The 30-week NOAEL is 10.7 ng/kg-day. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2.4.2.5.6. National Toxicology Program, 1982. 

2 National Toxicology Program (NTP, 1982) conducted a carcinogenic bioassay of TCDD 

3 on rats and mice. Fifty male and female Osborne-Mendel rats and male and female B6C3F1 

4 mice were treated twice per week with TCDD (purity not specified) in corn oil via oral gavage at 

5 doses of 0, 5, 25, or 250 ng/kg for rats and male mice (1.4, 7.1, 71 ng/kg-day adjusted for 

6 continuous exposure; administered doses multiplied by 2 and divided by 7) and 0, 20, 100, or 

7 1,000 ng/kg for female mice (5.7, 28.6, or 286 ng/kg-day adjusted for continuous dosing; 

8 administered doses multiplied by 2 and divided by 7) for 104 weeks. Seventy-five rats and mice 

9 of each sex served as vehicle controls. One untreated control group of 25 rats and mice of each 

10 sex was present in the TCDD treatment room and one untreated control group consisting of 

11 25 rats and mice of each sex were present in the vehicle-control room. Animals surviving until 

12 study termination were sacrificed at 105 or 108 weeks. A complete histopathological evaluation 

13 was conducted on all animals. 

14 Survival rates were not affected by TCDD exposure in rats or mice of either sex. Male 

15 rats exhibited a dose-related depression in mean body weight after week 55, while the females 

16 exhibited a dose-related body-weight depression after 45 weeks of TCDD exposure. However, 

17 the magnitude of the body weight response is not indicated. Mean body weights in male and 

18 female mice were comparable to the vehicle control group throughout the bioassay. Noncancer 

19 histopathologic findings included increased incidences of liver lesions (termed toxic hepatitis) 

20 from TCDD exposure, and were detected in the high-dose rats and high-dose mice of each sex. 

21 A LOAEL for TCDD of 1.4 ng/kg-day for a 104-week exposure duration is identified for 

22 increased incidences of liver lesions in mice of both sexes. A NOAEL cannot be determined for 

23 this study. 

24 

25 2.4.2.5.7. National Toxicology Program, 2006. 

26 Female Sprague-Dawley rats (81 control; 82 treatment group) were administered TCDD 

27 (purity >98%) in corn oil:acetone (99:1) via gavage at doses of 0, 3, 10, 22, 46, or 

28 100 ng/kg-day, 5 days per week for 105 weeks (0, 2.14, 7.14, 15.7, 32.9, or 71.4 ng/kg-day, 

29 adjusted for continuous exposure) (NTP, 2006). In addition to this primary group, a stop group 

30 of 50 animals was administered 100 ng/kg-day TCDD in corn oil:acetone (99:1) via gavage for 

31 30 weeks and then just the vehicle for the remainder of the study. Up to 10 rats per dose group 
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1/15/10 DRAFT 2-186 —DO NOT CITE OR QUOTE 

1 from the primary study were sacrificed and evaluated at 14, 31, or 53 (n = 8) weeks for 

2 biologically noteworthy changes in the incidences of neoplasms or non-neoplastic lesions in the 

3 liver, lung, oral mucosa, uterus, pancreas, thymus, adrenal cortex, heart, clitoral gland, ovary, 

4 kidney, forestomach, bone marrow, mesentery gland, and pituitary gland. All animals also 

5 received a complete necropsy and microscopic examination, and the following organs were 

6 weighed: the left kidney, liver, lung, left ovary, spleen, thymus (14 weeks only), and thyroid 

7 gland. At study termination, the number of surviving animals had declined to 25 in the control 

8 group and to 21, 23, 19, 22, and 21 in five treatment groups, respectively, due to accidental 

9 deaths, moribund animals, or death due to natural causes. 

10 Survival rate was not affected by TCDD treatment. Mean body weights in the high dose 

11 primary study group and the 100 ng/kg stop group were less than the vehicle control group after 

12 week 13 of the study. The mean body weights of animals in the 46 ng/kg-day group were less 

13 than in the vehicle control at study termination (2 years), whereas animals in the 22 ng/kg-day 

14 had lower mean body weights compared to controls during the last 10 weeks of study. In 

15 addition to body weight changes, liver weights were also impacted as a result of TCDD 

16 exposure. Absolute and relative liver weights were significantly (either p ≤ 0.01 or p ≤ 0.05) 

17 higher in all dose groups compared to controls at the 14- and 31-week evaluation period, whereas 

18 the relative liver weights were significantly (either p ≤ 0.01 or p ≤ 0.05) higher only at 

19 ≥10 ng/kg-day at 53 weeks. 

20 No clinical findings associated with TCDD treatment were observed. TCDD caused 

21 changes in thyroid hormone levels at 14, 31, and 53 weeks. The following changes were 

22 statistically significant (p ≤ 0.05) compared to the vehicle control: decrease in TT4 at doses 

23 ≥22 ng/kg-day at 14 and 31 weeks and at doses ≥46 ng/kg-day at 53 weeks; decrease in FT4 at 

24 doses ≥22 ng/kg-day at 14 and 31 weeks; increase in total T3 at doses ≥46 ng/kg-day at 14 and 

25 31 weeks and at doses ≥10 ng/kg-day at 53 weeks; and increase in TSH at doses ≥46 ng/kg-day 

26 at 14 weeks. There was a statistically-significant (p ≤ 0.05) increase in hepatocyte proliferation 

27 at 14 weeks (22 ng/kg-day group only); 31 weeks (all doses); and 53 weeks (≥46 ng/kg-day). 

28 There were statistically significant (p ≤ 0.01) dose-dependent increases in liver (includes EROD 

29 [CYP1A1-associated] activity; 7-pentoxyresorufin-O-deethylase [PROD; CYP2B-associated] 

30 activity; and acetanilide-4-hydroxylase [CYP1A2-associated] activity) and lung (EROD) 

31 cytochrome P450 enzyme activities in all treatment groups at all three evaluation periods 
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1/15/10 DRAFT 2-187 —DO NOT CITE OR QUOTE 

1 compared to the vehicle control group. The largest effect was an 82-fold induction of hepatic 

2 EROD activity in the 46 ng/kg-day group at 31 weeks. 

3 TCDD was detected at the greatest concentration in the liver, followed by fat tissue, with 

4 tissue concentration increasing in both of these tissues in a dose-dependent manner. TCDD 

5 tissue levels generally remained constant after the first measurement at week 14. Pathological 

6 examination at week 14 revealed increased incidences of hepatocellular hypertrophy in animals 

7 administered ≥10 ng/kg-day TCDD. Examinations at weeks 31 and 53 indicated that incidence 

8 and or severity of hepatocellular hypertrophy was increased at all treatment doses although 

9 incidences were statistically significant (p ≤ 0.05) only at ≥10 ng/kg-day doses. The incidence of 

10 non-neoplastic hepatic lesions (including inflammation, necrosis, multiple eosinophilic focus, 

11 diffuse fatty change, pigmentation, toxic hepatopathy) in the liver increased at doses 

12 ≥22 ng/kg-day beginning at 14 weeks. Severity of the lesions increased at 14 weeks at doses 

13 ≥46 ng/kg-day and were also observed at lower dose levels during later evaluation periods (31 

14 and 53 weeks). By terminal sacrifice, numerous non-neoplastic changes were noted in TCDD 

15 treated rats, even at the lowest dose tested. 

16 Noncancer cardiovascular and pulmonary effects were evident after 2 years of TCDD 

17 exposure. Significantly increased incidences of minimal to mild cardiomyopathy were seen in 

18 male and female rats at ≥10 ng/kg-day. In the lung, there was a significant (p ≤ 0.01) 

19 dose-dependent increase, when compared to the vehicle control, in the incidence of bronchiolar 

20 metaplasia of the alveolar epithelium at all dose groups in the primary study. 

21 A LOAEL for TCDD of 2.14 ng/kg-day adjusted dose for a 105-week exposure duration 

22 is identified in this study for significantly (either p ≤ 0.01 or p ≤ 0.05) increased absolute and 

23 relative liver weights, increased incidence of hepatocellular hypertrophy, and increased incidence 

24 of alveolar to bronchiolar epithelial metaplasia. A NOAEL cannot be determined for this study. 

25 

26 2.4.2.5.8. Rier et al., 2001a, b. 

27 Female rhesus monkeys (8 per treatment group) were administered 0, 5, or 25 ppt TCDD 

28 (purity not specified) in the diet for 4 years. Previously, Bowman et al. (1989b) determined that 

29 these dietary concentrations were equivalent to 0, 0.15, and 0.67 ng/kg-day, respectively. 

30 Thirteen years after termination of TCDD treatment, serum concentrations of TCDD and 

31 dioxin-like polyhalogenated aromatic hydrocarbons (PHAH) were measured in six control 
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1 monkeys, six monkeys treated with 0.15 ng/kg-day, and three monkeys treated with 

2 0.67 ng/kg-day (Rier et al., 2001a). Even after 13 years without treatment, there was 

3 significantly (p < 0.05) elevated serum levels of TCDD and other dioxin-like compounds in 

4 treated monkeys. There was a significant increase in triglycerides and total lipids in the serum of 

5 monkeys treated with either 0.15 or 0.67 ng/kg-day, but not in cholesterol or phospholipids. In 

6 addition to these 15 animals, 8 other female monkeys (4 treated with 0.67 ng/kg-day TCDD that 

7 died 7 to 11 years after treatment and 4 lead-treated animals with no history of PHAH exposure) 

8 were evaluated for endometriosis. Elevated serum concentrations of TCDD were not correlated 

9 with endometriosis. Increased serum levels of 3,3’,4,4’-tetrachlorobiphenyl (TCB), however, 

10 were associated with the presence and severity of endometriosis (p < 0.05). TCB was found in 

11 none of the animals without endometriosis, including TCDD-treated animals, nor was it found in 

12 control animals with endometriosis. Animals with elevated serum levels of TCB, 

13 pentachlorobiphenyl, and total serum analyte TCDD equivalents (TEQ) had an increased 

14 incidence of endometriosis, but severity was associated only with increased levels of TCB. EPA 

15 did not develop a LOAEL for TCDD for this study, because of DLC contamination. 

16 In a separate study that evaluated the same 15 monkeys 13 years after exposure, Rier et 

17 al. (2001b) examined effects on systemic immunity. Peripheral blood mononuclear cells 

18 (PBMC) obtained from untreated monkeys secreted no detectable levels of TNF-α in response to 

19 T-cell mitogen exposure. There was, however, a significant (p < 0.05) dose-dependent increase 

20 in TNF-α production in PBMC from the TCDD-treated monkeys. Although PBMC from treated 

21 monkeys with endometriosis produced more TNF-α than cells from unexposed controls without 

22 the disease (median 128 pg/mL compared to not detected; p < 0.01), PBMC from TCDD-treated 

23 animals without endometriosis also produced more TNF-α than controls (median 425 pg/mL, 

24 p < 0.067). TNF-α production from the animals without endometriosis, however, was much 

25 more variable and was not statistically significant compared to controls. In addition, there was a 

26 dose-related but statistically insignificant decrease in PBMC cytotoxicity against natural 

27 killer-sensitive RAJI cells in TCDD-treated animals compared to the unexposed controls. The 

28 results were again related to TCDD exposure and not the presence of endometriosis. TCDD 

29 alone was not associated with changes in PBMC surface antigen expression, but increased serum 

30 levels of TCDD. 1,2,3,6,7,8-Hexachlorodibenzofuran and 3,3’,4,4’,5-pentachlorobiphenyl were 

31 correlated with increased numbers of CD3+/CD25- and CD3-/CD25+ leukocytes, as well as 
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1 increased secretion of TNF-α in response to T-cell mitogen exposure. Although TNF-α

2 production is considered to be a general indicator of inflammation, relative adversity of increased 

3 TNF-α secreted by PBMCs in and of itself cannot be substantiated in the absence of concurrent 

4 physiological measurements of an inflammatory response. Therefore, neither a LOAEL nor 

5 NOAEL can be determined for this study. 

 6 

7 2.4.2.5.9. Sewall et al., 1993. 

8 Sewall et al. (1993) examined the impact of TCDD exposure on the hepatic epidermal 

9 growth factor receptor (EGFR) as a critical effect in hepatocarcinogenicity. In two separate 

10 experiments, groups of 6- to 8-week-old female Sprague-Dawley rats were randomly assigned to 

11 the following groups: control group, receiving saline and corn oil; a promoted group that 

12 received four different doses of TCDD along with saline; a DEN-only initiated control group; 

13 and a DEN and TCDD initiated and promoted group that received four different doses of TCDD. 

14 DEN was administered via intraperitoneal injection at a dose of 175 mg/kg [saline (S) vehicle] as 

15 the initiating agent to animals that were 70 days old. The control animals received saline only. 

16 In the first experiment, each treatment group (S/TCDD and DEN/TCDD) that included 

17 sham-operated or ovariectomized and intact animals were treated with TCDD (purity >98%) at 

18 125 ng/kg-day. In the second dose-response experiment, DEN-initiated and saline control 

19 treatment groups (intact animals, 84 days old) were administered TCDD (purity >98%) in corn 

20 oil via oral gavage once every 2 weeks for 30 weeks at doses equivalent to 0, 3.5, 10.7, 35.7, or 

21 125 ng/kg-day (n = 9). A week after the last treatment, all animals were sacrificed and livers 

22 were harvested and fixed for immunohistochemistry. Sections of the fixed liver were tested for 

23 EGFR binding, EGFR autophosphorylation, immunolocalization of EGFR, and hepatic cell 

24 proliferation. 

25 In the first experiment, intact animals treated with 125 ng/kg-day TCDD exhibited a 65% 

26 reduction in EGFR binding capacity. In contrast, the EGFR equilibrium maximum binding 

27 capacity (Bmax) of the ovariectomized rats was not statistically different from the ovariectomized 

28 control rats, and no changes in the Kd were detected in any treatment group. In the 

29 dose-response experiment with intact animals, a significant (p < 0.05) TCDD dose-dependent 

30 decrease in the Bmax of EGFR was shown. A two-factor, five-level ANOVA indicated that the 

31 effect of TCDD exposure on EGFR Bmax was significant (p = 0.0001), whereas, the effect of 
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1 DEN treatment on EGFR Bmax was not significant. Comparative analysis using Fisher’s 

2 protected least significant difference indicated that the lowest TCDD dose resulting in a 

3 statistically significant (p < 0.05) decrease in the EGFR Bmax was 10.7 ng/kg-day S/TCDD 

4 group. At the highest TCDD dose of 125 ng/kg-day, the EGFR Bmax was reduced by 38% 

5 compared to controls in both the DEN initiated and noninitiated groups. A two-factor, five-level 

6 ANOVA showed no significant effect on EGFR Kd in either the DEN- or the TCDD-treated 

7 groups. The EGFR autophosphorylation assay indicated that, with increasing TCDD dose, the 

8 amount of EGFR autophosphorylation in DEN/TCDD-treated animals decreased. The study 

9 authors state that this decrease is similar to the dose-response alterations observed for the EGFR 

10 Bmax. Additionally, EGFR autophosphorylation in control and 125 ng/kg-day noninitiated 

11 animals was similar to the corresponding dose levels for the DEN-treated animals, suggesting 

12 that DEN treatment did not affect the EGFR or the EGFR response to TCDD under the 

13 experimental conditions. The immunolocalization assay indicated that staining was more 

14 apparent in the centrilobular and midzonal regions of the liver in the DEN initiated control 

15 animals, whereas, the amount of hepatocyte plasma membrane staining in DEN/TCDD treated 

16 animals substantially decreased. The cell proliferation assay showed a decrease in the cell 

17 labeling index in the 3.5 ng/kg-day DEN/TCDD dose group that was statistically less (p ≤ 0.05) 

18 than the labeling index for the control group. In contrast, the labeling index for the 

19 125 ng/kg-day DEN/TCDD treatment group was significantly (p ≤ 0.05) higher compared to 

20 controls. Except for the low-dose (3.5 ng/kg-day) group, a clear dose-response trend (two 

21 mid-level doses were not statistically significant) was observed in the other three TCDD treated 

22 groups. 

23 The role of EGFR in TCDD-mediated hepatotoxicity is unknown, and as such, this 

24 endpoint cannot be unequivocally linked to TCDD-induced hepatotoxicity nor labeled as 

25 adverse. Thus, no LOAEL/NOAEL was established. A LOEL for TCDD of 3.5 ng/kg-day for a 

26 30-week exposure duration was identified in this study for a significant (p = 0.0001 using 

27 ANOVA) decrease in EGFR Bmax levels. A NOEL cannot be determined for this study. 

28 

29 2.4.2.5.10. Sewall et al., 1995. 

30 Sewall et al. (1995) studied the dose-response relationship for thyroid function alterations 

31 in female rats as a result of TCDD exposure. Groups of female Sprague-Dawley rats were 
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1 initiated with DEN at 70 days of age at a dose of 175 mg/kg in a saline vehicle via an i.p. 

2 injection. DEN was administered as a liver-initiating agent for a concurrent study to determine 

3 TCDD promotion of hepatic preneoplastic foci. Saline-treated animals served as controls. At 

4 84 days of age, both the DEN-initiated and the saline-noninitiated groups of animals were 

5 administered TCDD (purity >98%) or corn oil vehicle via oral gavage once every 2 weeks for 

6 30 weeks at dose levels equivalent to 0, 0.1, 0.35, 1.0, 3.5, 10.7, 35.7, or 125 ng/kg-day (n = 9 

7 per group). One week after the last TCDD treatment, the animals were sacrificed and the thyroid 

8 was removed and fixed for further analysis. Blood was drawn from the abdominal aortic vein, 

9 and the serum was isolated and preserved for hormone analysis. Liver was also removed and 

10 prepped for further analysis. Thyroid hormone analysis was performed to determine serum TSH, 

11 T3, and T4 levels using radioimmunoassay kits. Histological examination was conducted on 

12 eosin-stained sections of the thyroid tissue. RNA level in the hepatic tissue was determined 

13 using a reverse transcription polymerase chain reaction (RT-PCR) technique. 

14 TCDD treatment did not affect thyroid weight. A dose-dependent decrease in serum T4 

15 levels was observed in both noninitiated and DEN-initiated animals with T4 levels dropping 

16 significantly (p < 0.05) at the 35 and 125 ng/kg-day TCDD doses in the noninitiated group. 

17 Compared to the noninitiated control group, DEN alone did not significantly affect T4 levels. 

18 Serum T3 level in the 125 ng/kg-day treatment group was slightly elevated but was not 

19 significantly different from levels in the control group. TSH levels in DEN initiated rats were 

20 increased at a dose of 3.5 ng/kg-day. In the noninitiated group, TSH level in the 125 ng 

21 TCDD/kg-day group was 3.27 ± 0.34 ng/mL (n = 9) compared to 1.3 ± 0.18 ng/mL in the corn 

22 oil control group (n = 7). This result, in conjunction with the T4 data, demonstrates that TCDD 

23 had a similar effect on thyroid hormone levels in both the noninitiated and DEN initiated groups. 

24 Histological sections examined for nodular lesions or neoplasms exhibited thyroid follicular 

25 adenoma in one DEN/corn oil control animal. The DEN/TCDD-treated animals exhibited 

26 diffuse follicular hyperplasia, with the size of colloidal follicles decreasing with TCDD 

27 treatment. Other qualitative DEN/TCDD-related changes included increased frequency of 

28 abnormally shaped follicles. The study authors reported that image analysis demonstrated a 

29 significant (p = 0.013) TCDD dose-related decrease in mean follicle size along with a significant 

30 (p = 0.001) TCDD dose-related increase in parenchymal area. Additionally, like T4 and TSH 
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1 levels, DEN treatment alone or in combination with TCDD did not influence thyroid follicular or 

2 C-cell morphology. 

3 RT-PCR results for UGT1 and CYP1A1 mRNA levels indicated that the amount of 

4 UGT1 mRNA at the 125 ng/kg-day dose was approximately 2.5-fold higher compared to the 

5 concurrent controls. The study authors also stated that the maximal response for the UGT1 

6 mRNA levels was reached at a dose between 1.0 and 3.5 ng TCDD/kg-day. In contrast, the 

7 maximum induction of CYP1A1 mRNA was 260-fold higher at the 125 ng/kg-day compared to 

8 the concurrent controls. 

9 A LOAEL for TCDD of 35 ng/kg-day for a 30-week exposure duration was identified in 

10 this study for a significant (p < 0.05) decrease in T4 levels. The NOAEL for this study is 

11 10.7 ng/kg-day. 

 12 

13 2.4.2.5.11. Toth et al., 1979. 

14 Toth et al. (1979) examined the impact of TCDD exposure on the formation of liver 

15 tumors in male mice. Ten-week-old, outbred Swiss/H/Riop male mice were administered 

16 sunflower oil or TCDD (purity not specified; in sunflower oil) at 0, 7, 700 or 7,000 ng/kg (0, 1, 

17 100, or 1,000 ng/kg-day adjusted for continuous dosing; administered dose divided by 7; n = 38, 

18 44, 44, and 43, respectively) once per week via gastric tube for 1 year. Once exposure had 

19 ceased, animals were followed for the rest of their lives. After spontaneous death or when mice 

20 were moribund, autopsies were performed and all organs were examined histologically. 

21 Average life span in the 1,000 ng/kg-day dose group decreased considerably (72%) when 

22 compared to the control group. TCDD also caused dose-dependent, severe chronic and ulcerous 

23 skin lesions (12, 30, and 58% in the 1, 100, and 1,000 ng/kg-day dose groups, respectively) that 

24 was followed by generalized lethal amyloidosis (12, 23, and 40% in the 1, 100, and 

25 1,000 ng/kg-day dose groups, respectively). 

26 A LOAEL for TCDD of 1 ng/kg-day for 1-year exposure duration was identified in this 

27 study for severe chronic and ulcerous skin lesions (12% higher than controls), and generalized 

28 lethal amyloidosis (12% higher than controls). A NOAEL cannot be determined for this study. 

29 
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1 2.4.2.6. Chronic Studies (Cancer Endpoints) 

2 2.4.2.6.1. Kociba et al., 1978. 

3 As discussed above, Kociba et al. (1978) conducted a lifetime (2-year) feeding study of 

4 male and female Sprague-Dawley rats using doses of 0, 1, 10, and 100 ng/kg-day. There were 

5 50 males and 50 females in each group. 

6 With respect to the cancer endpoints examined, the most significant finding was an 

7 increase in hepatocellular hyperplastic nodules and hepatocellular carcinomas in female rats. 

8 The incidence of hepatocellular carcinomas was significantly elevated above the control 

9 incidence at the 100 ng/kg-day dose, whereas increased incidence of hyperplastic nodules was 

10 evident in the 10 ng/kg-day dose group. 

11 There have been two reevaluations of slides of liver sections from the Kociba et al. study 

12 (Squire, 1980; Sauer, 1990; Goodman and Sauer, 1992). The Squire Review was requested by 

13 EPA as an independent review of the slides. The Sauer Review was carried out using refined 

14 criteria for the diagnosis of proliferative hepatocellular lesions (Maronpot et al., 1986, 1989). 

15 Liver tumor incidences for the three evaluations are compared in Appendix F. Although there 

16 are some quantitative differences between the evaluations, the lowest detectable effect for liver 

17 tumor incidence is consistently observed at 10 ng/kg-day. 

18 In the 10 ng/kg-day dose group, significant increases in the incidence of hyperplastic 

19 nodules of the liver were observed in female rats (18/50 in the Kociba evaluation, 27/50 in the 

20 Squire evaluation). Two females (2/50) had hepatocellular carcinomas. In the 1990 reevaluation 

21 (Sauer, 1990; Goodman and Sauer, 1992), nine females (9/50) were identified with 

22 hepatocellular adenomas and none with carcinomas; thus only one-third of the previously 

23 observed “tumors” were identified when using the refined diagnostic criteria. As discussed 

24 below, the tumor reclassification of Goodman and Sauer (1992) was used in the dose-response 

25 modeling for the Kociba et al. (1978) data set. 

26 In addition to nodules in the liver, increased incidence of stratified squamous cell 

27 carcinoma of the tongue and nasal turbinates/hard palate, and keratinizing squamous cell 

28 carcinoma of the lung were also observed in female rats in the 100 ng/kg-day dose group. One 

29 possible cause for the induction of lung tumors in the Kociba feeding study may have been the 

30 aspiration of dosed feed into the lungs. However the promotion of lung tumors has been 

31 observed in mice treated systemically by intraperitoneal (i.p.) injections of TCDD (Beebe et al., 
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1 1995). In addition the induction of hyperplastic and metaplastic lesions in rats has been observed 

2 following chronic oral gavage treatment with TCDD (Tritscher et al., 2000). More recently, 

3 chronic oral exposure to HCDD resulted in the induction of lung tumors in treated female rats 

4 (Rozman, 2000). These data indicate that the induction of lung tumors in the Kociba was most 

5 likely primarily the result of systemic chronic dietary exposure to TCDD rather than due to a 

6 localized exposure to aspired dosed feed. 

7 There was no detectable increase in liver tumor incidences in male rats in any of the dose 

8 groups. The mechanism responsible for dioxin-mediated sex specificity for 

9 hepatocarcinogenesis in rats is not clear, but may involve ovarian hormones (Lucier et al., 1991). 

10 Although there was no increase in liver tumors in male rats in this study, in the 

11 100 ng/kg-day group, there was an increased incidence of stratified squamous cell carcinoma of 

12 the hard palate/nasal turbinate, stratified squamous cell carcinoma of the tongue, and adenoma of 

13 the adrenal cortex. 

14 Kociba et al. (1978) had reported that chemically related increases in preneoplastic or 

15 neoplastic lesions were not found in the 1 ng/kg-day dose group. However, Squire identified two 

16 male rats in the 1 ng/kg-day dose group with squamous cell carcinoma of the nasal 

17 turbinates/hard palate, and one of these male rats had a squamous cell carcinoma of the tongue. 

18 These are both rare tumors in Sprague-Dawley rats, and these sites are targets for TCDD, 

19 implying that 1 ng/kg-day may not represent a NOEL. However, no dose-response relationships 

20 were evident for tumors at these sites (Huff et al., 1991) 

21 There is considerable controversy concerning the possibility that TCDD-induced liver 

22 tumors are a consequence of cytotoxicity. Goodman and Sauer (1992) have extended the 

23 reevaluation of the Kociba slides to include liver toxicity data and have reported a correlation 

24 between the presence of overt hepatotoxicity and the development of hepatocellular neoplasms in 

25 female rats. With the exception of two tumors in controls and one each in the low- and mid-dose 

26 groups, all liver tumors occurred in livers showing clear signs of toxicity. However, male rat 

27 livers exhibit cytotoxicity in response to high TCDD doses, yet they do not develop liver tumors. 

28 Moreover, both intact and ovariectomized female rats exhibit liver toxicity in response to TCDD, 

29 yet TCDD is a more potent promoter in intact but not ovariectomized rats (Lucier et al., 1991). 

30 Therefore, if cytotoxicity is playing a role in liver tumorigenesis, other factors must also be 
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1 involved. Also, there is little information on the role of cytotoxicity in TCDD-mediated cancer 

2 at other sites such as the lung and thyroid. 

3 

4 2.4.2.6.2. Toth et al., 1979. 

5 In a study of 10-week-old outbred male Swiss/H/Riop mice, Toth et al. (1979) 

6 administered oral gavage TCDD doses of 0, 7, 700, and 7,000 ng/kg-day in sunflower oil weekly 

7 for 1 year (0, 1, 100, or 1,000 ng/kg-day adjusted for continuous dosing; see details above). All 

8 mice (100/group) were followed for their entire lives. The study authors identified the effective 

9 number of mice in each group to be the number of surviving animals when the first 

10 tumor-bearing animal was identified. The average lifespan of the control, low, mid and high 

11 dose groups was 588, 649, 633, and 424 days, respectively. 

12 In the 100 ng/kg-day dose group, liver tumor incidence was twice that of the control 

13 group and was statistically significant (p < 0.01%). A dose-related increase in liver tumor 

14 incidence was observed (18, 29, 48, and 30% in the control and three TCDD-treated groups, 

15 respectively) in all treated mice. Increases were not statistically significant, however, at 1 and 

16 1,000 ng/kg-day. The study authors also stated that spontaneous and induced liver tumors were 

17 not histologically different. Additionally, the ratio of benign hepatomas to hepatocellular 

18 carcinomas in the control group was not affected by treatment and an increase was observed only 

19 in the absolute number of liver tumors. Cirrhosis was not observed with the tumors. 

20 

21 2.4.2.6.3. NTP, 1982. 

22 As discussed above, the NTP (1982) study was conducted using Osborne-Mendel rats 

23 and B6C3F1 mice (NTP, 1982). Groups of 50 male rats, 50 female rats, and 50 male mice 

24 received TCDD as a suspension in corn oil:acteone (9:1) by gavage twice each week at doses of 

25 0, 5, 25, or 250 ng/kg-day (daily averaged doses of 0, 1.4, 7.1, or 71 ng/kg-day for rats and male 

26 mice and doses of 0, 5.7, 28.6, or 286 ng/kg-day for female mice. 

27 There were no statistically significant dose-related decreases in survival in any 

28 sex-species group. TCDD-induced malignant liver tumors occurred in the high-dose female rats 

29 and in male and female mice. These can be considered to result from TCDD exposure because 

30 they are relatively uncommon lesions in control Osborne-Mendel rats (male, 1/208; female, 

31 3/208), are seen in female rats and mice of both sexes, and their increasing incidence with 
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1 increasing dose is statistically significant (Cochran-Armitage trend test, p = 0.004). Because 

2 liver tumors were increased in both sexes of mice, this effect is not female-specific as was 

3 observed in rats. Interestingly, liver tumor incidences were decreased in female rats in both the 

4 NTP and Kociba low doses (not statistically significant compared with controls). For example, 

5 the combined control incidence data were 11/161 (7%) compared with 4/99 (4%) in the low-dose 

6 group. 

7 The incidences of thyroid gland (follicular cell) tumors were increased in all three dose 

8 groups in male rats. Because the responses in the two highest dose groups are highly significant, 

9 the statistically significant elevation of incidence in the lowest dose group (Fisher exact 

10 p-value = 0.042) is considered to be caused by exposure to TCDD, suggesting that thyroid tumor 

11 incidence may be the most sensitive site for TCDD-mediated carcinogenesis. Because 

12 71 ng/kg-day is above the maximum tolerated dose (MTD) (Huff et al., 1991), thyroid tumors 

13 occur at doses more than 50 times lower than the MTD. 

14 TCDD-induced neoplasms of the adrenal gland were observed in the 7.1 ng/kg-day/dose 

15 group in male rats and in high-dose female rats. Fibrosarcomas of the subcutaneous tissue were 

16 significantly elevated in high-dose female mice and female rats. One additional tumor type, 

17 lymphoma, was seen in high-dose female mice. Lung tumors were elevated in high-dose female 

18 mice; the increase was not statistically significant when compared with concurrent controls, but 

19 the increase was dose related (Cochran-Armitage trend test, p = 0.004). 

20 Huff (1992) concluded, based on the NTP bioassay results, that TCDD was a complete 

21 carcinogen and induced neoplasms in rats and mice of both sexes. As was observed in the 

22 Kociba study (Kociba et al., 1978), liver tumors were observed with greater frequency in treated 

23 female rats, but in male rats the thyroid appears to be the most sensitive (increased tumor 

24 incidence at doses as low as 1.4 ng/kg-day). 

25 

26 2.4.2.6.4. NTP, 2006. 

27 As discussed above, female Sprague-Dawley rats (81 control; 82 treatment group) were 

28 administered TCDD (purity >98%) in corn oil:acetone (99:1) via gavage at doses of 0, 3, 10, 22, 

29 46, or 100 ng/kg-day, 5 days per week for 105 weeks (0, 2.14, 7.14, 15.7, 32.9, or 

30 71.4 ng/kg-day, adjusted for continuous exposure) (NTP, 2006). In addition to this primary 

31 group, a stop-dose group of 50 animals was administered 100 ng/kg-day TCDD in corn 
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1 oil:acetone (99:1) via gavage for 30 weeks and then just the vehicle for the remainder of the 

2 study. At study termination, the number of surviving animals had declined to 25 in the control 

3 group and to 21, 23, 19, 22, and 21 in five treatment groups, respectively, due to accidental 

4 deaths, moribund animals, or death due to natural causes. 

5 Incidence of hepatocellular adenomas was significantly (p < 0.001) increased in the 

6 100 ng/kg-day dose group in the primary study and exceeded incidences seen in historical 

7 vehicle control range at study termination. A dose-related increase in the incidence of 

8 cholangiosarcoma was seen in the primary study group in animals receiving 22 ng/kg-day or 

9 higher doses of TCDD. The high dose group of 100 ng/kg-day had the highest incidence of 

10 cholangiosarcoma with a significant (p < 0.001) number of animals exhibiting multiple 

11 cholangiosarcomas. Such an incidence was not seen in historical vehicle controls. In contrast, 

12 only two cholangiosarcomas and hepatocellular adenomas were seen in the 100 ng/kg-day group 

13 in the stop-exposure study. 

14 In the lung, at 2 years, there was a significantly (p = 0.002) increased incidence of cystic 

15 keratinizing epithelioma in the 100 ng/kg-day dose group of the primary study, while there were 

16 no epitheliomas in the 100 ng/kg-day group of the stop-exposure study. There was also a 

17 significant (p ≤ 0.01) dose-dependent increase, when compared to the vehicle control, in the 

18 incidence of bronchiolar metaplasia of the alveolar epithelium at all dose groups in the primary 

19 study. Squamous metaplasia was also present in the 46 and 100 ng/kg-day dose groups in the 

20 primary study, and was also observed in the 100 ng/kg-day dose group in the stop-exposure 

21 study. 

22 A positive trend in the incidence of gingival squamous cell carcinoma of the oral cavity 

23 was seen at all doses (except 22 ng/kg-day), with the incidence significantly (p = 0.007) high in 

24 the 100 ng/kg-day dose group. In addition, the occurrence of this lesion in the 46 and 

25 100 ng/kg-day group of the primary study and 100 ng/kg-day group of the stop-exposure study 

26 exceeded the historical control range. The incidence of gingival squamous hyperplasia was 

27 significantly (either p ≤ 0.01 or p ≤ 0.05) increased in all dose groups of the primary study as 

28 well as the 100 ng/kg-day group of the stop-exposure study. 

29 In the uterus, at 2 years, there was a significantly (p = 0.032) higher rate of squamous cell 

30 carcinoma in the 46 ng/kg-day group compared to vehicle controls. In addition there were two 
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1 squamous cell carcinomas in the 100 ng/kg-day group of the stop-exposure study. No squamous 

2 cell carcinomas have been reported in historical vehicle controls. 

3 These results indicate that TCDD is carcinogenic to female Sprague-Dawley rats and 

4 causes tumors at multiple sites. 

5 

6 2.4.3. Summary of Key Data Set Selection for TCDD Dose-Response Modeling 

7 To meet the NAS concerns regarding transparency and clarity in the identification of 

8 TCDD studies for dose-response assessment, EPA has, in this section, developed and applied 

9 two sets of criteria, for animal bioassays and epidemiologic studies. EPA has collected and 

10 evaluated these studies, including studies from the 2003 Reassessment and newer studies found 

11 via literature searches and through public submissions. Tables 2-4 and 2-5 contain the final lists 

12 of key cancer and noncancer studies, respectively, that have met EPA’s inclusion criteria for 

13 epidemiologic data. Tables 2-6 and 2-7 provide the final lists of key studies that have met EPA’s 

14 inclusion criteria for animal bioassay data for cancer and noncancer studies, respectively. 

15 Collectively, these four tables contain the final set of key studies that EPA has used to develop 

16 noncancer and cancer dose-response assessments for TCDD in Sections 4 and 5 of this 

17 document. In Sections 4 and 5, additional evaluations are made to determine which 

18 study/endpoint data sets are the most appropriate for development of the RfD and OSF for 

19 TCDD, using statistical criteria, dose-response modeling results and decisions regarding 

20 toxicological relevance of the endpoints. The approaches taken to select the final candidate 

21 study/endpoint data sets are discussed in Sections 4 and 5 and are illustrated in Figures 4-1 and 

22 5-1 of those sections. 
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1 Table 2-1. Summary of epidemiologic cancer studies (key characteristics) 

 2 

Publication 

Length of 

follow-up Latency period Half-life for TCDD 

Fraction of TEQs 

accounted for by TCDD

NIOSH cohort studies 

Fingerhut et al., 1991 1942−1987 0, 20 years N/A N/A 

Steenland et al., 1999 1942−1993 0, 15 years N/A N/A 

Steenland et al., 2001 1942−1993 0, 15 years 8.7 years (Michalek et 

al., 1996) 

TCDD accounted for all 

occupational TEQ; 10% 

of background 

Cheng et al., 2006 1942−1993 0, 10, 15 years 8.7 years (Michalek et 

al., 1996), and CADM 

(Aylward et al., 2005a) 

N/A 

Collins et al., 2009 1942−2003 None 7.2 years (Flesch-Janys 

et al., 1996) 

N/A 

BASF cohort studies

Thiess et al., 1982 1953−1980 None N/A N/A 

Zober et al., 1990 1953−1987 Years since first 

exposure: 0−9, 

10−19, and 20+ 

N/A N/A 

Ott and Zober, 1996 1953−1991 None 5.8 years N/A 

Hamburg cohort studies

Manz et al., 1991 1952−1989 None, used 

duration of 

employment 

(<20, >20 years)

N/A N/A 

Flesch-Janys et al., 

1995 

1952−1992 None 7.2 years 

(Flesch-Janys et al., 

1994) 

Mean TEQ without 

TCDD was 155 ng/kg; 

mean TEQ with TCDD 

was 296.5 ng/kg 

Flesch-Janys et al., 

1998 

1952−1992 None 7.2 years (Flesch-Janys 

et al., 1996), also used 

decay rates that were 

function of age and fat 

composition 

Mean concentration of 

TCDD was 101.3 ng/kg; 

for TEQ (without TCDD) 

mean exposure was 89.3 

ng/kg 

Becher et al., 1998 1952−1992 0, 5, 10, 15 and 

20 years 

7.2 years (Flesch-Janys 

et al., 1996) took into 

account age and fat 

composition 

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

1/15/10 DRAFT 2-200 —DO NOT CITE OR QUOTE 

 1 

Table 2-1. Summary of epidemiologic cancer studies (key characteristics) 

(continued) 

Publication 

Length of 

follow-up Latency period Half-life for TCDD 

Fraction of TEQs 

accounted for byTCDD

Seveso cohort studies 

Bertazzi et al., 2001 1976−1996 Periods 

postexposure: 0, 

0−4, 5−9, 

10−14, 15−19 

N/A N/A 

Consonni et al., 2008 1976−2001 Periods 

postexposure: 0, 

0−4, 5−9, 

10−14, 15−19, 

20−24 

N/A N/A 

Warner et al., 2002 1976−1998 None 8 years (Pirkle et al., 

1989) 

N/A 

Chapaevsk cohort studies 

Revich et al., 2001 Crosssectional 

study 

(1995−1998) 

N/A N/A N/A 

Ranch Hand cohort studies 

Akhtar et al., 2004 1962−1999 None N/A N/A 

Michalek and Pavuk, 

2008 

1962−2004 None, but 

stratified by 

period of service

7.6 years N/A 

New Zealand cohort studies 

McBride et al., 2009a 1969−2004 None N/A N/A 

McBride et al., 2009b 1969−2004 None 7 years N/A 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-201 DRAFT: DO NOT CITE OR QUOTE Table 2-2. Epidemiology cancer study selection considerations and criteria Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow- up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. Ex

pri

TCD

quan

tha

res

rela

c

ass

Cancer Considerations C

NIOSH Cohort Studies Fingerhut et al., 1991 all cancer sites, site-specific analyses 

√ X X X 

√

√ 

Steenland et al., 1999  all cancer sites combined, site-specific analyses

√

√

√

√

√

√

Steenland et al., 2001 

all cancer sites combined 

√

√

√

√

√

√

Cheng et al., 2006 

all cancer sites combined 

√

√

√

√

√

√

Collins et al., 2009  all cancer sites combined, site-specific analyses

√

√ X 

√

√

√

BASF Cohort Studies Zober et al., 1990  all cancer sites combined, site-specific analyses

√

√ X X X 

√ 

Ott and Zober, 1996 

all cancer sites combined 

√

√

√

√

√

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

1/15/10 2-202 DRAFT: DO NOT CITE OR QUOTE Table 2-2. Epidemiology cancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow- up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. Ex

pri

TCD

quan

tha

res

rela

c

ass

Cancer Considerations C

Hamburg Cohort Manz et al., 1991 all cancer sites combines, site-specific analyses

√

√

√

√

√

√ 

Flesh-Janys et al., 1995  all cancer sites combined 

√

√

√

√

√

√

Flesh-Janys et al., 1998  all cancer sites combined, site-specific analyses

√

√

√

√

√

√

Becher et al., 1998 

all cancer sites combined 

√

√

√

√

√

√

Seveso Cohort Bertazzi et al., 2001  all cancer sites combined, site-specific analyses

√

√

√ X 

√

√ 

Pesatori et al., 2003  all cancer sites combined, site-specific analyses

√

√ X X 

√

√ 

Consonni et al., 2008  all cancer sites combined, site-specific analyses

√

√

√ X 

√

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

1/15/10 2-203 DRAFT: DO NOT CITE OR QUOTE Table 2-2. Epidemiology cancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow- up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. Ex

pri

TCD

quan

tha

res

rela

c

ass

Cancer Considerations C

Baccarelli et al., 2006 site specific analysis 

√

√ X 

√

√

√

Warner et al., 2002 

breast cancer incidence 

√

√

√

√

√

√

Chapaevsk Study Revich et al., 2001  all cancer sites combined, site-specific analyses X X X X 

√ X 

Ranch Hands Cohort Akhtar et al., 2004  all cancer sites combined, site-specific analyses

√ X 

√

√

√

√ 

Michalek and Pavuk, 2008  all cancer sites combined 

√ X 

√

√

√

√ 

Others tMannetje et al., 2005  all cancer sites combined, site-specific analyses

√ X 

√

√

√ X 

McBride et al., 2009b  all cancer sites combined, site-specific analyses

√ X X 

√ X 

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

1/15/10 2-204 DRAFT: DO NOT CITE OR QUOTE Table 2-2. Epidemiology cancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow- up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. Ex

pri

TCD

quan

tha

res

rela

c

ass

Cancer Considerations C

McBride et al., 2009a all cancer sites combined, site-specific analyses

√

√ X 

√ X 

√

Hooiveld et al., 1998  all cancer sites combined, site-specific analyses

√

√

√

√ X 

√

aThis study has been superseded and updated by Steenland et al. (2001). bBecher et al. (1998) assessed this same cohort taking cancer latency into account, thereby superseding this study. cIt is unknown whether the frequency of t(14;18)translocations in lymphocytes relates specifically to an increased risk of non-Hodgkin’

lack of obvious adverse effect, dose-response analyses for this outcome were not conducted. dNo dose-response associations were noted. 

√ = Consideration/criteria satisfied; X= Consideration/criteria not satisfied. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-205 DRAFT: DO NOT CITE OR QUOTE Table 2-3. Epidemiology noncancer study selection considerations and criteria Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow-up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. E

p

T

qu

t

re

 Noncancer Considerations 

NIOSH Cohort Steenland et al., 1999 mortality (noncancer) -ischemic heart disease 

√ X 

√

√

√

√ 

Collins et al., 2009  mortality (noncancer) 

√

√ X 

√

√

√

BASF Cohort Ott and Zober, 1996  mortality (noncancer) 

√

√ X 

√

√

√

Hamburg Cohort Flesch-Janys et al., 1995  mortality (noncancer) 

√

√

√

√

√

√

Seveso Cohort Eskenazi et al., 2002a 

menstrual cycle characteristics 

√

√

√

√

√

√

Eskenazi et al., 2002b  endometriosis X X X 

√ X 

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

1/15/10 2-206 DRAFT: DO NOT CITE OR QUOTE Table 2-3. Epidemiology noncancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow-up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. E

p

T

qu

t

re

Noncancer Considerations 

Eskenazi et al., 2003 birth outcomes X X X 

√

√

√

Warner et al., 2004  age at menarch 

√

√ X 

√

√

√

Eskenazi et al., 2005  age at menopause 

√

√ X 

√

√

√

Warner et al., 2007  ovarian function 

√

√ X 

√

√

√

Eskenazi et al., 2007  uterine leiomyoma 

√

√

√

√

√

√

Mocarelli et al., 2008 

semen quality 

√

√

√

√

√

√

Mocarelli et al., 2000  sex ratio 

√

√

√

√

√ X 

Baccarelli et al., 2008 

neonatal thyroid function 

√

√

√ X 

√

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

1/15/10 2-207 DRAFT: DO NOT CITE OR QUOTE Table 2-3. Epidemiology noncancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow-up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. E

p

T

qu

t

re

Alaluusua et al., 2004 oral hygiene 

√

√

√

√

√

√

Bertazzi et al., 2001  mortality (noncancer) 

√

√ X X 

√

√ 

Consonni et al., 2008  mortality (noncancer) 

√

√ X X 

√

√ 

Baccarelli et al., 2005  chloracne 

√

√

√

√

√

√

Baccarelli et al., 2002, 2004  immunological effects 

√

√

√

√

√

√

Chapaevsk Study Revich et al., 2001  mortality (noncancer) and reproductive health

√ X X X 

√

√ 

Ranch Hands Cohort Michalek and Pavuk, 2008  diabetes 

√ X 

√

√

√

√ 

Other McBride et al., 2009a 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-208 DRAFT: DO NOT CITE OR QUOTE Table 2-3. Epidemiology noncancer study selection considerations and criteria (continued) Methods use to ascertain health outcomes were unbiased, highly sensitive and specific. Risk estimates are not susceptible to biases from confounding exposures or from study design or statistical analysis. Association between TCDD and adverse health effect, with exposure- response relationship. Exposure assessment methodology clear and adequately characterizes individual- level exposures. Limitations and uncertainties in exposure assessment considered. Study size and follow-up large enough to yield precise estimates of risk and ensure adequate statistical power. Published in peer- reviewed literature with appropriate discussion of strengths, limitations. E

p

T

qu

t

re

mortality (noncancer) X X X 

√ X 

√

McBride et al., 2009b  mortality (noncancer) X 

√ X 

√ X 

√ 

Ryan et al., 2002  sex ratio X X X X 

√

√ 

aCategorical measures of TCDD suggest an inverse association between TCDD exposure and uterine fibroids. The observed direction o

associations precluded quantitative dose-response modeling. bThe somewhat arbitrary cut off age of 19 for statistically significant exposure associations results in a highly uncertain critical exposur

to determine whether effects are a consequence of the initial high exposure during childhood or a function of the cumulative exposurwindow. The differences between these two dose estimates are quite large. cChloracne is recognized to occur following high TCDD exposure levels. This study provides limited relevance to TCDD RfD develop

observed in the general population are much lower. 

√ = Consideration/criteria satisfied. X= Consideration/criteria not satisfied. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-209 DRAFT: DO NOT CITE OR QUOTE Table 2-4. Epidemiology studies selected for TCDD cancer dose-response modeling Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Mortality from all cancers USA, 1942–1993 NIOSH cohort including 3,538 occupationally exposed male workers at 8 plants in the United States; 256 cancer deaths Cumulative serum lipid TCDD concentrations (CSLC) based on work histories, job- exposure matrix, and concentration and age- dependent two- compartment model of elimination kinetics No exposure categories provided 256 cancer deaths The slope (β) was 3.3 × 10−6 for lag of 15 years excluding upper 5% of TCDD exposures. The slopes ranged two orders of magnitude depending on modeling assumption Available: age, year of birth, and race Risks adjusted for: year of birth, age, and race Confo

smokiconsidby anasmokismokicanceOther exposconsidby repremovat a timBased evaluawas nevidenconfo

Mortality from all cancers USA, 1942–1993 NIOSH cohort including 3,538 male workers, 256 cancer deaths CSLC based on work histories, job-exposure matrix, and a simple one- compartment first-order pharmacokineti c elimination model with 8.7- year half-life CSLC (ppt-years) <335 335

−520 

520

−1,212 

1,212

−2,896 

2,896

−7,568 

7,568

−20,455

≥20,455 

64 29 22 30 31 32 48 

1.00 1.26 (0.79

−2.00)

1.02 (0.62

−1.65)

1.43 (0.91

−2.25)

1.46 (0.93

−2.30)

1.82 (1.18

−2,82)

1.62 (1.03

−2,56) 

Available: date of birth and age Adjusted for: date of birth, and age was used as time scale in Cox model 

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

1/15/10 2-210 DRAFT: DO NOT CITE OR QUOTE Table 2-4. Epidemiology studies selected for TCDD cancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Mortality from all cancers combined Hamburg, Germany, production period was 1950

−1984 

and mortality follow-up extended through 1992 

Boehringer cohort including approximately 1,189 workers employed in the production of herbicides 

Cumulative TCDD serum lipid concentrations based on area under curve (in μg/kg years); backextrapolation to date of last employment took into account age and percent body fat; half-life value was 7.2 years 

Categorical exposures (Cox model) 0− <1 1− <4 4− <8 8− <16 16

− <64 

64+ Continuous exposure TCDD (μg/kg years) 

124 124 

1.0 1.12 (0.70

−1.80) 

1.42 (0.70

−2.85) 

1.77 (0.81

−3.86) 

1.63 (0.73

−3.64) 

2.19 (0.76

−6.29) 

β = 0.0089, p = 0.0047 

Available: year of entry, age of entry, duration of employment, birth cohort, β-HCH; TEQ other than TCDD Available: year of entry, age of entry, duration of employment, birth cohort, β-HCH; TEQ other than 

TCDD 

IncludU.S. EA largmodelThese modeldiffereinterv15, anwell amultipadditimodeldifferevariabyears deaths
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-211 DRAFT: DO NOT CITE OR QUOTE Table 2-4. Epidemiology studies selected for TCDD cancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Mortality and incidence for all cancers combined, as well as for specific cancer sites Ludwig- shafen, Germany, 1954

−1992 

BASF cohort, 243 men exposed from accidental release that occurred in 1953 during production of trichlorophenol, or who were involved in clean-up activities 

Cumulative TCDD serum lipid concentrations expressed in μg/kg based on TCDD half-life of 5.1-8.9 years, Cox regression model 

Internal comparisons based on continuous measure of TCDD. External comparisons exposure categories: <0.1, 0.1

−0.99, 

1.0

−1.99 

>2 μg/kg 

Internal cohort analysis31 cancer deaths 47 incident cancers External cohort analysesDeaths 8 8 8 7 

Date of 1st TCDD 

exposure 1.22 (95% CI: 1.00

−1.50) 

1.11 (95% CI: 0.91

−1.35) 

SMRs 0.8 (0.4

−1.6) 

1.2 (0.5

−2.3) 

1.4 (0.6

−2.7) 

2.0 (0.8

−4.0) 

Available: age, BMI, smoking status and history of occupational exposure to aromatic amines and asbestos 

IncludU.S. EPositinoted cancerespirAssocTCDD increafound currenLast paccoucohor
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-212 DRAFT: DO NOT CITE OR QUOTE Table 2-4. Epidemiology studies selected for TCDD cancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Breast cancer incidence Italy 1976–1998 981 women from zones A and B with available archive serum samples, 15 breast cancer cases TCDD serum lipid concentrations (ppt) collected between 1976 and 1981. For most samples collected after 1977, serum TCDD levels were back- extrapolated using a first- order kinetic model with a 9- year half-life. <20 ppt 20.1

−44 ppt 

44.1

−100 ppt 

>100 ppt Log10TCDD 

also modeled as continuous variable 

Cases 1 2 7 5 15 

1.0 1.0 (0.1

−10.8) 

4.5 (0.6

−36.8) 

3.3 (0.4

−28.0) 

2.1 (1.0

−4.6) 

Available: gravidity, parity, age at first pregnancy, age at last pregnancy, lactation, family history of breast cancer, age at menarche, current body mass index, oral contraceptive use, menarcheal status at explosion, menopause status at diagnosis, height, smoking, alcohol consumption. Adjusted for age, which was used as time scale in Cox model; other covariates were evaluated but were not identified as confounders. 

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

1/15/10 2-213 DRAFT: DO NOT CITE OR QUOTE Table 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

 b-TSH measured 72 hours after birth from a heel pick (routine screening for all newborns in the region Italy, 1976; children, 1994–2005 Population- based study: 1,041 singletons (56 from zone A, 425 from zone B and 533 from reference) born between Jan. 1, 1994- June 30, 2005. Plasma dioxin study: 51 children born to 38 women of fertile age who were part of the Seveso Chloracne Study. Based on zone of residence, estimated mean values from a previous study. Maternal plasma TCDD levels estimated at the date of delivery using a first-order pharmacokineti c model and elimination rate estimated in Seveso women (half-life =9.8 years). Population- based study: Reference Zone B Zone A Plasma dioxin study: Continuous maternal plasma TCDD 533 births 425 births 56 births Population-based study Mean b-TSH Reference: 0.98 (95% CI: 0.90

−1.08) 

Zone B: 1.66 (95% CI: 1.19

−2.31) 

Zone A: 1.35 (95% CI: 1.22

−1.49) 

Association between neonatal b-TSH with plasma TCDD: adjusted β = 0.75 (p < 0.001) 

Available: gender, birth weight, birth order, maternal age at delivery, hospital, type of delivery. There was limited evidence of confounding, so mean TSH results presented here are unadjusted. 

An aserum 

of mofound amonin the study
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-214 DRAFT: DO NOT CITE OR QUOTE Table 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Sperm conc. (million/ mL) Progressive motility (%) Serum E2 (pmol/L) Italy, 1976, 1998 135 exposed (from zone A) and 184 non-exposed men aged 

1−26 in 1976 were included. These subjects were selected from the cohort of 257 exposed and 372 unexposed people. 

Serum TCDD (in ppt) from 1976-1977 samples (for exposed men); background values were assumed for unexposed men based on serum analysis of residents in uncontaminated areas. 

TCDD quartiles 

 Mean values 

were compared between the exposed and comparison groups for sperm concentration, volume, motility and count, FSH, E2, LH, and 

Inhibin B. 

Available: age, abstinence time, smoking status, education, alcohol use, maternal smoking during pregnancy, employment status, BMI, chronic exposure to solvents and other toxic substances. Adjusted for smoking status, organic solvents, age at time of tests, BMI, alcohol use, education, employment status and abstinence (days) for sperm data. Hormone data not adjusted for education level, employment status, and abstinence time.

Resutimin(1

−9 

10

−1

1976
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-215 DRAFT: DO NOT CITE OR QUOTE Table 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Dental defects Seveso, Italy, Dental exams administered in 2001 among those exposed to TCDD in 1976 65 subjects <9.5 years old at time of Seveso explosion and residing in zones ABR; 130 subjects recruited from the non-ABR region (unexposed) Serum TCDD (ng/kg) from 1976 samples for those who resided in Zone ABR; no serum levels for non- ABR residents (unexposed). TCDD exposure represent levels as of 1976 (after accident) Non-ABR Zone 31

−226 

ng/kg 238

−592 

ng/kg 700

−26000 

ng/kg <5 years of age at time of accident Non-ABR Zone or 31

−226 

ng/kg serum TCDD 238-26,000 ng/kg serum TCDD 

10/39 1/10 5/11 9/15 25/75 

Dental defect % 26% 10% 45% 60% Odds Ratios (among those <5 years of age at time of accident) 1.0 2.4 (1.3

−4.5) 

Available: medical history, age, sex, education, smoking

Dosepatterwith in the howepopumuch prevadefecthose expo(10%Also hypoother aberrthese to allby A
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-216 DRAFT: DO NOT CITE OR QUOTE Table 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued) Health outcome Location, time period Cohort description Exposure assessment Exposure measures No. of cases/ deaths Effect Measure/ RR (95% CI) Risk factors C

Menstrual cycle characteristics: menstrual cycle length. Seveso, Italy, follow-up interview conducted in 1996-1997 of women exposed to TCDD in the 1976 accident Women who were <40 years from zones A or B in 1976, A positive association found among women who were pre- menarcheal at the time of accident (n=134) Serum TCDD (ng/kg) from 1976 samples. TCDD exposure level was back- extrapolated to 1976 using the Filser or the first-order kinetic models. Interquartile range was 64-322 ppt TCDD examined as continuous measure (per 10-fold increase in serum levels). Lengthening of the menstrual cycle by 0.93 days (95% CI: - 0.01, 1.86) Interview data: medical history, personal habits, work history, reproductive history, age, smoking, body mass index, alcohol and coffee consumption, exercise, illness, abdominal surgeries. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-217 DRAFT: DO NOT CITE OR QUOTE Table 2-6. Animal bioassays selected for cancer dose-response modeling Species/strain Sex exposure route/duration n Average daily dose levels (ng/kg-day) Cancer types Statistical significant tum

(pairwise with controls or tren

Rat/Sprague- Dawley Male/female Oral-lifetime feeding; 2 years 50 each (86 each in vehicle control group) 0, 1, 10, or 100 Females: liver, lung, oral cavity Males: adrenal, oral cavity, tongue Adrenal cortex: adenoma Liver: hepatocellular adenoma(s) o

carcinoma(s); hyperplastic nodule

Lung: keratinizing squamous cell cOral cavity: stratified squamous ce

carcinoma of hard palate or nasal 

Tongue: stratified squamous cell ca

Mouse/ B6C3F1 Male/female Oral-gavage twice per week; 104 weeks 50 each (75 each in vehicle control group) 0, 1.4, 7.1, or 71 for males; 0, 5.7, 28.6, or 286 for females Females: hematopoietic system, liver, subcutaneous tissue, thyroid Males: liver, lung Hematopoietic system: lymphoma 

Liver: hepatocellular adenoma or cLung: alveolar/bronchiolar adenom

carcinoma Subcutaneous tissue: fibrosarcoma Thyroid: follicular-cell adenoma 

Rat/Osborne- Mendel Male/female Oral-gavage twice per week; 104 weeks 50 each (75 each in vehicle control group) 0, 1.4, 7.1, or 71 Females: adrenal, liver, subcutaneous tissue, thyroid Males: adrenal, liver, thyroid Adrenal: cortical adenoma, or carci

adenoma, NOS Liver: neoplastic nodule or hepatoccarcinoma Subcutaneous tissue: fibrosarcoma Liver: neoplastic nodule or hepatoccarcinoma Thyroid: follicular-cell adenoma or 

Rat/Harlan Sprague- Dawley Female Oral-gavage 5 days per week; 2 years 53 or 54 0, 2.14, 7.14, 15.7, 32.9, or 71.4 Liver Lung Oral mucosa Pancreas Liver: hepatocellular adenoma Liver: cholangiocarcinoma Lung: cystic keratinizing epitheliom

Oral mucosa: squamous cell carcinPancreas: adenoma or carcinoma 

Mouse/Outbred Swiss/H/Riop Male Gastric intubation once per week; 1 year 43 or 44 (vehicle control group = 38) 0, 1, 100, or 1,000 Liver Liver: tumors 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-218 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Reproductive toxicity studies Monkey/ Rhesus Daily dietary exposure in female monkeys (3.5

−4 years) 

F (F0, F1, F2, F3) 

3 to 7 (F1) 0, 0.15, or 0.67 0.15 0.67 Reproductive 

and developmental effects 

Neurobehav(e.g., discrimreversal leaaffected) 

Mink Daily dietary 

exposure (132 days) 

F 12 0.03 (control), 

0.8, 2.65, 9, or 70 

None 2.65 Reproductive 

effects 

Reduced kit 

Rat/Holtzman Corn oil 

gavage (initial loading dose followed by weekly dose during mating, pregnancy, and lactation–about 10 weeks) 

F (F0) F and M (F1 and F2) 

12 (F0) Not specified (F1 and F2) 

0 or 16.5 None 16.5 

(maternal exposure) 

Reproductive and developmental effects 

Decreased dof the ventr(F1), decrea(percentage (F2) 

Mouse/ICR Sesame oil 

gavage (initial loading dose followed by weekly doses for 5 weeks) 

M (F0) 42 or 43 0, 0.095, or 

950 

0.1 100 Reproductive 

effects 

Decreased msex ratio (pemales) (F1) 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-219 DRAFT: DO NOT CITE OR QUOTE 1 Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Reproductive toxicity studies (continued) Rat/Wistar albino Olive oil gavage (daily for 45 days) M 6 0, 1, 10, or 100 None 1 Reproductive effects Reduced sp

production, reproductivweights 

Rat/Sprague- Dawley Daily dietary exposure (3 generations) F and M, (F0) F and M, (F1 and F2) 10–32 (F0) 22 (F1) 28 (F2) 0, 1, 10, or 100 1 10 Reproductive and developmental effects Decrease in 

decrease in of live pupsgestational decrease in survival, depostnatal boone or more 

Monkey/ Rhesus Daily dietary exposure (4 years) F 8 0, 0.15, or 0.67 None 0.15 Reproductive effects Increased in

endometrioranged from severe) 

Rat/Sprague- Dawley Maternal corn oil gavage (weekly on GD 14 and 21; PND 7 and 14) Offspring corn oil gavage (weekly for 11 months) F (F0) F (F1) 3 (F0) 10 (F1) 0, 0.14, 0.71, 7.14, or 28.6 0.14 0.71 Reproductive effects Decrease se

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

1/15/10 2-220 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Reproductive toxicity studies (continued) Rhesus monkey/ Cynomolgus Fed gelatin capsules (5 days/week for 12 months) F 6 (treatment) 5 (controls) 0, 0.71, 3.57, or 17.86 17.86 None Endometriosis effects Increased en

implant survincreased mminimum imdiameters, gregulatory cdysregulatio

Developmental toxicity studies Rat/Harlan Sprague- Dawley Corn oil gavage (GD 10

−16 

F (F0) 80

−88 (F1) 0, 25, or 100 None 25 Developmental 

effects 

Decreased pthe consum0.25% saccsolution (F1

Rat/CRL:WI (Han) 

Maternal daily dietary exposure for an estimated 20 weeks (12 weeks prior to mating through parturition) 

F (F0) M (F1) 

65 (F0 treatments) 75 (F0 controls) at study initiation; following interim sacrifice ~30 animals were allowed to litter; F1 on PND 21 was ~7 

0, 2.4, 8, or 46 None 2.4 

(maternal exposure) 

Reproductive and developmental effects 

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

1/15/10 2-221 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Developmental toxicity studies (continued) Rat/Sprague- Dawley Maternal corn oil gavage (GD 14 and 21; PND 7 and 14) Offspring corn oil gavage (weekly for 8 months) F (F0 and F1) 2 or 3 (F0) 7 (F1) 0, 7.14, or 28.6 None 7.14 Developmental effects Decreased s

estradiol lev

Rat/Sprague- Dawley Maternal single corn oil gavage (GD 8) Offspring exposed during gestation and lactation (35 days) F (F0) F and M (F1) 12 (F0) 50 or 60 (F1) 0, 20, 60, or 180 None 20 (maternal exposure) Developmental effects Abrogation 

dimorphic nbehavioral r(F1) 

Rat/ Han/Wistar and Long- Evans Maternal single corn oil gavage (GD 15) F (F0) F and M (F1) 4 to 8 (F0) 3F/3M per treatment group (F1) 0, 30, 100, 300, or 1,000 None 30 (maternal exposure) Developmental effects Reduced m

length of thmolar (F1) 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-222 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Developmental toxicity studies (continued) Mouse/ C57BL/6J, BALB/cByJ, A/J, CBA/J, C3H/HeJ, and C57BL/10J Maternal single corn oil gavage (GD 13) F (F0) F and M (F1a, b, c) Dams not specified (F0); 23

−36 (F1a); 

4−5 (F1b); 107

−110 

(F1c) 

0, 10, 100, or 1,000 

None 10 

(maternal exposure) 

Developmental effects 

Variation in morphology C57BL/10J females (F1decreased mshape and sC3H/HeJ mvariation in in C3H/HeJ 

Mouse/ddY Maternal olive 

oil gavage (weekly for 8 weeks prior to mating) 

F (F0) M (F1) 

7 (F0) 3 (F1 immunocytochemical analysis) 6 (F1 cell number count) 

0, 0.7, or 70 None 0.7 

(LOEL) (maternal exposure) 

Neurotoxicity Decreased s

immunoreain raphe nucoffspring (F

Mouse/NIH (pregnant and pseudopregnant) 

Maternal sesame oil gavage daily for 8 days (GD 1

−8) 

F 10 0, 2, 50, or 100 None 2 Developmental 

effects 

Decreased pand increasestradiol lev

Rat/Holtzman Maternal 

single olive oil gavage (GD 18) 

F (F0 and F1) 

4−7 (F0 and F1) 

0, 20, 60, or 180 

None 20 

(maternal exposure) 

Behavioral effects 

Decreased tresponses (F

Rat/Line C Maternal 

single corn oil gavage (GD 15) 

F (F0) F and M (F1) 

24

−32 

(treatment) 12

−48 

(controls) 

0, 30, 100, 300, or 1,000 

None 30 

(maternal exposure) 

Developmental effects 

Increase in (F1) 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-223 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Developmental toxicity studies (continued) Rat/Holtzman Maternal single corn oil gavage (GD 15) F (F0) M (F1) Not specified (F0) 5 males and 3 females (F1) 0, 12.5, 50, 200, or 800 800 (maternal exposure) None Immunotoxicity Decreased s

cellularity (

Rat/Holtzman Maternal single corn oil gavage (GD 15) F (F0) M (F1) 6 (F0) 5 males and 3 females (F1) 0, 12.5, 50, 200, or 800 12.5 (maternal exposure) 50 (maternal exposure) Developmental effects Decreased a

distance (F1

Rat/Harlan Sprague- Dawley Maternal corn oil gavage (GD 10

−16 

F(F0) ~4 (F0); 

80

−88 (F1) 

0, 25, or 100 Not 

determined 

Not determined 

Developmental effects 

Facilitatory radial arm m(F1) 

Rat/SpragueDawley Maternal corn oil gavage 

(GD 10

−16) 

F and M (F1) 

~15 (F0); 5−9 (F1) 

0, 25, or 100 25 100 Developmental 

effects 

Decreased tweight 

Rat/TCDDresistant Han/Wistar bred with TCDDsensitive Long-Evans 

Maternal corn oil gavage (GD 15) 

F (F0) M (F1) 

5−8 (F0) 0, 30, 100, 

300, or 1,000 

100 300 Reproductive 

effects 

Reduction isperm producauda epidireserves 

Mouse/C57/6NCji 

Maternal drinking water exposure (daily for 17-day lactational period) 

F (F0) F and M (F1) 

8 (F0) Not specified (F1) 

0, 1.14, or 11.3 1.14 

(NOEL) (maternal exposure) 

11.3 (LOEL) (maternal exposure) 

Immunotoxicity Increased su

to Listeria (and femalesthymic CD4(F1 males); 

spleen weig(F1 males) 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-224 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Acute toxicity studies Mouse/B6C3F1 Corn oil gavage (single exposure) F 20 0, 1, 5, 10, 50, 100, or 6,000 5 10 Immunotoxicity Increased m

influenza in7 days after TCDD expo

Rat/Long- Evans Corn oil gavage (4 consecutive days) F 14, 6, 12, 6, 6, 6, 6, 6, 6, and 4, respectively in control and treated groups 0, 0.1, 3, 10, 30, 100, 300, 1,000, 3,000, or 10,000 30 100 Thyroid effects Reduction i

levels 

Rat/Sprague- Dawley Corn oil gavage (single dose) F 4 (treated); 9 (control) 0, 0.6, 2, 4, 20, 60, 200, 600, 2,000, 5,000, or 20,000 0.6 (NOEL) 2 (LOEL) Enzyme induction Increased benzo(a)pyr

hydroxylase 

Rat/Sprague- Dawley Corn oil dose via oral gastric intubation (single dose) F 10 0, 3, 10, 30, 100, 300, 1,000, 3,000, 10,000, or 30,000 3 10 Hormonal effects Increased s
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-225 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Acute toxicity studies (continued) Rat/Sprague- Dawley Corn oil gavage or TCDD- contaminated soil (single dose) F 6 0, 15, 40, 100, 200, 500, 1,000, 2,000, or 5,000 in corn oil 0, 15, 44, 100, 220, 500, 1,100, 2,000, or 5,500 in contaminated soil None 15 (LOEL) Enzyme induction Induction o

arylhyrocarhydroxylase dose in both protocols) 

Mouse/ B6C3F1 (BALB/c (C57BL/6N (and DBA2 Corn oil gavage (single dose) M, F 10

−40 0, 5, 20, 100, 

or 500 

500 None Mortality and 

body weight changes 

No increaseof virus-inftreatment-rechanges in b

Rat/TCDDresistant Han/Wistar bred; TCDDsensitive Long-Evans 

Corn oil gavage (single dose) 

M, F 9

−11 30–100,000 100 300 General 

toxicological endpoints, organ weights, dental defects 

Reduction ilevels 

Rat/TCDDresistant Han/Wistar bred with TCDDsensitive Long-Evans 

Corn oil gavage (single dose) 

M, F 5

−6 Line A: 

30

−3,000,000 

Line B: 30

−1,000,000 

Line C: 30

−100,000 

100 300 General 

toxicological endpoints, organ weights, dental defects 

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

1/15/10 2-226 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Acute toxicity studies (continued) Mouse/ C57BL/6N CYP1A2 (+/+) wild-type Corn oil gavage (single dose) F Not specified 0, 30, 100, 300, 1000, 3000, or 10,000 300 1,000 Immunotoxicity Decreased a

response to 

Rat/Sprague- Dawley Corn oil gavage (single dose) F 5

−15 0, 0.05, 0.1, 1, 

10, 100, 1,000, or 10,000 

0.1 (NOEL) 

1 (LOEL) 

Liver effects Increase in 

EROD activCYP1A1 m

Subchronic toxicity studies Rat/SpragueDawley Corn oil gavage (daily 

for 28 days) 

F 5 0, 2.5, 25, 250, 

or 1,000 

250 1,000 Body and organ 

weight changes 

Decreased bincreased reweight and biochemicadecreased rthymus wei

Rat/SpragueDawley Corn oil gavage (daily 

for 28 days) 

F 5 0, 2.5, 25, 250, 

or 1,000 

2.5 25 Liver effects Alterations 

thymus, and histopatholo

Guinea pig/ Hartley 

Daily dietary exposure (90 days) 

M, F 10/sex 0, 0.12, 0.61, 

4.9, or 26 (males); 0, 0.12, 0.68, 4.86, or 31 (females) 

0.61 4.9 Body and organ 

weight changes 

Decreased b(male and fincreased reweights (madecreased rthymus wei

Mice/B6C3F1 Corn oil 

gavage (5 days/week for 13 weeks) 

F 5 0, 1.07, 3.21, 

10.7, 32.1, or 107 

None 1.07 

(LOEL) 

Body and organ weight changes; enzyme induction 

Increased EACOH and phosphotyroat all doses 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-227 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Subchronic toxicity studies (continued) Daily dietary exposure (13 weeks) M, F 6 0, 20, 200, or 2,000 Daily dietary exposure (13 weeks) M, F 6 0 or 200 Daily dietary exposure (13 weeks) M, F 6 0, 200, or 1,000 Rat/Iva:SIV 50-Sprague- Dawley Daily dietary exposure (13 weeks, 26, and 39 weeks) F 6 0 or 100 None 20 Liver effects Reduced he

vitamin A l

Rat/Sprague- Dawley Gavage loading/ maintenance doses (every 4 days for 14 days) M, F 6 0, 0.55, 307, or 1,607 0.57 327 Body and liver weight changes; hepatic cell proliferation Increased ab

relative live

Mouse/ B6C3F1 Corn oil gavage (5 days/week for 13 weeks) F Not specified 0, 0.32, 1.07, 10.7, or 107 None 0.32 (LOEL) Brain effects Induction o

of oxidative doses 

Rat/Harlan Sprague- Dawley Corn oil gavage (5 days/week for 13 weeks) F 6 0, 2.14, 7.14, 15.7, 32.9, or 71.4 None 2.14 (LOEL) Liver and brain effects Induction o

of oxidative doses in liv
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-228 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Subchronic toxicity studies (continued) Rat/Harlan Sprague- Dawley Corn oil gavage (5 days/week for 13 weeks) F 12 0, 7.14, 15.7, or 32.9 None 7.14 (LOEL) Brain effects Induction o

of oxidative doses 

Rat/Sprague- Dawley Corn oil gavage (5 days/week for 13 weeks) M, F 12 0, 0.71, 7.14, 71.4, or 714 7.14 71.4 Liver effects, body weight changes, and hematologic and clinical effects Reduced bo

and food coslight liver lymphoid dincreased urporphyrins aminolevuliincreased sephosphatase bilirubin 

Rat/F344 Corn oil gavage (2 days/week for 28 days) F 3 0, 0.71, 7.14, or 71.4 None 0.71 (LOEL) Clinical signs and histopathology Decreased C

number and liver 

Mouse/ B6C3F1 Corn oil gavage (5 days/week for 13 weeks) F Not specified 0, 0.11, 0.32, 1.07, 10.7, or 107.14 1.07 (NOEL) 10.7 (LOEL) Liver, lung, kidney, and spleen effects Increased h

superoxide 

Mouse/ B6C3F1 Corn oil gavage (5 days/week for 13 weeks) F 8

−15 0, 1.07, 10.7, 

107, or 321 

None 1.07 Immunotoxicity 

and organ weight 

Reduced anresponse to increased reweight 

Rat/SpragueDawley TCDD in diet (13 weeks) F 8 0, 14, 26, 47, 320, or 1,024 None 14 Multiple end- points Decreased a

relative thymdecreased lilevels 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-229 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Subchronic toxicity studies (continued) Guinea pig/ Hartley Corn oil gavage (weekly for 8 weeks) F 10 0, 1.14, 5.71, 28.6, or 143 1.14 5.71 Immunotoxicity Decreased t

leukocytes lymphocyte decreased athymus and increase in serum tetan

Mouse/ B6C3F1 Corn oil gavage (daily for 14 days) F 6

−8 0, 10, 50, 100, 

500, 1,000, or 2,000 

None 10 Immunotoxicity Reduction o

complemen

Chronic toxicity studies Rat/CDCOBS Corn oil gavage 

(weekly for 45 weeks) 

F 4 0, 1.43, 14.3, 

or 143 

None 1.43 Hepatic 

porphyria 

Increased uporphyrin e

Rat/SpragueDawley Loading/ maintenance 

dose (every 3 days for different durations up to 128 days) 

F 5 0, 0.85, 3.4, 

13.6, 54.3, or 217 (28-day duration) 

54.3 (28-day duration) 

217 (28-day duration) 

Body weight changes and changes in PEPCK activity and IGF-I levels 

Decreased bdecreased Pactivity, and IGF-I levels 

Rat/SpragueDawley Corn oil gavage 

(5 days/week for 30 weeks) 

F 6 0, 2.14, 7.14, 

15.7, 32.9, or 71.4 

None 2.14 

(LOEL) 

Brain effects Induction o

of oxidative doses 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 2-230 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Chronic toxicity studies (continued) Rat/Sprague- Dawley Daily dietary exposure (2 years) M, F 50 0, 1, 10, or 100 1 10 Multiple endpoints measured Increased u

porphyrins, hepatocelluand focal alhyperplasia 

Rat/Sprague- Dawley Biweekly gavage (30 weeks) F 9 0, 3.5, 10.7, 35, or 125 10.7 35 Body and organ weight changes, clinical chemistry, hepatocellular proliferation Increased re

weight 

Mouse/ B6C3F1; Rat/Osborne Mendel Corn oil gavage (2 days/week for 104 weeks) M, F 50 0, 1.4, 7.1, or 71 for rats and male mice; 0, 5.7, 28.6, or 286 for female mice None 1.4 Liver and body weight changes Increased in

liver lesions (males and 

Rat/Sprague- Dawley Corn oil gavage (5 days/week for 105 weeks) F 53 0, 2.14, 7.14, 15.7, 32.9, or 71.4 None 2.14 Liver and lung effects Increased ab

relative liveincreased inhepatocelluhypertrophyincidence obronchiolar metaplasia 

Monkey/ Rhesus Daily dietary exposure (4 years) F 8 0, 0.15, or 0.67 None 0.15 General toxicological endpoints and reproductive effects Elevated se

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

1/15/10 2-231 DRAFT: DO NOT CITE OR QUOTE Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued) Species/ strain Exposure protocol Sex (exposure group) n Average daily dose levels (ng/kg-day) NOAEL (ng/kg-day) LOAEL (ng/kg-day) Endpoint(s) examined LOAEL

Endp

Chronic toxicity studies (continued) Rat/Sprague- Dawley Biweekly gavage (30 weeks) F 9 0, 3.5, 10.7, 35, or 125 None 3.5 (LOEL) EGFR kinetics and auto- phosphorylation, hepatocellular proliferation Decrease in 

maximum bcapacity 

Rat/Sprague- Dawley Biweekly gavage (30 weeks) F 9 0, 0.1, 0.35, 1, 3.5, 10.7, 35, or 125 10.7 35 Thyroid function Decreased s

levels 

Mouse/Swiss/ H/Riop Sunflower oil gavage (weekly for 1 year) M 38

−44 0, 1, 100, or 

1,000 

None 1 Skin effects Dermal amy

skin lesions 

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

1/15/10 DRAFT 2-232 ―DO NOT CITE OR QUOTE 

1 

2 Figure 2-1. EPA’s process to select and identify in vivo mammalian and 

3 epidemiologic studies for use in the dose-response analysis of TCDD. EPA 

4 first conducted a literature search to identify studies published since the 2003 

5 Reassessment. Results were published and additional study submissions were accepted 

6 from the public. Next EPA developed TCDD-specific study inclusion criteria for in vivo 

7 mammalian studies and held a Dioxin Workshop where these criteria were discussed and 

8 refined. Third, EPA developed two final sets of study inclusion criteria, one for in vivo 

9 mammalian studies and another for epidemiologic studies. Finally, EPA applied these 

10 two sets of criteria to all studies from the literature search, public submissions, 2003 

11 Reassessment, and additional studies identified by EPA after the Dioxin Workshop 

12 through October 2009. The studies that met these criteria formed a list of key studies for 

13 EPA’s consideration in TCDD dose-response assessment. 

Studies Included in final list of 

key cancer and noncancer 

studies for quantitative doseresponse analysis of TCDD

Literature search for in vivo mammalian bioassays and 

epidemiologic TCDD studies (2000−2008)

Studies excluded

from quantitative

dose-response

analysis of TCDD 

Criteria

met?

Yes No

Initial TCDD-specific study inclusion criteria 

development for in vivo mammalian bioassays 

Dioxin workshop (2009) and expert refinement of 

TCDD study inclusion criteria for in vivo mammalian bioassays

Federal Register Notice; Web publication of literature 

search for public comment and submissions

Final literature collection (October, 2009)

Studies screened using TCDD study inclusion criteria: 

• Studies cited in 2003 Reassessment

• Studies identified via literature search results

• Studies submitted by the public

• Studies collected by EPA in 2009

Final development of two sets of TCDD study inclusion criteria:

• For in vivo mammalian bioassays

• For epidemiologic studies

Studies Included in final list of 

key cancer and noncancer 

studies for quantitative doseresponse analysis of TCDD

Literature search for in vivo mammalian bioassays and 

epidemiologic TCDD studies (2000−2008)

Studies excluded

from quantitative

dose-response

analysis of TCDD 

Criteria

met?

Yes No

Initial TCDD-specific study inclusion criteria 

development for in vivo mammalian bioassays 

Dioxin workshop (2009) and expert refinement of 

TCDD study inclusion criteria for in vivo mammalian bioassays

Federal Register Notice; Web publication of literature 

search for public comment and submissions

Final literature collection (October, 2009)

Studies screened using TCDD study inclusion criteria: 

• Studies cited in 2003 Reassessment

• Studies identified via literature search results

• Studies submitted by the public

• Studies collected by EPA in 2009

Final development of two sets of TCDD study inclusion criteria:

• For in vivo mammalian bioassays

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

1/15/10 DRAFT 2-233 ―DO NOT CITE OR QUOTE 

1 

2 Figure 2-2. EPA’s process to evaluate available epidemiologic studies using 

3 study inclusion criteria for use in the dose-response analysis of TCDD. EPA 

4 applied its TCDD-specific epidemiologic study inclusion criteria to all studies published 

5 on TCDD and DLCs. The studies were initially evaluated using five considerations 

6 regarded as providing the most relevant kind of information needed for quantitative 

7 human health risk analyses. For each study that satisfied most of these considerations 

8 and was published in the peer-reviewed literature, EPA then examined whether the 

9 exposures were primarily to TCDD and if the TCDD exposures could be quantified so 

10 that dose-response analyses could be conducted. Finally, EPA required that the effective 

11 dose and oral exposure be estimable: (1) for cancer, information is required on long-term 

12 exposures, (2) for noncancer, information is required regarding the appropriate time 

13 window of exposure that is relevant for a specific, nonfatal health endpoint, and (3) for 

14 all endpoints, the latency period between TCDD exposure and the onset of the effect is 

15 needed. Only studies meeting these criteria were included in EPA’s TCDD 

16 dose-response analysis. 

List of available epidemiologic studies on TCDD and DLCs

Evaluate study using five considerations:

• Methods used to ascertain health outcomes are clear and unbiased.

• Confounding and other potential sources of bias are addressed.

• Exposures based on individual-level estimates and uncertainties described.

• Statistical precision, power and study follow-up are sufficient.

• Exposure methods are described including exposure duration and latency. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Exposure 

windows and 

latency information 

available for RfD 

assessment?

Key study included 

for TCDD noncancer

dose-response 

assessment

Key study included

for TCDD cancer

dose-response

assessment

Long-term 

exposures and 

latency information 

available for cancer 

assessment?

Exposure 

primarily to TCDD 

and quantified?

No No

Most

elements of the five

considerations 

satisfied?

No

Yes

Yes

Yes

List of available epidemiologic studies on TCDD and DLCs

Evaluate study using five considerations:

• Methods used to ascertain health outcomes are clear and unbiased.

• Confounding and other potential sources of bias are addressed.

• Exposures based on individual-level estimates and uncertainties described.

• Statistical precision, power and study follow-up are sufficient.

• Exposure methods are described including exposure duration and latency. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Exposure 

windows and 

latency information 

available for RfD 

assessment?

Key study included 

for TCDD noncancer

dose-response 

assessment

Key study included

for TCDD cancer

dose-response

assessment

Long-term 

exposures and 

latency information 

available for cancer 

assessment?

Exposure 

primarily to TCDD 

and quantified?

No No

Most

elements of the five

considerations 

satisfied?

No

Yes

Yes

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

1/15/10 DRAFT 2-234 ―DO NOT CITE OR QUOTE 

1 

2 Figure 2-3. EPA’s process to evaluate available animal bioassay studies using study 

3 inclusion criteria for use in the dose-response analysis of TCDD. EPA evaluated all 

4 available in vivo mammalian bioassay studies on TCDD. Studies had to be published in 

5 the peer-reviewed literature. Next, to ensure working in the low-dose range for TCDD 

6 dose-response analysis, EPA applied dose requirements to the lowest tested average daily 

7 doses in each study, with specific requirements for cancer (≤1 μg/kg-day) and noncancer 

8 (≤30 ng/kg-day) studies. Third, EPA required that the animals were exposed via the oral 

9 route to only TCDD and that the purity of the TCDD was specified. Finally, the studies 

10 were evaluated using four considerations regarded as providing the most relevant kind of 

11 information needed for quantitative human health risk analyses from animal bioassay 

12 data. Only studies meeting all of these criteria and considerations were included in 

13 EPA’s TCDD dose-response analysis. 

List of available in vivo mammalian bioassay studies on TCDD

Evaluate study further using four considerations:

• Strain, gender, and age of test species is identified.

• Testing protocol, including duration and timing of dosing is clear.

• Study design is consistent with standard toxicological practices.

• Magnitude of animal responses is outside range of normal variability. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Lowest dose 

tested for noncancer 

endpoint <30

ng/kg-day?

Key study included 

for TCDD cancer and/or noncancer

dose-response assessment

Lowest 

dose tested for

cancer endpoint <1

μg/kg-day?

Oral

exposure to TCDD 

only with purity

specified?

No No

Were

elements of the four

considerations 

satisfied?

No

Yes

Yes

Yes

No

List of available in vivo mammalian bioassay studies on TCDD

Evaluate study further using four considerations:

• Strain, gender, and age of test species is identified.

• Testing protocol, including duration and timing of dosing is clear.

• Study design is consistent with standard toxicological practices.

• Magnitude of animal responses is outside range of normal variability. 

Study excluded 

from TCDD 

dose-response 

assessment

Study 

in peer-reviewed 

literature?

