DRAFT DO NOT CITE OR QUOTE January 2010 Agency/Interagency Review Draft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/INTERAGENC Y 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, OHThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE ii DISCLAIMER

This document is distributed solely for th e 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 Ag ency determination or policy. Mention of trade names or commercial pr oducts does not constitute endorsement or recommendation for use.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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

.... xviii LIST OF FIGURES ..................................................................................................................... xxi LIST OF ABBREVIATI ONS 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 SCI ENCE PLAN .............................................................................................. 1-4 1.3. OVERVIEW OF EPA’S RESPONSE TO NAS (2006a) “HEALTH RISKS FROM DIOXIN AND RELATE D COMP OUNDS: 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 TH E 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 SE TS FOR DOSE-RESPONSE ANAL YSIS................................................................................................................... 2-1 2.2. EPA’s RESPONSE TO NAS COMM ENTS ON TRANSPARENCY AND CLARITY IN THE SEL ECTION 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 Epidem iologic Studies ................................ 2-6 2.3.2. Study Inclusion Criteria for TC DD 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.......................................................... 2-20This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 Ha mburg 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.......................................................... 2-45This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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.......................................................... 2-61This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 re levance to dose-response modeling.

................................................................................... 2-67 2.4.1.1.1.7.1. t’ Mannetje et al., 2005—N ew 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 ep idem iologic 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.................. 2-79This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 cohor t in dose-response modeling....... 2-82 2.4.1.1.4. Discussion of general issues re lated 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. Summ ary 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 c ohort. ...................................................................... 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 Ha mburg 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 Wom en’s Health Study (SWHS)............................ 2-92 2.4.1.2.1.4.1. Eskenazi et al., 2002a—Menstru al 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. .................................................................... 2-95This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 outcom es................. 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 m enarche. ............................. 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 m enopause.

........................ 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 leiomyom a...................... 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— Neona tal thyroid function.......... 2-108 2.4.1.2.1.5.3.1. Study summary. ........................................................ 2-108This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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; Cons onni 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, 2 004—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)—M ortality 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—N oncancer 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 fo r dose-response analysis........... 2-122 2.4.1.2.1.8.2. McBride et al., 2009b—N oncancer mortality.

................. 2-122This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 m odeling for noncancer..................... 2-125 2.4.1.2.3. Summary of epidemiologic non cancer 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 relate d: 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. ............................................................. 2-156This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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............................................................................... 2-195This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 Selecti on 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 TH E USE OF TOXICOKINETICS IN DOSE-RESPONSE M ODELING A PPROACHES FOR TCDD

................................. 3-1 3.2. OVERVIEW OF EPA’S RESPONSE TO THE NAS COMMENTS ON THE USE OF TOXIC OKENTICS 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 Ani mals 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 Pharmac okinetic 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 pr edictions 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. .......................... 3-37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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3.3.4.3.2.5. Confidence in PBPK model pr edictions of dose metrics........... 3-40 3.3.4.4. Applicability of PK Models to De rive 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. Lim itations 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 Do se 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-RESPONS E 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 Assessmen t of Animal Bioassay Data ................... 4-12 4.2.4.1. Use of Kinetic Modeling for Ani mal 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-TETRAC HLORODIBENZO-p-DIOXIN (TCDD)......................................................................................................................... 5-1This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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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 Comme nts on the Qualitative Weight-ofEvidence Carcinogen Classification for TCDD................................................... 5-2 5.1.2.1. Summary Evaluation of Epidemiologi c 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 Carc inogenicity in Experimental Ani mals....................................................................................................... 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 ac tion of TCDD in rodents. ........................ 5-15 5.1.2.3.3.1. Liver tum ors............................................................................... 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. Summ ary 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 Comm ents on Cancer DoseResponse Modeling............................................................................................ 5-22 5.2.3. Updated Cancer Dose-R esponse Modeli ng for Derivation of Oral Slope Factor ................................................................................................................. 5-22 5.2.3.1. Dose-Response Modeling Based on Epidem iologic 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 St udies Published Since the 2003 Reassessm ent 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................. 5-33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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5.2.3.2.2.2. Calculation of human eq uivalent doses (HEDs)........................ 5-34 5.2.3.2.3. Dose-response m odeling 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 f actor 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 determ ining 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 acro ss chronic rodent bioassays............. 5-41 5.2.3.2.6.4. Hum an 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 a nd model uncertainty for POD derivation.

.................................................................................. 5-43 5.2.3.2.6.10. Statistical uncertain ty 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 Comme nts on Choice of Response Level and Characterization of the Statis tical Confidence Around Low Dose Model Predictions ..................................................................................... 5-45 5.2.3.4. EPA’s Response to the NAS Comme nts on Model Form s 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 tu morigenesis in experim ental 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. ................................................. 5-62This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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5.2.3.4.1.4.3. Effect of dose metric on linearity of response.................... 5-64 5.3. DERIVATION OF THE TCDD ORAL SL OPE FACTOR AND CANCER RISK ESTIMATES............................................................................................................... 5-66 5.3.1. Uncertainty in Estimation of Oral Slope Factors From Huma n 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 Epidem iological Studies................................................................................................................ 5-74 5.3.3. Approaches to Combining Estimates Fr om 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 QUANTITATI VE UN CERTAINTY ANALYSIS FROM NAS EVALUATI ON OF THE 2003 REASSESSMENT ............................................................ 6-1 6.1. INTRODUCTION ........................................................................................................ 6-1 6.1.1. Historical Context for Quanti tative Uncertainty Analysis................................... 6-1 6.1.2. Definition o f 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 Uncer tainties: Aleatoric and Epistem ic (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 a nd 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 TH E 2003 REASSESSMENT

........................................ 6-11 6.4. FEASIBILITY OF CONDUCTING A QUANTITATIVE UNCERTAINTY ANAL YSIS FOR TCDD ............................................................................................ 6-14 6.4.1. Feasibility of Conducting a Quantita tive Uncertainty Analysis under the RfD Methodology .............................................................................................. 6-14 6.4.1.1. Feasibility of Conducting a Quantita tive Uncertainty Analysis for the Point of Departure

..................................................................................... 6-15 6.4.1.2. Feasibility of Conducting a Quantit ative Uncertainty Analysis with Uncertainty Factors

................................................................................... 6-18 6.4.1.3. Conclusion on Feasibility of Quantit ative Uncertainty Analysis with the RfD Approach

..................................................................................... 6-20 6.4.2. Feasibility of Conducting a Quantita tive Uncertainty Analysis for TCDD Under the Dose-Response Methodology

........................................................... 6-20This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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6.4.2.1. Feasibility of Quantitatively Ch aracterizing the Uncertainties Encountered when Determ ining 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 Equi valence (TEQ) Exposures in Epidemiological St udies ........................................................................... 6-26 6.4.2.4. Uncertainty in Background F eed Exposures in Bioassays ....................... 6-27 6.4.2.5. Feasibility of Quantifying the Uncertainties Encountered W hen 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 (T rade-Off Between Confidence in Estim ated 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 Ca ncer...................................................................... 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 Can cer Slope Factors to Development of Predictive Huma n 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 BENC HMARK DOSE MODELING.......................................E-1

APPENDIX F: CANCER BENC HMARK DOSE MODELING................................................F-1

APPENDIX G: ENDPOINTS EXCLUDED FROM REFERENCE DOSE DERIVATION BASED ON TOXICO LOGICAL RELEVANCE ................... G-1

APPENDIX H: CANCER PRECURSOR BENCHMARK DOSE MODELING...................... H-1This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

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2-1. Summary of epidemiologic can cer studies (key characteristics)................................. 2-199 2-2. Epidem iology cancer study selec tion considerations and criteria ............................... 2-201 2-3. Epidem iology noncancer study sele ction considerations and criteria ......................... 2-205 2-4. Epidem iology 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 select ed for noncancer dose-response modeling.................. 2-218

3-1. Partition coefficients, tissue volumes , and volume of distri bution for TCDD in huma ns ........................................................................................................................... 3-55 3-2. Blood flows, permeability factors and resu lting half lives (t1⁄2 ) for perfusion losses for humans as represented by the TC DD 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 Em ond et al. (2006).............................. 3-60

3-8. Param eters of the PBPK model for TCDD.................................................................... 3-62 3-9. Regression analysis results for the relationship between log 10 serum TCDD at the midpoint of observations and the log 10 of the rate constant for decline of TCDD levels using Ranch Hand data........................................................................................ 3-65

3-10. Confidence in the PBPK mode l simulations of TCDD dose m etrics ............................ 3-65

3-11. Overall confidence associated with alternative dose metrics for cancer and noncancer dose-response m odeling fo r TCDD using rat PBPK model......................... 3-66

3-12. Overall confidence associated with alternative dose metrics for cancer and noncancer dose-response m odeling for TCDD using mouse PBPK model................... 3-66This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xix LIST OF TABLES (continued)

3-13. Contributors to the overall uncertainty in the selection and use of dose metrics in the dose-response m odeling of TCDD ba sed 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 m odeling of TCDD ba sed on mouse and human PBPK models...... 3-67 3-15. Impact of toxicokinetic modeling on the extrapolation of administered dose to HED, comparing the E mond PBPK a nd 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 an imal bioassay benchmark dose modeling ............................................................................................. 4-28 4-3. Summary of key animal study NOA ELs, 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 th e TCDD RfD using blood- concentration-based human equivalent doses

................................................................................................. 4-40 4-6. Qualitative analysis of the strengths and limitation s/uncertainties associated with animal bioassays possessing candidate poi nts-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), St eenland et al. (2001) and Ott and Zober (1996) from 2003 Reassessment Table 5-4

..................................... 5-79 5-2. Cox regression coefficient estimate a nd 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 c oncentrations, fat concentrations, risk specific dose estimates and equivalent oral slope factors based on upper 95 th percentile estimate of regression coeffici ent 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 c oncentrations, fat concentrations, risk specific dose estimates and equivalent oral slope factors based on best estimate of regression coefficient a 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

.................................. 5-81This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xx LIST OF TABLES (continued)

5-5. Kociba et al. (1978) male rat tumor incidence data a and blood concentrations for dose-response modeling................................................................................................. 5-82 5-6. Kociba et al. (1978) female rat tumor incidence data a and blood concentrations for dose-response modeling ........................................................................................... 5-82 5-7. NTP (1982) female rat tumor incidence data a and blood concentrations for doseresponse modeling ......................................................................................................... 5-83 5-8. NTP (1982) male rat tumor incidence data a and blood concentrations for doseresponse modeling ......................................................................................................... 5-83 5-9. NTP (1982) female mouse tumor incidence data a and blood concentrations for dose-response modeling................................................................................................. 5-84 5-10. NTP (1982) male mouse tumor incidence data a and blood concentrations for dose-response modeling................................................................................................. 5-84 5-11. NTP (2006) female rat tumor incidence data a and blood concentrations for doseresponse modeling b

........................................................................................................ 5-85 5-12. Toth et al. (1979) male mouse tumor incidence data a and blood concentrations for dose-response modeling................................................................................................. 5-85 5-13. Comparison of multi-stage modeling results across cancer bioassays usin g blood concentrations ................................................................................................................ 5-86 5-14. Individual tumor point s of departure and slope fact ors using blood concentrations ..... 5-88

5-15

. Multiple tumor points of departure and slope factors using bl ood concentrations........ 5-89

5-16. Illustrative RfDs based on tumo rigenesis in experimental anim als............................... 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-evalu ated by Goodman and Sauer (1992) a

........................... 5-92 5-19. Comparison of princi pal epidemiological studies.......................................................... 5-93 6-1. Key sources of uncertainty............................................................................................. 6-40

6-2. PODs and amenability for uncertainty quantification ................................................... 6-41This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxi LIST OF FIGURES

2-1. EPA’s process to select and identify in vivo mamm alian 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 TC DD .............................................. 2-233
2-3. EPA’s process to evaluate available an imal bioassay studies using study inclusion criteria for use in the dose -response analysis of TC DD .............................................. 2-234

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

m ass 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 or gan toxicity of TCDD................................................................. 3-72
3-6. Process of estimating a human-equi valent TCDD lifetime average daily oral exposure ( d H

) from an experimental animal average daily oral exposure ( d A ) 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. Schem atic 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 representati on of PBPK model for rat exposed to TCDD

.......................... 3-77

3-11

. Conceptual repres entation of PBPK model for rat deve lopmental exposure to TCDD.

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

3-12. TCDD distribution in the liver tissue............................................................................. 3-79This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxii LIST OF FIGURES (continued)

3-13. Growth rates for physiological change s occurring during gestation. (a) Placental growth during gestation ................................................................................................. 3-80

3-14. Comparisons of model predictions to e xperimental data using a fixed elim ination 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 conce ntration (ppb) during chronic exposure to TCDD at 50, 150, 500, 1,750 ng TCDD/BW using the inducible elimination rate model compared with th e experimental data measured at the end of exposure.............................................................................................................. 3-82
3-16. Model predictions of TCDD blood concen tration in 10 veterans (A-J) from Ranch Hand Cohort................................................................................................................... 3-83

3-17. Tim e 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 = natura l 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 vete rans and two highly exposed Austrian patients

........................................................................................................................... 3-86
3-20. Sensitivity analysis was performe d on the inducible elimination rate........................... 3-87

3-21. Experimental data (symbols) and m odel simulations (solid lines) of (A) blood, (B) liver and (C) adipose tissue concentra tions of TCDD af ter oral exposure to 150 ng/kg-day, 5 days/week for 17 weeks in mice........................................................ 3-88
3-22. Comparison of PBPK model simulati ons with experimental data on liver concentrations in m ice administ ered a single oral dose of 0.001

− 300 μ g 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 (c li) and fat (cf) concentrations following repetitive exposure to 0.1

− 450 ng TCDD/kg, 5 days/week for 13 weeks in m ice ........ 3-90
3-24. Comparison of experimental data (symbol s) 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 m ice ..................................... 3-91This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxiii LIST OF FIGURES (continued)

3-25. Comparison of experimental data (symbol s) and model predictions (solid lines) of (A) blood concentration, (B) liv er concentration, (C) adi pose tissue concentration (D) feces excretion (% do se) and (E) urin ary 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 (symbol s) and model predictions (solid lines) of (A) blood concentration, (B) liv er concentration, (C) adi pose tissue concentration (D) feces excretion (% do se) and (E) urin ary 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 (sym bols) on the distribution of TCDD after a sing le acute oral exposure to A

− B) 0.1, C

− D) 1.0 and E

− F) 10 μ g of TCDD/kg of body weight in mice................................................... 3-94

3-28. PBPK model simulation (solid lines) vs. experiment al data (symbols) on the distribution of TCDD aft e r a single dose of 24 μ g/kgBW on GD 12 in mice ............... 3-95
3-29. Comparison of the near-steady-st ate body burden simulated with CADM and Emond m odels for a daily dose ranging fr om 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 Em ond human PBPK model ................................................................. 3-97
3-31. TCDD serum concentration-time profile for lifetime, less-than-lifetime and gestational exposure scenarios, show ing cont inuous intake levels to fixed target concentration; profiles generate d 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 assessm ent 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 T CDD ................................................. 4-49
4-3. Exposure-response array fo r ingestion exposures to TCDD.......................................... 4-50

4-4. Candidate R fD array .....................................................................................................