Yes

No

Yes

No

Lowest dose 

tested for noncancer 

endpoint <30

ng/kg-day?

Key study included 

for TCDD cancer and/or noncancer

dose-response assessment

Lowest 

dose tested for

cancer endpoint <1

μg/kg-day?

Oral

exposure to TCDD 

only with purity

specified?

No No

Were

elements of the four

considerations 

satisfied?

No

Yes

Yes

Yes

No
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-1 —DO NOT CITE OR QUOTE 

1 3. THE USE OF TOXICOKINETICS IN THE DOSE-RESPONSE MODELING FOR 

2 CANCER AND NONCANCER ENDPOINTS 

 3 

 4 

5 A key recommendation from the National Academy of Sciences (NAS) for improving the 

6 2003 Reassessment was that U.S. Environmental Protection Agency (EPA) should justify its 

7 approaches to dose-response modeling for cancer and noncancer endpoints. Further, the NAS 

8 suggested that EPA incorporate the most up-to-date and relevant state of the science for 

9 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) dose-response assessment. 

10 While EPA believes that at the time of its release, the 2003 Reassessment offered a 

11 substantial improvement over the general state-of-the-science regarding dose-response modeling, 

12 EPA agrees that the justification of the approaches to dose-response modeling can be improved 

13 and the methodologies updated to reflect the most current EPA practices and science. In 

14 Section 3, EPA describes the use of toxicokinetic (TK) information in the dose-response 

15 modeling assessment of TCDD. Section 3.1 summarizes the NAS comments regarding the use 

16 of TK in the dose-response approaches for TCDD. Section 3.2 overviews EPA’s responses to 

17 the NAS comments. Section 3.3 discusses TCDD kinetics, including TK models developed to 

18 simulate disposition of this compound in rodents and humans (see Section 3.3.4), alternative 

19 measures of dose that could be used in a TCDD dose-response analysis and uncertainties in the 

20 TCDD dose estimates (see Section 3.3.5). Sections 4 and 5 of this document incorporate the TK 

21 information into noncancer and cancer dose response modeling, respectively. 

22 

23 3.1. SUMMARY OF NAS COMMENTS ON THE USE OF TOXICOKINETICS IN 

24 DOSE-RESPONSE MODELING APPROACHES FOR TCDD 

25 The NAS commented on the appropriate use of TK models in dose-response modeling 

26 for TCDD. Specifically, the committee requested that EPA consider using such models to 

27 provide refined estimates of dose, for example, as the underlying science and predictive 

28 capabilities of these models improved. 

29 

30 [Discussing Kinetic models]...the committee encourages further development and 

31 use of these models as data become available to validate and further develop them 

32 (NAS, 2006a, p. 59).

33 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-2 —DO NOT CITE OR QUOTE 

1 Although the NAS basically agreed with EPA’s use of body burden as a dose metric in 

2 the 2003 Reassessment (e.g., see NAS, 2006a, p. 7), the NAS was concerned about the 

3 limitations of first order kinetic models, such as the one used in the 2003 Reassessment, to 

4 estimate TCDD body burdens. 

5 

6 TCDD, other dioxins, and DLCs act as potent inducers of CYP, a property that 

7 can affect both the hepatic sequestration of these compounds and their half-lives. 

8 Hepatic sequestration of dioxin may influence the quantitative extrapolation of the 

9 rodent liver tumor results because the body-burden distribution pattern in highly 

10 dosed rats would differ from the corresponding distribution in humans subject to 

11 background levels of exposure. EPA should consider the possible quantitative 

12 influence of dose-dependent toxicokinetics on the interpretation of animal 

13 toxicological data (NAS, 2006a, p. 129). 

14 

15 The NAS also asked EPA to evaluate the impact of kinetic uncertainty and variability on 

16 dose-response assessment. The NAS committee asked EPA to use TK models to examine both 

17 interspecies and human interindividual differences in the disposition of TCDD, which would 

18 better justify EPA dose-response modeling choices. 

19 

20 The Reassessment does not adequately consider the use of a PBPK model to 

21 define species differences in tissue distribution in relation to total body burden for 

22 either cancer or noncancer end points (NAS, 2006a, p. 62). 

23 

24 EPA ...should consider physiologically based pharmacokinetic modeling as a 

25 means to adjust for differences in body fat composition and for other differences 

26 between rodents and humans (NAS, 2006a, p. 10). 

27 

28 The Reassessment does not provide details about the magnitudes of the various 

29 uncertainties surrounding the decisions EPA makes in relation to dose metrics 

30 (e.g., the impact of species differences in percentage of body fat on the steady31 state concentrations present in nonadipose tissues). The committee recommends 

32 that EPA use simple PBPK models to define the magnitude of any differences 

33 between humans and rodents in the relationship between total body burden at 

34 steady-state concentrations (as calculated from the intake, half-life, 

35 bioavailability) and tissue concentrations. The same model could be used to 

36 explore human variability in kinetics in relation to elimination half-life. EPA 

37 should modify the estimated human equivalent intakes when necessary (NAS, 

38 2006a, p. 73). 

39 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-3 —DO NOT CITE OR QUOTE 

1 Finally, the NAS asked EPA to use TK considerations to better justify its choice of dose 

2 metric. 

3 

4 EPA makes a number of assumptions about the appropriate dose metric and 

5 mathematical functions to use in the Reassessment’s dose-response analysis ... 

6 but does not adequately comment on the extent to which each of these 

7 assumptions could affect the resulting risk estimates...EPA did not quantitatively 

8 describe how this particular selection affected its estimates of exposure and 

9 therefore provided no overall quantitative perspective on the relative importance 

10 of the selection (NAS, 2006a, p. 51). 

11 

12 3.2. OVERVIEW OF EPA’S RESPONSE TO THE NAS COMMENTS ON THE USE OF 

13 TOXICOKENTICS IN DOSE-RESPONSE MODELING APPROACHES FOR 

14 TCDD 

15 In response to the NAS recommendations regarding TCDD kinetics and choice of dose 

16 metrics, this document presents an in depth evaluation of TCDD TK models, exploring their 

17 differences and commonalities and their possible application for the derivation of dose metrics 

18 relevant to TCDD. Initially, EPA discusses the application of first order kinetics to estimate 

19 body burden as a dose metric for TCDD. This first order kinetic model is used to predict TCDD 

20 body burden for all of the studies identified as Key Studies (see Section 2.4); this model uses a 

21 constant half-life to simulate the elimination of TCDD from the body. However, given the 

22 observed data indicating early influence of cytochrome P450 1A2 (CYP1A2) induction and 

23 binding to TCDD in the liver and later redistribution of TCDD to fat tissue, the use of a constant 

24 half-life for TCDD clearance following long term or chronic TCDD exposure is not biologically 

25 supported. Therefore, using half-life estimates based on observed terminal steady state levels of 

26 TCDD will not account for the possibility of an accelerated dose-dependent clearance of the 

27 chemical during early stages following elevated TCDD exposures. The biological processes 

28 leading to dose-dependent TCDD excretion are better described using physiologically based 

29 pharmacokinetic (PBPK) models than by simple first order kinetic models. Additionally, as part 

30 of its preparation for developing this document, EPA evaluated recent TCDD kinetic studies as 

31 NAS advocated. Although the NAS agreed with continued use of body burden metric as the 

32 dose metric of choice, EPA believes that the state-of-the-practice has advanced sufficiently to 

33 justify the consideration of alternative dose metrics (other than administered dose) based on an 

34 application of a physiologically-based TK model. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-4 —DO NOT CITE OR QUOTE 

1 EPA identified a number of advances in the overall scientific understanding of TCDD 

2 disposition; many of these are documented in a summary discussion introducing the section on 

3 TCDD kinetics (see Section 3.3). The increased understanding warranted an evaluation of 

4 current kinetic modeling of TCDD to determine if the use of such models would improve the 

5 dose-response assessment for TCDD. Justification of the final PBPK model choice is detailed in 

6 Section 3.3. Through the choice of a published PBPK model to estimate dose metrics for dioxin, 

7 EPA has addressed several of the NAS concerns. The PBPK model can be applied to estimate 

8 dose metrics other than body burden that may be more directly related to response, e.g., tissue 

9 levels, serum levels, blood concentrations, or dose metrics related to TCDD-protein receptor 

10 binding. The selected PBPK model included explicit description of physiological and 

11 biochemical parameters, therefore, it can also provide an excellent tool for investigating 

12 differences in species uptake and disposition of TCDD. One of the criteria used to select a 

13 PBPK model for TCDD kinetics was the availability of both human and animal models so that 

14 differences in species uptake and disposition of TCDD can be investigated. Additionally, the 

15 PBPK model includes quantitative information that is suitable for addressing the impact of 

16 physiological (e.g., body weight [BW] or fat tissue volume), or biochemical (e.g., induction of 

17 CYP1A2) variability on overall risk of TCDD between species, in response to another area of 

18 concern in the NAS report. The sensitivity analysis and uncertainty in dose metrics derived for 

19 the risk assessment of TCDD are also presented in Section 3.3. Detailed discussion on the 

20 uncertainty in choice of PBPK model-driven dose metrics is also provided in Section 3.3.

21 

22 3.3. PHARMACOKINETICS (PK) AND PK MODELING 

23 3.3.1. PK Data and Models in TCDD Dose-Response Modeling: Overview and Scope 

24 In general, the use of measures of internal dose in dose-response modeling is considered 

25 to be superior to that of administered dose (or uptake) because the former is more closely related 

26 to the response. The evaluation of internal dose, or dose metric, in exposed humans and other 

27 animals is facilitated by an understanding of pharmacokinetics (i.e., absorption, distribution, 

28 metabolism, and excretion). When measurements of internal dose (e.g., blood concentration, 

29 tissue concentration) are not available in animals and humans, pharmacokinetic models can be 

30 used to estimate them. The available data on the pharmacokinetics of TCDD in animals and 

31 humans have been reviewed (Van Birgelen and Van den Berg, 2000; U.S. EPA, 2003; NRC, 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-5 —DO NOT CITE OR QUOTE 

1 2006). It is evident based on these reviews and other analyses that three distinctive features of 

2 TCDD play important roles in determining its pharmacokinetic behavior, as discussed below: 

3 

4 TCDD is very highly lipophilic and thus is more soluble in fat or other relatively nonpolar 

5 organic media than in water. The n-octanol/water partition coefficient is a commonly6 used measure of lipophilicity equal to the equilibrium ratio of a substance’s concentration 

7 in n−octanol (a surrogate for biotic lipid) to the substance’s concentration in water (Leo 

8 et al., 1971). For TCDD, this coefficient is on the order of 10,000,000 or more (ATSDR, 

9 1998). It follows that the solubility of TCDD in the body’s lipid fraction, i.e., the fatty 

10 portions of various tissues, including adipose, organs, and blood, is extremely high. 

11 TCDD is very slowly metabolized compared to many other organic compounds, with an 

12 elimination half life in humans on the order of years following an initial period of 

13 distribution in the body (Carrier et al., 1995a; Michalek et al., 2002). Most laboratory 

14 animals used for toxicologic testing tend to eliminate TCDD much more quickly than 

15 people, although even in animals TCDD is eliminated much more slowly than most other 

16 chemicals. 

17  TCDD induces binding proteins in the liver that have the effect of sequestering some 

18 of the TCDD. The ability of TCDD to alter gene expression and the demonstration that 

19 the induction of CYP1A2 is responsible for hepatic TCDD sequestration suggest that 

20 both pharmacokinetic and pharmacodynamic events must be incorporated for a 

21 quantitative description of TCDD disposition (Santostefano et al., 1998). The induction 

22 of these proteins implies that TCDD tends to be eliminated more rapidly in the early 

23 years following short-term, high-level exposures than it is after those initial levels have 

24 declined. Recent efforts of pharmacokinetic modeling have supported the concentration25 dependent elimination of TCDD in animals and humans (Aylward et al., 2005b; Emond 

26 et al., 2006). 

27 

28 Sections 3.3.2 and 3.3.3 of this section present the salient features of TCDD 

29 pharmacokinetics in animals and humans, with particular focus on mechanisms and data of 

30 relevance to interspecies and intraspecies variability. Section 3.3.4 describes the various dose 

31 metrics for the dose-response modeling of TCDD and the characteristics of pharmacokinetic 

32 models potentially useful for estimating these metrics. Finally, Sections 3.3.5 and 3.3.6 

33 summarize the results of application of pharmacokinetic models to derive dose metrics as well as 

34 the uncertainty associated with the predictions of dose metrics used in dose-response modeling. 

35 Dose metrics derived via PBPK modeling approaches are utilized in Sections 4 and 5 of this 

36 document for noncancer and cancer TCDD dose-response modeling, respectively. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-6 —DO NOT CITE OR QUOTE 

1 3.3.2. PK of TCDD in Animals and Humans 

2 3.3.2.1. Absorption and Bioavailability 

3 When administered via the oral route in the dissolved form, TCDD appears to be well 

4 absorbed. Animal studies indicate that oral exposure to TCDD in the diet or in an oil vehicle 

5 results in the absorption of >50% of the administered dose (Nolan et al., 1979; Olson et al., 

6 1980). Human data from Poiger and Schlatter (1986) indicate that >87% of the oral dose (after 

ingestion of 105 ng [3 7 H]−2,3,7,8−TCDD [1.14 ng/kg BW] in 6 mL corn oil) was absorbed from 

8 the gastrointestinal tract. Lakshmanan et al. (1986), investigating the oral absorption of TCDD, 

9 suggested that it is absorbed primarily by the lymphatic route and transported predominantly by 

10 chylomicrons. 

11 Oral absorption is generally less efficient when TCDD is more tightly bound in soil 

12 matrices. Based on experiments in miniature swine, Wittsiepe et al. (2007) reported an 

13 approximately 70% reduction in bioavailability when TCDD was administered in the form of 

14 contaminated soil, relative to TCDD after extraction from the same soil matrix with solvents. 

15 Working with soil from the prominent contamination site at Times Beach, Missouri, Shu et al. 

16 (1988) reported an oral bioavailability of approximately 43% based on experiments in rats. 

17 Percent dose absorbed by the dermal route is reported to be less than the oral route, whereas 

18 absorption of TCDD by the transpulmonary route appears to be efficient (see, for example, 

19 Banks et al., 1990; Banks and Birnbaum, 1991; Nessel et al., 1992; Diliberto et al., 1996; 

20 U.S. EPA, 2003; Roy et al., 2008). 

21 

22 3.3.2.2. Distribution 

23 TCDD in systemic circulation equilibrates and moves into the tissues where it is then 

24 accumulated, bound, or eliminated. Whereas the bulk of the body tissues are expected to 

25 equilibrate in a matter of hours, the adipose tissue will approach equilibrium concentrations with 

26 blood much more slowly. Consistent with these assertions, a number of experimental and 

27 modeling studies in rats and humans have shown that TCDD has a large volume of distribution 

28 (Vd), i.e., the apparent volume in which it is distributed. The Vd corresponds to the volume of 

29 blood plus the product of internal tissue volumes and the corresponding tissue:blood partition 

30 coefficients. This parameter is a key determinant of the elimination rate of TCDD in exposed 

31 organisms. The tissue:blood partition coefficients of TCDD, in turn, are determined by the 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-7 —DO NOT CITE OR QUOTE 

1 relative solubility of TCDD in tissue and blood components (including neutral lipids, 

2 phospholipids, and water). 

3 Column 1 in Table 3-1 presents the tissue:blood partition coefficients for TCDD (Wang 

4 et al., 1997; Emond et al., 2005). Column 3 of this table lists the physical volume of each tissue, 

5 scaled to a person weighing 60 kg. The last column shows the implications of the tissue volumes 

6 and tissue:blood partition coefficients for the effective volumes of distribution for each tissue 

7 and for the body as a whole. It can be seen that, purely on the basis of solubility space, the fat 

8 should be expected to contain about 94% of the TCDD in the body, and that the body as a whole 

9 behaves as if it is about 1,200 liters in terms of blood-equivalents (i.e., approximately 22−fold 

10 larger than its physical volume). 

11 Maruyama et al. (2002) have published another set of tissue/blood partition coefficients 

12 for TCDD and other dioxin congeners based in part on observations of tissue concentrations 

13 measured in autopsy specimens from eight Japanese people without known unusual exposures to 

14 TCDD. Their estimates of TCDD partition coefficients seem to be rather large and variable, 

15 with a fat:blood value of 247 ± 78 (standard deviation [SD]), a liver:blood value of 9.8 ± 5.7 and 

16 a muscle:blood value of 18 ± 10.6. Depending on time of autopsy, tissue samples may not be an 

17 accurate source of information on observed, in vivo partition coefficients because weight loss is 

18 likely to occur pre and post mortem. In particular, a decline in fat stores volume could lead to an 

19 increased concentration of dioxin in fat in autopsy specimens relative to what would be observed 

20 in vivo. 

21 The calculations shown in Table 3-1 do not include the additional amount that will be 

22 bound to induced proteins in the liver. That induction and binding will tend to increase the 

23 contribution of the liver on the effective volume of distribution (Birnbaum, 1986). 

24 It is also of interest to point out some basic implications of the data in Table 3-1 for the 

25 expected rates of perfusion-mediated transfer of TCDD between blood and each of the 

26 organ/tissues. The rate of loss from a tissue (occurring primarily via blood flow) and the 

27 corresponding half-life can be calculated using the following equations: 

28 

Blood flow (liters / hour) -1 Rate constant for loss (hour ) = Tissue volume (liters) × Tissue / Blood Partition Coefficent

29 (Eq. 3-1) 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-8 —DO NOT CITE OR QUOTE 

( )

1/2

ln 2

t for tissue perfusion loss = Rate constant for loss 

ln(2) Tissue volume (liters) Tissue/Blood Partition Coefficent = Blood flow (liters/hour)× ×

1 (Eq. 3-2) 

2 

3 Because TCDD is highly lipophilic, its concentration in the aqueous portion of the blood 

4 is very small, and TCDD tends to partition from blood components into cellular membranes and 

5 tissues, probably in large part via diffusion. As a result, full equilibrium concentrations of 

6 TCDD are not attained by the end of the transit time through organs from the arterial to venous 

7 blood. For organs in which this occurs, diffusion coefficients or “permeability factors” have 

8 been estimated to assess the fractional attainment of equilibrium concentration that occurs by the 

9 time the blood leaving each organ reaches the venous circulation. Table 3-2 presents the 

10 permeability factors and implications for perfusion half-lives for TCDD, per Emond et al. (2005, 

11 2006). 

12 Despite the high lipid bioconcentration potential of TCDD, it does not always occur at 

13 the highest concentration in the adipose tissue (Poiger and Schlatter, 1986; Geyer et al., 1986; 

14 Abraham et al., 1988). Further, the ratios of tissue:tissue concentrations of TCDD and related 

15 compounds (e.g., the liver:adipose ratio) may not remain constant during nonsteady-state 

16 conditions. TCDD concentrations have been observed to decrease more rapidly in the liver than 

17 in adipose tissue. For example, Abraham et al. (1988) found that the liver:adipose tissue 

18 concentration ratio in female Wistar rats exposed to a subcutaneous TCDD dose of 300 ng/kg 

19 decreased from 10.3 at 1 day postexposure to 0.5 at 91 days postexposure. It should be noted 

20 that even at a ratio of 0.5, the amount of TCDD in the liver is greater than that based on lipid 

21 content of the tissue alone, consistent with the presence of hepatic TCDD binding proteins. The 

22 liver/adipose tissue ratio also was dose-dependent, such that the liver TCDD burden increased 

23 from ~11% of the administered dose at low doses (i.e., 1−10 ng/kg) to ~37% of the dose at an 

24 exposure level of 300 ng/kg. The increase in TCDD levels in liver, accompanied by a decrease 

25 in concentration in the adipose tissue, is a particular behavior to be considered in high dose to 

26 low dose extrapolations. This behavior is essentially a result of dose-dependent hepatic 

27 processes, as described below. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-9 —DO NOT CITE OR QUOTE 

1 3.3.2.3. Metabolism and Protein Binding 

2 The metabolism of TCDD is slow, particularly in humans, and it is thought to be 

3 mediated by the CYP1A2 enzyme that is inducible by TCDD (Ramsey et al., 1982; Wendling et 

4 al., 1990; Olson et al., 1994; Weber et al., 1997). The low rate of metabolism in combination 

5 with sequestration appear to account for the retention of TCDD in liver, and these processes 

6 collectively contribute to the long half-life for elimination of TCDD from the body. 

7 Dynamic changes in TCDD binding in liver and partitioning to fat have been studied 

8 extensively in rats and mice (Diliberto et al., 1995, 2001). Figure 3-1 shows observations by 

9 Diliberto et al. (1995) of the ratio of liver concentrations to adipose tissue concentrations for 

10 mice given doses spread over a 100−fold range and studied at four different times following 

11 exposure. It can be seen that even for the lowest dose studied the liver:fat concentration ratio is 

12 higher than would be expected based on the lipid contents of the tissues (i.e., 0.06:1, 

13 corresponding to the ratio of human liver:blood and fat:blood partition coefficients; see 

14 Table 3-1). Moreover, the relative concentration in the liver consistently rises with dose, with 

15 the steepest rise observed during the first two weeks after dosing. If the distribution of TCDD 

16 were governed solely by passive partitioning into fat, there should be no such change in relative 

17 concentrations with dose. However, data presented in Figure 3-1 illustrate that at longer time 

18 points, the ratio of TCDD in the liver to TCDD in fat decreases, indicating that a redistribution of 

19 the chemical occurs as time goes on for each applied dose. The redistribution of TCDD tissue 

20 levels from liver to fat with increasing time suggests that binding of the chemical in the liver 

21 (including via induction of CYP1A2) is an important kinetic consideration at early exposure 

22 points with relatively high applied doses. At steady state levels (longer than 35 days, and low 

23 applied doses), there seems to be a tendency for TCDD to redistribute to fat tissue. 

24 Experiments with CYP1A2 “knock-out” mice (i.e., congenic strains differing in only a 

25 single gene that is “knocked out” in one of the strains) indicate that the inducible binding of 

26 TCDD is attributable to CYP1A2 (Diliberto et al., 1997, 1999). As noted previously, this 

27 enzyme is believed to make an important contribution to metabolism of TCDD. Given the 

28 critical role of CYP1A2 induction in the kinetics of TCDD, dose-and time-dependent induction 

29 of this protein in rats has been examined and modeled (Wang et al., 1997; Santostefano et al., 

30 1998; Emond et al., 2004, 2006). Accordingly, the amount of CYP1A2 in the liver can be 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-10 —DO NOT CITE OR QUOTE 

1 computed as the time-integrated product of inducible production and a simple first-order loss 

2 process (Wang et al., 1997): 

3 

0 2 2A1A2t

dCYP S(t)K K C dt

4 = − (Eq. 3-3) 

5 

6 where CYP2A1 is the concentration of the enzyme, K2 is the rate constant for the first order loss, 

7 CA2t is the concentration of CYP1A2 in the liver, K0 is the basal rate of production of CYP1A2 in 

8 the liver, and S(t) is a multiplicative stimulation factor for CYP1A2 production in the form of a 

9 Hill-type function: 

10 

h

A2

( ) ( ) = 1 + (IC ) ( )

h

A2 Ah-TCDD

h

Ah-TCDD

In C S t

+ C

11 (Eq. 3-4) 

12 

13 where ICA2 corresponds to the concentration of the aryl hydrocarbon (Ah)-TCDD complex at 

14 which half of the maximum fold stimulation of CYP2A production is reached, and h, the Hill 

15 exponent, determines the curvature of the stimulation in relation to concentration of the 

16 Ah-TCDD complex at relatively low doses. A value of 0.6 as the Hill exponent has been used by 

17 Wang et al. (1997, 2000) and Emond et al. (2004, 2005, 2006), indicative of a negative 

18 cooperation, i.e., the curve is convex-upward (supralinear), depicting a faster increase in the low19 dose region compared to a straight line. Additional parameters in this expression include InA2, 

20 the maximum fold increase in the CYP1A2 synthesis rate over the basal rate that can occur at 

21 high levels of TCDD, and (CAh-TCDD), the concentration of TCDD bound to the aryl hydrocarbon 

22 receptor (AhR). This concentration in turn depends on the concentration of TCDD in the liver 

23 (CLif), the concentration of the AhR (AhLi) in liver, and the dissociation constant for the 

24 Ah-TCDD receptor complex, KDAh: 

25 

Li Lif

Ah-TCDD

DAh Lif

Ah C

C

K C

× = +

26 (Eq. 3-5) 

27 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 3.3.2.4. Elimination 

2 Elimination half-lives (i.e., the time taken for the concentration to be reduced to one-half 

3 of its initial level) of TCDD range from 11 days in the hamster to 2,120 days in humans 

4 (U.S. EPA, 2003). Hepatic metabolism and binding processes, fecal excretion, and accumulation 

5 in adipose tissue collectively determine the dose-dependent elimination half-lives in various 

6 species. Aylward et al. (2005a) depicted the relationship between the elimination rate versus 

7 initial level of lipid-corrected TCDD in serum for 36 people (see Figure 3-2). Even though this 

8 analysis was done using the initial TCDD level, rather than the geometric mean or midpoint level 

9 in the decline for each person, it indicated a concentration-dependency of the half-life and 

10 elimination of TCDD in exposed individuals. 

11 

12 3.3.2.5. Interspecies Differences and Similarities 

13 Among the pharmacokinetic determinants of TCDD, some are known to vary markedly 

14 between species whereas others are not characterized sufficiently in this regard. Overall, the 

15 qualitative determinants of the body burden and elimination half-lives appear to be similar across 

16 species. Based on empirical observations for TCDD as well as with other PCDFs, Carrier et al. 

17 (1995a,b) argued that in rats, monkeys, and humans, the dose-dependent changes in the fraction 

18 contained in liver and adipose tissue follow a similar pattern across species. The authors 

19 suggested that the half-saturation body burden is around 100 ng/kg and the plateau of liver dose 

20 (as fraction of body burden) appears to occur around 1,000 ng/kg. Literature also indicates that 

21 AhR is conserved phylogenetically (Nebert et al., 1991; Fujii-Kuriyama et al., 1995; Harper 

22 et al., 2002) and is present in mammalian species, including experimental animals and humans 

23 (Roberts et al., 1985, 1986; Manchester et al., 1987; Lorenzen and Okey, 1991; Okey et al., 

24 1994). These qualitative similarities in pharmacokinetic determinants and outcome support the 

25 use of animal data to infer general patterns of the pharmacokinetic behavior of TCDD in humans. 

26 However, quantitative differences in determinants, including physiological, physicochemical, 

27 and biochemical, need to be taken into account. Even though species-specific physiological 

28 parameters can be obtained from the literature, key data on species-specific biochemical 

29 parameters (particularly binding constants, maximal capacity, induction rates, and other 

30 parameters) are not available for humans at this time. However, these can be inferred by using a 

31 pharmacokinetic model fit to in vivo data on the rate of TCDD elimination from specific 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-12 —DO NOT CITE OR QUOTE 

1 compartments in humans (Carrier et al., 1995a, b; Emond et al., 2004, 2005, 2006; Aylward 

2 et al., 2005b). 

3 

4 3.3.3. PK of TCDD in Humans: Interindividual Variability 

5 The pharmacokinetics and tissue dose of TCDD in humans can exhibit variability within 

6 a population as a function of the interindividual variability in the key determinants. If a chronic, 

7 lifetime exposure is considered to be the relevant scenario for developing guidance for human 

8 exposure standards, the key determinants of concern are clearance, binding, and temporal 

9 changes in volume of distribution. When considering the interindividual variability in 

10 pharmacokinetics and dose metrics of TCDD, it is important to recognize that higher lipid11 corrected serum concentrations in highly exposed persons are associated with greater elimination 

12 rates, probably due to greater degrees of induction of CYP1A2 in the liver and possibly other 

13 related metabolic enzymes (Grassman et al., 2000; Abraham et al., 2002; Aylward et al., 2005b; 

14 Emond et al., 2006). 

15 The interindividual variability in fat content is a critical parameter given the 

16 pharmacokinetic characteristics of TCDD (see Section 3.3.2). Both metabolic elimination and 

17 elimination via the GI tract depend on the fraction of TCDD in the body that is available outside 

18 of adipose tissue. As body fat content rises, a smaller portion of the total body TCDD will be 

19 contained in the relatively available fraction outside of fat. Because elimination of TCDD by 

20 both metabolism and fecal excretion depends on the small proportion of TCDD that exists 

21 outside of fat tissue, people with larger proportions of body fat—including many older people—

22 will tend to require longer times to reduce TCDD levels by a given proportion than leaner people 

23 (Van der Molen et al., 1996, 1998; Rohde et al., 1999; Emond et al., 2006). 

24 The sections that follow highlight key aspects of interindividual variability in TCDD 

25 pharmacokinetics, with an emphasis on the available data related to elimination half-lives and 

26 volume of distribution. 

27 

28 3.3.3.1. Life Stage and Gender 

29 The influence of the variability of fat content in human population on the distribution and 

30 clearance of TCDD has been evaluated by several investigators. Figure 3-3 shows the results of 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-13 —DO NOT CITE OR QUOTE 

1 a rare experiment in which TCDD elimination via feces was measured in six people in relation to 

2 their body fat content (Rohde et al., 1999). 

3 There are data which suggestive an inverse dependency of half life on percent body fat. 

4 Observations of TCDD elimination rates in a small number of men and women in the Seveso 

5 cohort provide a modest opportunity to compare TCDD elimination rates on this basis. Based on 

6 the partition coefficients reported by Emond et al. (2006), the elimination rates for the men in the 

7 sampled group are expected to be greater than the elimination rates in the women. Based on 

8 calculations similar to those shown in Table 3-2, and fat proportions inferred from body mass 

9 indices using the equations of Lean et al. (1996), the Seveso men studied are expected to have an 

10 overall average of about 3.92% of their TCDD body burden outside of fat, whereas the women 

11 are expected to have an average of only 2.36% outside of fat. On this basis, the TCDD 

12 elimination rates in the men are expected to be 3.92/2.36 = 1.66 times faster than the elimination 

13 rates in the women. By comparison, Michalek et al. (2002) reported observed elimination rates 

14 in men and women that result in a slightly lower ratio: 

15 

1

1

men:0 111 year 0 010 (std.error) 1 56

women:0 071 year 0 010 (std.error)

. . . . .

−

−

± = ±

16 (Eq. 3-6)

17 

18 The central estimates for the elimination rates correspond to half lives of 6.5 and 9.6 years for 

19 men and women, respectively. 

A further point of comparison can be derived using the observed body mass index (BMI)8 20 

21 and TCDD elimination rate of each of the male Ranch Hand military veterans, whose TCDD 

22 elimination rates were observed between 9 and 33 years after their time in Vietnam. The average 

23 BMI over that time was 29.44 (based on 287 measurements for the 97 veterans, tabulated in three 

24 periods by Michalek et al., 2002), and their average age was about 44.5 for the measurements. 

25 Based on these data, the corresponding average estimated percent body fat is 29.7% using the 

26 Lean et al. (1996) formula for men. The observed average TCDD elimination rate constant for 

these men for the period was 0.092 year−1 27 ± 0.004 (standard error), corresponding to a half life of 

28 7.5 years. This half life is slightly longer than the central estimate of the half life of 6.2 years 

 

8

The body mass index, or BMI, is calculated as the body weight in kilograms divided by the square of the height in 

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

1/15/10 DRAFT 3-14 —DO NOT CITE OR QUOTE 

1 (i.e., ln(2)/0.111) for the smaller group of Seveso males with their slightly smaller estimated 

2 percent body fat. Figure 3-4 shows a simple plot of these data and a fitted unweighted regression 

3 line characterizing the relationship between estimated fat content and TCDD elimination rates. 

4 Variation in metabolic enzyme activities and other routes of loss is also likely to be important, 

5 but there is little human quantitative information available on these issues. 

6 More recently, Kerger et al. (2006) estimated the slope of the relationship between half7 life and age to be 0.12 years (95% confidence interval, 0.10−0.14), which corresponds to the rate 

8 of increase in TCDD half-life for each year of age. The authors speculated that although age 

9 explained most of the variance in the individual half-life trends, it was also correlated with 

10 TCDD concentration, BMI, and body fat mass. The regression model developed by these 

11 authors discriminated between the high and low TCDD exposures or concentrations. Thus, after 

12 accounting for the TCDD (concentration × age) term’s effect on the slope of age, the final model 

13 for TCDD concentration ≤ 700 ppt was 

14 

15 t . . Age 1 2 / = 0 35 0 12 + × (Eq. 3-7) 

16 For TCDD concentration >700 ppt, the final model was: 

17 

18 t . . Age 1 2 / = 0 35 0 088 + × (Eq. 3-8) 

19 

20 where t1/2 is the half-life and Age is the age at time of subsequent sampling. Pharmacokinetic 

21 information relevant to specific age groups is presented in the sections that follow. 

22 

23 3.3.3.1.1. Prenatal period. 

24 Data to estimate TCDD elimination rates for fetuses are not available. Levels of TCDD 

25 in fetal tissues for rats were experimentally estimated at different gestational periods and utilized 

26 in a developmental model by Emond et al. (2004). There is information on body composition 

27 that is relevant to prediction of TCDD dose to fetus. These data, summarized as part of the 

28 radiation dosimetry model of the International Commission on Radiological Protection, are 

29 consistent with the idea that early fetuses are nearly all water and less than 1% lipid, and lipid 

30 levels rise toward parity with protein near the time of normal delivery. 
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1 Bell et al. (2007b) reported that the disposition of TCDD into the fetus shows dose 

2 dependency, with a greater proportion of the dose reaching the fetus at lower doses of TCDD. 

3 Further, both CYP1A1 and CYP1A2 are highly inducible (~103−fold) in fetal liver, whereas 

4 CYP1A2 shows much lower induction (10−fold) in maternal liver. It has been speculated that 

5 this is due to the lower basal levels of CYP1A2 in fetal liver, as compared to maternal liver 

6 (Bell et al., 2007b). 

7 

8 3.3.3.1.2. Infancy and childhood. 

9 Hattis et al. (2003) describe the general pattern of change of body fat content with age in 

10 children. Central tendency values for percent body fat begin at about 12% at birth and rise 

11 steeply to reach about 26% near the middle of the first year of life. Fat content then falls to reach 

12 a minimum of approximately 15% at 5−8 years of age, followed by a sex-dependent “adiposity 

13 rebound” that takes females to about 26% body fat while the males remain near 16−17% on 

14 average by age 20. The interindividual variability distributions about these central values are 

15 complex, as some children experience the “adiposity rebound” earlier than others, and this 

16 creates patterns that are not simply interpretable as unimodal normal distributions. Hattis et al. 

17 (2003) did find it possible to fit distributions of body fat content inferred from NHANES skin 

18 fold measures to mixtures of two normal distributions for children between age 5 and 18. 

19 At least two groups of authors have published PBPK modeling results indicating 

20 generally more rapid clearance of TCDD in children than in adults, a trend that is consistent with 

21 the generally lower fat content of children (Kreuzer et al., 1997; Van der Molen et al., 2000; 

22 Leung et al., 2006). The rapid expansion of the adipose tissue compartment can contribute, in 

23 part, to the reduced apparent half-life in children (Clewell et al., 2004). This reduction may also 

24 be due to varying rates of metabolism and/or fecal lipid excretion (Abraham et al., 1996; 

25 Kerger et al., 2007). 

26 Furthermore, very young children have different modes and quantities of exposure 

27 compared to adults. Lakind et al. (2000) characterize distributions of milk intake for nursing 

28 infants to characterize distributions of TCDD exposure. This is also a corresponding route of 

29 loss of TCDD stores for lactating women, as described in Section 3.3.3.2 below. 

30 
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1 3.3.3.1.3. Adulthood and old age. 

2 The fraction of fat in relation to body weight in adulthood and old age can be computed 

3 as a function of the BMI and age (e.g., Lean et al., 1996): 

4 

5 % Body Fat . BMI . Age . (males) 1 33 0 236 20 2 = × + ×− (Eq. 3-9) 

6 % Body Fat (females) 1 21 0 262 6 7 = . BMI . Age . × + ×− (Eq. 3-10) 

7 

8 The above equations are the result of analysis of data based on underwater weighing of 

9 63 men and 84 women (age range 16.8−65.4). The salient observation with respect to TCDD for 

10 these data is that age and BMI-dependent variability in fat content have implications for the 

11 variability in TCDD elimination rates and internal dose among adults. 

12 

13 3.3.3.2. Physiological States: Pregnancy and Lactation 

14 Data on body fat content in pregnant women at various stages of gestation (Pipe et al., 

15 1979) have potential implications for TCDD elimination rates during pregnancy, even though the 

16 relationship between these parameters has not been formally analyzed. 

17 Lactation is viewed as an additional route of elimination for some chemicals such as 

18 TCDD. According to a recent study, a breast-feeding woman expels through lactation an 

19 estimated 8.76 kg fat per year [qf (kg/day), 0.8 kg milk/day with an average 3% lipid], and the 

20 partition coefficient between blood lipid and milk fat (KBM) for TCDD is 0.92 (Wittsiepe et al., 

21 2007; Milbrath et al., 2009). The estimated rate of elimination of TCDD due to breast-feeding 

22 (kbfed) can then be computed as follows (Milbrath et al., 2009): 

23 

100

f bfed

bfed

i

BM i

q Δt

k pbf K BW

× =

× ×

24 (Eq. 3-11) 

25 

26 where 

27 Δtbfed (unitless) = the fraction of the year during which the woman was actively breast28 feeding; 

29 pbfi = woman’s percent body fat; and 

30 BW = woman’s body weight in kg. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 Assuming no interaction between breast-feeding and other half-life determinants 

2 Milbrath et al. (2009), the authors predicted a half-life of 4.3 years for TCDD in a 30-year-old, 

3 nonsmoking woman with 30% body fat if she did not breast-feed that year, and a half-life of 

4 1.8 years if she breast-fed for 6 months. 

5 

6 3.3.3.3. Lifestyle and Habits 

7 One of the factors related to lifestyle and habits that could influence TCDD kinetics is 

8 smoking. Smoking has been reported to enhance the elimination of dioxin and dioxin-like 

9 compounds (Flesch-Janys et al., 1996; Ferriby et al., 2007). Milbrath et al. (2009) accounted for 

10 interindividual variation in body composition as well as smoking habits in an empirical model. 

11 The predicted half-life (years) for an individual i as a function of age, smoking status, and 

12 percent body fat i was as follows 

13 

1/2 (0 ) ( )

( )

( , , )[ ]

i

i

i age age i i

ref age

pbf t age smoke pbf age SF pbf

14 = + × ×× β β (Eq. 3-12) 

15 

16 where 

β age)0( 17 = intercept constant derived from regressed data; 

β age)( 18 = slope constant derived from regressed data; 

agei 19 = specific age i (years); 

pbfi 20 = individual percent body fat; 

)(ageref i 21 pbf = reference percent body fat; and 

22 SFi = the unitless, multiplicative smoking factor. 

23 

24 3.3.3.4. Genetic Traits and Polymorphism 

25 One particular genetic locus that is potentially related to TCDD pharmacokinetics and 

26 tissue dose is the gene for the AhR. Eight candidate AhR polymorphisms have been identified to 

27 date (Harper et al., 2002; Connor and Aylward, 2006). Given the role of AhR in regulating the 

28 induction of CYP1 isozymes (Baron et al., 1998; Toide et al., 2003; Connor and Aylward, 2006), 

29 the polymorphism might lead to interindividual differences in metabolic clearance, the 

30 significance of which would depend upon the dose, fat content, and exposure scenario. In this 

31 regard, it should be noted that the inducibility of aromatic hydrocarbon hydroxylase in human 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 tissues has been reported to be highly variable, up to 100−fold (Wong et al., 1986; Smart and 

2 Daly, 2000; Connor and Aylward, 2006). 

3 Finally, the scientific literature contains values of Kd (the dissociation constant of the 

4 TCDD−AhR complex) ranging from about 1 to much higher values (corresponding to lower 

5 binding affinity) (reviewed in Connor and Aylward, 2006). This provides suggestive evidence 

6 for a heterogeneous human AhR, with functionally important polymorphisms (Roberts et al., 

7 1986; Micka et al., 1997), even though some of the range may be attributed to experimental 

8 procedural differences and to other factors (Manchester et al., 1987; Lorenzen and Okey, 1991; 

9 Harper et al., 2002; Connor and Aylmard, 2006). 

10 The various pharmacokinetic processes and determinants (see Sections 3.3.2 and 3.3.3), 

11 individually or together, might influence the dose metrics of relevance to the dose-response 

12 modeling of TCDD. 

13 

14 3.3.4. Dose Metrics and Pharmacokinetic Models for TCDD 

15 3.3.4.1. Alternative Dose Metrics for Dose-Response Modeling 

16 The dose metric related to a toxicologic endpoint can range from the maximal 

17 concentration, the area under a time-course curve (AUC), or the time-averaged concentration of 

18 the toxic moiety in the body, blood, or target tissue, to an appropriate measure of the resulting 

19 interactions in the target tissue (e.g., receptor occupancy or functional biomarkers related to 

20 specific effects). A single dose metric, however, is unlikely to be sufficient for all endpoints and 

21 exposure durations. Further, the ideal dose metric chosen on the basis of the mode of action 

22 (MOA) may not be the dose metric for which model predictions can be obtained with a high 

23 level of confidence. Consideration of these issues is critical to the selection of the dose metrics 

24 of relevance to dose-response modeling of TCDD. 

25 Figure 3-5 lists a range of alternative dose metrics for TCDD in terms of their relevance 

26 based on considerations of pharmacokinetic mechanisms and MOA. The administered dose or 

27 daily intake (ng/kg-day) is the least relevant dose metric for dose-response modeling of TCDD. 

28 This dose adjusts only for body weight differences between species. When used with an 

additional uncertainty factor for kinetics (or BW3/4 29 ) and for dynamics, it can also account for 

30 allometrically-predicted pharmacokinetic (clearance) and pharmacodynamic differences between 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-19 —DO NOT CITE OR QUOTE 

1 species in deriving the human equivalent dose (HED). In effect, it facilitates the computation of 

2 administered dose associated with the same steady-state blood concentration of parent chemical 

3 in humans and rats by accounting for differences in metabolic clearance (assumed to be related 

4 to body surface area, with no corresponding temporal changes in the volume of distribution; see, 

5 for example, Krishnan and Andersen, 1991). Such calculations of HED for TCDD may not be 

6 appropriate given that (1) steady-state was not attained in all critical toxicological studies chosen 

7 for the assessment, (2) the clearance is mainly due to enzyme(s) and processes whose levels/rates 

8 do not necessarily vary across species or life stages as a function of body surface differences, and 

9 (3) there is a likelihood of change in volume of distribution over time. Furthermore, the use of 

10 administered dose does not explicitly account for the dose-dependent elimination of TCDD from 

11 tissues as demonstrated in multiple studies (reviewed in Sections 3.3.2 and 3.3.4). The use of 

12 administered dose in TCDD dose-response modeling is unlikely to facilitate the characterization 

13 of the true relationship between the response and the relevant measures of internal dose that are 

14 influenced by dose-dependent elimination and binding processes. Additionally, the use of 

15 administered dose to extrapolate across species or life stages would not effectively take into 

16 account the differences in fat content or the demonstrated dose-dependent and species-dependent 

17 differences in elimination half-life of TCDD. 

18 The other alternative dose metrics for TCDD include absorbed dose, body burden, serum 

19 or whole blood concentration, tissue concentration, and functional-related metrics of relevance to 

20 the MOA (e.g., receptor occupancy, change in protein levels). These measures can be calculated 

21 as a current (terminal), average (over a defined period), or integral quantity. The applicability of 

22 the integral measures, such as the AUC (i.e., the area under the curve of a plot of blood or 

23 plasma concentration vs. time), traditionally used for analyzing chronic toxicity data, is 

24 questionable in the case of TCDD because of differences in lifespan and uncertainties regarding 

25 the appropriateness of the duration to be specified for averaging the AUC in experimental 

26 animals and humans for certain critical effects (NRC, 2006). 

27 Among the alternative dose metrics, the absorbed dose accounts for differences in body 

28 weight as well as species-specific differences in bioavailability. However, this dose measure, 

29 while more closely related to the endpoint than administered dose, does not account for the 

30 physiological and biochemical mechanisms responsible for interspecies differences in internal 

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

1/15/10 DRAFT 3-20 —DO NOT CITE OR QUOTE 

1 Body burden, or more appropriately the body concentration, represents the amount of 

2 TCDD per kg body weight. TCDD body burdens, like other dose measures, can be determined 

3 as the peak, the average over the period of the bioassays, or the level at the end of the 

4 experiments. Thus, the terminal or average body burdens can be obtained either using data or 

5 pharmacokinetic models and used in dose-response modeling. The body burden is a measure of 

6 TCDD dose that reflects the net impact of bioavailability, uptake, distribution, and elimination 

7 processes in the organism. It is essentially a function of the volume of distribution and clearance 

8 processes, and as such it does take into account the temporal changes in volume of distribution as 

9 well as the concentration-dependent clearance. These are phenomena that are critical to the 

10 understanding of TCDD dose to the target. However, the body burden may not accurately reflect 

11 the tissue dose (NRC, 2006), and as such does not allow for analysis of species-specific 

12 differences in target organ sensitivity to TCDD. In essence, the body burden represents only an 

13 “overall average” of TCDD concentration in the body, without regard to the differential 

14 partitioning and accumulation in specific tissues, including the target tissue(s). 

15 Serum (or blood) concentration of TCDD is a dose metric that reflects both the body 

16 burden and the dose to target tissues. Serum or blood concentration, at steady-state, would be 

17 reflective of the impact of clearance processes, and expected to be directly proportional to the 

18 tissue concentrations of TCDD (NRC, 2006). This dose metric for lipophilic chemicals such as 

19 TCDD is often expressed as a lipid-normalized value, to adjust for varying serum lipid content 

20 (e.g., DeKoning and Karamus, 2000; Niskar et al., 2009), particularly in human biomonitoring 

21 studies, thus of relevance to dose-response modeling; however, the serum lipid-normalized 

22 concentrations of TCDD are not routinely collected and reported in animal toxicologic studies. 

23 The lipid-adjusted serum concentration, however, would be reflective of the lipid-adjusted 

24 concentration of TCDD in other organs (reviewed in Aylward et al., 2008) depending upon the 

25 extent of steady-state attained and the similarity of lipid composition across tissues in each 

26 species. In essence, the serum lipid-normalized measure is representative of the amount of 

27 TCDD per specified volume of total lipids, whereas the whole blood measure will be reflective 

28 of the ensemble of free, lipid-bound and protein-bound TCDD in plasma and erythrocytes, which 

29 may be species-specific. Even though these dose metrics are thought to be more closely and 

30 directly related to the tissue concentrations associated with an effect, a less direct association 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-21 —DO NOT CITE OR QUOTE 

1 might occur at increasing doses when nonlinear processes dominate the kinetics and distribution 

2 of TCDD into organs such as the liver. 

3 Tissue concentration of TCDD, as free, bound, or total TCDD, is a more relevant 

4 pharmacokinetic measure of dose, given that it provides a measure of exposure of the target cells 

5 to the chemical. In this regard, the CYP1A2-bound fraction may be considered as a relevant 

6 dose metric for certain toxic effects; however, the available data contain mixed results regarding 

7 the mechanistic linkage of this dose metric to toxicity and carcinogenicity (reviewed in Budinsky 

8 et al., 2006). In such cases, the use of alternative dose metrics (e.g., bound concentration as well 

9 as the serum concentration) in dose-response modeling could be considered. Other function10 related biomarkers and dose metrics could facilitate the additional consideration of 

11 pharmacodynamic aspects reflecting tissue- and species-specific sensitivity. These metrics 

12 represent the most relevant measures of tissue exposure and sensitivity to TCDD. 

13 Empirical time-course data on the alternative dose metrics of TCDD associated with 

14 epidemiologic and experimental (animal) studies are not available, requiring the use of 

15 pharmacokinetic models to obtain estimates of these dose metrics. These models may be simple, 

16 based on first order kinetics (see Section 3.3.4.2), or more complex based on physiochemical, 

17 biochemical, and physiological parameters for simulating uptake, distribution (including 

18 sequestration to proteins), and clearance of TCDD (see Section 3.3.4.3). 

19 

20 3.3.4.2. First-Order Kinetic Modeling 

21 Figure 3-6 illustrates the process of estimating a human-equivalent TCDD oral exposure 

22 from an experimental animal-administered dose, based on the assumption that body burden is the 

23 effective dose metric for TK equivalence across species. The primary assumption is that the 

24 time-weighted average (TWA) TCDD body burden over some critical time period is the 

proximate toxicokinetically-effective dose eliciting a toxicologic effect.9 25 The process consists of 

26 estimating the effective average body burden in the experimental animal over some time tA

27 (generally the experimental duration) using a TK model, then “back-calculating” a daily human 

28 exposure level that would result in that average body burden over some time tH (the human 

29 equivalent to tA). 

 

9

The conversion depicted in Figure 3-6 does not account for toxicodynamic differences between species. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-22 —DO NOT CITE OR QUOTE 

1 The following closed-form equation is the general formula used to calculate a TCDD 

2 terminal body burden in an experimental animal or human at time (t). 

3 

(1 ) ( ) (0)

kt d e fa BB t BB

k

− − 4 = + (Eq. 3-13) 

5 

6 where 

7 BB(t) = the body burden at time t (ng/kg); 

8 BB(0) = the initial body burden (ng/kg); 

9 d = the daily dose (ng/kg-day); 

k = the whole-body elimination rate (days−1 10 ); 

11 t = the time at which the body burden is determined (days); and 

12 fa = the fraction of oral dose absorbed (unitless). 

13 

For the experimental animal, BB(t) is 

A

A

tk

tk A

A A k

faed eBBtBB

AA

AA )1( )0()(

− − − 14 = + , and for 

humans, this parameter is 

H

H

tk

tk H

H H k

faed eBBtBB

HH

HH 1( ) )0()(

− − − 15 = + . 

16 

17 Setting BBH(t) = BBA(t) obtains the following expression: 

18 

A

A

tk

tk A

A

H

H

tk

tk H

H k

faed eBB

k

faed eBB

AA

AA

HH

HH )1( )0( 1( ) )0(

− − − − − = + − 19 + (Eq. 3-14) 

20 

21 Rearranging yields the general solution for dH. 

22 

AA HH

HH

AA tk

H

tk

A tk

tk

H

A

A

H

AH eBB eBB

e

e

fa

fa

k

k dd − −

−

−

+ −

−

− = ( )0 )0(

1( )

)1( 23 (Eq. 3-15) 

24 

25 Assuming that initial body burdens are very small compared to BB(t) and that the fraction of 

26 TCDD absorbed is the same for humans and experimental animals, and using the relationship 

2/1

)2ln(

t

k =

27 , where t1⁄2 is the whole-body half-life, a simplified solution for dH is obtained. 

28 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-23 —DO NOT CITE OR QUOTE 

1/2

1/2

(1 )

(1 )

A A

H H

k t

A

H A k t

H

t e d d

t e

−

−

− = − 1 (Eq. 3-16) 

2 

The term 1−e

−kt 3 is the daily fraction eliminated. Therefore, dH can be seen to be the 

4 average daily administered dose to the experimental animal times the ratio of the animal:human 

5 half-life times the ratio of the animal:human daily fraction eliminated over the respective times, 

6 tA and tH. For both species at (theoretical) steady state (t → ∞; daily fraction eliminated → 1), 

7 the latter ratio approaches unity, reducing the animal:human conversion factor to the ratio of the 

8 half-lives. The latter approach was used in the 2003 Reassessment for conversion of animal 

cancer slope factors to the human equivalent, where only lifetime exposures are relevant.10 9 

10 However, for less-than-lifetime exposures eliciting noncancer effects, specific values for 

11 tA and tH must be considered. Furthermore, Eq. 3-16 computes dH on the basis of terminal body 

12 burdens at times tA and tH. The more representative metric for toxicokinetic equivalence based 

13 on average body burden over the respective time periods is given in Eq. 3-17. 

14 

( ) ( )⎥

⎦

⎤ ⎢

⎣

⎡ − −+ − = + =−

− −

− −

∫∫ kt

e

k

fa d

kt

e BBde

tk

fa dde

t

BBtBB

t kt kt

k

t

k 1 1 1 )0()1( 1 1 )0()(

0 0

τ τ τ τ 15 (Eq. 3-17) 

16 

17 On the basis of average body burden as given in Eq. 3-17, is transformed again assuming 

18 minimal initial body burden (BB(0) ~ 0), as follows: 

19 

( )

( )

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ − −−

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ − −

= − −

−

HH

tk tk

H

H

AA

tk

H

A

AH

tk

e e

t

t

tk

e

t

t

dd

HH HH

AA

0 0 2/1

2/1

1

1 1

20 (Eq. 3-18) 

21 

22 where tH0 is the initial human exposure time. 

23 The value of tA is the duration of the experimental exposure period. For some gestational 

24 exposures, if a critical exposure window is defined, tA will be the duration of the critical 

 

10No conversions to human-equivalent exposures were attempted for other effects in the 2003 Reassessment. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 exposure window. The value of tH is the human-equivalent duration corresponding to tA. 

2 However, for less than lifetime exposure in humans, tH does not begin at 0, but must end at 

3 25,550 days (70 years) to include the terminal (pseudo) steady-state level, at which the BBH(t):

4 dH ratio is highest. The average is determined from the terminal end of the human exposure 

5 period because the daily exposure achieving the target blood concentration is smaller than for the 

6 same exposure period beginning at birth and is health protective for effects occurring after 

shorter-term exposure.11 7 Figure 3-7 depicts the relationship of daily dose to TWA body burden 

8 graphically for several exposure duration scenarios. For shorter durations occurring later in life, 

9 the average body burden over the exposure period does not differ substantially from the steady10 state value. Even for half-lifetime exposures, the deviation of the average from steady state is 

11 minimal. Only for lifetime exposures does the difference become more marked, but only by 

12 about 15%. Note that in the 2003 Reassessment, a constant value of 3,000 was used for BBH(t):

13 dH, based on the relationship of continuous exposure to theoretical steady-state body burden 

14 (t = lifetime, t1⁄2 = 2,593 days); this approach, while conservative, does not account for exposure 

15 scenarios of different durations and does not strictly reflect the average body burden dose metric. 

16 The simulation in Figure 3-7 is based on a unit daily exposure to humans, such that the 

17 target body burden represents BBH(tH):dH as a general scalar for calculating dH from any given

18 dA. Table 3-3 shows the resulting TK conversion factors for the rodent species and strains 

19 comprising the bulk of the experimental animals in TCDD studies. Monkey and mink values are 

20 not shown in this table because, for the former, only chronic exposures were evaluated and, for 

21 the latter, no TCDD half-life information is available. Monkey (Rhesus) half-life estimates 

22 range from about 200−500 days. A representative value of 365 days is used for this TCDD 

23 assessment. The dA to dH conversion factor for the chronic monkey exposures (3.5−4 years) in 

24 TCDD studies is 9.2−9.7 (BBA:dA = 279−263). 

25 Application of first order kinetics for the risk assessment of TCDD can only be used to 

26 estimate total body burdens or back-calculate administered dose from experimental data. Body 

27 burden calculations using first order kinetics is based on the assumption of a first order decrease 

28 in the levels of administered dose as function of time. In that sense, any loss of TCDD from the 

29 body is described by using a rate constant that is not specific to any biological process. This 

 

11 See the following section (3.3.4.3) for a more detailed discussion of this concept. 
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1/15/10 DRAFT 3-25 —DO NOT CITE OR QUOTE 

1 constant is usually estimated from estimates of half-life of TCDD. Assuming a constant half-life 

2 value for the clearance for long-term or chronic TCDD exposure is not biologically supported 

3 given the observed data indicating early influence of CYP1A2 induction and binding to TCDD 

4 and later redistribution of TCDD to fat tissue. Abraham et al. (1988) found that the liver:adipose 

5 tissue concentration ratio in female Wistar rats exposed to a subcutaneous TCDD dose of 

6 300 ng/kg decreased from 10.3 at 1 day postexposure to 0.5 at 91 days postexposure. 

7 Consequently, using half-life estimates based on observed steady-state levels of TCDD will not 

8 account for the possibility of accelerated dose-dependent clearance of the chemical at the early 

9 stages and thus would result in estimation of lower administered levels of the chemical. The 

10 dynamic change in half-life due to dose-dependent elimination at the early stages of TCDD 

11 exposure and its later redistribution to fat tissues for steady-state levels is better described using 

12 biologically-based models, such as the PBPK models and concentration- and age-dependent 

13 elimination (CADM) models (Carrier et al., 1995a, b; Emond et al., 2004, 2005, 2006; Aylward 

14 et al., 2005b). Additionally, these models provide estimates for other dose metrics (e.g., serum 

15 or tissue levels) that are more biologically relevant to response than administered dose or total 

16 body burden (see Section 3.3.4.3). 

17 

18 3.3.4.3. Biologically-Based Kinetic Models 

19 The development and evolution of biologically-based kinetic models for TCDD have 

20 been reviewed by EPA (2003) and Reddy et al. (2005). The initial PBPK model of Leung et al. 

21 (1988) was developed with the consideration of TCDD binding to CYP1A2 in the liver. The 

22 next level of PBPK models by Andersen et al. (1993) and Wang et al. (1997) used diffusion23 limited uptake and described protein induction by interaction of DNA binding sites. The models 

24 of Kohn et al. (1993) and Andersen et al. (1997) further incorporated extensive hepatic 

25 biochemistry and described zonal induction of CYP by TCDD. TCDD PBPK models have 

26 evolved to include detailed descriptions of gastrointestinal uptake, lipoprotein transport, and 

27 mobilization of fat, as well as biochemical interactions of relevance to organ-level effects (Kohn 

28 et al., 1996; Roth et al., 1994). Subsequently, developed PBPK models either used constant 

29 hepatic clearance rate (Wang et al., 1997, 2000; Maruyama et al., 2002) or implemented varying 

30 elimination rates as an empirical function of body composition or dose (Andersen et al., 1993, 

31 1997; Kohn et al., 1996; Van der Molen et al., 1998, 2000). The more recent pharmacokinetic 
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1 models explicitly characterize the concentration-dependent elimination of TCDD (Carrier et al., 

2 1995a, b; Emond et al., 2004, 2005, 2006; Aylward et al., 2005b). The biologically-based 

3 pharmacokinetic models describing the concentration-dependent elimination (i.e., the 

4 pharmacokinetic models of Aylward et al. [2005b] and Emond et al. [2005, 2006]) are relevant 

5 for application to simulate the TCDD dose metrics in humans and animals exposed via the oral 

6 route. The rationale for considering the application of Aylward et al. (2005b) and Emond et al. 

7 (2004, 2005, 2006) models for estimating dose metrics for possible application to TCDD risk 

8 assessment is based on the following considerations. 

9 

10 • Both models represent research results from the more recent peer-reviewed publications. 

11 • Both models are relatively simple and less parameterized than earlier kinetic models for 

12 TCDD. The Aylward et al. (2005b) model is based on two-time scale TCDD kinetics 

13 described by Carrier et al. (1995a), and the Emond et al. (2004, 2005, 2006) PBPK 

14 models are reduced versions of earlier complex PBPK models. Although simple, both 

15 the Aylward et al. (2005b) and Emond et al. (2004, 2005, 2006) models are still inclusive 

16 of important kinetic determinants of TCDD disposition. 

17 • Both models are uniquely formulated with dose-dependent hepatic elimination consistent 

18 with the physiological interpretations commonly accepted by the scientific community. 

19 • Both models and extrapolated human versions were tested against human data collected 

20 in a variety of human exposure scenarios (Alyward et al., 2005b; Emond et al., 2005). 

21 • Both models are capable of deriving one or more of the candidate dose-metrics that are of 

22 interest to EPA’s dose-response assessment of TCDD. 

23 

24 3.3.4.3.1. CADM model. 

25 3.3.4.3.1.1. Model structure. 

26 The pharmacokinetic model of Aylward et al. (2005b), referred to as the CADM model in 

27 this report, is based on an earlier model developed by Carrier et al. (1995a,b) that describes the 

28 dose-dependent elimination and half-lives of polychlorinated dibenzo-p-dioxins and furans. This 

29 model describes the TCDD levels in blood (body), liver, and adipose tissue. Blood itself is not 

30 characterized physically as a separate compartment within the model, and the distribution of 

31 TCDD to tissues other than adipose tissue and liver (usually less than 4%) is not accounted for 

32 by the model. The original structure of the Carrier et al. (1995a, b) model was modified by 

33 Aylward et al. (2005b) to include TCDD elimination through partitioning from circulating lipids 
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1 across the lumen of the large intestine into the fecal content (see Figure 3-8). The most recent 

2 version of the Carrier model (Aylward et al., 2005b, 2008) includes fecal excretion of TCDD 

3 from two routes: (1) elimination from circulating blood lipid through partitioning into the 

4 intestinal lumen; and (2) elimination of unabsorbed TCDD from dietary intake. 

5 A basic assumption of this model is that metabolic elimination of TCDD is a function of 

6 its current concentration in the liver. The current concentration of TCDD in the liver increases 

7 with increasing body burden in a nonlinear fashion as a result of the induction of (and binding of 

8 TCDD to) specific proteins (i.e., CYP1A2). Consequently, the fraction of TCDD body burden 

9 contained in the liver increases nonlinearly (with a corresponding decrease in the fraction 

10 contained in adipose tissues) with increasing body burden of TCDD (Carrier et al., 2005a). 

11 Of particular note is that the adipose tissue compartment of the model is considered to 

12 represent the lipid contained throughout the body. It then assumes that the concentrations of 

13 TCDD in lipids of plasma and various organs is essentially equivalent to that of adipose tissue, 

14 and as such these concentrations are included in the adipose compartment of the model. Even 

15 though this approximation is fairly reasonable given the available data, there is some concern 

16 that the adipose compartment of this model also includes the lipid content of the liver to some 

17 unknown extent. Removal of lipid volume from the liver would mathematically alter total 

18 hepatic concentration and therefore would affect the estimated levels of the chemical available 

19 for binding to proteins. 

20 Distribution in the body is modeled to occur between hepatic and adipose/lipid 

21 compartments, with the fraction of body burden in liver increasing according to a function that 

22 parallels the induction of the binding protein CYP1A2. Elimination is modeled to occur through 

23 hepatic metabolism (represented as a first-order process with rate constant K that decreases with 

24 age) and through lipid-based partitioning of unmetabolized TCDD across the intestinal lumen 

25 into the gut, which is also modeled as a first-order process. As the body burden increases, the 

26 amount of TCDD in the liver increases nonlinearly, resulting in an increased overall elimination 

27 rate. 

28 

29 3.3.4.3.1.2. Mathematical representation. 

30 The CADM model describes the distribution to tissues (including liver and adipose 

31 tissue) based on exchange from blood at time intervals of one month. The model is based on 
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1/15/10 DRAFT 3-28 —DO NOT CITE OR QUOTE 

1 quasi-steady-state-approximation, and thus it is also based on the consideration that the 

2 intertissue processes reach their equilibrium values “quasi-instantaneously.” In this regard, 

3 absorption and internal distribution reflective of kinetics at the cellular level (e.g., diffusion, 

4 receptor binding, and enzyme induction) likely occur on a relatively fast time scale (a few hours 

5 to a few days). However, the overall body concentration (i.e., body burden) varies slowly with 

6 time such that it remains virtually unchanged during short time intervals. 

7 The CADM model does not differentiate between binding to AhR and CYP1A2, and it 

8 lacks explicit descriptions of CYP1A2 induction, a key determinant of TCDD kinetics. 

9 However, the empirical equation in the CADM model is based on five parameters (i.e., fmin, fmax, 

10 K, Wa, and Wl; see Tables 3-4 and 3-5) that allow the successful description of the behavior of 

11 TCDD in liver and adipose tissue (i.e., TCDD half-lives in each compartment increase with 

12 decreasing body burden). This observation implies that the model adequately accounts for the 

13 ensemble of the processes. Essentially, the CADM model describes the rate of change in tissue 

14 concentrations of TCDD as a function of total body burden such that the global elimination rate 

15 decreases with decreasing body burden or administered dose. 

16 

17 3.3.4.3.1.3. Parameter estimation. 

18 The CADM model is characterized by its simplicity and fewer parameters compared to 

19 physiologically-based models. Reflecting this simplicity, hepatic extraction is computed with a 

20 unified empirical equation that accounts for all relevant processes (i.e., protein induction and 

21 binding). 

22 The key parameters (fmin, fmax, K, and ke) were all obtained by fitting to species-specific 

23 pharmacokinetic data. The physiological parameters (such as tissue weights) used in the model 

24 are within ranges documented in the literature. The fat content is described to vary as a function 

25 of age, sex, and BMI. However, the BMI of the model is not allowed to change during an 

26 individual simulation (which can range from 20 years to 70+ years) when in reality the 

27 percentage of fat in humans changes over time. None of the TCDD-specific parameters were 

28 estimated a priori or independent of the data set simulated by the model. 

29 
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1/15/10 DRAFT 3-29 —DO NOT CITE OR QUOTE 

1 3.3.4.3.1.4. Model performance and degree of evaluation. 

2 The CADM model was not evaluated for its capabilities in predicting data sets not used 

3 in its parameterization. In other words, one or more of the key input parameters (fhmin, fhmax, ke, 

4 K) was or were obtained essentially by fitting to the species-specific pharmacokinetic data, such 

5 that there was no “external” validation data set to which the model was applied. Despite the lack 

6 of emphasis on the “external” validation aspect, the authors have demonstrated the ability of the 

7 model to describe multiple data sets covering a range of doses and species. 

8 The visual comparison of the simulated data to experimental values suggests that the 

9 model could, to an approximate degree, correctly reproduce the whole set of data (e.g., 

10 pharmacokinetic [PK] profile over a range of dose and time) and not just part of the PK curve, 

11 essentially with the use of a single set of equations and parameters. 

12 The pharmacokinetic data sets for TCDD that were used to calibrate/evaluate the CADM 

13 model included the following: 

14 

15 • Adipose tissue and liver concentrations of TCDD following a single oral dose of 1 μg/kg 

16 in monkeys (McNulty et al., 1982); 

17 • Percent dose retained in liver for a total dose of 14 ng in hamsters (Van den Berg et al., 

18 1986); 

19 • Elimination kinetics of TCDD in female Wistar rats following a single subcutaneous dose 

20 of 300 ng/kg (data from Abraham et al., 1988); 

21 • Liver and adipose tissue concentrations (terminal measurements) in Sprague−Dawley rats 

22 given 1, 10 or 100 ng TCDD/kg bw during 2 years (Kociba et al., 1978); and 

23 • Serum lipid concentrations of TCDD over a period of several years in 54 adults (29 men 

24 and 25 women) from Seveso and in three Austrian patients (Aylward et al., 2005a). 

25 

26 For illustration purposes, Figure 3-9 shows model simulations of rat data from Carrier et 

27 al. (1995a). Figure 3-2 (see Section 3.3.2.4) depicts the human data that were used by the 

28 authors to support the concentration-dependent elimination concept; the model was 

29 parameterized to fit approximately to these data (Aylward et al., 2005a). 

30 The authors did not report any specialized analyses that quantitatively evaluated the 

31 uncertainty, sensitivity, and/or variability of CADM model parameters and structure. 

32 
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1/15/10 DRAFT 3-30 —DO NOT CITE OR QUOTE 

1 3.3.4.3.1.5. Confidence in CADM model predictions of dose metrics. 

2 The level of confidence associated with the predictability and reliability of absorbed dose 

3 and body burden for oral exposures in humans (as well as several animal species) by this model 

4 can be ranked as high (see Table 3-6). This model, however, does not account for the differential 

5 solubility of TCDD in serum lipids and adipose tissue lipids, nor does it account for the 

6 diffusion-limited uptake by adipose tissue. Due to these limitations, the confidence associated 

7 with the predictions of the serum lipid concentration of TCDD is considered medium, 

8 particularly when it is not documented that steady-state is reached during the critical toxicologic 

9 studies and human exposures. Furthermore, the CADM model does not facilitate the 

10 computation of TCDD concentrations in specific internal organs (other than liver and adipose 

11 tissue). The reliability of this model for simulating the liver concentration (free, bound, or total) 

12 of TCDD at low doses is considered to be low. This low confidence level is a result of the 

13 uncertainty associated with the key parameter ƒhmin. This parameter needs to be re-calibrated for 

14 each study/species/population to effectively represent the free fraction of TCDD in liver and the 

15 amount of TCDD contained in the hepatic lipids and bound to the liver proteins (whose levels 

16 might be reflective of background exposures of various sources; see Carrier et al., 1995a). The 

17 uncertainty related to the numerical value of this parameter in animals and humans—particularly 

18 at very low exposures—raises concern regarding the use of this model to predict TCDD 

19 concentration (free, bound, or total) in liver as the dose metric for dose-response modeling. 

20 Although the use of the parameter ƒhmax permits the prediction of the dose to liver at high doses, 

21 it does not specifically facilitate the simulation of the amount bound to the protein or level of 

22 induction in liver. Because the CADM model is not capable of simulating enzyme induction 

23 based on biologically-relevant parameters, its reliability for predicting the concentration of 

24 TCDD bound specifically to the AhR is not known. Finally, due to the lack of parameterization 

25 or verification with kinetic data in pregnant, lactating, or developing animals or humans, the 

26 CADM model is unlikely to be reliable in the current form for use in predicting potential dose 

27 metrics in these subpopulations or study groups that might form the basis of points of departure 

28 (PODs) for the assessment. 

29 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-31 —DO NOT CITE OR QUOTE 

1 3.3.4.3.2. PBPK model. 

2 3.3.4.3.2.1. Model structure. 

3 Emond et al. (2004, 2006) simplified the eight-compartment rat model of Wang et al. 

4 (1997) to a four-compartmental model (liver, fat, rest of body and placenta with fetal transfer) 

5 (Emond et al., 2004), and later to a three-compartment adult model (liver, fat, rest of the body) 

6 (Emond et al., 2006) (see Figures 3-10 and 3-11). Their rationale for simplification of the model 

7 was based on evaluating, critiquing, and improving all earlier PBPK models by Wang et al. 

8 (1997). In general, the main reason for the simplification was that extrapolation of a PBPK 

9 model to humans with these many (i.e., eight compartments) compartments would be 

10 problematic due to the limited availability of relevant human data for validation (Emond et al., 

11 2004). One major difference from earlier models, repeatedly emphasized by Emond et al. (2005, 

12 2006), was their description (included in their simplified PBPK models) of the dose-dependent, 

13 inducible elimination of TCDD. The rationale for including TCDD binding and induction of 

14 CYP1A2 into the model was earlier described by Santostefano et al. (1998). 

15 The most recent version of the rat and human PBPK models developed by Emond et al. 

16 (2006) describes the organism as a set of three compartments corresponding to real physical 

17 locations—liver, fat, and rest of the body—interconnected by systemic circulation (see 

18 Figure 3-10). The liver compartment includes descriptions of CYP1A2 induction, which is 

19 critical for simulating TCDD sequestration in liver and dose-dependent elimination of TCDD. In 

20 this model, the oral absorption of TCDD from the GI tract accounts for both the lymphatic (70%) 

21 and portal (30%) systems. 

22 The biological relationship between TCDD “sequestration” by liver protein and its 

23 “elimination” by the liver is not entirely clear. TCDD is metabolized slowly by unidentified 

24 enzymes. CYP1A2 is known to metabolize TCDD based on studies in CYP1A2 KO mice 

25 (Diliberto et al., 1997, 1999), in which the metabolic profile is different compared to wild-type 

26 mice. However, since several metabolites appear in the feces of CYP1A2 knock out mice, it is 

27 assumed that there are other enzymes involved in TCDD metabolism. TCDD binds to the AhR 

28 and induces not only CYP1A2, but also CYP1A1, CYP1B1, and several UGTs and transporters 

29 (Gasiwicz et al., 2008). Both hydroxylated and glucuronidated hydroxyl metabolites are found 

30 in the feces of animals treated with TCDD (Hakk et al., 2009). Because the exact enzymes 

31 involved with TCDD are unknown and yet the metabolism is induced by TCDD, an assumption 
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1/15/10 DRAFT 3-32 —DO NOT CITE OR QUOTE 

1 of increased the elimination rate of TCDD in proportion to the induction of CYP1A2 is made. In 

2 the PBPK model, CYP1A2 is needed because TCDD binds to rat, mouse, and human CYP1A2 

3 (Staskal et al., 2005; Diliberto et al., 1999). Thus CYP1A2 induction is necessary to describe 

4 TCDD pharmacokinetics due to TCDD binding. Hence, CYP1A2 can be used as a marker of 

5 Ah-receptor induction of “TCDD metabolizing enzymes.” Other models use AhR occupancy as 

6 a marker of induction of “TCDD metabolizing enzymes” (Andersen et al., 1997; Kohn et al., 

7 2001). 

8 Figure 3-11 depicts the structure of the PBPK model developing rat (Emond et al., 2004). 

9 

10 3.3.4.3.2.2. Mathematical representation. 

11 The key equations of the PBPK model of Emond et al. (2004) are reproduced in 

12 Text Boxes 3-1 and 3-2, whereas those from Emond et al. (2005, 2006) are listed in Table 3-7. 

13 The rate of change of TCDD in the various tissue compartments is modeled on the basis of 

14 diffusion limitation considerations. Accordingly, mass balance equations are used to compute 

15 the rate of change in the tissue (i.e., intracellular compartment) and tissue blood (i.e., 

16 extracellular compartment). The membrane transfer of TCDD is computed using a permeation 

17 coefficient-surface area cross product (PA) for each tissue. Metabolism and binding of TCDD to 

18 the AhR and inducible hepatic protein (CYP1A2) are described in the liver. The total mass in 

19 the liver was then apportioned between free dioxin (Clf) and bound forms of TCDD (see Figure 

20 3-12). The dose- and time-dependent induction of hepatic CYP1A2 in the liver is described per 

21 Wang et al. (1997) and Santostefano et al. (1998). Accordingly, the amount of CYP1A2 in the 

22 liver was computed as the time-integrated product of inducible production and a simple first23 order loss process (Wang et al., 1997): 

24 

0 2 () 1A2A2t

dCYP StK KC

dt

25 = − (Eq. 3-19) 

26 

27 In this expression, CYP1A2 is the concentration of the enzyme (nmol/g), K2 is the rate constant for 

28 the first order loss (liter/hour), CA2t is the concentration of CYP1A2 in the liver (nM/hour), K0 is 

29 the basal rate of production of CYP1A2 in the liver, and S(t) (unitless) is a multiplicative 
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1 stimulation factor for CYP1A2 production in the form of a Hill-type function (see 

2 Section 3.3.2.3): 

3 

( ) ( ) = 1 + ( )( )

h

A2 Ah-TCDD

h h

A2 Ah-TCDD

In C S t

IC C +

4 (Eq. 3-20) 

5 

6 where, S(t) is the stimulation function, InA2 is the maximum fold of CYP1A2 synthesis rate over 

7 the basal rate, CAh-TCDD is the amount of AhR occupied by TCDD, and ICA2 is the Michaelis8 Menten constant of CYP1A2 induction (nM). The dose-dependent or variable elimination of 

9 TCDD was described using the relationship: 

10 

12 12

1 2

⎡ ⎤ − = × ⎢ ⎥ ⎣ ⎦

induced basal

basal

CYP A CYP A KBILE LI Kelv

CYP A

11 (Eq. 3-21) 

12 

13 where CYP1A2induced is the concentration of induced CYP1A2 (nmol/mL), CYP1A2basal is the 

14 basal concentration of CYP1A2 (nmol/mL), and Kelv is the interspecies constant adjustment for 

the elimination rate (hour−1 15 ). 

16 There are various ways of formulating the dose-dependent elimination as a function of 

17 the level of CYP1A2, and the above equation (used by the authors) can be viewed as one means 

18 of describing this behavior quantitatively. The numerator in the equation above will always be 

19 greater than zero when there is TCDD in the system (including TCDD derived from either 

20 background exposures or defined external sources). Consequently, the rate of elimination will 

21 correspond to a nonzero value for situations involving TCDD exposures. The above equation, 

22 however, does not describe changes in elimination rate in direct proportionality with the 

23 CYP1A2 levels; also, the Kelv value by itself does not reflect a scalable basal metabolic rate. 

24 Rather, these two terms collectively describe the outcome related to the TCDD elimination 

25 processes, based on fitting to observations in rats (Santostefano et al., 1998). The impact of 

26 CYP1A2 induction and sequestration on binding and elimination of TCDD is simulated using the 

27 Emond et al. (2004) model. 
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1 The gestational model consisted of a fetal compartment, and the transfer of TCDD 

2 between the placental and fetal compartments was described as a diffusion-limited (rather than a 

3 perfusion-limited) process (see Text Boxes 3-1 and 3-2). 

4 

5 

Text Box 3-1. 

Variation of Body Weight with Age: 0 41

1402 5 Tim e

. Tim e B W (g ) B W in itia l . Tim e

⎛ ⎞ ×

= × ⎜ ⎟ ⎝ ⎠ +

Cardiac Output: 

0 75

60

1,000

.

BWmother Qc(mL / h) Qcc ⎛ ⎞

= × ⎜ ⎟ ⎝ ⎠

A factor of 60 corresponds to the conversion of minutes to hours, and 1,000 is the conversion of 

body weight from g to kg. 

Blood Compartment:

(( ) ( ) ( ) ( ) )) ( )

Cb(nmol / mL)

Qf Cfb Qre Creb Qli Clib Qpla Cplab Lymph Cb Clru

Qc

=

× + × + × + × + −×
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1 

2 

Text Box 3-2. 

Placenta Tissue Compartment 

(a) Tissue-blood subcompartment 

( ) ( ) dAplab nmol / h) Qpla(Ca Cplab PApla Cplab Cplafree dt 

= −+ −

Wplab

Aplab Cplab =

(b) Tissue cellular matrices 

( ) dApla dApla fet dAfet pla _ _ nmol / h) PApla(Cplab Cplafree dt dt dt

= −− +

( /) Apla Cpla nmol mL Wpla=

Free TCDD Concentration in Placenta 

max ( /) ( Plab Cplafree Cplafree nmol mL Clpla Cplafree Ppla Kdpla Cplafree

⎡ ⎛ ⎞ × ⎤

= − ×+ ⎢ ⎜ ⎟⎥ ⎝ ⎠ + ⎣ ⎦

Dioxin Transfer from Placenta to Fetuses 

_

_ ( /) Pla fetdAPla fet nmol h Cl Cpla dt

= ×

Dioxin Transfer from Fetuses to Placenta 

_

_ ( /) Pla fetdAfet Pla nmol h Cl CfetV dt

= ×

Fetal Dioxin Concentration (Fetuses 5 10 Per Litter) 

_ _ ( /) dAfet dAPla fet dAfet Pla nmol h

dt dt dt = −

Wfet

Afet hnmolCfet )/( =

( /) Cfet CfetV nmol mL Pfet =
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1 3.3.4.3.2.3. Parameter estimation. 

2 Table 3-8 lists the numerical values of the adult rat and human PBPK models of Emond 

3 et al. (2005, 2006). The values for key input parameters of the rat gestational model are 

4 summarized in Table 3-8 as well as Figure 3-13. 

5 The parameters for the rat model were obtained primarily from Wang et al. (1997) except 

6 that the value of affinity constant for CYP1A2 was changed from 0.03 to 0.04 nmol/mL to get 

7 better fit to experimental data (Emond et al., 2004) and the variable elimination parameter (Kelv) 

8 was obtained by optimization of model fit to kinetic data from Santostefano et al. (1998) and 

9 Wang et al. (1997) (Emond et al., 2005, 2006). Wang et al. (1997) used measured tissue weights 

10 whereas the tissue blood flows and tissue blood weights were obtained from International Life 

11 Sciences Institute (ILSI, 1994). The partition coefficients (which were similar to those of Leung 

12 et al. [1988, 1990]), the permeability x area (PA) value for tissues, the dissociation constant for 

13 binding to CYP1A2 (ICA2) and the Hill coefficient (h) were estimated using a two-stage process 

14 of fitting to dose-response and time-course data on TCDD tissue distribution (Wang et al., 1997). 

15 In the initial stage, the experimental data of arterial blood concentrations were used as input to 

16 the individual compartment to estimate the parameters; then, with the values obtained during 

17 stage one as initial estimates, those unknown parameters were re-estimated by solving the entire 

18 model at once using an optimization route (Wang et al., 1997). The receptor concentrations and 

19 dissociation constant of TCDD bound to AhR were obtained by fitting the model to TCDD tissue 

20 concentration combining with enzyme data reported by Santostefano et al. (1998) whereas the 

21 basal CYP1A2 in liver was based on literature data (Wang et al., 1997). 

22 The parameters for the human PBPK model were primarily based on the rat model (Wang 

23 et al., 1997; Emond et al., 2005, 2006). Specifically, the blood fraction in the tissues, the 

24 tissue:blood partition coefficients, tissue permeability coefficient, the binding affinity of TCDD 

25 to Ah and CYP, and the maximum binding capacity in the liver for AhR were all set equal to the 

26 values used in the rat model. The species-specific Kelv was estimated by fitting to human data 

27 (Emond et al., 2005). 

28 For the gestational rat model, the parameters describing the growth of the placental and 

29 fetal compartments as well as temporal change in blood flow during gestation were incorporated 

30 based on existing data. Exponential equations for the growing compartments were used (see 

31 Figure 3-13), except for adipose tissue for which a linear increment based on literature data was 
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1 specified. While physiological parameters for the pregnant rat were obtained from the literature, 

2 all other input parameters were set equal to that of nonpregnant rat (obtained from Wang et al. 

3 [1997]), see Tables 3-7 and 3-8. The current version of the rat gestational model contains 

4 parameters for variable elimination from Emond et al. (2006; Table 3-8), and still provides the 

5 essentially the same predictions as the original publication (Emond et al., 2004). 

6 

7 3.3.4.3.2.4. Model performance and degree of evaluation. 

8 The PBPK model of Emond et al. (2004, 2005, 2006) had parameters estimated by fitting 

9 to kinetic data, such that the resulting model consistently reproduced the kinetic data. The same 

10 model structure with a single set of species-specific parameters could reproduce the kinetics of 

11 TCDD following various doses and exposure scenarios not only in the rat but also in humans. 

12 The simulations of the PBPK model of Emond et al. (2006) have been compared with two sets of 

13 previously published rat data: blood pharmacokinetics following a single dose of 10 μg/kg (the 

14 dose corresponding to the mean effective dose for induction of CYP1A2) (Santostefano et al., 

15 1998) (see Figure 3-14); and hepatic TCDD concentrations during chronic exposure to 50, 100, 

16 500, or 1,750 ng/kg (Walker et al., 1999) (see Figure 3-15). It is relevant to note that the PBPK 

17 model of Emond et al. (2004, 2006) is essentially a reduced version of the Wang et al. (1997) 

18 model, and it therefore provides simulations of liver and fat concentrations of TCDD that 

19 deviated by not more than 10−15% of those of Wang et al. (1997). The nongestational model of 

20 Emond et al. (2004) simulated the kinetic data in liver, fat, blood and rest of body of female 

21 Sprague-Dawley rats given a single dose of 10 μg TCDD/kg (data from Santostefano et al., 

22 1996) and in liver and fat of male Wistar rats treated with a loading dose of 25 ng⁄kg followed by 

23 a weekly maintenance dose of 5 ng TCDD⁄kg by gavage (data from Krowke et al., 1989). 

24 The gestational rat PBPK model simulated the following PK data sets (Emond et al., 

25 2004): 

26 

27 • TCDD concentration in blood, fat, liver, placenta, and fetus of female Long−Evans rats 

28 given 1, 10, or 30 ng⁄kg, 5 days⁄week, for 13 weeks prior to mating followed by daily 

29 exposure through parturition (Hurst et al., 2000a); 

30 • TCDD concentration in tissues (liver, fat), blood, placenta and fetus determined on 

31 gestation day (GD) 16 and GD 21 following a single dose of 0.05, 0.8, or 1 μg/kg given 

32 on GD 15 to pregnant Long Evans rat (Hurst et al., 2000b); 
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1 • Maternal and fetal tissue concentrations on GD 9, GD 16 and GD 21 after a single dose 

2 of 1.15 μg TCDD/kg given to Long−Evans rats on GD 9 or GD 15 (Hurst et al., 1998); 

3 and 

4 • Fetal TCDD concentrations determined on GD 19 and GD 21 in rats exposed to 5.6 μg 

5 TCDD/kg on GD 18 (Li et al., 2006) 

6 

7 Furthermore, the scaled rat model was shown to be capable of simulating human data 

8 from the Austrian and Seveso subjects (see Figures 3-16 and 3-17). In this regard, it is useful to 

9 note that the computational version of the PBPK model of Emond et al. (2005, 2006) also 

10 contained the necessary equation to transform the model output of blood concentration into 

11 serum lipid adjusted concentration of TCDD. 

12 The human model of Emond et al. (2005) has advantages for improving the TCDD 

13 dosimetry used in existing human epidemiological studies because its PBPK structure naturally 

14 develops expectations for the redistribution of TCDD within the body (to stores in fat and liver) 

15 relative to metabolism. However, because the dose-dependency of metabolic elimination in the 

16 Emond et al. (2005) human model was not calibrated to human data, it is important to review the 

17 expectations of this model using a database of human observations that is as extensive as 

18 possible and a spread of internal TCDD concentrations that is as wide as possible. Thus, 

19 presented below is a juxtaposition of expectations of elimination rates for the Emond model with 

observations for two highly exposed Austrian patients and nine of 10 Ranch Hand veterans12 20 

21 used for the original “validation” comparisons in the Emond et al. (2005). 

22 Figure 3-18 shows the time course of the declines in TCDD serum concentrations in two 

23 highly-exposed Austrian subjects compared with expectations from the Emond et al. (2005) 

24 model. The comparison in Figures 3-17 and 3-18 indicates that the Emond model adequately 

25 describes the rate of TCDD elimination for the more highly exposed Austrian patients, but 

26 predicts a somewhat faster rate of decline than that observed for the less heavily exposed patient. 

27 Figure 3-19 shows the results of combining the simulated and observed rates of loss for a 

28 group of Austrian and Ranch Hand subjects evaluated by Emond et al. (2005), counting only one 

29 data point per person. The X-axis in this figure is the TCDD serum concentration at the 

 

12In preliminary comparisons, the simulation run for the 10th Ranch Hand veteran appeared anomalous and was 

therefore excluded from this summary. 
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1 midpoint of the observations for each subject. The error bars in the figure represent ±1 standard 

2 error. 

3 Table 3-9 presents the results of regression analyses of the observed rates of decline in 

4 relation to the estimated TCDD serum levels at the midpoint of the observations for each subject 

5 in the Ranch Hand study (see Figure 3-19). These results indicate that some appreciable dose 

6 dependency of TCDD elimination is unequivocally supported. However, the central estimate of 

7 the slope of the relationship between the log of the TCDD elimination rate and the log of the 

8 TCDD level is only about 75% of that expected under the Emond et al. PBPK model 

9 (i.e., 0.092 ÷ 0.123 = 0.748). 

10 Based on Table 3-9 and Figure 3-19, the expected slope from the Emond model 

11 (slope = 0.123) is about 2.8 standard errors above the regression line (slope = 0.092 with 

12 standard errors value of 0.011) indicated by the observations, suggesting that the departure is 

13 statistically significant. It can be seen that some appreciable dose dependency of TCDD 

14 elimination is unequivocally supported, but it is indicated that the dependency is somewhat less 

15 than presently incorporated into the Emond model. The difference might be slightly enhanced if 

16 the modest effects of the special measures taken to accelerate removal of TCDD from the 

17 Austrian subjects were subtracted from the observed elimination rates (i.e., oral administration of 

18 Olestra for both patients and LDL-apheresis for the more highly exposed patient). 

19 It would be desirable in the future to extend this juxtaposition to data for other intensively 

20 studied subjects. Figure 4 in Aylward et al. (2005a) (reproduced here as Figure 3-2) shows a plot 

21 of elimination rate versus initial level of lipid-corrected TCDD in serum for 36 people. This is 

22 not the most desirable comparison for characterizing the relationship, however, because the rate 

23 constant for loss should be related to the geometric mean or midpoint level in the decline for 

24 each person (rather than to the initial level) in order to be most accurate in relating current 

25 TCDD levels to elimination rates and to avoid possible “regression to the mean” type statistical 

26 errors due to measurement imperfections. Overall, the conclusion from this analysis is that it is 

27 not unreasonable to use the Emond model as it stands, but the model might be modestly 

28 improved by adapting it to (1) include the two nondose-dependent pathways of elimination 

29 documented in the Geusau papers (GI elimination via the feces and loss via the sloughing of skin 

30 cells), and (2) reducing the extent of loss via the dose-dependent metabolism pathway from the 
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1 liver (Geusau et al., 2002; Harrad et al., 2003) so that overall loss rates for the average 

2 elimination rates from the Ranch Hand veterans is maintained. 

3 A sensitivity analysis of inputs used to estimate inducible elimination rate for a single 

4 oral dose of 0.001 to 10 μg/kg in the rat indicated that the number of key parameters ranged from 

5 seven at the low dose region to 12 at the high dose (see Figure 3-20) (Emond et al., 2006). The 

6 sensitive parameters identified included the oral absorption parameters (KABS), volumes of liver 

7 and adipose tissue (WLIO, WFO), adipose tissue:blood partition coefficient (PF), and the basal 

8 CYP1A2 level (CYP1A2 1A2). At high doses, the most sensitive parameters also included those 

9 related to the maximal induction of CYP1A2 and AhR binding capacity (see Figure 3-20) 

10 (Emond et al., 2006). 

11 The gestational rat model described in Emond et al. (2004), upon reparameterization, 

12 could simulate the kinetics of TCDD in mice. The initial changes to the rat model parameters 

13 included: rest of the body:blood partition coefficient (PRE), basal concentration (CYP1A2_1A2), 

14 delay in induction time (CYP1A2_1TAU) and adipose tissue permeability coefficient (PAFF), in 

15 accordance with Wang et al. (2000) (see Table 3-8). Subsequently, four parameters (adipose 

16 tissue:blood partition coefficient, CYP1A2 affinity parameter, GI tract elimination transit 

constant (hour−1

) and the interspecies metabolic parameter Kelv (hour−1 17 ) were re-estimated based 

18 on visually fit of model simulations to the PK data from Deliberto et al. (2001), following an oral 

19 dose 150 ng TCDD/kg/day, 5 days/week for 17 weeks (see Table 3-7). The resulting mouse 

20 model is capable of reproducing the kinetics of TCDD in the adult (see Figures 3-21 through 

21 3-27), as well as, to a very limited extent, the kinetics during gestation (see Figure 3-28). 

22 

23 3.3.4.3.2.5. Confidence in PBPK model predictions of dose metrics. 

24 The PBPK model facilitates prediction of absorbed dose, body burden, and blood 

25 concentration of TCDD for oral exposures in adult humans and rats (adult and developing) with 

26 high confidence (see Table 3-10). The model output of blood concentration can be normalized to 

27 lipid content representative of the study group (species, sex, age, lifestage, and diet). However, 

28 the PBPK model of Emond et al. (2004, 2005, 2006) does not simulate plasma and erythrocyte 

29 TCDD concentrations separately, and it predicts tissue concentrations on the basis of 

30 tissue:whole blood partition coefficients and not on the basis of serum lipid-normalized values. 
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1 The reliability of this model for simulating the liver concentration of TCDD in rats is 

2 considered to be high but it is considered to be medium for humans. Although empirical data on 

3 bound or free concentrations were not used to evaluate model performance in humans, the 

4 biological phenomena (consistent with available data) related to the hepatic sequestration, 

5 enzyme induction, and dose-dependent elimination are described in the model. This is one of the 

6 situations where PBPK models are uniquely useful; that is, they permit the prediction of system 

7 behavior based on understanding of the mechanistic determinants, even though the required data 

8 cannot be directly obtained in the system (e.g., bound concentrations in the liver of exposed 

9 humans). For these dose measures (i.e., bound concentration and total liver concentration), the 

10 level of confidence can be further improved or diminished by the outcome of sensitivity analysis. 

11 In this regard, the results of a focused sensitivity analysis indicate that the most sensitive 

12 parameters of the human model are among the most uncertain (i.e., those parameters for which 

13 estimates were not obtained in humans) with respect to prediction of liver TCDD concentration, 

14 contrary to the animal model (see Section 3.3.6). 

15 With respect to the mouse model, however, the level of confidence is medium to low, 

16 given that it has not been verified extensively with blood, body burden, or tissue concentration 

17 time-course or dose-response data. However, the mouse PBPK model, based on the rat model 

18 that has been evaluated with several PK data sets, has been shown to reproduce limited liver 

19 kinetic data (see Figures 3-21 through 3-28; Boverhoff et al., 2005). The same model structure 

20 has been used for simulating kinetics of TCDD in humans successfully. Overall, the adult mouse 

21 model, given its biological basis combined with its ability to simulate TCDD kinetics in multiple 

22 species, is considered to exhibit a medium level of confidence for simulating dose metrics for use 

23 in high to low dose extrapolation and interspecies (mouse to human) extrapolation. Even though 

24 similar considerations are applicable to gestational model in mice, the confidence level is 

25 considered to be low since very limited comparison with empirical data has been conducted (see 

26 Figure 3-28). Despite the uncertainty in these predictions, the scaled rat gestational model, given 

27 its biological and mechanistic basis, might be of use in predicting dose metrics in these groups 

28 that might form the basis of PODs in certain key studies. 

29 
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1 3.3.4.4. Applicability of PK Models to Derive Dose Metrics for Dose-Response Modeling of 

2 TCDD: Confidence and Limitations 

3 Both the CADM and PBPK models describe the kinetics of TCDD following oral 

4 exposure to adult animals and humans by accounting for the key processes affecting kinetics, 

5 including hepatic sequestration phenomena, induction, and nonlinearity in elimination, and 

6 distribution in adipose tissue and liver. Both models can be used for estimating body burdens 

7 and serum lipid adjusted concentrations of TCDD. However, there are several differences 

8 between these two models. The PBPK model calculates the free and bound concentrations of 

9 TCDD in the intracellular subcompartment of tissues. The total or receptor-bound 

10 concentrations in liver are unambiguous and more easily interpretable with the PBPK model than 

11 with the CADM model. In addition, the PBPK model computes bound and total concentrations 

12 as a function of the free concentration in the intracellular compartment of the tissue. By contrast, 

13 the CADM model simulates the total concentration based on empirical consideration of hepatic 

14 processes. Consequently, the amount of TCDD bound to AhR or CYP1A2 cannot be simulated 

15 with the CADM model. The CADM model computes only the total TCDD concentration in 

16 liver, and describes TCDD elimination through partitioning from circulating lipids across the 

17 lumen of the large intestine into the feces, while the PBPK model accounts for this process 

18 empirically within its hepatic elimination constant. Elimination of TCDD via skin, a minor 

19 process, is not described by either model. Thus, dose-response modeling based on body burden 

20 of TCDD in adult animals and humans can be conducted with either of the models, provided the 

21 duration of the experiment is at least one month, due to limitations in the CADM model. As 

22 shown in Figure 3-29, the predicted slope and body burden over a large dose range are quite 

23 comparable (generally within a factor of two). 

24 Results of simulations of serum lipid concentrations or liver concentrations vary for the 

25 two models to a larger extent (up to a factor of 7), particularly for simulations of short duration. 

26 These differences reflect two characteristics of the PBPK model: first, quasi-steady-state is not 

27 assumed in the PBPK model; second, the serum lipid composition used in the model is not the 

28 same as the adipose tissue lipids. The CADM model does not account for differential solubility 

29 of TCDD in serum lipids and adipose tissue lipids, nor does it account for the diffusion-limited 

30 uptake by adipose tissue. Therefore, the PBPK model would appear to be superior to the CADM 
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1 model with respect to the ability to simulate serum lipid and tissue concentrations during 

2 exposures that do not lead to the onset of steady-state condition in the exposed organism. 

3 The CADM model is simple and based on fewer parameters than the PBPK model. 

4 Because the CADM model is constructed by fitting to data, its performance is likely to be 

5 reliable for the range of exposure doses, species, and life stages from which the parameter 

6 estimates were obtained. On the other hand, the PBPK model structure and parameters are 

7 biologically-based and can be adopted for each species and life stage. Accordingly, the PBPK 

8 model has been adopted to simulate the kinetics of TCDD in the fetus and in pregnant rats, as 

9 well as in adult humans and rats (Emond et al., 2004, 2005, 2006). The time step for calculation 

10 and dosing in the CADM model corresponds to 1 month. This requirement represents a 

11 constraint in terms of the use of this model to simulate a variety of dosing protocols used in 

12 animal toxicity studies. This requirement, however, is not a constraint with the PBPK models. 

13 So, simulating the body burden and serum lipid concentrations for a longer duration of exposure, 

14 either model would appear to be useful; but the PBPK model would be the tool of choice for 

15 simulating alternative dose metrics of TCDD (e.g., blood concentration, total tissue 

16 concentration, bound concentration) for various exposure scenarios (including single dose 

17 studies), routes and life stages in the species of relevance, to TCDD dose-response assessment, 

18 particularly, mice, rats, and humans. 

19 Two minor modifications, to enhance the biological basis, were made to the PBPK model 

20 of Emond et al. (2006), before its use in the computation of dose metrics for TCDD. The first 

21 one involved the recalculation of the volume of the rest of the body as follows: 

22 

23 WRE WLIB WLI WFB WF WLI WF WREB 0 (0.91 ( 0 0 0 0 0 0) (1 0)) = − × + ×+ + + (3-22) 

24 

25 where 

26 WRE0 = weight of cellular component of rest of body compartment (as fraction of 

27 body weight); 

28 WLI0 = weight of cellular component of liver compartment (as fraction of body 

29 weight); 

30 WF0 = weight of cellular component of fat compartment (as fraction of body 

31 weight); 
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1 WREB0 = weight of the tissue blood component of the rest of body compartment (as 

2 fraction of body weight); 

3 WLIB0 = weight of the tissue blood component of the liver compartment (as fraction 

4 of body weight); and 

5 WFB0 = weight of the tissue blood component of the fat compartment (as fraction of 

6 body weight). 

7 

8 In the original code, the weight of the rest of body compartment was calculated as the 

9 difference between 91% of body weight and the sum total of the fractional volumes of blood, 

10 liver tissue (intracellular component), and adipose tissue (intracellular component). The blood 

11 compartment in the PBPK model is not explicitly characterized with a volume; as a result, the 

12 total volume of the compartments is less than 91%. The recalculations shown above were used 

13 to address this problem. Given the very low affinity of TCDD for blood and rest of the body, 

14 reparameterizing the model resulted in less than a 1% change in output compared to the 

15 published version of the PBPK model for chronic exposure scenarios (Emond et al., 2006). 

16 The second minor modification related to the calculation of the rate of TCDD excreted 

17 via urine. The original model code computed the rate of excretion by multiplying the urinary 

18 clearance parameter with the concentration in the rest of the body compartment. Instead, the 

19 code was modified to use the blood concentration in this equation. This resulted in the 

20 re-estimation of the urinary clearance value in the rat and human models but it did not result in 

21 any significant change in the fit and performance of the original model. 

22 The revised parameter estimates of the rat, mouse, and human models are captured in 

23 Table 3-8 with a footnote. 

24 

25 3.3.4.5. Recommended Dose Metrics for Key Studies 

26 The selection of dose metrics for the dose-response modeling of key studies is largely the 

27 result of (1) the relevance of a dose metric on the basis of current knowledge of TCDD’s 

28 mechanism of action for critical endpoints and (2) the feasibility and reliability of obtaining the 

29 dose metric with available PK models. Secondarily, the goodness-of-fit of the dose-response 

30 models (which reflects the relationship of the selected internal dose measures to the response) 

31 can be used to inform selection of the most appropriate dose metric for use in deriving TCDD 

32 toxicity values. 
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1 Body burden—even though this metric is based on mechanistic considerations—is a 

2 somewhat distant measure of dose with respect to target tissue dose, and this metric represents 

3 the “overall” average concentration of TCDD in the body. However, a benefit of body burden is 

4 that this metric represents a dose measure for which the available PK models can provide highly 

5 certain estimates. Thus, the overall confidence associated with the use of body burden in TCDD 

6 assessment is categorized as medium. 

7 The confidence in the ability of PK models to simulate blood concentration as a dose 

8 metric is high, given that the models have been shown to consistently reproduce whole blood (or 

9 serum lipid-normalized) TCDD concentration profiles in both humans and rats. Considering the 

10 facts that the PBPK models simulate whole blood rather than the serum lipid-normalized 

11 concentrations of TCDD and that the study-specific values of serum lipid content are not known 

12 with certainty, it is preferable to rely on TCDD blood concentrations as the dose metric. The 

13 blood concentrations, if intended, can be normalized on the basis of appropriate total lipid levels. 

14 However, based on mechanistic considerations, the confidence in their use would be somewhat 

15 lower for hepatic effects. This conclusion reflects the concern regarding the inconsistent 

16 relationship between the two variables with increasing dose levels and the fraction of steady17 state attained at the time of observation. For other systemic effects related to tissue 

18 concentrations, the confidence in the use of TCDD serum or blood concentration is high, 

19 particularly for chronic exposures, given the absence of organ-specific nonlinear mechanisms. 

20 The tissue concentration typically cannot be calculated as a reliable dose metric with either PK 

21 model. One exception to this conclusion is the use of PBPK models to estimate levels in liver, a 

22 metric that is highly relevant based on MOA considerations. Finally, the bound concentration 

23 may be evaluated for receptor-mediated effects. This dose metric, of medium-low confidence, 

24 can be obtained with PBPK models for high dose-low dose and interspecies extrapolations. The 

25 alternative dose metrics for dose-response modeling of TCDD selected on the basis of MOA and 

26 PK modeling considerations are summarized in Tables 3-11 and 3-12. 

27 These measures of internal dose can be obtained as peak, average, integral (AUC), or 

28 terminal values. For chronic exposures (e.g., exposures reflected by the results of a cancer 

29 bioassay) in rodents, the terminal and average values would be fairly comparable. For less-than 

30 lifetime exposures, however, the terminal and average values will differ, and therefore an overall 

31 average or integrated value (AUC) would be more appropriate. Similarly, for developmental 
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1 exposures, these alternative dose metrics can be obtained with reference to the known or 

2 hypothesized exposure window of susceptibility. 

3 

4 3.3.5. Uncertainty in Dose Estimates 

5 3.3.5.1. Sources of Uncertainty in Dose Metric Predictions 

6 3.3.5.1.1. Limitations of available PK data. 

7 3.3.5.1.1.1. Animal data. 

8 The available animal data relate to blood, liver, and adipose tissue concentrations for 

9 certain exposure doses and scenarios. Although these data are informative regarding the dose10 and time-dependency of TCDD kinetics for the range covered by the specific studies (see 

11 Section 3.3.2), they do not provide the peak, average, terminal, or lipid-normalized values of 

12 dose metrics associated with the key studies selected for this assessment. The limited available 

13 animal PK data are useful, however, in the evaluation of the pharmacokinetic models (see 

14 Section 3.3.4). 

15 

16 3.3.5.1.1.2. Human data. 

17 The human data on potential dose metrics are restricted to the serum lipid-adjusted 

18 TCDD concentrations associated with mostly uncharacterized exposures (see Sections 3.3.2 and 

19 3.3.3). While these data are useful in estimating half-lives in exposed human individuals, they 

20 do not provide estimates of hepatic clearance or reflect target organ exposure. Some autopsy 

21 data have been used to infer the partition coefficients; however, these data were collected 

22 without quantification of the temporal nature of TCDD uptake (see Section 3.2). Despite the 

23 limitations associated with the available human data, there has been some success in using these 

24 data to infer the half-lives and elimination rates in humans using pharmacokinetic models 

25 (Aylward et al., 2005b; Carrier et al., 2005a; Emond et al., 2006). 

26 

27 3.3.5.1.2. Uncertainties associated with model specification. 

28 Uncertainty associated with model specification should be viewed as a function of the 

29 specific application, such as interspecies extrapolation, intraspecies variability, or high dose to 

30 low dose extrapolation. Because the use of pharmacokinetic models in this assessment is limited 

31 to interspecies extrapolation and high dose to low dose extrapolation, it is essential to evaluate 
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1 the confidence in predicted dose metrics for these specific purposes. For interspecies 

2 extrapolation, the PBPK and CADM models calculate differences in dose metric between an 

3 average adult animal and an average adult human. Both models have a biologically and 

4 mechanistically-relevant structure along with a set of parameters with reasonable biological 

5 basis, and reproduce a variety of pharmacokinetic data on TCDD in both rodents and humans. 

6 These models possess low uncertainty with respect to body burden, blood, and TCDD/serum 

7 (lipid) concentration for the purpose of conducting rat to human extrapolation. However, for 

8 other dose metrics, such as free, total, or bound hepatic concentrations, the uncertainty is higher 

9 in the CADM model compared to the PBPK model due to model specification differences related 

10 to the mechanisms of sequestration and induction in the liver (see Section 3.3.3). 

11 For the purpose of high dose to low dose extrapolation in experimental animals, 

12 confidence in both models is high with respect to a variety of dose metrics (see previous 

13 discussion). The high confidence results from the use of the PBPK models to reproduce a 

14 number of data sets covering a wide range of dose levels in rodents (rats, mice). This dose range 

15 likely covers that of most of the key toxicological studies. Given that the TCDD levels during 

16 and at the end of exposures were not measured in most of the key studies, use of the PBPK 

17 models is preferred because these models account for dose-dependent elimination, induction, and 

18 sequestration. Despite the empirical nature of the specification of these key processes in PBPK 

19 models, they essentially reproduce the dose-dependent behavior in rodents, supporting their use 

20 in deriving dose metrics for dose-response modeling of TCDD. Overall, the uncertainty 

21 associated with the use of the dose metrics (identified in Table 3-10) is less than the uncertainty 

22 associated with the use of administered dose of TCDD, for relating to the concentration within 

23 tissues to produce an effect. The administered dose does not take into account interspecies 

24 differences in the volume of distribution and clearance or the complex nonlinear processes 

25 determining the internal dose. 

26 The PBPK model of Emond et al. (2006) could benefit from further refinement and 

27 validation, including a more explicit consideration of nondose-dependent elimination pathways. 

28 As indicated in Section 4, there is some uncertainty associated with the way the elimination of 

29 TCDD is described in the existing human PBPK model. The current model essentially treats all 

30 TCDD elimination as related to dose dependent metabolism in the liver. In this regard, the 

31 classical and more recent PK data on TCDD may be useful in further improving the confidence 
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1 in their predictions. However, it is likely that there is nondose-dependent elimination of TCDD 

2 via feces and, to a lesser extent skin; juxtaposition of available elimination rate data with the 

3 PBPK model predictions suggests that the current PBPK model modestly overestimates the dose 

4 dependency of overall TCDD elimination. (The central estimate of the slope of the relationship 

5 between the log of the TCDD elimination rate and the log of the TCDD level is only about 

6 three−fourths of that expected using the unmodified PBPK model). Emond et al. (2005) 

7 acknowledge that the model did not describe the elimination of TCDD from the blood into the 

8 intestines, but it indirectly accounted for this phenomenon with the use of the optimized 

9 elimination rate. 

10 

11 3.3.5.1.3. Impact of human interindividual variability. 

12 The sources and extent of human variability suggested by the available data are presented 

13 in Section 3.3.3, although there is some discussion of the impact of individual differences in 

14 body fat content. The CADM model facilitates the simulation of body burden and serum lipid 

15 concentrations on the basis of BMI and tissue weights of people, and the PBPK model simulates 

16 alternative dose metrics in the fetus and in pregnant animals in addition to adult animals and 

17 humans. However, neither of these models has been parameterized for simulation of population 

18 kinetics and distribution of TCDD dose metrics. Therefore, at the present time, a quantitative 

19 evaluation of the impact of human variability on the dose metrics of TCDD is not feasible, and 

20 dose metric-based replacement of the default interindividual factor has not been attempted. 

21 

22 3.3.5.2. Potential Magnitude and Sources of Uncertainty in Dose Metrics 

23 3.3.5.2.1. Magnitude of uncertainty. 

24 The usefulness of the CADM and PBPK models for conducting high dose to low dose 

25 and interspecies extrapolations is determined by their reliability in predicting the desired dose 

26 metrics. The confidence, or conversely the magnitude of uncertainty, in the model predictions of 

27 dose metrics is dictated by the extent to which the model has been verified with empirical data 

28 relevant to the dose metric, supplemented by sensitivity and uncertainty analyses. Analysis of 

29 sensitivity or uncertainty has not been conducted with the CADM model. For the PBPK model, 

30 Emond et al. (2006) published the initial results from sensitivity analyses of acute exposure 

31 modeling (see Section 3.3.3). One of the objectives of a sensitivity analysis that is of highest 
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1 relevance to this assessment is to identify the most critical model parameters with respect to the 

2 model output (i.e., dose metric). 

3 If the model simulations have only been compared to entities that do not correspond to 

4 the moiety representing the dose metric, or if the comparisons have only been done for some but 

5 not all relevant dose levels, routes, and species, then the reliability in the predictions of dose 

6 metric can be an issue. The extent to which model results are uncertain will depend largely upon 

7 the extent to which the dose metric is measurable (e.g., serum concentrations of TCDD) or 

8 inferred (e.g., receptor-bound TCDD concentration). 

9 With respect to body burden and blood concentration, extrapolation uncertainty is low, 

10 and therefore the need for sensitivity and uncertainty analysis is less critical. For serum lipid11 based metric, the lipid content used for normalizing the animal and human blood concentrations 

12 will have a direct impact on the outcome. Because the PBPK models have directly evaluated 

13 blood or tissue concentration data of relevance to dose metric calculations in the species and life 

14 stage of interest to the assessment, extrapolation uncertainty is low and therefore confidence in 

15 these simulated dose metrics is high. For those dose metrics that are not directly measurable or 

16 are less easily determined by available calibration methods (e.g., free liver concentration, 

17 receptor bound concentration), sensitivity and uncertainty analyses are crucial to inform the 

18 reliability of the PBPK model predictions 

19 Sensitivity analysis for the PBPK model predictions of liver concentration of TCDD 

20 indicated hepatic CYP1A2 concentration is the most sensitive parameter in the rat model 

21 (Emond et al., 2006). In addition, the absorption parameters, basal concentration of CYP1A2, 

22 and adipose tissue:blood partition coefficients were identified as highly sensitive model 

23 parameters for simulations of human kinetics. These results indicate that the confidence in the 

24 use of the rat PBPK model for high dose to low dose extrapolation is high. Confidence is low for 

25 the purpose of rat to human extrapolation given that the values of these key human model 

26 parameters are uncertain. A similar inference can be made with respect to the concentration of 

27 bound TCDD in liver, suggesting that the use of bound concentration as a dose metric in the rat 

28 is reliable for high dose to low dose extrapolation, and less reliable for extrapolating from rat to 

29 human due to model sensitivity to parameters with high uncertainty. 

30 With regard to the predictability of body burden, the absorption and excretion parameters 

31 were among the sensitive parameters in the rat. Several other parameters were also identified as 
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1/15/10 DRAFT 3-50 —DO NOT CITE OR QUOTE 

1 being sensitive in humans. Despite the sensitivity to these parameters and the uncertainty 

2 associated with individual parameter estimates, the overall confidence in the model predictions 

3 of body burden appears to be high given the reproducibility of empirical data on tissue burdens 

4 and blood concentrations of TCDD in various experiments by both models. Similar conclusions 

5 can be drawn for serum lipid concentration of TCDD predicted by the PBPK model, except that 

6 the assigned value of lipid content will have additional, direct impact on this dose metric. 

7 Therefore, knowledge of the statistical representativeness of the lipid levels for rodents and 

8 humans, intended for use in normalization, will be crucial. In this regard, variability of the total 

9 lipid levels and the variability of the contribution of phospholipids and neutral lipids to the total 

10 lipid pool across species, lifestage and study groups is to be expected (Poulin and Theil, 2001; 

11 Bernert et al., 2007). 

12 Both conceptual uncertainty and prediction uncertainty are relevant to dose metrics used 

13 in the dose-response modeling. In the current context, conceptual uncertainty arises from the 

14 assumed relevance of the dose metrics to the MOA and target organ toxicity of TCDD. 

15 Prediction uncertainty reflects the lack of confidence in the model predictions of dose metrics. 

16 Tables 3-13 and 3-14 provide a qualitative ranking of the importance and magnitude of each 

17 dose metric with respect to these two sources of uncertainty. Overall, uncertainty is high for the 

18 use of administered dose and absorbed dose at one end of the spectrum and receptor occupancy 

19 at the other end of the spectrum. Based on the information presented in Table 3-13, for instances 

20 involving the use of rat and human PBPK models, the overall uncertainty associated with the use 

21 of body burden and serum concentrations are medium, whereas for hepatic effects, the use of 

22 liver concentration would be considered to be medium/low. While using mouse PBPK model 

23 along with the human model (see Table 3-14), the contribution of the prediction uncertainty to 

24 the overall uncertainty increases due to the limited comparison of the mouse model simulations 

25 with empirical data. 

26 

27 3.3.6. Use of the Emond PBPK Models for Dose Extrapolation from Rodents to Humans 

28 EPA has selected the Emond et al. (2004, 2005, 2006) PBPK models, as modified by 

29 EPA for this assessment, for establishing toxicokinetically-equivalent exposures in rodents and 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-51 —DO NOT CITE OR QUOTE 

humans.13 1 The 2003 Reassessment (U.S. EPA, 2003) presented a strong argument for using the 

2 relevant tissue concentration as the effective dose metric. However, no models exist for 

3 estimation of all relevant tissue concentrations. Therefore, EPA has decided to use the 

4 concentration of TCDD in blood as a surrogate for tissue concentrations, assuming that tissue 

5 concentrations are proportional to blood concentrations. Furthermore, because the RfD and 

6 cancer slope factor are necessarily expressed in terms of average daily exposure, the blood 

7 concentrations are expressed as averages over the relevant period of exposure for each endpoint. 

8 Specifically, blood concentrations in the model simulations are averaged from the administration 

9 of the first dose to the administration of the last dose plus one dosing interval unit in order to 

10 capture the peaks and valleys for each administered dose. That is, for daily dosing, 24 hours of 

11 TCDD elimination following the last dose is included in the average (the modeling time interval 

12 is one hour); for a weekly dosing protocol, a full week is included. In addition, because of the 

13 accumulation of TCDD in fat and the large differences in elimination kinetics between rodent 

14 species and humans, exposure duration plays a much larger role in TK extrapolation across 

15 species than for rapidly-eliminated compounds. Because of these factors, EPA is using discrete 

16 exposure scenarios that relate human and rodent exposure durations. The use of discrete 

17 exposure scenarios was introduced previously in Section 3.4.4.2 describing first-order kinetic 

18 modeling and is further described in the following paragraphs. This section concludes with a 

19 quantitative evaluation of the impact of exposure duration on the rodent-to-human TK 

20 extrapolation from both the human and rodent “ends” of the process. 

21 Figure 3-30 shows the TCDD blood concentration-time profile for continuous exposure 

22 at 0.01 ng/kg-day, as predicted by the Emond human PBPK model, and the target TCDD 

23 concentrations corresponding to the three discrete exposure scenarios used by EPA in this 

24 document. The target concentrations are those that would be identified in the animal bioassay 

25 studies that correspond to a particular POD (no-observed-adverse-effect level, lowest-observed26 adverse-effect level, or benchmark dose lower confidence bound) established for that bioassay. 

27 That is, the target concentrations represent the toxicokinetically-equivalent internal exposure to 

28 be translated into an equivalent human intake (or HED). 

 

13The models will be referred to hereafter as the “Emond human PBPK model” and the “Emond rodent PBPK 

model,” with variations when referring to individual species or components (e.g., gestational). 
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1 For the lifetime exposure scenario, the HED is “matched” to the lifetime average TCDD 

2 blood concentration from a lifetime animal bioassay result by determining the continuous daily 

3 intake that would result in that average blood concentration for humans over 70 years. 

4 For the gestational exposure scenario, the effective TCDD blood concentration (usually 

5 the peak) determined for the particular POD in a particular developmental study is matched to 

6 the average TCDD blood concentration over the gestational portion of the human gestational 

7 exposure scenario. The HED is determined as the continuous daily intake, starting from birth 

8 that would result in that average blood concentration over the 9-month gestational period for a 

9 pregnancy beginning at 45 years of age. The choice of 45 years as the beginning age of 

10 pregnancy is health protective of the population in that the daily exposure achieving the target 

11 blood concentration is smaller than for earlier pregnancies. 

12 For a less-than-lifetime exposure, the average TCDD blood concentration over the 

13 exposure period in the animal bioassay associated with the POD is matched to the average over 

14 the 5-year period that includes the peak concentration (58 years for an intake of 0.01 ng/kg-day). 

15 The HED is determined as the continuous daily intake that would result in the target 

16 concentration over peak 5-year period. The use of the peak is analogous to the approach in the 

17 2003 Reassessment, where the terminal steady-state body burden played the same role. The 

18 5-year average over the peak is taken to smooth out sharp peaks and more closely approximate a 

19 plateau. The choice of peak is health protective because humans of any age must be protected 

20 for short-term exposures, and the daily intake achieving a given TCDD blood concentration is 

21 smallest when matched to the peak exposure as opposed to an average over shorter durations. 

22 Thus, target concentrations for any exposure duration of less-than-lifetime must be averaged 

23 backwards from the end of the lifetime scenario, rather than from the beginning. The only 

24 exception would be if the short-term endpoints evaluated in the animal bioassay were associated 

25 with a specific life stage (such as for the gestational scenario). Note that this scenario lumps all 

26 exposures from 1 day to over 1 year in rodents into the same less-than-lifetime category. 

27 Conceptually, duration-specific scenarios could be constructed by defining equivalent rodent and 

28 human exposure durations. However, for the most part, defining duration equivalents across 

29 species is a somewhat arbitrary exercise, not generally based on physiologic or toxicologic 

30 processes, but relying primarily on fraction-of-lifetime conversions. EPA defines “lifetime” 

31 exposure as 2 years and 70 years for rodents and humans, respectively. So, a half-lifetime 
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1 equivalence of 1 year in rodents and 35 years in humans is defined easily. Also, considering a 

2 subchronic exposure to be 10–15% of lifetime, leads to an equivalence of 90 days in rodents and 

3 7–10 years in humans. However, in the practical sense with respect to the Emond human PBPK 

4 model predictions, the difference in the dose-to-target-concentration ratios are not significantly 

5 different from the peak 5-year average scenario, differing by less than 5%. 

6 The net effect of using three different scenarios for estimating the HED from rodent 

7 exposures is that, for the same target concentration, the ratio of administered dose (to the rodent) 

8 to HED will be larger for short-term exposures than for chronic exposures. Figure 3-31 is 

9 similar to Figure 3-30, except that it shows the relationship of daily intake to a fixed target 

10 TCDD blood concentration level. Figure 3-31 shows that, for human intakes of approximately 

11 0.01 ng/kg-day, the difference in the defined scenarios is 40% or less, with a lifetime-scenario 

12 daily intake of 0.014 ng/kg-day required to reach the same target concentration for a shorter-term 

13 exposure of 0.01 ng/kg-day. The corresponding daily intake for the gestational scenario is 

14 0.011 ng/kg-day. Because of the nonlinearities in the Emond human PBPK model, the 

15 magnitude of the difference between the lifetime and less-than-lifetime exposure scenarios 

16 increases at lower intake levels, but not to a substantial degree. 

17 The differential effect of short- and long-term exposures is much more accentuated at the 

18 rodent end of the exposure kinetic modeling. Analogous to the processes described in the 

19 previous section for first-order body burden (see Section 3.4.2.2), the TCDD blood concentration 

20 for single exposures is essentially the immediate absorbed fraction of the administered dose, 

21 which will be somewhat lower than the administered dose, while for chronic exposure, the 

22 TCDD blood concentration will reflect the long-term accumulation from daily exposure, which 

23 will be very much larger than the administered dose (expressed as a daily intake). Table 3-15 

24 shows the overall impact of TK modeling on the extrapolation of administered dose to HED, 

25 comparing the Emond PBPK and first-order body burden models. For comparison purposes, the 

26 administered dose is fixed at 1 ng/kg-day for all model runs. Large animal-to-human TK 

27 extrapolation factors (TKEF) are evident for short-term mouse studies, decreasing in magnitude 

28 with increasing exposure duration. The only exception is the slightly lower extrapolation factor 

29 for the mouse 1-day exposure, which is the result of the relatively short TCDD half-life (10 days) 

30 in mice and the use of the peak TCDD blood concentration as representative of single exposures, 

31 compared to the average TCDD blood concentration over the exposure period used for multiple 
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1/15/10 DRAFT 3-54 —DO NOT CITE OR QUOTE 

1 exposures. The TKEFs are lower for rats because of the slower elimination of TCDD in rats 

2 compared to mice. Also, because of the nonlinear kinetics inherent in the Emond PBPK model, 

3 the span of the HED (13-fold for mice) across these exposure durations is greater than the span 

4 of the lipid-adjusted serum concentration (LASC; 4-fold for mice). Because of the dose5 dependence of TCDD elimination in the Emond model, the TKEF becomes smaller with 

6 decreasing intake. The result of this nonlinearity is that, although Table 3-15 shows much lower 

7 TKEFs for the Emond PBPK model than for the first-order body burden metric, at much lower 

8 HED levels the two models give much closer predictions. 
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1/15/10 DRAFT 3-55 —DO NOT CITE OR QUOTE 

1 Table 3-1. Partition coefficients, tissue volumes, and volume of distribution 

2 for TCDD in humans 

3 

Tissue 

Tissue/blood 

partition 

coefficient 

Tissue volume 

(liters, for a 

60 kg person) 

Effective volume of 

distribution (Vd—liters of 

blood equivalent) 

Percent 

total Vd

Blood 1 3 3 0.25 

Fat 100 11.4 1.140 94.19 

Liver 6 1.56 9 0.77 

Rest of the body 1.5 38.64 58 4.79 

Total 54.6* 1.210 100.00 

4 

5 *The total tissue volume presented here represents only 91% of body weight because some of the weight and 

6 volume of the body is occupied by bone and other structures where TCDD uptake and accumulation do not occur to 

7 a significant extent. 

8 

9 Source: Wang et al. (1997), Emond et al. (2005, 2006). 

10 

11 

12 Table 3-2. Blood flows, permeability factors and resulting half lives (t1⁄2) for 

13 perfusion losses for humans as represented by the TCDD PBPK model of 

14 Emond et al. (2005, 2006) 

15 

Tissue 

Permeability (fraction of 

compartment blood flow)

Rate constant for 

compartmental 

elimination (hour−1) t1⁄2 (hrs) 

Fat 0.12 0.0049 143 

Liver 0.03 0.77 0.90 

Rest of the body 0.35 3.84 0.18 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 Table 3-3. Toxicokinetic conversion factors for calculating human equivalent 

2 doses from rodent bioassays 

3 

Mouse Rat (Wistar) Rat (other) Guinea pig 

Half-life (days)a

10 20 25 40 

Exposure 

duration (days) Conversion factor (CF)b BBA(tA):dA given in parentheses 

1 3882 (0.77) 3815 (0.79) 3802 (0.79) 3783 (0.79) 

7 1107 (2.71) 1020 (2.94) 1004 (2.99) 979 (3.07) 

14 681 (4.41) 587 (5.11) 569 (5.27) 543 (5.53) 

28 453 (6.62) 350 (8.56) 331 (9.06) 303 (9.90) 

90 307 (9.76) 186 (16.1) 163 (18.4) 130 (23.0) 

180 282 (10.6) 154 (19.5) 129(23.2) 93 (32.1) 

365 270 (11.1) 141 (21.3) 115(26.0) 77 (38.9) 

730 226 (11.3) 115 (22.2) 93 (27.4) 60 (42.5) 

4 a 5 Half-life for humans = 2,593 days (7.1 years). 

b 6 dH = dA/CF; BBH(tH):dH = 2,185 (1−180 days), 2,202 (365 days), 2,555 (730 days). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-57 —DO NOT CITE OR QUOTE 

1 Table 3-4. Equations used in the CADM PBPK model*

2 

Parameter Equation

Hepatic

Concentration

(ng/kg) body

body

l

body

hepatic CK

Cff f WQ C

+

− ∗ = +

)( (* max min

min

Fat

Concentration

(ng/kg) )) )( (1(* max min

min

body

body body

adipose CK

Cff f WaQ C

+

− ∗ = +−

Hepatic

Elimination )) )( _ (1(* max min

min

body

body

e body CK

Cff Exr Qkhepatic f

+

− ∗

+−∗=

Excretion via

gut of

Unchanged

TCDD

(Exsorption)

= *_ QkgutExr aa

Change of

TCDD due to

bodyweight

change )(

))()(( _ * tBWtBWdttBW ChangeTCDD QBW body

+ − =

Amount in

body as a

function of

time

body −+ body = ExrtQdttQ _)()( hepatic + _ gutExr + ChangeTCDD _ BW

Adipose tissue

growth 100

BMI Age *8.10)*23.0(*2.1 sex Wa

+ − =

Change of

hepatic

elimination

constant with

age

Agekkk ee 0 −= eslope *

3 *For abbreviations and parameters, see Table 3-5. 

4 Source: Aylward et al. (2005b). 
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1/15/10 DRAFT 3-58 —DO NOT CITE OR QUOTE 

1 Table 3-5. Parameters of the CADM model 

2 

 Parameter Value Units Comments/sources 

fhmin 0.01 unitless Minimum body burden fraction in liver 

fhmax 0.7 unitless Maximum body burden fraction in liver 

K 100 ng/kg Body burden at half-maximum of fraction 

liver 

ke Calculated per year ke = ke0 − ke_slope*(age) with enforced 

minimum of ke_min

ke0 0.85 per year CADM-mean hepatic elimination base rate at 

age 0 

ke_slope 0.011 per year Change in ke per year of age 

ke_min 0.2 per year Minimum hepatic elimination rate 

wa (adipose weight fraction) Calculated unitless wa = [(1.2*BMI)+0.23*Age-10.8*sex]/100 

wh (liver body weight fraction) 0.03 unitless Assumed constant 

ka (adipose clearance factor) 0.0025 per month Passive elimination rate from intestinal tract 

Monthly dose 0.15507069 ng per month 

Estimated absorption fraction 0.97 unitless From Moser and McLaghlan (2001) 

Body weight 70 kg Standard male weight 

Sex 1 unitless 1 = male; 0 = female 

Time of administration 840 months 

Initial Cbody 0.2 ng/kg Estimated background young adults UMDES 

sampling 

Absorbed monthly dose 1 0.150418569 ng per month 

3 

4 Note: The values of fhmin, fhmax, and K were obtained by best fit of the model simulations to the experimental data 

5 with the method of least squares (Carrier et al., 2005a). 

6 

7 Source: Aylward et al. (2005b). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-59 —DO NOT CITE OR QUOTE 

1 Table 3-6. Confidence in the CADM model simulations of TCDD dose 

2 metrics

3 

Dose metric Level of confidence 

Administered dose N/A 

Absorbed dose H 

Body burden H 

Serum lipid concentration M 

Total tissue (liver) concentration L 

Receptor occupancy (bound concentration) N/A 

4 

5 H = high, M = medium, L = low, NA = not applicable.
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-60 —DO NOT CITE OR QUOTE 

1 Table 3-7. Equations used in the TCDD PBPK model of Emond et al. (2006) 

2 

Aspect Equation 

Body weight 

growth with age 

0.41 () _ 0 1402.5 timetime BW g BW T time

⎛ ⎞ ×

= × ⎜ ⎟ ⎝ ⎠ +

Cardiac output 0.75

( / ) 60 1000

BW Qc mL hr QCCAR ⎛ ⎞ = × ⎜ ⎟ ⎝ ⎠

A factor of 60 corresponds to the conversion of minutes to hours, and 1,000 is conversion of BW 

from grams to kilograms. 

Blood 

compartment ( ) ( ) ( ) ( ) ( /) Qf Cfb Qre Creb Qli Clib lymph Cb CLURI

Cb nmol mL

Qc Qc

⎡ ⎤ × + × +× + × ⎣ ⎦ = −

Tissue compartment (fat, rest of the body) 

Tissue blood 

subcompartment ( /) ( ) dAtb Ct nmol mL Qt Ca Ctb PAt Ctb dt Pt

⎛ ⎞ = −− − ⎜ ⎟ ⎝ ⎠

Wtb

Atb mLnmolCtb )/( =

Tissue cellular 

matrices ( /) dAt Ct nmol mL PAt Ctb

dt Pt

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠

Wt

At mLnmolCt )/( = 

Liver tissue compartment 

Tissue blood 

subcompartment ( /) ( ) ( ) oral

dAlib nmol mL Qli Ca Clib PALI Clib Clifree input dt

= −− − +

WLIB

Alib mLnmolClib )/( =

Tissue cellular 

matrices ( / ) ( )( _ )

( /)

dAli nmol mL PALI Clib Clifree KBILE LI Clifree WLI dt

Ali Cli nmol mL

Wli

= − − ××

=

Free TCDD 

concentration in 

liver 

1 2 ( /) 1 2LIBMAX Clifree CYP A Clifree Clifree nmol mL Cli Clifree PLI KDLI Clifree KDLI A Clifree

⎡ ⎛ ⎞ × × ⎛ ⎞⎤

=− × + ⎢ ⎜ ⎟ + ⎜ ⎟⎥ ⎣ ⎝ ⎠ + + ⎝ ⎠⎦

Concentration 

bound to AhR 

in hepatic tissue 

( /) AhRbound

LIBMAX Clifree Ct nmol mL

KDLI Clifree

× = +

All other induction processes and equations have been described and presented by Wang et al. 

(1997). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-61 —DO NOT CITE OR QUOTE 

1 

Table 3-7. Equations used in the TCDD PBPK model of Emond et al. (2006) 

(continued) 

Aspect Equation 

Gastrointestinal absorption and distribution of TCDD to the portal lymphatic circulation 

Amount of 

TCDD 

remaining in 

lumen cavity 

( /) ( ) dLumen nmol hr KST KABS lumen intake

dt

=+ × + ⎡ ⎤ ⎣ ⎦

Lumen in the amount of TCDD remaining in the GI tract (nmol); intake is the rate of intake of 

TCDD during a subchronic exposure (nmol/hr). 

Amount of 

TCDD 

eliminated in the 

feces 

( /) dFeces nmol hr KST lumen

dt = ×

Absorption rate 

of TCDD to the 

blood via the 

lymphatic 

circulation 

( /) 0.7 dLymph nmol hr KABS lumen

dt

= ××

Absorption rate 

of TCDD by the 

liver via portal 

circulation 

( /) 0.3 dPortal nmol hr KABS lumen

dt

= ××

2 

3 Note: Key parameters and abbreviations are defined in Table 3-10.
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1/15/10 3-62 DRAFT: DO NOT CITE OR QUOTE Table 3-8. Parameters of the PBPK model for TCDD Parameter values Parameter Description Symbol Human nongestationala Human gestationala Mouse nongestational Mouse gestational 

no

Body weight (g) BW Calculated Calculated 23-28b 23-28 Cardiac output (mL/hour/kg) QCCAR 15.36c,d Calculated 275c 275c Tissue (intracellular) volumes (fraction of BW) Liver WLI0 Calculated Calculated 0.0549f 0.0549f Fat WF0 Calculated Calculated 0.069e Calculated Tissue blood volumes Liver (fraction of WLI0) WLIB0 0.266e 0.266e 0.266e 0.266e Fat (fraction of WF0) WFB0 0.05e 0.05e 0.05e 0.05 e Rest of body (fraction of WRE0) WREB0 0.03e 0.03e 0.03e 0.03 e Placenta tissue fraction of tissue blood weight (unitless) WPLAB0 N/A 0.5g N/A 0.5e Tissue blood flow (fraction of cardiac output) Liver QLIF 0.26c 0.26c 0.161f 0.161f Fat QFF 0.05c 0.05c 0.07h 0.07h Placenta QPLAF N/A Calculated N/A Calculated Tissue permeability (fraction of tissue blood flow) Liver PALIF 0.35e 0.35e 0.35e 0.35e Fat PAFF 0.12i 0.12i 0.12i 0.12i Placenta diffusional permeability fraction (unitless) PAPLAF N/A 0.3g N/A 0.03g Rest of body PAREF 0.03e 0.03e 0.03e 0.03e 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 3-63 DRAFT: DO NOT CITE OR QUOTE Table 3-8. Parameters of the PBPK model for TCDD (continued) Parameter values Parameter Description Symbol Human nongestationala Human gestationala Mouse nongestational Mouse gestational 

no

Partition coefficient Liver PLI 6e 6e 6e 6 e Fetus/blood partition coefficient (unitless) PFETUS N/A 4 j N/A 4j Placenta/blood partition coefficient (unitless) PPLA N/A 1.5j N/A 3g Fat PF 100e 100e 400i 400i Rest of body PRE 1.5e 1.5e 3k 3k Metabolism constants Urinary clearance elimination (mL/hour) CLURI 4.17E-08l 4.17E-08l 0.09i 0.09i Clearance - transfer from mother to fetus (mL/hour) CLPLA_FET N/A 16e N/A 0.17i Liver (biliary elimination and metabolism; hour-1) KBILE_LI Inducible Inducible Inducible Inducible Interspecies constant (hour-1) Kelv 0.0011i 0.0011i 0.4i 0.4i AhR Affinity constant in liver (nmol/mL) KDLI 0.1e 0.1e 0.0001e 0.0001e Binding capacity in liver (nmol/mL) LIBMAX 0.35e 0.35e 0.00035e 0.00035e Placenta binding capacity (nmol/mL) PLABMAX N/A 0.2j N/A 0.0002j Affinity constant protein (AhR) in placenta (nmol/mL) KDPLA N/A 0.1j N/A 0.0001j 
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1/15/10 3-64 DRAFT: DO NOT CITE OR QUOTE Table 3-8. Parameters of the PBPK model for TCDD (continued) Parameter values Parameter Description Symbol Human nongestationala Human gestationala Mouse nongestational Mouse gestational 

no

CYP1A2 induction parameters Dissociation constant CYP1A2 (nmol/mL) KDLI2 40j 40j 0.02i 0.02i Degradation process CYP1A2 (nmol/mL) CYP1A2_1OUTZ 1,600e 1,600e 1.6e 1.6e Dissociation constant during induction (nmol/mL) CYP1A2_1EC50 130e 130e 0.13e 0.13e Basal concentration of CYP1A2 (nmol/mL) CYP1A2_1A2 1,600e 1,600e 1.5k 1.5k First-order rate of degradation (hour-1) CYP1A2_1KOUT 0.1e 0.1e 0.1e 0.1e Time delay before induction process (hour) CYP1A2_1TAU 0.25e 0.25e 1.5k 1.5k Maximal induction of CYP1A2 (unitless) CYP1A2_1EMAX 9,300i 9,300i 600e 600e Other constants Oral absorption constant (hour-1) KABS 0.06i 0.06i 0.48i 0.48i Gastric nonabsorption constant (hour-1) KST 0.01

m 0.01

m 0.30i 0.30i 

aUnits for human nongestational parameters are L rather than mL and kg rather than g where applicable. bBody weight varies by study (Emond et al., 2004). cKrishnan (2008). dUnits are L/kg/hr. eWang et al. (1997). fILSI (1994). gFixed. hLeung et al. (1990). iOptimized. jEmond et al. (2004). kWang et al. (2000). lLawrence and Gobas (1997). mCalculated to estimate 87% bioavailability of TCDD in humans (Poiger and Schlatter, 1986). 
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1/15/10 DRAFT 3-65 —DO NOT CITE OR QUOTE 

1 Table 3-9. Regression analysis results for the relationship between log10

2 serum TCDD at the midpoint of observations and the log10 of the rate 

3 constant for decline of TCDD levels using Ranch Hand data 

4 

Item Aspect Value 

RSquare 0.894 

RsquareAdj 0.871 

Root mean square error 0.044 

Mean responses 0.130 

Summary of fit 

Observations (or sum weights) 11 

Intercept 

Estimate −0.054 

Standard deviation 0.026 

t ratio −2.07 

Prob>|t| 0.0679 

Log (TCDDpg/g) 

Estimate 0.092 

Standard error 0.011 

t ratio 8.28 

Parameter estimates 

Prob>|t| <0.0001 

5 

6 

7 Table 3-10. Confidence in the PBPK model simulations of TCDD dose 

8 metrics

9 

Dose metric Human model Rat model Mouse model 

Administered dose N/A N/A N/A 

Absorbed dose H H M 

Body burden H H M 

Serum (blood)concentration H H M 

Total liver concentration M/L H M 

Receptor occupancy (bound concentration) L L L 

10 

11 H = high, M = medium, L = low.
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-66 —DO NOT CITE OR QUOTE 

1 Table 3-11. Overall confidence associated with alternative dose metrics for 

2 cancer and noncancer dose-response modeling for TCDD using rat PBPK 

3 model

4 

End point 

Body 

burden 

Blood or serum 

concentration 

Liver 

concentration 

Bound 

concentration in 

liver 

Liver effects M H M/L 

Nonhepatic effects M H M/L 

5 

6 H = high, M = medium, L = low. 

7 

8 

9 Table 3-12. Overall confidence associated with alternative dose metrics for 

10 cancer and noncancer dose-response modeling for TCDD using mouse PBPK 

11 model

12 

End point 

Body 

burden 

Blood or serum 

concentration 

Liver 

concentration 

Bound 

concentration in 

liver 

Liver effects M M L 

Nonhepatic effects M M L 

13 

14 H = high, M = medium, L = low. 

15 

16 

17 Table 3-13. Contributors to the overall uncertainty in the selection and use 

18 of dose metrics in the dose-response modeling of TCDD based on rat and 

19 human PBPK models

20 

Dose metric Conceptual uncertainty Prediction uncertainty 

Administered dose H NA 

Absorbed dose H L 

Body burden M L 

Blood or serum concentration M L 

Tissue concentration L M 

Receptor occupancy L(?) H 

21 

22 H = high, M = medium, L = low, NA = not applicable, ? = if relevant to MOA of response. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-67 —DO NOT CITE OR QUOTE 

1 Table 3-14. Contributors to the overall uncertainty in the selection and use 

2 of dose metrics in the dose-response modeling of TCDD based on mouse and 

3 human PBPK models

4 

Dose metric Conceptual uncertainty Prediction uncertainty 

Administered dose H NA 

Absorbed dose H L 

Body burden M M 

Blood or serum concentration M M 

Tissue concentration L M⁄H 

Receptor occupancy L(?) H 

5 

6 H = high, M = medium, L = low, NA = not applicable, ? = if relevant to MOA of response. 

7 

8 

9 Table 3-15. Impact of toxicokinetic modeling on the extrapolation of 

10 administered dose to HED, comparing the Emond PBPK and first-order 

11 body burden models 

12 

1st-order BB Emond PBPK 

Exposure 

duration (days) 

HED 

(ng/kg−day) TKEF

LASC 

(ng/kg) 

HED 

(ng/kg−day) TKEF

Mouse 

1 2.57E-4 3,882 75.5 9.49E-4 1,054 

14 1.47E-3 681 64.4 8.17E-4 1,224 

90 3.25E-3 307 173 3.83E-3 261 

365 3.70E-3 270 248 6.66E-3 150 

730 4.43E-3 226 263 1.08E-2 93 

Rat

1 2.63E-4 3,802 110 1.87E-3 535 

14 1.76E-3 569 208 5.22E-3 192 

90 6.13E-3 163 599 2.81E-2 36 

365 8.68E-3 115 811 4.52E-2 22 

730 1.07E-2 93 853 6.47E-2 15 

13 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-68 —DO NOT CITE OR QUOTE 

1 

2 

3 

4 Figure 3-1. Liver/fat concentration ratios in relation to TCDD dose at 

5 various times after oral administration of TCDD to mice. 

6 

7 Source: Dilberto et al. (1995). 

.1 1 10

0

1

2

3

4

7 Day Liver/Fat

14 Day Liver/Fat

21 Day Liver/Fat

35 Day Liver/Fat

Dose μg/Kg

Ratio of Concentrations in Liver to Fat
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-69 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-2. First-order elimination rate fits to 36 sets of serial TCDD 

3 sampling data from Seveso patients as function of initial serum lipid TCDD. 

4 

5 Source: Aylward et al. (2005). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-70 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-3. Observed relationship of fecal 2,3,7,8-TCDD clearance and 

4 estimated percent body fat. 

5 

6 Source: Rohde et al. (1999). 

20 21 22 23 24 25 26

0.02

0.03

0.04

0.05

% Body Fat

Fract Fecal Elim/Year

y = 0.09735 - 0.00282x R^2 = 0.752
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-71 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-4. Unweighted empirical relationship between percent body fat 

4 estimated from body mass index and TCDD elimination half-life—combined 

5 Ranch Hand and Seveso observation. 

26 28 30 32 34 36 38

5.0

6.0

7.0

8.0

9.0

10.0

11.0

% Body Fat

TCDD Elimination Half-Life (Years)

y = - 1.89 + 0.314x R^2 = 0.998

Seveso Males

Ranch Hand Males

Seveso Females

Error bars are ± 1 standard error
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-72 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-5. Relevance of candidate dose metrics for dose-response modeling, 

3 based on mode of action and target organ toxicity of TCDD. 

4 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 3-73 DRAFT: DO NOT CITE OR QUOTE Figure 3-6. Process of estimating a human-equivalent TCDD lifetime average daily oral exposure (

experimental animal average daily oral exposure (dA) based on the body-burden dose metric. The a

represent mathematical conversions based on toxicokinetic modeling. BBA (TWA animal body burden) a

human body burden) are assumed to be toxicokinetically equivalent. See text for further explanation.

Experimental Applied Dose

k

faed Body eBBtBurden kt kt Rat )1( )0()(

−

− −

=

+

Body 

Burd

Body Burd

HumanEstimated Exposure

1/2

1/2

(1 )

(1 )

A A

H H

k t

A

H A k t

H

t e d d

t e

−

−

−

=

−

Experimental Applied Dose

k

faed Body eBBtBurden kt kt Rat )1( )0()(

−

− −

=

+

Body 

Burd

Body Burd

HumanEstimated Exposure

1/2

1/2

(1 )

(1 )

A A

H H

k t

A

H A k t

H

t e d d

t e

−

−

−

=

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

1/15/10 DRAFT 3-74 —DO NOT CITE OR QUOTE 

Exposure days

Body burden

0 5000 10000 15000 20000 25000

0 500 1000 1500 2000 2500 3000

target body burden

chronic exposure (BB:d = 2555)

half-chronic exposure (BB:d = 2202)

shorter exposures (BB:d = 2185)

1 

2 

3 Figure 3-7. Human body burden time profiles for achieving a target body 

4 burden for different exposure duration scenarios. BB:d is BBH(tH):dH in 

5 Figure 3-6. The curve depicted using the solid line illustrates the increase in the 

6 human body burden over time for a hypothetical human administered a daily 

7 TCDD dose where the time-weighted average human body burden estimate over 

8 the lifetime is equal to the target body burden attained in a rodent bioassay. When 

9 compared to shorter durations (dashed lines), a higher average daily TCDD dose 

10 is required to yield a time-weighted average human body burden over a lifetime 

11 that is equal to the target body burden attained in a rodent bioassay. The half12 chronic exposure scenario (depicted using a dashed line) is equivalent to a 1-year 

13 exposure in rodents. When compared to a chronic BBH, a lower value of dH is 

14 needed to attain the target body burden in a rodent bioassay when the time15 weighted average is over the last 35 years of life; the dose to plateau ratio is also 

16 smaller (i.e., dH,C < dH,SC to attain the target body burden in a rodent bioassay). 

17 The shorter exposure scenario is equivalent to most other shorter rodent exposure 

18 durations, from 1 day to subchronic, which are indistinguishable with respect to 

19 the BB:d ratio (subchronic shown). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-75 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-8. Schematic of the CADM structure. 

4 

5 Source: Aylward et al. (2005). 

TCDD

ABSORPTION

Liver Burden

() () ( ) Qt Qt fC h b hb = ∗

Blood

Adipose Burden

() () 1 ( ) Qt Qt fC a b hb = ∗− ⎡ ⎤ ⎣ ⎦

Concentration-Dependant Tissue-Specific

Hepatic metabolism 

with first-order rate 

constant ke

Fecal excretion 

with the first-order 

rate constant ka

DISTRIBUTION ELIMINATION

TCDD

ABSORPTION

Liver Burden

() () ( ) Qt Qt fC h b hb = ∗

Blood

Adipose Burden

() () 1 ( ) Qt Qt fC a b hb = ∗− ⎡ ⎤ ⎣ ⎦

Concentration-Dependant Tissue-Specific

Hepatic metabolism 

with first-order rate 

constant ke

Fecal excretion 

with the first-order 

rate constant ka

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

1/15/10 DRAFT 3-76 —DO NOT CITE OR QUOTE 

1 Figure 3-9. Comparison of observed and simulated fractions of the body 

2 burden contained in the liver and adipose tissues in rats. fh, fraction contained 

3 in liver (observation) (□); fh-sim, fraction contained in liver (simulation) (—); fat, 

4 fraction contained in the adipose tissue (observation) (◊); fat-sim, fraction contained 

5 in the adipose tissue (simulation) (---); and Cb, body concentration in ng TCDD/kg 

6 body wt. 

7 

8 Source: Carrier et al. (1995a); data from Abraham et al. (1988) measured 7 days 

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

1/15/10 DRAFT 3-77 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-10. Conceptual representation of PBPK model for rat exposed to 

4 TCDD. 

5 

6 Source: Emond et al. (2006). 

GI tract 

elimination

Oral 

absorption

Urinary 

excretion

Cellular matrices

Blood tissue

Cellular matrices

Liver

Cellular matrices

Blood tissue

Cellular matrices

Cellular matrices

Blood tissue

Cellular matrices

Rest of body

Fat

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

1/15/10 DRAFT 3-78 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-11. Conceptual representation of PBPK model for rat 

3 developmental exposure to TCDD. 

4 

5 Source: Emond et al. (2004). 

Oral absorption

Liver (AhR and CYP1A2 induction)

Fat

Rest of body

Placenta (AhR)

Fetus

Cipla_fet

Elimination GI tract

Venous blood

Arterial blood

Portal vein

Urinary

excretion

Oral absorption

Liver (AhR and CYP1A2 induction)

Fat

Rest of body

Placenta (AhR)

Fetus

Cipla_fet

Elimination GI tract

Venous blood

Arterial blood

Portal vein

Urinary

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

1/15/10 DRAFT 3-79 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-12. TCDD distribution in the liver tissue. 

4 

5 Source: Wang et al. (1997). 

Lipid fraction

CLip

Plasma 

CBb proteins

CTnb

Nonspecific 

bound

Tissue blood

Water PA

fraction

Tissue

CYP1A2-TCDD

TCDD-Ah

CBf

CTr

Ks

Kn

Q

CB

Q

CTB

KAB

Lipid fraction

CLip

Plasma 

CBb proteins

CTnb

Nonspecific 

bound

Tissue blood

Water PA

fraction

Tissue

CYP1A2-TCDD

TCDD-Ah

CBf

CTr

Ks

Kn

Q

CB

Q

CTB

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

1/15/10 DRAFT 3-80 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-13. Growth rates for physiological changes occurring during 

4 gestation. (a) Placental growth during gestation (calculated for n = 10 placenta). 

5 Experimental data from Sikov (1970). (b) Blood flow rate in Placental 

6 compartment during gestation. Experimental data from Buelke-Sam et al. 

7 (1982a, b). (c) Fat fraction of body weight during gestation. Experimental data 

8 came from Fisher et al. (1989), and (d) Fetal growth during gestation. 

9 Experimental data obtained from Sikov (1970). 

10 

11 

A 

a b

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

1/15/10 3-81 DRAFT: DO NOT CITE OR QUOTE Figure 3-14. Comparisons of model predictions to experimental data using a fixed elimination rate 

hepatic sequestration (A) and an inducible elimination rate model with (B) and without (C) hepatic sequestration. EXBL, experimental blood levels. Model predictions were compared with the data of Sa

al. (1998), where female rats were exposed to a single oral dose of 10 

μg of TCDD/kg BW. Error bars ar

Source: Edmond et al. (2006). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-82 —DO NOT CITE OR QUOTE 

1 

2 

3 Figure 3-15. PBPK model simulation of hepatic TCDD concentration (ppb) 

4 during chronic exposure to TCDD at 50, 150, 500, 1,750 ng TCDD/BW using 

5 the inducible elimination rate model compared with the experimental data 

6 measured at the end of exposure. 

7 

8 Source: Emond et al. (2006). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-83 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-16. Model predictions of TCDD blood concentration in 10 veterans 

3 (A-J) from Ranch Hand Cohort. 

4 Source: Emond et al. (2005). 

1

10

100

1000

10000

0 10 20 30 40

Time (year)

TCDD (ppt)

CBPPTRH

V40922

1

10

100

1000

10000

0 10 20 30 40

Time (Year)

TCDD (PPT)

CBPPTRH

V33457

1

10

100

1000

10000

0 5 10 15 20 25 30 35

Time (year)

TCDD (PPT)

CBPPTRH

V30208

1

10

100

1000

10000

0 5 10 15 20 25 30 35

Time (year)

TCDD (ppt)

CBPPTRH

V49044

1

10

100

1000

10000

100000

1000000

0 10 20 30 40

Time (year)

TCDD (ppt)

CBPPTRH

V31731

1

10

100

1000

10000

0 5 10 15 20 25 30

Time (year)

TCDD (PPT) 

CBPPTRH

V46286

1

10

100

1000

10000

0 10 20 30 40

Time (year)

TCDD (Year)

CBPPTRH

V36564

1

10

100

1000

10000

0 5 10 15 20 25

Time (year)

TCDD (ppt)

CBPPTRH

V52064

1

10

100

1000

10000

100000

0 10 20 30 40

Time (year)

TCDD (ppt)

CBPPTRH

V30172

1

10

100

1000

10000

100000

1000000

0 10 20 30 40

Time (year)

TCDD (ppt)

CBPPTRH

V30991

A B

C D

E F

G H

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

1/15/10 DRAFT 3-84 —DO NOT CITE OR QUOTE 

1 

2 

3 

4 

5 

6 Figure 3-17. Time course of TCDD in blood (pg/g lipid adjusted) for two 

7 highly exposed Austrian women (patients 1 and 2). Symbols represent 

8 measured concentrations, and lines represent model predictions. These data were 

9 used as part of the model evaluation (Geusau et al., 2002). 

10 

11 Source: Emond et al. (2005). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-85 —DO NOT CITE OR QUOTE 

1 

2 

3 

4 Figure 3-18. Observed vs. Emond et al. (2005) model simulated serum 

5 TCDD concentrations (pg/g lipid) over time (ln = natural log) in two 

6 Austrian women. Data from Geusau et al. (2002).

0 1 2 3 4

9

10

11

12

Ln(pt1 Em Mod Sim pg/g TCDD)

Ln(pt1 Obs pg/g TCDD)

Ln(pt2 Em Mod Sim pg/g TCDD)

Ln(pt2 Obs pg/g TCDD)

pg g p

Years After Exposure

Ln(pg/g TCDD)

y = 11.828 - 0.47875x R^2 = 0.999

y = 11.765 - 0.45316x R^2 = 0.772

y = 10.481 - 0.42255x R^2 = 1.000

y = 9.9713 - 0.24207x R^2 = 0.612

y = 11.8 − 0.48x R^2 = 0.999 ln(pt1 Em Mod Sim pg/g TCDD) 

y = 11.8 − 0.45x R^2 = 0.772 ln(pt1 Obs pg/g TCDD) 

y = 10.5 − 0.43x R^2 = 1.000 ln(pt2 Em Mod Sim pg/g TCDD) 

y = 10.0 − 0.24x R^2 = 0.612 ln(pt2 Obs pg/g TCDD)
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-86 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-19. Comparison of the dose dependency of TCDD elimination in the 

3 Emond model vs. observations of nine Ranch Hand veterans and two highly 

4 exposed Austrian patients. Circles are observed data.

1 2 3 4 5

0.00

0.10

0.20

0.30

0.40

0.50

Emond Mod Sim Ln(Decline/Yr)

Observed Ln(Decline/Yr)

Log(TCDD pg/g at Midpoint Obs)

Ln(TCDD Serum Decline/Yr)

y = - 0.101 + 0.123x R^2 = 0.995

y = - 0.054 + 0.092x R^2 = 0.884
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 3-87 —DO NOT CITE OR QUOTE 

1 

2 

3 

4 Figure 3-20. Sensitivity analysis was performed on the inducible elimination 

5 rate. The analysis was performed at 0.001 μg/kg (A) and at 10 μg/kg (B). 

6 

7 Source: Emond et al. (2006). 

A 

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

1/15/10 DRAFT 3-88 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-21. Experimental data (symbols) and model simulations (solid lines) 

3 of (A) blood, (B) liver and (C) adipose tissue concentrations of TCDD after 

4 oral exposure to 150 ng/kg-day, 5 days/week for 17 weeks in mice. Y-axis 

5 represents concentration in pg/g and X-axis represents time in days. 

6 

7 Source: Experimental data were obtained form Diliberto et al (2001). 

C 

B 

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

1/15/10 DRAFT 3-89 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-22. Comparison of PBPK model simulations with experimental 

3 data on liver concentrations in mice administered a single oral dose of 

4 0.001−300 μg TCDD/kg. The simulations and experimental data were obtained 

5 24 hour post-exposure. 

6 

7 Source: Data obtained from Boverhoff et al. (2005). 

1

10

100

1000

10000

100000

1000000

10000000

0.001 0.01 0.1 1 10 100 300

Dose (ug/kg)

Liver concentration (ppt)

Measured

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

1/15/10 DRAFT 3-90 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-23. Comparison of model simulations (solid lines) with 

3 experimental data (symbols) on the effect of dose on blood (cb), liver (cli) and 

4 fat (cf) concentrations following repetitive exposure to 0.1−450 ng TCDD/kg, 

5 5 days/week for 13 weeks in mice. 

6 

7 Source: Data obtained from Diliberto et al. (2001). 

0

0

1

10

100

1000

10000

100000

0.1 1.0 10.0 100.0 1000.0

Dose (ng/kg)

Concentration (ng/kg)

cb sim cb measured

cli sim cli measured

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

1/15/10 DRAFT 3-91 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-24. Comparison of experimental data (symbols) and model 

3 predictions (solid lines) of (A) blood, (B) liver and (C) adipose tissue 

4 concentrations of TCDD after oral exposure to 1.5 ng/kg-day, 5 days/week 

5 for 17 weeks in mice. Y-axis represents concentration in pg/g and X-axis 

6 represents time in days. 

7 

8 Source: Experimental data were obtained form Diliberto et al. (2001). 

A 

B 

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

1/15/10 DRAFT 3-92 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-25. Comparison of experimental data (symbols) and model 

3 predictions (solid lines) of (A) blood concentration, (B) liver concentration, 

4 (C) adipose tissue concentration (D) feces excretion (% dose) and (E) urinary 

5 elimination (% dose) of TCDD after oral exposure to 1.5 ng/kg-day, 

6 5 days/week for 13 weeks in mice. Y-axis represents concentration in pg/g and 

7 X-axis represents time in days. 

8 Source: Experimental data were obtained form Diliberto et al. (2001). 

A 

B 

C 

D 

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

1/15/10 DRAFT 3-93 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-26. Comparison of experimental data (symbols) and model 

3 predictions (solid lines) of (A) blood concentration, (B) liver concentration, 

4 (C) adipose tissue concentration (D) feces excretion (% dose) and (E) urinary 

5 elimination (% dose) of TCDD after oral exposure to 150 ng/kg-day, 

6 5 days/week for 13 weeks in mice. Y-axis represents concentration in pg/g and 

7 X-axis represents time in days. 

8 Source: Experimental data were obtained form Diliberto et al. (2001). 

A 

B 

C 

D 

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

1/15/10 DRAFT 3-94 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-27. PBPK model simulations (solid lines) vs. experimental data 

3 (symbols) on the distribution of TCDD after a single acute oral exposure to 

4 A−B) 0.1, C−D) 1.0 and E−F) 10 μg of TCDD/kg of body weight in mice. 

5 Liver and adipose concentration for each dose was measured after 72 hours. 

6 Y-axis represents the concentration in tissues (ng/g); insets A, C, and E represent 

7 liver tissue, whereas B, D, and F correspond to adipose tissue. X-axis represents 

8 the time in hours. 

9 Source: experimental data were obtained from Santostefano et al. (1996). 

A 

B 

C 

D 

E 

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

1/15/10 DRAFT 3-95 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-28. PBPK model simulation (solid lines) vs. experimental data 

3 (symbols) on the distribution of TCDD after a single dose of 24 μg/kgBW on 

4 GD 12 in mice. Concentrations expressed as ng TCDD/g tissue. (A) maternal 

5 blood, (B) maternal liver and (C) maternal adipose tissue. Y-axis represents the 

6 tissue concentration whereas X-axis represents the time in hours. 

7 . 

8 Source: Experimental data were obtained from (Abbott et al., 1996). 

A 

B 

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

1/15/10 DRAFT 3-96 —DO NOT CITE OR QUOTE 

1 

2 Figure 3-29. Comparison of the near-steady-state body burden simulated 

3 with CADM and Emond models for a daily dose ranging from 1 to 

4 10,000 ng/kg-day in rats and humans. The rat model was run for 13 weeks and 

5 the human model was run from age 20 to 30. The time-averaged concentration 

6 was used for each.

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1 10 100 1000 10000

Intake (ng/kg-day)

Body Burden per Body Weight (ng/kg)

CADM (human)

Emond (human)

CADM (rat)

Emond (rat)
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 3-97 DRAFT: DO NOT CITE OR QUOTE Year

TCDD blood concentration (ng/kg)

0 20 40 60

0 50 100 150 200 peak 5-year average concentration gestational average concentration lifetime average concentration Concentration-time profile Less-than-lifetime scenario Gestational scenario Lifetime scenario

1 2 Figure 3-30. TCDD serum concentration-time profile for lifetime, less-than-lifetime and gestationa3 scenarios, with target concentrations shown for each; profiles generated with Emond human PBPK 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 3-98 DRAFT: DO NOT CITE OR QUOTE Year

TCDD blood concentration (ng/kg)

0 20 40 60

0 50 100 150 200 250

0.014 ng/kg-day

0.011 ng/kg-day

0.01 ng/kg-day

Lifetime scenarioLess-than-lifetime scenarioGestational scenarioTarget concentration

Figure 3-31. TCDD serum concentration-time profile for lifetime, less-than-lifetime and gestationascenarios, showing continuous intake levels to fixed target concentration; profiles generated with EPBPK model. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-1 —DO NOT CITE OR QUOTE 

1 4. CHRONIC ORAL REFERENCE DOSE 

2 

3 

4 This section presents U.S. Environmental Protection Agency (EPA)’s response to the 

5 National Academy of Sciences (NAS) recommendations that EPA more explicitly discuss the 

6 modeling of noncancer endpoints and develop a reference dose (RfD) to address noncancer 

7 effects associated with oral 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) exposures. Section 2 

8 details the selection of the animal studies with the lowest TCDD doses associated with the 

9 development of adverse noncancer effects and the selection of relevant epidemiologic studies of 

10 adverse noncancer health effects. Section 3 discusses the kinetic modeling and estimation of 

11 human equivalent daily oral doses used in TCDD RfD development in this section. This section 

12 discusses the modeling of noncancer health effect data associated with TCDD exposure and the 

13 derivation of an RfD. Specifically, Section 4.1 summarizes the NAS comments on TCDD dose14 response modeling and EPA’s response, including justification of selected noncancer effects and 

15 statistical characterization of modeling results. Section 4.2 presents the TCDD dose-response 

16 modeling undertaken for identification of candidate points of departure (PODs) for derivation of 

17 an RfD. Finally, in Section 4.3, EPA develops an RfD for TCDD. 

18 

19 4.1. NAS COMMENTS AND EPA’S RESPONSE ON IDENTIFYING NONCANCER 

20 EFFECTS OBSERVED AT LOWEST DOSES 

21 The NAS recommended that EPA identify the noncancer effects associated with low dose 

22 TCDD exposures and discuss its strategy for identifying and selecting PODs for noncancer 

23 endpoints, including biological significance of the effects. 

24 

25 With respect to noncancer end points, the committee notes that EPA does not use 

26 a rigorous approach for evaluating evidence from studies. . . (NAS, 2006a, p. 47). 

27 

28 The Reassessment should describe clearly the following aspects: 

29 1. The effects seen at the lowest body burdens that are the primary focus for any 

30 risk assessment—the “critical effects.” 

31 2. The modeling strategy used for each noncancer effect, paying particular 

32 attention to the critical effects, and the selection of a point of comparison based 

33 on the biological significance of the effect; if the ED01 is retained, then the 

34 biological significance of the response should be defined and the precision of 

35 the estimate given... (NAS, 2006a, p. 187). 
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1 In this document, EPA has developed a strategy for identifying the noncancer data sets 

2 and PODs that represent the most sensitive and biologically relevant endpoints for derivation of 

3 an RfD for TCDD. EPA began this process by using the animal bioassays and human 

4 epidemiologic studies that met its study inclusion criteria as sources of these data sets. For all 

5 epidemiologic studies that were identified as suitable for further quantitative dose-response 

6 analyses in Section 2.4.3, EPA has chosen to identify PODs (i.e., estimates of a no-observed7 adverse-effect level [NOAEL], lowest-observed-adverse-effect level [LOAEL], or benchmark 

8 dose lower confidence bound [BMDL] when possible). Figure 4-1 shows EPA’s process to 

9 select and identify candidate PODs from these key epidemiologic studies. EPA first evaluated 

10 the dose-response information in the study to determine whether it provided an estimate of 

11 TCDD dose and an observed noncancer effect that was relevant for RfD derivation. If such data 

12 were available, then EPA identified a NOAEL or LOAEL as a candidate POD. For each of 

13 these, EPA applied a human kinetic model to estimate the continuous oral daily intake 

14 (ng/kg-day) associated with the POD that could be used in the derivation of an RfD (see Section 

15 4.2). If all of this information was available, then the result was included as a candidate POD. 

16 Figure 4-2 summarizes the strategy employed for identifying and selecting candidate 

17 PODs from the key animal bioassays identified in Section 2.4.3 for use in noncancer dose18 response analysis of TCDD. For each noncancer endpoint, EPA first evaluated the toxicologic 

19 relevance of each endpoint, rejecting those judged not to be relevant for RfD derivation. Next, 

20 initial PODs (NOAELs, LOAELs, and BMDLs) based on the first-order body burden metric (see 

21 Section 3.3.4.2) and expressed as human-equivalent doses (HEDs) were determined for all 

22 relevant endpoints (summarized in Table 4-3). Because there were very few NOAELs and 

23 BMDL modeling was largely unsuccessful due to data limitations, the next stage of evaluation 

24 was carried out using LOAELs only. Endpoints not observed at the LOAEL (i.e., reported at 

25 higher doses) with BMDLs greater than the LOAEL were eliminated from further analysis, as 

26 they would not be considered as candidates for the final POD on either a BMDL or 

27 NOAEL/LOAEL basis (i.e. the POD would be higher than the PODs of other relevant 

28 endpoints). In addition, all endpoints with HED estimates based on LOAELs (LOAELHEDs) 

29 beyond a 100-fold range of the lowest identified LOAELHED were eliminated from further 

30 consideration, as they would not be potential POD candidates either (i.e., the POD would be 

31 higher than the PODs of other relevant endpoints). For the remaining endpoints, EPA then 
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1 determined final potential PODs (NOAELs, LOAELs and BMDLs) based on TCDD blood 

2 concentrations obtained from the Emond rodent physiologically based pharmacokinetic (PBPK) 

3 models. HEDs were then estimated for each of these PODs using the Emond human PBPK 

model. From these HEDs, a PODHED was selected14 4 for each study as the basis for the candidate 

5 RfD, to which appropriate uncertainty factors (UFs) were applied following EPA guidelines. 

6 The resulting candidate RfDs were then considered in the final selection process for the RfD. 

7 Other endpoints occurring at slightly higher doses representing additional effects associated with 

8 TCDD exposure (beyond the 100-fold LOAEL range) were evaluated, modeled, and included in 

the final candidate RfD array15 9 to examine endpoints not evaluated by studies with lower PODs. 

10 In addition, BMD modeling based on administered dose was performed on all endpoints for 

11 comparison purposes. The final array of selected endpoints is shown in Table 4-4 (summary of 

12 BMD analysis) and Table 4-5 (candidate RfDs). 

13 The NAS recommended that EPA better justify the selection of response levels for 

14 endpoints used to develop risk estimates. The NAS commented on EPA’s decision to estimate 

15 an ED01 (effective dose eliciting x percent response) for noncancer bioassay/data set 

16 combinations as a comparative tool across studies, suggesting that EPA identify and evaluate the 

17 levels of change associated with adverse effects to define the benchmark response (BMR) level 

18 for continuous noncancer endpoints. 

19 

20 The committee notes that the choice of the 1% response level as the POD 

21 substantially affects ... the noncancer analyses.... The committee recommends 

22 that the Reassessment use levels of change that represent clinical adverse effects 

23 to define the BMR level for noncancer continuous end points as the basis for an 

24 appropriate POD in the assessment of noncancer effects (NAS, 2006a, p. 72). 

25 

26 The committee concludes that EPA did not adequately justify the use of the 1% 

27 response level (the ED01) as the POD for analyzing epidemiological or animal 

28 bioassay data for ... noncancer effects (NAS, 2006a, p. 18). 

29 

30 In the 2003 Reassessment (U.S. EPA, 2003), EPA was not attempting to derive an RfD 

31 when it conducted TCDD dose-response modeling. The 2003 Reassessment developed ED01

 

14In the standard order of consideration: BMDL, NOAEL, and LOAEL. 

15However, studies with a lowest dose tested greater than 30 ng/kg-day were not included in the expanded 

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

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1 estimates for noncancer effects in an attempt to compare disparate endpoints on a consistent 

2 response scale. Importantly, the 2003 Reassessment defined the ED01 as 1% of the maximal 

3 response for a given endpoint, not as a 1% change from control. Because RfD derivation is one 

4 goal of this document, the noncancer modeling effort undertaken here differs substantially from 

5 the modeling in the 2003 Reassessment. 

6 The NAS committee was concerned with the statistical power to determine the shape of 

7 the dose-response curve at doses far below observed dose-response information. EPA agrees 

8 that the shape of the dose-response curve in the low-dose region cannot be determined 

9 confidently when based on higher-dose information. An observed response above background 

10 near (or below) the BMR level is needed for discrimination of the shape of the curve and for 

11 accurate estimation of an EDx or BMDL. Although many of the ED01s presented in the 2003 

12 Reassessment were near the lowest dose tested, responses at the lowest doses were often high 

13 and much greater than a 1% response (i.e., 1% of the maximum response). The lack of an 

14 observed response near the BMR level is often a problem in interpretation of BMD modeling 

15 results. 

16 In this document, EPA has used a 10% BMR for dichotomous data for all endpoints 

17 except for developmental study designs that incorporate litter effects, for which a 5% BMR is 

18 used (U.S. EPA, 2000a). For continuous endpoints in this document, EPA has used a BMR of 

19 1 standard deviation from the control mean whenever a specific toxicologically-relevant BMR 

20 could not be defined. For the vast majority of continuous endpoints, EPA could not establish 

21 unambiguous levels of change representative of adversity, which EPA defines as “a biochemical 

22 change, functional impairment, or pathologic lesion that affects the performance of the whole 

23 organism, or reduces an organism's ability to respond to an additional environmental challenge” 

24 (U.S. EPA, 2009b). For body and organ weight change, EPA has previously established a BMR 

25 of 10% change, which also is used in this document. 

26 The NAS commented on EPA’s development of ED01 estimates for numerous study/data 

27 set combinations in the 2003 Reassessment, suggesting that EPA had not appropriately 

28 characterized the statistical confidence around such model predictions in the low-response region 

29 of the model. 

30 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 It is critical that the model used for determining a POD fits the data well, 

2 especially at the lower end of the observed responses. Whenever feasible, 

3 mechanistic and statistical information should be used to estimate the shape of the 

4 dose-response curve at lower doses. At a minimum, EPA should use rigorous 

5 statistical methods to assess model fit and to control and reduce the uncertainty of 

6 the POD caused by a poorly fitted model. The overall quality of the study design 

7 is also a critical element in deciding which data sets to use for quantitative 

8 modeling (NAS, 2006a, p. 18). 

9 

10 EPA should ... assess goodness-of-fit of dose-response models for data sets and 

11 provide both upper and lower bounds on central estimates for all statistical 

12 estimates. When quantitation is not possible, EPA should clearly state it and 

13 explain what would be required to achieve quantitation (NAS, 2006a, p. 10). 

14 

15 The NAS also commented that EPA report information describing the adequacy of dose16 response model fits, particularly in the low response region. For those cases where biostatistical 

17 modeling was not possible, NAS recommended that EPA identify the reasons. 

18 

19 The Reassessment should also explicitly address the importance of statistical 

20 assessment of model fit at the lower end and the difficulties in such assessments, 

21 particularly when using summary data from the literature instead of the raw data, 

22 although estimates of the impacts of different choices of models would provide 

23 valuable information about the role of this uncertainty in driving the risk estimates 

24 (NAS, 2006a, p. 73).