. 4-51
5-1. Mechanism of altered gene expression by AhR ............................................................ 5-96
5-2. TCDD’s hypothesized modes of ac tion in site-specific carcinogenesis ........................ 5-97This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxiv LIST OF FIGURES (continued)

5-3. EPA’s process to select and identify candidate OSFs from key animal bioassays for use in the cancer ri sk assessm ent 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 cholangiocarcinom a respons e data (NTP, 2006) with comparison to linear m odel fit ..................................................................................... 5-101

5-7. Weibull model fits to Kociba/G&S li ver tumor response data with alternative dose m etrics ................................................................................................................. 5-101
6-1. Back-casted vs. predicted TCDD serum levels for a worker subset.............................. 6-42
6-2. Distribution of in vivo unweight ed 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

........................................................... 6-44This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxv LIST OF ABBREVIATIONS AND ACRONYMS

2,4,5-T 2,4,5-trichlorophenoxyacetic acid 2,4-D 2,4-dich lorophenoxyacetic 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 B max 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 balanoprepu tial 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 D a :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 E 2 17 β -estradiol ED x effective dose eliciting x percent response EGFR epidermal growth factor receptorThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxvi LIST OF ABBREVIATIONS AN D ACRONYMS (continued)

EPA Environmental Protection Agency ER estrogen receptor EROD 7-ethoxyresorufin-O-deethylase ER α 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 stimul ating 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 LD 50 lethal dose eliciting x percent response LED lower confidence effective dose LED x lower bound of the 95% confidence interval on the dose that yields an x% effect LH luteinizing hormone LOAEL lowest-observed-adverse-effect level LOAEL HED 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 HealthThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxvii LIST OF ABBREVIATIONS AN D 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 activ ator inhibitor 2 PBMC peripheral bl ood 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 ri sk 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 breakThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxviii LIST OF ABBREVIATIONS AN D 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 TGF α 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 fo r the effective dose UF uncertainty factor UF A interspecies extrapolation factor UF D database factor UF H human interindi vidual variability UF L LOAEL-to-NOAEL UF UF S 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 doseThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxix PREFACE

This document contains the U.S. Environmen tal Protection Ag ency’s (EPA), Office of Research and Development, National Center for Environmental Assessment’s response to the National Academy of Scien ces (NAS, 2006) review of Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenz o-p-Dioxin (TCDD) and Related Compounds (U.S. EPA, 2003). Given that the key recommenda tions 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 unc ertainty analysis in TCDD dose-response

assessment.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxx 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 Res earch 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 KopylevThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxi 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; BioSim ulation Consulting, Newark, DE Claude EmondThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxii AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

INTERNAL REVIEWERS

National Center for Environmental Assessmen t, 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-Johns on, 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 ParticipantsThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxiii EXECUTIVE SUMMARY 1
2 3 OVERVIEW 4
The U.S. Environm ental Protection Agency (EPA) is committed to the development of 5 risk assessment information of the highest scientif ic integrity for use in protecting human health 6 and the environment. Scientific peer review is an integral component of the process EPA uses to 7 generate high quality toxicity and exposure assess ments of environmental contaminants. To this 8 end, EPA asked the National Academy of Sciences (NAS) to review its comprehensive human 9
health risk assessment extern al review draft entitled, Exposure and Human Health Reassessment 10 of 2,3,7,8-Tetrachlorodibenzo-p-Dio xin (TCDD) and Related Compounds (U.S. EPA, 2003; 11 “2003 Reassessment”). This current document, EPA’s Response to “Health Risks from Dioxin 12 and Related Compounds: Evaluation of the EPA Re assessment” Published by the National 13 Research Council of the National Academies, directly and technically responds to key comments 14 and recommendations pertaining to TCDD dose -response assessment published by the NAS in 15 their review (NAS, 2006a). This document onl y addresses issues pertaining to TCDD dose16 response assessment. 17 In response to the recommendations presented in the 2006 NAS review, EPA 18
Administrator Jackson released EPA’s “ Science Plan for Activities Related to Dioxins in the 19
Environment ” (“Science Plan”) on May 26, 2009. 1 There are five key components of the 20 Science Plan that pertain to EPA’s res ponse to the NAS comments on TCDD dose-response 21

assessment: 22
23
1. EPA will release a draft report that res ponds to the recommendations and comments 24 included in the NAS review of EPA’s 2003 Reassessment. 25

• EPA’s National Center for Environment Assessment (NCEA), in the Office of 26 Research and Development (ORD), will pr epare a limited response to key comments 27 and recommendations in the NAS report (d raft response to comments report). 28

• The draft response will focus on dose-respon se issues raised by the NAS and will 29 include an analysis of re levant new key studies. 30

• The draft response will be provided for public review and comment and independent 31 external peer review. 32

1 Available online at http://www.epa.gov/dioxin/scienceplan.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxiv • The draft response will also include an evaluation of some of the significant 1 recommendations that are difficult for EPA to address given the current state of 2 science, and a detailed rationa le for these conclusions. 3

• The peer review will be conducted by EPA’s Science Advisory Board (SAB), an 4 independent review body chartered under the Federal Advisory Committee Act. 5

• The draft response to comments report will be completed and released for public and 6 peer review by December 31, 2009. 7
2. EPA will provide the draft response to comments report for internal and external review. 8
3. The SAB will review the science conten t of the response to comments report. 9
4. EPA will review impacts of the draf t response to comments report on its 2003 10 Reassessment. 11
5. EPA will release the final response to comments report and focus on completion of the 12 2003 Reassessment. 13
14 This document responds to and addresses key NAS comments relating to TCDD dose15 response assessment. Three separate EPA activities address additional NAS comments 16 pertaining to toxicity equivale nce factors (TEFs) and background exposure levels. Information 17 on the application of the dioxin TEFs is publis hed elsewhere by EPA for both ecological (U.S. 18 EPA, 2008a) and human health (U.S, EPA, 2009a) risk assessment. As a consequence, EPA 19 does not directly address TEFs herein, but makes use of the concept of toxicity equivalence 20 (TEQ) 2 as applicable to the analys is of exposure dose in epidemio logic studies. Furthermore, 21 addressing the NAS recommendati ons pertaining to the assessment of human exposures to 22 TCDD and other dioxins, information on updated background levels of dioxin in the U.S. 23 population has been recently repor ted by EPA (Lorber et al., 2009). 24 The NAS identified three key recommendations requiring substantial improvement to 25 support a scientifically robust ch aracterization of human responses to exposures to TCDD. 26 These three key areas are (1) improved transparency and clarity in the selec tion of key data sets 27 for dose-response analysis, (2) further justification of approaches to dose-response modeling for 28 cancer and noncancer endpoints, and (3) improve d transparency, thoroughness, and clarity in 29 quantitative uncertainty analysis. NAS also en couraged EPA to calculate a Reference Dose 30 (RfD), and provided numerous specific co mments on various aspects of EPA’s 2003 31

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.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxv Reassessment. The three key recommendations sp ecifically pertain to dose-response assessment 1 and uncertainty analysis. Therefore, EPA’s respon se to the NAS in this document is focused on 2 these issues. EPA thoroughly considered the recommendations of the NAS and responds with 3
scientific and technical evaluation of TCDD dose–response data via: 4
5

• an updated literature search that iden tified new TCDD dose-response studies (see 6 Section 2); 7

• a kickoff workshop that included the particip ation of external experts in TCDD health 8 effects, toxicokinetics, dose-response assessm ent and quantitative un certainty analysis; 9 these experts discussed potential appro aches to TCDD dose-response assessment and 10 considerations for EPA’s res ponse to NAS (see Appendix A); 11

• detailed study inclusion criteria and processes for the selection of key studies (see 12 Section 2.3) and epidemiologic and animal bioassay data for TCDD dose-response 13 assessment (see Section 2.4.1/Appendix B and Section 2.4.2, respectively); 14

• kinetic modeling to quantify appropriate dos e metrics for use in TCDD dose-response 15 assessment (see Section 3); 16

• dose-response modeling for all appropriate noncancer and cancer data sets (see 17 Section 4.2/Appendix E and Section 5.2.3/Appendix F, respectively); 18

• thorough and transparent evaluation of the sele cted TCDD data for use in the derivation 19 of an RfD and an oral slope factor (OSF) (see Sections 4.2 and 5.2.3, respectively); 20

• the development of an RfD (see Section 4.3); 21

• the development of a revised OSF (see Sect ion 5.3) with an updated cancer weight of 22 evidence determination for TCDD based on EPA’s 2005 Cancer Guidelines (U.S. EPA, 23 2005) (see Section 5.1.2); 24

• consideration of nonlinear dose-response approaches for cancer, including illustrative 25 RfDs for cancer precursor events an d tumors (see Section 5.2.3.4) ; and 26

• discussion of the feasibility and utility of quantitative unc ertainty analysis for TCDD 27 dose-response assessment (see Section 6). 28
29
Each of the activities listed above is briefl y described in this Executive Summary, and is 30
described in detail in the relate d sections of this document. 31
32
33
34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxvi PRELIMINARY ACTIVITIES UNDERTAKEN BY EPA TO ENSURE THAT THIS 1 TECHNICAL RESPONSE REFLECTS TH E CURRENT STATE-OF-THE-SCIENCE 2 As part of the development of this docume nt, EPA undertook two activities that included 3 public involvement: an updated lite rature search and a scientific expert workshop. The adverse 4 health effects associated with TCDD exposures are documented extensively in epidemiologic 5 and toxicologic studies. As su ch, the database of relevant in formation pertaining to the dose6 response assessment of TCDD is vast and consta ntly expanding. Responding directly to the 7
NAS recommendation to use the most current and up-to-date scientific information related to 8 TCDD, EPA, in collaboration with Argonne Na tional Laboratory (ANL), developed an updated 9 literature database of peer-reviewed studies on TCDD toxicity, including in vivo mammalian 10 dose-response studies and epidem iologic studies. An initial literature search for studies 11 published since the 2003 Reassessment was conduc ted to identify studies published between the 12 year 2000 and October 31, 2008. EPA pub lished the initial lite rature search results in the Federal 13 Register in November 2008 and invited the public to review the list and submit additional peer14
reviewed relevant studies. Additional studie s identified by the public and through continued 15 work on this response have been incorporated into the final set of studies for TCDD dose16 response assessment (updated through October 2009) . EPA believes that the implementation of 17 this rigorous search strategy ensures that the most current and relevant studies were considered 18 for the technical response to NAS and TCDD dose-response assessment included herein. 19 To assist in responding to the NAS, EPA, in collaboration with ANL, convened a 20 scientific expert workshop (“Di oxin Workshop”) in February 2009 that was open to the public. 21 The primary goals of the Dioxin Workshop were to identify and address issues related to the 22 dose-response assessment of TC DD and to ensure that EPA’s response to the NAS focused on 23 the key issues while reflecting the most meani ngful science. EPA and ANL assembled expert 24 scientists and asked them to identify and discuss the technical challenges involved in addressing 25 the NAS comments, discuss approaches for addr essing these key recommendations, and to assist 26 in the identification of important published and peer-reviewed liter ature on TCDD. The 27 workshop was structured into seven scientific topic sessions as follows: (1) quantitative dose28 response modeling issues, (2) immunotoxicity, (3 ) neurotoxicity and nonreproductive endocrine 29 effects, (4) cardiovascular toxicity and hepatotoxicity, (5) can cer, (6) reproductive and 30 developmental toxicity, and (7) quantitative uncertainty analysis of dose-response. External 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxvii co-chairs (i.e., scientists who we re not members of EPA or ANL) were asked to facilitate the 1 sessions and then prepare summaries of discussi ons occurring in each session. The session 2
summaries formed the basis of a final works hop report (U.S. EPA, 2009c, Appendix A of this 3 document). Some of the key outcomes from the workshop include the following 4
recommendations: 5
6

• to further develop study selection criteria for evaluating the suita bility of developing 7 dose-response models based on animal bioassays and human epidemiologic studies; 8

• to use kinetic modeling to identify relevant dose metrics and dose conversions between 9 test animal species and humans, and between human internal dose measures and human 10 intakes; 11

• to consider newer human or animal (e .g., NTP, 2006) publications when evaluating 12 quantitative dose-response models for cancer; 13