25 

26 To address this concern, in this document EPA has reported the standard suite of 

27 goodness-of-fit measures from the benchmark dose modeling software (BMDS 2.1). These 

28 include chi-square p-values, Akaike’s Information Criterion (AIC), scaled residuals at each dose 

29 level and plots of the fitted models. In some cases, when restricted parameters hit a bound, EPA 

30 used likelihood ratio tests to evaluate whether the improvement in fit afforded by estimating 

31 additional parameters could be justified. Goodness-of-fit measures are reported for all key data 

32 sets in Appendix E. 

33 

34 4.2. NONCANCER DOSE-RESPONSE ASSESSMENT OF TCDD 

35 This section describes EPA’s current effort to conduct an evaluation of TCDD dose36 response for the noncancer endpoints from studies that met the study inclusion criteria. 

37 Discussions include benchmark dose modeling procedures, kinetic modeling, and POD 

38 candidates for derivation of the RfD. Section 4.2.1 describes how EPA has used physiologically-
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1 based pharmacokinetic (PBPK) modeling to estimate effective internal exposures as an 

2 alternative to using administered doses or body burdens based on first-order kinetics. 

3 Section 4.2.2 details the dose-response analysis of the epidemiologic data, with supporting 

4 information on kinetic modeling in Appendix D. Section 4.2.3 details the dose-response analysis 

5 for the animal bioassay data; Appendix E provides the BMDS input tables (see Section E.1) and 

6 output for all modeling, including blood concentrations (see Section E.2) and administered dose 

7 (see Section E.3). 

8 

9 4.2.1.1. Determination of Toxicologically Relevant Endpoints 

10 The NAS committee commented on the low dose model predictions and the need to 

11 discuss the biological significance of the noncancer health effects modeled in the 2003 

12 Reassessment. In selecting POD candidates from the animal bioassays for derivation of the 

13 candidate RfDs, EPA had to consider the toxicological relevance of the identified endpoint(s) 

14 from any given study. Some endpoints/effects may be sensitive, but lack general toxicological 

15 significance due to not being clearly adverse (defined in EPA’s Integrated Risk Information 

16 System glossary as “a biochemical change, functional impairment, or pathologic lesion that 

17 affects the performance of the whole organism, or reduces an organism’s ability to respond to an 

18 additional environmental challenge” (U.S. EPA, 2009)), being an adaptive response or not being 

19 clearly linked to downstream functional or pathological alterations. It is standard EPA RfD 

20 derivation practice not to base a reference value on endpoints that are not adverse or not 

21 precursors to an adverse effect. Studies meeting the study selection criteria with endpoints that 

22 were not considered for derivation of a candidate RfD (because they were not considered to be 

23 toxicologically relevant noncancer effects) are: Kitchin and Woods (1979) , Hassoun et al. (1998, 

24 2000, 2002, 2003), Burleson et al. (1996), Kuchiiwa et al. (2002), Mally and Chipman (2002), 

25 Vanden Heuvel et al. (1994), Devito et al. (1994), Lucier et al. (1986), Sugita-Konishi et al. 

26 (2003), and Sewall et al. (1993). Appendix G identifies the endpoints from these studies that 

27 were not considered to be toxicologically relevant (e.g., cytochrome P450 induction, oxidative 

28 stress measures, gap junction disruption, mRNA induction, brain serotonin levels) and provides 

29 the rationales for the toxicological relevance decisions on the endpoints. Note that for many of 

30 these studies, other endpoints were examined that are toxicologically relevant and were 

31 considered in the RfD derivation process. 
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1 

2 4.2.2. Use of Toxicokinetic Modeling for TCDD Dose-Response Assessment 

3 Given that TCDD accumulates in fat with continuous exposure and is eliminated slowly 

4 from the body, but at very different rates across species, EPA has determined that the standard 

5 UF approach or allometric scaling of body weight for interspecies extrapolation is not 

6 appropriate. Therefore, EPA has decided to use toxicokinetic modeling to estimate an effective 

7 internal dose for equivalence across species. The toxicokinetic models chosen by EPA are the 

8 rodent and human PBPK models described by Emond et al. (2004, 2006) and modified by EPA 

9 for this assessment as described in Section 3.3.4 (hereafter referred to as the “Emond [rodent or 

10 human] PBPK model”). Both the rodent and human models have a gestational component, 

11 which allow for more relevant exposure comparisons between general adult exposures and the 

12 numerous gestational exposure studies. Ideally, a relevant tissue concentration for each effect 

13 would be estimated. However, no models exist for estimation of all relevant tissue 

14 concentrations. Therefore, EPA has decided to use the concentration of TCDD in blood as a 

15 surrogate for tissue concentrations, assuming that tissue concentrations are proportional to blood 

concentrations.16 16 Furthermore, because the RfD is necessarily expressed in terms of average 

17 daily exposure, the blood concentrations are expressed as averages over the relevant period of 

18 exposure for each endpoint. For the animal bioassay studies, the relevant period of exposure is 

19 the duration of dosing, starting at the age of the animals at the beginning of the study. For 

20 humans, the relevant period of exposure is generally lifetime, which is defined as 70 years by 

21 convention. However, EPA varied the averaging time for the equivalent human blood 

22 concentrations to correspond to the test-animal exposure duration in the following manner. 

23 

• For correspondence with animal chronic exposures,17 24 the human-equivalent 

25 TCDD blood concentration is assumed to be the 70-year average. 

26 • For correspondence with animal gestational exposures, the human-equivalent 

27 TCDD blood concentration is assumed to be the average over 45 years for a 

28 female, beginning at birth, plus 9 months of gestational exposure. The choice of 

29 45 years to beginning of pregnancy is health protective of the population in that 

 

16As virtually all TCDD is found in the adipose fraction of tissues, or bound to specific proteins, an ideal better 

approach would be to account for the fat fraction of each tissue and protein binding; EPA has not found sufficient 

data to implement this approach. 

17Assumed to be ≥75% of nominal lifetime, or about 550 days in rodents. 
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1 the daily exposure achieving the target blood concentration is smaller than for 

shorter averaging times.18 2 

3 • For correspondence with any other animal exposure duration, the human4 equivalent TCDD blood concentration is assumed to be the average over the 

5 equivalent human exposure duration calculated backward from the peak exposure 

6 plateau at or near the end of the 70-year scenario. The average is determined 

7 from the terminal end of the human exposure period because the daily exposure 

8 achieving the target blood concentration is smaller than for the same exposure 

9 period beginning at birth and is health protective for effects occurring after 

10 shorter-term exposure. The determination of equivalent exposure durations across 

11 species is problematic and somewhat arbitrary, so EPA uses the average peak 

12 blood concentration as the human equivalent for all less-than-chronic animal 

exposures (other than gestational).19 13 For the first-order kinetics model, the 

14 average peak exposure is close to the theoretical steady-state asymptote (see 

15 Section 3.3.4.2). However, for the Emond human PBPK model used by EPA in 

16 this assessment, the timing of the peak exposure is dose-dependent and tends to 

17 decline after 60 years in some cases. Therefore, the 5-year average TCDD blood 

18 concentration that includes the peak (“5-year peak”) is used as the relevant dose19 metric for the PBPK model applications. 

20 

21 4.2.3. Noncancer Dose-Response Assessment of Epidemiological Data 

22 The following four epidemiologic studies describing noncancer endpoints were identified 

23 in Section 2.4.3 as studies to be evaluated for development of PODs for derivation of candidate 

24 RfDs: Baccarelli et al. (2008), Mocarelli et al. (2008), Alaluusua et al. (2004) and Eskenazi et al. 

25 (2002). Each of these studies described effects observed in the Seveso cohort (see study 

26 summaries in Section 2.4.1 and Table 2-5). Each study provided individual-level human 

27 exposure measures and an exposure window over which kinetic models could be used to quantify 

28 TCDD exposures for dose-response assessment. EPA used kinetic information to estimate 

29 group-mean daily TCDD intake rates for the exposure groups presented in these studies (see 

30 Appendix D for details). EPA focused on identifying NOAELs and LOAELs for these studies; 

31 EPA did not conduct Benchmark Dose modeling because the covariates identified by the study 

32 authors could not be incorporated by modeling the grouped response data. EPA’s development 

33 of PODs for these studies is described in this section and shown in Table 4-1. 

 

18See Section 3.3.4.2 for a discussion of this issue. 

19By comparison to a half-lifetime equivalent (1 year in rodents, 35 years in humans), the ratio of body burden 

(1st-order kinetic model) to oral intake does not differ significantly from the average-peak scenario; all shorter-term 

scenarios differ even less (see Section 3.3.4.2). These relationships, with respect to the 5-year peak, hold for the 

PBPK model results, as well (see Section 3). 
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1 4.2.3.1. Baccarelli et al. (2008) 

2 For Baccarelli et al. (2008), EPA was able to define a LOAEL as the group mean of 

3 39 ppt TCDD in neonatal plasma for thyroid stimulating hormone (TSH) values above 5 μU/mL. 

4 Baccarelli et al. (2008) did not estimate the equivalent oral intake associated with TCDD serum 

5 concentrations and gave only neonatal serum TCDD concentrations for the groups above and 

6 below the 5 μU/mL standard. The study authors, however, developed a regression model 

7 relating the level of TSH in 3-day-old neonates to TCDD concentrations in maternal plasma at 

8 birth (given as lipid-adjusted serum concentrations, LASC). The authors extrapolated maternal 

9 plasma concentrations from previous measurements using simple first-order pharmacokinetic 

10 model. Because there is limited information regarding the relationship between maternal and 

11 neonatal serum TCDD levels, EPA determined that there was too much uncertainty in estimating 

12 maternal intake from neonatal TCDD serum concentrations, directly. Therefore, EPA 

13 determined the maternal intake at the LOAEL from the maternal serum-TCDD/TSH regression 

14 model by finding the maternal TCDD LASC at which neonatal TSH exceeded 5 μU/mL. EPA 

15 then used the Emond PBPK model under the human gestational scenario (see Section 4.2.1) to 

16 estimate the continuous daily TCDD intake that would result in a TCDD LASC corresponding to 

17 a neonatal TSH of 5 μU/mL at the end of gestation, with the resulting maternal intake established 

18 as the LOAEL (0.024 ng/kg-day), shown in Table 4-1 as a candidate POD for derivation of 

19 candidate RfDs. The results of the PBPK modeling are shown in Appendix D. 

20 

21 4.2.3.2. Mocarelli et al. (2008) 

22 Mocarelli et al. (2008) reported decreased sperm concentrations (20%), decreased motile 

23 sperm counts (11%), and decreased serum estradiol (23%) in men who were 1–9 years old in 

24 1976 at the time of the accident (initial TCDD exposure event). Men who were 10–17 years old 

25 in 1976 were not affected, with the possible exception of reduced serum estradiol. Serum 

26 (LASC) TCDD levels were measured within one year of the initial exposure. Serum TCDD 

27 levels and corresponding responses were reported by quartile, with a reference group of less28 exposed individuals assigned a TCDD LASC value of 15 ppt (which was the mean of individuals 

outside the contaminated area). The lowest exposed group mean was 68 ppt (1st 29 quartile). 

30 Because effects were detected only among boys under the age of 10, EPA assumes there is a 

31 maximum 10-year critical exposure window for elicitation of these effects. However, for the 
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1 exposure profile, with a high initial pulse followed by an extended period of elimination with 

2 only background exposure, the estimation of an average exposure resulting in the effect is 

3 problematic. Therefore, EPA implemented a procedure for the estimation of the continuous 

4 daily TCDD intake associated with the LOAEL in the Mocarelli et al. (2008) study using a 

5 5-step process. 

6 

7 1. Using the Emond human PBPK model, the initial (peak) blood TCDD concentrations 

8 associated with the accident were back-calculated based on the time that had elapsed 

9 between the explosion and the serum collection. As serum measurements were taken 

10 within 1 year after the event, a lag time of 0.5 years was assumed. 

11 2. The oral exposure associated with the peak blood TCDD concentration (peak exposure) 

12 was calculated using the Emond PBPK model. 

13 3. Starting with the peak exposure and accounting for background TCDD intake, the 

14 average daily blood TCDD concentration experienced by a representative individual in 

15 the susceptible population (boys under 10 years old) was estimated using the Emond 

16 PBPK model. Assuming a random distribution of ages at the time of the event, the 

17 average age of the exposed male children would be 5 years. Consequently, a critical 

18 exposure window for the cohort was estimated to be, on average, 5 years (i.e., a boy aged 

19 5 years would remain in this exposure window for 5 more years until he was 10 years of 

20 age). 

21 4. Using the Emond PBPK model, the average daily TCDD intake rate needed to attain the 

22 5-year average blood TCDD concentration in a boy 10 years old was calculated. 

23 5. The LOAEL POD was calculated as the average of the peak exposure (0.032 ng/kg-day) 

24 and the 5-year average exposure (0.0080 ng/kg-day), resulting in LOAEL of 

25 0.020 ng/kg-day, shown in Table 4-1 as a candidate POD for derivation of a candidate 

26 RfD. However, neither of the extremes was used because (1) the peak exposure does not 

27 account for the continuing internal exposure from TCDD given its slow elimination, and 

28 (2) the 5-year average does not reflect the influence of the much higher peak exposure, 

29 which may be a significant factor in TCDD toxicity (Kim et al., 2003). 

30 

31 The results of the modeling are shown in Appendix D. 

32 

33 4.2.3.3. Alaluusua et al. (2004) 

34 For Alaluusua et al. (2004), the approach for estimation of daily TCDD intake is virtually 

35 identical to the approach used for the Mocarelli et al. (2008) data. Alaluusua et al. (2004) 

36 reported dental effects in male and female adults who were less than 5 years of age at the time of 

37 the initial exposure (1976). For the 75 boys and girls who were less than 5 years old at the time 
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1 of the accident, 25 (33%) were subsequently diagnosed with some form of dental enamel defect. 

2 For the 38 individuals who were older than 5, only 2 (5.3%) suffered dental enamel defects at a 

3 later date. A window of susceptibility of approximately 5 years is established. Serum 

4 measurements for this cohort were taken within a year of the accident. Serum TCDD levels and 

5 corresponding responses were reported by tertile, with a reference group of less-exposed 

6 individuals assigned a TCDD LASC value of 15 ppt (ng/kg); the tertile group means were 130, 

7 383, and 1830 ppt. The incidence of dental effects for the reference group was 26% (10/39). 

The incidence of dental effects in the 1st, 2nd and 3rd 8 tertile exposure groups was 10% (1/10), 

9 45% (5/11) and 60% (9/15), respectively. EPA judged that the NOAEL and LOAEL were 130 

10 and 383 ppt TCDD in serum. Following the same procedure used for the Mocarelli et al. (2008) 

11 study (see Section 4.2.3.2), EPA estimated the continuous daily human TCDD intake associated 

12 with each of the tertiles for both peak and average exposure across the critical exposure window, 

13 assuming that the average age of the susceptible cohort at the time of the accident was 2.5 years. 

14 Separate estimates for boys and girls were developed based on both the peak intake and average 

15 intake across the critical exposure window (see Appendix D for details of the PBPK modeling 

16 are shown in Appendix D). The estimated averaged daily intakes for the tertiles, averaged for 

17 boys and girls, are 0.20, 1.7 and 30 ng/kg-day for the peak exposure and 0.033, 0.15 and 

18 1.5 ng/kg-day for the critical exposure window average. A study NOAEL at the second tertile of 

19 0.12 ng/kg-day was identified as a candidate POD for derivation of a candidate RfD in Table 4-1. 

20 

21 4.2.3.4. Eskenazi et al. (2002) 

22 The approach used to estimate daily TCDD intake in Eskenazi et al. (2002) combines the 

23 approaches EPA used for Baccarelli et al (2008), Mocarelli et al. (2008) and Alaluusua et al. 

24 (2004). Eskenazi et al. (2002) reported menstrual effects in female adults who were 

25 premenarcheal at the time of the initial exposure (1976). In Rigon et al. (2009), the median age 

26 at menarche was shown to be 12.4 in Italian females with intergenerational decreases in age at 

27 menarche. Thus, EPA established a window of susceptibility of approximately 13 years for this 

28 analysis. The average age of the premenarcheal girls at the time of the initial exposure in 1976 

29 was 6.8 years, establishing an average critical-window exposure duration of 6.2 years for this 

30 cohort. Serum samples were collected within a year of the accident from this cohort. However, 

31 serum TCDD levels and corresponding responses were not reported by percentile and no internal 
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1 reference group was identified. As for Baccarelli et al (2008), Eskenazi et al. (2002) developed a 

2 regression model relating menstrual cycle length to plasma TCDD concentrations (LASC) 

3 measured in 1976. The model estimated that menstrual cycle length was increased 0.93 days for 

4 each 10-fold increase in TCDD LASC, with a 95% confidence interval of −0.01 to 1.86 days. 

5 EPA judged a 1-day increase in menstrual cycle length to be adverse; a normal menstrual cycle 

6 length is 28 days. EPA then determined the 1976 TCDD serum level corresponding to a 29-day 

7 menstrual cycle length in the exposed cohort from the regression model developed by Eskenazi 

8 et al. (2002). Using this serum level, the peak initial exposure and average exposure over the 

9 6.2 year window were calculated using the Emond human PBPK model, in the same manner as 

10 for Mocarelli et al. (2008) and Alaluusua et al. (2004). The resulting peak TCDD intake is 

11 3.2 ng/kg-day. The average exposure experienced by this cohort over the critical exposure 

12 window is estimated to be 0.12 ng/kg-day. The average of these two estimates is 1.64 ng/kg-day, 

13 which is designated as a LOAEL and shown in Table 4-1. Because the LOAEL is almost 

14 2 orders of magnitude higher than the LOAELs for Baccarelli et al (2008) and Mocarelli et al. 

15 (2008), it was not considered further as a candidate POD for derivation of the RfD. The results 

16 of the PBPK modeling are shown in Appendix D. 

17 

18 4.2.4. Noncancer Dose-Response Assessment of Animal Bioassay Data 

19 EPA followed the strategy illustrated in Figure 4-2 to evaluate the animal bioassay data 

20 for TCDD dose-response. For the administered average daily doses (ng/kg-day) in each animal 

21 bioassay, EPA identified NOAELs and/or LOAELs based on the original data presented by the 

22 study author. Section 2.4.2 identifies these values in the study summaries and in Table 2-7. 

23 These became candidate PODs for consideration in the derivation of an RfD for TCDD. The 

24 candidate RfD values associated with these candidate PODs are presented in Table 4-5. 

25 Additional PODs were identified using BMD modeling. All PODs were converted to HEDs 

26 using the Emond PBPK models. The remainder of this Section describes the steps in this process 

27 and concludes with the POD candidates from the animal bioassay data that were considered for 

28 derivation of the RfD. 

29 
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1 4.2.4.1. Use of Kinetic Modeling for Animal Bioassay Data 

2 Blood concentrations corresponding to the administered doses in each mouse or rat 

3 bioassay qualifying as a final RfD POD candidate were estimated using the appropriate Emond 

4 rodent PBPK model. In each case, the simulation was performed using the exposure and 

5 observation durations, body weights, and average daily doses from the original studies. For all 

6 multiple exposure protocols, the time-weighted average blood TCDD concentrations over the 

7 exposure period were used as the relevant dose metric. For single (gestational and 

8 nongestational) exposures, the initial peak blood TCDD concentrations were considered to be the 

9 most relevant exposure metric. Gestational exposures were modeled using the species-specific 

10 gestational component of the Emond rodent PBPK model. Bioassays employing exposure 

11 protocols spanning gestational and postpartum life stages were modeled by sequential 

12 application of the gestational and nongestational models. 

13 The Emond PBPK models do not contain a lactation component, so exposure during 

14 lactation was not modeled explicitly. Only one bioassay (Shi et al., 2007) considered as a POD 

15 candidate for RfD derivation included exposure during lactation. In Shi et al. (2007) pregnant 

16 animals were exposed weekly to TCDD throughout gestation and lactation. Exposure was 

17 continued in the offspring following weaning for 10 months. For assessment of maternal effects, 

18 the Emond gestational model was used, terminating at parturition. For assessment of long-term 

19 exposure in the offspring, the Emond nongestational model was used, ignoring prior gestational 

20 and lactational exposure, with the assumption that the total exposure during these periods was 

21 small relative to exposure in the following 10 months. The assumption is conservative in that 

22 effects observed in the offspring would be attributed entirely to adult exposures, which is 

23 somewhat less than the actual total exposure. 

24 The model code, input files and PBPK modeling results for each bioassay are reported in 

25 Appendix C. Note that the modeled output is given in terms of LASC. The corresponding 

26 TCDD whole blood concentrations, which are used directly as the toxiocologically-equivalent 

27 dose metric, are derived by multiplying the LASC by the lipid fraction in rodent serum (0.0033) 

28 and the fraction of blood that is serum (0.55). For the rat gestational model, only, the lipid 

29 fraction in serum is 0.0023, rather than 0.0033. 

30 These predicted TCDD blood concentrations were used for benchmark dose modeling of 

31 bioassay response data and determination of NOAELs and LOAELs. BMD modeling was 
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1 performed, as described in Section 3.5.2.2.1, by substituting the modeled blood concentrations 

2 for the administered doses and calculating the corresponding BMDL. For each of these LOAEL, 

3 NOAEL, or BMDL blood-concentration equivalents, corresponding HEDs were calculated using 

4 the Emond human PBPK model for the appropriate gestational or nongestational scenario as 

5 described previously (see Section 4.2.1). 

6 

7 4.2.4.2. Benchmark Dose Modeling of the Animal Bioassay Data 

8 Benchmark dose modeling was performed using BMDS 2.1, Build 06/16/09 to estimate 

9 BMDs and BMDLs for each study/endpoint combination. The input data tables for these 

10 noncancer studies are shown in Appendix E, Section E1, including both administered doses 

11 (ng/kg-day) and blood concentrations (ng/kg) and either incidence data for the dichotomous 

12 endpoints or mean and standard deviations for the continuous endpoints. (See Section 4.3.1 

13 [below] and Sections 3.3.4 and 3.3.5 for a description of the development of TCDD blood 

14 concentrations using kinetic modeling.) For the continuous endpoints, all available models were 

15 run separately using both the assumption of constant variance and the assumption of modeled 

16 variance. Saturated (0 degrees of freedom) model fits were rejected from consideration. 

17 Parameters in models with power or slope parameters were constrained to prevent supralinear 

18 fits, which EPA considers not to be biologically plausible and which often have undesirable 

19 statistical properties (i.e., the BMDL diverges towards zero). However, if the constrained 

20 parameters were estimated at their lower bounds, the unrestricted model was fit to the data, 

21 primarily for elucidation of the degree of supralinearity present in the data. Depending on the 

22 latter and the magnitude of the BMDL relative to the BMD, unrestricted model fits were 

23 occasionally deemed acceptable. Table 4-2 shows each model and any restrictions imposed. For 

24 the quantal/dichotomous endpoints, all primary BMDS dichotomous models were run. The 

25 alternative dichotomous models were fit to several data sets, but the results were very sensitive to 

26 the assumed independent background response and the fits were not accepted. The confidence 

27 level was set to 95% and all initial parameter values were set to their defaults in BMDS. For the 

28 continuous endpoints, one standard deviation was chosen as the default for the BMR when a 
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1 specific toxicologically-relevant BMR could not be defined. For the dichotomous endpoints, a 

BMR of 10% extra risk was used for all endpoints.20 2 

3 The model output tables in Appendix E show all of the models that were run, both 

4 restricted and nonrestricted, goodness-of-fit statistics, BMD and BMDL estimates, and whether 

5 bounds were hit for constrained parameters. After all models were run, the one giving the best 

6 fit was selected using the selection criteria in the current BMDS guidance (U.S. EPA, 2000a) 

7 where possible. Acceptable model fits were those with chi-square goodness-of-fit p-values 

8 greater than 0.1. For the continuous endpoints, the p-value for the homogenous variance test 

9 (Test 2) was used to determine whether the constant variance (p > 0.1) model or modeled 

10 (nonconstant) variance (p < 0.1) model should be used. As BMDS offers only one variance 

11 model, model fits for nonconstant variance models were not necessarily rejected if the variance 

12 model did not fit well (Test 3 p-value < 0.05). Within the group of models with acceptable fits, 

13 the nominal best fitting model was selected by determining the model with the lowest AIC value 

14 if the BMDLs were within a factor of 3; otherwise, the model with the lowest BMDL was chosen 

15 (U.S. EPA, 2000). However, particularly for continuous models, the fit of the model to the 

16 control mean and standard deviation and in the lower response range was assessed. Models with 

17 higher AICs but much better fit to the lower response data were often chosen over the nominally 

18 best-fitting model. Judgment was also used to assess the plausibility of BMDLs far below the 

19 BMD. In most of these cases, deficiencies in the response data resulted in rejection of the 

20 modeling results. 

21 For many data sets, no models satisfied the acceptance criteria and no clear BMD/BMDL 

22 selection could be made. In theses cases, model fits were examined on an individual basis to 

23 determine the reasons for the poor fits. On occasion, high doses were dropped and the models 

24 were refit. If the fit was still not acceptable, the NOAEL/LOAEL approach was applied to the 

25 study/data set combination. 

26 

27 4.2.4.3. POD Candidates from Animal Bioassays Based on HED 

28 Table 4-3 summaries the PODs that EPA estimated for each key animal study included 

29 for TCDD dose-response modeling. After estimating the blood TCDD concentration associated 

 

20 There were no developmental studies that accounted for litter effects, for which a 5% BMR would be used. 
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1 with a particular toxicity measure (NOAEL, LOAEL, or BMDL) obtained from a rodent 

2 bioassay, EPA estimated a corresponding HED using the Emond human PBPK model (described 

3 in Section 3). Table 4-3 summarizes the NOAEL, LOAEL, or BMDL (ng/kg) based on the 

4 administered animal doses for each key bioassay/data set combination. Table 4-3 also 

5 summarizes the continuous daily HED corresponding to these administered doses. The doses in 

6 Table 4-3 are defined as follows: 

7 

8 • Administered Dose NOAEL: Average daily dose (ng/kg-day) from the study 

9 • Administered Dose LOAEL: Average daily dose (ng/kd-day) from the study 

10 • Administered Dose BMDL: Dose from BMD modeling of the administered doses 

11 converted to a blood concentration (ng/kg) using the Emond rodent PBPK models 

12 • Emond Model NOAEL: Administered Dose NOAEL converted to an HED (ng/kg-day) 

13 using the Emond human PBPK model 

14 • Emond Model LOAEL: Administered Dose LOAEL converted to an HED (ng/kg-day) 

15 using the Emond human PBPK model 

16 • Emond Model BMDL: Dose from BMD modeling of the blood concentrations (ng/kg) 

17 converted to an HED (ng/kg-day) using the Emond human PBPK model 

18 

19 Tables showing the best model fit for each study/endpoint combination and the associated 

20 BMD/BMDL are shown in Appendix E. 

21 An evaluation of key BMD analyses is presented in Table 4-4. The BMD modeling was 

22 largely unsuccessful, primarily because of a lack of response data near the BMR, poor modeled 

23 representation of control values, or nonmonotonic responses yielding poor fits. BMDLs were 

24 often implausibly low (orders of magnitude below the LOAEL) or implausibly high. The 

25 comments column in Table 4-4 lists reasons for poor or implausible results. 

26 

27 4.3. RfD DERIVATION 

28 Table 4-5 lists all the studies and endpoints considered for derivation of the RfD. These 

29 studies were chosen from the entire list of candidate study/data set combinations (see 

30 Section 4.2) based on the toxicologic relevance of the endpoints and covering a range that 

includes three of the four human studies21 31 . Figure 4-3 (exposure-response array) shows all of the 

 

21 The RfD derived from the study of Eskenazi et al. (2002) was outside the RfD range presented in Table 4-5. 
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1 endpoints listed in Table 4-5 graphically in terms of PODs in human-equivalent intake units 

2 (ng/kg-day). The human study endpoints are shown at the far left of the figure and the rodent 

3 endpoints are arranged by category to the right. Figure 4-4 demonstrates the same endpoints 

4 arrayed by RfD value, showing the POD, applicable UFs and candidate RfD. 

5 Table 4-5 illustrates the study, species, strain and sex, study protocol, and toxicologic 

6 endpoints observed at the lowest TCDD doses. The table also identifies the human-equivalent 

7 BMDLs (when applicable), NOAELs and LOAELs, as well as the composite UF that applies to 

the specific endpoint, and finally, the corresponding candidate RfD.22 8 The NOAELS, LOAELs, 

9 and BMDLs are presented as HEDs, based on the assumption that blood concentration is the 

10 toxicokinetically-equivalent TCDD dose metric across species and serves as a surrogate for 

tissue concentration.23 11 For rats and mice, these estimates relied on the two Emond PBPK 

12 models—one for the relevant rodent species and one for the human—as described previously 

13 (see Sections 3.3.4.3). The two guinea pig studies that are included in Table 4-5 are given in 

14 HED units based on the first-order body burden model described in Section 3.3.4.2; there is 

15 currently no TCDD PBPK model for the guinea pig. The values listed for guinea pigs are not 

16 directly comparable to those for rats and mice but are probably biased low, as first-order body 

17 burden HED estimates for rats and mice are generally 2- to 5-fold lower than the corresponding 

18 PBPK model estimates. The LOAELs for the human studies also rely on the Emond PBPK 

19 model, as described in Sections 4.2.2 and 4.2.3. 

20 As is evident from the table, very few NOAELs and even fewer BMDLs have been 

21 established for low-dose TCDD studies. BMD modeling was unsuccessful for all of the 

22 endpoints without a NOAEL, primarily because of the lack of dose-response data near the BMR 

23 (see discussion in Section 4.2). Therefore, the RfD assessment rests largely on evaluation of 

24 LOAELs to determine the POD. 

25 The rows in Table 4-5 are arranged in order of increasing candidate RfD magnitude. 

26 Endpoints projected to occur at higher exposure levels are still considered for qualitative support 

27 of the effects shown in Table 4-5. 

28 

 

22Extra significant digits are retained for comparison prior to rounding to one significant digit for the final RfD. 23The procedures for estimating HEDs based on TCDD blood concentration are described in the preceding section. 
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1 4.3.1. Toxicological Endpoints 

2 As can be seen in Table 4-5, a wide array of toxicological endpoints has been observed 

3 following TCDD exposure, ranging from subtle developmental effects to overt chronic liver 

4 toxicity. Developmental effects in rodents include dental defects, delayed puberty in males, and 

5 several neurobehavioral effects. Reproductive effects reported in rodents include altered 

6 hormone levels in females and decreased sperm production in males. Immunotoxicity endpoints 

7 such as decreased response to SRBC challenge in mice and decreased delayed-type 

8 hypersensitivity response in guinea pigs are also observed. Longer durations of TCDD exposure 

9 in rodents elicit results such as organ and body weight changes, renal toxicity, and liver and lung 

10 lesions. Adverse effects in human studies are also observed, which include male reproductive 

11 effects, increased TSH in neonates, and dental defects in children. Analogous results have been 

12 observed in animal bioassays for each of these human endpoints. 

13 All but two of the study/endpoint combinations from animal bioassays listed in Table 4-5 

14 are on TCDD-induced toxicity observed in mice and rats; the other two study/endpoint 

15 combinations are effects in guinea pigs. Although the effects of TCDD have been investigated in 

16 several other species (i.e., hamsters, monkeys, and mink), those studies were not included for 

17 final POD consideration because the effect levels were greater than those in Table 4-5, or 

18 because the effects could not be attributed solely to TCDD exposure (i.e., confounding by 

19 dioxin-like compounds [DLCs]). 

20 Three human studies were also included for final POD consideration in the derivation of 

21 an RfD and are presented in Table 4-5 as candidate RfDs. All three human study/endpoint 

22 combinations are from studies on the Seveso cohort. The developmental effects observed in 

23 these studies were associated with TCDD exposures either in utero or in early childhood between 

24 1 and 10 years of age. Baccarelli et al. (2008) reported increased levels of TSH in newborns 

25 exposed to TCDD in utero, indicating a possible dysregulation of thyroid hormone metabolism. 

26 Mocarelli et al. (2008) reported decreased sperm concentrations, decreased motile sperm counts, 

27 and decreased serum estradiol in men who were 1–9 years old in 1976 at the time of the Seveso 

28 accident (initial TCDD exposure event). Alaluusua et al. (2004) reported dental effects in adults 

29 who were less than 9.5 years of age at the time of the initial exposure (1976). 

30 
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1 4.3.2. Exposure Protocols of Candidate PODs 

2 The studies in Table 4-5 represent a wide variety of exposure protocols, involving 

3 different methods of administration and exposure patterns across virtually all exposure durations 

4 and life stages. Both dietary and gavage administration have been used in rodent studies, with 

5 gavage being the predominant method. Gavage dosing protocols vary quite widely and include 

6 single gestational exposures, multiple daily exposures (for up to 2 weeks, intermittent schedules 

7 that include 5 days/week, once weekly, or once every 2 weeks), and loading/maintenance dose 

8 protocols, in which a relatively high dose is initially given followed by much smaller weekly 

9 doses. The intermittent dosing schedules require dose-averaging over time periods as long as 

10 2 weeks, which introduces uncertainty in the effective exposures. In other words, the high unit 

11 dose may be more of a factor in eliciting the effect than the average TCDD tissue levels over 

12 time. Although the loading/maintenance dose protocols are designed to maintain a constant 

13 internal exposure, these protocols are somewhat inconsistent with the constant daily TCDD 

14 dietary exposures associated with human ingestion patterns. 

15 The epidemiologic studies conducted in the Seveso cohort represent exposures over 

16 different life stages including gestation, childhood, and young adulthood. The Seveso exposure 

17 profile is essentially a high initial pulse TCDD exposure followed by a 20−30 year period of 

18 elimination. Effects are realized, or measured, 10−20 years following the initial exposure; the 

19 critical exposure window for susceptibility varies with effect and is often unknown. Therefore, 

20 the effective exposure profiles for the Seveso cohort studies vary considerably. For the 

21 Mocarelli et al. (2008) and Alaluusua et al. (2004) studies where early childhood exposures 

22 proximate to the initial event are associated with the outcomes, there is some uncertainty as to 

23 the magnitude of the effective doses. Although the effects are associated with TCDD exposure 

24 in the first 10 years of life, it is not clear to what extent the initial peak exposure is primarily 

25 responsible for the effects. It is also not clear if averaging exposure over the critical window is 

26 appropriate given the large difference between initial TCDD body burden and body burden at the 

27 end of the critical exposure window. The LOAELs for both Mocarelli et al. (2008) and 

28 Alaluusua et al. (2004) are calculated as the average of the peak exposure and average exposure 

29 across the critical exposure window (see Section 4.2 for details). For the gestational exposure 

30 study (Baccarelli et al., 2008), the critical exposure window is strictly defined and relatively 

31 short (9 months) and occurs long after the initial exposure (15−20 years). In addition, the 
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1 maternal serum TCDD measurements were taken 10−15 years after the initial exposure and are 

2 proximate to the actual pregnancies, allowing for less uncertainty in the kinetic extrapolation to 

3 time of birth. The narrow critical exposure window at a much later time than the initial exposure 

4 (where the TCDD elimination curve is flattening) is assumed to lead to a relatively steady-state 

5 exposure over the critical time period with much less uncertainty in the magnitude of the 

6 effective dose. With the exception of Eskenazi et al (2002) (see section 4.2), the effective doses 

7 for other effects reported for the Seveso cohort (see Section 2.4.1.1.1.4) have not been quantified 

8 and are not represented in Table 4-5 because no critical exposure windows can be identified or 

9 individual exposure estimates were not reported. 

10 

11 4.3.3. Uncertainty Factors (UFs) 

12 The UF column in Table 4-5 shows the composite (total) UF that would be applied to the 

13 POD for each endpoint. For the animal bioassays, a UF of 3 for the toxicodynamic component 

14 of the interspecies extrapolation factor (UFA) was applied to all PODs. For both animal and 

15 human studies, when a NOAEL was used as the POD, a factor of 10 was applied for human 

16 interindividual variability (UFH). For all of the animal bioassay endpoints lacking a NOAEL, a 

17 UF of 10 for the LOAEL-to-NOAEL UF (UFL) was included. For the human LOAELs, a UFL of 

18 3 was applied because sensitive populations were identified. A subchronic-to-chronic UF (UFS) 

19 of 1 and a database factor (UFD) of 1 are applied to all endpoints. A rationale for each UF is 

20 provided for the derivation of the RfD below. 

21 

22 4.3.4. Human Studies 

23 For selection of the POD, the human studies are given the highest consideration, as 

24 quality human data are always preferred. Although the lower end of the candidate RfD 

25 distribution is dominated by mouse studies (comprising 6 of the 7 lowest rodent-based RfDs), 

26 EPA considers these candidate RfD estimates to be much more uncertain than human candidate 

27 RfD estimates. The LOAELHEDs identified in mouse bioassays are low primarily because of the 

28 large toxicokinetic interspecies extrapolation factor used for mice. The ratio of administered 

29 dose to HED (Da:HED) ranges from 65 to 1227 depending on the duration of exposure. The 

30 Da:HED for mice is, on average, about 4 times larger than that used for rats. In addition, each 
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1 one of the mouse studies has other qualitative limitations and uncertainties (discussed above and 

2 in Table 4-4) that make them poor candidates as the basis for the RfD. 

3 Most of the other rodent studies between the first six mouse studies and the human 

4 studies of Mocarelli et al (2008) and Baccarelli et al. (2008) are of small size, using 10 or fewer 

5 animals per dose group and are considered too uncertain on which to base the final RfD. Two of 

6 the rat bioassays–Bell et al (2007) and NTP (2006)--however, are of particular note. Both of 

7 these studies were very well designed and conducted, using 30 or more animals per dose group 

8 (see Table 4-6). Bell et al (2007) evaluated several reproductive and developmental endpoints 

9 starting exposure well before mating and continuing through gestation. NTP (2006) is the most 

10 comprehensive evaluation of TCDD chronic toxicity in rodents to date, evaluating dozens of 

11 endpoints at several time points in all major tissues. The toxicokinetic extrapolation to humans, 

12 however, is still very uncertain. Despite the overall strength of the Bell and NTP studies, EPA 

13 still considers the human data to be a better basis for the RfD. 

14 Baccarelli et al. (2008) reported increased levels of TSH in newborns exposed to TCDD 

15 in utero, indicating a possible dysregulation of thyroid hormone metabolism. The study authors 

16 related TCDD concentrations in neonatal blood to TSH levels, reporting group mean TCDD 

17 concentrations associated with TSH levels above or below 5 μ-Units TSH per mL of serum 

18 (5 μU/mL). The 5 μU/mL standard was established by the World Health Organization (WHO, 

19 1994) as an indicator of potential iodine deficiency (and potential thyroid problems) in neonates. 

20 Baccarelli et al. (2008) also showed, in graphical form, how the TSH distribution in each of three 

21 categorical exposure groups (reference, zone A, and zone B—representing increasing TCDD 

22 exposure) shifted to higher TSH values with increasing exposure. The individuals comprising 

23 the above 5 μU/mL group were from all three categorical exposure groups, not just from the 

24 highest exposure group. Therefore, EPA was able to designate a LOAEL independently of the 

25 nominal categorical exposure groups; the LOAEL is designated as the group mean of 39 ppt 

26 TCDD in neonatal plasma as a LOAEL for TSH values above 5 μU/mL. The daily oral intake at 

27 the LOAEL is estimated to be 0.024 ng/kg-day (see Section 4.2.3.1). A NOAEL is not defined 

28 because it is not clear what maternal intake should be assigned to the group below 5 μU/mL. 

29 Mocarelli et al. (2008) reported decreased sperm concentrations (20%), decreased motile 

30 sperm counts (11%), and decreased serum estradiol (23%) in men who were 1–9 years old in 

31 1976 at the time of the Seveso accident (initial TCDD exposure event). The sperm 
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1 concentrations and motile sperm counts in men who were 10–17 years old in 1976 were not 

2 affected; marginal serum estradiol reductions were reported in this group. Serum (LASC) TCDD 

3 levels were measured within one year of the initial exposure. Serum TCDD levels and 

4 corresponding responses were reported by quartile, with a reference group of less-exposed 

5 individuals assigned a TCDD LASC value of 15 ppt (which was the mean of the TCDD LASC 

6 reported in individuals outside the contaminated area). The lowest exposed group mean was 

68 ppt (1st 7 -quartile). Mean sperm concentrations and motile sperm counts were reduced about 

8 20% from the reference group. Further decrease in these values was slight and reached a 

9 maximum of about 33%. Although a decrease in sperm production of 20% would not have 

10 clinical significance for an individual, EPA considers a 20% shift in the population mean to be of 

11 biological significance. Therefore, EPA has designated the lowest exposure group (68 ppt) as a 

12 LOAEL, which translates to a continuous daily oral intake of 0.020 ng/kg-day (see 

13 Section 4.2.3.2). The reference group is not designated as a NOAEL because there is no clear 

14 zero-exposure measurement for any of these endpoints, particularly considering the contribution 

15 of background exposure to DLCs, which futher complicates the interpretation of the reference 

16 group response as a true “control” response (see discussion in Section 4.4). However, males less 

17 than 10 years old can be designated as a sensitive population by comparison to older males who 

18 were not affected. 

19 Alaluusua et al. (2004) reported dental effects in male and female adults were were less 

20 than 9.5 years of age, but not older, at the time of the initial exposure (1976) in Seveso. EPA 

21 used the same approach to estimate daily TCDD intake as was used for the Mocarelli et al. 

22 (2008) data; a window of susceptibility of about 5 years was established. Serum measurements 

23 for this cohort were taken within a year of the accident. Serum TCDD levels and corresponding 

24 responses were reported by tertile, with a reference group of less-exposed individuals assigned a 

25 TCDD LASC value of 15 ppt (ng/kg); the tertile group means were 130, 383, and 1,830 ppt. 

26 Both a NOAEL and LOAEL can be defined for this study. The NOAEL is 0.12 ng/kg-day, 

27 corresponding to the TCDD LASC of 130 ppt at the first tertile. The LOAEL is 0.93 ng/kg-day 

28 at the second tertile. The children in this cohort less than 5 years old can be designated as a 

29 sensitive population by comparison to older individuals who were not affected relative to the 

30 reference group. 

31 
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1 4.3.5. Derivation of the RfD 

2 The two human studies, Baccarelli et al. (2008) and Mocarelli et al. (2008), have similar 

3 LOAELs of 0.024 and 0.020 ng/kg-day, respectively. Together, these two studies constitute the 

4 best foundation for establishing a POD for the RfD, and are designated as co-principal studies. 

5 Therefore, increased TSH in neonates (Baccarelli et al., 2008) and male reproductive effects 

6 (decreased sperm count and motility, increased estradiol) are designated as cocritical effects. 

7 Although the exposure estimate used in determination of the LOAEL for Mocarelli et al. (2008) 

8 is more uncertain than the Baccarelli et al. (2008) exposure estimate, the slightly lower LOAEL 

9 of 0.020 ng/kg-day from Mocarelli et al. is designated as the POD. A composite UF of 30 is 

10 applied to account for lack of a NOAEL (UFL = 10) and human interindividual variability 

(UFH = 3); the resulting RfD in standard units is 7 × 10−10 11 mg/kg-day. Table 4-7 presents the 

12 details of the RfD derivation. 

13 

14 4.4. UNCERTAINTY IN THE RfD 

15 Exposure assessment is a key limitation of the epidemiologic studies (of the Seveso 

16 cohort) used to derive the RfD. The Seveso cohort exposure profile consists of an initial high 

17 dose followed by a drop in body burden to background levels over a period of about 20 years, at 

18 which time the effects were observed. This exposure scenario is a mismatch with the constant 

19 daily intake scenario addressed by the RfD methodology. The determination of an effective 

20 average daily dose from the Seveso exposure scenario requires an understanding of the critical 

21 time-window of susceptibility and the influence of the peak exposure on the occurrence of the 

22 observed effects, particularly when the peak exposure is high relative to the average exposure 

23 over the critical exposure window. For one of the principal studies (Mocarelli et al., 2008), a 

24 maximum susceptibility exposure window can be identified based on the age of the population at 

25 risk. However, the influence of the peak exposure on the effects observed 20 years later is 

26 unknown and the biological significance of averaging the exposure over several years, with 

27 internal exposure measures spanning a 4.5-fold range, is unknown. EPA, in this assessment, has 

28 averaged intermittent exposures for rodent bioassays over weekly dosing intervals, but the peak 

29 and average body burdens varied by less than 50%. EPA has not developed guidance for larger30 interval averaging. Furthermore, because there is an assumption of a threshold level of exposure 

31 below which the effects are not expected to occur, averaging over large intervals could include 
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1 below-threshold exposures. The process used by EPA to estimate the LOAEL exposure for the 

2 Mocarelli study is a compromise between the extremes; as such, there is some uncertainty in the 

3 estimate, perhaps in the range of 3- to 10-fold in either direction. This uncertainty also holds for 

4 the LOAEL determined for the dental effects reported in Alaluusua et al. (2004) and the 

5 increased menstrual cycle length reported in Eskenazi et al. (2002a; see Section 4.2.3.4); in both 

6 of those studies, the uncertainty is greater, as the difference between peak and average internal 

7 exposures is an order of magnitude or more. The LOAEL for increased TSH in neonates 

8 (Baccarelli et al., 2008), however, is less uncertain because the critical exposure window is much 

9 narrower (9 months) and the developmental exposures occurred 10 to 15 years after the initial 

10 exposure, when internal TCDD concentrations for the pregnant women were leveling off; that is, 

11 exposure over the critical window was more constant. However, there is uncertainty in the 

12 magnitude of the exposures because they were extrapolated from serum measurements taken 

13 several years earlier. 

14 Another source of uncertainty using human epidemiologic data is the lack of completely 

15 unexposed populations. The available TCDD epidemiologic data were obtained by comparing 

16 populations that experienced elevated TCDD exposures to populations that experienced lower 

17 exposures, rather than to a population with no TCDD exposure. An additional complicating 

18 factor is coexposure to DLCs, which can behave in the same way as TCDD. Although the 

19 accidental exposure to the Seveso women’s cohort was virtually 100% TCDD, background 

20 exposure was largely to DLCs. Eskenazi et al. (2004) reported that TCDD comprised only 20% 

21 of the total toxicity equivalence (TEQ) in the serum of the reference group that was not exposed 

22 as a result of the factory explosion, which implies that the effective background exposure could 

23 have been as much as 5-fold higher. The higher background exposure could be significant at the 

24 lower TCDD exposure levels, with the effect diminishing as TCDD exposure increased. For 

25 dose-response modeling, the effect of a higher background dose (i.e., total TEQ), if included, 

26 would be to shift the response curve to the right (responses associated with higher exposures) 

27 but, primarily, would reduce the spread of the exposures, which would tend to alter the shape of 

28 the dose response towards sublinear. Both the right shift and the more sublinear shape would 

29 result in higher EDx estimates, such as BMDs and BMDLs, from fitting dose-response models. 

30 However, for determination of a LOAEL, which is the case for all the human studies in 

31 Table 4-5, the impact may be minimal, as the LOAEL depends only on establishing that an effect 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-25 —DO NOT CITE OR QUOTE 

1 of sufficient magnitude was observed at some TCDD exposure level. In this case, the effect of 

2 the increased effective background exposure would be to inflate the “control” (zero-TEQ) 

3 response, providing the threshold for the response had been exceeded. The potential impact of 

4 an inflated control response would be to mask a significant effect of the added TCDD exposure, 

5 when the latter effect is determined by comparison to the reference group response. To 

6 compensate for this, EPA has been somewhat conservative in interpreting the magnitude of 

7 responses defining LOAELs for the Seveso cohort studies. The actual magnitude of the impact 

8 of the DLC background exposure is impossible to assess without knowing the true (TEQ-free) 

9 background response. 

10 A primary strength of the TCDD database is that analogous effects have been observed in 

11 animal bioassays for most of the human endpoints, increasing the overall confidence in the 

12 relevance to humans of the effects reported in rodents and the association of TCDD exposure 

13 with the effects reported in humans. Table 4-5 shows that low dose TCDD exposures are 

14 associated with a wide array of toxicological endpoints in rodents including developmental 

15 effects, reproductive effects, immunotoxicity and chronic toxicity. Effects reported in human 

16 studies are similar, including male reproductive effects, increased TSH in neonates and dental 

17 defects in children; other human health effects such as female reproductive effects and chloracne 

18 have been observed at higher exposures (see Section 2.4.1). Other effects reported in rodent 

19 studies such as liver toxicity and overt immunological endpoints have not been reported in 

20 human studies. However, with respect to immunological effects, Baccarelli et al (2002, 2004) 

21 evaluated immunoglobin and complement levels in the sera of TCDD-exposed individuals from 

22 the Seveso cohort and found slightly reduced immunoglobulin in the highest exposure groups but 

23 no effect on other immunoglobulins or on C3 or C4 complement levels. The latter finding 

24 indicates that at least one immunological measure in humans is not a sensitive endpoint, as it is 

25 for mice, with large reductions in serum complement at low exposure levels (White et al., 1986). 

26 Although there is a substantial amount of qualitative concordance of effects between 

27 rodents and humans, quantitative concordance is not evident in Table 4-5. The differential 

28 sensitivity of mice and humans for the serum complement endpoint is one example. Other 

29 examples of differential sensitivity are developmental dental effects and thyroid hormonal 

30 dysregulation. Developmental dental defects are relatively sensitive effects in rodents, appearing 

31 at exposure levels in mice (Keller et al., 2007, 2008a,b) more than an order of magnitude lower 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-26 —DO NOT CITE OR QUOTE 

1 than effect levels in humans (Alaluusua et al., 2004). In contrast, thyroid hormone effects are 

2 seen in rats (Crofton et al., 2005) at 30-fold higher exposures than for humans (Baccarelli et al., 

3 2008). Male reproductive effects (sperm production) occur in rats (Latchoumycandane and 

4 Mathur, 2002) and humans (Mocarelli et al., 2008) at about the same dose. To what extent these 

5 differential sensitivities depend on specifics of the comparison, such as species (mouse vs. rat), 

6 life-stage (e.g., fetal vs. adult), endpoint measure (e.g., thyroxine [T4] vs. TSH) or magnitude of 

7 the lowest dose tested, cannot be determined, so strong conclusions about quantitative 

8 concordance cannot be made. 

9 Considering the issue of lowest tested dose, the general lack of NOAELs and acceptable 

10 BMDLs is a primary weakness of the rodent bioassay database. None of the 6 most sensitive 

11 rodent studies in Table 4-5, spanning a 30-fold range of LOAELs, had defined NOAELs or 

12 BMDLs. For only 4 of the next 10 rodent studies were NOAELs or BMDLs established. In 

13 addition, many of these LOAELs are characterized by relatively high responses with respect to 

14 the control population, so it is not certain that a 10-fold lower dose (based on the application of 

15 UFL of 10) would be approximately equivalent to a NOAEL. A major reason for the failure of 

16 BMD modeling was that the responses were not “anchored” at the low end; first response levels 

17 were far from the BMR (see Table 4-4). Another major problem with the data was non18 monotone and flat response profiles. The small dose-group sizes and large dose intervals 

19 probably contributed to many of these response characteristics that prevented successful BMD 

20 modeling. Larger study sizes with narrower dose intervals at lower doses are still needed to 

21 clarify rodent response to TCDD. 

22 Lower TCDD doses have been tested in rodents but almost entirely for investigation of 

specialized biochemical endpoints24 23 that would not be considered to be adverse health effects 

24 (see Appendix G). There is, however, a fundamental limit to the lowest dose of TCDD that can 

25 be tested meaningfully, as TCDD is present in feed stock and accumulates in unexposed animals 

26 prior to the start of a study. This issue is illustrated by the presence of TCDD in tissues of 

27 unexposed control animals, often at significant levels relative to the lowest tested dose in low 

28 dose studies (Vanden Heuvel et al., 1994; Ohsako et al., 2001; Bell et al., 2007a; see 

 

24Enzyme induction, oxidative stress indicators, mRNA levels, etc. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-27 —DO NOT CITE OR QUOTE 

1 Text Box 4-1). Some DLCs have been measured in animal feeds and are anticipated to 

2 accumulate in unexposed test animals further complicating the interpretation of low dose studies. 

3 

4 

5 

6 

7 Table 4-6 compares the qualitative strengths and limitations/uncertainties associated with 

8 the top animal bioassays listed in Table 4-5. 

Text Box 4-1. 

TCDD tissue levels in control animals are rarely reported either explicitly or implicitly. 

Vanden Heuvel et al. (1994), however, reported TCDD concentrations in livers of control 

animals (10-week-old female Sprague-Dawley rats) of 0.43 ppt (ng/kg) compared to 0.49 ppt in 

the livers of animals given a single oral TCDD dose of 0.1 ng/kg. Assuming proportionality of 

liver concentration to total body burden, the body burden of untreated animals was 87.8% of 

that of treated animals. The equivalent administered dose for untreated animals (d0) can be 

calculated as equal to 0.878 × (0.1 + d0) , assuming proportionality of body burden to 

administered dose and that all animals started with the same TCDD body burdens. The 

calculation yields a value of 0.72 ng/kg for d0, which represents the accumulated TCDD from 

all sources in these animals prior to being put on test. This value would raise the nominal 

0.1 ng/kg TCDD dose 8-fold to 0.82 ng/kg. The next higher dose of 1 ng/kg would be nearly 

doubled to 1.72 ng/kg. The impact on higher doses would be negligible. Bell et al. (2007a) 

reported slightly higher levels (0.66 ppt) in the livers of slightly older untreated pregnant female 

Sprague-Dawley rats (mated at 16–18 weeks of age and tested 17 days later). 

Ohsako et al. (2001) reported TCDD concentrations in the fat of offspring of untreated 

pregnant Holtzman rats that were 46% of the TCDD fat concentrations in animals exposed in 

utero to 12.5 ng/kg (single exposure on GD 15). This level of TCDD would imply a very large 

background exposure, but quantitation based on simple kinetic assumptions probably would not 

reflect the more complicated indirect exposure scenario 

Bell et al. (2007a) also reported concentrations of 0.1 and 0.6 ppt TCDD measured in 

two samples of feed stock. Assuming that the average of 0.35 ppt is representative of the entire 

supply of feed stock and a food consumption factor of 10% of body weight per day, the average 

daily oral exposure from feed to these animals would be 0.035 ng/kg. Discrimination of 

outcomes from longer-term repeated exposures might be problematic at exposure levels around 

0.1 ng/kg-day. Background exposure was not much of an issue for Bell et al. (2007a), as the 

lowest TCDD exposure level was 2.4 ng/kg-day (28-day dietary exposure). 

 NTP (2006), however, found virtually no TCDD in the tissues of untreated animals or 

in the feed stock. In all of these studies, except the 28-day exposure in Bell et al. (2007a), 

control animals were gavaged with corn oil vehicle. TCDD concentrations in corn oil were not 

reported in any of the studies.
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-28 —DO NOT CITE OR QUOTE 

1 Table 4-1. POD candidates for epidemiologic studies of TCDD 

2 

Study POD (ng/kg-day) Critical effects 

Alaluusua et al., 2004 7.20E-02a (NOAEL) Dental effects in adults exposed to TCDD in childhood 

Baccarelli et al., 2008 2.40E-2b (LOAEL) Elevated TSH in neonates 

Eskenazi et al., 2002 1.66E+0c (LOAEL) Increased length of menstrual cycle in women exposed 

to TCDD in childhood 

Mocarelli et al., 2008 2.00E-2d (LOAEL) Decreased sperm count and motility and increased 

estradiol in men exposed to TCDD in childhood 

3 a 4 Mean of peak exposure (0.15 ng/kg-day) and average exposure over 10-year critical window (0.0093 ng/kg-day). 

b 5 Maternal exposure corresponding to neonatal TSH concentration exceeding 5 μU/mL. 

c 6 Mean of peak exposure (3.2 ng/kg-day) and average exposure over 10-year critical window (0.12 ng/kg-day). 

d 7 Mean of peak exposure (0.035 ng/kg-day) and average exposure over 10-year critical window (0.0078 ng/kg-day). 

8 

9 

10 Table 4-2. Models run for each study/endpoint combination in the animal 

11 bioassay benchmark dose modeling 

12 

Model Restrictions imposed 

Continuous models 

Exponential M2-M5, 

not grouped 

Adverse direction specified according to the response data; power ≥1 

Hill Adverse direction is automatic; n > 1 

Linear Adverse direction is automatic; degree of polynomial = 1 

Polynomial Adverse direction is automatic; degree of polynomial = 2; restrict the sign of 

the power to non-negative or non-positive, depending on the direction of the 

responses 

Power Adverse direction is automatic; power ≥1 

Dichotomous models 

Gamma Power ≥1 

Logistic None 

Log-Logistic Slope ≥1 

Log-Probit None 

Multistage Beta ≥0, 2nd degree polynomial 

Probit None 

Weibull Power ≥1 

13 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-29 DRAFT: DO NOT CITE OR QUOTE Table 4-3. Summary of key animal study NOAELs, LOAELs, and BMDLs for different dose metri

day) Administered dosea 1st-order body burden HEDb Bloo

Study Endpoint NOAEL LOAEL BMDLd NOAEL LOAEL BMDLd NOAEL Amin et al., 2000 Saccharin preference ratio, female - 2.50E+01 5.15E+01 - 2.51E+04 5.17E+04 - Bell et al., 2007a Balano-preputial separation in male pups - 2.40E+00 1.25E-01 - 1.41E+02 7.36E+00 - Cantoni et al., 1981 Urinary coproporhyrins - 1.43E+00 2.82E-01 - 1.65E+02 3.25E+01 - Chu et al., 2001 Tissue weight changes 2.50E+02 1.00E+03 - 8.28E+04 3.31E+05 - - Chu et al., 2007 Liver lesions 2.50E+00 2.50E+01 - 8.28E+02 8.28E+03 - 3.56E-0Crofton et al., 2005 Serum T4 3.00E+01 1.00E+02 3.01E+01 4.69E+04 1.56E+05 4.70E+04 1.73E-0Croutch et al., 2005 Decreased body weight 5.43E+01 2.17E+02 - 1.33E+04 5.30E+04 - - DeCaprio et al., 1986 Decreased body weight 6.10E-01 4.90E+00 - 9.05E+01 7.27E+02 - - Fattore et al., 2000 Decreased hepatic retinol - 2.00E+01 - - 3.25E+03 - - Fox et al., 1993 Increased liver weight 5.73E-01 3.27E+02 - 2.31E+02 1.32E+05 - - Franczak et al., 2006 Abnormal estrous cycle - 7.14E+00 - - 8.57E+02 - - Hojo et al., 2002 DRL response per min - 2.00E+01 1.10E+02 - 7.60E+04 4.18E+05 - Ikeda et al., 2005a Sex ratio - 1.65E+01 - - 2.60E+03 - - Ishihara et al., 2007 Sex ratio 1.00E-01 1.00E+02 - 3.15E+01 3.15E+04 - - Kattainen et al., 2001 3rd molar length - 3.00E+01 2.14E+00 - 1.14E+05 8.15E+03 - Keller et al., 2007a, 2008a, 2008b Missing mandibular molars - 1.00E+01 9.17E+00 - 3.88E+04 3.56E+04 - Kociba et al., 1976 Liver and hematologic effects and body weight changes 7.14E+00 7.14E+01 - 1.13E+03 1.13E+04 - 1.60E-0
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-30 DRAFT: DO NOT CITE OR QUOTE Table 4-3. Summary of key study NOAELs, LOAELs, and BMDLs for different dose metrics (ng/k

(continued) Administered dosea 1st-order body burden HEDb Bloo

Study Endpoint NOAEL LOAEL BMDLd NOAEL LOAEL BMDLd NOAEKociba et al., 1978 Liver and lung lesions, increased urinary porphyrins 1.00E+00 1.00E+01 7.30E-01 9.31E+01 9.31E+02 6.80E+01 6.46E-0Latchoumycandane and Mathur., 2002 Sperm production - 1.00E+00 1.56E-02 - 2.59E+02 4.03E+00 - Li et al., 1997 Increased serum FSH 3.00E+00 1.00E+01 - 1.14E+04 3.80E+04 - 2.97E-0Li et al., 2006 Hormone levels (serum estradiol) - 2.00E+00 1.11E+02 - 4.06E+03 2.27E+05 - Markowski et al., 2001 FR2 revolutions - 2.00E+01 5.81E+01 - 6.40E+04 1.86E+05 - Maronpot et al., 1993 Increased relative liver weight 1.07E+01 3.50E+01 - 1.28E+03 4.18E+03 - - Miettinen et al., 2006 Cariogenic lesions in pups - 3.00E+01 4.99E+00 - 1.14E+05 1.90E+04 - Murray et al., 1979 Fertility index in f2 generation 1.00E+00 1.00E+01 - 1.06E+02 1.06E+03 - 2.96E-0NTP, 1982 Liver lesions - 1.39E+00 1.38E+01 - 2.98E+02 2.97E+03 - NTP, 2006 Liver and lung lesions - 2.14E+00 1.40E+00 - 1.96E+02 1.28E+02 - Nohara et al., 2000 Decreased spleen cellularity 8.00E+02 - - 3.04E+06 - - 5.34E+0Ohsako et al., 2001 Anogenital distance in pups 1.25E+01 5.00E+01 1.22E+01 4.75E+04 1.90E+05 4.62E+04 2.87E-0Seo et al., 1995 Decreased thymus weight 2.50E+01 1.00E+02 - 2.51E+04 1.00E+05 - 1.69E-0Sewall et al., 1995 Serum T4 1.07E+01 3.50E+01 5.20E+00 1.28E+03 4.18E+03 6.20E+02 5.15E-0Shi et al., 2007 Serum estradiol in female pups 1.43E-01 7.14E-01 2.24E-01 1.67E+01 8.32E+01 2.61E+01 4.71E-0
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-31 DRAFT: DO NOT CITE OR QUOTE Table 4-3. Summary of key study NOAELs, LOAELs, and BMDLs for different dose metrics (ng/k

(continued) Administered dosea 1st-order body burden HEDb Bloo

Study Endpoint NOAEL LOAEL BMDLd NOAEL LOAEL BMDLd NOAESimanainen et al., 2002 Decreased serum T4 1.00E+02 3.00E+02 - 3.80E+05 1.14E+06 - - Simanainen et al., 2003 Decreased thymus weight and change in EROD activity 1.00E+02 3.00E+02 - 3.80E+05 1.14E+06 - - Simanainen et al., 2004 Decreased daily sperm production 1.00E+02 3.00E+02 - 3.80E+05 1.14E+06 - - Smialowicz et al., 2004 Decreased antibody response to SRBCs 3.00E+02 1.00E+03 - 1.16E+06 3.88E+06 - - Smialowicz et al., 2008 PFC per 10^6 cells - 1.07E+00 7.19E+01 - 2.29E+02 1.54E+04 - Toth et al., 1979 Skin lesions - 1.00E+00 6.85E-02 - 2.70E+02 1.85E+01 - VanBirgelen et al., 1995a,b Decreased liver retinyl palmitate - 1.40E+01 9.89E+02 - 2.27E+03 1.61E+05 - Vos et al., 1973 Decreased delayed-type hypersensitivity response to tuberculin 1.14E+00 5.71E+00 - 2.02E+02 1.01E+03 - - White et al., 1986 Decreased serum complement - 1.00E+01 2.89E+01 - 4.49E+03 1.30E+04 - Yang et al., 2000 Increased endometrial implant survival 1.79E+01 - - 4.73E+02 - - - aAverage administered daily dose over the experimental exposure period. bHED based on 1st-order body burden model described in Section 3.2.4.4. cHED based on Emond rodent and human PBPK models described in Section 3.3.6. dBMR = 0.1 for quantal endpoints and 1 standard deviation control mean for continuous endpoints, except for body and organ weights, 

relative deviation from control mean. 

- = value not established or not modeled. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-32 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) Study NOAEL/ LOAEL Endpoint Control response First responseb Max responsec Model fit detail BMD/ BMDL 

PFC per spleen (n = 15) 

─ 24% ↓

(0.5 SD) 

89% ↓ Continuous power, 

unrestricted (p = 0.27) 

6.54E+3 2.07E+3 

BMDL low dosconstrahit lowe

Smialowicz et al., 2008 (mouse) 

None/ 

2.41E+2 

PFC per 10^6 cells (n = 8

−15) 

─ 24% ↓

(0.5 SD) 

9.3-fold ↓ Continuous power 

unrestricted, constant variance (p = 0.48) 

1.05E+3 1.19E+2 

Constancontrol 35%; pvarianc

serum estradiol (n = 10) 

─ 2.0-fold ↑

(0.8 SD) 

2.4-fold ↑ Continuous linear 

(p = 0.16) 

8.85E+3 2.96E+3 

BMDL (1.25); dose; nmodel f

Li et al., 2006 (mouse) 

None/ 

8.75E+1 

serum progesterone (n = 10) 

─ 33% ↓

(2.0 SD) 

61% ↓ Continuous Hill 

(p = 0.44) 

8.95E-1 6.31E-3 

No resp(>1) for varianclower bimplaus

skin lesions (n = 38

−44) 

0/38 5/44 25/43 Dichotomous log-logistic 

(p = 0.67) 

1.1E-2 4.7E-4 

Constraimplaus

Dichotomous log-logistic, unrestricted (p = 0.98) 

2.60E+2 3.18E+0 

Supraliimplaus

dermal amyloidosis (n = 38

−44) 

0/38 5/44 17/43 Dichotomous log-logistic 

(p = 0.02) 

8.3E+3 4.8E+3 

Poor fitbound h

Toth et al., 1979 (mouse) 

None/ 

3.15E+2 

Dichotomous log-logistic, unrestricted (p = 0.90) 

2.67E+2 2.93E+0 

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

1/15/10 4-33 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

daily sperm production (n = 6) 

─ 29% ↓

(1.0 SD) 

41% ↓ Continuous Hill, 

restricted (p = 0.98) 

3.39E+2 1.51E-2 

Near mconstraimplausdeviatiostandar

Latchoumycandane and Mathur, 2002 (rat) 

None/ 

4.37E+2 

Continuous Hill, unrestricted (p = 0.96) 

3.32E+2 8.77E-3 

Slightly implaus

NTP, 1982 (mouse) 

None/ 

4.20E+2 

Toxic hepatitis; males (n = 50) 

1/73 5/49 44/50 Dichotomous multistage 

(p = 0.01) 

8.3E+2 3.7E+2 

No acceBMDL 

Total hemolytic complement activity (CH50) (n = 8) 

─ 41% ↓

(2.6 SD) 

81% ↓ Continuous 

Hill, restricted (p = 0.002) 

4.76E+3 8.56E+2 

Poor fitconstraBMDL 

White et al., 1986 (mouse) 

- 

6.03E+2 

Continuous Hill, unrestricted (p = 0.07) 

8.17E+1 7.55E+1 

SupraliBMDL 

Keller et al., 2007, 2008a, b (mouse) 

None/ 

2.96E+2 

Missing molars (n = 23

−36) 

0/29 2/23 30/30 Dichotomous 1° multistage 

(p = 0.26) 

6.01E+2 4.20E+2 

Poor fit sensitivamenab

Shi et al., 2007 (rat) 

1.88E+2 5.92E+2 

Serum estradiol in female pups (n = 10) 

─ 38% ↓

(0.4 SD) 

62% ↓ Continuous exponential 

(M4) (p = 0.69) 

4.45E+2 1.95E+2 

Adequa

Cantoni et al., 1981 (rat) 

None/ 

1.02E+3 

Urinary uroporhyrins (n = 4) 

─ 2.4-fold ↑

(5.7 SD) 

87-fold ↑ Continuous exponential 

(M2) (p = 0.0003) 

2.07E+3 1.52E+3 

No respnon-convariancSD; BM
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-34 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

Urinary copro- porhyrins (n = 4) 

─ 2.4-fold ↑

(3.1 SD) 

4.0-fold ↑ Continuous exponential 

(M4) (p = 0.49) 

2.94E+2 9.93E+1 

Cantoni et al., No resp1981 (rat, continued) 

None/ 

1.02E+3 (cont.) 

 Continuous power, 

unrestricted (p = 0.61) 

1.52E+1 2.31E-6 

SupraliBMDLeffect 

Hepatocyte hypertrophy (n = 53

−54) 

0/53 19/54 52/53 Dichotomous multistage 

(p = 0.03) 

5.01E+2 4.33E+2 

Poor fit

Alveolar metaplasia (n = 52

−54) 

2/53 19/54 46/52 Dichotomous log-logistic 

(p = 0.72) 

3.58E+2 2.07E+2 

No resp

Oval cell hyperplasia(n = 53

−54) 

0/53 4/54 53/53 Dichotomous probit 

(p = 0.23) 

3.13E+3 2.64E+3 

Relativdose grintercepimplaus

Dichotomous Weibull (p = 0.08) 

3.15E+3 2.25E+3 

Margin

Gingival hyperplasia (n = 53

−54) 

1/53 7/54 16/53 Dichotomous log-logistic 

(p = 0.06) 

3.22E+3 2.25E+3 

Poor fithit; BM

Dichotomous loglogistic, unrestricted (p = 0.66) 

3.88E+2 6.95E-3 

Supraliimplaus

Eosinophilic focus, multiple (n = 53

−54) 

3/53 8/54 42/53 Dichotomous probit 

(p = 0.46) 

3.08E+3 2.68E+3 

RelativBMDL 

NTP, 2006 (rat) 

None/ 

1.41E+3 

Liver fatty change, diffuse (n = 53

−54) 

0/53 2/54 48/53 Dichotomous Weibull 

(p = 0.72) 

2.16E+3 1.57E+3 

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

1/15/10 4-35 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

Liver necrosis (n = 53

−54) 

1/53 4/54 17/53 Dichotomous log-probit, 

unrestricted (p = 0.80) 

4.13E+3 1.93E+3 

AdequaBMDL 

Liver pigmentation (n = 53

−54) 

4/53 9/54 53/53 Dichotomous log-probit 

(p = 0.96) 

1.36E+3 1.04E+3 

Adequa

NTP, 2006 (rat, continued) 

None/ 

1.41E+3 (cont.) 

toxic hepatopathy (n 

= 53

−54) 

0/53 2/54 53/53 Dichotomous multistage, 

restricted (p = 0.81) 

2.11E+3 1.70E+3 

BMDL fit 

Dichotomous loglogistic, restricted  (p = 0.79) 

1.96E+3 1.22E+3 

Adequabound h

Bell et al., 2007a (rat) 

None/ 

2.00E+3 

Balano-preputial separation in male pups (n = 30 [dams]) 

1/30 5/30 15/30 

Dichotomous loglogistic, unrestricted  (p = 0.51) 

1.81E+3 2.64E+2 

Adequa(slope = 

Uroporphyrin per creatinine, females (n = 5) 

─ 15% ↑

(0.48 SD) 

89% ↑ Continuous linear 

(p = 0.79) 

7.20E+3 5.12E+3 

BMDL fit 

Urinary coproporphyrins, females (n = 5) 

─ 67% ↑

(5.1 SD) 

78% ↑ Continuous exponential 

(M4, non-constant var) (p = 0.01) 

8.6E+2 4.0E+2 

Poor fitBMDL 

Liver lesions (n = 50) 

No data 

Kociba et al., 1978 (rat) 

8.53E+2 3.94E+3 

Lung lesions (n = 50) 

No data 

Markowski et al., 2001 (rat) 

None/ 

1.23E+3 

FR5 run opportunities (n = 4

−7) 

─ 10% ↓

(0.21 SD) 

51% ↓ Continuous Hill 

(p = 0.94) 

1.37E+3 7.21E+2 

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

1/15/10 4-36 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

Continuous power, unrestricted (p = 0.13) 

2.11E+3 8.15E-12 

Saturat= 0.39)

FR2 revolutions (n = 4

−7) 

─ 9% ↓

(0.15 SD) 

43% ↓ Continuous Hill 

 (p = 0.65) 

1.46E+3 4.76E+2 

Constrabound) 

Continuous power, unrestricted (p = 0.16) 

4.54E+3 8.15E-12 

Supraliimplaus

Markowski et al., 2001 (rat, continued) 

None/ 

1.23E+3 

(cont.) 

FR10 run opportunities (n = 4

−7) 

─ 15% ↓

(0.24 SD) 

57% ↓ Continuous exponential 

(M2) (p = 0.30) 

6.77E+3 2.28E+3 

BMDL 

DRL reinforce per min (n = 12) 

─ 55% ↑

(1.0 SD) 

80% ↑ Continuous exponential 

(M4) (p = 0.054) 

1.04E+3 4.94E+0 

Poor fitlowest 

Hojo et al., 2002 (rat) 

None/ 

1.29E+3 

DRL response per min (n = 12) 

─ 105% ↓

(2.4 SD) 

105% ↓ Continuous exponential 

(M4) (p = 0.48) 

3.02E+2 7.55E+0 

No respresponsratio >> 

Murray et al., 1979 (rat) 

6.19E+2/ 

3.24E+3 

fertility in f2 gen. (no litters) (n = 20) 

4/32 0/20 9/20 Dichotomous multistage 

(p = 0.08) 

1.50E+3 7.50E+2 

Poor fitrespons

3rd molar length in 

pups (n = 4

−8) 

─ 15% ↓

(4.2 SD) 

27% ↓ Continuous Hill, 

restricted (p < 0.01) 

2.48E+2 1.33E+2 

No respConstra

Kattainen et al., 2001 (rat) 

None/ 

1.76E+3 

 Continuous Hill, 

unrestricted (p < 0.01) 

2.01E+5 - 

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

1/15/10 4-37 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

3rd molar eruption in 

pups (n = 4

−8) 

1/16 3/17 13/19 Dichotomous loglogistic, restricted 

(p = 0.98) 

1.90E+3 1.05E+3 

Kattainen et Constraal., 2001 (rat, continued) 

None/ 

1.76E+3 

(cont.) 

Dichotomous loglogistic, unrestricted (p = 0.95) 

1.53E+3 1.46E+2 

Suprali

Cariogenic lesions in pups (n = 4

−8) 

25/42 23/29 29/32 Dichotomous loglogistic, restricted 

(p = 0.60) 

1.13E+3 4.09E+2 

Constranear macontrol 

Miettinen et al., 2006 (rat) 

None/ 

1.76E+3 

Dichotomous loglogistic, unrestricted (p = 0.73) 

3.91E+1 - 

Supralicould n

Ano-genital distance in male pups (n = 5) 

─ 12% ↓

(1.0 SD) 

17% ↓ Continuous Hill, 

restricted (p = 0.26) 

3.63E+3 8.05E+2 

Constranear ma

Ohsako et al., 2001 (rat) 

8.45E+2/ 

2.76E+3 

 Continuous Hill, 

unrestricted (p = 0.11) 

4.74E+3 4.52E+2 

Suprali

Saccharin consumed, female, (0.25%) (n = 10) 

─ 22% ↓

(0.3 SD) 

66% ↓ Continuous linear 

(p = 0.55) 

7.22E+3 4.81E+3 

BMDL model, bound 

Continuous power, unrestricted (p = NA) 

6.61E+3 2.70E+3 

Saturat= 0.74) 

Amin et al., 2000 (rat) 

None/ 

2.67E+3 

Saccharin consumed, female (0.50%) (n = 10) 

─ 49% ↓

(0.7 SD) 

80% ↓ Continuous linear 

(p = 0.06) 

8.02E+3 5.18E+3 

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

1/15/10 4-38 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

Continuous power, unrestricted (p = NA) 

5.19E+3 9.41E+2 

Saturat= 0.40) 

Saccharin preference ratio, female (0.25%) (n = 10) 

─ 29% ↓

(1.8 SD) 

33% ↓ Continuous linear 

(p = 0.002) 

9.17E+3 4.39E+3 

BMDL BMR; nLOAEL 

Saccharin preference ratio, female (0.50%) (n = 10) 

─ 39% ↓

(1.1 SD) 

54% ↓ Continuous linear 

(p = 0.14) 

6.43E+3 4.03E+3 

BMDL responsmodel, bound 

Amin et al., 2000 (continued) 

None/ 

2.67E+3 

(cont.) 

 Continuous power, 

unrestricted (p = NA) 

2.05E+3 1.26E-5 

Saturat= 0.28) 

Crofton et al., 2005 (rat) 

1.91E+3/ 

5.10E+3 

Serum T4, (n = 4

−14) 

─ 29% ↓

(1.9 SD) 

51% ↓ Continuous exponential 

(M4) (p = 0.94) 

2.86E+3 1.67E+3 

No resp

─ 9.1% ↓

(0.6 SD) 

40% ↓ Continuous Hill 

(p = 0.90) 

5.68E+3 1.98E+3 

Constraotherwi

Sewall et al., 1995 (rat) 

3.92E+3 9.16E+3 

Serum T4 (n = 9) 

 Continuous Hill, 

unrestricted (p = 0.90) 

5.35E+3 1.09E+3 

Supraliotherwi

Maze errors per block, female (n = 10) 

─ 22% ↓

(0.2 SD) 

34% ↓ Continuous linear 

(p = 0.16) 

5.53E+3 3.63E+3 

BMDL responsmodel, bound 

Schantz et al., 1996 (rat) 

None/ 

None 

 Continuous power, 

unrestricted (p = NA) 

2.02E+3 8.11E-6 

Saturat= 0.37)

Li et al., 1997 (rat) 

1.46E+2/ 

4.40E+2 

FSH in female rats (n = 10) 

─ 3.6-fold ↑

(2.0 SD) 

19-fold ↑ Continuous power, 

restricted (p < 0.01) 

1.11E+5 7.50E+4 

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

1/15/10 4-39 DRAFT: DO NOT CITE OR QUOTE Table 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (c

Study NOAEL/ LOAEL Endpoint Control response First response Max response Model fit detail BMD/ BMDL 

Hepatitic retinol (n = 8) 

─ 44% ↓

(0.74 SD) 

96% ↓ Continuous exponential 

(M4) (p < 0.01) 

1.37E+4 1.85E+3 

Poor fit 

Continuous power, unrestricted (p = 0.01) 

1.03E+5 7.92E+4 

Poor fit

Hepatitic retinyl palmitate (n = 8) 

─ 80% ↓

(1.4 SD) 

99% ↓ Continuous exponential 

(M4) (p < 0.01) 

7.79E+4 2.01E+4 

Poor fit

Van Birgelen et al., 1995ab (rat) 

None/ 

3.97E+3 

Continuous power, unrestricted (p = 0.24) 

2.90E+1 3.25E–2 

Supraliimplaus

aConverted to whole blood concentrations as described in Section 3 prior to determining HEDs in Table 4-5. bMagnitude of response at first dose where response differs from control value (in the adverse direction); continuous response magnitud

control; quantal response given as number affected/total number. 

cMagnitude of response maximally differing from control value (in the adverse direction). 

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

1/15/10 4-40 DRAFT: DO NOT CITE OR QUOTE Table 4-5. Candidate points of departure for the TCDD RfD using blood-concentration-based hum

equivalent doses Study Species, strain (sex, if not both) Protocol Endpoint NOAELHED (N) or BMDLHED (B) (ng/kg-day) LOAEL

(ng/kg-d

Li et al., 2006 Mouse, NIH (F) Gavage GD 1–3; n = 10 Hormone levels in pregnant dams (decreased progesterone, increased estradiol) - 1.6E-0Smialowicz et al., 2008 Mouse, B6C3F1 (F) 90-day gavage; n = 8–15 Decreased SRBC response - 6.4E-0Keller et al., 2007, 2008a, bb Mouse, CBA/J and C3H/HeJ Gavage GD 13; n = 23–36 (pups) Missing molars, mandibular shape changes in pups - 9.8E-0Toth et al., 1979 Mouse, Swiss/ H/Riop (M) 1-year gavage; n = 38–44 Dermal amyloidosis, skin lesions - 1.0E-0Latchoumy- candane and Mathur, 2002 Rat, Wistar (M) 45-day oral pipetting; n = 6 Decreased sperm production - 1.7E-0NTP, 1982 Mouse, B6C3F1 (M) 2-year gavage; n = 50 Liver lesions - 2.2E-0White et al., 1986 Mouse, B6C3F1 (F) 14-day gavage; n = 6–8 Decreased serum complement - 2.8E-0Li et al., 1997 Rat, S-D (F, 22 day-old) Single gavage; n = 10 Increased serum FSH 3.0E-03 (N) 1.7E-0DeCaprio et al., 1986 Guinea pig, Hartley 90-day dietary; n = 10 Decreased body weight, organ weight changes (liver, kidney, thymus, brain) 4.1E-03d (N) 3.3E-0Shi et al., 2007 Rat, S-D (F) 11-month gavage; n = 10 Decreased serum estradiol 4.7E-03 (N) 5.0E-03 (B) 2.8E-0Markowski et al., 2001 Rat, Holtzman Gavage GD 18; n = 4–7 Neurobehavioral effects in pups (running, lever press, wheel spinning) - 5.2E-0Hojo et al., 2002 Rat, S-D Gavage GD 8; n = 12 Food-reinforced operant behavior in pups - 5.5E-0Vos et al., 1973 Guinea pig, Hartley (F) 8-week gavage; n = 10 Decreased delayed-type hypersensitivity response to tuberculin 6.4E-03d (N) 3.2E-0
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-41 DRAFT: DO NOT CITE OR QUOTE 1 Table 4-5. Candidate points of departure for the TCDD RfD using blood-concentration-based hum

equivalent doses (continued) Study Species, strain (sex, if not both) Protocol Endpoint NOAELHED (N) or BMDLHED (B) (ng/kg-day) LOAEL

(ng/kg-d

Cantoni et al., 1981 Rat, CD-COBS (F) 45-week gavage; n = 4 Increased urinary porhyrins - 6.5E-0Bell et al., 2007 Rat, CRL:WI (Han) (M) 17 week dietary; n = 30 Delay in onset of puberty 7.4E-03 (B) 1.1E-0Miettinen et al., 2006 Rat, Line C Gavage GD 15; n = 3–10 Cariogenic lesions in pups - 8.9E-0Kattainen et al., 2001 Rat, Line C Gavage GD 15; n = 4–8 Inhibited molar development in pups - 9.0E-0NTP, 2006 Rat, S-D (F) 2-year gavage; n = 53 Liver and lung lesions - 1.4E-0Amin et al., 2000 Rat, S-D Gavage GD 10–16; n = 10 Reduced saccharin consumption and preference - 1.7E-0Schantz et al., 1996 Rat, S-D (F) Gavage GD 10–16; n = 10 Altered maze performance - 1.7E-0Mocarelli et al., 2008 Human (M) Childhood exposure; n = 157 Decreased sperm conc, sperm motility and increased estradiol, as adults

- 2.0E-0

Baccarelli et al., 2008

Human infants Gestational 

exposure; n = 51

Increased TSH in newborn infants - 2.4E-0

Ohsako et al., 2001 

Rat, Holtzman Gavage GD 15; 

n = 5 

Decreased ano-genital distance in male pups 2.9E-02 (N) 1.8E-0

Murray et al., 1979 

Rat, S-D 3-generation dietary Reduced fertility and neonatal survival (f 0 

and f 1) 

3.0E-02 (N) 3.9E-0

Chu et al., 2007 Rat, S-D (F) 28-day gavage, 

n = 5 

Liver lesions 3.6E-02 (N) 5.8E-0

Van Birgelen et al., 1995 

Rat, S-D (F) 13-week dietary; 

n = 8 

Decreased liver retinyl palmitate - 5.3E-0
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-42 DRAFT: DO NOT CITE OR QUOTE Table 4-5. Candidate points of departure for the TCDD RfD using blood-concentration-based hum

equivalent doses (continued) Study Species, strain (sex, if not both) Protocol Endpoint NOAELHED (N) or BMDLHED (B) (ng/kg-day) LOAEL

(ng/kg-d

Kociba et al., 1978 Rat, S-D (F) 2-year dietary; 

n = 50 

Liver and lung lesions, increased urinary porhyrins 

6.5E-02 (N) 6.5E-0

Sewall et al., 1995 

Rat, S-D (F) 30-week gavage; 

n = 9 

Decreased serum T4 5.2E-01 (N) 

7.3E-02 (B) 

1.8E+0

Fattore et al., 2000 

Rat, S-D 13-week dietary; 

n = 6 

Decreased hepatic retinol - 8.01E-

Seo et al., 1995 Rat, S-D Gavage GD 10–16; 

n = 10 

Decreased serum T4 and thymus weight 1.7E-01 (N) 9.3E-0

Crofton et al., 2005 

Rat, Long-Evans (F) 

4-day gavage; n = 4

−14 

Decreased serum T4 1.7E-01 (N) 7.6E-0

Alaluusua et al., 2004 

Human Childhood exposure; 

n = 48 

Dental defects 1.2E-01h (N) 9.3E-0

aExcept where indicated, UF

A = 3 (for dynamics), UFH = 10, UFL = 10. bResults from 3 separate studies with identical designs combined. 

cUFL = 1 (NOAEL or BMDL). dHED determined from 1st-order body burden model; no PBPK model available for guinea pigs. e Mean of peak exposure (0.0319 ng/kg-day) and average exposure over 10-year critical window (0.00802 ng/kg-day). fUFH = 3, UFL = 10. gMaternal exposure corresponding to neonatal TSH concentration exceeding 5 μU/mL. hMean of peak exposure (0.200 ng/kg-day) and average exposure over 10-year critical window (0.0335 ng/kg-day). i Mean of peak exposure (1.71 ng/kg-day) and average exposure over 10-year critical window (0.153 ng/kg-day). jUFH = 3. 

S-D = Sprague-Dawley. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-43 DRAFT: DO NOT CITE OR QUOTE Table 4-6. Qualitative analysis of the strengths and limitations/uncertainties associated with anima

possessing candidate points-of-departure for the TCDD RfD Study Strengths Limitations 

Li et al., 2006 • Female reproductive effects (i.e., early embryo loss and changes in serum progesterone and estradiol) were tested at multiple exposure times—early gestation, preimplantation, and peri- to postimplantation. 

• Relatively small sample size of mouse dams/dose 

employed (n = 10) 

StudrelevTCDreprsequobse

Smialowicz et al., 2008 

• Sheep red blood cell (SRBC) plaque forming cell 

assay is highly sensitive and reproducible across laboratories when examining TCDD 

• Small sample size of mice/dose employed (n = 8) 

• Only female mice were tested 

• Thymus and spleen weights were only other 

immune response-related endpoints tested 

Studdataimmlabo

Toth et al., 1979 • Large sample size of mice/dose employed 

• One-year exposure duration 

• Reporting of findings is terse and lacks sufficient 

detail (e.g., materials and methods, thorough description of pathological findings, etc.) 

• Only male mice were tested for amyloidosis and 

skin lesions 

Studbaseskin michum

Latchoumycandane and Mathur, 2002 

• Compared to epididymal sperm counts, the 

testicular spermatid head count provides better quantitation of acute changes in sperm production and can indicate pathology 

• Small sample size of rats/dose employed (n = 6) 

• Oral pipette administration of TCDD may be a less 

efficient dosing method than gavage 

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

1/15/10 4-44 DRAFT: DO NOT CITE OR QUOTE 1 Table 4-6. Qualitative analysis of the strengths and limitations/uncertainties associated with anima

possessing candidate points-of-departure for the TCDD RfD (continued) Study Strengths Limitations 

Keller et al. 2007, 2008a,b 

• Six different inbred mouse strains were utilized 

• Large sample size of mouse offspring/dose/strain 

evaluated 

• Low TCDD dose levels used compared to typical 

mouse studies allowed for identification of subtle sensitivity differences in presence of absence of third molars, variant molar morphology, and mandible structure in offspring 

• Unknown sample size of mouse dams/dose/strain 

employed 

• All inbred strains possessed sensitive b allele at 

the Ahr locus (i.e., a potentially resistant subpopulation was not evaluated for comparison 

purposes) 

• Morphological dental and mandibular changes 

induced by TCDD at earlier or later gestational dosing dates are unknown because of single gavage administration on GD 13 

• Difficulties breeding A/J mice led to abandonment 

of that strain in the analysis (Keller et al., 2008a, b) 

Studbaseindueffe

NTP, 1982 • Large sample size of mice and rats/dose employed 

• Comprehensive 2-year bioassay that assessed body 

weights, clinical signs, and pathological changes in multiple tissues and organs 

• Elevated background levels of hepatocellular 

tumors in untreated male mice 

• Gavage exposure was only 2 days/week 

One chroTCD

White et al., 1986 • Total hemolytic complement (CH50) is 

representative functional assay of the complete complement sequence

• Small sample size of rats/dose employed (n = 6

−8)

• Individual complement factors may be 

significantly depleted without affecting CH50 activity (only C3 is measured)

• TCDD used for dosing was of unknown purity 

Firseffe

Shi et al., 2007 • Study design evaluated TCDD effects on aging 

female reproductive system (i.e., exposure began in utero and spanned across reproductive lifespan) 

• Several female reproductive endpoints were 

evaluated, including cyclicity, endocrinology, serum hormone levels, and follicular reserves 

• Relatively small sample size of rats/dose 

employed (n = 10) 

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

1/15/10 4-45 DRAFT: DO NOT CITE OR QUOTE Table 4-6. Qualitative analysis of the strengths and limitations/uncertainties associated with anima

possessing candidate points-of-departure for the TCDD RfD (continued) Study Strengths Limitations 

Markowski et al., 2001 

• Low TCDD dose levels used allowed for subtle 

behavioral deficits to be identified in rat offspring. 

• Several training sessions on wheel apparatuses 

were extensive. 

• Neurobehavioral effects are exposure-related and 

cannot be attributed to motor or sensory deficits. 

• Unknown sample size of rat dams/dose employed. 

• Small sample size of rat offspring/dose evaluated 

(n = 4-7). 

• TCDD used for dosing was of unknown purity and 

origin. 

• Neurobehavioral effects induced by TCDD at 

earlier or later gestational dosing dates are unknown because of single gavage administration on GD 18. 

• Although BMD analysis was conducted, the model 

parameters were not constrained according to EPA guidance, so the results cannot be used. 

StudinfoneurTCDneursigndeve

DeCaprio et al., 1986 

• Subchronic oral dosing duration up to 90 days. 

• Male and female guinea pigs tested 

• Relatively small sample size of guinea pigs/dose 

employed (n = 10) 

• No histopathological analyses performed 

• TCDD used for dosing was of unknown purity 

Stan

Vos et al., 1973 • Three different animal species tested (guinea pigs, 

mice, rats) 

• Effects of TCDD tested on both cell-mediated and 

humoral immunity 

• Small sample size of animals/dose employed in 

each experiment (n = 5

−10) 

• Only female guinea pigs and rats were tested, and 

only male mice were tested 

• Only one experimental assay was utilized to assess 

cell-mediated and humoral immunity in each animal species; humoral immunity was only investigated in guinea pigs 

• TCDD used for dosing was of unknown purity 

Earlimmlabo

Cantoni et al., 1981 

• Experiments were designed to test qualitative and 

quantitative composition and the course of urinary excretion in TCDD-induced porphyria 

• Small sample size of rats/dose employed (n = 4) 

• Concurrent histological changes with tissue 

porphyrin levels were not examined 

• TCDD used for dosing was of unknown purity 

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

1/15/10 4-46 DRAFT: DO NOT CITE OR QUOTE Table 4-6. Qualitative analysis of the strengths and limitations/uncertainties associated with anima

possessing candidate points-of-departure for the TCDD RfD (continued) Study Strengths Limitations 

Bell et al., 2007 • Large sample size of both rat dams and offspring/dose employed 

• Several developmental effects tested 

• Batch-to-batch variation of up to 30% in TCDD 

concentration in the diet 

• Chronic dosing of dams does not accurately define 

gestational period when fetus is especially sensitive to TCDD-induced toxicity 

Studdatatoxianim

Hojo et al., 2002 • Low TCDD dose levels used allowed for subtle 

behavioral deficits to be identified in rat offspring. 

• Preliminary training sessions in operant chamber 

apparatuses were extensive. 

• Neurobehavioral effects are exposure-related and 

cannot be attributed to presence of learning or discrimination deficits. 

• Relatively small sample size of rat dams/dose 

employed (n = 12). 

• Small sample size of rat offspring/dose evaluated 

(n = 5-6). 

• Neurobehavioral effects induced by TCDD at 

earlier or later gestational dosing dates are unknown because of single gavage administration on GD 8. 

• Although BMD analysis was conducted, the 

model parameters were not constrained according to EPA guidance, so the results cannot be used. 

StudinfoneurTCDneursigndeve

NTP, 2006 • Chronic exposure duration with several interim 

sacrifices 

• Large number of dose groups with close spacing 

• Large number of animals per dose group 

• Comprehensive suite of endpoints evaluated 

• Comprehensive biochemical, clinical and 

histopathological tests and measures 

• Detailed reporting of results, with individual 

animal data presented as well as group summaries 

• Evaluation of background exposure to TCDD and 

DLCs in feed 

• Single species, strain and sex 

• Lowest dose tested too high for establishing 

NOAEL 

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

1/15/10 DRAFT 4-47 —DO NOT CITE OR QUOTE 

1 Table 4-7. Basis and derivation of the TCDD reference dose 

2 

Principal study detail 

Study POD (ng/kg-day) Critical effects 

Mocarelli et al., 2008 0.020 (LOAEL) Decreased sperm count (20%) and motility (11%) 

and decreased estradiol (23%) in men exposed to 

TCDD during childhood 

Baccarelli et al., 2008 0.024 (LOAEL) Elevated TSH (> 5 μU/mL) in neonates 

RfD derivation 

POD 0.020 ng/kg-day (2.0E-8 mg/kg-day) 

UF 30 (UFL = 10, UFH = 3) 

RfD 7 × 10-10 (7E-10) mg/kg-day (2.0E-8 ÷ 30) 

Uncertainty factors 

LOAEL-to-NOAEL 

(UFL) 

10 No NOAEL established; cannot quantify lower exposure 

group in Baccarelli et al. (2008); magnitude of effects at 

LOAEL sufficient to require a 10-fold factor. 

Human interindividual 

variability 

(UFH) 

3 A factor of 3 (100.5) is used because the effects were 

elicited in sensitive populations. A further reduction to 

1 was not made because the sample sizes were relatively 

small, which, combined with uncertainty in exposure 

estimation, may not fully capture the range of 

interindividual variability. 

Interspecies extrapolation 

(UFA) 

1 Human study. 

Subchronic-to-chronic 

(UFS) 

1 Chronic effect levels are not well defined for humans; 

however, animal bioassays indicate that developmental 

effects are the most sensitive, occurring at doses lower 

than other effects noted in chronic studies. Considering 

that exposure in the principal studies encompasses the 

critical window of susceptibility associated with 

development, an UF to account for exposure duration is 

not warranted. 

Database sufficiency 

(UFD) 

1 The database for TCDD contains an extensive range of 

human and animal studies that examine a 

comprehensive set of endpoints. There is no evidence to 

suggest that additional data would result in a lower 

reference dose. 

3 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-48 —DO NOT CITE OR QUOTE 

1 

2 Figure 4-1. EPA’s process to select and identify candidate PODs from key 

3 epidemiologic studies for use in the noncancer risk assessment of TCDD. For 

4 each noncancer study that qualified for TCDD dose-response assessment using 

5 the study inclusion criteria, EPA first evaluated the dose-response information 

6 developed by the study authors for whether the study provided noncancer effects 

7 and TCDD dose data for a toxicologically relevant endpoint. If such data were 

8 available, then EPA identified a NOAEL or LOAEL as a candidate POD. Then, 

9 EPA used a human kinetic model to estimate the continuous oral daily intake 

10 (ng/kg-day) for the candidate POD that could be used in the derivation of an RfD 

11 based on the study data. If all of this information was available, then the result 

12 was included as a candidate POD. 

List of key noncancer epidemiologic studies for 

quantitative dose-response analysis of TCDD

Does the 

study provide data 

on noncancer effects and TCDD

dose for determining a POD on

a toxicologically relevant 

endpoint? 

Exclude study from 

POD candidate estimation Include as POD candidate

No

Yes

Identify a study NOAEL or LOAEL 

as a candidate POD

Use kinetic model to estimate 

continuous oral daily intake (ng/kg-day)

in the affected study population

List of key noncancer epidemiologic studies for 

quantitative dose-response analysis of TCDD

Does the 

study provide data 

on noncancer effects and TCDD

dose for determining a POD on

a toxicologically relevant 

endpoint? 

Exclude study from 

POD candidate estimation Include as POD candidate

No

Yes

Identify a study NOAEL or LOAEL 

as a candidate POD

Use kinetic model to estimate 

continuous oral daily intake (ng/kg-day)

in the affected study population
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 4-49 —DO NOT CITE OR QUOTE 

1 

2 Figure 4-2. EPA’s process to select and identify candidate PODs from key 

3 animal bioassays for use in noncancer dose-response analysis of TCDD. For 

4 each noncancer endpoint found in the studies that qualified for TCDD dose-response assessment 

5 using the study inclusion criteria, EPA attempted to estimate six candidate PODs, i.e., estimates of 

6 a NOAEL, LOAEL, and BMDL using administered average daily doses (ADD) and using blood 

7 concentrations from the animal kinetic model. Benchmark dose modeling was not always possible 

8 due to poor model fits. Then for all of the candidate PODs that were estimated, HEDs were 

9 estimated using the human kinetic model. Next, the toxicological relevance of each candidate 

10 POD was evaluated relative to its usefulness in human health risk assessment. Finally, the lowest 

11 group of the toxicologically relevant candidate PODs are selected for final use in derivation of an 

12 RfD. 

Include NOAEL/LOAEL/BMDL 

as a POD candidate

Exclude endpoint 

as a POD candidate

List of key noncancer animal studies for 

quantitative dose-response analysis of TCDD

No

Determine NOAEL, LOAEL, and BMDL (if possible) human equivalent dose 

(HED) based on 1st-order body burden for each study/endpoint combination

Estimate a Human Equivalent Dose (HED) 

corresponding to each blood concentration NOAEL, LOAEL, or BMDL 

using the Emond human PBPK model

Is the endpoint less 

than the minimum 

LOAEL X 100?

Determine a NOAEL, LOAEL, and BMDL (if possible) for each 

study/endpoint combination, based on blood concentrations from the 

Emond rodent PBPK model

Is the 

endpoint observed

at the LOAEL?

No

Yes

Is the 

endpoint under consideration 

toxicologically

relevant?

Is the BMDL less 

No than the LOAEL?

Yes

Yes

No

Include NOAEL/LOAEL/BMDL 

as a POD candidate

Exclude endpoint 

as a POD candidate

List of key noncancer animal studies for 

quantitative dose-response analysis of TCDD

No

Determine NOAEL, LOAEL, and BMDL (if possible) human equivalent dose 

(HED) based on 1st-order body burden for each study/endpoint combination

Estimate a Human Equivalent Dose (HED) 

corresponding to each blood concentration NOAEL, LOAEL, or BMDL 

using the Emond human PBPK model

Is the endpoint less 

than the minimum 

LOAEL X 100?

Determine a NOAEL, LOAEL, and BMDL (if possible) for each 

study/endpoint combination, based on blood concentrations from the 

Emond rodent PBPK model

Is the 

endpoint observed

at the LOAEL?

No

Yes

Is the 

endpoint under consideration 

toxicologically

relevant?

Is the BMDL less 

No than the LOAEL?

Yes

Yes

No
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-50 DRAFT: DO NOT CITE OR QUOTE 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02

Alaluusua et al., 2004 (H)

Baccarelli et al., 2008 (H)

Moccarelli et al., 2008 (H)

Li et al., 1997 (R)

Crofton et al., 2005 (R)

White et al., 1986 (M)

Chu et al., 2007 (R)

Fattore et al., 2000 (R)

Seo et al., 1995 (R)

Smialowicz et al., 2008 (M)

Van Birgelen et al., 1995a (R)

Sewall et al., 1995 (R)

Cantoni et al., 1981 (R)

NTP, 1982 (M)

Kociba et al., 1978 (R)

Toth et al., 1979 (M)

NTP, 2006 (R)

Latch. and Mathur, 2002 (R)

Shi et al., 2007 (R)

Murray et al., 1979 (R)

Hojo et al., 2002 (R)

Kattainen et al., 2001 (R)

Keller et al., 2007, 2008a,b (M)

Markowski et al., 2001 (R)

Miettinen et al., 2006 (R)

2,3,7,8-TCDD Ingestion Rate

(Human-equivalent dose, ng/kg-d)

Less than 

1 year

Greater than 

Human 1 year Develo

 Reproductive 

Figure 4-3. Exposure-response array for ingestion exposures to TCDD. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 4-51 DRAFT: DO NOT CITE OR QUOTE 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06

Moccarelli et al. 2008

Baccarelli et al. 2008

Alaluusua et al. 2004

Li et al. 2006

Smialowicz et al. 2008

Keller et al. 2007, 2008a,b

Toth et al. 1979

Latchoumycandane & Mathur 2002

NTP 1982

White et al. 1986

Li et al. 1997

DeCaprio et al. 1986

Shi et al. 2007

Markowski et al. 2001

Hojo et al. 2002

Vos et al. 1973

Cantoni et al. 1981

Bell et al. 2007

Miettinen et al. 2006

Kattainen et al. 2001

NTP 2006

Amin et al. 2000

Schantz et al. 1995

Ohsako et al. 2001

Murray et al. 1979

Chu et al. 2007

Van Birgelen et al. 1995a

Kociba et al

Sewall 

F

Oral Exposure (mg/kg-day)

UFL UfH UFA RfDPoin

Human

Animal Bioassays

Figure 4-4. Candidate RfD array. 

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

1/15/10 DRAFT 5-1 —DO NOT CITE OR QUOTE 

1 5. CANCER ASSESSMENT 

2 

3 

4 5.1. QUALITATIVE WEIGHT-OF-EVIDENCE CARCINOGEN CLASSIFICATION 

5 FOR 2,3,7,8-TETRACHLORODIBENZO-p-DIOXIN (TCDD) 

6 5.1.1. Summary of National Academy of Sciences (NAS) Comments on the Qualitative 

7 Weight-of-Evidence Carcinogen Classification for 2,3,7,8-Tetrachlorodibenzo8 p-Dioxin (TCDD) 

9 In its charge, the National Academy of Scinces (NAS) was requested to comment 

10 specifically on U.S. Environmental Protection Agency (EPA)’s conclusion that 

11 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is best characterized as “carcinogenic to humans.” 

12 While indicating that distinction between the categories of “carcinogenic to humans” and “likely 

13 to be carcinogenic to humans” is “...based more on semantics than on science...” 

14 (NAS, 2006a, p. 141) and recommending that EPA “...spend its energies and resources on more 

15 carefully delineating the assumptions used in quantitative risk estimates for TCDD...” 

16 (NAS, 2006a, p. 141) rather than on the qualitative cancer descriptor for TCDD, the NAS 

17 provided the following comments: 

18 

19 ...the classification of dioxin as “carcinogenic to humans” versus “likely to be 

20 carcinogenic to humans” depends greatly on the definition and interpretation of 

21 the specific criteria used for classification, with the explicit recognition that the 

22 true weight of evidence lies on a continuum with no bright line that easily 

23 distinguishes between these two categories. The committee agreed that, although 

24 the weight of epidemiological evidence that dioxin is a human carcinogen is not 

25 strong, the human data available from occupational cohorts are consistent with a 

26 modest positive association between relatively high body burdens of dioxin and 

27 increased mortality from all cancers. Positive animal studies and mechanistic data 

28 provide additional support for classification of dioxin as a human carcinogen. 

29 However, the committee was split on whether the weight of evidence met all the 

30 necessary criteria described in the cancer guidelines for classification of dioxin as 

31 “carcinogenic to humans.” EPA should summarize its rationale for concluding 

32 that dioxin satisfies the criteria set out in the most recent cancer guidelines for 

33 designation as either “carcinogenic to humans” or “likely to be carcinogenic to 

34 humans (NAS, 2006a, p. 140). 

35 

36 If EPA continues to designate dioxin as “carcinogenic to humans,” it should 

37 explain whether this conclusion reflects a finding that there is a strong association 

38 between dioxin exposure and human cancer or between dioxin exposure and a key 

39 precursor event of dioxin’s mode of action (presumably AhR binding). If EPA’s 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 finding reflects the latter association, EPA should explain why that end point 

2 (e.g., AhR binding) represents a “key precursor event (NAS, 2006a, p. 141). 

3 

4 5.1.2. EPA’s Response to the NAS Comments on the Qualitative Weight-of-Evidence 

5 Carcinogen Classification for TCDD 

6 A cancer descriptor is used to express the conclusion of the weight of evidence regarding 

7 the carcinogenic hazard potential of a compound. EPA agrees with the NAS committee that 

8 cancer descriptors represent points along a continuum of evidence. Relatedly, EPA 

9 acknowledges that there are gradations and borderline situations that cannot be communicated 

10 through a descriptor and are best clarified by a full weight of evidence narrative. 

11 The 2003 Reassessment contains a detailed discussion of TCDD carcinogenicity in both 

12 humans (Part II, Chapter 7a; 8) and animals (Part II, Chapter 6; 8) as well as an overall summary 

13 of TCDD carcinogenicity (Part III, Chapter 2.2.1). Since the release of the 2003 Reassessment, 

14 the database pertaining to TCDD carcinogenicity has been strengthened and expanded by 

15 numerous publications (see U.S. EPA, 2008b), including a new chronic bioassay in female rats 

16 (NTP, 2006) and several new follow-up epidemiological investigations (see Section 2.4.1 and 

17 references therein). Many of these studies have been published subsequent to the NAS review. 

18 These new data are summarized and evaluated in Section 2.4 of this document. 

19 As noted by the NAS, the 2003 Reassessment was released prior to EPA’s publication of 

20 the U.S. EPA Guidelines for Carcinogen Risk Assessment (“2005 Cancer Guidelines”; U.S. EPA, 

21 2005). Using EPA’s guidance at the time of its release (U.S. EPA, 1996a), the 2003 

22 Reassessment determined that the available evidence was sufficient to classify TCDD as a 

23 “human carcinogen.” The 1996 guidance suggested “human carcinogen” to be an appropriate 

24 descriptor of carcinogenic potential when there is an absence of conclusive epidemiologic 

25 evidence to clearly establish a cause-and-effect relationship between human exposure and 

26 cancer, but there are compelling carcinogenicity data in animals and mechanistic information in 

27 animals and humans demonstrating similar modes of carcinogenic action. 

28 The 2005 Cancer Guidelines (U.S. EPA, 2005) are intended to promote greater use of the 

29 increasing scientific understanding of the mechanisms that underlie the carcinogenic process. 

30 The 2005 Cancer Guidelines expand upon earlier guidance applied in the 2003 Reassessment and 

31 encourage the use of chemical- and site-specific data versus default options, the consideration of 

32 mode of action information and understanding of biological changes, fuller characterization of 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-3 —DO NOT CITE OR QUOTE 

1 carcinogenic potential, and consideration of differences in susceptibility. The 2005 Cancer 

2 Guidelines also emphasize the importance of weighing all of available evidence in reaching 

3 conclusions about the human carcinogenic potential of an agent. As noted above, additional 

4 information on TCDD carcinogenicity has been published since the release of the 2003 

5 Reassessment. This information has expanded the TCDD database and provided additional 

6 support for conclusions made in the 2003 Reassessment regarding the carcinogenic potential of 

7 TCDD. 

8 Under the 2005 Cancer Guidelines (U.S. EPA, 2005), TCDD is characterized as 

9 carcinogenic to humans, based on the available data as of 2009. The 2005 Cancer Guidelines 

10 indicate that this descriptor is appropriate when there is convincing epidemiologic evidence of a 

11 causal association between human exposure and cancer or when all of the following conditions 

12 are met (a) there is strong evidence of an association between human exposure and either cancer 

13 or the key precursor events of the agent’s mode of action, but not enough for a causal 

14 association, and (b) there is extensive evidence of carcinogenicity in animals, and (c) the mode(s) 

15 of carcinogenic action and associated key precursor events have been identified in animals, and 

16 (d) there is strong evidence that the key precursor events that precede the cancer response in 

17 animals are anticipated to occur in humans and progress to tumors, based on available biological 

18 information. 

19 As noted above, the NAS commented that EPA should “...explain whether this 

20 conclusion reflects a finding that there is a strong association between dioxin exposure and 

21 human cancer or between dioxin exposure and a key precursor event of dioxin’s mode of action 

22 (presumably AhR binding)” (NAS, 2006a). When evaluating the carcinogenic potential of a 

23 compound, EPA employs a weight of evidence approach in which all available information is 

24 evaluated and considered in reaching a conclusion. The following sections provide a summary 

25 of EPA’s weight of evidence evaluation for TCDD. 

26 

27 5.1.2.1. Summary Evaluation of Epidemiologic Evidence of TCDD and Cancer 

28 The available occupational epidemiologic studies provide convincing evidence of an 

29 association between TCDD exposure and all cancer mortality. Among the strongest of these are 

30 the studies of over 5,000 U.S. chemical manufacturing workers (the National Institute for 

31 Occupational Safety and Health [NIOSH] cohort) (Fingerhut et al., 1991; Steenland et al., 1999, 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 2001; Aylward et al., 1996; Cheng et al., 2006; Collins et al., 2009); a study of nearly 

2 2,500 German workers involved in the production of phenoxy herbicides and chlorophenols (the 

3 Hamburg cohort) (Becher et al., 1996, 1998; Manz et al., 1991; Nagel et al., 1994; Flesch-Janys 

4 et al., 1995, 1998); a study of more than 2,000 Dutch workers in two plants involved in the 

5 synthesis and formulation of phenoxy herbicides and chlorophenols (the Dutch cohort) (Bueno 

6 de Mesquita et al., 1993; Hooiveld et al., 1998); a smaller study of roughly 250 workers involved 

7 in a chemical accident cleanup (the BASF cohort) (Thiess et al., 1982; Zober et al., 1990; Ott and 

8 Zober, 1996); and an international study of more than 18,000 workers exposed to phenoxy 

9 herbicides and chlorophenols (Saracci et al., 1991; Kogevinas et al., 1997) including newer 

10 studies of smaller subsets of these workers (t’Mannetje et al., 2005; McBride et al., 2009a, b). 

11 The findings from these studies have been thoroughly described either in the 2003 Reassessment 

12 or in Section 2.4.1 of this document. 

13 As noted in Section 2.4, there are considerable challenges inherent in addressing potential 

14 sources of confounding from smoking and co-exposure to other carcinogens, (which could 

15 produce inflated or spurious associations), the healthy worker effect, (which could result in 

16 attenuated effects through comparison with a referent background with an inappropriately high 

17 background risk), and quantifying exposure to the populations included in many of these 

18 retrospective studies. The more recent studies of these cohorts have made significant advances 

19 in reducing the potential for bias from the healthy worker effect through use of internal cohort 

20 analyses and/or controlling for potential confounders through statistical adjustment, restriction, 

21 and use of internal comparisons. Although some exposure assessment uncertainties remain, 

22 some of these studies have also collected individual-level TCDD exposure estimates that allow 

23 quantification of effective dose necessary for dose-response modeling. Overall, the occupational 

24 data provide consistent support for an association between exposure to TCDD and increased 

25 cancer mortality. 

26 Additional epidemiologic evidence supporting an association between TCDD exposure 

27 and cancer comes from studies investigating the morbidity and mortality of residents exposed to 

28 TCDD following an accidental release from a chemical plant near Seveso, Italy (the Seveso 

29 cohort) (Bertazzi et al., 1989, 1993, 1997, 2001; Pesatori et al., 1998, 2003; Consonni et al., 

30 2008; Warner et al., 2002). Pesatori et al. (2003) and Consonni et al. (2008) were not available 

31 at the time the 2003 Reassessment was released. Among individuals with relatively high 
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1/15/10 DRAFT 5-5 —DO NOT CITE OR QUOTE 

1 exposure at Seveso (Zones A and B combined), all-cancer mortality in the 20-year post-accident 

2 period and all-cancer incidence in the 15-year post-accident period failed to exhibit significant 

3 departures from the expected (Bertazzi et al., 2001; Pesatori et al., 2003; Consonni et al., 2008). 

4 However, an increased risk of all-cancer mortality was noted among men 15−20 years after first 

5 exposure; not only is the association similar in magnitude to other studies (relative risk 

6 [RR] = 1.3; 95% confidence interval [CI] = 1.0−1.7) but also emphasizes the importance of 

7 consideration of latency (Bertazzi et al., 2001). Furthermore, associations between TCDD and 

8 some specific cancer sites were detected in this cohort, including increased incidence (based on 

9 15 years of follow-up) and mortality (based on 20 years follow-up) from lymphatic and 

10 hematopoietic neoplasms in both males and females from Zones A and B (Consonni et al., 2008). 

11 This excess was primarily due to non-Hodgkin’s lymphoma. Additionally, there was an increase 

12 in lung and rectal cancer mortality in men (Bertazzi et al., 2001) and limited evidence of 

13 increased liver cancer incidence in women based on the 15-year follow-up study (Bertazzi et al., 

14 1993). In a separate analysis of 981 women in Zone A, breast cancer incidence (n = 15) was 

15 associated (a 2-fold increase for a 10-fold increase in serum TCDD) with TCDD measurements 

16 first collected in 1976 and 1977 (Warner et al., 2002). The authors also reported a 2−3-fold 

17 increase in all cancer incidence (n = 21) for the two upper quartiles of TCDD exposure. 

18 Overall, the newer studies of the Seveso cohort have reported significant increases in 

19 cancer incidence and elevations in cancer mortality that were not evident in earlier studies of this 

20 cohort. While these studies demonstrate an association between TCDD exposure and different 

21 types of cancer, one of the main limitations is the small number of cancer cases to assess 

22 site-specific associations with TCDD exposure. Ongoing studies in that cohort should help 

23 further elucidate potential risk for specific cancer types (and other endpoints) associated with 

24 TCDD exposures among this population. 

25 

26 5.1.2.1.1. Evidence for causality. 

27 The evidence for causality for cancer from the human studies is briefly summarized in the 

28 paragraphs that follow and is based on recommendations from the 2005 Cancer Guidelines. It 

29 should be noted that there are methodological limitations of the epidemiologic studies that may 

30 temper some of the conclusions regarding causality. These limitations include limited statistical 

31 power, exposure assessment uncertainty, and lack of control of confounders (e.g., dioxin-like 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-6 —DO NOT CITE OR QUOTE 

1 compounds and smoking) in some studies. There is additional uncertainty regarding lack of 

2 organ specificity in TCDD associated cancers, as the most consistent results occurred for 

3 all-cancer mortality. Despite these uncertainties, many of the more recent studies have greatly 

4 improved exposure assessments compared to earlier studies of the same cohorts and have 

5 addressed the potential for confounding and other types of biases. 

6 Temporality—exposure must precede the effect for causal inference. Given the long 

7 induction period for many types of cancers, exposure must precede the effect with a sufficient 

8 latency (i.e., typically 15−20 years for environmental carcinogens). In all the occupational 

9 studies reviewed (with the exception of McBride et al., 2009a), TCDD exposure has preceded 

10 the effect with sufficient latency to be considered causally associated. In the studies of the 

11 Seveso cohort, the follow-up exposure period has now reached 20 years, a latency sufficient to 

12 address some carcinogenic endpoints. Since most of the studies are based on occupational 

13 exposures or accidental releases into the environment, temporality is more readily established 

14 due to the obvious determination of the specific exposure windows prior to disease onset. 

15 Strength of Association—refers to the magnitude of measures of association such as the 

16 ratio of incidence or mortality (e.g., standardized mortality ratio [SMRs], standardized incidence 

17 ratios, RRs, or odds ratios) irrespective of statistical significance. Effect estimates that are large 

18 in magnitude are less likely to be due to chance, bias, or confounding. Reports of modest risk, 

19 however, do not preclude a causal association and may reflect an agent of lower potency, lower 

20 levels of exposure or attenuation due to nondifferential exposure misclassification. The four 

21 occupational cohorts with the highest exposures (NIOSH, Hamburg, Dutch, and BASF) 

22 consistently showed statistically significant, although moderate, elevations in cancer mortality. 

23 When the data were combined, the SMR for all four subcohorts was 1.4 [95% CI = 1.2−1.6] 

24 (IARC, 1997). Based on findings from the International Agency for Research on Cancer (IARC) 

25 Working Group, increases in all cancer (combined) mortality of the magnitude reported for 

26 TCDD have rarely been found in occupational cohort studies (IARC, 1997). Although these 

27 estimates are higher than the all-cancer mortality results among Seveso men (RR = 1.1; 

28 95% CI = 1.0−1.3), they are comparable to the risk estimated in this population (RR = 1.3; 

29 95% CI = 1.0-1.7) 15−20 years after first exposure. These consistent results comparable in 

30 magnitude from the occupational cohorts and Seveso population are not likely due to chance. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-7 —DO NOT CITE OR QUOTE 

1 The occupational cohort studies also show an increased risk for lung cancer in the 

2 previously mentioned four subcohorts. The relative risk for lung cancer in the combined highly 

3 exposed subcohorts was estimated to be 1.4 (95% CI = 1.1−1.7) (IARC, 1997). This is 

4 consistent with the lung cancer mortality findings for the highest exposed group of men in 

5 Seveso (RR = 1.3; 95% CI = 1.0−1.7). Additionally, there was an increase in rectal cancer 

6 mortality in the Seveso cohort (RR = 2.4; 95% CI = 1.2−4.6) (Bertazzi et al., 2001) with a 

7 corresponding increase in incidence. Consistent relative risks of more than two were also 

8 detected for rectal cancer in the Hamburg and New Zealand cohorts, but increased risks were not 

9 found in the other cohorts. Although there was limited evidence of increased incidence or 

10 morality from hepatobiliary cancers across the cohorts, liver cancer incidence was elevated in the 

11 15-year postaccident period among women in the Seveso cohort (RR = 2.4; 95% CI = 1.1−5.1, 

12 Warner et al., 2002). An association in this population was also detected for between breast 

13 cancer incidence (RR = 2.1; 95% CI = 1.0–4.6) and serum TCDD levels (per a 10-fold increase 

14 in serum TCDD). Although findings were based on small numbers, three- and four-fold 

15 increased risks of soft tissue sarcoma were detected among the NIOSH (Collins et al., 2009) and 

16 New Zealand cohorts (McBride et al., 2009a). No other cases of this very rare cancer were 

17 detected in the exposed populations from the other cohorts. 

18 Elevated risk of lymphohemopoietic cancer mortality was noted among the Seveso cohort 

19 (RR = 1.7; 95% CI = 1.2, 2.5) (Consonni et al., 2008). Increased SMRs for lymphohemopoietic 

20 cancer comparable in magnitude (range: 1.6−2.2) were also detected among the Hamburg and 

21 New Zealand occupational cohorts, but limited evidence (range: 1.0 to 1.2) of increased 

22 mortality was found in the BASF, NIOSH and Ranch Hands employees (Ott and Zober, 1996; 

23 Steenland et al., 1999; Akhtar et al., 2004). Most of the lymphohemopoietic cancer mortality 

24 risk was reportedly due to non-Hodgkin’s lymphoma in most of the cohorts. Relative risks for 

25 non-Hodgkin’s lymphoma among TCDD exposed populations from the NIOSH, Hamburg, New 

26 Zealand, Dutch, and Seveso cohorts ranged from 1.2 to 3.8. Although statistical power was 

27 limited in most of these studies, relative risks exceeded 3.0 for non-Hodgkin’s lymphoma in 

28 three of these cohorts (Flesch-Janys et al., 1998; Consonni et al., 2008; Hooiveld et al., 1998). 

29 Consistency—the observation of the same site-specific effect across several independent 

30 study populations strengthens an inference of causality. Despite differences across occupational 

31 cohorts, most studies have consistently reported increases in all-cancer mortality with TCDD 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-8 —DO NOT CITE OR QUOTE 

1 exposure. Several of these studies have also reported increases in lung cancer related to TCDD 

2 exposure. As noted above, there is also suggestive evidence of an increased risk in all-cancer 

3 and lung cancer mortality among the Seveso cohort consistent in magnitude to the occupational 

4 cohorts. Elevated risk of lymphohemopoietic cancer mortality consistent in magnitude 

5 (range: 1.6−2.2) was also detected among the Seveso, Hamburg and New Zealand cohorts. An 

6 increased risk for non-Hodgkin’s lymphoma was found in two of the occupational cohorts as 

7 well as in the Seveso cohort, although the relative risks largely did not achieve statistical 

8 significance. Among those studies detecting an association, consistent two-fold relative risks 

9 were found for rectal cancer (McBride et al., 2009a; Flesch-Janys et al., 1998; Bertazzi et al., 

10 2001) and relative risks in excess of three were detected for soft tissue sarcoma (McBride et al., 

11 2009a; Collins et al., 2009). 

12 Biological Gradient—refers to the presence of a dose-response and/or duration-response 

13 between a health outcome and exposure of interest. Several of the occupational cohort studies 

14 (Ott and Zober, 1996; Flesch-Janys et al., 1998; Manz et al., 1991; Steenland et al., 1999; 

15 Michalek and Pavuk, 2008) found evidence of a dose-response relationship for all cancers and 

16 various TCDD exposure measures. The SMR analyses based on internal comparisons within the 

17 occupational cohorts show a biological gradient by comparing highly TCDD exposed workers to 

18 low or unexposed workers. A biological gradient was also demonstrated in the Seveso cohort by 

19 comparing highly exposed individuals (Zones A and B) to individuals in lower exposure zones 

20 (Zones C and R). Warner et al. (2002) also reported evidence of a dose-response trend for breast 

21 cancer and increasing TCDD exposures. 

22 Biological Plausibility—refers to the observed effect having some biological link to the 

23 exposure. Most evidence suggests that toxic effects of TCDD are mediated by interaction with 

24 the aryl hydrocarbon receptor (AhR). AhR is a highly conserved protein among mammals, 

25 including humans (Fujii-Kuriyama et al., 1995; Harper et al., 2002; Nebert et al., 1991). Several 

26 hypothesized modes of action have been presented for TCDD-induced tumors in rodents, all 

27 involving AhR activation. The available evidence does not preclude the relevance of these 

28 hypothesized modes of action to humans. 

29 Specificity—as originally intended, this refers to increased inference of causation if a 

30 single site effect, as opposed to multiple effects, is observed and associated with exposure. 

31 Based on current biological understanding, this is now considered one of the weaker guidelines 
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1/15/10 DRAFT 5-9 —DO NOT CITE OR QUOTE 

1 for causality. As stated in the 2005 Cancer Guidelines, given the current understanding that 

2 many agents cause cancer at multiple sites, and cancers have multiple causes, the absence of 

3 specificity does not detract from evidence for a causal effect. Given that the most consistent 

4 findings associating TCDD and cancer are for all-cause cancer mortality, epidemiological 

5 evidence suggests that TCDD lacks specificity for particular tumor sites. A key event in 

6 TCDD’s mechanism of action is binding to and activating AhR; however, downstream events 

7 leading to tumor formation are uncertain and may likely be tissue specific. Given that the AhR is 

8 highly conserved within species and is expressed in various human tissues, the lack of tumor site 

9 specificity does not preclude a determination of causality. 

10 In summary, EPA finds the available epidemiological information provides strong 

11 evidence of an association between TCDD exposure and human cancer that cannot be reasonably 

12 attributed to chance or confounding and other types of bias, and with a demonstration of 

13 temporality, strength of association, consistency, biological plausibility, and a biological 

14 gradient. Additional evidence from animal studies and from mechanistic studies (described 

15 below) provides additional support for the classification of TCDD as carcinogenic to humans. 

16 

17 5.1.2.2. Summary of Evidence for TCDD Carcinogenicity in Experimental Animals 

18 An extensive database on the carcinogenicity of TCDD in experimental animals is 

19 described in detail in Part II, Chapter 6 of the 2003 Reassessment. There is substantial evidence 

20 that TCDD is carcinogenic in experimental animals based on long-term bioassays conducted in 

21 both sexes of rats and mice (Kociba et al., 1978; NTP, 1982, 2006) and in male hamsters (Rao et 

22 al., 1988). Additionally, National Toxicology Program (NTP, 2006) has completed a new 

23 chronic bioassay in female Sprague Dawley rats. These studies are summarized in Section 2.4.2 

24 of this document. All studies have produced positive results, with TCDD increasing the 

25 incidence of tumors at sites distant from the site of treatment and at doses well below the 

26 maximum tolerated dose. In both sexes of rodents, when administered by different routes and at 

27 low doses, TCDD caused tumors at multiple sites; tumors were observed in liver, lung, 

28 lymphatic system, soft tissue, nasal turbinates, hard palate, thyroid, adrenal, pancreas, and 

29 tongue. The most consistent and best characterized carcinogenic responses to TCDD are in the 

30 rodent liver, lung, and thyroid (discussed below in Section 5.1.2.3). 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-10 —DO NOT CITE OR QUOTE 

1 5.1.2.3. TCDD Mode of Action 

2 The 2005 Cancer Guidelines defines the term “mode of action” as “a sequence of key 

3 events and processes, starting with interaction of an agent with a cell, proceeding through 

4 operational and anatomical changes, and resulting in cancer formation.” A “key event” is an 

5 empirically observable precursor step that is itself a necessary element of the mode of action or is 

6 a biologically based marker for such an element. Mode of action is contrasted with “mechanism 

7 of action,” which implies a more detailed understanding and description of events, often at the 

8 molecular level. In the case of TCDD, the terms ‘mechanism of action’ and ‘mode of action’ are 

9 often used interchangeably in the scientific literature in reference to TCDD’s interaction with the 

10 AhR. A thorough discussion of TCDD’s interaction with the AhR can be found in the 2003 

11 Reassessment (Part II, Chapter 2; Part III, Chapter 3), and is summarized below (see 

12 Section 5.1.2.3.1). 

13 Most evidence suggests that the majority of toxic effects of TCDD are mediated by 

14 interaction with the AhR. EPA considers interaction with the AhR to be a necessary, but not 

15 sufficient, event in TCDD carcinogenesis. The sequence of key events following binding of 

16 TCDD to the AhR and that ultimately leads to the development of cancer is unknown. 

17 Therefore, in the strictest sense, TCDD’s interaction with the AhR does not constitute a “mode 

18 of action” as defined by the 2005 Cancer Guidelines because information about the progression 

19 of necessary events is lacking. However, AhR binding and activation by TCDD is considered to 

20 be a key event in TCDD carcinogenesis. 

21 

22 5.1.2.3.1. The aryl hydrocarbon receptor (AhR). 

23 While substantial evidence suggests that most toxic effects of TCDD are mediated by 

24 interaction with the AhR, less is known about the complex responses that result in tumor 

25 formation. Nonetheless, a picture is emerging wherein TCDD is considered a 

26 “receptor-mediated carcinogen” in laboratory animals (see Figure 5-1), acting in a manner 

27 similar to peroxisome proliferators, phorbol esters, or estrogen (Woods et al., 2007). 

28 The mechanism of action of TCDD has been extensively studied. TCDD activates the 

29 AhR, a member of the basic helix-loop-helix, Per-Arnt-Sim (bHLH-PAS) family of transcription 

30 factors. AhR is present in most cell types and in the unactivated state is cytosolic and exists in a 

31 complex with chaperone proteins, such as heat shock protein 90 (Hsp90). Binding of TCDD to 
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1 AhR leads to nuclear translocation and heterodimerization with its partner protein Arnt, another 

2 bHLH-PAS family member. The AhR:Arnt heterodimer binds to specific cognate DNA 

3 sequence elements known as dioxin/xenobiotic response elements (DRE/XRE) present in the 

4 regulatory region of specific genes. Binding of the AhR:Arnt heterodimer to these elements, and 

5 subsequent recruitment of transcription coactivator complexes, leads to increased transcription of 

6 specific genes, known as “target genes.” There is a battery of genes affected in this manner and 

7 targets include certain xenobiotic-metabolizing enzymes, such as cytochrome P450 (CYP)1A1, 

8 CYP1A2, CYP2B1, and UDP-glucuronosyltransferase (UGT)1A6 (reviewed in Schwartz and 

9 Appel [2005]). In addition, genes affected by the TCDD/AhR-complex code for both inhibitory 

10 and stimulatory growth factors and their gene products affect cellular growth, differentiation and 

11 homeostasis and have been shown to contribute to carcinogenicity as well as other forms of 

12 toxicity (reviewed in Popp et al. [2006]). 

13 Detailed molecular biology research has been performed to identity the extent of the 

14 genes regulated by AhR (Woods et al., 2007); however a complex and still ill-defined profile 

15 remains. Additionally, it is important to note that the extent of the response of individual genes 

16 may not correlate with site-specific tumorigenicity. For example, while TCDD is ineffective as a 

17 tumor promoter in ovariectomized rats and does not stimulate liver cell proliferation in these 

18 animals, it is still capable of inducing CYP1A2 in roughly the same magnitude as in the intact 

19 female rats (Lucier et al., 1991). Similarly, CYP1A1 induction by TCDD is very similar in male 

20 and female rats even though males are almost completely resistant to TCDD carcinogenicity 

21 (Wyde et al., 2002). 

22 Some of AhR’s effects on gene expression may be the result of interaction with other 

23 transcription factors (such as the retinoblastoma protein [Ge and Elferink, 1998], NF-κB [Tian et 

24 al., 1999] or with the tyrosine kinase c-Src (Blankenship and Matsumura, 1997]) rather than via 

25 direct interaction with DNA. By far the most extensive studies involving cross talk between 

26 AhR and another transcription factor are those involving the estrogen receptor alpha (ERα). The 

27 anti-estrogenic properties of TCDD have been well-documented, beginning with the 

28 observations that TCDD repressed estradiol function in rat uterus and liver. The AhR-ERα

29 cross-talk can be manifested at several levels including direct interaction, association of the 

30 receptors with the other’s response element and altered metabolism of estradiol by AhR ligand 

31 (Takemoto et al., 2004). The interactions between AhR/Arnt- and estrogen receptor-dependent 
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1 signaling pathways, which mediate anti-estrogenic effects of dioxins and dioxin-like 

2 polychlorinated biphenyls (PCBs; Bock, 1994), is probably causal for the well-documented 

3 gender-specificity of the carcinogenic effects of these agents (e.g., hepatocarcinogenicity of 

4 TCCD in female as opposed to male rats) (Lucier et al., 1991). In addition, cross-talk between 

5 AhR/Arnt and other nuclear receptors, their coactivators, and corepressors, has been described. 

6 In fact, cross-talk has been reported for AhR and numerous signaling pathways involved in a 

7 broad range of physiological processes. The molecular mechanisms by which the AhR interferes 

8 with these signaling networks are multifaceted and occur at multiple levels of regulation (many 

9 beyond transcriptional control) (Haarmann-Stemmann et al., 2009). 

10 Pertinent to human risk assessment, there are wide inter- and intraspecies differences in 

11 the toxicological responses to TCDD (Ema et al., 1994; Poland and Glover, 1990; Poland et al., 

12 1994) some of which can be explained by polymorphisms in AhR. For instance there is a 

13 10-fold difference in susceptibility to TCDD-induced toxicity between the TCDD-sensitive 

14 C57BL/6 and the resistant DBA/2 strains of mice (Poland and Glover, 1980) that can be 

15 explained by polymorphic variations in the ligand-binding domain and in the C-terminal region 

16 of the AhR molecule of each strain (Dolwick et al., 1993). Depending on the system examined, 

17 the estimated affinity of binding of TCDD (and related compounds) to the human AhR is about 

18 10-fold lower than that observed to the AhR from “responsive” rodent species and is comparable 

19 to that observed to the AhR from “nonresponsive” mouse strains (Ramadoss and Perdew, 2004). 

20 This reduced affinity is due, in part, to a single amino acid substitution within the ligand binding 

21 domain of the human and “nonresponsive” mouse AhRs (Ramadoss and Perdew, 2004). 

22 However, there is considerable tissue and species variability in response to TCDD that cannot be 

23 ascribed solely to polymorphisms of the AhR gene (Pohjanvirta and Tuomisto, 1994; Geyer et 

24 al., 1997). Although the affinity of binding of TCDD and related compounds to the human AhR 

25 is reduced compared with rodent AhRs, the qualitative and quantitative rank-order potency of 

26 these chemicals is similar. 

27 

28 5.1.2.3.1.1. Other AhR considerations. 

29 In addition to the potent agonist TCDD, there are many other exogenous ligands for the 

30 AhR, including certain polycyclic aromatic hydrocarbons, polychlorinated dibenzofurans, and 

31 PCBs (Bock, 1994). Several natural and endogenous compounds are also regulators of AhR 
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1 (Chiaro et al., 2008). The classes of endogenous compounds that have been shown to induce 

2 CYP1 and/or activate AhR include: (a) tryptophan metabolites, other indole-containing 

3 molecules, and phenylethylamines (Gielen and Nebert, 1971); (b) tetrapyrroles such as bilirubin 

4 and biliverdin; (c) sterols such as 7-ketocholesterol and the horse steroid equilenin; (d) fatty acid 

5 metabolites, including at least six different prostaglandins (Seidel et al., 2001) and lipoxin A4; 

6 and (e) the ubiquitous second messenger cAMP (reviewed in McMillan and Bradfield [2007] and 

7 Barouki et al. [2007]). The existence of multiple ligands with varying affinity and responses 

8 suggests that “selective receptor modulators” (or SRMs) of the AhR exist. SRMs are ligands for 

9 a receptor that, upon binding, elicit a conformational change in the receptor that results in 

10 differential recruitment of coregulatory molecules to the target gene promoter region, thereby 

11 imparting a different biological activity relative to the prototypical ligand. This phenomenon has 

12 been most studied for nuclear receptors such as the ERα with the classic example being 

13 tamoxifen, which has estrogen-like activity in the uterus but anti-estrogen-like effects in the 

14 breast. Thus, the relative abilities of compounds to stimulate gene expression or other effects 

15 vary in promoter- and cell type-specific manners. It is now apparent that SRMs exist for the 

16 AhR as well (SAhRMs, Fretland et al. [2004]). For example, 

17 6-methyl-1,3,8-trichlorodibenzofuran (6-MCDF), a SAhRM whose structure is similar to that of 

18 the prototypical AhR agonist TCDD, can induce CYP1A1 gene expression in liver but does not 

19 lead to the toxic responses associated with TCDD (Fritz et al., 2009). 

20 It has been demonstrated that AhR-deficient (AhR-/-) mice show no signs of toxicity at 

21 doses of TCDD approximating the lethal dose eliciting 50% response (LD50) dose (200 μg/kg) in 

22 mice expressing the AhR (Fernandez-Salguero et al., 1996). However, a single high exposure of 

23 2,000 μg/kg to AhR-deficient mice produced several minor lesions including scattered necrosis 

24 and vasculitis in the liver and lungs. These data suggest that a pathway leading to toxicity exists, 

25 albeit at very high doses, that is independent of the AhR. However, these data also indicate that, 

26 at least in mice, the major in vivo effects of TCDD are mediated through the AhR. The finding 

27 of carcinogenicity in hamsters (Rao et al., 1988) is of special interest since hamsters have been 

28 found to be relatively resistant to the lethal effects of TCDD (Henck et al., 1981; Olson et al., 

29 1980). To date, there have been no chronic bioassay studies of TCDD carcinogenicity in 

30 AhR-deficient transgenic animals. 
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1 There are additional insights into the complexity of TCDD’s mechanism of action 

2 involving AhR. Some biochemical responses to TCDD treatment in isolated cells have been 

3 reported in cells lacking Arnt, in cells expressing a mutated Arnt protein and in cells with highly 

4 reduced levels of AhR (Puga et al., 1992; Kolluri et al., 1999), implying either a non nuclear role 

5 of the AhR in mediating these events or an AhR-independent process. 

6 Additionally, recent studies have linked AhR activation in the absence of exogenous 

7 ligand to a multitude of biological effects, ranging from control of mammary tumorigenesis to 

8 regulation of autoimmunity (reviewed in Hahn et al., 2009). Finally, constitutively activated 

9 AhR in rodents has been shown to induce stomach tumors (Andersson et al., 2002). This 

10 indicates that AhR activation alone (i.e., in the absence of ligand) is sufficient to induce tumors. 

11 

12 5.1.2.3.2. TCDD as a tumor promoter. 

13 The role of TCDD as a tumor promoter is discussed in the 2003 Reassessment (Part II, 

14 Chapter 6). The following is a brief summary of the information regarding TCDD as a tumor 

15 promoter. 

16 TCDD is typically designated as a nongenotoxic carcinogen because it does not damage 

17 DNA directly through the formation of DNA adducts, is negative in most short-term assays for 

18 genotoxicity, and is a potent tumor promoter and a weak initiator or noninitiator in multistage 

19 models for chemical carcinogenesis (Pitot et al., 1980; Graham et al., 1988; Lucier et al., 1991; 

20 Clark et al., 1991; Flodstrom and Ahlborg, 1991; Poland et al., 1982). However, mechanisms 

21 have been proposed that support the possibility that TCDD might be indirectly genotoxic, either 

22 through the induction of oxidative stress or by altering the DNA-damaging potential of 

23 exogenous and endogenous compounds, such as estrogens. In addition, there have been 

24 numerous reports demonstrating TCDD-induced modifications of growth factor signaling 

25 pathways and cytokines in experimental animals and cell culture systems. Some of the altered 

26 signaling pathways include those for epidermal growth factor, transforming growth factor alpha, 

27 glucocorticoids, estrogen, tumor necrosis factor-alpha, interleukin 1-beta, plasminogen 

28 inactivating factor-2, and gastrin. Many of these pathways are involved in cell homeostasis, 

29 proliferation, and differentiation and provide plausible mechanisms responsible for the 

30 carcinogenic actions of TCDD. 

31 
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1 5.1.2.3.3. Hypothesized modes of action of TCDD in rodents. 

2 TCDD has been shown to consistently induce multiple tumors in both sexes in several 

3 rodent species. These tumors are observed in various tissues, including (but not limited to): 

4 liver, lung, thyroid, lymphatic system, soft tissue, nasal turbinates, hard palate, adrenal, pancreas, 

5 and tongue. While the mode of action of TCDD in producing cancer has not been elucidated for 

6 any tumor type, the best characterized carcinogenic actions of TCDD are in rodent liver, lung, 

7 and thyroid. The hypothesized mode of action for each of these three tumor types is briefly 

8 discussed below and is described in Figure 5-2. The hypothesized sequence of events following 

9 TCDD interaction with the AhR is markedly different for each of these three tumor types. No 

10 detailed hypothesized mode of action information exists for any of the other reported tumor 

11 types. Further, no single definitive mode of action of TCDD-mediated carcinogenicity has been 

12 identified. 

13 

14 5.1.2.3.3.1. Liver tumors. 

15 The mode of action of TCDD in producing liver cancer in rodents has not been 

16 elucidated. One hypothesized mode of carcinogenic action of TCDD in the liver is mediated 

17 through hepatotoxicity. Generically speaking, TCDD activation of the AhR leads to a variety of 

18 changes in gene expression, which then lead to hepatotoxicity, followed by compensatory 

19 regenerative cellular proliferation and subsequent tumor development. The details of the 

20 mechanism of TCDD-induced hepatotoxicity have not been fully determined but both CYP 

21 induction and oxidative stress have been postulated to be involved (Maronpot et al., 1993; 

22 Viluksela et al., 2000). The enhanced cell proliferation arising from either altered gene 

23 expression or hepatotoxicity, or both, could be the principal factor leading to promotion of 

24 hepatocellular tumors (Whysner and Williams, 1996). The sensitivity of female rat liver to 

25 TCDD, which apparently does not extend to the mouse, depends on ovarian hormones (Lucier et 

26 al., 1991; Wyde et al., 2001). This sensitivity has been ascribed to induction of estradiol 

27 metabolizing enzymes (Graham et al., 1988) and is hypothesized to lead either to generation of 

28 reactive metabolites of endogenous estrogen or to active oxygen species of estrogens. Oxidative 

29 DNA damage has been implicated in liver tumor promotion (Umemura et al., 1999). 

30 A dose-response relationship exists for TCDD-mediated hepatotoxicity, and this parallels 

31 the dose-response relationship for tumor formation (or formation of foci of cellular alteration as a 
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1 surrogate of tumor formation). However, the dose-response relationship for other 

2 TCCD-induced responses such as enhanced gene expression is different from the dose-response 

3 for tumor formation in terms of both efficacy and potency (see Popp et al. [2006] for review). It 

4 is important to note that differences in potency between events (i.e., gene expression versus cell 

5 proliferation) does not necessary imply alternative mechanisms of action. 

6 

7 5.1.2.3.3.2. Lung tumors. 

8 The mode of action of TCDD in producing lung cancer in rodents (predominantly 

9 keratinizing squamous cell carcinoma, [Larsen, 2006]) has not been elucidated. One 

10 hypothesized mechanism of the carcinogenic action of TCDD in the lung involves disruption of 

11 retinoid homeostasis in the liver. Retinoic acids and their corresponding nuclear receptors, the 

12 retinoic acid receptors (RARs) and the retinoid X receptors (RXRs), work together to regulate 

13 cell growth, differentiation, and apoptosis. It is hypothesized that TCDD, through activation of 

14 the AhR, can affect parts of the complex retinoid system and/or other signaling systems 

15 regulated by, and/or cross-talking with, the retinoid system (reviewed in Nilsson and Hakansson, 

16 2002). These effects are then hypothesized to lead to lung tumor development; however, the 

17 mechanisms underlying this hypothesis are not well-defined. Pulmonary squamous proliferative 

18 lesions have been reported following oral exposure to TCDD in rats (Tritscher et al., 2000). In 

19 general, squamous metaplasia with some inflammation is associated with significant forms of 

20 injury via inhalation of toxic compounds but is also seen with vitamin A deficiency (Tritscher et 

21 al., 2000) and gives some credence to this hypothesis. 

22 Another hypothesized mechanism for the carcinogenic action of TCDD in the lung is 

23 through induction of metabolic enzymes. Through activation of AhR and subsequent induction 

24 of metabolizing enzymes (such as CYP1A1), TCDD may enhance bioactivation of other 

25 carcinogens in lung (Tritscher et al., 2000). There have been few studies to support this 

26 hypothesis; however, in a long-term continuous-application study of carcinogenesis using 

27 airborne particulate extract (APE), squamous cell carcinoma occurred in 8 of 17 AhR+/+ mice 

28 (47%) while no tumors were found in AhR-/- mice (Matsumoto et al., 2007). In addition 

29 CYP1A1 was induced in AhR+/+ mice but not in AhR-/- mice in this study. These results 

30 suggest that AhR plays a significant role in APE-induced carcinogenesis in AhR+/+ mice and 
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1 CYP1A1 activation of carcinogenic polycyclic aromatic hydrocarbons (the primary carcinogenic 

2 component of APE) is also of importance. 

3 

4 5.1.2.3.3.3. Thyroid tumors. 

5 The mode of action of TCDD in producing thyroid cancer in rodents has not been 

6 elucidated. It is hypothesized that TCDD increases the incidence of thyroid tumors through an 

7 extrathyroidal mechanism. The prevailing hypothesis for the induction of thyroid tumors by 

8 TCDD involves the disruption of thyroid hormone homeostasis via induction of Phase II 

9 enzymes UGTs in the liver (reviewed in Brouwer et al., 1998) by an AhR-dependent 

10 transcriptional mechanism (Bock et al., 1998; Nebert et al., 1990). This induction of hepatic 

11 UGT results in increased conjugation and elimination of thyroxine (T4), leading to reduced 

12 serum T4 concentrations. T4 synthesis is controlled by the thyroid stimulating hormone (TSH) 

13 which is under negative and positive regulation from the hypothalamus, pituitary, and thyroid via 

14 thyrotrophin-releasing hormone, TSH, T4, and triiodothyronine. Consequently, the reduced 

15 serum T4 concentrations lead to a decrease in the negative feedback inhibition on the pituitary 

16 gland. This would then lead to a rise in secreted TSH and stimulation of the thyroid. The 

17 persistent induction of UGT by TCDD and the subsequent prolonged stimulation of the thyroid 

18 could result in thyroid follicular cell hyperplasia and hypertrophy of the thyroid, thereby 

19 increasing the risk of progression to neoplasia. Increases in blood TSH levels are consistent with 

20 prolonged stimulation of the thyroid and may represent an early stage in the induction of thyroid 

21 tumors identified in animal bioassays. Statistically significant increases in neonatal blood TSH 

22 levels have been recently been reported in children born to TCDD-exposed mothers in the 

23 Seveso cohort (Baccarelli et al., 2008; discussed in Section 2.4.1.1.1.4.4). Support for this 

24 hypothesis comes from several studies showing that TCDD decreases serum total thyroxine and 

25 free thyroxine concentrations in rats following both single dose and repeated dose exposures 

26 (Bastomsky, 1977; Brouwer et al., 1998; Pohjanvirta et al., 1989; Potter et al., 1983, 1986; 

27 Sewall et al., 1995; Van Birgelen et al., 1995). Further support comes from studies of transgenic 

28 animals in which TCDD exposure resulted in a marked reduction of total thyroxin and free T4 

29 levels in the serum of AhR+/- mice but not AhR-/- mice (Nishimura et al., 2005). Additionally, 

30 gene expression of UGT1A6, CYP1A1, and CYP1A2 in the liver was markedly induced by 

31 TCDD in AhR+/- but not AhR-/- mice (Nishimura et al., 2005). 
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1 5.1.2.3.4. Summary of TCDD mode of action in rodents. 

2 Overall, there are inadequate data to support the conclusion that any of the particular 

3 mode of action hypotheses described above is operant in TCDD-induced carcinogenesis. 

4 However, the wealth of scientific evidence available indicates that most, if not all, of the 

5 biological and toxic effects of TCDD are mediated by the AhR. Although the receptor may be 

6 necessary for the occurrence of these events, it is not sufficient because other proteins and 

7 conditions are known to affect the activity of the receptor and its ability to alter gene expression 

8 or to induce other effects. Certain studies could be interpreted to indicate AhR-independent 

9 mechanisms, although these studies have not clearly ruled out involvement of the AhR. The 

10 only consistent, but limited, evidence for TCDD-induced effects that do not involve the AhR 

11 comes from studies using AhR-deficient transgenic animals. Here however, only minor effects 

12 occurred following treatment with extremely high doses of TCDD. Thus, a toxic response to 

13 TCDD has AhR interaction as a key event, but there are various species-, cell-, development-, 

14 gender-, and disease-dependent differences in the cellular milieu that can affect the nature and 

15 extent of the response observed. 

16 The findings that many AhR-modulated effects are regulated with distinct specificity 

17 supports the understanding that the molecular and cellular pathways leading to any particular 

18 toxic event are extremely complex. Precise dissection of these events represents a considerable 

19 challenge, especially in that a toxic response may depend on timely modulation of several genes 

20 rather than of just one particular gene, and possibly modulation of these genes in several rather 

21 than just one cell type or tissue. 

22 While a defined mechanism at the molecular level or a defined mode of action for 

23 TCDD-induced carcinogenicity is lacking, EPA concludes the following 

24 

25 • interaction with the AhR is a necessary early event in TCDD carcinogenicity in 

26 experimental animals. 

27 • through interaction with the AhR, TCDD modifies one or more of a number of cellular 

28 processes, such as induction of enzymes, changes in growth factor and/or hormone 

29 regulation, and/or alterations in cellular proliferation and differentiation. 
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1 • AhR activation is anticipated to occur in humans and to progress to tumors. AhR is 

2 present in human cells and tissues, studies using human cells are consistent with the 

3 hypothesis that the AhR mediates TCDD toxicity and no data exist to suggest that the 

4 biological effects of AhR activation by TCDD are precluded in humans. 

5 • non-AhR mediated carcinogenic effects of TCDD are possible. 

6 

7 5.1.3. Summary of the Qualitative Weight of Evidence Classification for TCDD 

8 Under the 2005 Cancer Guidelines (U.S. EPA, 2005), TCDD is characterized as 

9 carcinogenic to humans, based on the available data as of 2009. This conclusion is based on 

10 • Multiple occupational epidemiologic studies showing strong evidence of an association 

11 between TCDD exposure and increased mortality from all cancers. 

12 • Epidemiological studies showing an association between TCDD exposure and certain 

13 cancers in individuals accidentally exposed to TCDD in Seveso, Italy. 

14 • Extensive evidence of carcinogenicity at multiple tumor sites in both sexes of multiple 

15 species of experimental animals. 

16 • General scientific consensus that the mode of TCDD’s carcinogenic action in animals 

17 involves AhR-dependent key precursor events and proceeds through modification of one 

18 or more of a number of cellular processes, such as induction of enzymes, changes in 

19 growth factor and/or hormone regulation, and/or alterations in cellular proliferation and 

20 differentiation. 

21 • The human AhR and rodent AhR are similar in structure and function and human and 

22 rodent tissue and organ cultures respond to TCDD in a similar manner and at similar 

23 concentrations. 

24 • General scientific consensus that AhR activation is anticipated to occur in humans and to 

25 progress to tumors. 

26 

27 5.2. QUANTITATIVE CANCER ASSESSMENT 

28 5.2.1. Summary of NAS Comments on Cancer Dose-Response Modeling 

29 5.2.1.1. Choice of Response Level and Characterization of the Statistical Confidence Around 

30 Low Dose Model Predictions 

31 The NAS commented on the low dose model predictions in the 2003 Reassessment, 

32 including EPA’s development of ED01 (effective dose eliciting x percent response) estimates for 

33 numerous study/endpoint combinations. The committee also suggested that EPA had not 

34 appropriately characterized the statistical confidence around such model predictions in the low35 response region of the model. 
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1 The committee concludes that EPA did not adequately justify the use of the 1% 

2 response level (the ED01) as the POD for analyzing epidemiological or animal 

3 bioassay data for both cancer and noncancer effects. The committee recommends 

4 that EPA more explicitly address the importance of the selection of the POD and 

5 its impact on risk estimates by calculating risk estimates using alternative 

6 assumptions (e.g., the ED05) (NAS, 2006a, p. 18) 

7 

8 It is critical that the model used for determining a POD fits the data well, 

9 especially at the lower end of the observed responses. Whenever feasible, 

10 mechanistic and statistical information should be used to estimate the shape of the 

11 dose-response curve at lower doses. At a minimum, EPA should use rigorous 

12 statistical methods to assess model fit, and to control and reduce the uncertainty of 

13 the POD caused by a poorly fitted model. The overall quality of the study design 

14 is also a critical element in deciding which data sets to use for quantitative 

15 modeling (NAS, 2006a, p. 18). 

16 

17 EPA should ... assess goodness-of-fit of dose-response models for data sets and 

18 provide both upper and lower bounds on central estimates for all statistical 

19 estimates. When quantitation is not possible, EPA should clearly state it and 

20 explain what would be required to achieve quantitation (NAS, 2006a, p. 10). 

21 

22 The NAS also suggested that EPA report information describing the adequacy of dose23 response model fits, particularly in the low-response region. For those cases where biostatistical 

24 modeling was not possible, the NAS recommended that EPA identify the reasons. 

25 

26 The Reassessment should also explicitly address the importance of statistical 

27 assessment of model fit at the lower end and the difficulties in such assessments, 

28 particularly when using summary data from the literature instead of the raw data, 

29 although estimates of the impacts of different choices of models would provide 

30 valuable information about the role of this uncertainty in driving the risk estimates 

31 (NAS, 2006a, p. 73). 

32 

33 5.2.1.2. Model Forms for Predicting Cancer Risks Below the Point of Departure (POD) 

34 The NAS focused much of its review on EPA’s derivation of a cancer slope factor. 

35 Specifically, the NAS commented extensively on the selection of the appropriate point of 

36 departure (POD) and the extrapolation of dose response modeling below the POD. 

37 The NAS questioned EPA’s choice of a linear, nonthreshold model for extrapolating risk 

38 associated with exposure levels below the POD, concluding that the current scientific evidence 

39 was sufficient to justify the use of nonlinear methods when extrapolating below the POD for 
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1 TCDD carcinogenicity. The committee further recommended that EPA include a nonlinear 

2 model for low dose cancer risk estimates as a comparison to the results from the linear model. 

3 

4 The committee concludes that EPA’s decision to rely solely on a default linear 

5 model lacked adequate scientific support. The report recommends that EPA 

6 provide risk estimates using both nonlinear and linear methods to extrapolate 

7 below PODs (NAS, 2006a, p. 5). 

8 

9 After reviewing EPA’s 2003 Reassessment and additional scientific data 

10 published since completion of the Reassessment, the committee unanimously 

11 agreed that the current weight of scientific evidence on the carcinogenicity of 

12 dioxin is adequate to justify the use of nonlinear methods consistent with a 

13 receptor-mediated response to extrapolate below the POD. The committee points 

14 out that data from NTP released after EPA generated the 2003 Reassessment 

15 provide the most extensive information collected to date about TCDD 

16 carcinogenicity in test animals, and the committee found the NTP results to be 

17 compelling. The committee concludes that EPA should reevaluate how it models 

18 the dose-response relationships for TCDD... (NAS, 2006a, p. 16). 

19 

20 Because EPA’s assumption of linearity at doses below the 1% excess risk level 

21 for carcinogenic effects of TCDD, other dioxins, and DLCs is central to the 

22 ultimate determination of regulatory values, it is important to critically address the 

23 available scientific evidence on the most plausible shape of the dose-response 

24 relationship at doses below the POD (LED01). On the basis of a review of the 

25 literature, including the detailed review prepared by EPA and presented in Part II 

26 of EPA’s Dioxin Risk Assessment and new literature available since the last EPA 

27 review, the committee concludes that, although it is not possible to scientifically 

28 prove the absence of linearity at low doses, the scientific evidence, based largely 

29 on mode of action, is adequate to favor the use of a nonlinear model that would 

30 include a threshold response over the use of the default linear assumption (NAS, 

31 2006a, p. 122). 

32 

33 On the whole, the committee concluded that the empirical evidence supports a 

34 nonlinear dose-response below the ED01, while acknowledging that the possibility 

35 of a linear response cannot be completely ruled out. The Reassessment 

36 emphasizes the lack of such nonlinear models, hence its adoption of the approach 

37 of linear extrapolation below the POD level. Although this approach remains 

38 consistent with the cancer guidelines (EPA 2005, see also Appendix B), EPA 

39 should acknowledge the qualitative evidence of nonlinear dose response in a more 

40 balanced way, continue to fill in the quantitative data gaps, and look for 

41 opportunities to incorporate mechanistic information as it becomes available. The 

42 committee recommends adopting both linear and nonlinear methods of risk 

43 characterization to account for the uncertainty of dose-response relationship shape 

44 below ED01 (NAS, 2006a, p. 72). 
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1 5.2.2. Overview of EPA Response to NAS Comments on Cancer Dose-Response Modeling 

2 EPA agrees with the NAS that the approaches to cancer dose-response modeling for 

3 TCDD should be clearly communicated and justified. Furthermore, due to the abundance of new 

4 information on TCDD carcinogenicity published since the 2003 Reassessment, EPA has 

5 reevaluated the cancer dose-response modeling for TCDD presented in the 2003 Reassessment. 

6 As detailed below in Section 5.2.3, EPA has conducted an updated cancer dose-response 

7 assessment for TCDD that incorporates key NAS recommendations discussed in this document, 

8 reflects the current state-of-the science in cancer dose-response modeling and integrates new 

9 TCDD carcinogenic information. Detailed responses to the NAS comments summarized above 

10 are found in Section 5.2.3.3. 

11 The 2003 Reassessment presents an extensive dose-response assessment of TCDD and 

12 provides a comprehensive summary of dose-response relationships. The analyses and 

13 discussions synthesized a considerable breadth of data and model types, highlighting the 

14 strengths and weaknesses of the then-available scientific information. Modeling included both 

15 administered dose and steady state body burden dose metrics, taking into account variation in 

16 half-lives of TCDD across species. These body burden calculations used a simple one17 compartment kinetic model based on the assumption of a first-order decrease in the levels of 

18 administered dose as a function of time. An excess risk of 1% was chosen to model the cancer 

19 data, but comparative results were also shown for 5% and 10% excess risk (see Table 8-2 of the 

20 2003 Reassessment). Dose response was also explored thoroughly for a number of in vitro and 

21 biochemical endpoints in addition to the in vivo data analyses, and ranges of these values were 

22 presented (see Figures 8-1, 8-2 and 8-3 of the 2003 Reassessment). Thus, the 2003 

23 Reassessment provides an initial evaluation of the carcinogenic database for TCDD and serves as 

24 the foundation for the analyses presented below. 

25 

26 5.2.3. Updated Cancer Dose-Response Modeling for Derivation of Oral Slope Factor 

27 The following sections describe the dose-response analysis of the cancer data from 

28 epidemiologic cohort studies (see Section 2.4.1) and rodent bioassays (see Section 2.4.2), 

29 concluding with the derivation of oral slope factors for TCDD based on epidemiologic data (see 

30 Section 5.2.3.1) and rodent bioassay data (see Section 5.2.3.2). 
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1 5.2.3.1. Dose-Response Modeling Based on Epidemiologic Cohort Data 

2 The 2003 Reassessment included dose-response analyses and the development of oral 

3 slope factors from the following three occupational cohorts: the NIOSH cohort, the Hamburg 

4 cohort, and the BASF cohort. In this document, EPA determined that specific studies from each 

5 of these cohorts (Steenland et al., 2001; Ott and Zober, 1996; Becher et al., 1998) met the 

6 epidemiologic study inclusion criteria (see Section 2.3.1 and Section 2.4.1). In Section 5.2.3.1.1, 

7 the oral slope factors derived from these studies in the 2003 Reassessment are reviewed. Since 

8 the publication of the 2003 Reassessment, additional cancer epidemiology studies based on these 

9 cohorts have been published in the peer-reviewed literature. Of these, Cheng et al. (2006) met 

10 the epidemiologic study inclusion criteria (see Section 2.3.1 and Section 2.4.1). In 

11 Section 5.2.3.1.2, EPA evaluates the suitability of deriving an oral slope factor from this study 

12 and derives oral slope factor estimates. 

13 Another study that met the current epidemiologic study inclusion criteria (Warner et al., 

14 2002) was also briefly discussed in the 2003 Reassessment, but an oral slope factor (OSF) was 

15 not derived from that study. In Section 5.2.3.1.2.2, EPA discusses its unsuccessful attempt to use 

16 the categorical results published by Warner et al. (2002) to develop an oral cancer risk estimate. 

17 

18 5.2.3.1.1. Evaluation of Epidemiologic Studies in the 2003 Reassessment for OSF Derivation. 

19 In the 2003 Reassessment, EPA reported dose-response modeling results for three 

20 epidemiologic studies of human occupational cohorts: the NIOSH cohort with data published by 

21 Steenland et al. (2001); the Hamburg cohort with data published by Becher et al. (1998); and the 

22 BASF cohort with data published by Ott and Zober (1996). Each of these studies is summarized 

23 in Section 2.4.1 of this document and in the 2003 Reassessment (Part II, Chapter 8; Part III, 

24 Chapter 5). Furthermore, EPA has evaluated the suitability of these studies for use in TCDD 

25 dose-response modeling and concluded that each of these studies meet the inclusion criteria for 

26 epidemiology studies presented in Section 2.3.1. 

27 Each of these studies reports all cancer mortality as an outcome. Steenland et al. (2001) 

28 and Becher et al. (1998) analyzed cohorts of primarily male workers who experienced 

29 occupational exposures to TCDD over long periods of time, while Ott and Zober (1996) studied 

30 a cohort of primarily male workers who were exposed to high TCDD concentrations at a single 

31 point in time due to an industrial accident. 
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1 The authors of all three of these studies measured and then back-extrapolated TCDD 

2 levels in a subset of workers to estimate exposures during employment and then the authors used 

3 this information to estimate exposures in the remainder of the cohort. These measured TCDD 

4 samples generally were collected decades after the last known occupational exposure. In each 

5 study, the authors relied on TCDD measures in the cohort to back-calculate serum lipid or body 

6 fat levels of TCDD using a simple one-compartment kinetic model based on the assumption of a 

7 first-order decrease in the levels of exposure dose as a function of time. The assumed half-life of 

8 TCDD used in the models varied from study to study. None of the studies sampled TCDD levels 

9 from the entire cohort; for example, Ott and Zober collected samples from 138/243 workers 

10 (57% of the cohort), which was the highest percentage of workers sampled among the three 

11 studies. Steenland et al. (2001) and Becher et al. (1998) used the measured and back12 extrapolated TCDD concentrations to estimate the exposures that were associated with various 

13 occupations within the cohort, and subsequently used this information to develop exposure 

14 matrices (i.e., the TCDD load per unit time for an occupation) that then could be used to estimate 

15 the cumulative dioxin dose for each cohort member. Ott and Zober (1996) used regression 

16 procedures with data on time spent at various occupational tasks to estimate TCDD levels for all 

17 members of the cohort. Following the estimation of worker exposures in each of these three 

18 studies, the studies’ authors divided these cohorts into exposure subgroups based on the 

19 estimated TCDD levels. 

20 In the 2003 Reassessment, EPA identified a POD based on a 1% response in cancer 

21 mortality (ED01) for the Steenland et al. (2001), and the Ott and Zober (1996) studies. EPA 

22 extrapolated from this POD to lower doses using a straight line drawn from the POD to the 

23 origin—zero incremental dose, zero incremental response—to give a probability of extra risk. 

24 Because there was insufficient evidence to support an assumption of nonlinearity, EPA chose to 

25 develop these models using a linear model. 

26 

27 5.2.3.1.1.1. Steenland et al. (2001).

28 Steenland et al. (2001) developed dose-response models based on TCDD exposures and 

29 all cancer mortalities from eight plants in the NIOSH cohort. Serum lipid levels of TCDD in 

30 1988 were measured in 193 workers at one of these plants. Steenland and coauthors relied on a 

31 first-order kinetic model (assuming a constant 8.7 year half-life) to back-extrapolate to serum 
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1 TCDD levels at the time of the last occupational exposure. The study authors assigned exposure 

2 estimates to each of the 3,538 workers in the cohort based on a job-exposure matrix. This matrix 

3 was based on (1) an estimated level of contact with TCDD, (2) the degree of TCDD 

4 contamination of the products the workers produced, and (3) the fraction of a workday during 

5 which the worker likely contacted the TCDD-contaminated products. They then estimated each 

6 worker’s serum TCDD levels as an area under the concentration curve (AUC) for lipid-adjusted 

7 serum levels over time. The mortality analysis was conducted on 256 cancer decedents. 

8 Several different dose-response models were fit to these data to provide estimates of fatal 

9 cancer risk. The best-fitting model was a Cox regression exposure-response model using the 

10 log(AUC) of TCDD lipid concentration (ppt-year) lagged by 15 years as the exposure metric. 

11 Steenland and colleagues also developed a piecewise linear regression model with no lag, in 

12 which two separate linear slopes were estimated. This analysis assumed a background exposure 

13 of 0.5 pg/kg-day. The lipid concentrations were converted to body burdens by dividing by 4. 

14 The central tendency estimate and lower bound ED01s from the piecewise linear model and their 

15 associated cancer slope factors for the most sensitive endpoint (male cancer mortality) are 

16 presented in Table 5-1. 

17 

18 5.2.3.1.1.2. Becher et al. (1998).

19 Based on the Hamburg cohort, Becher et al. (1998) reported a dose-response analysis for 

20 all fatal cancers combined. The mortality analysis was conducted in 1992 on 124 cancer 

21 decedents. The exposure variable in the study was the integrated blood levels for TCDD 

22 concentration over time (AUC, ng/kg-years), as estimated by Flesch-Janys et al. (1998); these 

23 were converted to body burdens by dividing by 4. Estimates of the half-life of TCDD, based on 

24 the sample of 48 individuals with repeated measures, were incorporated into a model that back25 extrapolated TCDD exposures to the end of the employment after accounting for the workers’ 

26 ages and body fat percentages. These estimated exposure measures were then applied to the 

27 entire cohort, which consisted of all 1,189 regular male employees who were employed for at 

28 least 3 months between 1952 and 1984 at the Boehringer chemical plant in Hamburg, Germany. 
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1 Becher et al. (1998) used a Cox regression approach for the dose-response modeling and 

developed three models: a multiplicative model, an additive model, and a power model.25 2 The 

3 response variable in each model was the SMR for total cancer mortality. The models were 

4 calculated with lag times of up to 20 years. The multiplicative model provided the best fit; 

5 however, the study authors judged the fits for all three models to be acceptable. The model 

6 results were used to calculate unit risk estimates derived as the risk of cancer death through age 

7 70 given a daily dose of 1 pg/kg body weight of TCDD minus the risk given no exposure to 

8 TCDD. These calculations were based on background German cancer mortality rates. The 

9 model results were used to calculate cancer risk estimates. The lower bound estimates on the 

10 dose were not available for models published by Becher et al. due to the absence of statistical 

11 parameter measures. The central tendency estimate ED01s from the three statistical models and 

12 their associated cancer slope factors are presented in Table 5-1. 

13 

14 5.2.3.1.1.3. Ott and Zober (1996).

15 In the 2003 Reassessment, EPA also developed a dose-response analysis based on a study 

16 reported by Ott and Zober (1996) for cancer incidence and mortality experienced by 243 men, 

17 who were exposed to TCDD in 1953 during an accident at the BASF plant in Germany. The 

18 cohort was followed through 1992. TCDD blood lipid levels were available for 138 of these 

19 men 30 years after the accident. These levels were back-extrapolated and used to estimate the 

20 AUC for TCDD. Body burdens (ng/kg) were estimated by dividing AUC by 4, and steady-state 

 

25The “multiplicative model” set relative risk (RR) equal to exp(βd), where the dose d is the AUC. The “additive 

model” set RR = 1+βd, and the “power model” set RR = exp(β log (kd+1)). The values β and k are estimated 

parameters. 
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body burdens were estimated assuming a constant half-life of approximately 7.1 years.26 1 Ott and 

2 Zober (1996) used Cox proportional hazard approaches to estimate cancer mortality risk per unit 

TCDD dose.27 3 The central tendency estimate and lower bound ED01s from the modeling and 

4 their associated cancer slope factors are presented in Table 5-1. 

5 

6 5.2.3.1.2. Evaluation of Epidemiologic Studies Published Since the 2003 Reassessment for 

7 OSF Derivation. 

8 Two additional epidemiological studies that met the study inclusion criteria (see 

9 Section 2.3) for use in dose response modeling as set forth in this document are evaluated in this 

10 section for the estimation of cancer risk estimates. Each of these studies is summarized in 

11 Section 2.4.1 of this document. 

12 

13 5.2.3.1.2.1. Cheng et al. (2006).

14 As discussed in Section 2.4.1.1.1, Cheng et al. (2006) analyzed the relationship between 

15 TCDD dose and all cancer mortality for the same subset of NIOSH workers as analyzed 

16 previously by Steenland et al. (2001). In contrast to Steenland et al., Cheng et al. (2006) used the 

17 “concentration- and age-dependent elimination model” (concentration- and age-dependent 

18 elimination [CADM], discussed in Section 3.3; see also Aylward et al. [2005a]), rather than a 

19 constant 8.7-year half-life, and calculated serum-derived TCDD estimates for use in dose20 response analysis. An important feature of CADM is that it incorporates concentration- and age21 dependent elimination of TCDD from the body, meaning that the effective half-life of TCDD 

 

26Based on the initial body burden (B0) EPA estimated the body burden at time t using the following formula: 

-

0 ()=

k t

e Bt Be , where ke is an elimination constant equal to ln(2)/(half-life in years). This implies that the AUC at 

time T after initial exposure is 0 = (1- )

-k Te

e

B

AUC e

k

. T in this case was 39 years (time from the accident in 1953 to 

the follow-up in 1992). Dividing by a lifetime of 71 years (mean age in 1954, 33 years, plus 38 years from 1954 to 

the followup in 1992) yields the lifetime mean body burden as: 

(1 )

71

-k T o e mean

e

B

B = -e

k

. In the 2003 Reassessment, EPA converted the steady-state body burden to units of equivalent 

initial dose by dividing by the constant 1 -

(1- )

71

k T

e

e

e k

. With the given values for half-life and T, that constant is 

0.1411 and 1/(the constant) is 7.09. 

27The model from Ott and Zober has risk proportional to dose eβ × with β = ln(1.22). The corresponding slope for the 

mean (steady-state) body burden is 7.0851 * log(1.22) * 0.001 (the 0.001 converts nanograms to micrograms). 
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1 elimination varies based on exposure history, body burden, and age of the exposed individuals. 

2 As discussed in Section 3.3, the use of the CADM model to simulate TCDD kinetics in the 

3 NIOSH cohort results in time-integrated body burden estimates four to five times greater than 

4 those obtained with the simple first-order model, with smaller differences between the two 

5 methods at lower exposures. 