• to consider both linear and nonlinear modeling in the cance r dose-response analysis. 14
15 The discussions held during the Dioxin Workshop helped inform, guide, and focus EPA’s 16 response to NAS. 17
18
EPA’S APPROACH TO CONSIDERING TRANSPARENCY AND CLARITY IN THE 19 SELECTION OF KEY STUDIES AND DA TA SETS FOR DOSE-RESPONSE 20 MODELING 21 One of the key NAS recommendations to EP A was to utilize a cl ear and transparent 22 process for the selection of key studies and data sets for dose-response assessment. EPA agrees 23
with the NAS and believes that clear delineati on of the study selection process and decisions 24 regarding key studies and data sets will facilitat e communication of critical decisions made in the 25 TCDD dose-response assessment. EPA developed detailed processes and TCDD-specific 26 criteria for the selection of key dose-response studies. These criteria are based on common 27 practices and current guidance for point of departure (POD) identification and RfD and OSF 28 derivation while also considering issues specifical ly related to TCDD. Following the selection of 29 key studies, EPA employed additional processe s to further select and identify cancer and 30 noncancer datasets from these key studies for use in dose-response an alysis of TCDD. 31 Figure ES-1 presents EPA’s study evaluati on process for the epidemiologic studies 32 considered for TCDD dose-response assessment, in cluding specific study incl usion criteria (see 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxviii Section 2.3). EPA applied TC DD-specific epidemiologic study inclusion criteria to all 1 epidemiologic studies published on TCDD and di oxin-like compounds (DLCs) that had been 2 identified in the TCDD literature database (s ee Section 2.4.1, Appendix B). The studies were 3 initially evaluated using five considerations (s ee Figure ES-1) that provi de the most relevant 4 kinds of information needed for quantitative human health risk analys es. Additionally, EPA 5 examined whether the human exposures were primarily to TCDD and if the TCDD exposures 6 could be quantified so that dos e-response analyses could be conducted. Finally, EPA required 7 that the effective dose be estimable: (1) fo r cancer, information is required on long-term 8 exposures, and (2) for noncancer, information is required regarding the ap propriate time window 9 of exposure that is relevant for a specific, nonfatal hea lth endpoint. Theref ore, the study should 10 include an appropriate latency pe riod between TCDD exposure and the onset of the effect. Only 11 studies meeting these criteria were include d in EPA’s TCDD dose-response analyses (see 12 Section 2.4.3). 13 Figure ES-2 presents EPA’s study eval uation process for mammalian bioassays 14 considered for TCDD dose-response assessment, in cluding the specific st udy inclusion criteria 15 (see Section 2.3). EPA applied TCDD-specific in vivo mammalian bioassay study inclusion 16 criteria to all of the bioassay studies of TCDD that had been identified in the TCDD literature 17 database (see Section 2.4.2). After ascertaini ng that a study had been published in the peer18 reviewed literature, EPA applied dose requirement s to the lowest tested average daily doses in 19 each study, with specific requirements for cancer ( ≤ 1 μ g/kg-day) and noncancer ( ≤ 30 ng/kg-day) 20 studies to ensure that only low-dose TCDD bi oassays would be considered. These dose 21 requirements were used to eliminate those studies that would not be selected for development of 22
an RfD or an OSF because the lowest doses tested were too high re lative to other TCDD 23 bioassays. EPA also required that the bioassays exposed the anim als via the oral route to TCDD 24 only and that the purity of the TCDD was specifi ed. Finally, the studies were evaluated using 25 four considerations (see Figure ES-2) regarded as providing the most relevant information for 26 development of quantitative human health risk an alyses from animal bioassay data. Only the 27 bioassay studies meeting these criteria and cons iderations were included in EPA’s TCDD dose28 response analyses (see S ection 2.4.3). 29 Applying the study inclusion criteria for both epidemiologic and mammalian bioassay 30
datasets resulted in a list of key noncancer and cancer studie s that were considered for 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xxxix quantitative dose-response anal yses of TCDD. Endpoints from these studies that were not 1 considered to be toxicologi cally relevant were eliminat ed from consideration (see 2 Section 4.2.1.1, Appendix G). The study/endpoint dataset combinations from the remaining 3 studies were then subjected to dose-response as sessment, and PODs for use in developing RfDs 4
or OSFs were identified. PODs included no-obs erved-adverse-effect levels (NOAELs), lowest5 observed-adverse-effect levels (LOAELs) or lo wer bound benchmark dose levels (BMDLs). The 6
most sensitive PODs were selected as candi dates for derivation of the RfD and OSF. 7
8
USE OF KINETIC MODELING TO ESTIMATE TCDD DOSES 9 NAS recommended that EPA utilize state-of-the-science approaches to finalize the 2003 10 Reassessment. Although NAS concurred with EP A’s use of first-order body burden models in 11 the 2003 Reassessment, analyses of recent TC DD literature and comments by experts at the 12 Dioxin Workshop suggested that th e understanding of TCDD kineti cs had increased significantly 13
since the release of EPA’s 2003 Reassessment. These advances led to the development of 14 several pharmacokinetic models for TCDD (e .g., Emond et al., 2004, 2005, 2006; Aylward et al., 15 2005a) and resulted in EPA’s incorporation of TCDD kinetics in the dose-response assessment 16
of TCDD. 17 The evaluation of internal dose in exposed hum ans and other species is facilitated by an 18 understanding of pharmacokinetics (i.e., absorption, distribution, me tabolism, and excretion). 19 TCDD pharmacokinetics are influenced by three distinctive features : (1) TCDD is highly 20 lipophilic, (2) TCDD is slowly metabolized, and (3) TCDD induces binding proteins in the liver. 21 The overall impact of these factor s results in preferential storag e of TCDD in adipose tissue, a 22 long half-life of TCDD in blood due to slow metabolism, and sequest ration in liver tissue when 23 binding induction becomes significant. As these kinetic features c ontrol target tissue levels of 24 dioxin, they become important in relating toxicity in animals to possible effects in humans. 25 Consideration of pharmacokinetic mechanisms is critical to the selection of the dose 26 metrics of relevance to dose-response modeli ng of TCDD. Earlier assessments for TCDD, 27 including the 2003 Reassessment, used estimates of body burden as the dose metric for 28 extrapolation between animals and humans. Th ese body burden calculations used a simple one29 compart ment kinetic model based on the assumption of a first-order decrea se in the levels of 30
administered dose as a function of time. Howeve r, the assumption of a c onstant half-life value 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xl for the clearance of TCDD from long-term or chronic exposure is not well-supported 1 biologically given the dose-dependant elimin ation observed in rodents and humans. The 2 dynamic disposition and redistributi on of TCDD between blood, fat, and liver as a function of 3
time and dose is better described using biologically-based models . Additionally, these models 4 provide estimates for other dose metrics (e.g., seru m or tissue levels) that are more biologically 5 relevant to response than body burden estima ted based on an assumption of first-order 6
elimination over time. 7 EPA considered the following possible dos e metrics for TCDD: administered dose, 8 absorbed dose, first-order body burden, serum or whole blood concentration, tissue 9 concentration, and functional-rela ted metrics of relevance to the mode of action (MOA) (e.g., 10 receptor occupancy) (see Section 3.3.4.1). After careful evaluation of these dose metrics, EPA 11
chose to use TCDD concentration in whole bl ood as the dose metric for assessing TCDD dose 12 response in this document; blood concentrati on reflects both the body burden and the dose to 13 target tissues. EPA used the time-weighted av erage whole-blood concentration over the relevant 14 exposure periods for all continuous dosing protocols, dividing th e area under the time-course 15 concentration curve (AUC) by the exposure duration. 3 16 Several biologically-based kinetic models fo r TCDD exist in the literature. The more 17 recent pharmacokinetic models explicitly charac terize the concentration-dependent elimination 18 of TCDD (Carrier et al., 1995a , b; Emond et al., 2004, 2005, 2006; Ay lward et al., 2005a). The 19 biologically-based pharmacokinetic models describing the concen tration-dependent elimination 20 (i.e., the pharmacokinetic models of Aylward et al. [2005a] and Emond et al. [2005, 2006]) are 21 relevant for application to simulate the TCDD do se metrics in humans and animals exposed via 22 the oral route. The rationale for considering th e application of the Aylw ard et al. (2005a) and 23 Emond et al. (2004, 2005, 2006) models was largely ba sed on the fact that both models reflect 24 research results from recent peer-reviewed public ations, and both models are formulated with 25 dose-dependent hepatic elimination consistent with the physiologica l understanding of TCDD 26
kinetics. 27