6 Following the estimation of dose using the CADM-derived AUC values, Cheng and 

7 colleagues derived dose-response estimates for the NIOSH cohort using Cox regression and 

8 piecewise linear modeling. Their results are summarized in Table 5-2. For comparison, the Cox 

9 regression coefficient from the analysis conducted by Steenland et al. (2001), which relied on a 

10 first-order elimination rate model assuming a constant 8.7-year half-life, is also shown on the 

11 first line of the table. As in Steenland et al. (2001), Cheng et al. (2006) found a much stronger 

12 relationship between cancer mortality and exposure metrics lagged 15 years compared to the 

13 relationships for unlagged exposures. Cheng et al. (2006) also noted that the dose-response 

relationship plateaued above the 95th 14 percentile of exposure. For exposures lagged 15 years, the 

regression coefficient of the linear slope derived by Cheng et al. (2006) was 3.3 × 10−6 15 per 

16 ppt-year lipid-adjusted serum TCDD (in Table III in their analysis—the standard error of this 

regression coefficient was 1.4 × 10−6 17 ). The upper 5% of the exposure range (individuals 

18 ≥252,950 ppt-year lipid adjusted serum TCDD) was excluded in estimating this slope. Because 

19 this exclusion reduces the upper portion of the response where the slope is shallow, this likely 

20 better represents the slope in the region of the curve where the fatal cancer risk is increasing with 

21 dose, which is the equivalent of dropping the highest dose in an animal bioassay. 

22 To develop an OSF for TCDD, EPA used data and equations from the Cheng et al. (2006) 

23 in its calculations as follows 

24 

• Upper 95th 25 percentile slope (β95). To estimate β95 of this regression coefficient, EPA 

26 summed the regression coefficient (β) and the product of 1.96 and the standard error of 

the regression coefficient, yielding an estimate of 6.0 × 10−6 27 per ppt-year lipid adjusted 

28 serum TCDD, as follows 

29 β95 = β + *96.1 SE
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1 • Background cancer mortality risk estimate (R0). EPA used an R0 of 0.112 as reported by 

Cheng et al. (2006)28 2 

3 • Risk in the exposed group associated with a 1% extra risk of fatal cancer (Rexp). EPA 

4 estimated Rexp to be 0.12088, using the following relationship for extra risk: 

0

0exp

1

01.0

R

RR

−

− 5 =

6 • Incremental cancer mortality risk in the exposed population based on a 1% extra risk 

(RD). RD, was then calculated to be 8.9 × 10−3 7 using the following equation: 

8 RD = Rexp − R0

9 • Cumulative TCDD concentration in the fat compartment for a 1% extra risk (AUC01). 

10 EPA then estimated AUC01 using Equation 3 from Cheng et al. (2006): 

11 AUC01 = ln(RD + R0/R0)/β95

12 Cheng et al. (2006) assume that the TCDD concentration in fat is the same as ppt-yr lipid 

adjusted serum concentration. The AUC01 was calculated to be 1.26 × 104 13 ppt-yr. 

14 • Oral slope factor associated with 1% extra risk [OSF(AUC01)]. AUC01 of 

1.26 × 104 15 ppt-yr is used by EPA as a POD for linear extrapolation to zero. OSF(AUC01) 

is calculated to be 7.92 × 10−7 16 by dividing 0.01 by the AUC01. OSF(AUC01) is linear 

17 with the TCDD concentration in fat. Ingested TCDD doses, however, are not linear with 

18 the predicted TCDD fat concentrations in the Emond pharmacokinetic model. Thus, the 

19 OSF(AUC01) that is linear with TCDD in the fat is not linear with ingested TCDD dose. 

20 

21 Thus, to estimate the fatal cancer risk associated with an oral intake of TCDD, estimates 

22 of both the average TCDD concentration in the fat resulting from the oral intake and the risk 

23 using the OSF(AUC01) are needed. Next in this report, EPA presents estimates of OSFs for 

specific TCDD intake rates based on target risk levels (RLs) of 1 × 10−3

, 1 × 10−4, 1 × 10−5 24 , 

1 × 10−6

, and 1 × 10−7 25 , using the following calculations: 

26 

27 • Area under the TCDD fat concentration curve associated with a target risk level 

28 (AUCRL) (ppt-yr). For each target risk level, EPA calculated an AUCRL by dividing the 

target risk level by the OSF(AUC01) [i.e., RL/7.92 × 10-7 (ppt-yr)-1 29 ]. 

30 • Lifetime averaged concentration of TCDD in the fat compartment associated with the 

31 target risk level (FATRL) (ng/kg). The AUCRL estimates were then further divided by 

 

28In Table IV, Cheng et al. (2006) report two estimates of background fatal cancer risk, R0, for males aged 75 years: 

0.112 and 0.124. A R0 estimate of 12.4% was used by Steenland et al. (2001), and 11.2%, as estimated for all males 

in the 1999–2001 Surveillance Epidemiology and End Result data set. EPA chose to use 11.2% as this reflects more 

current cancer mortality rates in the U.S. population. 
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1 70 years to identify (FATRL) (ng/kg). This step essentially reverses the integration 

2 undertaken to calculate AUC01. 

3 • Continuous daily TCDD intake DRL (ng/kg-day) associated with a target risk level over a 

4 lifetime. Using the Emond human physiologically based pharmacokinetic (PBPK) 

5 model, EPA estimated the DRL necessary to achieve the FATRL. Table 5-3 shows the 

6 ingested TCDD doses (DRL) corresponding to these target risk levels. 

7 • Oral slope factor at the target risk level (OSFRL) (per mg/kg-day). Table 5-3 also shows 

that at the target risk levels, the associated OSFRLs range from 3.7 × 105

 to 1.3 × 106 8 per 

mg/kg-day. These are calculated as OSFRL = RL/DRL × 106 9 . 

10 

11 Table 5-4 shows analogous results based on the best estimate of regression coefficient 

(3.3 × 10−6 12 ) for all fatal cancers as reported by Cheng et al. (2006) by comparing lipid-adjusted 

13 serum concentrations, fat concentrations, risk specific dose estimates and equivalent oral slope 

factors for risk levels of 1 × 10−3

, 1 × 10−4, 1 × 10−5, 1 × 10−6, and 1 × 10−7 14 . 

15 

16 5.2.3.1.2.2. Warner et al. (2002).

17 Warner et al. (2002) is a study of 981 females exposed to elevated TCDD levels 

18 following the Seveso accident of 1976. The TCDD exposure pattern involving a single period of 

19 elevated TCDD exposures followed by an extended period of lower ambient level TCDD 

20 exposures and elimination is similar to that of the BASF cohort (Ott and Zober, 1996). TCDD 

21 levels, measured or estimated in blood lipids shortly after the time of the accident, were available 

22 for all women. These women were divided into four exposure groups of <20, 20−44, 44.1−100, 

23 and >100 ppt. In this cohort, 21 total cancers have been observed; 15 of these were breast cancer 

24 cases and 3 were thyroid cancer cases. Cox proportional hazards modeling showed that the 

25 hazard ratio for breast cancer associated with a 10-fold increase in serum TCDD levels (log10

26 (TCDD)) was significantly increased to 2.1 (95% CI = 1.0−4.6). Rate ratios (95% CI) for cancer 

27 incidence in these 4 groups were 1.0, 1.0 (0.2−5.5), 2.2 (0.5−10.8) and 2.5 (0.5−11.8). Using a 

28 Cox proportional hazards model and assuming continuous exposure, the rate ratio was 1.7 

29 (0.9−3.4) for each 10-fold increase in serum TCDD; that is, a log10 transformation of the 

30 exposure estimates in their analysis was presented. They reported a test for trend of p = 0.09. 

31 EPA attempted to estimate an ED01 from the modeled results of Warner et al. (2002) from 

32 the statistically significant hazard ratio for breast cancer. However, EPA had to estimate the 

33 slope of the tangent to the log-linear relationship. Because the slope of a log-linear relationship 
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1 is not constant but varies with dose, and because the lowest exposure measure was well-above 

2 the 1% response region of interest, EPA could not confidently estimate the tangent to the 

3 log-linear relationship. The transformation of the log10 dose units to linear units of TCDD 

4 yielded an implausibly low ED01 and correspondingly high cancer risk that was inconsistent with 

5 a visual inspection of the untransformed plot. EPA was not confident in these values for health 

6 risk assessment because of uncertainties in the transformation in the low response region of the 

7 original model. Thus, EPA did not derive an ED01 or oral slope factor for this study. 

8 

9 5.2.3.2. Dose-Response Modeling Based on Animal Bioassay Data 

10 Figure 5-3 provides a summary of the process EPA has utilized to select and identify 

11 candidate TCDD OSFs from key animal bioassays that were identified in Section 2.4.3 of this 

12 document. For each in vivo animal cancer study that qualified for TCDD dose-response 

13 assessment using the study inclusion criteria specified in Section 2.3.2, EPA first selected the 

14 species/sex/tumor data set combinations that had been characterized as having statistically 

15 significant increases in tumor incidence by either a pair-wise test between the treated group and 

16 the controls or by a trend test showing increases in tumors with increases in dose. Next, EPA 

17 used the Emond animal kinetic model discussed in Section 3 to estimate blood concentrations 

18 corresponding to each study’s average daily administered doses for use in dose response 

19 modeling. Benchmark dose lower confidence bounds (BMDL01s) were then estimated for the 

20 blood concentrations by (1) using the multistage cancer model for each species/sex/tumor 

21 combination within each study and (2) using a Bayesian Markov Chain Monte Carlo framework 

22 that assumes independence of tumors, modeling all tumors together for each species/sex 

23 combination within each study. The final selected models were subjected to goodness-of-fit tests 

24 and visual inspections of fit to the raw data. Thus, for each sex/species combination within each 

25 study, this process generated a BMDL01 for each single tumor type and another BMDL01 for the 

26 combined tumors. Finally, using the Emond human kinetic model discussed in Section 3, human 

27 equivalent doses (BMDLHEDs) were then estimated for each of the BMDL01s and, using a linear 

28 extrapolation, OSFs were calculated by OSF = 0.01/BMDLHED. The lowest OSF for a 

29 species/sex combination for either a single tumor type or all combined tumors was selected as a 

30 candidate OSF for TCDD cancer assessment. These steps in Figure 5-3 are further described in 

31 detail in the following sections. 
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1 5.2.3.2.1. Selection of key data sets. 

2 Based on the study selection criteria outlined in Section 2.3.2 (see Figure 2-3), EPA 

3 selected four animal bioassays for use in the cancer dose-response assessment for TCDD (see 

4 Table 2-6 and Section 2.4.2 for detailed study descriptions). Three of these studies (Kociba et 

5 al., 1978; NTP, 1982; Toth et al., 1979) were evaluated in the 2003 Reassessment, while one 

6 study (NTP, 2006) was published after the 2003 Reassessment was released. The NTP (2006) 

7 study was specifically called out by the NAS (2006a) report as cancer bioassay data that EPA 

8 should evaluate prior to completing its TCDD dose-response assessment. As discussed below, 

9 EPA has chosen to conduct dose-response modeling for a number of tumor types from each of 

10 the sex/species combinations in these studies in order to maximize the amount of information 

11 available to support OSF derivation. Because tumors occurred in multiple sites in the exposed 

12 animals, each tumor type was considered separately (individual tumor models) and were also 

13 combined into composite tumor incidence dose estimates (multiple tumor models). 

14 The tumor incidence tables for these four bioassays are shown in Tables 5-5 through 5-8. 

15 The data in these tables are summarized from each study’s reference publication and are the 

16 species/sex/tumor incidence data used for TCDD dose-response modeling in this report. In the 

17 case of the Kociba et al. (1978) female rat combined hepatocellular adenomas and carcinomas 

18 only, EPA used data from a reanalysis of the pathology slides that was published by Goodman 

19 and Sauer (1992). The data sets in Tables 5-5 through 5-12 were selected because they had been 

20 characterized by the study authors as having statistically significant increases in tumor incidence 

21 by either a pair-wise test between the treated group and the controls or by a trend test showing 

22 increases in tumors with increases in dose. One exception is that Goodman and Sauer (1992) did 

23 not statistically analyze the revised tumor incidence data from their reanalysis of the Kociba et 

24 al. (1978) female rat combined hepatocellular adenomas and carcinomas, so a Fischer’s Exact 

25 Test to evaluate the statistical significance of those data was used in this document. In the case 

26 of the NTP (2006) study only, information was available regarding the length of time that the 

27 animals stayed on test (105 weeks); animals who died within the first year were censored from 

28 analysis in this document because animals who died within the first year were not considered to 

29 have been alive long enough to develop tumors. Therefore, those animals were not included in 

30 the denominators in Table 5-11. These adjusted incidence data were used in the analysis of 
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1 tumor dose-response for NTP (2006) in this document. The tumor incidence data in Tables 5-5 

2 through 5-12 include 

3 

4 • nasal, tongue and adrenal tumors in males, and liver, nasal and lung tumors in females 

5 from the Kociba et al. (1978) 2-year study of Sprague-Dawley rats, 

6 • liver, thyroid and adrenal tumors in males, and subcutaneous tissue, liver, adrenal and 

7 thyroid tumors in females and from the NTP (1982) 2-year study of Osborne-Mendel rats, 

8 • lung and liver tumors in males, and subcutaneous tissue, hematopoietic system, liver and 

9 thyroid tumors in females from the NTP (1982) 2-year study of B6C3F1 mice 

10 • liver, oral mucosa, pancreas and lung tumors in females from the NTP (2006) 2-year 

11 study of Sprague-Dawley rats, and 

12 • liver tumors in males from the Toth et al. (1979) 1-year study of Swiss/H/Riop mice. 

13 

14 For each cancer endpoint, the reported (administered) doses from each study were converted, 

15 where necessary, to average daily doses in ng/kg-day (e.g., doses administered 5 days/week were 

16 adjusted by multiplying by 5 and dividing by 7 to get average daily doses). These doses were 

17 then subjected to kinetic modeling to generate blood concentrations for use in TCDD dose18 response modeling. 

19 

20 5.2.3.2.2. Dose adjustment and extrapolation methods for selected data sets. 

21 5.2.3.2.2.1. Dose metric estimation for dose-response modeling. 

22 Tables 5-5 through 5-12 show the blood concentrations that were used in TCDD dose23 response modeling of the animal bioassay data. Based on kinetic analysis (see Section 3), a 

24 choice for whole blood concentration of TCDD was made for the purpose of dose extrapolation 

25 between animals and humans. In order to estimate blood concentrations for each study selected, 

26 the Emond PBPK model was run using ACSLX® software, version 2.5.0.6 (see Section 3). 

27 Depending on the selected study, either rat or mouse versions of the model were used. In each 

28 case, the simulation was performed using the exposure and observation durations, the body 

29 weights, and the adjusted doses from the original studies. Details of PBPK model input 

30 parameters are given for each study’s m-file in Appendix C.2. In the case of Toth et al. (1979) 

31 study, which dosed the animals for a year and then followed up for the lifetime of the animal, 

32 only the one-year simulation was performed. The m-files were used to run the appropriate PBPK 
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1 model to estimate time-averaged, maximum, and terminal (end of exposure) lipid adjusted blood 

2 serum concentration (see Appendix C.3). Other model simulated dose metrics such as 

3 concentrations for liver, fat, Ah-receptor bound in liver, body burden, and the time at which the 

4 maximum concentration was reached for each dose metric are also reported for illustrative 

5 purposes in Appendix C.3. The complete results for each study modeled are shown in 

6 Appendix C.3. 

7 Applying the Emond PBPK model to each study/endpoint combinations in Tables 5-5 

8 through 5-12, whole blood concentrations were derived by converting the model estimated lipid9 adjusted to whole blood concentration using model constants for the lipid fraction in blood serum 

10 for rodents (0.0033) and the blood serum fraction (0.55). Once the Emond modeling runs were 

11 performed, the model outputs of whole blood concentrations were used as dose metrics to 

12 estimate BMDLs (in ng/kg). To obtain BMDLs, benchmark dose modeling was performed as 

13 described below by substituting the model simulated dose metrics (whole blood concentrations) 

14 for the original study doses and calculating the corresponding BMDL (results appear below and 

15 in Appendix F). 

16 

17 5.2.3.2.2.2. Calculation of human equivalent doses (HEDs). 

18 Human equivalent doses (ng/kg-day), corresponding to each BMDL (ng/kg) were 

19 calculated using the Emond human PBPK model (see Section 3) and are denoted as BMDLHEDs. 

20 The Emond human PBPK model was run for 70 years assuming a constant daily dose starting 

21 from birth. The model concentrations were averaged over both the entire 70 year lifetime 

22 (lifetime average) and over the five years surrounding the peak concentration (five-year average) 

23 (see Section 3.3.1, describing first order body burden estimation). The human equivalent doses 

24 were estimated by adjusting the daily dose model input until the time-averaged whole blood 

25 concentration matched the associated alternative dose BMDL (derived earlier from animal PBPK 

26 model). For animal studies which lasted longer than 540 days, the lifetime average was used; for 

27 studies lasting less than 540 days, the five year average was used. The process was iterative and 

28 continued until the modeled human concentration was within 1% of the BMDL. In general, 

29 however, the concentrations matched to within 0.1%. 

30 
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1 5.2.3.2.3. Dose-response modeling approaches for rodent bioassays. 

2 5.2.3.2.3.1. Modeling of individual tumors. 

3 EPA’s BMDS Software, version 2.1 was used to estimate the BMDL01s for each of the 

4 species/sex/tumor combinations, using the blood concentrations and incidence data shown in 

5 Tables 5-5 through 5-12. Each data set was modeled using the multistage cancer model, and a 

6 BMDL01 in blood concentration was estimated. The multistage model has been used by EPA in 

7 the majority of its quantitative cancer assessments because it is statistically robust and able to 

8 provide good fits to a wide range of dose-response patterns. It is also consistent with the 

9 multistage nature of the carcinogenic process. The mathematical form of the multistage model is 

10 

 P(d) = 1 – exp[–(q0 + q1d + q2d2

 + ... + qkdk 11 )] (Eq. 5-3) 

12 

13 where 

14 P(d) = lifetime excess risk (probability) of cancer at dose d

15 qi = parameters estimated in fitting the model, i = 1, ..., k. 

16 

17 To estimate the BMD01s and BMDL01s, BMDS was run with all parameters set to their 

18 defaults; up to three degrees of freedom were specified for the dichotomous, multistage cancer 

19 model; and a 95% confidence level. A 1% benchmark response (BMR) was used for each tumor 

20 type to ensure the PODs were estimated in the linear portion of the dose-response curve. The 

21 BMDL01 (ng/kg) was then converted to a BMDLHED (ng/kg-day) using the Emond human model, 

and an OSF in units of (mg/kg-day)−1

 was calculated by, OSF = 0.01/BMDLHED ×106 22 . It may be 

23 noted that the OSF is linear with the blood concentration, and is not linear with ingested TCDD 

24 doses; thus, using the Emond et al. pharmacokinetic model, risk-specific doses of TCDD intake 

25 (ng/kg-day) corresponding to the target risk levels would need to be provided for use in human 

26 health risk assessment. In the following sections, results are presented for the models that 

27 provided the best overall fit to the data as judged by comparison of likelihood ratios for models 

28 that had an acceptable fit (chi-squared goodness of fit statistic p > 0.05). 

29 

30 5.2.3.2.3.2. Multiple tumor (Bayesian) models. 

31 Statistically significant increased tumor incidences were observed at multiple sites in 

32 male and/or female rats (Kociba et al., 1978; NTP, 1982, 2006) and male and female mice (NTP, 
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1 1982) following oral exposures to TCDD. With this multiplicity of tumors, the concern is that a 

2 potency or risk estimate based solely on one tumor site (e.g., the most sensitive site) may 

3 underestimate the overall cancer risk associated with exposure to this chemical. Relevant 

4 approaches in the 2005 Cancer Guidelines (U.S. EPA, 2005) for characterizing total risk include 

5 the following: (1) analyze the incidence of tumor-bearing animals, or (2) combine the potencies 

6 associated with significantly elevated tumors at each site. The NRC (1994) concluded that an 

7 approach based on counts of animals with one or more tumors (tumor-bearing animals) would 

8 tend to underestimate overall risk when tumor types occur independently, and thus an approach 

9 based on combining the risk estimates from each separate tumor type should be used. On 

10 independence of tumors, NRC (1994) stated “...a general assumption of statistical independence 

11 of tumor-type occurrences within animals is not likely to introduce substantial error in assessing 

12 carcinogenic potency.” 

13 Because potencies are typically upper bound estimates, summing such upper bound 

14 estimates across tumor sites is likely to overstate the overall risk. Therefore, following the 

15 recommendations of the NRC (1994) and the 2005 Cancer Guidelines (U.S. EPA, 2005), a 

16 statistically valid upper bound on combined risk was derived, assuming independence, in order 

17 to gain some understanding of the overall risk resulting from tumors occurring at multiple sites. 

18 In the case of TCDD, tumors are thought to be independent across the sites found in these three 

19 studies because: (1) they are in different organs and tissues, specifically liver, lung, thyroid, 

20 subcutaneous tissue, oral cavity, tongue, pancreas, adrenal cortex and the hematopoietic system; 

21 (2) different kinds of tumors were found, even within the same organ (e.g., both 

22 cholangiocarcinomas and hepatocellular adenomas were found in female rat livers in NTP, 

23 2006); and (3) the tumors found in these studies were not progressive (i.e., they did not 

24 metastasize to other sites in the body). It is important to note that this estimate of overall 

25 potency describes the risk of developing tumors at any combination of the sites and is not the 

26 risk of developing tumors at all sites simultaneously. 

27 For modeling individual tumor data, the multistage model is specified as shown in the 

28 previous section (see Eq. 5-3). Under the assumption of independence, the model for the 

29 combined (or composite) tumor risk is still multistage, with a functional form that has the sum of 

30 stage-specific multistage coefficients as the corresponding multistage coefficient. 

 31 
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 Pc(d) = 1 − exp[-(Σq0i + dΣq1i + d2

Σq2i + ... + dm 1 Σqmi)], for i = 1,..., k, (Eq. 5-4) 

2 

3 where k = total number of sites. 

4 The resulting equation for fixed extra risk (BMR) is polynomial in dose (when logarithms 

5 of both sides are taken) and can be solved in a straightforward manner for the combined BMD. 

6 However, the current version of BMDS cannot estimate confidence bounds for this combined 

7 BMD. 

8 Therefore, a Bayesian approach to finding confidence bounds on the combined BMD was 

9 implemented using WinBUGS (Spiegelhalter et al., 2003). WinBUGS software is freely 

10 available and implements Markov Chain Monte Carlo (MCMC) computations. Use of 

11 WinBUGS has been demonstrated for derivation of a distribution of BMDs for a single 

12 multistage model (Kopylev et al., 2007) and is easily generalized (Kopylev at al., 2009) to derive 

13 the distribution of BMDs for the combined tumor load, following the NRC (1994) methodology 

14 described above. The advantage of a Bayesian approach is that it produces a distribution of 

15 BMDs that allows better characterization of statistical uncertainty. For the current analysis, a 

16 diffuse (high variance or low tolerance) Gaussian prior restricted to be nonnegative was used. 

17 The posterior distribution was based on three simulation chains with 50,000 burn-in (i.e., the 

18 initial 50,000 iterations were dropped) and a thinning rate of 20, resulting in 150,000 interactions 

total. The median and 5th 19 percentile of the posterior distribution provided the BMD01 (central 

20 estimate) and BMDL01 (lower bound) for combined tumor load, respectively. 

21 The methodology above was applied to the statistically significant dose-response data 

22 from Kociba et al. (1978), NTP (1982), and NTP (2006) (see Section 2.3.2 for data set selection 

23 criteria). As with the risk estimates generated for individual tumor sites, the combined analysis 

24 used the internal dose metric, whole blood concentration (see Section 3). For the combined 

25 tumors for each sex/species combination, a BMDL01 in blood concentrations was estimated. The 

26 BMDL01 (ng/kg) was then converted to a BMDLHED (ng/kg-day) using the Emond human model, 

and an OSF in units of (mg/kg-day)−1

 was calculated by, OSF = 0.01/BMDLHED × 106 27 . It may be 

28 noted that the OSF factor is linear with the blood concentration, and is not linear with ingested 

29 TCDD doses; thus, using the Emond et al. pharmacokinetic model, risk-specific doses of TCDD 

30 intake (ng/kg-day) corresponding to the target risk levels would need to be provided for use in 

31 human health risk assessment. 
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1 5.2.3.2.4. Results of dose-response modeling for rodent bioassays. 

2 Table 5-13 presents the benchmark dose modeling results for both the individual tumors 

3 and the combined tumors based on blood concentrations. The p-values in the table are for a 

4 chi-square goodness of fit statistic with significance of p > 0.05. Goodness of fit was acceptable 

5 at p > 0.05 for all models. The difference in log likelihood (dLL) statistic documents the 

6 difference in log likelihoods between stages of the models in cases where the stage is above 1; it 

7 shows the difference between the stage in the table and the lower stage. For example, for the 

8 NTP (2006) liver cholangiocarcinomas, twice the difference of 2.92 would be >3.84, the test 

statistic from the assumed chi-square distribution,29 9 with p = 0.95, justifying the choice of 

10 3 stages over 2 stages. The best fitting multistage models include a 1-stage (linear) model for all 

11 of the individual tumor data sets from Kociba et al. (1978), NTP (1982), and Toth et al. (1979), 

12 as well as for the pancreatic and oral mucosa tumors in NTP (2006), a 2-stage model for the lung 

13 tumors in NTP (2006), and a 3-stage model for the liver cholangiocarcinoma and liver adenoma 

14 data sets from NTP (2006). For the Toth et al. (1979) liver tumors, the model fit to all of the data 

15 was poor, and the highest dose group was dropped in order to achieve an acceptable fit 

16 (p = 0.29). The BMD01s and BMDL01s (ng/kg) were estimated from these multistage models for 

17 the individual tumors. BMD01s and BMDL01s (ng/kg) were also provided in Table 5-13 for the 

18 combined tumors for each sex/species combination within a study. These were estimated from 

19 the distributions of BMD01s produced by the Bayesian MCMC simulation (see 

20 Section 5.2.3.1.2.3.2). The BMD01s and BMDL01s (ng/kg) for the combined tumors in 

21 Table 5-13 are the mean and lower 95% percentile values from these distributions, respectively. 

22 

23 5.2.3.2.4.1. Individual tumor models. 

24 Table 5-14 shows the BMDLHEDs (ng/kg-day) that were estimated from the BMDL01s in 

25 Table 5-13 using the Emond human model (see Section 5.2.3.1.2.2.2) and the OSFs calculated 

by, OSF = 0.01/BMDLHED × 106

 to convert the OSF to (mg/kg-day)−1 26 units. BMDS results, 

27 details of the model fits and dose-response graphics for all endpoints are shown in Appendix F. 

 

29The chi-square distribution with 1 degree of freedom is the correct distribution only under standard conditions 

(e.g., no boundary parameters in null hypothesis). Thus, the correct distribution for the situation where the 

parameter of interest is on the boundary, as happens with testing for the order of the multistage model, and, possibly 

nuisance parameters (estimated parameters of the model), is very difficult to derive (Self and Liang, 1987). 

Therefore the p-value of chi-square with one degree of freedom is used as the best available choice. 
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1 Although only the blood concentration results are presented in this section, for comparison 

2 purposes, Appendix F also provides modeling results for the studies’ administered average daily 

3 doses. Table 5-14 lists the OSFs in decreasing value. It can be seen that liver tumors in male 

mice yield the highest slope factors; OSF values are 5.9 ×106

 and 5.2 ×106 4 per mg/kg-day in 

5 NTP (1982) and Toth et al. (1979), respectively. The OSFs for the new NTP (2006) study in 

female rats are two orders of magnitude lower, ranging from 1.8 ×104

 to 1.8 ×105 6 per mg/kg-day, 

7 representing the lowest OSFs for TCDD from the individual tumor models. 

8 

9 5.2.3.2.4.2. Multiple tumor (Bayesian) models. 

10 Table 5-15 shows the BMDLHEDs (mg/kg-day) that were estimated from the BMDL01s in 

11 Table 5-13 using the Emond human model (see Section 5.2.3.1.2.2.2) and the OSFs calculated 

by, OSF = 0.01/BMDLHED ×106

 to convert the OSF to (mg/kg-day)−1 12 units. Table 5-15 lists the 

13 OSFs in decreasing value. It can be seen that the combined liver and lung tumors in male mice 

yield the highest OSF value of 9.4 ×106 14 per mg/kg-day from NTP (1982), and the combined 

adrenal, tongue and nasal tumors in male rats yield the lowest OSF value of 3.2 ×105 15 from 

16 Kociba et al. (1978). The OSF for the combined liver, oral mucosa, lung, and pancreatic tumors 

in female rats from the newer NTP (2006) study is 4.4 ×105 17 . 

18 

19 5.2.3.2.5. Summary evaluation of slope factor estimates from rodent bioassays. 

20 To estimate a range of candidate TCDD OSFs from the animal data, dose-response 

21 modeling of the four chronic rodent bioassays identified in Section 2.4.3 was conducted. Dose22 response modeling was performed using whole blood concentrations, and BMDLHED values 

23 (ng/kg-day) were derived for the 25 species/sex/endpoint data sets individually (see Table 5-14) 

24 and for seven species/sex combined tumor data sets (see Table 5-15). 

25 For each sex/species combination within a study, the combined tumor OSFs presented in 

26 Table 5-15 represent the highest OSFs that have been derived from the animal cancer bioassay 

27 multistage models. The most sensitive species and sex is male mice, for which the estimated 

BMDLHED for combined tumors is 1.1 × 10-3 28 ng/kg-day. This result, which is derived under the 

29 assumption that multiple tumor types occur independently in the exposed animals, is, as 

30 expected, lower than the BMDLHED for the most sensitive individual tumor. 
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1 Based on these results, EPA believes that a credible value for the BMDLHED derived from 

the animal studies lies in the range shown in Table 5-15 between 3.1 × 10-2 2 and 

1.1 × 10−3

 ng/kg-day. These values, which correspond to oral slope factor values of 3.2 × 105 3 

and 9.4 × 106 4 per mg/kg-day, respectively, encompass the range at which elevated cancer risks 

5 can be detected for the most sensitive species, sex, and endpoints in the animal bioassay data. 

6 As noted above in Sections 5.2.3.1.2.2 and 5.2.3.1.2.3, the OSFs shown in this section are 

7 linear with blood concentration. The OSFs shown in Tables 5-14 and 5-15 correctly depict the 

8 cancer risks generated from the multistage models for their specific associated BMDLHEDs. 

9 However, ingested TCDD doses are not linear with the predicted TCDD blood concentrations 

10 generated by the Emond pharmacokinetic model. Thus, the OSFs associated with the ingested 

11 doses are not linear with ingested dose. If the OSFs derived in this section were to be used in 

human health risk assessment, target risk levels (e.g., 1 × 10−3

, 1 × 10−4, 1 × 10−5, 1 × 10−6 12 , etc.) 

13 would need to be identified. Then, the Emond et al. pharmacokinetic model could be used to 

14 generate risk-specific doses of TCDD intake (ng/kg-day) corresponding to the target risk levels. 

15 

16 5.2.3.2.6. Qualitative uncertainties in slope factor estimates from rodent bioassays. 

17 This section presents a qualitative discussion of the uncertainties associated with 

18 calculating the OSF for TCDD from chronic animal bioassay data. Discussions on the feasibility 

19 of conducting a quantitative uncertainty analysis for TCDD using dose-response methods are 

20 provided in Section 6.4.2 of this document. 

21 

22 5.2.3.2.6.1. Quality of studies relied upon for determining POD. 

23 EPA considers the overall quality and breadth of the studies used for the cancer dose24 response analysis to be excellent. All of the studies were published in the peer-reviewed 

25 literature, and two of them were conducted by NTP (1982, 2006). Kociba et al. (1978) and Toth 

26 et al. (1979) are older studies, but appear to have been conducted according to good laboratory 

27 practice standards. The control and dose group sample sizes were relatively large, ~50 animals 

28 or more per group for all of the studies except for Toth et al. (1979), where the dose group sizes 

29 were ~40 animals per group. All four studies exposed the test animals via the oral route to 

30 TCDD alone, as was stipulated in EPA’s study inclusion criteria. Collectively, these four studies 

31 reported development of numerous cancer endpoints (tumors) in both sexes in two strains of rats 
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1 (Sprague-Dawley and Osborne-Mendel) and two strains of mice (i.e., B6C3F1, Swiss/H/Riop). 

2 The overall high quality of these studies and the strong, positive association between TCDD 

3 exposure and cancer suggests that study quality is not a major contributing factor to uncertainty 

4 in the risk estimates. 

5 

6 5.2.3.2.6.2. Interpretation of results from studies relied upon for determining POD. 

7 As discussed in Section 3.4.3.2.1, questions arose about the interpretation of liver tumor 

8 responses in female rats in the Kociba et al. (1978) study. Three re-evaluations of the slides have 

9 been reported (Kociba et al., 1978; Squire, 1980; Goodman and Sauer, 1992). The decision to 

10 use the Goodman and Sauer (1992) evaluation was based on the fact that the authors used the 

11 most current tumor classification procedures. The incidence of hepatocellular adenomas and 

12 carcinomas (individually and combined), however, did vary (sometimes widely) for each dose 

13 group across the three evaluations. Although the state-of-the-science is reflected in the incidence 

14 data used for analysis, a small degree of additional uncertainty (i.e., above that associated with 

15 data collection and reporting errors) could be associated with the female liver tumor risk 

16 estimates from the Kociba et al. (1978) study due to this variability. No controversy has arisen 

17 with regard to the interpretation of the NTP (1982, 2006), or Toth et al. (1979) tumor 

18 identification and classification. 

19 

20 5.2.3.2.6.3. Consistency of results across chronic rodent bioassays. 

21 The existence of four high-quality chronic bioassays for TCDD increases confidence and 

22 reduces uncertainty in the cancer OSFs. Considered together, these studies tested two species 

23 and both sexes of mice and rats, and a wide range of well-characterized tumor types. All four 

24 studies were consistent in observing increases (at some dose level) in rates of liver tumors (in 

25 both species and sexes). While tumors at other sites were observed (and those sites varied across 

26 study, species, and sex), the liver tumors were consistently the most sensitive indicators of 

27 carcinogenic response (with respect to BMDLHED estimates). Lung tumors were also 

28 consistently observed across three of the studies, in male mice in the NTP (1982) study and in 

29 female rats in Kociba et al. (1978) and NTP (2006). As discussed above, the two most sensitive 

30 single-tumor endpoints as judged by BMDL01 values were associated with elevated liver tumor 

31 risks, followed by lung, lymphoma or leukemia, thyroid and adrenal cancers. The consistency of 
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1 tumor types and sensitivities across endpoints and studies lends confidence to the multistage 

2 modeling results. 

3 

4 5.2.3.2.6.4. Human relevance of rodent tumor data. 

5 There is some concordance in the tumor responses observed in the rodent test species and 

6 humans, however, the most sensitive tumor site in the animals, the liver, has not been associated 

7 with cancer from TCDD exposures in humans. On the other hand, lung cancer and leukemia 

8 have been are found both in the animal studies and in epidemiologic studies of exposed workers. 

9 The consistency across sex, species and strains in the animal studies suggests that the occurrence 

10 of several of these tumors, in particular, liver and lung tumors is not an idiosyncratic response of 

11 a particular combination of species, strain, or sex. As discussed in Section 5.2.1, the likely AhR 

12 related carcinogenic mechanism is credible for humans as well as for rodent species. 

13 

14 5.2.3.2.6.5. Relevance of rodent exposure scenario. 

15 The four chronic rodent bioassays exposed the test animals for ~2 years, the majority of 

16 their lifespans. The exception is the Toth et al. (1979) study, where the animals were exposed 

17 only for one year, but were kept on the study for a second year before they were evaluated for 

18 cancer. These lifetime bioassays in animals have long been used by EPA to assess potential 

19 lifetime exposures and effects in humans. However, in the case of TCDD, the half life of TCDD 

20 in the body for rats, mice, and humans is very different (see Section 3). Thus, there is a 

21 significant amount of uncertainty in the use of rat and mouse data to develop OSFs for human 

22 cancer risk assessment of TCDD. 

23 

24 5.2.3.2.6.6. Impact of background TCDD exposures. 

25 It is known that TCDD has been found in the feed used in animal bioassays, and that this 

26 is a confounding factor, particularly in older studies. The effect of TCDD in the diets of test 

27 species has the potential to be quite significant given the low levels of TCDD at which adverse 

28 effects have been observed. Insofar as that is an issue, the risks associated with TCDD 

29 exposures in the animal bioassays, and therefore the OSFs, would be biased high, which could be 

30 the case for the NTP (1982), Kociba et al. (1978) and Toth et al. (1979) studies. The impact of 

31 this issue is that the newer study, NTP (2006), accounted for TCDD exposures in the animal 
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1 feed. Thus, there is likely to be less uncertainty in the TCDD dose-response information 

2 presented in NTP (2006) than in the other three studies conducted before 1990. 

3 

4 5.2.3.2.6.7. Choice of endpoint for POD derivation. 

5 As noted above, the liver tumor PODs represent the most sensitive single-tumor endpoint 

6 across the four cancer bioassays. Thus, the liver cancer endpoints must be seriously considered 

7 for derivation of a TCDD OSF. As discussed in the previous section, EPA has also developed 

8 Bayesian dose-response estimates for combined tumors, which yield BMDL01 values slightly 

9 lower than those for any individual tumor type. Although it is the most conservative choice to 

10 select the lowest combined tumor POD for OSF derivation, there are uncertainties associated 

11 with the multiple tumor analysis. The assumption of independence of tumors across sites is 

12 reasonable, particularly since the tumors from TCDD do not metastasize. However, the 

13 independence assumption lacks hard evidence and needs further laboratory confirmation. 

14 

15 5.2.3.2.6.8. Choice of animal-to-human extrapolation method. 

16 The analyses presented here have used the Emond human kinetic model for extrapolating 

17 dose from animals to humans (as discussed in Section 3.4.2). The rationale for this choice is that 

18 the blood concentration metric most accurately reflects the concentration of TCDD in the various 

19 tissues. As discussed in Section 3.4.3.2.4, use of the blood concentration dose metric results in 

20 critical dose estimates (HEDs) that are considerably lower (10- to more than 100-fold) than those 

21 derived based on administered dose. This does not reflect bias in the blood-based measure; 

22 rather it is a reflection of the highly nonlinear biokinetics of TCDD in the body. EPA has also 

23 explored the impacts of using other dose metrics, including liver bound TCDD concentration 

24 calculated based on the Emond model. As discussed in Section 3.4.3.2.6.2, this also results in 

25 HED estimates much lower than those obtained based on administered dose. 

26 

27 5.2.3.2.6.9. Choice of model for POD and model uncertainty for POD derivation. 

28 The bioassay-based cancer dose-response assessment in this section has used the 

29 multistage model which is the standard model choice for such assessments and has been the basis 

30 for most of EPA’s cancer risk assessments. In that sense, there is no associated uncertainty for 

31 model choice. 
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1 There is some model choice uncertainty associated with instances of lack of fit. When 

2 the multistage model does not adequately describe the observed pattern of responses (typically 

3 determined by examining the p-value for lack of fit), a decision must be made about possible 

4 adjustments, including the dropping of higher dose groups thought to be less relevant to the 

5 estimation of low-dose slopes. In this analysis, poorer fits (p-values less than 0.10) were 

6 observed in four cases, all from NTP (1982) (see Table 5-13). The lowest BMDL01 associated a 

7 low p-value (p = 0.09) was for the lung tumors in the NTP (1982) male mouse, the third lowest 

8 POD behind the liver PODs in the individual tumor data sets. The other instances were for 

9 adrenal cortex and thyroid follicular cell adenomas in male rats and for subcutaneous tissue in 

10 female mice in the NTP (1982) study. In those instances, the p-values were 0.06, 0.06, and 0.09, 

11 respectively; the fits were considered adequate for describing the low-dose response patterns and 

12 estimating slope factors. These poorly fit data sets contribute to uncertainty in the combined 

13 tumor PODs. The lowest BMDL01 in the combined tumors is for the male mice combined liver 

14 and lung tumors, thus estimates from this sex/species combination from NTP (1982) is highly 

15 uncertain and impacts its choice as a POD. 

16 

17 5.2.3.2.6.10.Statistical uncertainty in model fits. 

18 Every model fit to a data set is associated with some inherent statistical uncertainty. For 

19 this reason, bounds were calculated and used for OSF derivation (e.g., lower bounds on 

20 benchmark doses, in this case the BMDL01s). Those bounds account for uncertainties associated 

21 with finite samples of test animals, both in terms of the number of dose groups and of the 

22 number of animals per dose group. Valid and accepted statistical procedures have been applied 

23 to ascertain the impact of those limitations on the estimates of interest. That being the case, the 

24 statistical uncertainties associated with finite samples have been adequately addressed. 

25 

26 5.2.3.2.6.11.Choice of risk level for POD derivation. 

27 The BMR level that has been used for the POD in deriving the cancer OSF is one percent 

28 extra risk. This choice is supported by the fact that risks need to be low enough so that the linear 

29 extrapolation from them reflects low-dose behavior, reducing the impact of any higher-dose 

30 changes (inflexions) in the dose-response. The multistage model has the characteristic that it can 

31 be approximated by a linear model at low enough doses. Thus, a risk level in the range of one 
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1 percent is generally suitable for characterizing that approximate linear function, while not falling 

2 too far below the observed range of the data. 

3 

4 5.2.3.3. EPA’s Response to the NAS Comments on Choice of Response Level and 

5 Characterization of the Statistical Confidence Around Low Dose Model Predictions 

6 The NAS was concerned with the statistical power to determine the shape of the dose 

7 response curve at low doses, well below observed dose-response information. EPA shares this 

8 concern in that the shape of the dose-response curve in the low-dose region cannot be determined 

9 with confidence when based on higher dose information. 

10 When tumor data are used for dose-response modeling, a POD is obtained from the 

11 modeled tumor incidences. When assessing carcinogenicity using a linear extrapolation 

12 approach from a POD, a balance must be struck between staying within the range of the 

13 observations and obtaining a representative estimate of the low-dose slope. Traditional cancer 

14 bioassays, with approximately 50 animals per group, can typically support modeling down to an 

15 increased incidence of 1–10%; epidemiologic studies, with larger sample sizes, below 1%. For 

16 the TCDD animal cancer bioassay data, most of the low-dose tumor incidence responses are 

17 under 10% (relative to controls), with some as low as 2%. After evaluating the magnitude of the 

18 uncertainty in BMDL01s against the impact of using BMDL10s, EPA has chosen to use a 1% 

19 BMR in all cases, determining that the uncertainty bounds on the BMDL01 values are reasonable 

20 (see Appendix F for details). For comparison purposes, BMDL01s, BMDL05s, and BMDL10s are 

21 presented for all modeled tumor incidences in Appendix F. 

22 In the analysis of the animal cancer bioassays presented in this document, the multistage 

23 cancer model was applied with a linear dose extrapolation to zero. EPA used a 1% excess risk 

24 estimate, i.e., a BMDL01, as the POD for development of candidate TCDD cancer oral slope 

25 factors using a Bayesian multitumor approach (see Section 5.2.3.2. The advantage of a Bayesian 

26 approach is that it produces a distribution of BMDs that allows better characterization of 

27 statistical uncertainty. 

28 Central tendency and lower bound slope factor estimates are part of the standard BMDS 

29 multistage cancer model and are included in each output file for the animal bioassay single tumor 

30 analyses in Appendix F. Central tendency BMDs are also reported for the results of the animal 
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1 bioassay multitumor analysis (see Table 5-13). Central tendency slope factor estimates are given 

2 for all the qualifying epidemiological studies as well (see Tables 5-1 and 5-4). 

3 

4 5.2.3.4. EPA’s Response to the NAS Comments on Model Forms for Predicting Cancer Risks 

5 Below the POD 

6 The NAS offered extensive comments on the cancer dose-response modeling in the 2003 

7 Reassessment. Although epidemiologic and rodent bioassay data are useful for the evaluation of 

8 the dose-response curve within the range of the observed response data, they have traditionally 

9 not been useful sources of information for identifying a threshold or for estimating the shape of 

10 the dose-response curve below the POD. Rather, mechanistic toxicological data have been the 

11 evidentiary sources of choice for those types of analyses. As noted above, any quantitative 

12 estimation of carcinogenic risk associated with TCDD exposure requires low-dose extrapolation 

13 of experimental data. Unfortunately, the shape of the dose-response curve in the low dose region 

14 is unknown. 

15 Several of the analyses of epidemiological cohort data evaluated the fit of different dose16 response models to the data. Log-dose models accentuate the importance of low-dose low17 magnitude responses and can yield implausible results. The most relevant models used in these 

18 studies are the untransformed-dose Cox regression models, which are the most similar to the 

19 multistage (1-degree) model used for animal bioassay analysis. Better results have been obtained 

20 in the cohort analyses when the flattening of the hazard-ratio curve is taken into account. The 

21 latter has been modeled explicitly by Steenland et al. (2001), who use a piecewise linear model 

22 and implicitly by Cheng et al. (2006), who drop out a percentage of the high-dose response data 

23 and fit a linear model to the remainder. Importantly, the analyses of the epidemiologic cohorts 

24 presented in Section 5.2.3.1 are limited to evaluation and reanalyses of published data as 

25 reported by the study authors. EPA does not have access to the raw data from these 

26 epidemiologic studies and, therefore, could not conduct de novo analyses. 

27 

28 5.2.3.4.1. Choice of extrapolation approach 

29 5.2.3.4.1.1. TCDD and receptor theory.

30 TCDD is considered to be a receptor-mediated carcinogen in animals. Nearly all TCDD 

31 experimental data are consistent with the hypothesis that the binding of TCDD to the AhR is the 
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1 first step in a series of biochemical, cellular, and tissue changes that ultimately lead to toxic 

2 responses observed in both experimental animals and humans (Part II, Chapter 2 of the 2003 

3 Reassessment). Ligand-receptor binding, like any bimolecular interaction, obeys the law of mass 

4 action as originally formulated by A.J. Clark (Limbird, 1996). The law of mass action predicts 

5 the fractional receptor occupancy at equilibrium as a function of ligand concentration. Fractional 

6 occupancy (Y) is defined as the fraction of all receptors that are bound to ligand: 

7 

Y = [TCDD − AhR ]

[ AhR ]TOT

= [TCDD − AhR ]

[AhR ] + [TCDD − AhR ]

= [TCDD ]

[TCDD ] + K d 8 (Eq. 5-5) 

9 

10 where [TCDD] is the concentration of the ligand, [AhR] is the concentration of the receptor and 

11 [TCDD-AhR] is the amount of liganded receptor. The equilibrium dissociation constant Kd

12 describes the affinity of the interaction and is the concentration of TCDD that results in 50% 

13 receptor occupancy. This simple equation defines a rectangular hyperbola, which is the 

14 characteristic shape of the vast majority of biological dose-response relationships. 

15 In certain cases, no response occurs even when there is some receptor occupancy. This 

16 suggests that there may be a threshold phenomenon that reflects the biological “inertia” of the 

17 response (Ariens et al., 1960). In other cases, a maximal response occurs well before all 

18 receptors are occupied, a phenomenon that reflects receptor “reserve” (Stephenson, 1956). 

19 Therefore, the law of mass action cannot by itself fully explain the effect or response observed 

20 after TCDD interacts with AhR. The ligand-receptor complex is associated with a signal 

21 transduction or effector system. In the case of the AhR, this effector system can be considered to 

22 be the transcriptional machinery itself. The key feature of this formulation is that a response is 

23 proportional, or a function of, the number of receptors occupied. 

24 Furthermore, for a ligand such as TCDD that elicits multiple receptor-mediated effects, 

25 one cannot assume that the binding-response relationship for a simple effect (such as enzyme 

26 induction) will necessarily be identical to that for a different and more complex effect (such as 

27 cancer). The cellular cascades of events leading to different complex responses (e.g., altered 

28 immune function, developmental effects, or cancer) are different, and other rate-limiting events 

29 likely influence the final biological outcome resulting in different dose-response curves. Thus, 

30 even though TCDD binding to AhR is assumed to be the initial event leading to a spectrum of 
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1 biological responses, TCDD-AhR binding data may not always correlate with the dose-response 

2 relationship observed for particular effects. 

3 A receptor-based mechanism would predict that, except in cases where the concentration 

4 of TCDD is already high (i.e., [TCDD]~Kd), incremental exposure to TCDD will lead to some 

5 increase in the fractional occupancy of AhR. However, as discussed above, it cannot be assumed 

6 that an increase in receptor occupancy will necessarily elicit a proportional increase in all 

7 biological response(s), because numerous molecular events contributing to the biological 

8 endpoint are integrated into the overall response. That is, the final biological response could be 

9 considered as an integration of a series of interdependent dose-response curves with each curve 

10 dependent on the molecular dosimetry for each particular step. Dose-response relationships that 

11 will be specific for each endpoint must be considered when using mathematical models to 

12 estimate the risk associated with exposure to TCDD. It remains a challenge to develop models 

13 that incorporate all the complexities associated with each biological response as the modes of 

14 action for various toxicological endpoints appear to vary greatly. For TCDD, extensive 

15 experimental data from studies using animal and human tissues indicate that cell- or tissue16 specific factors determine the quantitative relationship between receptor occupancy and the 

17 ultimate biological response. This would suggest that the parameters for each mathematical 

18 model might only apply to a single biological response within a given tissue and species, making 

19 extrapolation to other systems challenging. 

20 

21 5.2.3.4.1.2. Low-dose extrapolation: threshold or no threshold?

As indicated in the 2005 Cancer Guidelines,30 22 toxicity reference values for human 

23 noncancer endpoints have historically been estimated based on a no-observed-adverse-effect 

24 level (NOAEL) or lowest-observed-adverse-effect level (LOAEL) from animal bioassay studies. 

25 This terminology suggests a biological population threshold beneath which no harm is 

26 anticipated. Reference values such as the oral reference dose (RfD) or inhalation reference 

27 concentration (RfC) are derived by applying uncertainty factors (UFs) to a POD. Depending on 

28 the nature of available data and modeling choice, a POD can be selected from values other than 

 

30As stated in the 2005 Cancer Guidelines (U.S. EPA, 2005): “For effects other than cancer, reference values have 

been described as being based on the assumption of biological thresholds. The Agency’s more current guidelines 

for these effects (U.S. EPA, 1996a, 1998b), however, do not use this assumption, citing the difficulty of empirically 

distinguishing a true threshold from a dose-response curve that is nonlinear at low doses.” 
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1 an NOAEL or LOAEL, such as an EDx, or a benchmark dose (BMD) or its BMDL. An RfD is 

2 described as “likely to be without appreciable risk” but the probabilistic language has not as yet 

3 been operationalized. There is no quantitative definition of “appreciable” and no mechanism to 

4 compute risk as a function of dose, so as to ascertain that the risk is indeed not appreciable. The 

5 risk at the RfD is not calculated, and it cannot be calculated within the current UF framework. 

6 Instead, a hazard quotient is computed as the ratio of a given exposure to the RfD, or a margin of 

7 exposure is estimated as the ratio of the POD to the human exposure level. 

8 Cancer endpoints are predominantly thought to have no population biological threshold. 

9 Although the terminology “threshold/nonthreshold” is still common in cancer dose-response 

10 discussions, the 2005 Cancer Guidelines propose a different terminology, whereby “nonlinear 

11 models” are those whose dose-response slope is zero at or above zero. In the natural language, 

12 and indeed in data analysis, it is difficult to distinguish the following situations: 

13 

14 • The response approaches zero as dose goes to zero, versus 

15 • The response slope goes to zero as dose goes to zero (nonlinear model). 

16 

This use of “nonlinear” is acknowledged to be idiosyncratic.31 17 The NAS review (NAS, 

18 2006a) does not consistently apply the terminology from the 2005 Cancer Guidelines, nor does it 

19 consistently distinguish the above two circumstances: “...the observed data are more consistent 

20 with a sublinear response that approaches zero at low doses rather than a linear dose response” 

21 (NAS, 2006a). The point of a nonlinear model in the sense of the 2005 Cancer Guidelines is that 

22 the response slope approaches zero. Both linear and nonlinear responses approach zero at low 

23 dose (in the absence of background). Since the terms “linear,” “sublinear,” and “nonlinear” 

24 invite confusion in this context, the following terminology is used in this document: 

25 

 

31From the 2005 Cancer Guidelines (U.S. EPA, 2005): “The term ‘nonlinear’ is used here in a narrower sense than 

its usual meaning in the field of mathematical modeling. In these cancer guidelines, the term ‘nonlinear’ refers to 

threshold models (which show no response over a range of low doses that include zero) and some nonthreshold 

models (e.g., a quadratic model, which shows some response at all doses above zero). In these cancer guidelines, a 

nonlinear model is one whose slope is zero at (and perhaps above) a dose of zero. .... Use of nonlinear approaches 

does not imply a biological threshold dose below which the response is zero.”
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1 Threshold Model: There is some threshold T > 0 such that the probability of response for 

2 any dose less than or equal to T is zero, and the probability is nonzero for any dose 

3 greater than T. 

4 Linear/ Linear above Threshold Model: For the linear model, the probability of response 

5 is proportional to the dose. For the linear over threshold model, the probability of 

6 response is zero for a dose below the threshold, and it is proportional to the excess dose 

7 over the threshold otherwise. 

8 Nonlinear Model: Any model that is not linear. 

9 Supralinear/ Supralinear above Threshold Model: For the supralinear model, the slope of 

10 the probability of response decreases as dose increases; in other words, the second 

11 derivative of the response curve is negative. For the supralinear above threshold model, 

12 the second derivative is negative above the threshold, and the response probability is zero 

13 below the threshold. 

14 Sublinear/Sublinear above Threshold Model: For the sublinear model, the slope of the 

15 probability of response increases as dose increases; in other words, the second derivative 

16 of the response curve is positive. For the sublinear above threshold model, the second 

17 derivative is positive above the threshold, and the response probability is zero below the 

18 threshold. 

19 Zero Slope at Zero Model: The slope of the response curve is zero at or above dose zero. 

20 

21 All of these models may be understood in an individual or population sense. According 

22 to the 2005 Cancer Guidelines, the trigger for applying the basic RfD methodology for cancer 

23 endpoints is sufficient evidence for the “zero slope at zero” model for the population. By 

24 definition, any sublinear, supralinear, or linear model above the threshold is a zero slope at zero 

25 (“ZS@Z”) model. 

26 The relation between individual and population models is not simple. Suppose for 

27 purposes of illustration that each individual has a threshold, and that the threshold values are 

28 uniformly distributed in the population, for some neighborhood of zero. (Note that “in the 

29 neighborhood of zero” has the mathematical meaning that all values are sufficiently close to 

30 zero; i.e., for some δ >0, the thresholds are uniformly distributed over the interval [0,δ].) 

31 Then (see Text Box 5-1) 

32 

33 A. If each person’s response function is linear for a dose above the threshold, then the 

34 population dose-response curve is quadratic, and the slope of the population curve is zero 

35 for dose zero (ZS@Z). 
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Text Box 5-1. Individual vs. Population 

Thresholds 

Suppose each individual has a threshold T, below 

dose T the probability of response is zero, and 

suppose that the values for T are uniformly 

distributed in some neighborhood of zero. 

A. Suppose that for dose δ > T, the probability of 

response is proportional to (δ − T). For dose δ

small enough to be in this neighborhood, the 

population response probability is proportional 

to: 

∫T∈(0,δ) (δ− T) dT = 

δ∫T∈(0,δ) dT − ∫T∈(0,δ) T dT = δ2 − δ2

/2 = δ2/2. 

The slope of the response function is found by 

taking the derivative with respect to δ: 

(d/dδ) δ2

/2 = δ, which goes to zero as δ → 0. 

B. Suppose now that the response probability at 

dose δ is proportional to ln(δ) – ln(T), all else 

being the same. The population response 

probability at dose δ is: 

 

∫T∈(0,δ) (ln(δ)− ln(T)) dT. 

To evaluate this, note that 

∫T∈(0,δ) ln(T) dT = δ ln(δ) − δ. 

Hence the integral is: 

δ ln(δ) – [ δ ln(δ) – δ] = δ. 

The derivative of this with respect to δ is 1, 

which goes to 1 as δ → 0.

2 B. If each person’s response function is log 

4 linear, for a dose above the threshold, 

6 then the population dose-response curve 

8 is linear without a threshold, and the 

10 slope of the population curve is positive 

12 for dose zero. 

14 

16 Of course these are not the only 

18 possibilities; in general, the population dose20 response curve depends on (1) the distribution of 

22 individual thresholds in the neighborhood of 

24 zero, (2) the dose-response curve for each 

26 individual, and (3) the dose metric. 

28 On the nature or the distribution of 

30 individual thresholds, often referred to as the 

32 population tolerance distribution, there is 

34 ongoing debate as to how receptor kinetics 

36 influence the shape of that distribution. Even 

38 within an individual, there is a lack of consensus 

40 as to whether receptor kinetics confer linear or 

42 sublinear attributes to downstream events, or 

44 whether receptor kinetics, themselves, are linear, 

46 sublinear, or supralinear. Whatever the nature of 

48 the form of receptor kinetics, it may have little or 

49 no influence on the ultimate population response. The kinetics of receptors is in the domain of 

50 the individual, rather than the population. As described previously, receptor kinetics are 

51 governed by the law of mass action, which leads to a low-dose proportional (linear) response 

52 model, generally modeled by some form of Hill function, the linear form being Michaelis53 Menten kinetics. Linearity can be violated by a number of inhibitory or stimulatory processes, 

54 but is a fundamental conserved characteristic in living systems. There is no a priori reason to 

55 believe that the shape of the dose-response curve in an individual has any relationship to the 

56 shape of the population response, particularly for quantal endpoints. Lutz and Gaylor (2008) 

57 present a strong argument for considering the population response in terms of the more 
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1 traditional tolerance distribution, which is likely the result of more variable factors than the 

2 shape of receptor kinetics. Perhaps more to the point, receptor activation is only the first of 

3 many events in the path to the apical event (a tumor in this example). Because there are 

4 undoubtedly numerous additional downstream events that must occur before the apical effect is 

5 observed, there are many opportunities for interindividual variability to become manifest in the 

6 tolerance distribution. Even at the first step, a more likely contributor to interindividual 

7 variability than the shape of the response is the dose resulting in the response, as measured by the 

8 ED50 (the aforementioned equilibrium dissociation constant Kd, Km in the Michaelis-Menten 

9 formulation), which shifts the response curve. Factors that influence shifts in response curves 

10 are generally modeled as normal or log-normal distributions and will most likely confer a log11 normal shape on the population tolerance distribution, particularly if there are a number of 

12 dependent sequential steps or distinct subpopulations (Lutz, 1999; Hattis et al., 1999; Hattis and 

13 Burmaster, 1994). 

14 Pertaining to the shape of the dose-response curve, because the criterion for applying the 

15 RfD model is a zero slope at dose zero, the role of dose metric must be fully appreciated. A 

16 slope is simply the ratio of a change on the vertical axis (the probability of response) relative to a 

17 change on the horizontal axis (the dose). Changing the dose metric from the dose to the 

18 logarithm of the dose dramatically changes this ratio. As dose goes to zero, the rate of change of 

19 log(dose) becomes infinite. Therefore, ANY dose-response relation with a finite slope at zero 

20 will appear to have a zero slope when graphed against log(dose). Text Box 5-2 illustrates this 

21 point for the mass action dose response, which has been proposed for receptor-mediated modes 

22 of action. 

23 To see how the discussion over threshold/nonthreshold might play out for TCDD, 

24 consider the equilibrium dissociation constant Kd for TCDD, which measures the binding affinity 

25 of TCDD to the AhR. Lower values indicate higher binding affinity and (other things being 

26 equal) greater risk. For Han/Wistar rats, the value Kd = 3.9 is reported; human values are 

27 reported as Kd =9.6 ± 7.8(0.3 – 38.8 with 15 of 67 donors without detectable binding) (Connor 

28 and Aylward, 2006). 

29 If AhR binding is necessary for carcinogenesis, then the majority of a human population 

30 may be much less susceptible than Han/Wistar rats, whereas a population threshold, if it exists, 

31 might be well below the Han/Wistar rat threshold (see Section 4.4.2.9). The NAS contends that 
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1 

2 

3 

4 an AhR-mediated mode of action indicates a threshold dose-response relation (NAS, 2006a). 

5 Presumably, the value of the threshold, if it exists, depends on the AhR binding affinity. 

Text Box 5-2. Logarithmic Transform of Mass Action Hyperbolic Model 

According to the operational model (Black and Leff, 1983; Motulsky and Christopoulos, 2003), the fractional 

occupancy of receptors by ligands follows a mass action law; the concentration of ligand-occupied receptors 

[LR] is equal to the product of the concentration of receptors [R] and the concentration of ligands [L], divided 

by the ligand concentration plus the binding affinity. This is a hyperbolic function written as [LR] = [R][L]/([L] 

+ Kd). 

The efficacy of a ligand can vary by tissue and endpoint; 

this is usually expressed as a hyperbolic function of 

[LR]; (TOP-BOTTOM) [LR]/([LR] + KE) where KE is the concentration of [LR] producing the effect halfway 

between TOP and BOTTOM. Combining these expressions gives: 

 [L](TOP-BOTTOM) (τ/(τ+1)) 

Response([L]) = ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯, 

 [L] + Kd/(τ+1) 

where τ is the tissue-/endpoint-specific ‘transducer constant’ [R]/KE. Writing δ for dose [L], this is proportional 

to the hyperbolic equation f(δ) = δ/(δ + K); K = Kd/(τ + 1). 

Switching the dose metric to δ* = log(δ), 

f(δ) becomes f(exp(δ*)) = exp(δ*)/[exp(δ*) + K]. 

f(δ) and f(δ*) are graphed below, for K = 1. 

f(δ) f(exp(δ*)) 

 

Although these represent the same dose-response relation, the graph of f(exp(δ*)) has zero slope as dose goes to 

zero (δ* goes to − ∞). In fact, df/dδ* = (df/dδ)(dδ/dδ*), and dδ/dδ* = eδ*→ 0 as δ* → −∞. The change in 

response relative to the change in log dose becomes infinitesimal, since log dose changes infinitely fast as dose 

goes to zero. f(δ*) is not a zero slope at zero model.

 
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1 Arguing for a population threshold in this case requires two types of information: 

2 

3 1. The distribution of the individual thresholds induced by, inter alia (among other things), 

4 the individual Kd values; and 

5 2. The dose-response function for values above the threshold induced by Kd. 

6 

7 Without this information, the two possibilities {A,B} enumerated on Pages 5-50 through 

8 5-51 cannot be distinguished, and the default linear relationship applies response probability is 

9 modeled as a linear function of dose, for dose near zero. However, from the 2005 Cancer 

10 Guidelines: “When adequate data on mode of action provide sufficient evidence to support a 

11 nonlinear mode of action for the general population (emphasis added) and/or any subpopulations 

12 of concern, a different approach—a reference dose/reference concentration that assumes that 

13 nonlinearity—is used.” In current terminology, the reference dose methodology applies if there 

14 is sufficient evidence supporting a “zero slope at zero” model; otherwise, the linear nonthreshold 

15 model applies by default. 

16 In principle, the choice between the above models could fall within the purview of dose17 response modeling. However, standard statistical methods encounter well-known difficulties in 

18 detecting thresholds. Without going into detail, suffice to say that the maximum likelihood 

19 estimate of response probability when no responses are observed in a finite sample is always 

20 zero. That said, some researchers have attempted to identify thresholds (Mackie et al., 2002; 

21 Alyward et al., 2003) or nonlinearity (Hoel and Portier, 1994) by means of parameter estimation 

22 of appropriate models. A review of 344 rodent bioassays on 315 chemicals led to the following 

23 conclusion by Hoel and Portier (1994): 

24 

25 We have also found that the oft-held belief that genotoxic compounds typically 

26 follow a linear dose-response pattern and that nongenotoxic compounds follow a 

27 nonlinear or threshold dose response pattern is not supported by the data. In fact 

28 we find the opposite with genotoxic compounds differing from linearity more 

29 often than nongenotoxic compounds. 

30 

31 The choice between a linear and “zero slope at zero” model in current practice does not 

32 fall under dose-response model fitting, it is made on the basis of a structured narrative as set 

33 forth in the 2005 Cancer Guidelines (U.S. EPA, 2005): 
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1 In the absence of sufficiently, scientifically justifiable mode of action information, 

2 EPA generally takes public health-protective, default positions regarding the 

3 interpretation of toxicologic and epidemiologic data: animal tumor findings are 

4 judged to be relevant to humans, and cancer risks are assumed to conform with 

5 low dose linearity. ... The linear approach is used when: (1) there is an absence of 

6 sufficient information on modes of action or (2) the mode of action information 

7 indicates that the dose-response curve at low dose is or is expected to be linear. 

8 Where alternative approaches have significant biological support, and no 

9 scientific consensus favors a single approach, an assessment may present results 

10 using alternative approaches. A nonlinear approach can be used to develop a 

11 reference dose or a reference concentration. 

12 

13 5.2.3.4.1.3. Extrapolation method.

14 The 2005 Cancer Guidelines (U.S. EPA, 2005) emphasize that the method used to 

15 characterize and quantify cancer risk from a chemical is determined by what is known about the 

16 MOA of the carcinogen and the shape of the cancer dose-response curve. 

17 The NAS was critical of EPA’s decision to apply linear low-dose extrapolation for 

18 TCDD cancer assessment in the 2003 Reassessment and encouraged the use of a nonlinear 

19 approach. The 2005 Cancer Guidelines state that a nonlinear approach should be used when 

20 “there are sufficient data to ascertain the mode of action and conclude that it is not linear at low 

21 doses and the agent does not demonstrate mutagenic or other activity consistent with linearity at 

22 low doses.” 

23 Receptor modeling theory (as outlined in the 2003 Reassessment, Part II, Chapter 8) 

24 indicates that exogenous compounds which operate through receptor binding mechanisms, such 

25 as TCDD, will follow a linear dose-response binding in the 1−10% receptor occupancy region. 

26 This theory has been supported by empirical findings and suggests that the proximal biochemical 

27 effects (such as enzyme induction) and transcriptional reactions for TCDD may also follow 

28 linear dose-response kinetics. More distal toxic effects could take any one of multiple forms 

29 (i.e., linear, sublinear, supralinear or threshold) depending on (1) the toxic mechanism; 

30 (2) location on the dose-response curve; and (3) interactions with other processes such as 

31 intracellular protein binding and cofactor induction/repression. 

32 In the case of TCDD, many adverse effects experienced at low exposure levels have too 

33 much data variability to distinguish on a statistical basis (goodness-of-fit) between dose-response 

34 curve options, and whether the dose-response is linear, sublinear or supralinear. For tumor 
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1 responses, with the exception of squamous cell carcinoma of the oral mucosa and adenomas or 

2 carcinomas of the pancreas, which were fit with a linear multistage model, the tumor endpoints 

3 in the NTP (2006) study using female Sprague-Dawley (S-D) rats are all best fit with a sublinear 

4 model (i.e., the multistage model fits to tumor incidence data were second or third degree; see 

5 Table 5-13 and Appendix F). For all other tumor incidence data from all other cancer bioassays 

6 that met the study inclusion criteria (NTP, 1982, Toth et al., 1979, and Kociba et al., 1978), the 

7 multistage model fit was linear (first degree), when based on either administered dose or 

8 modeled blood concentrations (see Appendix F). 

9 Another issue of potential importance when evaluating the shape of the dose-response 

10 curve for low dose effects is the concept of “interacting background.” The concept of interacting 

11 background refers to a pathological process in the exposed population that shares a causal 

12 intermediate with the toxicant being evaluated. On this issue, a recent NAS committee (NAS, 

13 2009) contended that 

14 

15 ...the current EPA practice of determining “nonlinear” MOAs does not account 

16 for mechanistic factors that can create linearity at low dose. The dose-response 

17 relationship can be linear at a low dose when an exposure contributes to an 

18 existing disease process (Crump et al.1976, Lutz 1990). Effects of exposures that 

19 add to background processes and background endogenous and exogenous 

20 exposures can lack a threshold if a baseline level of dysfunction occurs without 

21 the toxicant and the toxicant adds to or augments the background process. Thus, 

22 even small doses may have a relevant biologic effect. That may be difficult to 

23 measure because of background noise in the system but may be addressed through 

24 dose-response modeling procedures. Human variability with respect to the 

25 individual thresholds for a nongenotoxic cancer mechanism can result in linear 

26 dose-response relationships in the population (Lutz 2001) (NAS, 2009, p. 130). 

27 

28 AhR activation could be considered a causal intermediate in several disease processes. 

29 Recent studies have linked AhR activation in the absence of exogenous ligand to a multitude of 

30 biological effects, ranging from control of mammary tumorigenesis to regulation of 

31 autoimmunity (reviewed in Hahn et al., 2009). While the level of background activation of AhR 

32 by endogenous compounds (and/or exogenous compounds other than TCDD) in the human 

33 population is unknown, given the ubiquitous nature of several of the known endogenous and 

34 exogenous AhR ligands, it is reasonable to assume that a certain baseline level of AhR activation 
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1 exists in the population. The degree to which TCDD exposure augments this baseline level of 

2 AhR activation is unknown. 

3 The 2005 Cancer Guidelines (U.S. EPA, 2005) recommend that the method used to 

4 characterize and quantify cancer risk from a chemical be determined by what is known about the 

5 mode of action of the compound and the shape of the cancer dose-response curve. The linear 

6 approach is used if the mode of action is not understood (U.S. EPA, 2005). In the case of 

7 TCDD, (1) the mode of action of TCDD-induced carcinogenesis beyond potential AhR 

8 activation is unknown; (2) information is lacking to determine confidently the shape of the dose9 response curves for various adverse endpoints (including cancer) in humans or experimental 

10 animals; (3) there is undoubtedly a certain level of interacting background (i.e., AhR activation 

11 by endogenous ligands) in the human population; (4) many of the rodent cancer dose-response 

12 relationships (NTP, 1982; Toth et al., 1979; Kociba et al., 1978) are linear (first degree 

13 multistage model fit) when based on either administered dose or modeled blood concentrations; 

14 and (5) higher human interindividual variability compared to experimental rodents will tend to 

15 shift the shape of the dose-response towards linear (relative to rodents). Therefore, a linear low16 dose extrapolation approach was used to estimate human carcinogenic risk associated with 

17 TCDD exposure. 

18 

19 5.2.3.4.1.4. Consideration of nonlinear methods.

20 While the 2005 Cancer Guidelines deem linear extrapolation to be most appropriate for 

21 TCDD, EPA has carefully considered the NAS recommendation to provide risk estimates using 

22 both linear and nonlinear methods. 

23 The 2005 Cancer Guidelines state 

24 

25 For cases where the tumors arise through a nonlinear mode of action, an oral 

26 reference dose or an inhalation reference concentration, or both, should be 

27 developed in accordance with EPA’s established practice for developing such 

28 values ... This approach expands the past focus of such reference values 

29 (previously reserved for effects other than cancer) to include carcinogenic effects 

30 determined to have a nonlinear mode of action. 

31 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 In this section, EPA presents two illustrative examples of RfD development for 

2 carcinogenic effects of TCDD. Each of these examples focuses on data derived from animal 

3 bioassays as described in Section 2.4.2. 

4 

5 5.2.3.4.1.4.1. Illustrative RfDs based on tumorigenesis in experimental animals. 

6 TCDD has been shown to be a multisite carcinogen in both sexes of several species of 

7 experimental animals. It also has been shown to be carcinogenic to humans. Most of the 

8 available quantitative human epidemiologic data related to TCDD carcinogenesis are for all 

9 cancer mortality. Mortality is a frank effect and is generally considered to be inappropriate for 

10 RfD development, therefore, the illustrative example below utilizes available evidence from 

11 experimental animals. Table 5-16 presents candidate PODs and RfDs for TCDD carcinogenicity 

12 based on combined tumor responses from the animal bioassays described in Section 2.4.2. The 

13 PODs from the NTP (1982, 2006) and Kociba et al. (1978) animal studies were derived from 

14 Bayesian multitumor dose-response modeling (as described in Section 5.2.3.2, Table 5-15) using 

15 a BMR of 1%. Because only TCDD-induced liver tumors were reported by Toth et al. (1979), 

16 the BMR of 1% (POD) from that study was generated using a first degree linear multistage 

17 model (see Table 5-14). Following BMD modeling, BMDLHEDs were then estimated using 

18 alternative dose metrics from the Emond model as described in Section 3. The illustrative RfDs 

19 were derived by dividing the BMDLHEDs by appropriate uncertainty factors. In each instance, a 

20 total UF of 30 was applied, comprising factors of 3 for the toxicodynamic component of the 

21 interspecies extrapolation factor (UFA) and a factor of 10 for human interindividual variability 

22 (UFH). 

23 As shown in Table 5-16, the illustrative RfDs for TCDD-induced tumors range from 

24 3.6E-11 for liver and lung tumors in male mice (NTP, 1982) to 1.0E-9 for adrenal cortex, tongue 

25 and nasal/palate tumors in male rats (Kociba et al., 1978). This illustrative RfD range for TCDD 

26 tumorigenesis falls within the range of candidate RfDs for noncancer TCDD effects presented in 

27 Table 4-5. 

28 

29 

30 
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1 5.2.3.4.1.4.2. Illustrative RfDs based on hypothesized key events in TCDD’s MOAs for liver 

2 and lung tumors. 

3 As described in Section 5.1, most evidence suggests that the majority of toxic effects of 

4 TCDD are mediated by interaction with the AhR. EPA considers interaction with the AhR to be 

5 a necessary, but not sufficient, event in TCDD carcinogenesis. The sequence of key events 

6 following binding of TCDD to the AhR and that ultimately leads to the development of cancer is 

7 unknown. While the mode of action of TCDD in producing cancer has not been elucidated for 

8 any tumor type, the best characterized carcinogenic actions of TCDD are in rodent liver, lung, 

9 and thyroid. The hypothesized sequence of events following TCDD interaction with the AhR is 

10 markedly different for each of these three tumor types. Additionally, no detailed hypothesized 

11 mode of action information exists for any of the other reported tumor types. 

12 The endpoints selected for this illustration were evaluated to provide insight into the 

13 quantitative relationships between tumor development and precursor events in TCDD-induced 

14 carcinogenesis. The endpoints described below may or may not be biologically adverse in 

15 themselves; the intent herein was to consider TCDD-induced biochemical and cellular changes 

16 that could lead to subsequent tumor development. 

17 In the following exercise, illustrative RfDs were derived for key events in TCDD’s 

18 hypothesized modes of action in the liver and lung. No appropriate dose-response data were 

19 identified for key events in TCDD’s hypothesized MOA for thyroid tumors in a 

20 sex/species/strain that has been shown to develop thyroid tumors (i.e., female B6C3F1 mice and 

21 male and female Osborne-Mendel rats [NTP, 1982]). 

22 As this is an illustrative exercise only, only studies that were originally identified in 

23 Section 2 for potential noncancer dose-response modeling were evaluated here (see Section 2.4.2 

24 for study details). There may be additional studies available in the literature that would further 

25 inform the dose-response assessment of these endpoints. 

26 Additionally, for animal model consistency, only results from studies conducted in 

27 female S-D rats are presented here. The majority of the available information on TCDD 

28 carcinogenicity (and TCDD carcinogenic precursor events) comes from studies conducted in 

29 female S-D rats and the most recent TCDD carcinogenicity study was conducted in female S-D 

30 rats (NTP, 2006). While both Kociba et al. (1978) and NTP (2006) have conducted TCDD 

31 carcinogenicity studies in female S-D rats, different substrains were used; this difference in 
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1 substrain may have resulted in the different carcinogenic responses reported from the two 

2 studies. While the carcinogenicity of TCDD in female S-D rats has been well characterized, this 

3 animal model does not exhibit the full suite of tumor responses reported for TCDD (for instance, 

4 female S-D rats have not been shown to develop thyroid tumors). Additionally, the most 

5 sensitive single tumor response in female S-D rats from NTP (2006) is squamous cell carcinoma 

6 of the oral mucosa (see Section 5.2.3.2), a tumor type for which no mode of action information 

7 exists. Therefore, the illustrative RfDs described below may not be protective against all tumor 

8 types. 

9 For each endpoint, PODs for illustrative cancer RfD development were identified as 

10 described for the noncancer RfD derivation in Section 4. Briefly, for the endpoints identified 

11 below, the NOAELHEDs and/or LOAELHEDs were determined based on EPA analysis of the 

12 original data presented by the study author (see Section 2.4.2 for details) and by application of 

13 the Emond PBPK models as described in Section 3.3.4. BMDLHEDs were determined as 

14 described in Section 4.2 for all data sets amenable to BMD modeling. Modeling outputs for the 

15 endpoints are presented in Appendices E and G as noted in Table 5-17. The illustrative RfDs 

16 were derived by dividing the POD by appropriate uncertainty factors as indicated in Table 5-17. 

17 

18 5.2.3.4.1.4.2.1. Liver tumors. 

19 Figure 5-4 presents one hypothesized mode of action for TCDD-induced liver tumors in 

20 rats. TCDD activation of the AhR leads to a variety of changes in gene expression, including 

21 increased CYP1A1 mRNA and subsequent increases in CYP1A1 activity. These alterations in 

22 gene expression are hypothesized to lead to hepatotoxicity, followed by compensatory 

23 regenerative cellular proliferation and subsequent tumor development. The details of the 

24 mechanism of TCDD-induced hepatotoxicity have not been fully determined but both CYP 

25 induction and oxidative stress have been postulated to be involved (Maronpot et al., 1993; 

26 Viluksela et al., 2000). Additionally, oxidative DNA damage has been implicated in liver tumor 

27 promotion (Umemura et al., 1999). The enhanced cell proliferation arising from either altered 

28 gene expression or hepatotoxicity, or both, could be the principal factor leading to promotion of 

29 hepatocellular tumors (Whysner and Williams, 1996). 

30 A dose-response relationship exists for TCDD-mediated hepatotoxicity, and this parallels 

31 the dose-response relationship for tumor formation (or formation of foci of cellular alteration as a 
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1 surrogate of tumor formation). However, the dose-response relationship for other 

2 TCCD-induced responses such as enhanced gene expression is different from the dose-response 

3 for tumor formation in terms of both efficacy and potency (see Popp et al. [2006] for review). 

4 A representative endpoint for each of the hypothesized key events following AhR 

5 activation for TCDD-induced liver tumors was identified and is shown in Figure 5-4. Illustrative 

6 RfDs based on each representative endpoint are shown in Table 5-17. 

7 

8 5.2.3.4.1.4.2.2. Lung tumors. 

9 Far less is known about TCDD’s mode of action in the lung. Figure 5-5 presents two 

10 hypothesized modes of action for TCDD-induced lung tumors in rats. The first hypothesized 

11 mode of action of TCDD in the lung involves disruption of retinoid homeostasis in the liver. 

12 Retinoic acids and their corresponding nuclear receptors, the RARs and the RXRs, work together 

13 to regulate cell growth, differentiation, and apoptosis. It is hypothesized that TCDD, through 

14 activation of the AhR, can affect parts of the complex retinoid system and/or other signaling 

15 systems regulated by, and/or cross-talking with, the retinoid system (reviewed in Nilsson and 

16 Hakansson, 2002). These effects are then hypothesized to lead to lung tumor development, 

17 however the mechanisms underlying this hypothesis are not well-defined. The second 

18 hypothesized mechanism for the carcinogenic action of TCDD in the lung is through induction of 

19 metabolic enzymes. Through activation of AhR and subsequent induction of metabolizing 

20 enzymes (such as CYP1A1), TCDD may enhance bioactivation of other carcinogens in lung 

21 (Tritscher et al., 2000). However, there are few studies to support this hypothesis. 

22 Representative endpoints could only be identified for two of the hypothesized key events 

23 following AhR activation for TCDD-induced lung tumors. These endpoints are presented in 

24 Figure 5-5. Illustrative RfDs based on each of these two representative endpoints are shown in 

25 Table 5-17. There is insufficient information to form any conclusions on the quantitative 

26 progression to tumorigenicity or on the relative protection afforded by preventing the key events 

27 shown. 

28 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 5.2.3.4.1.4.2.3. Limitations of illustrative RfDs based on hypothesized key events in TCDD’s 

2 MOAs for liver and lung tumors. 

3 A trend for increasing RfD values that follows the progression of endpoints towards the 

4 production of tumors is evident. However, there are a number of factors that prevent making 

5 strong conclusions based on this exercise. These limitations include the following 

6 

7 • This example addresses only two tumor types in one species, strain and sex (female S-D 

8 rats), with little information available on the hypothesized mode of action for lung 

9 tumors. No mode of action information is available for the most sensitive tumor type in 

10 this animal model (squamous cell carcinoma of the oral mucosa). Therefore, it is 

11 possible that the illustrative RfDs presented in this example would not be protective 

12 against all tumor types in female S-D rats. Importantly, other animal models have been 

13 shown to be more sensitive to TCDD-induced carcinogenesis based on combined tumor 

14 analysis (see Section 5.2.3.2); an RfD based on tumorigenesis in this animal model may 

15 not be protective against tumorigenesis in other, more sensitive, animal models (or, by 

16 extension, in humans). 

17 • Several of the BMDLs are based on poorly-fitting models, such that the RfD is based on 

18 a LOAEL, which is not a particularly good measure for comparison across endpoints 

19 (e.g., LOAELs are subject to influences of dose spacing in bioassays). Furthermore, the 

20 hepatotoxicity BMDL based on a dichotomous 10% BMR, is not directly comparable to 

21 all the other BMDLs based on a continuous 1 standard-deviation BMR (Crump, 2002). 

22 • The endpoints selected as representative of each hypothesized key event may not be the 

23 most appropriate choices. These particular endpoints were chosen because they were the 

24 most sensitive indicator (i.e., lowest POD) from the available data or were the only 

25 available choice based on a lack of data for other effects related to the hypothesized key 

26 event. 

27 • The optimum timing of these events may not be reflected in the endpoints selected. 

28 Almost certainly, changes in gene expression are early events, such that a single 

29 exposure should be relevant, as in the mRNA changes reported after a single TCDD 

30 exposure (Vanden Heuvel et al., 1994), although it is not known whether the magnitude 

31 of these changes would be altered after longer-term exposure, or whether longer-term 

32 exposure would be more relevant to downstream events. Similarly, single exposures for 

33 induction of CYP enzymes would seem to relevant as a measure of the immediate effect, 

34 but it may be longer-term repeated CYP activity that is important for longer-term 

35 downstream events; Table 5-17 shows a nominal order-of-magnitude difference in effect 

36 levels for similar effect magnitudes (ca. 20-fold) from single exposures (Kitchin and 

37 Woods, 1979) and long-term exposures (53-weeks; NTP, 2006). The relevant exposure 

38 durations for oxidative stress and later effects are longer term, so a measurement of 

39 oxidative stress at 90-days in a rodent may be appropriate; Wyde et al. (2001) suggest 

40 that induction of 8-oxo-dG DNA adducts are a result of longer-term oxidative stress 

41 because of the lack of effect of single exposures. Hepatotoxicity and hepatocellular 
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1 proliferation events would appear at successively later times, but the effective exposure 

2 levels would depend heavily on the endpoints chosen to represent those events and the 

3 time at which they were measured. The toxic hepatopathy endpoint reported in NTP 

4 (2006), is a general measure of mild to moderate liver toxicity, but is measured only at 

5 the end of the study when tumors have already appeared. Hepatocyte hypertrophy, 

6 measured at 31weeks may be more duration-relevant, but may not indicate actual 

7 hepatocellular toxicity. 

8 • The lowest of the tested doses may well be much higher, given that all animal diets are 

9 contaminated to a certain extent by TCDD, resulting in initial TCDD body burdens in all 

10 animals. Vanden Heuvel et al. (1994) reported TCDD liver concentrations in control 

11 animals almost as high as for the low-dose group, which could equate to a significant 

12 increase in the actual exposure experienced by the low-dose group. A similar effect on 

13 the low-dose group (0.45 ng/kg) in Kitchin and Woods (1979) is possible, although they 

14 did not report control animal tissue concentrations. Higher exposure levels or longer15 term exposures would not be affected to the same degree, as administered TCDD levels 

16 would likely be large compared to initial body burden or low-level feed stock exposure. 

17 

18 Given the limitations described above, establishing an unambiguous progression of 

19 effects is extremely problematic given the lack of sufficient data. Identifying a RfD that could 

20 be considered to be protective against tumorigenesis in humans based on these data and models 

21 is subject not only to the determination of effective low doses for the RfDs in Table 5-17 but also 

22 to the determination of effective exposures that could be considered to be protective of all other 

23 tumor types in female S-D rats as well as all other animal models. The latter would entail 

24 identifying precursors that are sufficient in themselves for progression to tumorigenesis for all 

25 tumor types. Given the disparate sequence of hypothesized key events following TCDD-induced 

26 AhR activation for the tumor types for which some information is available, AhR 

27 binding/activation is the only key event that is likely to be shared across tumor types. No 

28 appropriate quantitative data on AhR binding/activation by TCDD in relevant animal models 

29 were located; therefore, an illustrative RfD based on TCDD AhR activation could not be 

30 developed. 

31 Simon et al. (2009) present a similar analysis for the liver tumors observed in the NTP 

32 (2006) study, showing a progression of effects from early biochemical events to irreversible liver 

33 toxicity, culminating in tumorigenesis. While illustrative of the putative tumor-promoting MOA 

34 for TCDD, the limitations of using such an approach within the context of an assessment of the 

35 overall carcinogenic risk of TCDD as detailed above still apply. Simon and colleagues also 
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1 present RfDs for liver tumors and several precursor endpoints. All the RfDs presented in Simon 

2 et al. (2009) are essentially equivalent and are 1 to 3 orders of magnitude higher than the RfDs 

3 for equivalent endpoints presented in Table 5-17. These discrepancies are partly due to the fact 

4 that the Emond PBPK models (Emond et al., 2004, 2005, 2006; see also Section 3.3.4) used in 

5 this document predicts lower TCDD intakes for similar tissue concentrations than the CADM 

6 kinetic model (Carrier et al., 1995a, b; Aylward et al., 2005b) used by Simon and colleagues. 

7 However, a larger contributor to these discrepancies is the use of a chemical-specific adjustment 

8 factor (CSAF) of 0.1 for the toxicodynamic component of the interspecies uncertainty factor by 

9 Simon et al. (2009), while EPA used an uncertainty factor of 3. EPA does not find that the in 

10 vitro evidence presented by Simon et al. in support of a CSAF of 0.1 for interspecies 

11 toxicodynamics meets the burden of proof necessary for a reduction in this uncertainty factor. 

12 

13 5.2.3.4.1.4.3. Effect of dose metric on linearity of response. 

14 EPA agrees that there is evidence for sublinearity in the TCDD carcinogenic response in 

15 rats. The lung and liver tumor endpoints in the NTP (2006) study using female S-D rats are best 

16 fit with a sublinear model. Except for squamous cell carcinoma of the oral mucosa and 

17 combined carcinomas and adenomas of the pancreas, the multistage model fits to tumor 

18 incidence data were second or third degree (see Table 5-13 and Appendix F). Figure 5-6 shows 

19 the multistage model fit for cholangiocarcinomas as a representative example. For all other 

20 tumor incidence data in all other cancer bioassays, the multistage model fit was linear (first 

21 degree), when based on either administered dose or modeled blood concentrations (see 

22 Appendix F). In further evaluating the combined liver tumor incidence for female S-D rats in 

23 Kociba et al. (1978) (as re-evaluated by Goodman and Sauer, 1992; “Kociba/G&S data”), using 

24 an unconstrained dichotomous Hill model, a progression from supralinearity to linearity to 

25 sublinearity is obtained when fitting the model to the data based on progressively more relevant 

26 dose metrics. Figure 5-7 shows the Hill model fits to the Kociba/G&S data based on 

27 administered dose, modeled blood concentrations and modeled liver AhR-bound 

concentrations.32 28 Details of the model fits are presented in Table 5-18. The Hill coefficient 

29 parameter values are also given in the caption to Figure 5-7. A Hill coefficient of 1 indicates 

 

32The modeled TCDD compartmental concentrations were obtained using the Emond rodent PBPK model described 

in Section 3. 
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1 linearity, while values less than 1 indicate supralinearity and values greater than 1 indicate 

2 sublinearity. The TCDD blood concentration metric is used in this document as it was 

3 determined to be the most relevant toxicokinetically-equivalent metric across species. However, 

4 as discussed in Section 3, the AhR-bound TCDD concentration may be the most relevant metric 

5 for liver effects, but this metric is also the most uncertain. Uncertainty aside, the tendency for 

6 the model fits to move towards sublinearity with increasing relevance of dose metric suggests 

7 that the “true” tumorigenic response may be sublinear in female S-D rat liver. The progression 

8 towards sublinearity is likely to be due to the failure of the less-relevant metrics to reflect the 

9 dose-dependent elimination of TCDD in the liver. The range of the administered doses in 

10 Kociba et al. (1978) is much larger than that of the effective internal TCDD concentrations. This 

11 “stretches out” the response relative to dose, resulting in a supralinear model fit. The blood12 concentration metric (as determined using the Emond PBPK model) partially accounts for dose13 dependent elimination, but does not account for the partition of effective (AhR-bound) and 

ineffective (CYP1A2-bound) TCDD in the liver33 14 , thus yielding only a partial correction to the 

15 shape of the dose-response curve (see Section 3). Thus, hypothesizing that the “true” response 

16 for liver tumors is sublinear in female S-D rats and assuming that the same mechanisms are 

17 operant in humans, a zero-slope at zero dose relationship could be proposed for liver tumors, 

18 allowing for an RfD approach for liver tumorigensis (as described above). This approach, 

19 however, would not be considered to be protective against all tumors given the lack of 

20 appropriate mode of action information for many of the known tumor types, particularly 

21 squamous cell carcinoma of the oral mucosa in this animal model. 

22 On balance, however, the evidence is only suggestive and the arguments are speculative. 

23 Even if a sublinear response could be shown for rodent tumors, the greater interindividual 

24 variability in the human population would tend to shift the response towards linear (Lutz, 1999; 

25 Hattis, 1996). Hattis (1996), however, points out that, assuming a lognormal distribution of 

26 susceptibilities, increasing the variance may result in a linear or supralinear shape for a large 

27 portion of the response curve but never brings the relationship to a true low-dose linear form. 

28 How close to zero the linearity extends and how much it encompasses the risks of concern 

29 depends on the specifics of the particular human variability scenario. 

 

33 Modeling the response against the whole liver concentrations results in a supralinear fit similar to administered 

dose (Hill coefficient = 0.71). 
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1 5.3. DERIVATION OF THE TCDD ORAL SLOPE FACTOR AND CANCER RISK 

2 ESTIMATES 

3 EPA was able to derive candidate OSFs for all cancer mortality from human 

4 epidemiologic studies as well as for individual and combined tumor incidence from rodent 

5 cancer bioassays. Each of these studies were selected for TCDD dose-response modeling using 

6 the study inclusion criteria outlined in Section 2. The derivation of these OSFs can be found for 

7 the epidemiologic data in Section 5.2.3.1 and for the rodent bioassay data in Section 5.2.3.2. 

The OSFs based on epidemiologic studies from three cohorts ranged from 3.75 × 105 8 to 

2.5 × 106 9 per mg/kg-day (see Tables 5-1 and 5-3). For the animal data, OSFs based on 

10 individual tumors were developed for 25 study/sex/endpoint combinations, and the results ranged 

from 1.8 × 104

 to 5.9 × 106 11 per mg/kg-day (see Table 5-14). The OSFs based on combined 

tumors were developed for 7 study/sex combinations, and the results ranged from 3.2 × 105 12 to 

9.4 × 106 13 per mg/kg-day (see Table 5-15). 

14 As recommended by expert panelists at EPA’s 2009 Dioxin Workshop (U.S. EPA, 

15 2009c) and in the 2005 Cancer Guidelines (U.S. EPA, 2005), EPA has chosen to give higher 

16 consideration to the human epidemiologic data rather than the animal bioassay data in 

17 developing an OSF for TCDD. Candidate OSFs derived from the human data are consistent with 

18 the animal bioassay OSFs; specifically, the human OSFs fall within the same range as the animal 

19 bioassay OSFs. Because all the human and animal studies were considered to be of high quality 

20 and yielded similar ranges of OSFs, EPA has chosen to rely on the epidemiologic data for OSF 

21 derivation. 

22 The strengths and limitations of the five epidemiological studies meeting the inclusion 

23 criteria for cancer dose-response modeling are summarized in Table 5-19. Among the human 

24 studies, the occupational TCDD exposures in the NIOSH and Hamburg cohorts are assumed to 

25 be reasonably constant over the duration of occupational exposure. In contrast, the TCDD 

26 exposure patterns in the Seveso and BASF cohorts are associated with industrial accidents; as a 

27 consequence, the exposure patterns are acute, high dose followed by low-level background 

28 exposure. Such exposure patterns similar to those experienced by the BASF and Seveso cohorts 

29 have been shown to yield higher estimates of risk when compared to constant exposure scenarios 

30 with similar total exposure magnitudes (Kim et al., 2003; Murdoch and Krewski, 1988; Murdoch 

31 et al., 1992). Thus, EPA has judged that the NIOSH and Hamburg cohort response data are more 
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1 relevant than the BASF and Seveso data for assessing cancer risks from continuous ambient 

2 TCDD exposure in the general population. 

3 The NIOSH (Steenland et al., 2001; Cheng et al., 2006) and Hamburg (Becher et al., 

4 1998) cohort studies report cumulative TCDD levels in the serum for cohort members. The most 

5 significant difference among the Cheng et al. (2006) analysis and those of Steenland et al. (2001) 

6 and Becher et al. (1998) is the method used to back-extrapolate exposure concentrations based 

7 on serum TCDD measurements. Steenland et al. (2001) and Becher et al. (1998) back8 extrapolated exposures and body burdens using a first-order model with a constant half-life. In 

9 contrast, Cheng et al. (2006) back-extrapolated body burdens using a kinetic modeling approach 

10 that incorporated concentration- and age-dependent elimination kinetics. 

11 Although all three of these are high-quality studies, the kinetic modeling used by Cheng 

12 et al. (2006) is judged to better reflect TCDD pharmacokinetics, as currently understood, than the 

13 first-order models used by Steenland et al. (2001) and Becher et al. (1998). EPA believes that 

14 the representation of physiological processes provided by Cheng et al. (2006) is more realistic 

15 than the assumption of simple first-order kinetics and this outweighs the attendant modeling 

16 uncertainties. Furthermore, the use of kinetic modeling is consistent with recommendations both 

17 by the NAS and the Dioxin Workshop panel. 

18 However, as discussed in Section 3.3.2, the kinetic model that they employed does have 

19 certain limitations, including the fact that it has been calibrated based on a relatively small 

20 number of human subjects. In addition, their kinetic model does not allow body mass index 

21 (BMI; and hence fat content) to vary with age, which may bias the model results. Nonetheless, 

22 EPA prefers the increased technical sophistication of the dose estimates used in the cancer 

23 mortality risk estimates derived from Cheng et al. (2006) to those derived from Steenland et al. 

24 (2001). 

25 EPA, therefore, has decided to use the results of the Cheng et al. (2006) study for 

26 derivation of the TCDD OSF based on total cancer mortality as calculated by EPA using 

27 data and models from the Cheng et al. (2006) study as described in Section 5.2.3.1.2. 

28 Table 5-3 shows the oral slope factors at specific target risk levels (OSFRLs) which range 

from 3.7 × 105 to 1.3 × 106 29 per (mg/kg-day). EPA recommends the use of an OSF of 

1.3 × 106 per (mg/kg-day) when the target risk range is 10−5 to 10−7 30 . 

31 
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1 5.3.1. Uncertainty in Estimation of Oral Slope Factors From Human Studies

2 A substantial degree of uncertainty is associated with the estimation of slope factor 

3 values and cancer risk specific doses for TCDD based on the epidemiological studies. In some 

4 instances, the influence of a given factor is theoretically amenable to analysis, but such 

5 investigation is limited by the availability of sufficiently detailed data to support such an 

6 analysis. In other cases, only very broad ranges can be placed on the uncertainty associated with 

7 a given feature of the analysis, or uncertainties must be discussed qualitatively. 

8 

9 5.3.1.1. Uncertainty in Exposure Estimation 

10 The major technical challenge within each of the epidemiological studies was developing 

11 relevant and precise estimates of exposure. While Warner et al. (2002) collected blood samples 

12 relatively close to the time of the Sevesso accident and could reasonably estimate peak exposures 

13 based on these collected samples, in the case of the Becher et al. (1998), Ott and Zober (1996), 

14 Steenland et al. (2001), and Cheng et al. (2006) studies, the major exposure issues included the 

15 following 

16 

17 • Selecting (an) appropriate dose metric(s) for dose-response modeling, 

18 • Estimating serum TCDD levels for the entire cohort based on measurements from a 

19 smaller number of the subjects in the cohort collected long after the occupational 

20 exposures had occurred, and then assigning exposures to the remaining members of the 

21 cohort based on qualitative job classifications. 

22 • Estimating time-weighted average tissue doses (e.g., lipid-average serum concentration 

23 over time) based on single samples taken at one point in time. (Except for the Becher et 

24 al. [1998] analysis where one of the study strengths was their estimate of TCDD half life, 

25 which utilized repeated measurements from a subset of their cohort). 

26 

27 In the Becher et al. (1998), Steenland et al. (2001), and Cheng et al. (2006) studies, dose28 response modeling was performed using ppt-years lipid-adjusted serum concentration as the 

29 primary dose metric for TCDD. The choice of serum concentration was natural in the sense that 

30 the serum TCDD was the only direct measurement of exposure or dose that was available. In 

31 addition, as discussed in Section 3.3.4, serum concentration is a reasonable index of total tissue 

32 concentration (target organ dose), and lipid-adjusted serum concentration provides a reasonable 

33 index of TCDD in the fatty components of tissues. Ott and Zober (1996) used ng/kg body 
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1 weight at the time of the accident as the primary dose metric, and EPA later converted these to 

2 units of ppt-years lipid-adjusted serum concentration. 

3 The decision to use cumulative serum concentrations (ppt-years) as the primary dose 

4 metric for carcinogenicity is based on the understanding that time weighted concentrations (over 

5 a chronic exposure period) are the most appropriate dose measures for cancer risk assessment. 

6 This may not be strictly true if cancer induction by TCDD is considered to be a “threshold 

7 process.” However, as discussed in Section 5.2, there are reasonable grounds to believe that the 

8 assumption of low-dose linearity is reasonable for TCDD, especially when calculating 

9 population risks where the effects of interindividual variability must be taken into account. 

10 In addition to the issue of low-dose thresholds, the rationale for using cumulative dose 

11 metrics also can fail at high doses if the adverse response in question involves a step that is 

12 saturable (e.g., where there is a maximum level of response that cannot be exceeded owing to a 

13 rate-limited process). There is some evidence for such a phenomenon in the NIOSH cohort 

14 where cancer risks in the highest exposure group (>50,000 ppt-years) appear to saturate, and the 

15 response decreases at this level. Steenland et al. (2001) suggest that the apparent saturation of 

16 dose-response in this cohort may be due, at least partially, to exposure misclassification, rather 

17 than to an actual reduction in response per unit exposure. 

18 The uncertainty associated with differences in the exposure patterns is important to 

19 consider across the five epidemiologic studies. Steenland et al. (2001), Cheng et al. (2006), and 

20 Becher et al. (1998) studied cohorts exposed to elevated TCDD levels over a long period of time, 

21 while Ott and Zober (1996) and Warner et al. (2002) studied cohorts exposed to TCDD levels 

22 significantly above background at one point in time but the exposures and likely the TCDD body 

23 burdens declined significantly following these periods of elevated exposure. Both these chronic 

24 and acute exposures can be analyzed in terms of cumulative exposure to TCDD. Use of such a 

25 metric requires an assumption that the “actual” cancer potency associated with a cumulative dose 

26 where much of the dose is received at a single point in time and then gradually eliminated would 

27 be similar to the cancer potency of the same cumulative dose received over a longer period of 

28 time and also gradually eliminated. While EPA believes that such an assumption is not 

29 unreasonable, the experiment of Kim et al. (2003), which showed statistically significant 

30 increase in liver effects due to a peak TCDD dose when compared to chronically-dosed Sprague31 Dawley rats administered the same levels of TCDD when measured as a cumulative dose, 
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1 suggests that additional analyses of cumulative and peak TCDD dose measures may need to be 

2 conducted. 

3 There are uncertainties associated with the approaches used to estimate TCDD exposures 

4 in the members of the occupational epidemiologic studies for which no measurement data were 

5 available. To impute TCDD levels for workers without measured samples, all four occupational 

6 epidemiologic studies matched workers for whom measured TCDD samples had never been 

7 reported to workers with measured TCDD levels based on job histories. The NIOSH cohort is 

8 used to illustrate some of the uncertainties. In the NIOSH cohort, the subset of workers (roughly 

9 five percent of the total cohort) with blood serum data comprised surviving members of the 

10 cohort (in 1988), and therefore, their age distribution would have differed from the rest of the 

11 cohort. For each worker in this subset, the following data were available: (1) job classification 

12 information, (2) employment history, and (3) serum TCDD measures. All of the workers in this 

13 subset were employed at a single plant where the work histories were less detailed than at other 

14 plants, and many of the workers at this plant had the same job title and were employed during the 

15 same calendar period. There is an assumption that workers with same job title and work history 

16 were exposed to the same TCDD levels within a plant and across plants; this obviously does not 

17 account for exposure heterogeneity. 

18 Both Steenland et al. (2001) and Cheng et al. (2006) addressed the potential for exposure 

19 measurement error in TCDD estimates and possible exposure misclassification. For the highest 

20 exposure workers, Steenland et al. (2001) and Cheng et al. (2006) found weak, “noisy,” and/or 

21 negative exposure-response relationships. Steenland et al. (2001) suggests that possible 

22 explanations for this observation include the saturation of effects at the upper end of the dose23 response curve, instability of the TCDD exposure estimates based on the limited number of 

24 highly exposed individuals, and the increased probability of exposure misclassification for 

25 workers whose job histories indicate the highest exposures. As Steenland et al. (2001) reported, 

26 some of the highest exposures might have been inaccurately estimated because they occurred in 

27 workers exposed to short-term, high-dose exposures during spill clean-up. Cheng et al. (2006) 

28 used sensitivity analyses to examine this measurement error issue and evaluated the potential for 

29 exposure misclassification by using ln-transformed TCDD ppt-years. The authors also removed 

30 all observations with exposures within the lower and upper 1, 2.5, or 5th percentiles of the 

31 TCDD ppt-year distribution and also removed observations within just the upper 1, 2.5, or 5th 
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1 percentile of TCDD ppt-years. These sensitivity analyses provided similar results. An 

2 additional concern is that exposure errors might distort the exposure distribution in the 

3 population, which generally spreads the response out over a wider dose range. This serves to 

4 increase the variance of the regression model, altering both the POD and the corresponding OSF. 

5 Becher et al. (1998) only considered workers from a single plant but their analysis 

6 included workers employed in five different job locations within the plant. The influence of 

7 worker location on slope factor estimates does not appear to be further explored and may 

8 represent a source of uncertainty. 

9 To estimate long-term body burden metrics from the serum TCDD measurements, 

10 Steenland et al. (2001) employed simple first order kinetic elimination rate model with a half-life 

11 of 8.7 years. Limitations of this approach include (1) the average elimination half-life among the 

12 study subjects may not be 8.7 years given differences between the study population and the 

13 Ranch Hand population from which the value was estimated, (2) use of a single-value estimate 

14 fails to take into account the inherent variability in elimination half life among the individual 

15 workers, and (3) it fails to take into account variations in elimination kinetics throughout the 

16 lifetime of the exposed worker due to change in body fat, age, etc. The impact of these potential 

17 sources of bias on the estimates of time-integrated body burden cannot be quantitatively 

18 assessed. However, Steenland et al. (2001) noted that modest changes in elimination half-life (to 

19 7.1 years) had only a very small impact on risk estimates. 

20 Cheng et al. (2006) estimated past body burdens using the CADM approach (described in 

21 Section 3) (Aylward et al., 2005a, b) rather than a half-life estimate. As noted above, the 

22 incorporation of concentration- and age-dependent elimination into this approach has significant 

23 advantages over the use of a constant elimination half-life. However, as discussed in 

24 Section 3.3, the CADM has only been subject to limited testing against human validation data 

25 sets, so the degree to which its advantages are realized in practice cannot be easily assessed. 

26 There are no available human data in the low dose region, the region of interest to this 

27 assessment, to compare with the CADM (or Emond) model predictions. 

28 Becher et al. (1998) developed half life estimates based on multiple TCDD blood 

29 measures in 48 individuals from this cohort. These half life estimates were then used to back 

30 calculate TCDD concentrations at the end of each worker’s employment, accounting for age and 

31 percentage of body fat. This cohort-specific information may provide a better exposure estimate 
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1 than Steenland et al. (2001) or Ott and Zober (1996) who used similar kinetic approaches. 

2 However, the comparison of the accuracy of the exposure estimates across the cohorts is not 

3 easily assessed. There are several assumptions and important uncertainties involved in modeling 

4 TCDD exposures in these cohorts. The study authors have invoked different kinetic assumptions 

5 when extrapolating measured levels of TCDD in sera backward in time to estimate higher 

6 chronic or peak dosage (i.e., there is uncertainty in these back-calculations that includes 

7 assumptions regarding elimination kinetics). There is also uncertainty in applying such estimates 

8 to other members of the cohort based on similar characteristics (e.g., job category). 

9 

10 5.3.1.2. Uncertainty in Shape of the Dose-Response Curve 

11 Another source of uncertainty is the nature of the dose-response curve in the low dose 

12 region of interest for risk assessment for environmental exposures (e.g., 5−10 ng/kg-day). Most 

13 of the dose-response curves appear reasonably linear in this region. These epidemiologic data, 

14 however, are based on occupational studies in which exposures were often several orders of 

15 magnitude higher than environmental exposures. Data from these studies are quite sparse in the 

16 low dose region, and only one study examined uncertainty due to the low dose region. Steenland 

17 and Deddens (2003) attempted to analyze this region specifically by fitting threshold curves to 

18 the NIOSH data in which there was no extra risk from exposure until some specific level. 

19 However, this model did not fit as well as models without a threshold. In general, the usual 

20 assumption of linearity in the low dose region seems reasonable when using epidemiologic data 

21 given the lack of data in this region that precludes the rejection of linearity. 

22 There is uncertainty in the extrapolation of the OSF to the low dose region (e.g., 

23 <1 ng/kg-day). EPA developed the cancer assessment in this document assuming the slope in the 

24 low-dose region of the dose-response curve is linear; the decision was made due to the lack of 

25 sufficient evidence to support an assumption of nonlinearity as outlined in the 2005 Cancer 

26 Guidelines (U.S. EPA, 2005). Similarly, there is uncertainty as to whether a threshold exists for 

27 TCDD-induced toxicity leading to tumorigenesis and the dose associated with such a threshold, 

28 if it exists, is unknown. EPA chose to model this dose-response without a threshold because 

29 there is insufficient evidence to support an assumption of a threshold. 

30 It also is noteworthy that the shapes of the exposure-response in several of these studies, 

31 based on the published statistical models, is indicative of a response that tends to tail off or 
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1 “plateau” at high cumulative exposures to TCDD. This phenomenon has been seen in many 

2 studies of occupational carcinogens, and may reflect a number of things including exhaustion of 

3 people susceptible to cancer, saturation of biological pathways which are part of the pathway to 

4 cancer, and increased error measurement of dose at high levels biasing dose-response towards 

5 the null (Stayner et al., 2003). 

6 

7 5.3.1.3. Uncertainty in Defining the Reference Population 

8 Another source of uncertainty using human epidemiologic data is due to the lack of 

9 completely unexposed populations. Epidemiologic data in TCDD studies is based on comparing 

10 health outcomes from populations which experienced elevated TCDD exposures to outcomes 

11 from populations which experienced lower exposures, rather than to a population with zero 

12 TCDD exposure (They also compared outcomes in members of the cohort that were more highly 

13 exposed to TCDD to outcomes in members that were less exposed to TCDD). This lack of a 

14 completely unexposed population in epidemiology studies is inevitable given that truly 

15 unexposed human populations do not exist. Therefore, the extra risk calculated in the form of 

16 ED01s, is inevitably the extra risk above a low background exposure due to dioxins in the general 

17 environment. Typically, the general population in western countries where the epidemiologic 

18 studies have been done have had serum levels on the order of 5 ppt. Hence, the extra risks may 

19 be considered as those incurred by added exposure above these background doses. For example, 

20 an ED01 of 18 ng/kg, which yields an excess risk of cancer mortality of 1%, would mean 

21 18 ng/kg above a typical background level of 5 ng/kg (5 ppt). 

22 

23 5.3.1.4. Uncertainty in Cancer Risk Estimates 

24 It is important to remember the overall uncertainty associated with cancer risk estimates 

25 for TCDD can be approximately bounded by evaluating the spread of estimates from the several 

26 epidemiological studies that have been conducted. In the 2003 Reassessment, EPA noted that 

27 the range of ED01/lower bound of the 95% confidence interval on the dose that yields a 1% effect 

28 (LED01) values identified in the then-available dose-response studies (Steenland et al., 2001; 

29 Becher et al., 1998; Ott and Zober, 1996) implied an approximate range of all-cancer mortality 

30 slope factors between 1.2 to 2.5 per ng/kg-day. The equivalent oral cancer slope factors resulting 

31 from the analysis of the newer Cheng et al. (2006) study are below and at the low end of this 
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range (0.37−1.2 per ng/kg-day for fatal cancer risks between 1 × 10−3

 and 1 × 10−7 1 ). This 

2 suggests that the highest quality epidemiological studies all yield risk estimates that agree to 

3 within about a factor of 7. This relatively close agreement between the studies of the different 

4 cohorts lends additional confidence to the slope factor derivation. EPA notes that each of these 

5 estimates is derived from an occupational cohort, and significant differences likely exist (e.g., 

6 exposure levels and routes) between these cohorts and those exposed to TCDD environmentally 

7 (i.e., primarily through the food chain). 

8 

9 5.3.2. Other Sources of Uncertainty in Risk Estimates From the Epidemiological Studies 

10 There are a number of other aspects of the Steenland et al. (2001), Cheng et al. (2006), 

11 Becher et al. (1998), and Ott and Zober (1996) studies that may contribute uncertainty to the 

12 cancer risk estimates. First, all studies that meet the criteria (with the exception of Warner et al., 

13 2002) measure cancer mortality rather than cancer incidence. This presumably biases the slope 

14 factor downward relative to that which would be calculated for cancer incidence, which would 

15 give a truer picture of the total health impacts of TCDD exposures on the general population. In 

16 the NIOSH cohort, roughly one-third of the fatal cancers were from lung cancer. Because of the 

17 high case mortality rate associated with lung cancer during the period of cohort evaluation, the 

18 slope factor estimated for cancer mortality might not be much lower than that calculated for 

19 cancer incidence. Estimation of cancer incidence in the general population associated with 

20 TCDD exposure would require assumptions related to the relative survival and age-specific 

21 cancer risks in the exposed population compared to the NIOSH cohort or the Hamburg cohort; 

22 insufficient data are available to support such an analysis. 

23 The general issues associated with potential confounding effects were noted and 

24 discussed in the 2003 Reassessment. In addition to smoking and lifestyle factors that might 

25 affect cancer risks, intra-individual variation in TCDD kinetics and susceptibility could also 

26 affect the relationship between exposure and cancer risk. One specific example of such a 

27 confounding factor would be variation in TCDD elimination half-lives. For example, if a large 

28 proportion of the 256 observed cancer deaths occurred in NIOSH workers with longer half lives, 

29 this could bias the slope factor downward because higher doses would be associated with cancer 

30 cases in the deceased workers. 
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1 Another source of uncertainty in the exposure/dose assessment in the Steenland et al. 

2 (2001), Cheng et al. (2006), Becher et al. (1998), and Ott and Zober (1996) studies is the limited 

3 of information related to exposure to dioxin-like compounds (DLCs). In addition, the Hamburg 

4 cohort was also exposed to hexachlorocyclohexane (HCH) and lindane. The oral slope estimates 

5 derived by these studies attribute all the observed excess cancer mortality solely to TCDD 

6 exposures. This assumes that occupational exposure was entirely to TCDD, with no other 

7 occupational exposure to dioxins and furans. However, because TCDD typically occurs as a 

8 component of a mixture with other DLCs, this assumption could leads to a positive bias in the 

9 slope factors estimates derived from these epidemiologic studies if confounding is present. The 

10 magnitude of the potential bias can be estimated in a general way through the estimation of risks 

11 for plausible mixtures of DLCs and TCDD exposures in the cohort with the same composition as 

12 the Steenland et al. (2001) and Cheng et al. (2006) studies, but the detailed data required to 

13 perform such an analysis on the NIOSH cohort are not available. 

14 Cheng et al. (2006) assessed the impact of possible confounding by conducting excluding 

15 individual plants in the modeling. If the estimated cancer risks as a function of exposure did not 

16 change too much when specific facilities were left out, then confounding was deemed unlikely. 

17 Cheng et al. (2006) likewise found little variation in risks based on these analyses. In addition to 

18 the slope factor estimated for TCDD, Becher et al. (1998) also evaluated the slope based on 

19 TEQs. They found a dose-response effect for TCDD but not for TEQ (excluding TCDD) which 

20 suggests that confounding by DLCs did not occur. 

21 There is adequate evidence to believe age, gender, and body fat content all can have a 

22 significant impact on elimination kinetics and consequent cancer risks associated with TCDD 

23 exposure (U.S. EPA, 2003). The Hamburg cohort accounted for impacts in their kinetic analysis. 

24 There may be gender differences that affect susceptibility to TCDD exposure, and the NIOSH 

25 cohort and the Becher et al. (1998) analysis were comprised almost exclusively of men, so these 

26 differences were not systematically addressed. There are few quantitative data on which to 

27 examine the potential variation of these modifying factors on exposure estimates; however 

28 several of the studies have reported similar results based on variable half-life estimates. 

29 Finally, none of these cancer cohorts contained any children, and the unique sensitivities 

30 of infants, toddlers, and children were not addressed. Aside from differences in exposure 

31 patterns and body fat content, the unique developmental status of children may result in a 
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1 substantially different profile of health risks (and magnitudes of those risks) than can be 

2 addressed by simply compensating on the basis of differences in body weight, food intake, etc. 

3 Further, because EPA could not develop an estimate from the Warner et al. (2002) cohort, none 

4 of the studies for cancer dose-response analyzed contained a significant percentage of women. 

5 Thus, the generalizability of the slope factor estimates to women and children is uncertain. 

6 

7 5.3.3. Approaches to Combining Estimates From Different Epidemiologic Studies 

8 Meta-analyses and pooled analyses are two common approaches for combining 

9 epidemiologic study data. Meta-analyses are a useful way to combine epidemiologic data from 

10 different studies and derive a common estimate of effect, particularly when there are a large 

11 number of comparable studies that are fairly homogenous as to make them impossible to 

12 combine. A meta-analysis often involves a weighted average of effect measures, dose-response 

13 coefficients, or ED01s. 

14 Unlike a meta-analysis, a pooled analysis combines the original exposure and health 

15 outcome data across multiple studies, enabling a fit of new models to the data which were not 

16 used in the original publications. Whereas a pooled analysis of the four different cohorts 

17 considered here would be useful to explore the functional form and fit of models (either 

18 statistical or multistage) across all four cohorts, this would entail a lengthy undertaking and is not 

19 being contemplated here, due in part to concerns about the confidence in the results of such an 

20 undertaking. 

21 

22 5.3.3.1. The Crump et al. (2003) Meta-analysis 

23 Crump et al. (2003) published a meta-analysis that incorporated data from the three 

24 studies EPA used in the quantitative dose-response modeling presented in the 2003 

25 Reassessment (U.S. EPA, 2003). These three study populations were the NIOSH (Steenland et 

26 al., 2001), the Hamburg (Becher et al., 1998), and the BASF (Ott and Zober, 1996) cohorts. The 

27 data for the NIOSH study included six additional years of follow-up and improved TCDD 

28 exposure estimates that had not been applied to EPA’s dose-response modeling in the 2003 

29 Reassessment. This study examined the relationship between TCDD exposure and all-cancer 

30 mortality. SMR statistics that had been used in all three studies were applied. 
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1 The Crump et al. (2003) analysis was based on published data, and therefore, selection of 

2 the dose metric was limited to how aggregated data had been presented in the publications. For 

3 the NIOSH component of the analysis, the exposure data were based on worker-specific data and 

4 specific processes performed at each plant (Steenland et al., 2001). The previous approach 

5 assigned workers that had broad categories of exposure duration with the same cumulative serum 

6 level, and did not take into account the particular plant or the job assignment within the plant. 

7 The Crump et al. (2003) approach did take into account when exposure occurred in relation to 

8 the follow-up interval. The TCDD exposure metric used was a cumulative serum lipid 

9 concentration (CSLC). For the Hamburg cohort, Crump et al. (2003) used an average value from 

10 the exposure ranges provided in Flesch-Janys et al. (1998). For the BASF cohort, arithmetic 

11 averages for the dose categories were converted to TCDD CSLC intakes by dividing them by 

12 0.25 (average body fat of 25%) and a decay rate that corresponded to a half-life of 7 years. 

13 The outcome variable for the dose-response modeling was all cancer mortality, and 

14 CSLC was the independent variable. Crump et al. (2003) performed a series of trend tests to 

15 determine the lowest dose for which a statistically significant trend in SMR could be shown and 

16 all other lower doses. These tests also examined the highest dose in which there was no 

17 statistically significant trend using data from this dose and all other lower doses. Estimates of 

18 ED10, ED05, and ED01 for TEQ with respect to the lifetime probability of dying from cancer were 

19 calculated. This calculation assumed a first-order elimination process with a half-life of 

20 7.6 years, a 50% systemic uptake of ingested dioxin, that dioxin concentration in serum lipid is a 

21 suitable measure for dioxin concentration in all lipid, and that all dioxin is sequestered in lipid 

22 (which comprises 25% of body weight). Age-specific mortality rates in the presence of dioxin 

23 exposure were then generated. Life-table methodology was used to calculate lifetime risks of 

24 cancer mortality. 

25 Based on the modeling results, the hypothesis of a baseline SMR of 1.0 was rejected, and 

26 the linear model produced an SMR estimate of 1.17 (95% CI = 1.04−1.30) from these studies. 

27 The dose-response curves for the three studies were not homogeneous. Namely, the points from 

28 the BASF cohort fell below the predicted curve. Because the heterogeneity was not judged to be 

29 extreme by different statistical tests, however, the investigators used a common model in a 

30 combined analysis of the data from the three studies. The linear model provided an adequate fit 

of the data, and the slope associated with CSLC-ppt was 6.3 × 10−6

 (95% CI = 8.8 × 10−7 31 to 
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1.3 × 10−5 1 ). Based on goodness of fit analysis, the preferred estimate of ED01 was 7.7 pg/kg/day, 

2 which was six times higher than the estimate derived Steenland et al. (2001). 

3 

4 5.3.3.2. EPA’s Decision Not to Conduct a Meta-analysis 

5 From a statistical perspective, meta-analyses may not be very reliable when applied to a 

6 small number of studies. Crump et al. (2003) used only three studies. Had EPA undertaken a 

7 meta-analysis for the studies that met its criteria, most of the weight would come from the two 

8 large studies on the NIOSH and Hamburg cohorts. However, such an analysis relies on an 

9 assumption of a normally distributed between-study effect. This normality assumption cannot be 

10 assessed with only three observations, yet the meta-analysis estimate is highly sensitive to this 

11 distributional assumption (Higgens et al., 2009). Because of this limitation and the imprecision 

12 of the between-study variance estimate, statisticians often recommend forgoing meta-analysis in 

13 favor of discussing the individual studies when few studies are available (Cox, 2006; Higgens et 

14 al., 2009). Based on these considerations, EPA decided not to undertake a meta-analysis in this 

15 document. 

16 As noted previously, Crump et al. (2003) has conducted a meta-analysis of the three 

17 cohorts considered here, i.e., the NIOSH, Hamburg, and BASF cohorts. However, Crump et al. 

18 modeled SMR data in which the cohorts were compared to the general population, rather than on 

19 internal exposure-response analyses as relied upon in this document. Their analysis included a 

20 total of 15 different SMRs from the three studies. A prior analysis of the dose-responses by 

21 Becher et al. (1998) was used (i.e., the categorical SMR analysis by Flesch-Janys et al. [1998]). 

22 Additionally, a prior analysis of the NIOSH cohort (Steenland et al., 1999) in which SMRs were 

23 calculated was used. Crump et al. (2003) found that a linear dose-response gave a good fit to the 

24 data, and used that for deriving an ED01. However, they found that a supra-linear dose-response 

25 provided a better fit to the data, but rejected the supra-linear model (a power model) because of 

26 an infinite slope at zero dose. In the original publications by Becher et al. (1998) and Steenland 

27 et al. (2001), both observed a supra-linear dose-response trend. Crump et al. (2003) concluded 

28 that the ED01 was 45 pg/kg-day, similar to the ED01 of 8 pg/kg-day calculated by Steenland et al. 

29 (2001) using the same dietary units (pg/kg-day). 
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1 Table 5-1. Cancer slope factors calculated from Becher et al. (1998), 

2 Steenland et al. (2001) and Ott and Zober (1996) from 2003 Reassessment 

3 Table 5-4 

4 

Study 

ED01 (LED01) 

(ng/kg) 

Cancer slope factor per

ng/kg-day above 

backgrounda

 (UCL) 

Hamburg cohort 

Power model 

Becher et al. (1998) 

6 (N.A.) 5.1 (N.A.) 

Hamburg cohort 

Additive model 

Becher et al. (1998) 

18.2 (N.A.) 1.6 (N.A.) 

Hamburg cohort 

Multiplicative model 

Becher et al. (1998) 

32.2 (N.A.) 0.89 (N.A.) 

NIOSH cohort 

Piecewise linear model 

Steenland et al. (2001) 

18.6 (11.5) 1.5 (2.5) 

BASF cohort, from Ott 

and Zober (1996), 

multiplicative 

50.9 (25.0) 0.57 (1.2) 

5 a 6 Assumes 25% of body weight is lipid; in humans 80% of dioxin dose is absorbed from the normal 

7 diet; the TCDD half-life is 7.1 years in humans. Background all cancer mortality rate calculated 

8 through lifetable analysis to 75 years. Summary results are for male all cancer risk, because the 

9 male lifetime (to 75 years) all cancer risk is greater than for females, leading to correspondingly 

10 higher cancer slope factors. As detailed in Part III, Chapter 8, RelRisk(ED01) = 0.99 + 

0.01/Risk(0 dose) 11 . Based on the manner in which the dose-response data were calculated using Cox 

12 regression rate ratio analyses, risks are given as cancer slope factors for 1 pg/kg-day above 

13 background, assumed 5 ppt TCDD in lipid. 

14 UCL = upper confidence limit. 

15 

16 Source: U.S. EPA (2003; Part III, Chapter 5, Table 5-4). 
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1 Table 5-2. Cox regression coefficient estimate and incremental risk for 

2 NIOSH cohort data as presented in Cheng et al. (2006) compared with 

3 Steenland et al. (2001) 

4 

Incremental riska

Model 

Cox regression 

coefficient estimate 

(ppt-year)−1 R0 = 0.124 R0 = 0.112 

Steenland et al. (2001) result 

Piecewise linearb 1.5 × 10−5 7.0 × 10−4 6.3 × 10−4

Unlagged exposure, Cheng et al. (2006) 

Piecewise linearc 1.4 × 10−6 6.5 × 10−5 5.9 × 10−5

Linear, lower 95% of observations 1.6 × 10−6 7.4 × 10−5 6.7 × 10−5

Linear, full data −8.9 × 10−9d <0 <0 

Lagged exposure (15 years), Cheng et al. (2006) 

Linear, lower 95% of observations 3.3 × 10−6 1.2 × 10−4 1.1 × 10−4

Linear, full data 1.7 × 10−8d 6.3 × 10−7 5.7 × 10−7

5 a 6 Assumes 5 ppt serum lipid TCDD concentration for 75 years (unlagged) or 60 years (taking into account a 15-year 

7 lag). 

b 8 Cox regression coefficient from the Steenland et al. (2001) analysis as reported in the 2003 Reassessment, Part III, 

9 pp. 5–34, males, no lag. The value of this coefficient for the piecewise linear modeling was not explicitly reported 

10 by Steenland et al. (2001). 

c 11 Piecewise linear with cutpoint set at maximum likelihood for model fit, 452,000 ppt-years. 

12 dNot statistically significantly different from 0. 

13 

14 Source: Cheng et al. (2006; Table IV). 
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1 Table 5-3. Comparison of lipid-adjusted serum concentrations, fat 

2 concentrations, risk specific dose estimates and equivalent oral slope factors 

based on upper 95th percentile estimate of regression coefficienta 3 of all fatal 

cancers reported by Cheng et al. (2006) for risk levels of 1 × 10−3, 1 × 10−4 4 , 

1 × 10−5, 1 × 10−6, and 1 × 10−7 5 

6 

Risk level 

(RL) 

AUCRL, 

(ppt-yr) 

FATRL

(ng/kg) 

Risk specific dose 

(DRL) (ng/kg-day) 

Equivalent oral slope 

factors (OSFRL) per 

(mg/kg-day) 

1 × 10−3 1.26 × 10−3 1.803 × 101 2.73 × 10−3 3.7 × 105

1 × 10−4 1.26 × 102 1.803 × 10 1.23 × 10−4 8.1 × 105

1 × 10−5 1.26 × 101 1.803 × 10−1 8.57 × 10−6 1.2 × 106

1 × 10−6 1.26 × 10 1.803 × 10−2 7.77 × 10−7 1.3 × 106

1 × 10−7 1.26 × 10−1 1.803 × 10−3 7.62 × 10−8 1.3 × 106

7 a 8 Based on regression coefficient of Cheng et al. (2006; Table III), excluding observations in the upper 5% range 

(≥252,950 ppt-year lipid adjusted serum TCDD) of the exposures; where reported β = 3.3 × 10−6 9 ppt-years and 

standard error = 1.4 × 10−6 10 . Upper 95%tile estimate of regression coefficient (β95) calculated to be 

6.04 × 10−6 = (3.3 × 10−6) + 1.96 × (1.4 × 10−6 11 ). 

12 

13 

14 Table 5-4. Comparison of lipid-adjusted serum concentrations, fat 

15 concentrations, risk specific dose estimates and equivalent oral slope factors 

based on best estimate of regression coefficienta 16 of all fatal cancers reported 

by Cheng et al. (2006) for risk levels of 1 × 10−3, 1 × 10−4, 1 × 10−5, 1 × 10−6 17 , 

and 1 × 10−7 18 

19 

Risk level 

(RL) 

AUCRL, 

(ppt-yr) 

FATRL

(ng/kg) 

Risk specific dose 

(DRL) (ng/kg-day) 

Equivalent oral 

slope factors 

(OSFRL) per 

(mg/kg-day) 

1 × 10−3 2.31 × 103 3.303 × 101 6.63 × 10−3 1.5 × 105

1 × 10−4 2.31 × 102 3.303 × 10 2.63 × 10−4 3.8 × 105

1 × 10−5 2.31 × 101 3.303 × 10−1 1.67 × 10−5 6.0 × 105

1 × 10−6 2.31 × 10 3.303 × 10−2 1.44 × 10−6 6.9 × 105

1 × 10−7 2.31 × 10−1 3.303 × 10−3 1.40 × 10−7 7.1 × 105

20 a 21 Based on regression coefficient of Cheng et al. (2006; Table III), excluding observations in the upper 5% range 

(≥252,950 ppt-year lipid adjusted serum TCDD) of the exposures; where reported β = 3.3 × 10−6 22 ppt-years. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-82 —DO NOT CITE OR QUOTE 

Table 5-5. Kociba et al. (1978) male rat tumor incidence dataa 1 and blood 

2 concentrations for dose-response modeling 

3 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 860 3,945 21,334 

Stratified squamous cell 

carcinoma of hard palate or nasal 

turbinates 

0/85 0/50 0/50 4/50b

Stratified squamous cell 

carcinoma of tongue 

0/85 1/50 1/50 3/50b

Adenoma of adrenal cortex 0/85 0/50 2/50 5/50b

4 a 5 Source: Kociba et al. (1978; Table 4). 

b 6 Statistically significant by Fischer Exact Test (p < 0.05). 

7 

8 

Table 5-6. Kociba et al. (1978) female rat tumor incidence dataa 9 and blood 

10 concentrations for dose-response modeling 

11 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 853 3,942 21,246 

Hepatocellular adenoma(s) or 

carcinoma(s) 

2/86 1/50 9/50a 18/45b

Stratified squamous cell 

carcinoma of hard palate or 

nasal turbinates 

0/86 0/50 1/50 4/49b

Keratinizing squamous cell 

carcinoma of lung 

0/86 0/50 0/50 7/49b

12 a 13 Source: Kociba et al. (1978; Table 5). Incidence for Hepatocellular adenomas or carcinomas is from Goodman and 

14 Sauer (1992; Table 1); EPA calculated statistical significance as the study authors did not provide this. 

b 15 Statistically significant by Fischer Exact Test (p < 0.05). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-83 —DO NOT CITE OR QUOTE 

Table 5-7. NTP (1982) female rat tumor incidence dataa 1 and blood 

2 concentrations for dose-response modeling 

3 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 1,072 3,111 16,207 

Subcutaneous tissue: fibrosarcoma 0/75 2/50 3/50 4/49b

Liver: neoplastic nodule or 

hepatocellular carcinoma 

5/75c 1/49 3/50 14/49b

Adrenal: cortical adenoma, or 

carcinoma or adenoma, NOS 

11/73c 9/49 5/49 14/46b

Thyroid: follicular-cell adenoma 3/73c 2/45 1/49 6/47 

4 a 5 Source: NTP (1982; Table 10). 

b 6 Statistically significant by Fischer Exact Test (p < 0.05). c 7 Statistically significant trend by Chochran-Armitage test (p < 0.05). 

8 

9 

Table 5-8. NTP (1982) male rat tumor incidence dataa 10 and blood 

11 concentrations for dose-response modeling 

12 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 1,072 3,116 16,272 

Liver: neoplastic nodule or 

hepatocellular carcinoma 

0/74b 0/50 0/50 3/50 

Thyroid: follicular-cell adenoma or 

carcinoma 

1/69b 5/48c 8/50c 11/50c

Adrenal cortex: adenoma 6/72 9/50 12/49b 9/49 

13 a 14 Source: NTP (1982; Table 9). 

b 15 Statistically significant trend by Chochran-Armitage test (p < 0.05). c 16 Statistically significant by Fischer Exact Test (p < 0.05). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-84 —DO NOT CITE OR QUOTE 

Table 5-9. NTP (1982) female mouse tumor incidence dataa 1 and blood 

2 concentrations for dose-response modeling 

3 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 1,064 3,184 17,406 

Subcutaneous tissue: fibrosarcoma 1/74b 1/50 1/48 5/47c

Hematopoietic system: lymphoma 

or leukemia 

18/74b 12/50 13/48 20/47c

Liver: hepatooellular adenoma or 

carcinoma 

3/73b 6/50 6/48 11/47c

Thyroid: follicular-cell adenoma 0/69b 3/50 1/47 5/46c

4 a 5 Source: NTP (1982; Table 15). 

b 6 Statistically significant trend by Chochran-Armitage test (p < 0.05). c 7 Statistically significant by Fischer Exact Test (p < 0.05). 

8 

9 

Table 5-10. NTP (1982) male mouse tumor incidence dataa 10 and blood 

11 concentrations for dose-response modeling 

12 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium 

dose (ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 420 1,240 6,118 

Lung: alveolar/bronchiolar adenoma 

or carcinoma 

10/71b 2/48 4/48 13/50 

Liver: hepatocellular adenoma or 

carcinoma 

15/73b 12/49 13/49 27/50c

13 a 14 Source: NTP (1982; Table 14). 

b 15 Statistically significant trend by Chochran-Armitage test (p < 0.05). c 16 Statistically significant by Fischer Exact Test (p < 0.05). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-85 —DO NOT CITE OR QUOTE 

Table 5-11. NTP (2006) female rat tumor incidence dataa 1 and blood 

concentrations for dose-response modelingb 2 

3 

Vehicle 

control 

(ng/kg) 

Low 

dose 

(ng/kg) 

Low-med 

dose (ng/kg)

Median 

dose 

(ng/kg) 

Med-high 

dose (ng/kg) 

High dose 

(ng/kg) 

System: 

morphology: 

topography 0 1,408 3,137 5,393 9,129 16,361 

Liver: 

cholangiocarcinoma 

0/49c 0/48 0/46 1/50 4/49 25/53c

Liver: 

hepatocellular 

adenoma 

0/49c

 0/48 0/46 0/50 1/49 13/53c

Oral mucosa: 

squamous cell 

carcinoma 

1/49c

 2/48 1/46 0/50 4/49 10/53c

Pancreas: adenoma 

or carcinoma 

0/48c 0/48 0/46 0/50 0/48 3/51 

Lung: cystic 

keratinizing 

epithelioma 

0/49c

 0/48 0/46 0/49 0/49 9/52c

4 a 5 Source: NTP (2006; Table A3a). 

b 6 Incidence adjusted for animals <365 days on study. c 7 Statistically significant by Poly-3 Test (p < 0.05).

8 

9 

Table 5-12. Toth et al. (1979) male mouse tumor incidence dataa 10 and blood 

11 concentrations for dose-response modeling 

12 

Vehicle control 

(ng/kg) 

Low dose 

(ng/kg) 

Medium dose 

(ng/kg) 

High dose 

(ng/kg) 

Morphology: topography 0 315 7,814 50,105 

Liver tumors 7/38 13/44 21/44b 13/43 

13 a 14 Source: Toth et al. (1979; Table 1). 

b

Statistically significant by Chi2 15 Test (p < 0.01). 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 5-86 DRAFT: DO NOT CITE OR QUOTE Table 5-13. Comparison of multi-stage modeling results across cancer bioassays using blood conce

Study Species Sex Morphology: topography Multi-stage modeling*: stage, GoF 

p-value, LL differenc

Stratified squamous cell carcinoma of hard palate or nasal turbinates 1, 

p = 0.81 

Stratified squamous cell carcinoma of tongue 1, 

p = 0.47 

Adenoma of adrenal cortex 1, 

p= 0.78 

Male 

Combined tumors Bayesian analysis 

Hepatocellular adenoma(s) or carcinoma(s) 1, 

p = 0.24, 

Stratified squamous cell carcinoma of hard palate or nasal turbinates 1, 

p = 0.97 

Keratinizing squamous cell carcinoma of lung 1, 

p = 0.63 

Kociba et al., 1978 

Rat 

Female 

Combined tumors Bayesian analysis 

Subcutaneous tissue: fibrosarcoma 1, 

p = 0.18 

Liver: neoplastic nodule or hepatocellular carcinoma 1, 

p = 0.22 

Adrenal: cortical adenoma, or carcinoma or adenoma, NOS 1, 

p = 0.34 

Thyroid: follicular-cell adenoma 1, 

p = 0.57 

Female 

Combined tumors Bayesian analysis 

Liver: neoplastic nodule or hepatocellular carcinoma 1, 

p = 0.85 

Thyroid: follicular-cell adenoma or carcinoma 1, 

p = 0.06 

Adrenal cortex: adenoma 1, 

p = 0.06 

Rat 

Male 

Combined tumors Bayesian analysis 

Subcutaneous tissue: fibrosarcoma 1, 

p = 0.93 

Hematopoietic system: lymphoma or leukemia 1, 

p = 0.98 

Liver: hepatocellular adenoma or carcinoma 1, 

p = 0.34 

Thyroid: follicular-cell adenoma 1, 

p = 0.09, no improvement with 

higher orders 

NTP, 1982 

Mouse Female 

Combined tumors Bayesian analysis 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 5-87 DRAFT: DO NOT CITE OR QUOTE Table 5-13. Comparison of multi-stage modeling results across cancer bioassays using blood conce

(continued) Study Species Sex Morphology: topography Multi-stage modeling*: stage, GoF 

p-value, LL differenc

Lung: alveolar/bronchiolar adenoma or carcinoma 1, 

p = 0.09 

Liver: hepatocellular adenoma or carcinoma 1, 

p = 0.93 

NTP, 1982 cont. 

Mouse cont. 

Male 

Combined tumors Bayesian analysis 

Liver: cholangiocarcinoma 3, 

p = 0.99, dLL = 2.93 

Liver: hepatocellular adenoma 3, 

p = 0.93, dLL = 2.10 

Oral mucosa: squamous cell carcinoma 1, 

p = 0.27 

Pancreas: adenoma or carcinoma 1, 

p= 0.64 

Lung: cystic keratinizing epithelioma 2, 

p = 0.51, dLL = 3.55 

NTP, 2006 

Rat Female 

Combined tumors Bayesian analysis 

Toth et al., 1979 

Mouse Male Liver: tumors 1, 

p = 0.29 

*Analysis uses a chi-square goodness of fit statistic for differences in the log likelihoods (p > 0.05). 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-88 —DO NOT CITE OR QUOTE 

1 Table 5-14. Individual tumor points of departure and slope factors using 

2 blood concentrations 

3 

Study Tumor Site (Sex/Species) 

BMDLHED

(ng/kg-day) 

OSF 

(per mg/kg-day) 

NTP, 1982 Liver: adenoma or carcinoma (male mice) 1.7E−03 5.9E+6 

Toth et al., 1979 Liver tumors (male mice) 1.9E−03 5.2E+6 

NTP, 1982 Lung: adenoma or carcinoma (male mice) 6.6E−03 1.5E+6 

Kociba et al., 1978 Liver: adenoma or carcinoma (female rats) 1.2E−02 8.6E+5 

NTP, 1982 Hematopoietic: lymphoma or leukemia (female mice) 1.5E−02 6.6E+5 

NTP, 1982 Thyroid: follicular cell adenoma (male rats) 1.9E−02 5.3E+5 

NTP, 1982 Liver: neoplastic nodule or hepatocellular carcinoma 

(female rats) 

2.1E−02 4.9E+5 

NTP, 1982 Adrenal: cortical adenoma or carinoma or adenoma, 

NOS (female rats) 

2.4E−02 4.2E+5 

NTP, 1982 Liver: adenoma or carcinoma (female mice) 2.4E−02 4.1E+5 

NTP, 1982 Adrenal cortex: adenoma (male rats) 4.4E−02 2.3E+5 

NTP, 1982 Subcutaneous fibrosarcoma (female rats) 5.3E−02 1.9E+5 

NTP, 2006 Oral mucosa: squamous cell carcinoma (female rats) 5.5E−02 1.8E+5 

NTP, 1982 Thyroid: adenoma (female mice) 5.6E−02 1.8E+5 

NTP, 1982 Thyroid: follicular cell adenoma (female rats) 6.4E−02 1.6E+5 

NTP, 1982 Subcutaneous fibrosarcoma (female mice) 7.2E−02 1.4E+5 

Kociba et al., 1978 Lung: carcinoma (female rats) 8.0E−02 1.2E+5 

Kociba et al., 1978 Adenoma of adrenal cortex (male rats) 8.5E−02 1.2E+5 

Kociba et al., 1978 Nasal/Palate: carcinoma (female rats) 1.2E−01 8.2E+4 

Kociba et al., 1978 Tongue: carcinoma (male rats) 1.4E−01 7.0E+4 

NTP, 1982 Liver: neoplastic nodule or hepatocellular carcinoma 

(male rats) 

1.5E−01 6.7E+4 

Kociba et al., 1978 Nasal/Palate: carcinoma (male rats) 1.6E−01 6.3E+4 

NTP, 2006 Liver: cholangiocarcinoma (female rats) 2.9E−01 3.5E+4 

NTP, 2006 Pancreas: adenoma or carcinoma (female rats) 3.4E−01 2.9E+4 

NTP, 2006 Lung: cystic keratinzing epithelioma (female rats) 4.1E−01 2.4E+4 

NTP, 2006 Liver: hepatocellular adenoma (female rats) 5.6E−01 1.8E+4 

4 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-89 —DO NOT CITE OR QUOTE 

1 Table 5-15. Multiple tumor points of departure and slope factors using blood 

2 concentrations 

3 

Study Sex/species: tumor sites 

BMDLHED

(ng/kg-day) 

OSF 

(per mg/kg-day) 

NTP, 1982 Male mice: liver adenoma and carcinoma, lung 1.1E−03 9.4E+6 

NTP, 1982 Female mice: liver adenoma and carcinoma, thyroid 

adenoma, subcutaneous fibrosarcoma, all lymphomas 

5.3E−03 1.9E+6 

NTP, 1982 Female rats: liver neoplasitc nodules, liver adenoma 

and carcinoma, thyroid follicular cell adenoma ,adrenal 

cortex adenoma or carcinoma 

5.7E−03 1.8E+6 

Kociba et al., 1978 Female rats: liver adenoma carcinoma, oral cavity, 

lung 

7.3E−03 1.4E+6 

NTP, 1982 Male rats: thyroid follicular cell adenoma, adrenal 

cortex adenoma 

9.6E−03 1.0E+6 

NTP, 2006 Female rats: liver cholangiocarcinoma, hepatocellular 

adenoma, oral mucosa squamous cell carcinoma, lung 

cystic keratinizing epithelioma, pancreas adenoma, 

carcinoma 

2.3E−02 4.4E+5 

Kociba et al., 1978 Male rats: adrenal cortex adenoma, tongue carcinoma, 

nasal/palate carcinoma 

3.1E−02 3.2E+5 

4 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 5-90 DRAFT: DO NOT CITE OR QUOTE Table 5-16. Illustrative RfDs based on tumorigenesis in experimental animals Study Species, strain (sex) Protocol Endpoint BMDLHEDa (ng/kg-day) 

NTP, 1982 Mouse, B6C3F1, male 2-year gavage; 

n = 50 

Liver adenoma and carcinoma, lung 1.1E-3 

NTP, 1982 Mouse, B6C3F1, 

female 

2-year gavage; n = 50 

Liver adenoma and carcinoma, thyroid adenoma, subcutaneous fibrosarcoma, all lymphomas 

5.3E-3 

NTP, 1982 Rat, OsborneMendel, female 2-year gavage; 

n = 50 

Liver neoplasitc nodules, thyroid follicular cell adenoma, liver adenoma and carcinoma, adrenal cortex adenoma or carcinoma 

5.7E-3 

Toth et al., 1979 Mouse, Swiss/ 

H/Riop, male 

1-year gavage (1-year average); n = 38

−44 

Liver tumors 6.1E-3 

Kociba et al., 1978 

Rat, S-D, female 2-year dietary; 

n = 50 

Liver adenoma carcinoma, oral cavity, lung 7.3E-3 

NTP, 1982 Rat, OsborneMendel, male 2-year gavage; 

n = 50 

Thyroid follicular cell adenoma, adrenal cortex adenoma 9.6E-3 

NTP, 2006 Rat, S-D, female 2-year gavage; 

n = 53 

Liver cholangiocarcinoma, hepatocellular adenoma, oral mucosa squamous cell carcinoma, lung cystic keratinizing epithelioma, pancreas adenoma, carcinoma 

3.1E-2 

Kociba et al., 1978 

Rat, S-D, male 2-year dietary; 

n = 50 

Adrenal cortex adenoma, tongue carcinoma, nasal/palate carcinoma 

3.1E-2 

aBMR = 0.01. 

bUF = 30; UF

A = 3, UFH = 10. 
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 5-91 DRAFT: DO NOT CITE OR QUOTE Table 5-17. Illustrative RfDs based on hypothesized key events in TCDD’s MOAs for liver and lun

Key event Endpoint and exposure duration NO(A)ELHED (ng/kg-day) LO(A)ELHED (ng/kg-day) BMDLHEDa (ng/kg-day) RfDb (mg/kg-day) Liver tumors Changes in gene expression CYP1A1 mRNA, 1 day 1.8E-05 3.4E-04 1.1E-01c (Appendix G) 6E-13 d 

Benzo(a)pyrene hydroxylase (BPH) activity (CYP1A1), 1 day 9.2E-04 6.0E-03 4.6E-04c (Appendix G) 2E-11d 

Changes in gene expression EROD (CYP1A1), 53 weeks none 1.4E-01 9.5E-03c (Appendix G) 3E-10d 

DNA single-strand breaks, 90 days none 3.3E-02 2.2E-02c (Appendix G) 7E-10d TBARS, 90 days - - 4.4E-02 (Appendix G) 2E-09d 

Oxidative stress Cytochrome C reductase, 90 days - - 8.8E-02 (Appendix G) 3E-09 d 

Toxic hepatopathy, 2 years none 1.4E-01 1.8E-01c (Appendix E) 5E-09e Hepatotoxicity 

Hepatocyte hypertrophy, 31 weeks 9.3E-02 3.3E-01 8.8E-03 (Appendix E) 3E-10 d 

Hepatocellular proliferation Labeling index, 31 weeks none 1.4E-01 6.6E-02c (Appendix G) 2E-09 d Lung tumors Metabolic enzyme induction EROD (CYP1A1), 53 weeks none 1.4E-01 2.9E-04 (Appendix G) 1E-11d Retinoid homeostatsis Hepatic retinol and retinyl palmitate, 90 days none 1.1E+00 1.7E-01c (Appendix E) 6E-09 d aBMR for continuous endpoints – 1 standard deviation; for quantal endpoints – 10%. bBolded NOAEL, LOAEL, or BMDL is selected POD; poorly-fitting BMDLs above the LOAEL not used. cPoor BMD model fit or no good model fit. dUF = 30; UF

A – 3, UFH – 10. eUF = 300; UFA – 3, UFH – 10; UFL – 10. 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-92 —DO NOT CITE OR QUOTE 

1 Table 5-18. Dichotomous Hill model fits to combined adenoma and 

2 carcinoma data from Kociba et al. (1987) as re-evaluated by Goodman and 

Sauer (1992)a 3 

4 

TCDD dose metric 

Background

response ED50

b 

Hill 

coefficient 

Chi-square GOF 

p-value 

Administered dose 0.0193 173 0.682 0.185 

Blood concentration 0.0193 57.1 0.951 0.155 

Liver AhR-bound concentration 0.0193 79.2 1.365 0.270 

Whole liver concentration 0.0193 2.51 × 104 0.714 0.172 

5 a

Calculations performed in S-PLUS® 6.2 for Windows® 6 . b 7 ng/kg tissue concentration except for administered dose, which is ng/kg BW per day (ng/kg-day).
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-93 —DO NOT CITE OR QUOTE 

1 Table 5-19. Comparison of principal epidemiological studies 

2 

Strengths Weaknesses Study 

Cumulative TCDD levels in the serum were 

estimated on an individual-level basis for 

the 3,538 workers. 

Evaluated effect of lag periods (0 and 15 

years). 

Measured and back-extrapolated TCDD 

concentrations to refine and quantify job 

exposure matrices, which were then used to 

estimate dioxin cumulative dose for each 

member of their entire cohort.

Internal cohort comparisons (Cox regression 

model).

Background exposure estimated.

• Exposure to other chlorinated hydrocarbons 

(dioxin like compounds). 

• Extrapolation of dose from a small subset 

(roughly 5%, n = 170) of the cohort. 

• Serum fat or body fat levels of TCDD were 

back-calculated using a simple first-order 

model. Half-life of TCDD is variable but 

simulated as a constant. Changes in the lipid 

fraction of body weight or presence/absence 

of genetic differences in humans that alter the 

distribution and metabolism of TCDD were 

not considered. 

• Serum lipid levels of TCDD in 1988 were 

measured only at one of the eight plants in 

the study. No follow-up measures. The 

estimates of dose are based on blood samples 

taken decades after exposure. 

NIOSH cohort 

Steenland et al. 

(2001) 

Cumulative TCDD levels in the serum were 

estimated on an individual-level basis for 

the 3,538 workers. 

TCCD dose estimates were simulated with a 

kinetic model that included considerations 

of exposure intensity and age-dependent 

body weight and fat levels. 

Evaluated effect of lag periods (0 and 15 

years). 

Background exposure estimated. 

Stratified risk estimates for smoking and 

nonsmoking. 

Race and age adjustments. 

Internal cohort noted an inverse-dose 

response for high-exposure groups and thus 

excluded the data resulting in better 

associations. 

• Extrapolation of dose from a small subset 

(roughly 5%, n = 170) of the cohort. 

• The authors reported the CADM model 

provided an improved fit over the onecompartmental model, but no evidence was 

reported regarding any formal test of 

statistical significance. 

• Serum lipid levels of TCDD in 1988 were 

measured only at one of the eight plants in 

the study. No follow-up measures. The 

estimates of dose are based on blood samples 

taken decades after exposure. 

• Exposure to other chlorinated hydrocarbons 

(dioxin like compounds). 

• No consideration for recent exposures to 

TCDD, changes in the lipid fraction of body 

weight or presence/absence of genetic 

differences in humans that alter the 

distribution and metabolism of TCDD could 

cause misclassification. 

NIOSH cohort 

Cheng et al. 

(2006) 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-94 —DO NOT CITE OR QUOTE 

1 

Table 5-19. Comparison of principal epidemiological studies (continued) 

Strengths Weaknesses Study 

• Repeated TCDD measures in serum in 48 

individuals. Used to estimate half-life for 

study cohort. Took into account the age 

and body fat percentage of the workers. 

Measured and back-extrapolated TCDD 

concentrations to quantify exposures for the 

remaining cohort members using 5 different 

working areas of the plant.

• Evaluated effect of lag periods up to 20 

years. 

• Multiple statistical models used to evaluate 

fatal cancer slope estimates.

• Background exposure estimated. 

• Exposure to other chlorinated hydrocarbons 

(dioxin like compounds), HCH, and lindane. 

• Extrapolation of dose from a small subset 

(roughly 4%, n = 1,189) of the cohort. 

• Serum fat or body fat levels of TCDD were 

back-calculated using a simple first-order 

model. Presence/absence of genetic 

differences in humans that alter the 

distribution and metabolism of TCDD were 

not considered. 

• Serum lipid levels of TCDD for only 275 

workers. 

Becher et al. 

(1998); 

Hamburg 

Cohort 

• Both internal and external analyses. 

• Adjustment for age, BMI, and smoking. 

• Both cancer incidence and cancer mortality 

data available, although results somewhat 

discordant, with steeper dose-response seen 

for cancer mortality. 

• Acute dose due to accident may not be 

comparable to chronic dose accumulated over 

a long time, as in most environmental 

exposures. 

• Relatively small number of cancer deaths 

compared to NIOSH and Hamburg cohorts 

(n = 31). 

• Serum TCDD levels measured 30 years after 

accident, requiring extrapolation back in time 

to estimate cumulative dose over time. 

• Serum TCDD levels measured only on a 

sample of the cohort (138 out of 243), 

requiring assumptions about similarities in 

exposure scenario for other workers to 

estimate their exposure 

Ott and Zober 

(1996) 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-95 —DO NOT CITE OR QUOTE 

Table 5-19. Comparison of principal epidemiological studies (continued) 

Strengths Weaknesses Study 

• TCDD levels measured in all 891 members 

of this female cohort. 

• Most TCDD measurements based on 

observed levels in stored serum at the time 

of the accident in 1976, no extrapolation 

needed to estimate past levels. 

• Internal analyses. 

• Evaluates female cancer incidence, other 

studies evaluate male cancer mortality. 

• Presumed adjustment for age and potential 

breast cancer confounders (15 of 21 cancers 

were breast cancer). 

• Acute dose due to accident may not be 

comparable to chronic dose accumulated over 

a long time, which is typical of most 

environmental exposures. 

• Did not evaluate different lag periods. 

• Not clear if any adjustment for confounders. 

• Small number of cancers (n = 21). 

• Doses known in 1976, require assumptions 

about excretion over time to estimate 

cumulative dose (9 year half life assumed), 

presumed metric of primary interest. No 

more recent TCDD concentration data used. 

• Reported log10 transformation of the exposure 

estimates in their regression analysis. 

Warner et al. 

(2002) 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-96 —DO NOT CITE OR QUOTE 

1 

2 Figure 5-1. Mechanism of altered gene expression by AhR. The regulation of 

3 gene expression by TCDD in mammalian cells requires binding of the xenobiotic 

4 to the aryl hydrocarbon receptor (AhR). The AhR is part of a multi-protein 

5 complex that includes heat shock proteins and various kinases and other post6 translational modifying factors. Upon ligand binding, the AhR heterodimerizes 

7 with the aryl hydrocarbon receptor nuclear translocator (Arnt) and binds to dioxin 

8 response elements (DREs) found in target genes. Alternatives to DRE-dependent 

9 gene expression exist whereby the AhR complex associates with other 

10 transcription factors and results in a cross-talk between these systems. The 

11 culmination of regulation of AhR targets genes (both increases ad decreases in 

12 transcription) results in an alteration in cellular phenotypes, including changes in 

13 intracellular metabolism and changes in cell cycle regulation.
This document is a draft for review purposes only and does not constitute Agency policy

1/15/10 5-97 DRAFT: DO NOT CITE OR QUOTE Figure 5-2. TCDD’s hypothesized modes of action in site-specific carcinogenesis. See text for detail

instance, the solid arrows depict pathways that are well-established and are associated with low uncertaindashed arrows represent connections that are less established and are associated with higher uncertainty.
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-98 —DO NOT CITE OR QUOTE 

1 

2 Figure 5-3. EPA’s process to select and identify candidate OSFs from key 

3 animal bioassays for use in the cancer risk assessment of TCDD. 

4 For each cancer study that qualified for TCDD dose-response assessment using the study inclusion criteria, 

5 EPA first selected the species/sex/tumor combinations with statistically significant increases in tumor 

6 incidence by either a pair-wise test between the treated group and the controls or by a trend test showing 

7 increases in tumors with increases in dose. Next, EPA used an animal kinetic model to estimate blood 

8 concentrations corresponding to the study average daily administered doses for use in dose response 

9 modeling. BMDL01’s were then estimated for the blood concentrations by, (1) using the linearized 

10 multistage model for each species/sex/tumor combination within each study, and (2) using the linearized 

11 multistage model within a Bayesian Markov Chain Monte Carlo framework that assumes independence of 

12 tumors and modeling all tumors together for each species/sex combination within each study. Using the 

13 human kinetic model, human equivalent doses (BMDLHEDs) were then estimated for each of the BMDL01s 

14 and oral slope factors were calculated by OSF = 0.01/BMDLHED. The lowest OSF for a species/sex 

15 combination for either a single tumor type or all tumors combined was selected as a candidate OSF for 

16 TCDD risk assessment. 

Include tumor data set Exclude tumor data set

Final list of key cancer animal bioassay studies for quantitative 

dose-response analysis of TCDD

For each 

species/sex/tumor combination,

were increases in tumor incidence with

dose statistically significant either pair-wise

or by a trend test?

Yes No

Conduct linear multi-stage doseresponse modeling using kinetic 

doses and derive a BMDL01 for 

each species/sex/tumor 

combination within a study 

Using the animal kinetic model, estimate blood concentrations 

corresponding to average daily administered doses 

Is the 

derived OSF the lowest for 

a species/sex combination 

in a given study? 

Include as candidate OSF Exclude as candidate OSF

Yes No

Assuming independence of 

tumors, apply multiple-tumor modeling

using kinetic doses and derive

a BMDL01 for each species/sex

combination within a study

Using the human kinetic model, estimate a Human Equivalent Dose (BMDLHEDs) for 

each BMDL01 and derive a candidate OSF by dividing 0.01 by the BMDLHEDs

Include tumor data set Exclude tumor data set

Final list of key cancer animal bioassay studies for quantitative 

dose-response analysis of TCDD

For each 

species/sex/tumor combination,

were increases in tumor incidence with

dose statistically significant either pair-wise

or by a trend test?

Yes No

Conduct linear multi-stage doseresponse modeling using kinetic 

doses and derive a BMDL01 for 

each species/sex/tumor 

combination within a study 

Using the animal kinetic model, estimate blood concentrations 

corresponding to average daily administered doses 

Is the 

derived OSF the lowest for 

a species/sex combination 

in a given study? 

Include as candidate OSF Exclude as candidate OSF

Yes No

Assuming independence of 

tumors, apply multiple-tumor modeling

using kinetic doses and derive

a BMDL01 for each species/sex

combination within a study

Using the human kinetic model, estimate a Human Equivalent Dose (BMDLHEDs) for 

each BMDL01 and derive a candidate OSF by dividing 0.01 by the BMDLHEDs
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 5-99 —DO NOT CITE OR QUOTE 

1 

2 Figure 5-4. Representative endpoints for each of the hypothesized key events 

3 following AhR activation for TCDD-induced liver tumors. 

Liver

TCDD

AhR

Changes in

gene expression

Oxidative

stress

Hepatotoxicity

Hepatocellular

proliferation

Adenoma

and

carcinoma

Labeling index, 31 weeks

NTP, 2006

Toxic hepatopathy, 2 years

NTP, 2006

TBARS, 90 days

Hassoun et al., 2000

CYP1A1 mRNA, 1 day

Vanden Heuvel et al., 1994

EROD (CYP1A1), 53 weeks

NTP, 2006

Liver

TCDD

AhR

Changes in

gene expression

Oxidative

stress

Hepatotoxicity

Hepatocellular

proliferation

Adenoma

and

carcinoma

Labeling index, 31 weeks

NTP, 2006

Toxic hepatopathy, 2 years

NTP, 2006

TBARS, 90 days

Hassoun et al., 2000

CYP1A1 mRNA, 1 day

Vanden Heuvel et al., 1994

EROD (CYP1A1), 53 weeks

NTP, 2006
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1 

2 

3 Figure 5-5. Representative endpoints for two hypothesized key events 

4 following AhR activation for TCDD-induced lung tumors. 

Lung

TCDD

AhR

Changes in

gene expression

Retinoid

homeostatsis

Metabolic

enzymes

(Cyps, COX2)

Cocarcinogens

Adenoma

and

carcinoma

Proliferation

Toxicity

Hepatic retinol, 90 days

Van Birgelen et al., 1995a

EROD (CYP1A1), 53 weeks

NTP, 2006

Lung

TCDD

AhR

Changes in

gene expression

Retinoid

homeostatsis

Metabolic

enzymes

(Cyps, COX2)

Cocarcinogens

Adenoma

and

carcinoma

Proliferation

Toxicity

Hepatic retinol, 90 days

Van Birgelen et al., 1995a

EROD (CYP1A1), 53 weeks

NTP, 2006
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dose (ng/kg-day)

cumulative response

1 5 10 50 100

0.0 0.2 0.4 0.6 0.8 1.0

Multistage model fit

Linear fit

1 

2 Figure 5-6. Multistage model fit to cholangiocarcinoma response data (NTP, 

3 2006) with comparison to linear model fit. 

Exposure measure

Tumor incidence

0 20 40 60 80 100

0.0 0.2 0.4 0.6 0.8 1.0

administered dose (ng/kg-day)

blood concentration (ng/kg)

AHR-bound concentration (ng/kg)

4 

5 Figure 5-7. Weibull model fits to Kociba/G&S liver tumor response data 

6 with alternative dose metrics. Weibull powers are 0.68, 0.95, and 1.4 for the 

7 administered dose, blood concentration and AhR-bound concentration fits, 

8 respectively. 

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

1/15/10 DRAFT 6-1 —DO NOT CITE OR QUOTE 

1 6. FEASIBILITY OF QUANTITATIVE UNCERTAINTY ANALYSIS 

2 FROM NAS EVALUATION OF THE 2003 REASSESSMENT 

3 

4 

5 6.1. INTRODUCTION 

6 This section focuses on the third area for improvement in the 2003 Reassessment that was 

7 identified by the National Academy of Sciences (NAS) review committee (NAS, 2006a), i.e., 

8 improving transparency, thoroughness, and clarity in quantitative uncertainty analysis. 

9 Although the NAS committee summarized the shortfalls in the 2003 Reassessment categorically, 

10 the elaborations within their report often contain the qualification “if possible” and do not take a 

11 position with regard to the feasibility of many of its suggestions. With appreciation for the 

12 extent of information available for dioxin, the goal of this section is to circumscribe the 

13 feasibility of a data-driven quantitative uncertainty analysis for 

14 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) dose-response assessment. Following brief 

15 highlights of the evolution of quantitative uncertainty analysis for such applications, this section 

16 lays out definitions of key terms, reviews U.S. Environmental Protection Agency (EPA)’s 

17 position regarding cancer and noncancer endpoints, summarizes the NAS critique, and evaluates 

18 the feasibility of quantitative uncertainty analysis for TCDD within the framework of EPA’s 

19 reference dose (RfD) and cancer dose-response methodologies. 

20 

21 6.1.1. Historical Context for Quantitative Uncertainty Analysis 

22 The basic methods of probabilistic risk assessment were developed in the aerospace 

23 program in the 1960s, and they found their first full-scale application in the Nuclear Regulatory 

24 Commission’s Reactor Safety Study of 1975—including accident consequence analysis and 

25 uncertainty analysis (U.S. NRC, 1975). This study, commonly referred to as the Rasmussen 

26 Report after its lead author, is considered to be the first modern probabilistic risk assessment. In 

27 the aftermath of the 1979 Three Mile Island accident, a new generation of probabilistic risk 

28 assessments (PRAs) appeared in which some of the methodological problems of the 1975 study 

29 were avoided. These advances were reflected in the Commission’s Fault Tree Handbook

30 (U.S. NRC, 1981) and PRA guide (U.S. NRC, 1983), which shored up and standardized much of 

31 the risk assessment methodology. An extensive chapter of the latter was devoted to uncertainty 

32 and sensitivity analysis. These documents formed the basis for standards and guidelines 
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1 established by other agencies, including the U.S. Department of Energy (U.S. DOE, 1992) and 

2 National Aeronautics and Space Administration (NASA, 2002). 

3 In 1991, a set of U.S. Nuclear Regulatory Commission (U.S. NRC) studies known as 

4 NUREG 1150 used structured expert judgment to quantify uncertainty and set new standards for 

5 uncertainty analysis, in particular with regard to expert elicitation (U.S. NRC, 1991). This was 

6 followed by a joint U.S.-European Union (EU) program for quantifying uncertainty in accident 

7 consequence models. Expert judgment methods were further elaborated in those evaluations, as 

8 well as screening, dependence modeling and sensitivity analysis (EC, 2009). Studies building 

9 off of this work have performed a large-scale uncertainty analysis of European consequence 

10 models and provided extensive guidance on identifying important variables; selecting, 

11 interviewing and combining experts; propagating uncertainty; inferring distributions on model 

12 parameters; and communicating results, as documented by Goossens et al. (1996, 1997, 1998, 

13 2001a-g) and others (Brown et al., 1997; Harper et al., 1995, 2002). 

14 The National Research Council (NRC) has been a persistent voice in urging the 

15 government to enhance its risk assessment methodology since its report on risk assessment in the 

16 federal government (NRC, 1983). The Council’s 1989 report, Improving Risk Communication, 

17 inveighed against minimizing the existence of uncertainty and noted the importance of 

18 considering the distribution of exposure and sensitivities in a population (NRC, 1989). The issue 

19 of uncertainty was a clear concern in subsequent reports, including those assessing human 

20 exposure to airborne pollutants (NRC, 1991). Building on its evaluation of Issues in Risk 

21 Assessment (NRC, 1993), the landmark study Science and Judgment in Risk Assessment (NRC, 

22 1994) gathered many of these themes in a plea for quantitative uncertainty analysis as “the only 

23 way to combat the ‘false sense of certainty,’ which is caused by a refusal to acknowledge and 

24 (attempt to) quantify the uncertainty in risk predictions.” A subsequent report Estimating The 

25 Public Health Benefits of Proposed Air Pollution Regulations (NRC, 2002) identified three 

26 barriers to the broad acceptance of recent EPA health benefit analyses: (1) the large amount of 

27 uncertainty inherent in these analyses, (2) the manner in which EPA deals with this uncertainty, 

28 and (3) “... projected health benefits are often reported as absolute numbers of avoided death or 

29 adverse health outcomes without a context of population size or total numbers of outcomes.” 

30 The Council encouraged EPA to “explore alternative options for incorporating expert judgment 

31 into its probabilistic uncertainty analyses.” 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 6-3 —DO NOT CITE OR QUOTE 

1 In 2006, the Office of Management and Budget released a draft bulletin proposing 

2 technical guidance for risk assessments produced by the federal government (OMB, 2006). An 

3 NRC (2007) review found many shortfalls in this proposal and recommended its retraction, and a 

4 revision was undertaken. In an early 2009 report, Science and Decisions: Advancing Risk 

5 Assessment, the NRC committee on improving risk analysis encouraged EPA to harmonize 

6 approaches for cancer and noncancer dose-response assessment (NRC, 2009), which involves 

7 uncertainty issues discussed in this section. Even more recently, EPA released a draft white 

8 paper, Using Probabilistic Methods to Enhance the Role of Risk Analysis in Decision Making

9 (U.S. EPA, 2009d). Although not focused specifically on quantitative uncertainty analysis, there 

10 is overlap with the issues treated here, and relevant insights are anticipated from ongoing efforts 

11 in this area. 

12 

13 6.1.2. Definition of Terms 

14 For purposes of this study, the following definitions are adopted: 

15 

16 Uncertainty Characterization: This consists of a Structured Uncertainty Narrative and, if 

17 the uncertainty is supported by quantitative models, Quantitative Uncertainty Analysis.

18 Structured Uncertainty Narrative: This identifies assumptions conditional on which 

19 uncertainty is to be characterized and delineates the type of arguments with supporting 

20 evidence that buttress these assumptions. 

21 Joint Distribution/Marginal Distribution: For a set of uncertain quantities, a joint 

22 distribution is an assignment of probabilities (or probability densities) for each possible 

23 combination of values of these quantities. Each uncertain quantity has a marginal 

24 distribution, that is, an assignment of probabilities (or probability densities) to each 

25 possible value of that quantity. Assigning a marginal distribution to each quantity is not 

26 equivalent to assigning a joint distribution to the set of quantities, unless the quantities 

27 are independent; in this case the joint distribution is just the product of the margins. 

28 Quantitative Uncertainty Analysis: This is a quantification of the uncertainty attending 

29 the use of quantitative models. It applies to a mathematical model of physical 

30 phenomena, some of whose parameter values are not known with certainty. A joint 

31 distribution is assigned to uncertain model parameters and propagated through the model 

32 to yield a joint distribution over the model output. Thus, a quantitative uncertainty 

33 analysis always has a joint distribution over model outputs as its result. 

34 Qualitative/Informal Uncertainty Analysis: This assembles the arguments and evidence 

35 and provides an assessment of their plausibility in terms of verbal modifiers. The 

36 meaning of verbal modifiers such as “likely/unlikely” or “plausible/implausible” in the 

37 natural language is indeterminate and context dependent. The way in which these 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 6-4 —DO NOT CITE OR QUOTE 

1 qualifiers combine in the natural language requires critical attention from a quantitative 

2 viewpoint (if A is likely and B is likely and C is likely, is A and B and C likely?). Before 

3 the advent of personal computers, various shorthand techniques were developed for 

4 computing system risk. In control theory schemes of ‘interval probabilities’ were 

5 proposed which could be propagated through a system to yield bounds on system 

6 reliability. Whereas these bounds originally reflected accuracy of shorthand 

7 approximations of complex formulae, their offspring have been proposed as 

8 quantifications of uncertainty. Alternative notions of uncertainty are also proposed with 

9 the goal of simplifying the assessment and computational burden or capturing putative 

10 features of uncertainty which are overlooked in probability theory. These include 

11 possibility theory, fuzzy numbers, qualitative algebra, imprecise probabilities, belief 

12 functions, certainty factors, and the like. Nonmonotonic reasoning systems attempt to 

13 capture reasoning about knowledge, or reasoning from partial knowledge; they include 

14 default logic, defeasible logic, abductive logic, autoepistemic logic, to name a few. This 

15 is not the place to discuss foundational issues, except to remark that the practitioner 

16 should carefully explore the whole range of alternatives and critically examine the 

17 operational meaning of the primitive notions in each alternative. 

18 Sensitivity Analysis: If a quantitative model uses “nominal values” for various input 

19 parameters, a sensitivity analysis is performed by choosing different values for these 

20 parameters and re-running the model to assess the impact of changes in these parameters 

21 on model output. Applicable methods include one- and two-at-a-time methods, design of 

22 experiments and Morris’s method. They aim at estimating first-, and perhaps 

23 higher-order effects with a minimal number of model runs, by systematically varying the 

24 nominal values. In large uncertainty analyses, sensitivity analysis is used to screen 

25 variables for in-depth uncertainty quantification, and thus is part of a quantitative 

26 uncertainty analysis. As a note, the NAS committee report does not distinguish between 

27 uncertainty and sensitivity analysis. In fields which have not developed a tradition in 

28 uncertainty quantification, the spread of values generated by a sensitivity analysis is 

29 sometimes presented as a representation of uncertainty (Murphy et al., 2004). That may 

30 or may not be the case; the question is moot so long as the uncertainty on model input 

31 parameters is not quantified. Systematically varying input values is not the same as 

32 sampling input parameter values from their uncertainty distributions. In any event, a 

33 systematic approach to parameter variation is essential; simply changing a few values 

34 happenstance and generating different output does not serve a scientific purpose, and 

35 inevitably raises questions of selection bias. That said, if alternative values recommend 

36 themselves, then running these through the models can help sensitize users to parameter 

37 variations. 