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).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xli Of the two selected models, the pharmac okinetic model developed by Emond et al. 1 (2004, 2005, 2006) is more physiologically-based, as compared to the Aylward et al. (2005a) 2 model, and models the blood compartment directly in the rat, mouse, and human; there are also 3 gestational and life-time nongestational forms of the Emond PBPK m odel. Therefore, in this 4 document, EPA chose the Emond rodent physiol ogically-based pharmacokinetic (PBPK) model 5
to estimate blood TCDD concentrations base d on administered doses (see Section 3.3.4, 6 Appendix C). EPA assumed that the same blood TCDD levels that led to effects in animals 7 would also lead to effects in humans; theref ore, the Emond human PBPK model was used to 8 estimate the lifetime average dail y oral doses (consistent with the chronic RfD and OSF) that 9 would correspond to the blood TCDD concentrati ons estimated to have occurred during the 10 animal bioassays. EPA used the same Emond human PBPK model to estimate the lifetime 11 average daily doses that would correspond to th e TCDD blood or tissue concentrations reported 12 in the epidemiological studies. These estimates are the Human Equivalent Doses (HEDs) that 13 are used to develop candidate RfDs and OSFs for TCDD. 14
Because TCDD elimination is inducible in th e Emond model, ratios of daily averaged 15 intake to long-term blood concentrations are not linear. OSFs based on internal dose measures 16 (i.e., blood or fat concentrations) will be lin ear only with respect to blood or fat TCDD 17 concentrations; however, ingested TCDD doses are not linear with TCDD fat or blood 18
concentrations in the Emond PBPK model. Thus, an OSF that is linear with TCDD in the fat is 19 not linear with the ingested TCDD dose. A cons equence, then, of usi ng the Emond PBPK model 20 is that risk-specific TCDD intake rates corresponding to target risk levels ne ed to be tabulated for 21
use in human health risk assessment. 22
23
DERIVATION OF AN RfD FOR TCDD 24 The NAS specifically recommended that EP A derive an RfD for TCDD. Through a 25 transparent study selection process, EPA identif ied key studies from both human epidemiologic 26 and animal bioassay studies. To select can didate PODs for its Rf D methodology, EPA applied 27 additional processes to the key human epidemiol ogic studies and animal bioassays. Figure ES-3 28 (exposure-response array) shows all of the candi date PODs graphically in terms of human29 equivalent intake (ng/kg-day). The human study endpoints are shown at the far left of the figure 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlii and, to the right, the rodent e ndpoints are arranged by the followi ng study categories: less than 1 1 year, greater than 1 year, reproductive, and developmental. 2 For each noncancer epidemiologic study that EPA selected as key, EPA evaluated the 3 dose-response information developed by the st udy authors to determine whether the study 4 provided noncancer effects and TCDD-relevant exposure data fo r a toxicologically-relevant 5 endpoint. If such data were available, EPA id entified a NOAEL or LOAEL as a candidate POD. 6 Then, EPA used the Emond human PBPK model to estimate the continuous oral daily intake 7 (ng/kg-day) that would lead to the relevant blood TCDD concen trations associated with the 8
candidate POD that could be used in the derivati on of an RfD. If all of this information was 9 available, then the result was included as a candidate POD. 10 Through this process, EPA identified h ealth effects from the following four 11 epidemiologic studies to be considered as th e basis for the RfD: Es kenazi et al. (2002) 12 (reproductive—increased length of menstrual cycle), Alaluusua et al. (2004) (developmental— 13 tooth development), Mocarelli et al. (2008) (rep roductive—decreased sperm concentrations), and 14 Baccarelli et al. (2008) (developmental—increase d thyroid-stimulating hormone levels in 15 neonates). All four studies are from the Seveso cohort, whose members were exposed 16 environmentally to high peak concentrations of TCDD as a consequence of an industrial 17 accident. This complicated the estimation of aver age daily doses associated with these specific 18 endpoints, however EPA was able to calculate candidate PODs fo r derivation of an RfD from 19
each of these human studies (see Section 4.2.3). 20 Figure ES-4 summarizes the strategy employed for identifying and selecting candidate 21 PODs from the key animal bioassays EPA identif ied for use in noncancer dose-response analysis 22 of TCDD (see Section 4.2.4). For each non cancer endpoint, EPA first evaluated the 23 toxicological relevance of each endpoint, rejectin g those judged not to be relevant for RfD 24 derivation. Next, initial PODs (NOAELs, LOAELs, and BMDLs) based on the first-order body 25 burden metric, and expressed as continuous human -equivalent oral dail y doses (HEDs), were 26 determined for all relevant endpoints. Because there were very few NOAELs, and BMDL 27 modeling was largely unsuccessful due to data limitations, the next st age of evaluation was 28 carried out using LOAELs only. Endpoints not observed at the L OAEL (i.e., reported at higher 29 doses) with BMDLs greater than the LOAEL were elim inated from further analysis, as they 30
would not be considered as candidates for th e final POD on either a BMDL or NOAEL/LOAEL 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xliii basis (i.e. the POD would be higher than the PODs of other relevant endpoi nts). In addition, all 1 endpoints with HEDs for LOAELs (LOAEL HED s) beyond a 100-fold range of the lowest 2
identified LOAEL HED were eliminated from fu rther consideration, as they would not be potential 3 POD candidates either (i.e. the POD would be highe r than the PODs of othe r relevant endpoints). 4 For the remaining endpoints, EPA then determined final potential PODs (NOAELs, LOAELs, 5 and BMDLs) based on TCDD blood concentratio ns obtained from the Emond rodent PBPK 6
models. HEDs were then estimated for each of these PODs using the Emond human PBPK 7 model. From these HEDs, a POD HED was selected for each study as the ba sis for the candidate 8 RfD, to which appropriate uncertainty factor s were applied following EPA guidelines. The 9 resulting candidate RfDs were then considered in the final selection process for the RfD. Other 10 endpoints occurring at slightly hi gher doses representing additi onal effects associated with 11 TCDD exposure (beyond the 100-fold LOAEL range ) were evaluated, modeled, and included in 12 the final candidate RfD array to examine endpoint s not evaluated by studies with lower PODs. 13 In addition, BMD modeling based on administer ed dose was performed on all endpoints for 14 comparison purposes. 15 For selection of the POD to serve as the basis of the RfD, EPA gave the epidemiologic 16 studies the highest consideration because human data are preferred in the derivation of an RfD, 17 given that the underlying epidemiologic and an imal bioassay data all met EPA’s selection 18 criteria. This preference for epidemiologic study data also is consistent with reccomendations of 19 panelists at the Dioxin Workshop. The lower end of the candidate POD distribution is dominated 20 by mouse studies, with mouse studies comprising si x of the first seven animal bioassays yielding 21
the lowest PODs. EPA has less confidence in th e values derived from these mouse bioassays 22 than the values derived from rat and human studies. The primary reason that the mouse 23
LOAEL HED s are low is the large toxicokinetic interspe cies extrapolation fact or applied to mouse 24
data in the Emond PBPK model. In addition, each of these first seven rodent studies has other 25 qualitative limitations and uncertainties that make them poor candidates as the basis for the RfD. 26
Most of the other rodent studi es yielding POD values higher than the first seven animal 27 bioassay studies and lower than th e human studies of Mocarelli et al (2008) and Baccarelli et al. 28 (2008) are of small size, using 10 or fewer animals per dose group and are considered too 29
uncertain on which to base the final RfD. However, two of th e rat bioassays—Bell et al (2007) 30 and NTP (2006)—were very well designed and c onducted, using 30 or more animals per dose 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xliv group (see Section 4.3.4). Bell et al (2007) ev aluated several reproductive and developmental 1 endpoints initiating TCDD exposures well before mating and continuing them through gestation. 2 NTP (2006) is the most comprehensive evaluation of TCDD chronic toxicity in rodents to date, 3 evaluating dozens of endpoints at several time points in all major tissues. It is EPA’s judgement 4 that the toxicokinetic extrapolation of the result s of these two studies to humans, however, is still 5 less certain than the use of human data. Despit e the overall strength of the Bell and NTP studies, 6
EPA considers the human data to be a better basis for the TCDD RfD. 7
The most relevant human PODs are based on the Baccarelli et al. ( 2008) and Mocarelli et 8 al. (2008) studies, which exhibited similar LOAELs of 0.024 and 0.020 ng/kg-day, respectively. 9 For Baccarelli et al. (2008), EPA defined a LOAEL as the gr oup mean of 39 ppt TCDD in 10 neonatal plasma which corresponds to thyroi d-stimulating hormone (TSH) values above 11 5 μU/mL. Baccarelli et al. did not estimate the equivalent oral intake associated with TCDD 12 serum concentrations, rather they provided ne onatal serum TCDD concentrations for the groups 13 above and below 5 μU/mL. EPA estimated the ma ternal intake at the LOAEL from a maternal 14 serum-TCDD/TSH regression model presented in Baccarelli et al. by estimating the maternal 15 TCDD lipid adjusted serum concentration (LASC) at which neonatal TSH exceeded 5 μU/mL. 16
EPA then used the Emond PBPK model to es timate the continuous daily TCDD intake that 17 would result in this TCDD LASC. The resulting pr edicted maternal daily intake rate established 18 the LOAEL (0.024 ng/kg-day). EPA did not defi ned a NOAEL because it is not clear what 19 maternal intake should be assigne d to the group below 5 μU/mL. 20 For Mocarelli et al. (2008), EPA defined a L OAEL as the lowest exposed group mean of 21 68 ppt (1 st -quartile) corresponding to decreased sperm concentrations, decreased motile sperm 22 counts, and decreased serum estrad iol in men who were 1–9 years ol d at the time of the Seveso 23 accident (initial TCDD exposure event). TC DD LASC levels were measured within 24 approximately one year of the initial exposure ev ent. Because effects were only observed in men 25 who were under 10 years of age at the time of exposure, EPA assumed a maximum 10-year 26 critical exposure window for elicitation of thes e effects. EPA has estim ated a continuous daily 27 oral intake of 0.020 ng/kg-day associated with the designated LOAEL from the lowest exposure 28 group (68 ppt), (see Section 4.2.3.2). The reference group is not designated as a NOAEL 29 because there is no clear zero-exposure measuremen t for any of these endpoints, particularly 30 considering the contribution of background expos ure to DLCs, which futher complicates the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlv interpretation of the reference group response as a true “control” res ponse (see discussion in 1 Section 4.4). However, males less than 10 years old can be designated as a sensitive population 2 by comparison to older males who were not affected. 3 The two human studies, Baccarelli et al. (2008) and Mocarelli et al. (2008), have similar 4 LOAELs of 0.024 and 0.020 ng/kg-day, respectively. Together, these two studies constitute the 5 best foundation for establishing a POD for the RfD, and are designated as co-principal studies. 6 Therefore, increased TSH in neonates (Baccarell i et al., 2008) and male reproductive effects 7 (decreased sperm count and motility, increased estrad iol) are designated as cocritical effects. 8 Although the exposure estimate used in determination of the LOA EL for Mocarelli et al. (2008) 9
is more uncertain than the Baccar elli et al. (2008) e xposure estimate, the slightly lower LOAEL 10 of 0.020 ng/kg-day from Mocarelli et al. is designated as the POD. 11 EPA used a composite UF of 30 for both studies. EPA applied a factor of 10 for UF L to 12
account for lack of a NOAEL. EPA also applied a factor of 3 (10 0.5 ) for UF H to account for 13
human interindividual vari ability because the effects were el icited in sensitive populations. A 14
further reduction to 1 was not made because the sample sizes in these two epidemiologic studies 15 were relatively small, which, combined with uncertainty in exposure estimation, may not fully 16 capture the range of interindividu al variability. The resulting RfD for TCDD in standard units is 17
7 × 10 − 10 mg/kg-day (Table 4- 7 deta ils the RfD derivation). 18 19 WEIGHT-OF-EVIDENCE STATEMENT FOR CARCINOGENICITY 20 The NAS recommended that EPA update its cancer classification for TCDD and the 21 weight-of-evidence (WOE) statement to reflect the current st ate of the science and incorporate 22 the latest EPA Cancer Guidelines (U.S. EPA, 2005). Several notable new studies addressing 23 TCDD’s carcinogenic potential have been published since the release of EPA’s 2003 24 Reassessment, including several new studies of the Seveso epidemiologic cohort and an NTP 25 2-year cancer bioassay in female rats (NTP, 2006). 26
Under the 2005 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005) TCDD is 27
characterized as carcinogenic to humans , based on the available data as of 2009 (see 28 Section 5.1.2). When evaluating the carcinoge nic potential of a compound, EPA employs a 29 WOE approach in which all available information is evaluated and considered. In the case of 30 TCDD, EPA based the classification on numerous lines of evidence, including: multiple 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlvi occupationally- and accidentally-exposed epidemiologic cohorts showing an association between 1 TCDD exposure and certain cancers or increased mortality from all cancers; extensive evidence 2 of carcinogenicity at multiple tumor sites in bot h sexes of multiple species of experimental 3 animals; consensus that the mode of TCDD’s carcinogenic action in an imals involves aryl 4 hydrocarbon receptor (AhR)-dependent key precur sor events and proceeds through modification 5
of one or more of a number of cellular processes; the human AhR and rodent AhR are similar in 6 structure and function, and huma n and rodent tissue and organ cultures respond to TCDD in a 7 similar manner and at similar concentrations; and general scientific consensus that AhR 8 activation is anticipated to occur in humans and to progress to tumors. 9 10 DERIVATION OF CANDIDATE OSFs FR OM EPIDEMIOLOGIC STUDIES AND 11 ANIMAL BIOASSAYS 12 There are several well-studied occupationally-exposed epidemiologic cohorts showing an 13
association between TCDD and in creased all-cancer mortality, and several epidemiologic 14 cohorts exposed to TCDD as a consequence of industrial accidents s howing an association 15 between TCDD and cancer or cancer mortality (see Section 5.2.3.1). The 2003 Reassessment 16 included cancer dose-response analys es based on the following th ree occupational cohorts: the 17 NIOSH cohort, an occupational cohort subject to chronic TCDD exposures (Steenland et al., 18 2001); the Hamburg cohort, an occupational coho rt also subject to chronic TCDD exposures 19 (Becher et al., 1998); and the BASF cohort, an occupational cohort subject to peak TCDD 20 exposures through clean-up followi ng an industrial accident (Ott and Zober, 1996). In this 21 document, EPA determined that each of these studies met the epidemiologic study inclusion 22 criteria. Thus, after further evaluating the O SFs presented in the 2003 Reassessment for these 23 three studies, EPA accepted those OSF estimates and retained them as candidate OSFs in this 24
document. EPA also determined that two additional studies met the epidemiologic study 25 inclusion criteria: Cheng et al . (2006) (NIOSH cohort) and Warn er et al. (2002) (Seveso cohort, 26 a cohort exposed environmentally as a consequence of an industrial accident). EPA was unable 27 to derive a credible OSF from the data presente d by Warner et al. (2002) but did derive an OSF 28 from Cheng et al. (2006), as deta iled in Text Box ES-1. In Tabl e ES-1, EPA presents estimates 29 of OSFs for specific TCDD intake rates ba sed on target risk levels of 1 × 10 − 3 , 1 × 10 − 4 , 1 × 10 − 5

, 30
1 × 10 − 6 , and 1 × 10 − 7 based on Cheng et al. (2006). 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlvii 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 wher e the fatal cancer risk is incr easing 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 95 th percentile slope ( β 95 ) of 6.0 × 10 − 6 per ppt-year lipid adjusted serum TCDD.

• Background cancer mortality risk estimate ( R 0 ) of 0.112.

• Risk in the exposed group associated w ith a 1% extra risk of fatal cancer ( R exp ) of 0.12088.

• Incremental cancer mortality risk in the exposed population based on a 1% extra risk ( R D ) of 8.9 × 10 − 3 using the equation, R D = R exp − R 0

• Cumulative TCDD concentration in the fat compartment for a 1% extra risk (AUC 01 ) using the following formula corresponding to equations used in Cheng et al. (2006):
AUC 01 = LN( R D + R 0 / R 0 )/ β 95 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(AUC 01 )], calculated to be 7.92 × 10 − 7 by dividing 0.01 by the AUC 01 . OSF(AUC 01 ) 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(AUC 01 ) 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 (AUC RL ) (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 (FAT RL ) (ng/kg). The AUC RL estimates were then further divided by 70 years to identify (FAT RL ) (ng/kg). This step essentially reverses the integration undertaken to calculate AUC 01 .

• Continuous daily TCDD intake (D RL )(ng/kg-day) associated with a targ et risk level over a lifetime. Using the Emond pharmacokinetic model, EPA estimated the D RL necessary to achieve the FAT RL .

• Oral slope factor at the target risk level (OSF RL ) (per mg/kg-day). At the target risk levels, the associated OSF RL s range from 3.7 × 10 5 to 1.3 × 10 6 per mg/kg-day. These are calculated as OSF RL = RL/D RL × 10 6

.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlviii EPA also identified candidate OSFs for TCDD from key animal bioassays (see 1 Section 5.2.3.2). Based on the incl usion criteria, EPA selected f our key rodent cancer bioassays 2 suitable for quantitative dose-response assessment . These included Kociba et al. (1978), NTP 3 (1982), and Toth et al. (1979) th at were evaluated in the 2003 Reassessment, and the new NTP 4 (2006) rat chronic bioassay. EPA conducted dose-response modeling for each tumor type 5 separately (individual tumor m odels) as well as composite tumor incidence dose estimates 6 (multiple tumor models). The tumor types that EPA analyzed are shown in Table ES-2. 7 For each in vivo animal cancer study that qualified for TCDD dose-response assessment, 8 EPA selected the species/sex/tumor dataset comb inations characterized as having statistically 9 significant increases in tumor incidences, then us ed the Emond rodent PBPK model to estimate 10 blood concentrations corresponding to each study’s average daily administered dose for use in 11 dose-response modeling. BMDL 01 s were then estimated for the blood concentration by two 12 different methodologies: (1) using the multistage cancer model for each species/sex/tumor 13 combination within each study, and (2) using a Bayesian Markov Chain Monte Carlo framework 14 that assumes independence of tumors, modeling all tumors together for each species/sex 15 combination within each study. The final selected models were subjected to goodness-of-fit tests 16 and visual inspection of fit to the raw data. Thus, for each sex/species combination within each 17 study, EPA generated a BMDL 01 for each single tumor type and another BMDL 01 for the 18 combined tumors. Using the Emond human PBPK model, BMDL HED s were then calculated for 19
each of the BMDL 01 s, and using a linear extrapolation, OSFs were calculated by 20
OSF = 0.01/BMDL HED The highest OSF for a species/sex combination for either a single tumor 21 type or all combined tumors was selected as a candidate OSF. The OSF candidates from the key 22 animal bioassays are shown in Table ES-2. 23
24
DERIVATION OF TCDD ORAL SLOPE FACTOR AND RISK ESTIMATES 25 EPA was able to derive OSFs for tumor incidence data from four animal cancer 26 bioassays, as well as cancer mort ality data from four epidemiol ogical cohort studies that were 27 selected for TCDD dose-response modeling using the study inclusi on criteria (see Section 5.3). 28 For the animal data, OSFs based on individual tumors were developed for 25 study/sex/endpoint 29 combinations, and the results ranged from 1.8 × 10 4 to 5.9 × 10 6 (per mg/kg-day). The OSFs 30 based on combined tumors were developed for seven study/sex combinations, and the results 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE xlix ranged from 3.2 × 10 5 to 9.4 × 10 6 (per mg/kg-day). EPA also developed OSFs based on four 1 epidemiologic studies from thr ee cohorts, ranging from 3.75 × 10 5 to 2.5 × 10 6 (per mg/kg-day). 2
EPA has chosen to use the human data over the animal data as recommended by expert 3 panelists at EPA’s 2009 Dioxin Workshop (U.S. EP A, 2009c) and in the 2005 Cancer Guidelines 4 (U.S. EPA, 2005). OSFs derived from the human data are consistent with the animal bioassay 5 results; human OSFs fall within the same range as the animal bioassay OSFs. 6 Among the human studies, the occupationa l TCDD exposures in the NIOSH and 7 Hamburg cohorts are assumed to be reasonably constant over the dur ation of occupational 8 exposure. In contrast, the TCDD exposure pattern for the Seveso and BASF accidents is acute, 9 high dose, followed by low-level background exposure. Such exposure patterns similar to those 10 experienced by the BASF and Seveso cohorts have been shown to yield higher estimates of risk 11 when compared to constant exposure scenarios with similar total expos ure magnitudes (Kim et 12 al. 2003; Murdoch and Krewski, 1988; Murdoch et al, 1992). Thus, EPA has judged that the 13 NIOSH and Hamburg cohort response data are more relevant than the BASF and Seveso data for 14 assessing cancer risks from continuous ambien t TCDD exposure in the general population. 15 The NIOSH (Steenland et al. 2001; Cheng et al . 2006) and Hamburg (Becher et al. 1998) 16 cohort studies report cumulative TCDD levels in the serum for cohort members. The most 17 significant difference among the Cheng et al. (2006) analysis and t hose of Steenland et al. (2001) 18 and Becher et al. (1998) is th e method used to back-extrapolat e exposure concentrations based 19
on serum TCDD measurements. Steenland et al . (2001) and Becher et al. (1998) back20 extrapolated exposures and body burdens using a firs t-order model with a cons tant half-life. In 21 contrast, Cheng et al. (2006) back-extrapolate d body burdens using a kinetic modeling approach 22 that incorporated concentration- and age-dependent elimination kinetics. 23 Although all three of these are high-quality st udies, the kinetic modeling used by Cheng 24 et al. (2006) is judged to bette r reflect TCDD pharmacokinetics, as currently understood, than the 25 first-order models used by Steen land et al. (2001) and Becher et al. (1998). EPA believes that 26 the representation of physiological processes provid ed by Cheng et al (200 6) is more realistic 27 than the assumption of simple first-order kinetics and this outweighs the attendant modeling 28 uncertainties. Furthermore, the use of kinetic m odeling is consistent with recommendations both 29 by the NAS and the Dioxin Workshop panel. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE l Therefore EPA has selected the results from the Cheng et al. (2006) study for derivation 1 of the TCDD OSF (see Section 5.3). Table ES-1 shows the oral sl ope factors at specific target 2 risk levels (OSF RL s) which range from 3.7 × 10 5 to 1.3 × 10 6 per (mg/kg-day). EPA 3
recommends the use of an OSF of 1.3 × 10 6 per (mg/kg-day) when the target risk range is 10 − 5 to 4
10 − 7 . 5
6
CONSIDERATION OF NONLINEAR DO SE-RESPONSE APPROACHES FOR 7 CANCER 8 The NAS focused much of its review on EP A’s derivation of a cancer slope factor, 9 commenting extensively on the extrapolation of dose-response modeling below the POD. The 10 NAS questioned EPA’s choice of a linear, nonthreshold model for extrapolating risk associated 11 with exposure levels below the POD, concluding that the current scientific evidence was 12 sufficient to justify the use of nonlinear methods when extrapol ating below the POD for dioxin 13 carcinogenicity. 14 While, based on the 2005 Cancer Guidelines, EPA deemed linear extrapolation to be 15 most appropriate for TCDD, EPA carefully consid ered the NAS recommenda tion to provide risk 16 estimates using both linear and nonlinear methods . In this document, EPA has evaluated the 17 information available for identifying a thresh old and for estimating the shape of the dose18 response curve below the POD (see Section 5.2.3.4) . EPA presents a hypothetical sublinear 19 dose-response modeling example of rodent carcin ogenicity. EPA also pr esents two illustrative 20 examples of RfD development (i.e., nonlinear me thod) for carcinogenic effects of TCDD, using 21 data derived from animal bioassays. EPA derives illustrative RfDs for cancer based on 22 combined tumor response and also on hypothesized key events in TCDD’s MOA for female rat 23 liver and lung tumors. EPA identifies a num ber of limitations that prevent making strong 24
conclusions based on the nonlinear do se-response modeling exercises. 25
26 FEASIBILITY OF QUANTITATIVE UNCERTAINTY ANALYSIS 27 EPA also addresses the third key recomme ndation of the NAS, specifically, improving 28 transparency, thoroughness, and clarity in quantitative uncertainty analysis (see Section 6). In 29 summary, NAS suggested that EPA should 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE li • describe and define (quan titatively to the extent possible) the variability and 1 uncertainty for key assumptions used for each key endpoint-specific risk 2 assessment (choices of data set, POD, model, and dose metric), 3

• incorporate probabilistic mode ls to the extent possible to represent the range of 4 plausible values, 5

• clearly state it when quan titation is not possible a nd explain what would be 6 required to achieve quantitation (NAS, 2006a, p. 9). 7
8 Although the NAS summarized th e shortfalls in the 2003 Rea ssessment categorically, the 9 elaborations within their report often contain the qualification “if possible” and do not take a 10 position with regard to the feasibility of many s uggestions. With appreciation for the extent of 11 information available for dioxin, EPA’s goal herein was to examine the feasibility of a 12 data-driven quantitative uncertainty analys is for TCDD dose-response assessment. 13 In examining feasibility of quantitative uncertainty an alysis, EPA recognized that 14 different kinds of uncertainty require different statistical treatment. Cognitive uncertainty 15 concerns uncertainty that can be expressed as probabilities and may be operationalized using 16 either frequentist or Bayesian approaches. Fo r example, classical statistical methods yield 17 distributions on model parameters which reflect sa mple fluctuations, assuming that the model is 18 true. This type of uncertainty can be taken into account in the BMDL estimation. Also, for 19 TCDD epidemiologic data, the dose reconstructi on often involves assumptions that may be 20 amenable to data-driven uncertainty analysis if sufficient data can be retrieved; back21 extrapolated TCDD levels, biological half-life, body fat, a nd background levels are example 22
variables that could be included in such an analysis. In addi tion, a Monte Carlo analysis has 23 been examined to develop quantit ative uncertainty dist ributions for the RfD (e.g., Swartout et al., 24 1998). Given a set of animal bioassay data, quantifying dose-response uncertainty may be 25 approached in different ways. The differences reflect different types of uncertainty that are 26 captured. A recent evaluation enumerates the fo llowing possible methodologi es (Bussard et al., 27 2009): 28
29 Benchmark Dose Modeling (BMD) : Choose the ‘best’ model, and 30 assess uncertainty assum ing this model is true. Supplemental results can compare 31 estimates obtained with different models , and sensitivity analyses can investigate 32 other modeling issues. 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE lii Probabilistic Inversion with Isotonic Regression (PI-IR) : Define 1 model-independent ‘observati onal’ uncertainty, and look for a model that captures 2 this uncertainty by assuming the select ed model is true and providing for a 3 distribution over its parameters. 4 Non-Parametric Bayes (NPB) : Choose a prior mean response (potency) 5 curve (potentially a “non-informative prior” ) and a precision parameter to express 6 prior uncertainty over all in creasing dose-response relati ons, and update this prior 7 distribution with the bioassay data. 8 Bayesian Model Averaging (BMA) (as considered here): Choose an 9 initial set of models, a nd then estimate the parame ters of each model with 10 maximum likelihood. Use classical methods to estimate parameter uncertainty, 11 given the truth of the model. Determ ine a probability weight for each model 12 using the Bayes Information Criterion (BIC ), and use these weights to average the 13 model results. 14 15
The first of the above methods involves standa rd classical statistical methods and captures 16 sampling uncertainty conditional on the truth of th e model used. The other methods are “exotic” 17 in the sense that they attempt to capture uncertain ty that is not conditional on the truth of a given 18 model. In this response document, EPA has not applied such methods , but recognizes that 19 quantitative uncertainty analysis is possible in these cases. 20
In contrast to c ognitive uncertainty, Volitional uncertainty concerns uncertainty regarding 21
choices on the best course of action to take ; volitional uncertainty cannot be analyzed by 22 sampling from a probability distribution and, thus , is not amenable to a complete quantitative 23 uncertainty analysis. Some of the choices ma de in TCDD dose-response assessment that are 24
volitional include: choice of occ upational cohort data set or bi oassay data set; choice of PODs 25 (e.g., ED 01 , ED 05 , and ED 10 ); choice of species, strain, or se x within an animal bioassay; and 26 choice of dose metric (e.g., administered dos es, blood concentrations, lipid-adjusted serum 27 concentrations). These volitional uncertaint ies cannot be quantifie d by sampling an input 28 distribution. However, EPA be lieves that NAS was requestin g that dose-response modeling 29 results be shown for specific choices of interest to TCDD assessment. To this end, for the cancer 30 dose-response modeling, BMDLs are reported fo r 1, 5, and 10% extra risk levels, and a 31 comparison is provided of linear and nonlinear do se-response assessments for cancer. For the 32 noncancer dose-response modeling, different model forms are run and contrasted. Finally, 33
TCDD kinetic doses from the Emon d et al. (2005, 2006) PBPK model that is primarily used in 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE liii the technical analysis in this document are comp ared with those predicted by the Aylward et al. 1 (2005a) model. 2 Uncertainty quantification is an emerging area in science. There are many examples of 3 highly vetted and peer-reviewed uncertainty analyses based on st ructured expert judgment. 4 Under this process, experts in effect synthesi ze a wide diversity of in formation in generating 5 their subjective probability distri butions. Where considerable da ta exist for an environmental 6 pollutant, such as for the well-studied TCDD, it is natural to ask whether these extensive data can 7 be leveraged more directly in uncertainty quantific ation. This is an area where research could be 8 focused. Additional research topics relevant to dioxin that could further inform health 9 assessments include population variability of biok inetic constants and threshold mechanisms for 10 the mass action model. Further data and improve d methodologies in these areas, combined with 11 developments illustrated elsewhere in this report, will help reduce or better quantify uncertainties 12 and strengthen EPA’s understand ing of potential health impli cations of environmental TCDD 13

exposures. 14
15Table ES-1. Comparison of lipid-a djusted serum concentrations, fat 1 concentrations, risk specific dose esti mates and equivalent oral slope factors 2 based on upper 95 th percentile estimate of regressi on coefficienta of all fatal 3 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) AUC RL , (ppt-yr) FAT RL (ng/kg) Risk specific dose (D RL ) (ng/kg-day) Equivalent oral slope factors (OSF RL ) per (mg/kg-day)
1 × 10 − 3 1.26 × 10 − 3 1.803 × 10 1 2.73 × 10 − 3 3.7 × 10 5

1 × 10 − 4 1.26 × 10 2 1.803 × 10 1.23 × 10 − 4 8.1 × 10 5

1 × 10 − 5 1.26 × 10 1 1.803 × 10 − 1 8.57 × 10 − 6 1.2 × 10 6

1 × 10 − 6 1.26 × 10 1.803 × 10 − 2 7.77 × 10 − 7 1.3 × 10 6

1 × 10 − 7 1.26 × 10 − 1 1.803 × 10 − 3 7.62 × 10 − 8 1.3 × 10 6
7

a Based on regression coefficient of Cheng et al. (2006; Table III), excluding observations in the upper 5% range 8 ( ≥ 252,950 ppt-year lipid adjusted serum TC DD) of the exposure s; where reported β = 3.3 × 10 − 6 ppt-years and 9 standard error = 1.4 × 10 − 6 . Upper 95th percentile estimate of regression coefficient ( β 95 ) calculated to be: 10 6.04 × 10 − 6 = (3.3 × 10 − 6 ) + 1.96 × (1.4 × 10 − 6 ). 11This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE lv Table ES-2. Tumor points of departure and oral slope factors using blood 1 concentrations 2 3

Study Sex/species: tumor sites BMDL HED a (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 BMDL HED s are from the multiple tumor analys es, with the exception of Toth et al. (1979) which is the result of 5 modeling a single tumor site. 6This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE lvi 1 Figure ES-1. EPA’s process to evaluate available epidemiologic studies using 2 study inclusion criteria for use in the dose-response analysis of TCDD. EPA 3 applied its TCDD-specific epidemiologic study inclusion criteria to all studies published 4 on TCDD and DLCs. The studies were initia lly evaluated using five considerations 5 regarded as providing the most relevant kind of information needed for quantitative 6 human health risk analyses. For each study that satisfied most of these considerations 7 and was published in the peer-reviewed lite rature, EPA then examined whether the 8 exposures were primarily to TCDD and if the TCDD exposures could be quantified so 9 that dose-response analyses could be conducte d. Finally, EPA require d that the effective 10 dose and oral exposure be estimable: (1) for cancer, information is required on long-term 11 exposures, (2) for noncancer, information is required regarding the appropriate time 12 window of exposure that is relevant for a sp ecific, nonfatal health endpoint, and (3) for 13 all endpoints, the latency period between TCDD exposure and the onset of the effect is 14 needed. Only studies meeting these crit eria were included in EPA’s TCDD dose15 response analysis. 16 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 YesThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE lvii 1 Figure ES-2. EPA’s process to evaluate available animal bioassay studies using 2 study inclusion criteria for use in th e dose-response analysis of TCDD. EPA 3 evaluated all available in vivo mammalian bioassay studies on TCDD. Studies had to be 4 published in the peer-rev iewed literature. Next, to ensu re working in the low-dose range 5 for TCDD dose-response analysis, EPA applie d dose requirements to the lowest tested 6 average daily doses in each study, with specific requirements for cancer ( ≤ 1 μ g/kg-day), 7 and noncancer ( ≤ 30 ng/kg-day) studies. Third, EPA required that the animals were 8 exposed via the oral route to only TCDD and that the purity of the TCDD was specified. 9 Finally, the studies were eval uated using four consideratio ns regarded as providing the 10 most relevant kind of information needed fo r quantitative human health risk analyses 11 from animal bioassay data. Only studies mee ting all of these criteria and considerations 12 were included in EPA’s TCDD dose-response analysis. 13 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

NoThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 lviii DRAFT: DO NOT CITE OR QUOTE1.0E-041.0E-031.0E-021.0E-011.0E+001.0E+011.0E+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) Ohsako et al., 2001 (R) Amin et al., 2000 (R) Schantz et al., 1996 (R) Li et al., 2006 (M) Bell et al., 2007a (R)