38 Cognitive Uncertainty: This concerns uncertainty regarding what is the case. This may 

39 be conceived as uncertainty over the set of possible worlds. Uncertainty over possible 

40 worlds may be represented as probability. Two interpretations or operationalizations of 

41 the probability formalism are current, the objective or frequentist interpretation and the 

42 subjective or Bayesian interpretation. These interpretations are not mutually exclusive, 

43 as subjective probabilities can and often do track relative frequencies. 
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1 Volitional Uncertainty: This concerns uncertainty regarding what to do. In the natural 

2 language, being unsure which course of action to choose is also called “uncertainty.” 

3 Insofar as uncertainty on the best course of action can be translated into a claim about the 

4 state of the world, volitional uncertainty can be translated into cognitive uncertainty. A 

5 regulatory body charged with setting a speed limit is obliged to make a decision. The 

6 decision may be cautious or reckless, well or poorly motivated, wise or foolish; but it 

7 cannot be true or false. Since the decision makes no claim about the state of the world, it 

8 cannot be uncertain in the cognitive sense. The uncertainty cannot be analyzed by 

9 sampling from some distribution. However, if the decision is based on the claim that the 

10 chosen speed limit minimizes accidents while maintaining a prescribed traffic volume, 

11 that claim may be uncertain and may be subjected to quantitative uncertainty analysis. A 

12 discretionary decision of a regulatory body may entrain cognitive uncertainty, but it 

13 becomes amenable for quantitative uncertainty analysis only when it is linked to a claim 

14 about the state of the world. 

15 Aleatoric/Epistemic Uncertainty: This distinction is also termed Variability/Uncertainty. 

16 A variable whose uncertainty is aleatoric for a given population takes different, uncertain, 

17 values for each member of the population. If its uncertainty is epistemic, it takes the 

18 same uncertain value for all members of the population. Issues involving uncertainty and 

19 variability or epistemic and aleatory uncertainty translate into issues of dependence, when 

20 conducting a quantitative uncertainty analysis (see Section 6.1.3.3). In its Science and 

21 Judgment report, NRC (1994) correctly remarks that “the amount of variability is 

22 generally itself an uncertain parameter.” It is natural to ask whether a given uncertainty 

23 is aleatoric or epistemic, whereas it is awkward to ask whether this uncertainty is 

24 uncertain or variable—which explains the preference for the epistemic/aleatoric 

25 terminology. 

26 

27 6.1.3. Basic Requirements of a Quantitative Uncertainty Analysis

28 The uncertainty propagation can be performed by some rough estimation, as for example 

29 the delta method (Oehlert, 1992) or in rare cases it can be performed analytically, as in simple 

30 error propagation. Most often, however, it will be performed using Monte Carlo simulation. A 

31 joint distribution is assigned to the parameters of a quantitative model and then propagated 

32 through the model by sampling repeatedly from this joint distribution, computing model output 

33 and generating a distribution of model output. Every uncertainty analysis is conditional on initial 

34 assumptions. A “complete” uncertainty analysis is an unattainable goal; the best that can be 

35 done in practice is to identify and motivate the assumptions that are used. This section is not a 

36 how-to guide, but a to-do list of key tasks involved in any quantitative uncertainty analysis. 

37 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 6-6 —DO NOT CITE OR QUOTE 

1 6.1.3.1. Quantitative Model 

2 The starting point of any quantitative uncertainty analysis is a mathematical model or 

3 prescription for calculating quantities of interest. A structured narrative explains the choice of 

4 quantitative models. If some values of input parameters for this calculation are not known with 

5 certainty, then the question arises: “What is the uncertainty attending the use of this model?” 

6 This is the question a quantitative uncertainty analysis seeks to answer. 

7 

8 6.1.3.2. Marginal Distributions over Model Parameter 

9 If the model parameters are directly measurable with sampling error, then the sampling 

10 distribution may itself be used in the quantitative uncertainty analysis. If the model parameters 

11 are fit to data that are sampled from a known or hypothesized distribution, then by resampling 

12 this distribution and refitting the model, distributions over the model parameters may be 

13 constructed. Physically-based simulation models, such as pharmacokinetic models or 

14 environmental transport models, may be solved analytically if equilibrium reaction rates (the 

15 transfer coefficients) are constant. If these rates are not constant, as when concentrations are 

16 near saturation levels, then simulation is indicated. Structured expert judgment has been applied 

17 for uncertainty quantification within the engineering community since the time of the Rasmussen 

18 Report. More recently, this approach has been “test-driven” by EPA in assessing health effects 

19 of fine particulates (Walker et al., 2009), and its potential application has been identified in the 

20 Agency’s Guidelines for Carcinogen Risk Assessment, commonly referred to as the cancer 

guidelines (U.S. EPA, 2005).34 21 

22 

23 6.1.3.3. Dependence Between Parameter Uncertainties: Aleatoric and Epistemic (Uncertainty 

24 and Variability) 

25 Two uncertain quantities are independent if knowledge about one of them does not alter 

26 our uncertainty regarding the other. The quantities are dependent if they are not independent. 

27 The role of dependence modeling in quantitative uncertainty analysis must be addressed. To 

28 illustrate, cigarette smoking and body fat are both thought to influence biomarkers for toxic 

 

34The U.S. EPA (2005) cancer guidelines state: “In many of these scientific and engineering disciplines, researchers 

have used rigorous expert elicitation methods to overcome the lack of peer-reviewed methods and data.... These 

cancer guidelines are flexible enough to accommodate the use of expert elicitation to characterize cancer risks, as a 

complement to the methods presented in the cancer guidelines. According to NRC (2002), the rigorous use of 

expert elicitation for the analyses of risks is considered to be quality science.” 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 response to dioxin exposure, such as ethoxyresorufin-O-deethylase (EROD) activity (Pereg et al., 

2 2002). Both cigarette smoking and percent body fat in a random individual sampled from a 

target population are uncertain.35 3 However, these uncertainties are not independent, inasmuch as 

4 smokers tend to have less body fat (Vanni et al., 2009). 

5 Issues involving uncertainty and variability or epistemic and aleatory uncertainty 

6 translate into issues of dependence, when conducting a quantitative uncertainty analysis. For 

7 example, a constant used to estimate the biokinetic behavior of dioxin may be uncertain. If it is 

8 believed to be the same for every member of the population, the uncertainty is termed 

9 “epistemic.” In a quantitative uncertainty analysis, this factor would be sampled from its 

10 uncertainty distribution on each Monte Carlo run and used for all members of the population. 

11 Body fat, in contrast, is aleatoric. We do not sample one value from the body fat distribution and 

12 use this value for all members of the population on each Monte Carlo run. Instead we sample a 

13 body fat value for each individual on each run. Because body fat is correlated with other 

14 relevant variables (e.g., smoking, sex, age, and socioeconomic status), all of these variables 

15 should be sampled in a manner that reflects their dependences. Kinetic constants whose 

16 uncertainty is epistemic are completely correlated across individuals: if the value is 0.5 for one 

17 individual, it is 0.5 for everyone. Body fat values vary from individual to individual, and they 

18 are correlated through a host of other variables. 

19 

20 6.1.3.4. Model Uncertainty 

21 All models, being idealizations, are false; on this there is no uncertainty to quantify. 

22 However, the choice of model may constrain the ability to represent uncertainty in observable 

23 phenomena. Thus, in a linear low-dose model, uncertainty over a cancer slope factor may be 

24 quantified, but uncertainty regarding changes in slope at distinct low-dose regimes cannot be 

25 captured. When the model choice imposes severe and potentially unwelcome constraints on 

26 uncertainty quantification, this must be addressed. Distributions over model parameters may be 

 

35Because dioxins generally distribute to body fat/lipid, the percent body fat is often used to estimate body burden; a 

default value of 25% is common (Connor and Aylward, 2006). However, body fat percentage varies widely 

between individuals, from a minimum essential level (e.g., 2% for men, 10% for women) to obesity (e.g., 38% or 

more for men, 42% for women). Considering that current estimates suggest 30% of the U.S. population are obese, 

an uncertainty analysis of dioxin risk in this population should sample individuals from their gender/body fat 

distribution and correlate this with other known or suspected covariates influencing toxic response (such as diet, 

smoking, natural and endogenous ligands, disease, and age). 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 selected and evaluated based on their ability to reflect uncertainty distributions over observable 

2 phenomena predicted by the models. In such cases, the uncertainty propagated through the 

3 quantitative model is not strongly model-dependent. In other cases, multiple model alternatives 

4 may be applied, whose “probability of being the true model” is known or assumed. Since 

5 different models can always be regarded as specializations of more general models, the 

6 distinction between parameter and model uncertainty is sometimes more apparent than real. The 

7 multistage and Weibull dose-response models both contain the model Pr(x) = γ + (1 − γ) 

(1 − e−β1x 8 ) as a sub model, to which they collapse if other parameters are zero (multistage) or one 

9 (Weibull). Recalling that the function 1/(1 + x) is first order equivalent to (1 − x) for small x, the 

10 same may be said for logistic models as well. In this case, these models could easily be 

11 parameterized within one family, rendering the choice between them a choice of parameter 

12 values. Similarly, the choice between sub-, supra-, and linear models is sometimes reduced to 

13 parameter estimation within a more general class of model (Hoel and Portier, 1994). 

14 In other cases, the reduction of model uncertainty to parameter uncertainty is less natural. 

15 For example, according to the “chemoprotection model” of Greenlee et al. (2001), dioxin’s 

16 binding to the aryl hydrocarbon receptor (AhR) inhibits proliferation in tumor cells and thus 

17 suppresses mammary tumors. Dose-dependent protection and cancer induction can both be true, 

18 each applying to different tissues. Although mathematical models exhibiting these twin features 

19 are under development (Kohn and Melnick, 2002), these models are not yet readily estimable 

20 from data, and the choice between them is referred to the structured narrative. 

21 

22 6.1.3.5. Sampling Method 

23 All sampling on a computer is “pseudo random.” Significant issues arise in choosing a 

24 method for sampling high-dimensional distributions with dependence. If evaluating the 

25 quantitative model is very time consuming, various ‘quasi random’ schemes may be applied, 

26 including Latin hypercube sampling, importance sampling, and Hammersley sampling. These 

27 methods involve trade-offs between economy and accuracy of the dependence modeling. 

28 

29 6.1.3.6. Method for Extracting and Communicating Results 

30 When a large quantitative uncertainty analysis has been performed, the method for 

31 identifying important contributors and communicating this information to users is not 
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1 straightforward. Analysts have proposed many ways to quantify the uncertainty contribution of 

one variable, or set of variables, on others,36 2 and his/her decision at this juncture may strongly 

3 impact the “take-home” message from the study. An importance measure that averages over an 

4 entire sample space may obscure the features of real interest. For example, the drivers of cancer 

5 induction at low doses might be different from the drivers at high doses. If the drivers of 

6 low-dose cancer induction are of interest, then importance measures that average over all doses 

7 should not be considered. 

8 

9 6.2. EPA APPROACHES FOR ORAL CANCER AND NONCANCER ASSESSMENT 

10 EPA typically develops different types of toxicity information in its oral cancer and 

11 noncancer dose-response assessments, although efforts to harmonize these approaches continue 

12 to be made. Noncancer endpoints are usually assessed using the RfD methodology to derive a 

13 threshold below which there is likely no appreciable risk. (Note “risk” is used here simply as a 

14 general term indicating the potential for adverse effects.) In contrast, cancer endpoints are 

15 commonly assessed using a dose-response function with the probability of excess risk above 

16 background modeled as a linear function of dose, for doses down to zero. The RfD method 

17 relies on a point of departure (POD), and the dose-response method uses a POD if the linear 

18 model is chosen by default. From the U.S. EPA (2005) guidelines, cancer endpoints can also be 

19 assessed using the RfD methodology if the proof burden is satisfactorily met (previously 

20 described in Section 5.2.3.3). 

21 Toxicity reference values have generally been derived for human noncancer endpoints 

22 based on a no-observed-adverse-effect level (NOAEL) or lowest-observed-adverse-effect level 

23 (LOAEL) from animal bioassay studies. This terminology suggests a biological population 

24 threshold beneath which no harm is anticipated. Reference values such as the RfD or reference 

 

36A few examples may suffice. The standard Pearson correlation coefficient measures the linear dependence 

between two variables, positive or negative. The rank or Spearman correlation coefficient measures the monotone 

dependence. The correlation ratio measures the (unsigned) variance contribution of an explanatory variable on a 

target variable. The regression coefficient measures the expected change in standard (not natural!) units of a target 

variable, per standard unit change in an explanatory variable and assumes this expected change is independent of the 

values of the explanatory variables. Multiple correlation measures the correlation between a given variable and its 

best linear predictor based on another set of variables. The partial correlation of two variables given a set of other 

variables is their correlation after discounting the influence of the other variables. The correlation ratio, multiple 

correlation and the regression coefficient are not symmetric, the correlation multiple regression are always 

non-negative (Saltelli et al., 2000; Kurowicka and Cooke, 2006). 
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1 concentration are derived by applying uncertainty factors (UFs) to a POD. Depending on the 

2 nature of available data and modeling choice, a POD can be selected from values other than a 

3 NOAEL or LOAEL, such as an EDx (effective dose eliciting x percent response), or a benchmark 

4 dose (BMD) or its lower confidence bound (BMDL). The BMD is the dose that induces a 

5 benchmark response (BMR), which is often chosen to represent a 5 or 10% increase in excess 

6 risk above background. The POD is divided by one or more uncertainty factors that represent 

7 knowledge gaps (see Section 6.4.1.2 for details on specific types of UFs). 

8 An RfD is described as “likely to be without appreciable risk” but the probabilistic 

9 language has not as yet been operationalized. There is no quantitative definition of 

10 “appreciable” and no mechanism to compute risk as a function of dose, so as to ascertain that the 

11 risk is indeed not appreciable. In addition, different participants in discussions over 

12 threshold/nonthreshold models for dioxin may have different perspectives regarding how to 

13 define “appreciable risk.” Under the current POD/UF framework, dose-response functions are 

14 not provided for calculating the actual risk at or above the RfD. Instead, to provide a “risk 

15 indicator” for use in screening for health hazards, a hazard quotient (HQ) is computed as the 

16 ratio of a given exposure to the RfD, or a margin of exposure (MOE) is estimated as the ratio of 

17 the POD to the human exposure level. 

18 For the cancer endpoint, an oral cancer slope factor may be derived for human health risk 

19 assessment, typically based on tumor incidence data from an animal bioassay or on cancer 

20 incidence or deaths from an epidemiologic study. In U.S. EPA’s Cancer Guidelines, cancer is 

21 predominantly thought to have no population biological threshold and a linear extrapolation to 

22 zero is applied from the POD based on extra risk above background, i.e., the probability of the 

23 endpoint decreases linearly in dose from the POD to zero or to a population background level. In 

24 the absence of sufficient evidence supporting low-dose cancer induction, the linear model is 

25 applied as a default. Cancer endpoints can also be evaluated using a “nonlinear” model. In this 

26 case, the proof burden clearly rests on the nonlinear model; it must have a preponderance of 

27 evidence to override the health-protective default choice, as described in U.S. EPA Cancer 

28 Guidelines. U.S. EPA (2005) cancer guidelines state, “When adequate data on mode of action 

29 provide sufficient evidence to support a nonlinear mode of action for the general population

30 (emphasis added) and/or any subpopulations of concern, a different approach—a reference 

31 dose/reference concentration that assumes that nonlinearity—is used.” In current terminology, 
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1 the RfD methodology applies to the cancer endpoint if there is sufficient evidence supporting a 

2 “zero slope at zero” model; otherwise, the linear nonthreshold model applies by default. (See 

3 Section 5.2.3.3 for a detailed discussion of linear vs. nonlinear extrapolations below the observed 

4 data, population vs. individual thresholds, and for the how U.S. EPA (2005) Cancer Guidelines 

5 are applied in choosing dose-response model forms for risk assessment.) 

6 

7 6.3. HIGHLIGHTS OF NAS REVIEW COMMENTS ON UNCERTAINTY 

8 QUANTIFICATION FOR THE 2003 REASSESSMENT 

9 The NAS (2006a, b) identified a number of uncertainty characterization issues for the 

10 2003 Reassessment; key sources of uncertainty for which quantification is suggested are 

11 highlighted in Table 6-1. The discussion in this section focuses on comments related to dose 

12 response. 

13 There are several nuances in the NAS position relative to the need for substantial 

14 improvement in transparency, thoroughness, and clarity in quantitative uncertainty analysis for 

15 the 2003 Reassessment. These nuances concern whether the nonlinear model (note that the NAS 

16 committee uses “sublinear” and “nonlinear” interchangeably) is scientifically better supported 

17 than the linear model, and if the sublinear model is better supported, whether this is based on 

18 data or on apodictic knowledge (knowledge without uncertainty) of the mode of action (MOA). 

19 The NAS committee does not distinguish between individual and population dose-response 

20 models; however the criteria from U.S. EPA Cancer Guidelines clearly apply to population 

21 models. Assuming that the AhR-mediated MOA implies a threshold for each individual, the step 

22 to a population “zero slope at zero” model requires the following, as identified and discussed in 

23 detail in Section 5.2.3.3: 

24 

25 1. The distribution the individual thresholds induced by the MOA, and 

26 2. The dose-response function for values above the thresholds. 

27 

28 This information can either come from data, or from apodictic knowledge of the MOA, 

29 but the burden of proof clearly rests on the nonlinear model. This section summarizes the NAS 

30 committee’s overall positions. Responses to specific suggestions are given in Section 6.4 and 
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1 summarized in Section 6.5. Several excerpts of specific comments from NAS (2006a) illustrate 

2 key issues. 

3 The NAS committee favors the nonlinear model with a threshold: 

4 

5 ...the committee concludes that, although it is not possible to scientifically prove 

6 the absence of linearity at low doses, the scientific evidence, based largely on 

7 mode of action, is adequate to favor the use of a nonlinear model that would 

8 include a threshold response over the use of the default linear assumption. 

9 (p. 126)

10 

11 The committee does not state whether the threshold applies to the population, or whether each 

12 individual has his/her own threshold. 

13 The NAS also comments on whether the nonlinear model should be used to compare with 

14 the health-protective linear default: 

15 

16 Because the committee concludes that the data support the hypothesis that the 

17 dose-response relationship for dioxin and cancer is sublinear, it recommends that 

18 EPA include a nonlinear model for cancer risk estimates, but that EPA also use 

19 the current linear models for comparative purposes. (p. 12) 20 

21 The committee does not suggest what the (population/individual) threshold might be, nor how it 

22 might be supported on the basis of data. Rather, the apodictic knowledge that there is a 

23 (population/individual) threshold places the dioxin risk assessment within the RfD framework, 

24 using a HQ or MOE as the basis for indicating the potential risks from exposure. The committee 

25 further asks for a quantitative characterization of the range of uncertainty: 

26 

27 The committee determined that the available data support the use of a nonlinear 

28 model, which is consistent with receptor-mediated responses and a potential 

29 threshold, with subsequent calculations and interpretation of MOEs. EPA’s sole 

30 use of the default assumption of linearity and selection of ED01 as the only POD 

31 to quantify cancer risk does not provide an adequate quantitative characterization 

32 of the overall range of uncertainties associated with the final estimates of cancer 

33 risk. (p. 17) 

34 

35 Regarding the preponderance of evidence, the committee indicates that quantitative 

36 evidence will not decide the linearity/nonlinearity (nonthreshold/threshold) issue, but knowledge 

37 (without uncertainty) of the MOA should be used: 
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1 Quantitative evidence of nonlinearity below the POD, the ED01, will never be 

2 available because the POD is chosen to be at the bottom end of the available 

3 dose-response data. ... EPA should give greater weight to knowledge about the 

4 mode of action and its impact on the shape of the dose-response relationship. 

5 (p. 126)

6 

7 The comment continues, with the committee implicitly acknowledging that there is no 

8 evidence arguing against linearity, but that the lack of evidence should not justify using the linear 

9 model: 

10 

11 The committee considers that the absence of evidence that argues against linearity 

12 is not sufficient justification for adopting linear extrapolation, even over a dose 

13 range of one or two orders of magnitude or to the assumption of linearity through 

14 zero, which would not normally be applied to receptor-mediated effects. (p. 126)

15 

16 Whereas EPA has applied its own guidance on cancer risk assessment and adopted 

17 linearity as a health-protective default in the absence of sufficient evidence (volitional 

18 uncertainty), the NAS committee views the absence of evidence as imposing a burden of proof 

19 on the linear model (cognitive uncertainty). (As a note, terminology issues and conceptual 

20 inconsistencies within the NAS report illustrate the importance of clarity and are not unexpected, 

21 given the complexity of these issues and the nature of a committee process.) 

22 The thrust of the NAS remarks regarding transparency, thoroughness and clarity in 

23 quantitative uncertainty analysis relevant to dose-response can be summarized as follows: 

24 

25 1. The uncertainty of cancer risks due to dioxin exposure should be quantified. 

26 2. Dioxin cancer risk should be treated either as a threshold phenomenon, thus following the 

27 basic RfD methodology, or should be modeled using a sublinear dose-response function 

28 below the observed data, with the linear model used for comparison. 

29 3. The POD should be subjected to quantitative uncertainty analysis. 

30 
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A similar point of view has been indicated by others.37 1 Detailed suggestions regarding specific 

2 improvements for quantitative uncertainty analysis in the 2003 Reassessment are outlined in the 

3 next section and summarized in Section 6.5. 

4 

5 6.4. FEASIBILITY OF CONDUCTING A QUANTITATIVE UNCERTAINTY 

6 ANALYSIS FOR TCDD 

7 This section focuses on uncertainty analysis for TCDD dose response, which involves a 

8 range of issues as highlighted in Table 6-1. 

9 

10 6.4.1. Feasibility of Conducting a Quantitative Uncertainty Analysis under the RfD 

11 Methodology 

12 This discussion applies to all noncancer endpoints of TCDD, and to cancer endpoints 

13 insofar as they fall under the RfD methodology. An RfD is obtained through the following steps 

14 

15 1. Choose a POD, then 

16 2. Apply uncertainty factors (UFs) to account for knowledge shortfalls. 

17 

18 The method of uncertainty factors harkens back to the engineering practice of safety 

19 factors (Lehman and Fitzhugh, 1954). To illustrate, if the reference load for an engineered 

20 structure is X, then engineers might design the structure to withstand load 3X, using a safety 

21 factor of 3 to create a margin of safety. If the structure functions in a corrosive environment, 

22 another factor could be multiplied to guarantee safety for that condition, and another factor could 

23 be applied for heat, another for vibrations, and so on. The choice of values is simply based on 

24 good engineering practice, i.e., reflecting what has worked in the past. Although safety factors 

25 are still common in engineering, they are giving way to probabilistic design in many 

26 applications. The reason is that compounding safety factors quickly leads to overdesigning. 

27 Compounding safety margins for spaceflight systems may render them too heavy to fly. As our 

28 understanding of a system increases, it becomes possible to guarantee the requisite safety by 

29 leveraging our scientific understanding of the materials and processes. That of course requires 

 

37For example, from Popp et al. (2006): “Overall, the evidence indicates that (1) TCDD causes cancer via a 

receptor-mediated process; (2) this dose-response is non-linear; and (3) a threshold region exists for TCDD-induced 

cancer below which adverse effects are unlikely to occur.”
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1 formulating clear probabilistic safety goals and developing the techniques to demonstrate 

2 compliance. 

3 The engineering community has never sought to account for uncertainty by treating 

4 safety factors as random variables and assigning them (marginal) distributions; such an approach 

5 would not counteract the overdesigning inherent in safety factors. Many authors, including the 

6 recent national committee for Science and Decisions (NRC, 2009), have advocated just such a 

7 probabilistic approach to the apparent “overdesigning” of the RfD when multiple UFs are used in 

8 its derivation. 

9 The NAS committee that evaluated the 2003 Reassessment does not discuss how to 

10 perform uncertainty analysis. But their call for substantial improvement in quantitative 

11 uncertainty analysis with TCDD falling under the RfD framework entails examining the 

12 feasibility of quantitative uncertainty analysis within this framework. (Note that the EPA 

13 Integrated Risk Information System database uses uncertainty factors without probabilistic 

14 interpretations; some context for this is offered in Section 6.4.1.2.) 

15 

16 6.4.1.1. Feasibility of Conducting a Quantitative Uncertainty Analysis for the Point of 

17 Departure 

18 The POD plays a role in both the noncancer RfD methodology and the cancer 

19 dose-response methodology. The POD can be selected from various options, such as a NOAEL 

20 or LOAEL, a BMDL, or an EDx. The feasibility of quantitative uncertainty analysis for each of 

21 these three options is considered below. 

22 As described by Swartout et al. (1998), “The NOAEL is the highest of the tested doses in 

23 a toxicological experiment that is judged not to have caused an adverse effect” (with dose 

24 expressed as a dose rate, in mg/kg-day). A quantitative uncertainty analysis for a NOAEL or 

25 LOAEL encounters the following problem. In an experiment involving a small, positive 

26 response, the probability of seeing no response can be calculated using a binomial model with 

27 the number of exposed animals and the observed number of responses. However, in an 

28 experiment with no response, the probability of having observed a response cannot be calculated 

29 without assuming a response probability. Such an assumption could not be based on observed 

30 data. If the analysis is restricted to experiments showing a positive response, the results will be 

31 biased. The probability of a higher NOAEL or higher LOAEL can be computed, but not that of a 
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1 lower NOAEL or LOAEL. In other words, the probability that an experiment with a positive 

2 result may have yielded a null response can be estimated, but not the probability that an 

3 experiment with a null response might have yielded a positive response. 

4 In addressing uncertainty quantification for a BMDL or EDx, two questions must be 

5 distinguished regarding the response: 

6 

7 1. What is the distribution of possible doses that causes an x% increase over background? 

8 2. What is the distribution for possible values of increase over background caused by a 

9 given dose? 

10 

11 The BMD is defined as the dose that realizes a BMR. It is an estimate from bioassay data 

12 that requires choosing a BMR and fitting a dose-response curve. The BMR, being a choice, is 

13 not amenable to quantitative uncertainty analysis, but the choice can be motivated in a structured 

14 narrative. The BMDL is the lower confidence limit (e.g., 5%) that can be found based on the 

15 uncertainty in the parameters of the dose-response relationship. Thus, the BMDL is addressed to 

16 the first question above, and represents in this case the 5% lower confidence band of the 

17 distribution of possible doses causing an x% increase over background. In the standard 

18 approach, the uncertainty captured by the BMDL is sampling uncertainty conditional on the truth 

19 of the dose-response model. Different models might fit the data equally well yet lead to different 

20 BMDLs. 

21 The BMDL is also influenced by the constraints imposed on the parameter fitting. 

22 Suppose that the slope is expected to be greater than one, and that the maximum likelihood 

23 estimate of the slope is slightly greater than one. Since the constraint is not binding, the 

24 constrained and unconstrained model would have the same Akaike Information Criterion and 

25 would be equivalent in this sense. However, computing the BMDL with the slope constraint can 

26 lead to very different values than without this constraint. In the latter case, slope values less than 

27 one contribute to the uncertainty in the dose causing the BMR (see Cooke, 2009). 

28 The EDx can also be taken as a POD. It is similar in spirit to the BMD; however, as used 

29 here, the term EDx applies when the dose causing an x% extra risk over background has actually 
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been observed, not estimated from a fitted dose-response model.38 1 The observations are subject 

2 to sample fluctuations, and if the experiment on which the EDx is based were repeated, different 

3 values might be found. It is helpful to consider a numerical example. Suppose a background 

4 response rate of 10% is established based on many observations of nonexposed individuals. In a 

5 given experiment, involving say 100 individuals given dose d, 14 individuals responded. The 

6 percent increase x over background (extra risk) is found by solving 14/100 = 10/100 + x 

7 × 90/100, or x = 4.4%. We conclude that d = ED4.4. We may assume that if the experiment 

8 were repeated with 100 new individuals sampled independently from the whole population, the 

9 response would be given by a binomial distribution with parameters (14, 100). The number of 

10 responses might be greater or smaller than four, there is a 16% chance of observing 10 or fewer 

11 responses. The response to dose d would not be distinguished from the background in that case, 

12 and a higher dose would be used for the POD. 

13 The uncertainty analysis of EDx as the POD involves addressing the second question 

14 above, without a quantitative dose-response model. A quantitative uncertainty analysis is 

15 hampered, however, by the possibility that dose d would produce a response less than or equal to 

16 the background, in which case the POD is indeterminate—another experiment with a different 

17 dose would be chosen as the POD. A true quantitative uncertainty analysis of EDx as the POD 

18 would thus require a full bioassay experimental design, with binomial sampling of response rates 

19 at each dose level in the assay. Absent that, quantitative uncertainty analysis is not possible. 

20 The interplay of choice and estimation ingredients in the POD depends on the type of 

21 POD. The main features of the above discussion are captured in Table 4-2. This table notes that 

22 the BMDL captures the uncertainty caused by sampling fluctuations given that the dose-response 

23 model is true. Other methods are available to compute the BMDL using (1) model-independent, 

24 observable uncertainty; (2) nonparametric Bayesian dose-response models; or (3) Bayesian 

25 model averaging (Cooke, 2009). Only the EDx can be subject to a quantitative uncertainty 

26 analysis, and then only if a full bioassay data set is available. 

27 

 

38This definition of EDx is adopted to distinguish the modeled response (BMD) and the observed response (EDx), 

and it is more restrictive than usages common in the literature. 
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1 6.4.1.2. Feasibility of Conducting a Quantitative Uncertainty Analysis with Uncertainty 

2 Factors 

3 Uncertainty factors are chosen based on a structured narrative characterizing knowledge 

4 shortfalls involving 

5 

6 1. Interspecies extrapolation, (UFA: from animal data to humans).

7 2. Intraspecies extrapolation (UFH: to account for human interindividual variability, 

8 considering sensitive subgroups).

9 3. LOAEL to NOAEL extrapolation (UFL: to estimate the dose corresponding to no adverse 

10 effect from a reported LOAEL). 

11 4. Subchronic to chronic extrapolation (UFS: to estimate effects of chronic exposures from a 

12 subchronic study).

13 5. Database deficiency (UFD: to extrapolate from an incomplete data set, e.g., in terms of 

14 endpoints assessed or study design, i.e., from a poor to a sufficient or rich data context). 

15 

16 The standard chronic RfD can represent a sensitive human (H) response to a toxic 

17 substance under chronic (C) exposure conditions. Suppose a BMDL POD were based on animal 

18 (A) data from a subchronic (S) study. The database for that chemical could be rich (R), e.g., 

19 involving multiple (and at least sensitive) species/strains, both genders, multiple life stages, with 

20 multiple endpoints observed under sound study designs. Or the data could be poor (P), with 

21 limited measurements from only a subchronic animal study (ASP) forming the basis for 

22 estimating a general reference value for humans (including sensitive subgroups) under chronic 

23 exposure conditions. For that case, the UF method would be applied as follows 

24 

DSA UFUFUFUF H

ASP RfD

×××

25 = (Eq. 6-1) 

26 

27 where UFA, UFS, UFD, and UFH are the uncertainty factors for extrapolating from animals to 

28 humans (UFA), subchronic to chronic exposure conditions (UFS), without adequate endpoint 

29 coverage (UFD), and considering sensitive human subpopulations (UFH). It is possible to assign 

30 distributions to the UFs in Eq. 6-1, and to perform a Monte Carlo analysis to produce a 

31 quantitative uncertainty distribution over the dose or value likely to be without appreciable risk 
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to humans for chronic exposures. Many authors have proposed such an approach,39 1 and the 

2 recent NRC (2009) report on science and decisions emphatically counsels EPA to move in this 

3 direction. 

4 The idea of using a Monte Carlo analysis to develop quantitative uncertainty distributions 

5 for the RfD is simple, but the data on which the UFs are based and the assumptions that would 

6 need to be made should be further explored. For example, it is assumed that the extrapolation 

7 from subchronic to chronic exposure (UFS) is the same whether applied to animals or humans, 

8 and whether applied to sufficient (rich) or deficient (poor) data contexts. Swartout et al. (1998) 

9 write “Within the current RfD methodology, UFS does not consider differences among species, 

10 endpoints, or severity of effects; the same factor is applied in all cases.” In addition, due to the 

11 paucity of relevant human data, the same authors suggest the use of other endpoints as surrogates 

12 in estimating the extrapolation from animals to humans, UFA. Further, few data exist in humans 

13 to accurately portray the interindividual variability in humans represented by UFH. Much of the 

14 data gathered to date on distributions of UFs have aggregated across other extrapolations; that is, 

15 data from subchronic to chronic ratios are aggregated over different species and different data 

16 contexts. Finally, it may be noted that an important issue is the data on which empirical 

17 distributions of response ratios are based. Brand et al. (1999, 2001) studied the sampling 

18 behavior of response ratios and raise concerns with regard to their informativeness. 

19 Detailed analyses of the data underlying a Monte Carlo uncertainty analysis of Eq. 6-1 

20 would afford the possibility of verifying at least some of the assumptions and numerical 

21 estimations such an analysis must make. Even if the assumption that the same UFS is applicable 

22 for all species, endpoints, and effect severities is thought to be biological plausible, the question 

23 of whether a given set of chemicals reflect this assumption, and hence are suitable for Monte 

 

39There has been considerable work on giving a probabilistic interpretation of the UFs, including by Abdel-Rahman 

and Kadry (1995), Vermeire et al. (1999), Baird et al. (1996), Swartout et al. (1998), Slob and Pieters (1998), Evans 

and Baird (1998), Calabrese and Gilbert (1993), Calabrese and Baldwin (1995), Hattis et al. (2002), Kang et al. 

(2000), and Pekelis et al. (2003). These evaluations can be considered to frame what might be called a random 

chemical approach. Several authors adduce properties based on log normal distributions. Insightful studies by 

Kodell and Gaylor (1999; Gaylor and Kodell, 2000) suggest that uncertainty factors are independent log normal 

variables. Combining uncertainty factors involves multiplying the median values, and combining the “error factors” 

according to the formula KS×H = exp[1.6449 × √(σS

2

 + σH

2

)], where σS

2

, σH

2 are the variances of ln(UFS) and 

ln(UFH). Thus UFS × UFH is a lognormal variable with Median(UFS × UFH) = Median(UFS) × Median(UFH), and 

95th percentile given by Median(UFS × UFH) × KS × H. If US and UH each have an error factor or 10, then the error 

factor of UFS × UFH is not 100 but 25.95. Several authors suggest that multiplying uncertainty factors might overprotect. Recent proposals from the National Research Council reflect the random chemical concept, and they inherit 

its problems (NRC, 2009). 
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1 Carlo analysis of Eq. 6-1, can only be decided by data evaluation. Data are the ultimate arbiter 

2 of whether quantitative uncertainty analysis with uncertainty factors, as currently envisioned, has 

3 sufficient evidentiary support. 

4 

5 6.4.1.3. Conclusion on Feasibility of Quantitative Uncertainty Analysis with the RfD 

6 Approach 

7 A quantitative uncertainty analysis of the POD is not feasible for PODs based on 

8 NOAELs or LOAELs. For the BMDL, such an analysis is not appropriate because the BMDL is 

9 already a quantile from an uncertainty distribution of the BMD. However, this uncertainty 

10 distribution can be obtained in different ways that capture different aspects of uncertainty. 

11 Quantitative uncertainty analysis is feasible if the POD is based on the EDx (as defined above) 

12 and is supported by a full set of bioassay data. A quantitative uncertainty analysis based on a 

13 probabilistic interpretation of uncertainty factors in their present form invokes strong 

14 assumptions. The data on which the distributions of uncertainty factors are based could be used 

15 to check at least some of these assumptions. 

16 

17 6.4.2. Feasibility of Conducting a Quantitative Uncertainty Analysis for TCDD Under the 

18 Dose-Response Methodology 

19 Quantitative uncertainty analysis starts with a mathematical model and seeks to quantify 

20 the uncertainty attending the use of this model. Dose-response relations are mathematical 

21 models expressing the probability of response as a mathematical function of dose. For several 

22 decades, the uncertainty attending the use of dose-response models has been an abiding concern 

23 in many sectors, including the chemical and nuclear industries as well as the public health sector. 

24 Given a set of animal bioassay data, quantifying dose-response uncertainty may be approached in 

25 different ways. The differences reflect different types of uncertainty that are captured. A recent 

26 evaluation enumerates the following possible methodologies (Bussard et al., 2009): 

27 

28 Benchmark Dose Modeling (BMD): Choose the ‘best’ model, and 

29 assess uncertainty assuming this model is true. Supplemental results can compare 

30 estimates obtained with different models, and sensitivity analyses can investigate 

31 other modeling issues. 
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1 Probabilistic Inversion with Isotonic Regression (PI-IR): Define 

2 model-independent ‘observational’ uncertainty, and look for a model that captures 

3 this uncertainty by assuming the selected model is true and providing for a 

4 distribution over its parameters. 

5 Non-Parametric Bayes (NPB): Choose a prior mean response (potency) 

6 curve (potentially a “non-informative prior”) and a precision parameter to express 

7 prior uncertainty over all increasing dose-response relations, and update this prior 

8 distribution with the bioassay data. 

9 Bayesian Model Averaging (BMA) (as considered here): Choose an 

10 initial set of models, and then estimate the parameters of each model with 

11 maximum likelihood. Use classical methods to estimate parameter uncertainty, 

12 given the truth of the model. Determine a probability weight for each model 

13 using the Bayes Information Criterion, and use these weights to average the model 

14 results. 

15 

16 The first of the above methods involves standard classical statistical methods and 

17 captures sampling uncertainty conditional on the truth of the model used. The other methods are 

18 “exotic” in the sense that they attempt to capture uncertainty that is not conditional on the truth 

19 of a given model. All have been subjected to peer review and published, but they do not enjoy 

20 the market penetration of the standard classical methods. The Bayesian models involve 

21 subjective choices of prior distributions. Insofar as the final result is largely independent of the 

22 choice of prior, these methods conform to the current starting point of focusing on data-driven 

23 methods and not appealing to structured expert judgment. (Structured expert judgment can also 

24 be considered an exotic method; an explanation of this approach falls outside the scope of this 

25 report.) 

26 It should be emphasized that the four approaches above were illustrated as part of an 

27 integrated bench-testing exercise using simplified data for demonstration purposes. Application 

28 to actual, complicated data sets would provide additional insights into the feasibility of each per 

29 the nature of those data. 

30 Many steps are involved in arriving at such a starting point, to frame the extrapolation of 

31 data from animal bioassays to human reference values together with consideration of 

32 epidemiological data from studies of workers (routine exposures) or the general public (including 

33 dietary exposures and those via discrete poisonings or accidental releases). Major issues are 

34 summarized below. 

35 
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1 6.4.2.1. Feasibility of Quantitatively Characterizing the Uncertainties Encountered when 

2 Determining Appropriate Types of Studies (Epidemiological, Animal, Both, and 

3 Other) 

4 The risk assessor must choose the data set(s) that will serve as a starting point for 

5 dose-response modeling. With respect to TCDD, a wealth of animal bioassay data exist in the 

6 scientific literature, across species ranging from rats, mice, guinea pigs, and hamsters to mink, 

7 dogs and monkeys, and a variety of tissues, organs, and systems. In addition, there are historical 

8 human data such as from clinical/case reports, accidental releases, and occupational exposures, 

9 including epidemiological data for several cohorts. Historical data are usually attended with 

10 large uncertainties regarding the doses actually received by individuals. Both clinical/case 

11 reports and epidemiological data pose issues with regard to possible confounders. Different 

12 TCDD studies have assessed various endpoints, ranging from chloracne to hepatic enzyme 

13 induction, abnormal fasting glucose, diabetes, lung and cardiovascular effects, neurological 

14 effects, developmental delay, dental defects, sex ratio, epididymal sperm count, endometriosis, 

15 non-Hodgkin’s lymphoma, susceptibility to infection, depression, hostility, and anger, among 

16 others. Confounders that have been evaluated in the epidemiological studies include gender, 

17 body mass index, age, cigarette and alcohol consumption, and hair and eye color (Eskenazi et al., 

18 2002a, b; Pereg et al., 2002; Baccarelli et al., 2005, 2006). 

19 There is disagreement in the literature over the nature, scope, and quality of the historical 

20 and evolving epidemiological data for TCDD. Popp et al. (2006) put forth a position similar to 

21 that taken by the NAS: 

22 

23 According to IARC [1997], the strongest overall evidence for the carcinogenicity 

24 of TCDD is for all cancers combined, rather than for any specific site. The 

25 relative risk for all cancers combined in the most highly exposed and 

26 longer-latency subcohorts is 1.4. Although IARC indicated that this relative risk 

27 does not appear likely to be explained by confounding, relative risks of this 

28 magnitude for studies of other substances, particularly with a lower confidence 

29 interval at or near one, are generally found to be the result of confounders. Few 

30 examples (perhaps only smoking and ionizing radiation in atomic bomb 

31 survivors) exist of agents known to cause an increase in cancers at many sites. 

32 This lack of precedent for a multi-site carcinogen without particular sites 

33 predominating, combined with the very small excess relative risks, means that the 

34 epidemiological findings must be treated with caution. 

35 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 On the other hand, U.S. EPA Cancer Guidelines express a clear preference for 

2 epidemiological studies over animal data. The question here is whether quantitative uncertainty 

3 analyses based on bioassay data and different epidemiological data can be combined in some 

4 overall uncertainty assessment. Diverse studies are sometimes combined in meta-analyses. 

5 Different studies have different protocols, and ignoring these differences can distort results. To 

6 illustrate, placental EROD activity was 4-fold higher in a group of highly polychlorinated 

7 biphenyl (PCB)-exposed Inuit women compared to a group of women from southern Quebec 

8 with lower exposure. However the differences in EROD activity disappeared when smoking was 

9 taken into account. Had tobacco consumption been unknown in one of the groups, spurious 

10 conclusions would have been drawn (Pereg et al., 2002). Can uncertainty arising from 

11 combining studies with different protocols be taken into account in quantitative uncertainty 

12 analysis? The question is similar to that of accounting for uncertainty due to missing covariates 

13 in Cox regression (see Section 6.4.2.2). 

14 Standard statistical tools will be of no avail, as they quantify uncertainty in model 

15 parameters estimated from data. However, exotic methods, such as Bayesian methods, 

16 probabilistic inversion, or structured expert judgment may be applicable. These methods can be 

17 applied when a quantitative model predicts other phenomena, even though these phenomena 

18 could not be used to estimate the model. The question of whether such methods could remain 

19 sufficiently tethered to data, or whether structured expert judgment is unavoidable, is a subject 

20 for future research. 

21 

22 6.4.2.2. Uncertainty in TCDD Exposure/Dose in Epidemiological Studies 

23 There are many types of epidemiological studies including case controls, longitudinal and 

24 cross-sectional cohorts, and simple exposed/unexposed studies. The variety of mathematical 

25 models includes Cox proportional hazard, Poisson regression, and logistic regression. The 

26 outputs vary accordingly: relative risk ratios, odds ratios, and standardized mortality ratios 

27 (SMR, ratio of observed to expected deaths). Exposure uncertainties often concern back-casted 

28 exposures based on current serum lipid concentrations, estimated/self reported dietary habits, 

29 fish consumption, placenta lipid concentrations, and other measures. 

30 Uncertainty in exposure is often compensated by coarsening the output, e.g., SMRs 

31 instead of dose-dependent risk factors. Packages computing the outputs routinely produce 
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1 confidence intervals that reflect sampling fluctuations, assuming truth of the model. Additional 

2 uncertainty could be factored in with exotic methods. A significant issue in epidemiological 

3 studies is the effect of omitted covariates. Omitted covariates in Cox regression will bias the 

4 estimates of effects of included covariates. If the omitted covariates are independent of the 

5 included covariates, the bias is toward zero in absolute value (Bretagnolle and Huber-Carol, 

6 1988); if the omitted covariates are not independent, little can be said. 

7 With regard to individual studies, it might be possible to identify specific opportunities 

8 for uncertainty quantification. This is illustrated with the study of Steenland et al. (2001) of 

9 more than 3,500 male workers exposed to TCDD-contaminated products at eight U.S. chemical 

10 plants. Each worker was assigned an exposure score based on estimated level of contact with 

11 TCDD, the degree of TCDD contamination of product at each plant over time, and the fraction of 

12 a workday in contact with the product. For 170 workers, the serum TCDD levels were also 

13 measured. The serum levels were back-extrapolated to the last time of exposure using a constant 

14 biological half life, and regressed on the exposure scores. This regression model was used to 

15 predict the dose in all workers, and predicted dose was correlated with cancer mortality. 

16 Figure 6-1 shows a scatter plot of back-casted versus predicted TCDD serum levels for the 

17 170 workers on which the regression was based. 

18 Given a predicted TCDD level, the uncertainty on the back-casted TCDD value could be 

19 inferred from such data by various techniques. A key question is whether the actual cancer 

20 mortalities among 170 back-casted workers are randomly placed in the conditional distribution 

21 given predicted TCDD. Imagine, in other words, that the mortalities among the 170 back-casts 

22 are colored red in the above picture. At any given level of TCDD prediction, are the red points 

23 evenly distributed, or are they shifted to the right? In principle, the correlation between mortality 

24 and back-casted TCDD level, given the predicted level, could be estimated. This amounts to 

25 estimating heteroscedasticity in the regression model. Then, for each of the 3,538 workers, given 

26 his predicted TCDD level, we could sample a back-casted TCDD level, appropriately correlating 

27 with mortality, and recompute the dose response analysis. Repeating this many times we could 

28 build up a distribution for excess lifetime cancer mortality risk. 

29 It is instructive to step through similar issues with regard to biological half life, 

30 background and body fat. The Steenland et al. (2001) analysis assumed a constant TCDD 

31 biological half life (8.7 years). A distribution over this half life could plausibly be developed 
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1 from published sources. Assuming this half life is constant for all workers, but uncertain 

2 (epistemic uncertainty), this distribution could easily supplement the previous distribution: first 

3 sample a half life (to be applied to all workers), then estimate the regression model for this half 

4 life, and sample back-casted TCDD levels given each worker’s exposure score, taking account of 

5 correlation with mortality. This works if the half life uncertainty is epistemic. However, since 

6 the half life is estimated from data, it is more reasonable to suppose that the half life varies from 

7 worker to worker (aleatoric uncertainty). Here again the correlation with mortality must be taken 

8 into account, indeed it seems reasonable to suppose that the 256 cancer deaths involved workers 

9 with longer half lives. However, there is no way ex post of determining the biological half life in 

10 the deceased workers. 

11 Background exposure and body fat are similar to half life. The study authors held 

12 background level constant at the median level (6.1 ppt, range 2.0−19.7) for 79 nonexposed 

13 workers from whom blood was also drawn (see also Section 6.4.2.4). The full distribution of 

14 TCDD levels for these nonexposed workers could be used as well. Is it reasonable to suppose 

15 that responsive workers have background levels that are sampled randomly from this 

16 distribution, or might they not plausibly come from the high end of the distribution? The 

17 analysis also assumed a constant percentage of body fat (30%), whereas body fat percentage 

18 varies in the general population, e.g., for men this has been reported to range from 2 to 38% or 

19 more (see Footnote 32). The body fat distribution in the worker population could have been 

20 ascertained, but again the question arises, are the responsive workers sampled randomly from 

21 this distribution? 

22 These three factors, variable half life, variable background, variable body fat percentage, 

23 might conspire to make the effective dose level among the responsive workers significantly 

24 higher than would appear in a study that assumes these factors to be constant. However, such 

25 concerns cannot be addressed in a quantitative uncertainty analysis, unless cancer mortality can 

26 be correlated with these variables. In an optimal study design, this information could be 

27 retrieved from the data, but optimality is a luxury that is seldom afforded. The optimal should 

28 not be the enemy of the good; it might be possible to estimate these correlations in some other 

29 defensible manner, in which case the effect of exposure uncertainty could be quantified and 

30 propagated. Such an analysis would involve substantial effort and should not be undertaken 

31 under assumptions that are themselves implausible. Experimental protocols do not currently 
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1 require such uncertainty quantification; perhaps they should and perhaps someday they will. In 

2 any event Steenland et al. (2001) must be applauded for conscientiously identifying these key 

3 issues. 

4 

5 6.4.2.3. Uncertainty in Toxicity Equivalence (TEQ) Exposures in Epidemiological Studies 

6 Toxicity equivalence factors (TEFs) are used to infer the health effects of dioxin-like 

7 compounds based on their relative potencies compared to TCDD. These factors are not known 

8 with certainty, and the question arises whether uncertainty in TEFs can be incorporated into a 

9 quantitative uncertainty analysis. The process of deriving TEFs applied by the World Health 

10 Organization (WHO, 2005) is described in Van den Berg et al. (2006). Distributions of relative 

11 potencies (REPs) were developed from the scientific literature, with preference for in vivo 

12 studies, as supplemented by in vitro studies. An expert panel used a consensus process to select 

13 a TEF value for each congener, in half log steps “Thus, the TEF is a central value with a degree 

14 of uncertainty assumed to be at least ± half a log, which is one order of magnitude. However, it 

should be realized that TEF assignments are usually within the 50th to 75th 15 percentile of the REP 

distribution, with a general inclination toward the 75th 16 percentile in order to be health protective” 

17 (Van den Berg et al., 2006) (see Figure 6-2). 

18 The WHO considers the uncertainty in TEFs to span one order of magnitude (presumably 

19 log uniformly distributed). It would be tempting to use the distributions in Figure 6-2 to quantify 

20 uncertainty in the TEFs in a quantitative uncertainty analysis. However, the issue of dependence 

21 in this case is daunting. For example, should values of 1,2,3,7,8,-pentachlorodibenzofuran and 

22 2,3,4,7,8-pentachlorodibenzofuran be sampled independently? The choice of dependence 

23 structure will have a large effect. As described by Van den Berg et al. (2006), the differences in 

24 REPs reflects differences in dosing regimens, species, endpoints, mechanisms, and calculation 

25 methods. In a quantitative uncertainty analysis one must insure that these are not double 

26 counted. 

27 

28 Reasons for significant differences in REPs for the same congener can be caused 

29 by the use of different dosing regimens(acute vs. subchronic), different endpoints, 

30 species, and mechanisms(e.g., tumor promotion caused by at least two different 

31 mechanisms as for mono-ortho-substituted PCBs), as well as different methods 

32 used for calculating REPs. Thus, different methodological approaches used in 

33 different studies clearly provide uncertainties when deriving and comparing REPs. 
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1 If future study designs to derive REPs were more standardized and similar, the 

2 variation in REPs when using the same congener, endpoint, and species might be 

3 expected to be smaller (Van den Berg et al., 2006). 4 

5 Although the TEFs themselves and the distributions underlying them are based on expert 

6 judgment, it is possible to incorporate these into a quantitative uncertainty analysis; however, it 

7 is not simply a matter of taking the distributions in Figure 6-2 to predict the results, with 

8 uncertainty, of exposure to dioxin-like compounds. The issues of dependence and double 

9 counting must first be addressed. Inasmuch as the distributions are the result of expert judgment, 

10 this would reasonably involve structured expert judgment as well. (Procedures for this type of 

11 assessment have been developed and applied, and it would entail a significant level of effort.) 

12 

13 6.4.2.4. Uncertainty in Background Feed Exposures in Bioassays 

14 TCDD is not produced intentionally but rather is formed as a byproduct of volcano 

15 eruptions, forest fires, manufacturing of steel and certain chemicals (including some pesticides 

16 and paints), pulp and paper bleaching, exhaust emissions, and incineration. It enters the food 

17 supply primarily via aerial transport and deposition of emissions, and it bioaccumulates in animal 

18 fat. In general, food of animal origin contributes to about 80% of the overall human exposure. 

19 For example, Schecter et al. (1997) measured dioxins in pooled food samples collected in 1995 

20 from supermarkets across the United States. Reported as parts per trillion (ppt) toxicity 

21 equivalences (TEQs), fresh water fish had the highest level (1.43); followed by butter (1.07); 

22 hotdog/bologna (0.54); ocean fish (0.47); cheese (0.40); beef (0.38); eggs (0.34); ice cream 

23 (0.33); chicken (0.32); pork (0.32); milk (0.12); and vegetables, fruits, grains, and legumes 

24 (0.07). (More recent exposure studies by Lorber et al. [2009] and others have updated these 

25 older estimates of dietary levels.) 

26 These results illustrate that a person’s dietary intake of dioxins depends on the relative 

27 intake of foods with high or low levels of contamination, and human background levels will vary 

28 accordingly. The same applies to experimental animals in bioassays, although in those cases the 

29 background intake can in principle be controlled. Some of the effects of TCDD and other AhR 

30 agonists in enhancing the early initiation stages of cancers are considered to occur as a result of 

31 prenatal exposures that are not included in the standard National Toxicology Program (NTP) 

32 bioassay protocol (Brown et al., 1998; Muto et al., 2001). Further, to enhance reproducibility 
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1 and keep statistical fluctuations to a minimum, the standard NTP assays are deliberately run on 

2 groups of animals that are relatively uniform genetically, fed uniform diets, and have the 

3 minimum possible exposures to toxicants other than the agent(s) being tested. This tends to 

4 reduce the potential for observing the consequences of potential interactive effects that might 

5 occur in the diverse human population with its variety of dietary and other exposures to a wide 

6 range of potentially interacting substances and conditions. 

7 A critical question is the extent to which the background exposure influences the 

dose-response curve, and how this background should be taken into account.40 8 One idea, 

9 articulated in the recent NRC (2009) report on science and decisions, involves an “interacting 

10 background.” This can be implemented by computing a virtual dose B which, according to the 

11 selected dose-response model, would explain a chosen fraction of the background response. If 

12 the chosen model for dose δ is f(δ), the model can be adapted to account for an interacting 

13 background by writing f*(δ) = f(δ + B) − f(B). This can alter the model’s behavior at zero dose. 

For example, if f(δ) = δn

/(δn + EC50n), the derivative d(f)/d(δ) is nδn-1EC50n/(δn + EC50n)

2 14 , 

15 which goes to zero as δ→0, if n > 1. However, replacing δ with (δ + B) evidently changes the 

derivative at zero to nBn−1

EC50n/(Bn + EC50n 16 ). This model is not yet estimable from data, as we 

17 have no way of choosing from the available animal data the fraction of background response to 

18 be explained by the model when applied to humans (although judgments could be made if we 

19 had better information about the details of the processes that are involved in causing various 

20 human health effects). However, as a conceptual model, it serves to remind us that the manner 

21 of accounting for background exposures can influence a model’s behavior in the low-dose 

22 region. 

23 

24 6.4.2.5. Feasibility of Quantifying the Uncertainties Encountered When Choosing Specific 

25 Studies and Subsets of Data (e.g., Species and Gender) 

26 Species, strain, gender, life stage, and other characteristics of experimental animals are 

27 selected for a given study based on previous knowledge (e.g., of the species sensitivity, 

 

40“Effects of exposures that add to background processes and background endogenous and exogenous exposures can 

lack a threshold if a baseline level of dysfunction occurs without the toxicant and the toxicant adds to or augments 

the background process. Thus, even small doses may have a relevant biologic effect. That may be difficult to 

measure because of background noise in the system but may be addressed through dose-response modeling 

procedures” (NRC, 2009).
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1 availability of strains having little genetic variation for the endpoints in question, relevance of 

2 the MOA, and degree to which the endpoints are similar for humans). Many other decisions are 

3 made in designing a bioassay study; will the animals be sacrificed at the termination of the study 

4 (if not a lifetime study), or will they be allowed to live out their natural lives? What dosing 

5 regimen should be applied? How will the animals be fed and handled? Although such questions 

6 may engender uncertainty in the minds of the experimenters, and reviewers; such uncertainty is 

7 not amenable for quantitative uncertainty analysis unless and until there are quantitative models, 

8 with parameters estimable from data, that can predict the effect of these choices on the response 

9 function. 

10 

11 6.4.2.6. Feasibility of Quantifying the Uncertainties Encountered when Choosing Specific 

12 Endpoints for Dose-Response Modeling

13 Standard experimental protocols guide the selection of exposure/dosing conditions for a 

14 given bioassay, including the amount, delivery vehicle, route, timing, dosing frequency and 

15 duration, and dose spacing. The goal is to find the dose range where the experimental animals 

16 begin to respond adversely, to help anchor the lower end of the dose-response relationship, and 

17 to avoid multiple experiments in which all or none of the animals respond. A common 

18 recommendation is that the dose levels be chosen such that the increments in probability of 

19 response are roughly equal. Hence, the choice of endpoint, spacing, and number of animals 

20 should be made with these factors in mind. Of particular importance is the number of animals at 

21 each dose level in relation to the choice of endpoint and probability of response. Using more 

22 animals at the lower dose levels increases the probability of seeing some animals respond; on the 

23 other hand, this approach will give higher weight to the low-dose responses in model fitting and 

24 uncertainty quantification. Including many experiments at low dose with no expected response 

25 could provide deceptive graphs. 

26 One of the most clearly nonlinear responses to high-dose dioxin exposure cited by the 

27 committee is cholangiocarcinoma (bile duct cancer) in rats (see Figure 6-3). The model 

Prob(dose) = dosen

/(dosen + EC50n 28 ) with Hill coefficient n was fit to these data, yielding a 

29 maximum likelihood estimate of n = 2.79 (Hattis, 2009). For n > 1, this is a zero slope at zero 

30 dose model (see Section 5.2.3.3). If we choose a linear extrapolation below the POD at dose 

31 3.82 ng/kg-day with probability of response = 0.02, then at dose 1.7 ng/kg-day, the predicted 
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1 probability of response is 0.009; with 46 rats exposed to this dose we expect to see no responses, 

2 which is what we see. However, sublinear models will yield a higher probability of seeing 

3 nothing. A graph like Figure 6-3 would suggest that the linear extrapolation was missing the 

4 experiments at doses 1.7 and 0.52 ng/kg-day, indicating lack of fit, whereas the null response is 

5 what we should expect on the linear model. 

6 The conclusion with regard to the feasibility of this quantitative uncertainty analysis 

7 echoes that of the previous paragraph: such uncertainty is not amenable for quantitative analysis 

8 unless and until there are quantitative models, with parameters estimable from data, that predict 

9 the effect of these choices on the response function, although sensitivity analyses can be done 

10 showing the consequences of assuming different amounts of interacting background within the 

11 context of a specific nonlinear model. 

12 

13 6.4.2.7. Feasibility of Quantifying the Uncertainties Encountered when Choosing a Specific 

14 Dose Metric (Trade-Off Between Confidence in Estimated Dose and Relevance of 

15 MOA) 

16 The concept of dose is not straightforward. To review, U.S. EPA (2005) Cancer 

17 Guidelines provide the following taxonomy: 

18 

19 • Exposure is contact of an agent with the outer boundary of an organism. 

20 • Exposure concentration is the concentration of a chemical in its transport or 

21 carrier medium at the point of contact. 

22 • Dose is the amount of a substance available for interaction with metabolic 

23 processes or biologically significant receptors after crossing the outer boundary of 

24 an organism. 

25 • Potential dose is the amount ingested, inhaled, or applied to the skin. 

26 • Applied dose is the amount of a substance presented to an absorption barrier and 

27 available for absorption (although not necessarily having yet crossed the outer 

28 boundary of the organism). 

29 • Absorbed dose is the amount crossing a specific absorption barrier (e.g., the 

30 exchange boundaries of skin, lung, and digestive tract) through uptake processes. 

31 • Internal dose is a more general term, used without respect to specific absorption 

32 barriers or exchange boundaries. Delivered dose is the amount of the chemical 

33 available for interaction by any particular organ or cell.

34 
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1 Using the applied dose or absorbed dose would yield statistically more powerful results 

2 and enable more precise predictions. If it is not possible to measure these, then other available 

3 dose metrics such as potential dose or exposure are used. Due to toxicokinetic variability, 

4 different individuals receiving the same exposure may not have the same applied or absorbed 

5 dose. Hence, use of an exposure metric would add variability to the predicted results. The dose 

6 metric should be selected that (1) has the most proximate possible causal relation to the 

7 production of an adverse health endpoint, and (2) is most easily related to the units of (external) 

8 exposure that will be the basis for assessing and controlling human exposures. 

9 

10 6.4.2.8. Feasibility of Quantifying the Uncertainties Encountered When Choosing Model 

11 Type and Form 

12 U.S. EPA (2009d) draft white paper on probabilistic methods notes: “There is no 

13 consensus on any one well-accepted general methodology for dealing with model uncertainty, 

14 although there are various examples of efforts to do so.” Model uncertainty was introduced in 

15 Section 6.1.3.4. Many statistical techniques are available to evaluate model adequacy or to 

16 choose a “best” model. Although it is tempting to qualify such deliberations as “uncertainty that 

17 a model is true,” one must remember that all models, being idealizations, are false. Ultimately, 

18 one is interested in uncertainty with regard to observable phenomena, not with regard to models. 

19 Models are merely tools for describing the phenomena. Nonetheless, the choice of a model 

20 constrains the ways in which uncertainty can be represented, and the question addressed in this 

21 paragraph is: How should one deal with these constraints? A recent study of uncertainty 

22 modeling in dose response (Cooke, 2009) addresses precisely this issue and provides ample 

23 technical detail to frame possible options. 

24 Before elaborating on exotic approaches to model uncertainty, it is well advised to 

25 appreciate a feature in the standard statistical treatment of uncertainty. Consider a model based 

26 on experimental data, typically bioassay data, in which a certain number of study subjects are 

27 exposed to varying doses of a test substance, and in which the numbers of subjects exhibiting a 

28 response are tallied. Values for the parameters in the model are chosen by the principle of 

29 maximal likelihood: those values are chosen which render the data as likely as possible. 

30 According to standard practice, a model is chosen that best fits the data according to one of the 
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accepted criteria, such as reduced R2 1 , or the Akaike Information Criterion. There might be many 

2 incompatible models that are nearly as good. 

3 One can ask the following: If the experiments on which the model is based were repeated, 

4 sampling the same number of experimental subjects from the distribution posited by the model, 

5 how much could our parameter estimates change? This is described by a joint distribution over 

6 the model’s parameters, which captures sampling uncertainty under the assumption that the 

7 model is true. Now, all models are false, and as our sample sizes grow the lack of fit in the 

8 model becomes increasingly apparent. At the same time, the sample fluctuations in parameter 

9 estimates—assuming the model is true—become ever smaller. In very large epidemiological 

10 studies, standard statistical methods can produce razor-thin confidence bands in this way, which 

fail to capture experts’ uncertainty regarding observable phenomena.41 11 

12 The exotic methods sketched in the beginning of Section 6.4.2 may be viewed as attempts 

13 to deal with this feature. Probabilistic inversion methods were deployed on a large scale in the 

14 joint U.S. NRC-EU uncertainty analyses noted in Section 6.1. Distributions over model 

15 parameters are intended to capture an antecedently defined uncertainty over observable 

16 phenomena predicted by the model. This method was applied in dispersion and deposition 

17 modeling and further environmental transport models (including uptake) for radionuclides. In 

18 most cases, the observable uncertainty was based on structured expert judgment, but it has also 

19 been based on binomial uncertainty in bioassay studies. A potential drawback is that it may not 

20 prove possible to capture the observable uncertainty in this way with a classically best-fitting 

21 model, and new models may be required. 

22 Nonparametric Bayesian methods arose in the biomedical and reliability fields. They 

23 start with a prior distribution over all nondecreasing dose-response functions, and update these 

24 with observations from a bioassay study. No further assumptions regarding parametric form are 

25 introduced, but the prior distribution remains important for doses outside the range of 

26 observations. Bayesian model averaging starts with a prior distribution over a set of candidate 

27 models, and updates this distribution with bioassay data. The method is flexible and intuitive, 

28 though attenuation of the effect of the prior on the posterior must be verified. 

 

41See for example Tuomisto et al. (2008, Table 6) for a comparison of experts’ uncertainty in health effects of fine 

particulates with uncertainties derived from sampling uncertainty from large epidemiological studies. 
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1 All these approaches represent attempts to capture “extramodel uncertainty,” that is, 

2 uncertainty that is not conditional on the truth of the model. This is an active research area, and 

3 the last word on capturing extramodel uncertainty in quantitative uncertainty analysis has not 

4 been spoken. A major effort with regard to dioxin would be indicated when the strengths and 

5 weakness of the exotic methods are well understood. 

6 

7 6.4.2.9. Threshold MOA for Cancer 

8 The NAS committee avers that knowledge of the AhR binding MOA entails that there is 

9 a response threshold for TCDD cancer induction. The differences between individual and 

10 population thresholds are not discussed, but the following two possibilities are distinguishable: 

11 

12 1. The threshold is the same for each individual; since human variability in AhR binding 

13 affinity is rather large (see Section 5.2.3.3), this entails that the threshold is not affected 

14 by the binding affinity. 

15 2. The threshold varies across individuals and is related to the individual AhR binding 

16 affinity. 

17 

18 These two positions are different. As shown in Section 5.2.3.3, it is quite possible that 

19 each individual in a population has a threshold whereas the population dose-response relation is 

20 linear. Because the NAS committee does not distinguish which of these positions it holds, the 

21 feasibility of quantitative uncertainty analysis is examined here for both. 

22 

23 i. Quantitative uncertainty analysis concerns a mathematical model. In case (i), this model 

24 would show how the existence of the AhR binding would induce a threshold, 

25 independently of the strength of the binding. Assessing the feasibility of quantitative 

26 uncertainty analysis must await the elaboration of such a model. 

27 ii. In this case, it must be shown that the distribution of thresholds, and the dose-response 

28 function above the threshold, is such as to induce a population “zero slope at zero dose” 

29 (ZS@Z) model. Recall, the burden of proof is on this (ZS@Z) model. Scoping the 

30 population variability with regard to AhR-mediated mechanisms in general, and dioxin 

31 sensitivity in particular, is an active area of research. It involves phenotyping human 

32 AhR-mediated responsiveness and relating this to polymorphisms in the human 

33 population. Harper et al. (2002) report that a 10-fold variation in binding affinity of AhR 

34 for TCDD in human placental tissue did not reveal any polymorphisms, suggesting that 

35 the relation between phenotypical and genotypical variation is tenuous at best. Tuomisto 

36 et al. (1999) demonstrate large variations in efficacy in two rat strains whose binding 
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1 affinity is similar (Long-Evans, Kd = 3.4, Han/Wistar, Kd = 3.9 [Connor and Alyward, 

2 2006]), and they also show that this variation is endpoint-specific. The responses in both 

3 strains are similar for cytochrome P450 (CYP)1A1 induction, but very dissimilar for 

4 thymus atrophy, serum bilirubin, and mortality. Toide et al. (2003) suggest that common 

5 biochemical measures of EROD activity might be mediated by CYP1B1 and CYP1A2. 

6 The differences in serum bilirubin at doses around 10 μg/kg are about a factor of 30. 

7 Han/Wistar rats seldom die at this dose, while mortality of Long Evans rats is about 50%. 

8 The mechanisms are not understood. 

9 

10 Although the mass action dose-response model does not have a threshold, it is possible 

11 that certain enzymes block the receptor binding, and until these are overwhelmed, no response 

12 occurs. The availability of such enzymes may vary from individual to individual, and may or 

13 may not covary with the dissociation constant Kd. Pursuing these lines of research may result in 

14 a convincing demonstration of a population (ZS@Z) model. Such a model would express the 

15 individual threshold in terms of parameters that could be estimated with uncertainty from the 

16 data. 

17 

18 6.4.2.10.Feasibility of Quantifying the Uncertainties Encountered when Selecting the BMR 

19 The NAS committee explicitly requested that the uncertainty attending the choice of a 

20 BMR be quantified. First of all, simply plugging other values in for the BMR does not constitute 

21 a quantitative analysis of uncertainty. The plugged-in values must be sampled from some 

22 uncertainty distribution. Since this concerns volitional uncertainty, there is no underlying 

23 distribution from which to sample, unless the choice of BMR is related to some claim about the 

24 state of the world. 

25 

26 6.5. CONCLUSIONS 

27 The main conclusions regarding the feasibility of quantitative uncertainty analysis are 

28 first summarized in relation to specific suggestions made by the NAS committee. A suggested 

29 research agenda follows. 

30 6.5.1. Summary of NAS Suggestions and Responses 

31 On page 130 of their report (NAS, 2006a), NAS makes specific suggestions regarding 

32 uncertainty quantification. These are reformatted and presented in italics below. Following each 
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1 suggestion, a summary of the discussion in this section is given, with reference to the section in 

2 which it is addressed. 

3 

4 EPA should have addressed quantitatively the following sources of uncertainty: 

5 

6 • Basis for risk quantification: 

7 1. bioassay data, 

8 2. occupational cohort data. 

9 

10 Response: (a) Classical statistical methods yield distributions on model parameters 

11 which reflect sample fluctuations, assuming that the model is true. This type of 

12 uncertainty is taken into account in the BMDL. Exotic methods can account for 

13 uncertainty which is not conditional on the truth of a model, at least for bioassay data 

14 (see Section 6.4.2). (b) For epidemiological data, the dose reconstruction often involves 

15 assumptions which may support data driven uncertainty analysis, if sufficient data can 

16 be retrieved. Examples discussed above include back-casted TCDD level, biological 

17 half life, body fat and background (see Section 6.4.2.2). Uncertainty in the choice of 

18 bioassay data sets or choice of occupational cohort data sets is volitional, and is not 

19 quantified by sampling an input distribution. To be amenable for quantitative 

20 uncertainty analysis, the choice must be linked to a statement about the state of the 

21 world (see Section 6.1.1). 

22 • Epidemiology data to use: 

23 1. risk estimate developed with data aggregated from all suitable studies, 

24 2. risk estimate or estimates developed using each study individually. 

25 • Factors affecting extrapolation from occupational to general population cohorts, 

26 including differences in baseline health status, age distribution, the healthy worker 

27 survivor effect, and background exposures. 

28 

29 Response: (a) Quantitative uncertainty analysis based on meta-analysis data poses 

30 challenges owing to differences in study protocols. Exotic methods might take us further, 

31 the question is whether the restriction to data driven methods (as opposed to expert 

32 judgment or Bayesian methods) could be maintained (see Sections 6.4.2.2 and 6.4.2.3). 

33 (b) If the general population is characterized by distributions over known confounders 

34 whose coefficients are estimated from the epidemiological studies, then uncertainty over 

35 these coefficients can be extracted with the methods mentioned in Section 6.4.2.1. 

36 Uncertainty due to missing covariates is intractable for data driven uncertainty analysis 

37 (see Section 6.4.2.2). 

38 • Bioassay data to use: 

39 1. risk estimate developed with the single data set implying the greatest risk (that is, 

40 single study, tumor site, gender), 
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1 2. risk estimate developed with multiple data sets satisfying an a priori set of 

2 selection criteria. 

3 

4 Response: (a) Echoing response 1.b, uncertainty in choice of data sets is volitional and is 

5 not quantified by sampling an input distribution. To be amenable for quantitative 

6 uncertainty analysis the choice must be linked to a statement about the state of the world 

7 (see Section 6.1.1). (b) The issue here is similar to the meta-analysis addressed in (2.a). 

8 • Dose-response model: 

9 1. linear dose response, 

10 2. nonlinear dose. 

11 

12 Response: (a) Presumably the low dose extrapolation is meant. When the linear model is 

13 used by default, the uncertainty is volitional. To be amenable for quantitative uncertainty 

14 analysis the choice must be linked to a statement about the state of the world (see 

15 Section 6.1.1). The EDx as POD for the linear extrapolation can be subjected to 

16 quantitative uncertainty analysis, if based on sufficient bioassay data. (b) presumably 

17 ‘nonlinear dose response’ is meant. It is possible that human thresholds exist, and that 

18 the distribution of thresholds can be characterized in the human population. In as much 

19 as the mechanisms for this are not yet understood, there is no quantitative model 

20 expressing threshold as a function of parameters which could be estimated, with 

21 uncertainty, from data. This currently limits the application of uncertainty quantification 

22 (see Section 6.4.2.9). 

23 • Dose metric: 

24 1. average daily intake, 

25 2. area under the blood concentration-time curve, 

26 3. lifetime average body burden, 

27 4. peak body burden, 

28 5. other. 

29 

30 Response: The dose metric is chosen to maximize causal proximity to the endpoint, 

31 while maintaining the link to measured exposure (see Section 6.4.2.7). There may be 

32 uncertainty with regard to which metric is optimal. If an inappropriate metric is chosen 

33 in a bioassay study, this would be expressed in noisier responses which would tend to 

34 suppress the dependence of endpoint on dose. A data driven quantitative uncertainty 

35 analysis of dose metric would require a mathematical model expressing endpoints as a 

36 function, inter alia, of dose metric, with parameters estimated from data. 

37 • Dose metric—biological measure: 

38 1. free dioxin, 

39 2. bound dioxin. 

40 
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1 Response: The issue is whether all TCDD available for AhR binding, or only the bound 

2 TCDD, should be used as a dose metric. Binding affinity is determined by more factors 

3 than genetic polymorphisms and these other factors are poorly understood (see 

4 Section 6.4.2.9). A quantitative uncertainty analysis must await the formulation of a 

5 quantitative model expressing binding affinity in terms of parameters which can be 

6 estimated from data. 

7 • POD: 

8 a. ED10, 

9 b. ED05, 

10 c. ED01

11 

12 Response: Uncertainty in choosing a POD is volitional. Uncertainty in the value of an 

13 EDx can be quantified in a data driven manner if sufficient bioassay data is at hand (see 

14 Section 6.4.1.1). 

15 • Value from ED distribution to use: 

16 1. ED, 

17 2. lower confidence bound value for the ED (LED), 

18 3. upper confidence bound for the ED (UED). 

19 

20 Response: Given that uncertainty on the POD is quantified, it is a trivial exercise to 

21 derive a distribution of the slopes of a linear low dose extrapolation, and hence a 

22 distribution of a risk specific dose. This would seem preferable to choosing between a 

23 lower, upper, or nominal value. 

24 • Where alternative assumptions or methodologies could not be ruled out as implausible or 

25 unreasonable, EPA could have estimated the corresponding risks and reported the 

26 impact of these alternatives on the risk assessment results. The potential impacts of four 

27 sources of uncertainty are discussed below. 

28 • The full range of plausible parameter values for the dose-response functions used 

29 to characterize the dose-response relationship for the three occupational cohort 

30 studies selected by EPA (Ott and Zober, 1996; Becher et al., 1998; Steenland et 

31 al., 2001). 

32 • Use of other points of departure, not just the ED01 (or LED01), to develop a CSF. 

33 • Alternative dose-response functional forms as well as goodness of fit of all 

34 models, especially at low doses. 

35 • Uncertainty introduced by estimation of historical occupational exposures. 

36 

37 Response: (a) The study of Steenland et al. (2001) was selected to illustrate the 

38 possibilities and limitations of quantitative uncertainty analysis for this type of study (see 

39 Section 6.4.2.2). (b) The possibilities for uncertainty quantification with regard to the 

40 POD are discussed in Section 6.4.1.1 and in the POD bullet above. (c) Goodness of fit at 
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1 any measured dose is evaluated in standard packages. There may be different models 

2 with comparable goodness of fit at observed doses which differ strongly at doses outside 

3 the measured range. Extra model uncertainty, that is, uncertainty which is not conditional 

4 on the truth of any given model, is addressed by the exotic methods (see Section 6.4.2). 

5 (d) The feasibility of quantifying uncertainty in occupational exposure is study specific. 

6 The example of Steenland et al. (2001) was discussed in some detail (see 

7 Section 6.4.2.2). In general, the problem is not so much quantifying the exposure 

8 uncertainty, but in quantifying the dependence between the endpoints and the exposure 

9 uncertainty. 

10 

11 6.5.2. How Forward? Beyond RfDs and Cancer Slope Factors to Development of 

12 Predictive Human Dose-Response Functions 

13 Uncertainty quantification is an emerging area in science. There are many examples of 

14 highly vetted and peer-reviewed uncertainty analyses based on structured expert judgment. 

15 Under this process, experts in effect synthesize a wide diversity of information in generating 

16 their subjective probability distributions. Where considerable data exist for an environmental 

17 pollutant, such as for the well-studied TCDD, it is natural to ask whether these extensive data can 

18 be leveraged more directly in uncertainty quantification. This is an area where research could be 

19 focused. The requisite knowledge does not yet exist, but there are promising lines of attack. It is 

20 therefore not a question of convening blue-ribbon panels to reveal the proper approach; instead 

21 multiple approaches should be encouraged to try out new ideas and share experiences. 

22 An important idea that has been pioneered in Europe is to organize bench-test exercises 

23 where different approaches are applied to a common problem. This focuses the discussion on 

24 real issues and builds a community of capable practitioners. Such initiatives have proven much 

25 more productive than simply supporting individual researchers to explore their ideas. 

26 Areas for which bench-test exercises might be appropriate include: 

27 

28 • Testing “exotic methods” for capturing model uncertainty 

29 • Combining bioassay and epidemiological data for uncertainty quantification 

30 • Applicability of structured expert judgment, e.g., for low-dose extrapolation 

31 • Conducting dependence modeling, dependence inference, and dependence elicitation 

32 (such as with regard to TEFs). 

33 

34 Looking beyond compounds for which considerable data exist, there will always be a 

35 need to evaluate new substances. The target will be a simple method that: 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 1. Can yield predictions of toxicological indicators with uncertainty via a valid probabilistic 

2 mechanism. 

3 2. Could evolve from approaches based on similarities (such as a random chemical model) 

4 under which the new substance could be seen as a random sample from a reference 

5 distribution of chemicals considered sufficiently similar, e.g., in terms of structure, 

6 physicochemical properties, and biological activity (potency). 

7 3. Is nondisruptive to the traditional process. 

8 

9 This last feature is very important because as a practical matter, any collective push 

10 forward in this area must extend from (not abruptly depart from) current methodology. For 

11 example, the discussion surrounding uncertainty factors suggests that a probabilistically valid 

12 inference system could substantially differ from the current system. Nonetheless, to meld with 

13 current practice, it must initialize on the current system and allow this system to evolve in a 

14 measured fashion. 

15 Additional research topics relevant to dioxin that could further inform health assessments 

16 include population variability of biokinetic constants, threshold mechanisms for the mass action 

17 model, and polymorphism at frequencies lower than 1%. Further data and improved 

18 methodologies in these areas, combined with developments illustrated elsewhere in this report, 

19 will help reduce uncertainties and strengthen our understanding of potential health implications 

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

1/15/10 DRAFT 6-40 —DO NOT CITE OR QUOTE 

1 Table 6-1. Key sources of uncertainty 

2 

Selection of endpoint and of species/strain, gender, life stage, other subject characteristics 

 - critical effect 

 - sensitivity (e.g., species, life stage) 

 - human relevance 

Selection of key study(ies): human data and bioassays (strength, inclusion criteria) 

 - epidemiological studies, clinical/case reports (exposure estimate) 

 - adequacy of study design, statistical power (exposure term, histopathology) 

 - human relevance of bioassays (TK, MOA, endpoint) 

 - data uncertainty, confidence in data; database deficiencies 

Use of TK, dosimetry; body burden; species differences, cross-species extrapolation 

 - bioavailability, dose dependence 

 - half life, life stage, body fat, other compartments, age, other factors 

 - body burden (peak, steady state, lifetime average) 

 - physiologically-based pharmacokinetic (PBPK) modeling 

 - scaling (human equivalents), adjustments (default and non-default; with TD) 

Selection of dose metric 

 - intake (averaging time) 

 - background (what place on the dose-response curve) 

 - free vs. receptor-bound TCDD 

 - tissue-specific concentration 

 - lipid-normalized level 

 Selection of POD 

 - selection (e.g., NOAEL/LOAEL, BMDL, ED01, 05, 10) 

 - derivation method (e.g., BMD) 

 - choice of model form (e.g., Hill, Weibull) 

 - statistical uncertainty at/confidence in POD 

Selection of dose-response model (e.g., biologically based, multi-stage) and of BMR 

 - biological plausibility, MOA 

 - model type and form, alternative functional forms 

 - range of plausible parameter values 

 - goodness of fit, especially at low doses 

Selection of low-dose extrapolation approach 

 - linear/nonlinear 

 - threshold/nonthreshold 

Human population variability 

 - subpopulations (e.g., occupational, general public, sensitive groups) 

 - polymorphisms 

 - life stage, other features 

 - individual vs. population threshold 

Characterization of risk/effect 

 - adversity of effect (vs. in normal range of variation and adaptation) 

 - uncertainty factors (TK; TD; chemical-specific vs. default; justification) 

 - consistency of methods for endpoints with common MOA 

 - back-extrapolation from occupational data 

 - MOE, RfD; beyond a point estimate for SF 

3 PBPK = physiologically-based pharmacokinetic; SF = slope factor; TD = toxicodynamic; TK = 

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

1/15/10 DRAFT 6-41 —DO NOT CITE OR QUOTE 

1 Table 6-2. PODs and amenability for uncertainty quantification 

2 

POD Data profile Choice Uncertainty quantification 

LOAEL Experimental dose 

level from set of 

exposure-response data 

Choose set of 

exposure-response 

measurements 

No 

NOAEL Experimental dose 

level from set of 

exposure-response data 

Choose set of 

exposure-response 

measurements 

No 

BMDL Estimate from 

bioassay data 

Choose BMR, choose 

dose-response relation 

No, the BMDL is a quantile of 

an uncertainty distribution 

assuming that the 

dose-response model is true 

EDx Estimate from set of 

exposure-response data 

Choose bioassay 

experiments to estimate EDx

Yes, if full bioassay data are 

available 

3 
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 6-42 —DO NOT CITE OR QUOTE 

1 

2 

3 

4 

5 

6 Figure 6-1. Back-casted vs. predicted TCDD serum levels for a worker 

7 subset. 

8 

9 Source: Steenland et al. (2001). Predicted TCDD 

Back-Extrapolated TCDD 
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1/15/10 DRAFT 6-43 —DO NOT CITE OR QUOTE 

1 

2 Figure 6-2. Distribution of in vivo unweighted REP values in the 2004 database. 

3 

4 Source: Van den Berg et al. (2006), reprinted with permission from Haws et al. (2006).
This document is a draft for review purposes only and does not constitute Agency policy. 

1/15/10 DRAFT 6-44 —DO NOT CITE OR QUOTE 

1 

2 Human equivalent dose (ng/kg-day), from simple body weight−1/4 projection 

3 

4 Figure 6-3. Plot of observed rat cholagiocarcinoma incidence and central 

5 estimate of Hill Model fit to the data vs. human equivalent dose. 

6 

7 Source: Hattis (2009). 

8 

0 2 4 6 8 10 12 14 16 18 20

0.0

0.1

0.2

0.3

0.4

0.5

Obs Fract Bile Duct Ca in Rats

Hill Mod Fit--Central Est of N (25/53)

(4/49)

(1/50)

(0/49, 48 and 46)

Fraction developing cholangiocarcinoma (bile duct cancer) 
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37 uncertainty assessment for internal dosimetry. Washington, DC, Brussels-Luxembourg; NUREG/CR-6571, EUR 

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

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12 Federal Register 61:17960–18011. 

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14 from: http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=12479. 

15 U.S. EPA. (2000) Benchmark dose technical guidance document. October. External review draft. Washington, DC. 

16 EPA/630/R-00/001. 

17 U.S. EPA. (2003) Exposure and human health reassessment of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and 

18 related compounds [NAS review draft]. Volumes 1−3. National Center for Environmental Assessment, Washington, 

19 DC; EPA/600/P-00/001 Cb. Available at: http://www.epa.gov/nceawww1/pdfs/dioxin/nas-review/. 

20 U.S. EPA. (2005) Guidelines for carcinogen risk assessment. Risk Assessment Forum, Washington, DC. 

21 EPA/630/P-03/001F. Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=116283. 

22 U.S. EPA. (2008a) Framework for application of the toxicity equivalence methodology for polychlorinated dioxins, 

23 furans, and biphenyls in ecological risk assessment. Risk Assessment Forum, Washington, DC; EPA/100/R-08/004 

24 U.S. EPA. (2008b) 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-response studies: preliminary literature 

25 search results and request for additional studies. National Center for Environmental Assessment, Cincinnati, OH; 

26 EPA/600/R-08/119. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=199923. 

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28 EPA/630/R-00/0001F. 

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30 Washington, DC. Available at: http://www.epa.gov/ncea/iris. 

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32 Environmental Assessment, Cincinnati, OH; EPA/600/R-09/027. 

33 U.S. EPA. (2009c) Using probabilistic methods to enhance the role of risk analysis in decision-making with case 

34 study examples. Risk Assessment Forum, Washington, DC. EPA/100/R-09/001. 

35 U.S. EPA. (2010) Recommended toxicity equivalency factors (TEFs) for human health risk assessments of dioxin 

36 and dioxin-like compounds [external review draft]. Risk Assessment Forum, Washington, DC; NCEA-C-2100. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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39 laboratory animals. Environ Health Perspect 5:149–162. 
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36 Goettingen minipigs. Chemosphere 67(9):S355−S364. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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28 large company. Occup Environ Med 40(7):586–594. 

29