2,3,7,8-TCDD Ingestion Rate (Human-equivalent dose, ng/kg-d)LOAELNOAELBMDLDosing RangeLess than1 yearGreater than1 yearDevelopmentalHumanReproductive1Figure ES-3. Exposure-response arrayfor ingestion exposures to TCDD.23This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE lix 1 Figure ES-4. EPA’s process to select and identify candidate PODs from key animal 2 bioassays for use in noncancer dose-response analysis of TCDD. For each noncancer 3 endpoint found in the studies that qualif ied for TCDD dose-response assessment using 4 the study inclusion criteria, EPA first determ ined if the endpoint was toxicologically 5 relevant. If so, EPA determined the NOAE L, LOAEL, and BMDL Human Equivalent 6 Dose (HED) based on 1 st -order body burdens for each endpoint. These potential PODs 7 were examined for statistical relevance a nd included when the endpoint was observed at 8 the LOAEL. If the BMDL was less than th e LOAEL, and if the endpoint was less than 9 the minimum LOAEL × 100, EPA then calcul ated NOAELs, LOAELs, or BMDLs based 10 on blood concentrations from the Emond rodent PBPK model. Then, for all of the 11 candidate PODs, HEDs were estimated usi ng the Emond human PBPK model. Finally, 12 the lowest group of the toxicologically releva nt candidate PODs wa s selected for final 13 use in derivation of an RfD. 14 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 1 st -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 than the LOAEL? No

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 1 st -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 than the LOAEL? No

Yes

Yes NoThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-1 1. INTRODUCTION 1
2 3 Dioxins and dioxin-like compounds (DLCs), including polychlor inated dibenzo-dioxins, 4 polychlorinated dibenzofurans, and polych lorinated biphenyls are structurally and 5 toxicologically related halogenate d dicyclic aromatic hydrocarbons. 4 Dioxins and DLCs are 6
released into the environment from several industrial sources such as chemical manufacturing, 7 combustion, and metal processing; from individu al activities including the burning of household 8 waste; and from natural processes such as fore st fires and volcanic activ ity. Dioxins and DLCs 9 are widely distributed throughout the environment and typically occur as chemical mixtures. 10 Additionally, they do not readily degr ade; therefore, levels persist in the environment, build up in 11 the food chain, and accumulate in the tissues of animals. Human exposure to these compounds 12 occurs primarily through the ingestion of contaminated fo ods (Lorber et al., 2009). 13 The health effects from exposures to dioxins and DLCs have been documented 14 extensively in epidemiological and toxico logical studies. 2,3,7,8-Tetrachlorodibenzo- p -dioxin 15 (TCDD) is one of the most toxic members of this class of compounds and has a robust 16 toxicological database. Characteri zation of TCDD toxicity is critical to the risk assessment of 17
mixtures of dioxins and DLCs because it has been selected repeatedly as the “index chemical” to 18
serve as the basis for standardiza tion of the toxicity of componen ts in a mixture of dioxins and 19 DLCs. The dose-response information for TCDD is used to evaluate risks from exposure to 20 mixtures of DLCs (Van den Berg et al., 1998, 20 06; also see the World Health Organization’s 21 Web site for the dioxin toxicity equivalence factors [TEFs]), 5 therefore, it is imperative to 22 correctly assess the dose res ponse of TCDD and understand the uncertainties and limitations 23
therein. 24 In 2003, the U.S. Environmental Protection Agency (EPA) produced an external review 25 draft of the multiyear comprehens ive reassessment of dioxin expos ure and human health effects 26 entitled, Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlor odibenzo-p-Dioxin 27 (TCDD) and Related Compounds (U.S. EPA, 2003). This draf t report, herein called the “2003 28 Reassessment,” consisted of (1) a scientific revi ew of information rela ting to sources of and 29 exposures to TCDD, other dioxin s, and DLCs in the environment; (2) detailed reviews of 30

4 For further information on the chem ical structures of these compounds, see U.S. EPA (2003, 2008a). 5 Available at http://www.who.int/ipcs/assessment/tef_update/en/.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-2 scientific information on the health effects of TCDD, other dioxins, and DLCs; and (3) an 1 integrated risk characterization for TCDD and related compounds. 2 In 2004, EPA asked the National Research Coun cil of the National Academy of Sciences 3 (NAS) to review the 2003 Reassessment. The NAS Statement of Task was as follows 4
5

6 In 2006, the NAS published its review of EPA’s 2003 Reassessment entitled Health Risks from 7 Dioxin and Related Compounds: Eval uation of the EPA Reassessment (NAS, 2006a). 8
9
1.1. SUMMARY OF KEY NAS (2006a) COMMENTS ON DOSE-RESPONSE 10 MODELING IN THE 2003 REASSESSMENT 11 While recognizing the effort that EPA e xpended to prepare the 2003 Reassessm ent, the 12
NAS committee identified three ke y areas that they believe requi re substantial improvement to 13 support a scientifically robust risk as sessment. These three key areas are 14
15 The National Academies’ National Research Coun cil 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 scien tifically robust and whether there is a clear delineation of all substantial uncertainties and vari ability. 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 asso ciated likelihood estimates for identified human health outcomes; EPA’s quantitative uncertainty an alysis; EPA’s selection of studies as a basis for its assessments; and gaps in scientific knowl edge. The study will also address the following aspects of EPA’s 2003 Reassessm ent: (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 diox ins and the uncertainties a ssociated 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 ex posures, taking into considerati on the Institute of Medicine’s report Dioxin and Dioxin-Like Compounds in the Food Supply: Strategies to Decrease Exposure. The committee will focu s particularly on the risk ch aracterization section of EPA’s 2003 Reassessment report and will endeavor to ma ke 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 co mmittee will identify gaps in scientific knowledge that are critical to understanding dioxin r eassessment (NAS, 2006a, p. 43, Box 1-1).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-3 • transparency and clarity in selection of key data sets for analysis; 1

• justification of approaches to dose-response modeling for cancer and noncancer 2 endpoints; and 3

• transparency, thoroughness, and clarity in quantitative uncertainty analysis. 4
5 In their Public Summary, the NAS made the following overall recommendations to aid 6 EPA in addressing their key concerns: 7
8

• EPA should compare cancer risks by using nonlin ear models consistent with a receptor 9 mediated mechanism of action and by using epidemiological data and the new National 10 Toxicology Program (NTP) animal bioassay data (NTP, 2006). The comparison should 11 include upper and lower bounds, as well as cent ral estimates of risk. EPA should clearly 12 communicate this information as part of its risk characterizati on (NAS, 2006a, p. 9). 13

• EPA should identify the most important data sets to be used for quantitative risk 14 assessment for each of the four key end points (cancer, immunot oxicity, reproductive 15 effects, and developmental effects). EPA s hould specify inclusion criteria for the studies 16 (animal and human) used for derivation of the benchmark dose (BMD) for different 17 noncancer effects and potentially for the deve lopment of RfD (reference dose) values and 18 discuss the strengths and limitations of those key studies; describe and define 19 (quantitatively to the extent possible) the variability and uncertainty for key assumptions 20 used for each key end-point-specific risk assess ment (choices of data set, POD [point of 21 departure], model, and dose metric); incor porate probabilistic models to the extent 22 possible to represent the range of plau sible values; and assess goodness-of-fit of 23 dose-response models for data sets and pr ovide both upper and lower bounds on central 24 estimates for all statistical estimates. When quantitation is not possible, EPA should 25 clearly state it and explain what would be required to achieve qua ntitation (NAS, 2006a, 26 p. 9). 27

• When selecting a BMD as a POD, EPA shoul d provide justification for selecting a 28 response level (e.g., at the 10%, 5% , or 1% level). In either case, the effects of this 29 choice on the final risk assessment values should be illustrated by comparing point 30 estimates and lower bounds derived fr om selected PODs (NAS, 2006a, p. 9). 31

• EPA should continue to use body burden as th e preferred dose metric but should also 32 consider physiologically based pharmacokine tic modeling as a means to adjust for 33 differences in body fat composition and for other differences between rodents and 34 humans (NAS, 2006a, p. 9). 35

• Although EPA addressed many sources of vari ability and uncertain ty qualitatively, the 36 committee noted that the 2003 Reassessment woul d be substantially improved if its risk 37 characterization included more quantitative approaches. Failure to characterize 38 variability and uncertainty thoroughly can convey a false sense of precision in the 39 conclusions of the risk assessment (NAS, 2006a, p. 5). 40This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-4 Importantly, the NAS encouraged EPA to calculate an RfD as the 2003 Reassessment 1
does not contain an RfD derivation. The committee suggested that 2
3 ...estimating an RfD would provide useful guidance to risk managers to help 4 them (1) assess potential hea lth risks in that portion of the population with intakes 5 above the RfD, (2) assess risks to popul ation subgroups, such as those with 6 occupational exposures, and (3) estimate th e contributions to risk from the major 7 food sources and other environmental sour ces of TCDD, other dioxins, and DLCs 8 for those individuals with high intakes (NAS, 2006a, p. 6). 9 10 The NAS made many thoughtful and specific re commendations throughout their review; 11 additional NAS recommendations and comments pe rtaining to the dose-response assessment of 12 TCDD will be presented and addressed in various sections throughout this document. 13
14
1.2. EPA’S SCIENCE PLAN 15 In response to recommendations presented in the 2006 NAS r eview, EPA Administrator 16
Jackson released EPA’s “ Science Plan for Activities Related to Dioxins in the Environment ” 17 (“Science Plan”) on May 26, 2009. 6 There are five key components of the Science Plan that 18 pertain to EPA’s response to the NAS comments on TCDD dose-response assessment: 19
20
1. EPA will release a draft report that res ponds to the recommendations and comments 21 included in the NAS review of EPA’s 2003 Reassessment. 22

• EPA’s National Center for Environmental Asse ssment in the Office of Research and 23 Development, will prepare a limited res ponse to key comments and recommendations 24 in the NAS report (draft response to comments report). 25

• The draft response will focus on dose-respon se issues raised by the NAS and will 26 include an analysis of re levant new key studies. 27

• The draft response will be provided for public review and comment and independent 28 external peer review. 29

• The draft response will also include an evaluation of some of the significant 30 recommendations that are difficult for EPA to address given the current state of 31 science, and a detailed rationa le for these conclusions. 32

• The peer review will be conducted by EPA Science Advisory Board, an independent 33 review body chartered under the Federal Advisory Committee Act. 34

6 Available at http://www.epa.gov/dioxin/scienceplan.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-5 • The draft response to comments report will be completed and released for public and 1 peer review by December 31, 2009. 2
2. EPA will provide the draft response to comments report for internal and external review. 3
3. The Science Advisory Board will review the science content of the response to comments 4 report. 5
4. EPA will review impacts of the draf t response to comments report on its 2003 6 Reassessment. 7
5. EPA will release the final response to comments report and focus on completion of the 8 2003 Reassessment. 9
10 This document comprises EPA’s report that responds to the recommendations and 11 comments on TCDD dose-response assessment in cluded in the NAS review of EPA’s 2003 12 Reassessment. This document focuses on TCDD only. 13
14
1.3. OVERVIEW OF EPA’S RESPONSE TO NAS (2006a) “HEALTH RISKS FROM 15 DIOXIN AND RELATED COMPOUNDS: EVALUATION OF EPA’s 2003 16 REASSESSMENT” 17 In their key recomm endations, the NAS co mmented that EPA should thoroughly justify 18 and communicate approaches to dos e-response modeling, increase transparency in the selection 19 of key data sets, and improve the communicatio n of uncertainty (particularly quantitative 20 uncertainty). They also encouraged EPA to calculate an RfD. These main areas of improvement 21 refer to issues specifically re lated to TCDD dose-response assessm ent (and uncertainty analysis); 22 therefore, as noted in the Scie nce Plan, EPA’s response to the NAS is particularly focused on 23
these issues. 24 EPA thoroughly considered the recommendati ons of the NAS and responds with 25
scientific and technical evaluation of TCDD dose–response data via: 26
27

• an updated literature search that identified new TCDD dose -response studies (see Section 28 2); 29

• a kickoff workshop that included the particip ation of external experts in TCDD health 30 effects, toxicokinetics, dose-response assessm ent and quantitative un certainty analysis; 31 these experts discussed potential appro aches to TCDD dose-response assessment and 32 considerations for EPA’s res ponse to NAS (see Appendix A); 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-6 • detailed study inclusion criteria and processes for the selectio n of key studies (see Section 1 2.3) and epidemiologic and animal bioassay data for TCDD dose-response assessment 2 (see Section 2.4.1/Appendix B and Section 2.4.2, respectively); 3

• kinetic modeling to quantify appropriate dos e metrics for use in TCDD dose-response 4 assessment (see Section 3); 5

• dose-response modeling for all appropriate noncancer and cancer data sets (see 6 Section 4.2/Appendix E and Section 5.2.3/Appendix F, respectively); 7

• thorough and transparent evaluation of the sele cted TCDD data for use in the derivation 8 of an RfD and an oral slope factor (OSF) (see Sections 4.2 and 5.2.3, respectively); 9

• the development of an RfD (see Section 4.3); 10

• the development of a revised OSF (see Sect ion 5.3) with an updated cancer weight of 11 evidence determination for TCDD based on EPA’s 2005 Cancer Guidelines (U.S. EPA, 12 2005) (see Section 5.1.2); 13

• consideration of nonlinear dose-response approaches for cancer, including illustrative 14 RfDs for cancer precursor events an d tumors (see Section 5.2.3.4) ; and 15

• discussion of the feasibility and utility of quantitative uncertainty analysis for TCDD 16 dose-response assessment (see Section 6). 17 18 Each of these activities is described in detail in subsequent sections of this document. 19 It should be noted that three separate EP A activities address additional TCDD issues, 20 specifically related to the application of dioxin TEFs a nd to TCDD and DLC background 21 exposure levels. Information on the applicati on of the dioxin TEFs is published elsewhere by 22 EPA for both ecological (U.S. EPA, 2008a) and hu man health risk assessment (U.S. EPA, 23 2009a). As a consequence, EPA does not directly address TEFs herein, but makes use of the 24 concept of toxicity equivalence 7 as applicable to the an alysis of exposure dose in 25 epidemiological studies. Furthermore, this document does not address the NAS 26 recommendations pertaining to the assessment of human exposures to TCDD and other dioxins. 27 Information on updated background levels of dioxi n in the U.S. population has been recently 28 reported (Lorber et al., 2009). 29 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-7 1.3.1. TCDD Literature Update 1 EPA has developed a literature database of peer-reviewed studies on TCDD toxicity, 2 including in vivo mammalian dose-response studie s and epidem iological studies. An initial 3 literature search for studies publ ished since the 2003 Reassessme nt was conducted by the U.S. 4 Department of Energy’s Argonne National Laborat ory (ANL) through an Interagency Agreement 5 with EPA. ANL used the online National Library of Medicine database (PubMed) and identified 6 studies published between the year 2000 and October 31, 2008. Supporting references published 7
since the release of th e 2003 Reassessment were also id entified. Supporting studies were 8
classified as studies pertaining to TCDD kinetic s, TCDD mode-of-action, in vitro TCDD studies, 9 and TCDD risk assessment approaches. The literatu re search strategy explicitly excluded studies 10 addressing (1) analytical/detection data and cellu lar screening assays; (2) environmental fate, 11 transport and concentration data; (3) di oxin-like compounds and toxic equivalents; 12 (4) nonmammalian dose-response data; (5) human exposure analyses only, including body 13 burden data; and (6) combustor or incinerator or other facility-related assessments absent 14 primary dose-response data. EPA published the initi al literature search results in the Federal 15 Register on November 24, 2008 (73 FR 70999; N ovember 24, 2008) and invited the public to 16
review the list and submit addi tional peer-reviewed in vivo ma mmalian dose-response studies for 17 TCDD, including epidemiological studies that were absent from the list (U.S. EPA, 2008b). The 18 literature search results and subsequent s ubmissions were used during a 2009 scientific 19 workshop, which was open to the public and feat ured a panel of experts on TCDD toxicity and 20 dose-response modeling (discussed below). A dditional studies identifi ed during the workshop 21 and those collected by EPA scientists during th e development of this report through October 22 2009 have been incorporated into the final set of studies for TCDD dose-response assessment. 23
24 1.3.2. EPA’s 2009 Workshop on TCDD Dose Response 25 To assist EPA in responding to the NAS, EP A, and ANL convened a scientific workshop 26 (the “Dioxin Workshop”) on February 18

− 20, 2009, in Cincinnati, Ohio. The goals of the 27 Dioxin Workshop were to identify and address is sues related to the dose-response assessment of 28
TCDD and to ensure that EPA’s response to the NAS focused on th e key issues and reflected the 29 most meaningful science. The Dioxin Workshop included seven scientific sessions: quantitative 30 dose-response modeling issues, immunotoxicity, neurotoxicity and nonreproductive endocrine 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-8 effects, cardiovascular toxicity and hepatotoxicity, cancer, reproductive and developmental 1 toxicity, and quantitative uncertain ty analysis of dose-response. During each session, EPA asked 2 a panel of expert scientists to perform the following tasks: 3
4

• Identify and discuss the technical challenge s involved in addressing the NAS comments 5 related to the dose-response issues with in each specific session topic and the TCDD 6 quantitative dose-response assessment. 7

• Discuss approaches for addressi ng the key NAS recommendations. 8

• Identify important published, indepe ndently peer-reviewed literature

― particularly 9 studies describing epidemiologi cal studies and in vivo mamma lian bioassays expected to 10 be most useful for informing EPA’s response. 11
12 The sessions were followed by open commen t periods during which members of the 13 audience were invited to address the expert pane ls. The session’s Panel Co-chairs were asked to 14 summarize and present the resu lts of the panel discussions

― including the open comment 15 periods. The summaries incorporated points of agreement as well as minority opinions. Final 16 session summaries were prepared by the session Pa nel Co-chairs with input from the panelists, 17 and they formed the basis of a final workshop report (U.S. EPA, 2009c, Appendix A of this 18 report). Because the sessions were not designed to achieve consensus among the panelists, the 19 summaries do not necessarily represent consensus opinions; rather reflect the core of the panel 20
discussions. Some of the ke y discussion points from the wo rkshop that influenced EPA’s 21 development of this document are listed below (see Appendix A for detail): 22
23

• In the development of study selection criteria , more relevant expos ure-level (i.e., dose) 24 decision points using tissue concen trations could be defined. 25

• A linear approach to body-burden estima tion, which was utilized in the 2003 26 Reassessment (U.S. EPA, 2003), does not fully c onsider key toxicokine tic issues related 27 to TCDD

― e.g., sequestration in the liver and fa t, age-dependent elimination, and 28 changing elimination rates over time. Thus, kinetic/mechanistic modeling could be used 29 to quantify tissue-based metrics. In consid ering human data, lipid-adjusted serum levels 30 may be preferable over body burden, alt hough the assumptions used in the back 31 calculation of the body burden in epidemiologic cohorts are of concern. In considering 32 rat bioassay data, lipid-adjusted body-bur den estimates may be preferable. 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-9 • New epidemiological studies on noncancer endpo ints have been published since the 2003 1 Reassessment that may need to be consider ed (e.g., thyroid dysfunc tion literature from 2 Wang et al. [2005] and B accarelli et al. [2008]). 3

• The 1% of maximal response (ED 01 ) that was utilized in th e 2003 Reassessment has not 4 typically been used in dose-response assessment. Some alternative ideas were as follows: 5 (1) the POD should depend on the specific e ndpoint; (2) for continuous measures, the 6 benchmark response (BMR) could be based on the difference from control and consider 7 the adversity level; and (3) for incidence da ta, the BMR should be set to a fixed-risk 8 level. 9

• The quantitative dose-response modeling for can cer could be based on human or animal 10 data. There are new publications in the literature for four epidemiological cohort studies 11 (Dutch cohort, NIOSH cohort, BASF accide nt cohort, and Hamburg cohort). The 12 increase in total cancers could be consider ed for modeling human cancer data. However, 13 non-Hodgkin’s lymphoma and lung tumors are the main TCDD-related cancer types seen 14 from human exposure. In reviewing the rat data, the NTP (2006) data sets are new and 15 can be modeled. Although the liver and lungs are the main target organs, modeling all 16 cancers, as well as using tumor incidence in lie u of individual rats as a measure, should 17 be considered. 18

• Both linear and nonlinear model functions should be considered in the cancer 19 dose-response analysis because there are da ta and rationales to support use of either 20 below the POD. 21

• For quantitative uncertainty analysis, consid er the impacts of c hoices among plausible 22 alternative data sets, dose metrics, models, a nd other more qualitative choices. Issues to 23 consider include how much difference these choices make and, also, how much relative 24 credence should be put toward each alternat ive as a means to gauge and describe the 25 landscape of imperfect knowledge w ith respect to possibilities for the true dose response. 26 This may be difficult to do quant itatively because the factors are not readily expressed as 27 statistical distributions. However, th e rationale for accepting or questioning each 28 alternative in terms of the available supporting evidence, co ntrary evidence, and needed 29 assumptions, can be delineated. 30
31
1.3.3. Overall Organization of EPA’s Re sponse to NAS Recommendations 32
The rem ainder of this document is divided into five sections that address the three 33 primary areas of concern resulting from the NAS (2006a) review. Section 2 describes EPA’s 34 approach to the recommendation for transparen cy and clarity during se lection of key data 35

sets

― including criteria for the se lection of key dose-response studies, evaluations of the 36 important epidemiologic studies and animal bioa ssays, and a summary of the key studies used 37 for subsequent dose-response modeling. Sections 3, 4, and 5 present EPA’s response to the NAS 38 recommendation to better justify the approaches used in dose-response modeling of TCDD. 39This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 1-10 Section 3 discusses the toxi cokinetic modeling EPA conducte d to support the dose-response 1 analyses. Section 4 presents EPA’s approach to noncancer data set selection, dose-response 2 modeling, derivation of an RfD for TCDD, and contains a qualitative discussion of the 3
uncertainties associated with the RfD. Section 5 presents an updated cancer weight-of-evidence 4 summary, EPA’s approach to cancer data set se lection, dose-response modeling, derivation of an 5 OSF for TCDD, and a qualitative discussion of the uncertainties associated with the OSF, 6 including an evaluation of alternative approaches to can cer assessment of TCDD. Finally, 7 Section 6 discusses the feasibility of conducting a quantitative uncertainty analysis of TCDD 8 dose response. 9This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-1 2. TRANSPARENCY AND CLARITY IN THE SELECTION OF KEY DATA SETS 1 FOR DOSE-RESPONSE ANALYS IS 2
3 4 This section focuses on addre ssing transparency and clarity in the study selection process 5 and on identifying key data se ts for 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) dose-response 6 analysis. Section 2.1 summarizes the Nationa l Academy of Sciences (NAS) committee’s 7 comments specifically regarding this issue. Se ction 2.2 presents U.S. Environmental Protection 8 Agency’s (EPA’s) response to those comment s and describes EPA’s approach to ensuring 9 transparency and clarity in se lection of studies for subseque nt dose-response analyses. 10
Section 2.3 describes the TCDD- specific study inclusion criteri a and evaluation process EPA 11 used in this document for determining the elig ibility of both epidemiologic and experimental 12 animal studies for TCDD dose-response analysis . Section 2.4.1 summarizes epidemiologic data 13 and evaluates the suitability of these data for TCDD dose-response analyses. Section 2.4.2 14 summarizes animal bioassay data that have met the study inclusi on criteria for TCDD 15 dose-response assessment. Fina lly, Section 2.4.3 identifies key TCDD epidemiologic and animal 16 bioassay studies that were determined using the study inclusion criteria. Then it previews the 17 evaluation processes used to further identify stud y/endpoint combination da ta sets for developing 18 TCDD toxicity values for noncan cer (see Section 4) and cancer (see Section 5) effects. 19
20 2.1. SUMMARY OF NAS COMMENTS ON TRANSPARENCY AND CLARITY IN 21 THE SELECTION OF KEY DATA SE TS FOR DOSE-RESPONSE ANALYSIS 22 The NAS committee proposed that EPA deve lop a clear and readily understandable 23 methodology for evaluating and including epidemio logic and animal bioassay data sets in 24 dose-response evaluations. The NAS committee recommended the development and application 25 of transparent initial cr iteria to judge whether or not specif ic epidemiologic or animal bioassay 26 studies be included in TCDD dose-response analysis. 27 Specific NAS comments on the topic of study evaluation and inclus ion criteria include 28
29 EPA should specify inclusion criteria for the studies (animal and human) used for 30 derivation of the benchmark dose (BMD ) for different noncancer effects and 31 potentially for the development of RfD values and discuss the strengths and 32 limitations of those key studies (NAS, 2006, p. 27). 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-2 ...in its [EPA’s] evaluation of the epidemio logical literature of carcinogenicity, it 1 did not outline eligibility requirements or otherwise provide the criteria used to 2 assess the methodological qua lity of other included studies (NAS, 2006, p. 56.) 3 With regard to EPA’s review of the animal bioassay data, the committee 4 recommends that EPA establish clear criter ia for the inclusion of different data 5 sets (NAS, 2006, p. 191). 6 ...the committee expects that EPA could substantially improve its assessment 7 process if it more rigorously evaluated th e quality of each study in the database 8 (NAS, 2006, p. 56). 9 EPA could also substantially improve th e clarity and presentation of the risk 10 assessment process for TCDD...by using a summary table or a simple summary 11 graphical representation of the key data sets and assumptions...(NAS, 2006, 12 p. 56). 13
14
2.2. EPA’S RESPONSE TO NAS COMMENTS ON TRANSPARENCY AND CLARITY 15 IN THE SELECTION OF KEY DATA S ETS FOR DOSE-RESPONSE ANALYSIS 16 EPA agrees with the NAS committee regardi ng the need for a transparent and clear 17 process for selecting studies and key data sets for TCDD dose-response analyses. The 18 delineation of the study selection process and decisions regarding key data sets will facilitate 19 communication regarding critical decisions made in the TCDD dose-response assessment. In 20 keeping with the NAS committee’s recommendati on to use a transparent process and improve 21 clarity and presentation of the risk assessmen t process for TCDD, Figure 2-1 overviews the 22 approach that EPA has used in this document to develop a final list of key cancer and noncancer 23 studies for quantitative dose-response analysis of TCDD. The steps in Figure 2-1 are further 24 explained below. 25
26
Literature search for in vivo mamm alian and epidemiologic TCDD studies 27 (2000

− 2008) : EPA conducted a literatur e search to identify p eer-reviewed, dose-response 28 studies for TCDD that have been published since the 2003 Reassessment. This search 29 included in vivo mammalian and epidemiol ogical studies of TCDD from 2000 to 2008. 30 Additional details describing the conduct of this literature search are presented in 31 Section 1.3.1 of this document. 32 Federal Register Notice—Web publication of literature search for p ublic comment : 33 In November 2008, EPA published a list of ~500 c itations from results of this literature 34 search (U.S. EPA, 2008b) and invited the publ ic to review this preliminary list of 35 dose-response citations for use in TCDD dose -response assessment. EPA requested that 36 interested parties identify and submit peer-r eviewed studies for TCDD that were absent 37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-3 from this list. All additional studies subm itted to EPA were included in the final TCDD 1 literature database considered by EPA for TCDD dose-respon se analysis. 2 Initial study inclusion criteria development for TCDD in vivo mammalian 3 bioassays : EPA developed an initial set of draf t criteria for evaluating the extensive 4 TCDD database of in vivo mammalian bioassays . These initial inclusion criteria had 5 three purposes. First, they provided a transparent and rigorou s evaluation of the scientific 6 quality of each study in EPA’s database, a de ficiency in the 2003 Reassessment identified 7 by the NAS committee. Second, given the va st TCDD mammalian bi oassay database, 8 they provided a transparent method for initia lly screening studies to be considered for 9 TCDD dose-response analyses. Third, they served as a starti ng point for discussions of 10 study inclusion criteria by expert panelists who were convened by EPA for its scientific 11 workshop on TCDD dose-response analysis (the Dioxin Workshop), described next (also 12 see the workshop report in Appendix A). 13 Dioxin Workshop and expert refinement of TCDD in vivo mammalian bioassay 14 inclusion criteria : In February 2009, EPA convened “A Scientific Workshop to Inform 15 EPA’s Response to NAS Comments on the Health Effects of Dioxin in EPA’s 2003 16 Dioxin Reassessment.” The goals of this 3-da y public and scientif ic workshop were to 17 identify and address issues related to the dose-response assessment of TCDD. Sessions at 18 the workshop examined toxicities associated with TCDD, issues related to developing 19 dose-response estimates based on these data and associated uncertainties. At the 20 workshop, EPA presented the draft set of study inclusion criteria for evaluating the 21 extensive TCDD in vivo mammalian bioassay li terature and asked wo rkshop panelists to 22 discuss these criteria and ma ke recommendations for their re vision. Further details on 23 this workshop are presented in Section 1.3.2 of this document, and the complete report 24 from this workshop is available in Appe ndix A, including detailed summaries of the 25 panels’ comments on the inclus ion criteria in relation to th e various toxic endpoints that 26 were discussed. 27 Final development of inclusion criteri a for TCDD in vivo mammalian studies : Based 28 on discussions at the Dioxin Workshop, the in itial draft inclusion criteria for evaluating 29 the TCDD mammalian bioassay literature were revised and are presented in Section 2.3.2 30 (see Figure 2-3). An initial cr ite rion is that studies for c onsideration must be publically 31 available and published in a peer-reviewed sc ientific journal. Because the methodology 32 EPA uses to develop reference doses (RfDs) and cancer oral slope factors (OSFs) relies 33 on identification of studies repor ting potential adverse effects at low doses (relative to the 34 overall database), another important cr iterion shown in Section 2.3.2 identifies a 35 maximum value for the lowest TCDD dose test ed in a bioassay. This maximum value 36 was used to eliminate those studies that coul d not be selected for development of an RfD 37 or an oral slope factor because tested doses were too high relative to other TCDD 38 bioassays. 39 Development of inclusion criteria for epidemiologic studies: Following the Dioxin 40 Workshop, EPA determined that an evaluation process was also needed for inclusion of 41 epidemiologic studies for TCDD dose-response assessment. These criteria were 42 developed and are detailed in Section 2.3.1 (see Figure 2-2). Analogous to animal 43 bioassay data, epidemiologic studies for consid eration must also be publically available 44This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-4 and published in a peer-reviewed scientific journal. In addition to assessing the 1 methodological considerations relative to epidemiologic cohorts and studies (e.g., 2 statistical power and precisi on of estimates, considerati on of latency periods), a key 3 consideration for use of a data set in TC DD dose-response modeling is characterization 4 of the exposure assessment methodology, a nd specifically, whether this methodology 5 allowed assignment of individual- level exposures within a study. 6 Final literature collection (October 2009): Additional literature was collected as it was 7 identified by EPA following the Dioxin Wo rkshop through October 2009 to ensure the 8 consideration of all recently publ ished data for this report. 9 Studies screened using inclusion criteria : The two sets of TCDD-specific study 10 inclusion criteria presented in Section 2.3 were used to evaluate all studies included in the 11 2003 Reassessment, studies identified in the 2000

− 2008 literature search, studies 12 identified through public comment and subm ission, and studies collected in 2009 as 13 identified by EPA during the development of this document. Section 2.4 presents results 14 of EPA’s evaluation of epidemiologic and mammalian bioassay literature for both cancer 15 and noncancer endpoints. 16 Final list of key cancer and noncancer studies for quantitative dose-response 17 analysis of TCDD : Application of the study inclusi on criteria conclude s in Section 2.4 18 with development of a list of key noncancer and cancer studies that were considered for 19 quantitative dose-response analys es of TCDD in Sections 4 a nd 5, respectively. In those 20 sections, points of departure (PODs) are de veloped and evaluated for all study/endpoint 21 combinations from these final key study lists, and key data sets and PODs for the 22 development of TCDD toxicity values are identified. 23
24
2.3. STUDY INCLUSION CRITERIA FOR TCDD DOSE-RESPONSE ANALYSIS 25 One of the three ma jor recommendations made by the NAS (2006a) committee was that 26 EPA should provide greater clarity and transparency on the selection of studies that were used in 27 the quantitative dose-response mode ling of TCDD in the 2003 Rea ssessment. In this section, 28 EPA describes TCDD-specific study in clusion criteria that have been developed to evaluate 29 epidemiologic studies and animal bioassays for TCDD dose-response assessment. These criteria 30 reflect EPA’s goal of developing an RfD and a cancer OSF for TCDD through a transparent 31 study selection process; they are intended to be used by EPA for TCDD dose-response 32 assessment only. These criteria were appl ied to each of the ~500 studies listed in Preliminary 33 Literature Search Results and Request for Additional Studies on 2,3,7,8-Tetrachlorodibenzo-p34 Dioxin (TCDD) Dose-Response Studies (U.S. EPA, 2008b); studies identified and submitted by 35 the public and by participants in the Dioxin Wo rkshop (U.S. EPA, 2009c); studies included in 36This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-5 the 2003 Reassessment, and other relevant publishe d studies collected by EPA scientists through 1
October 2009. 2 EPA has undertaken different approaches fo r epidemiologic versus in vivo animal 3 bioassay study evaluation and key data set selection. The signifi cant differences between animal 4
and human health effects data and their use in EPA risk asse ssment support development of 5 separate criteria for study incl usion and different approaches to study evaluation. For the vast 6 majority of compounds on EPA’s Integrated Ri sk Information System (IRIS), cancer and 7 noncancer toxicity values have b een derived using animal bioassay data; therefore, approaches to 8 dose-response modeling and POD selection from in vivo mammalian bioassays have been 9 standardized and codified (U.S. EPA, 2008c). The study criteria shown below and in Figure 2-3 10 for animal bioassay data reflect EPA’s preferen ces for TCDD-specific study inclusion, some of 11 which are based on common practices and guidance for POD selec tion and RfD and OSF 12 derivation. Far fewer IRIS toxicity values have been derived from hu man data, although some 13 examples do exist. For example, benzene, beryllium and compounds, chromium IV, and 14 1,3-butadiene have RfDs, Referen ce Concentrations, Inhalation Unit Risks and/or OSFs all based 15 on occupational cohort data and the methyl me rcury RfD is based on high fish consuming 16 cohorts (U.S. EPA, 2009b). The modeling and interpretation of such human data have been 17 conducted on a case-by-case basis because each cohor t is uniquely defined and has its own set of 18 exposure conditions, significant confounders, and bi ases that may need to be considered in 19 dose-response modeling. For TCDD, not all data are from occupationa l cohorts, but include 20 cohorts exposed for relatively short time periods to high concentrations as a consequence of 21 industrial accidents, a scenario that has not commonly been used to establish chronic EPA 22 toxicity values. 23
Because of these differences in data characteristics, diverg ent selection approaches are 24 used in this document to presen t and evaluate the epidemiologi c studies (see Se ction 2.3.1) and 25 the in vivo animal bioassays (see Section 2.3.2) . In Section 2.4.1, a ll of the available 26 epidemiologic studies on TCDD are summarized and evaluated for suitability for dose-response 27 modeling using the TCDD-specific study inclusion criteria below and shown in Figure 2-2; only 28 studies meeting the inclusion crite ria are presented as key studies in Section 2.4.3 (see Tables 2-4 29 and 2-5 for the cancer and noncancer endpoints, respectively ). In Section 2.4.2, because 30 summarizing and showing the evaluation of the thousands of available animal bioassays on 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-6 TCDD was prohibitive, only studies first meeting the in vivo an imal bioassays study inclusion 1 criteria below (and shown in Figure 2-3) are summ arized. These studies are also presented as 2 key studies in Section 2.4.3 (see Tables 2- 6 and 2-7 for cancer and noncancer endpoints, 3 respectively). 4
5
2.3.1. Study Inclusion Criteria for TCDD Epidemiologic Studies 6 This section identifies the process EPA used to select epidemiologic studies for defining 7
candidate P ODs for TCDD dose-response modeling. These criteria are based on EPA’s 8 approaches for deriving OSFs and RfDs. A discu ssion of the considerations used in selecting 9 epidemiologic data for quantitative dose-response modeling is valuable, particularly given EPA’s 10 preference to use high-quality human studies over animal studies because such human studies are 11 regarded as providing the most relevant informa tion needed for quantitative human health risk 12 analyses (U.S. EPA, 2005). As described by Hertz-Picciotto ( 1995), key components needed for 13 the use of an epidemiologic study as a basis fo r quantitative risk assessment include issues 14 regarding exposure assessment (a well-quantifie d exposure assessment with exposures linked to 15 individuals) and study qual ity (“strong biases,” for example with respect to inclusion criteria for 16 membership in the cohort and follow-up proced ures “ruled out or unlikely” and “confounding 17 controlled or likely to be limited”). The strength of the associati on, either within the full study or 18 within a high exposure subgroup, can also be cons idered in the evaluatio n of suitability for 19 dose-response modeling (Hertz-Pic ciotto, 1995). Stayner et al. ( 1999), however, note that even 20
weak associations could be useful in terms of providing an estimate of a potential upper bound 21 for a quantitative risk estimate. 22 EPA’s method for applying the TCDD study inclus ion criteria to epidemiologic data is 23 detailed below and in Figure 2-2. Based on the framework discussed above, EPA evaluated the 24 available epidemiologic cohorts and studies based on the five following considerations: 25
26 1. The methods used to ascertain health outco mes are clearly identified and unbiased, with 27 high sensitivity and specificity. 28 2. The risk estimates generated from the st udy are not susceptible to important biases 29 arising from an inability to control for potential confounding exposures or other sources 30 of bias arising from either study design or statistical analysis. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-7 3. The study demonstrates an association be tween TCDD and an adverse health effect 1 (assuming minimal misclassificat ion of exposure and absence of important biases) with 2 some suggestion of an exposure-response relationship. 3 4. The exposure assessment methodology is clear ly described and can be expected to 4 provide adequate characterization of exposur e, with assignment of individual-level 5 exposures within a study (e.g., base d on biomarker data, or based on a 6 job-exposure-matrix approach). Limitations and uncertainties in th e exposure assessment 7 are considered. 8 5. The size and follow-up period of a co hort study are large enough and long enough, 9 respectively, to yield sufficiently precise estimates for use in development of quantitative 10 risk estimates and to ensure adequate statis tical power to limit the possibility of not 11 detecting an association that might be present (i.e., to avoid Type II Errors due to failing 12 to reject the null hypothesis when the null hypot hesis is true). Similar considerations 13 regarding sample size and statistical precisi on and power apply to case-control studies. 14
15 Three specific study inclusion criteria were used to select studies for further evaluation 16 and potential TCDD quantitativ e dose-response assessment 17
18

• The study is published in the peer-reviewed scientific literatu re and includes an 19 appropriate discussion of strengths and limitations. 20

• The exposure is primarily to TCDD, rather than dioxin-like compounds (DLCs), and is 21 properly quantified so that dose-response relationships can be assessed. 22

• The effective dose and oral exposure must be reasonably estimable. The measures of 23 exposure must be consistent with the curre nt biological understa nding of dose. For 24 TCDD dose-response assessment, it is critical that reported dose is consistent with a dose 25 that is likely to be toxicologically releva nt. The timing of the measurement of effects 26 (i.e., the response) also must be consistent with current biological understanding of the 27 effect and its progression. 28 For cancer endpoints, EPA assumes that cumulative TCDD dose estimates are 29 toxicologically relevant measures. Thus , cancer studies must provide information 30 about long-term TCDD exposure levels. Further, EPA reasons that measures of 31

cancer o ccurrence or death need to allow for examination of issues of latency 32 between the end of effective expos ure and cancer detection or death. 33 For noncancer endpoints, exposure estimates and analysis must allow for examination 34 of issues of latency and other issues regarding the appropriate time window of 35 exposure relevant for specific endpoints. Also, to be consistent with the RfD 36 methodology, the response must be to a nonfatal endpoint. 37
38This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-8 Those studies that met the aforementioned c onsiderations and incl usion criteria (see 1 Sections 2.4.1, 2.4.3, and Appendix B) were then subjected to further consideration for 2 quantitative dose-res ponse analyses. 3
4
2.3.2. Study Inclusion Criteria for TCDD In Vivo Mammalian Bioassays 5 This section identifies the criteria EPA a pplied to select nonhuman in vivo m ammalian 6 studies for defining candidate PODs for use in TCDD dose-response modeling. These inclusion 7 criteria are based on EPA’s approaches for deri ving OSFs and RfDs from bioassay data (U.S. 8 EPA, 2005). EPA agrees with the NAS committee re garding the utility of an oral RfD and the 9 need for reevaluation of the OSF for TCDD, specifi cally in light of data that have been published 10
since the 2003 Reassessment was re leased. RfDs and OSFs are gene rally derived using data sets 11 that demonstrate the occurrence of adverse effects, or their precursors, in low-dose range for that 12 chemical. RfDs and OSFs are derived from a health protective perspective for chronic 13 exposures. Thus, when a group of studies is available on a chemical for which a number of 14
effects are observed at various doses across thos e studies, the studies usin g the lowest exposures 15 that show effects will typically drive the RfD an d OSF derivations, all other considerations being 16 equal. Studies conducted at higher exposures re lative to other availabl e studies are used as 17 supporting evidence for the final RfD or OSF sin ce they were conducted at doses too high to 18 impact the numeric derivations of toxicity valu es. EPA expresses RfDs and OSFs in terms of 19 average daily doses, usually as mg/kg-day and per mg/kg-day, respectively. Thus, the study 20 inclusion criteria for the animal bioassay data pr esented in this section include requirements that 21 average daily exposures in the studies are with in a low dose range where, relative to other 22 studies, they could be considered for devel opment of a toxicity value. These low-dose 23 requirements do not imply that TCDD studies co nducted at higher doses are of poor quality, 24 simply that they are not quantitat ively useful in the development of toxicity values because other 25 studies with lower exposures will drive th e RfD and OSF derivations under current EPA 26 practice. Because EPA has identified ~2,000 studi es on TCDD that may be considered for this 27 purpose, the development and application of thes e study inclusion criteria has been critical to 28 moving the risk assessment process forward. 29 EPA’s method for applying study in clusion criteria for mamma lian bioassays is detailed 30 below and in Figure 2-3. The first study inclusion criterion is that the st udy is published in the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-9 peer-reviewed scientific literature. Then, two specific study inclusi on criteria were used to select 1 studies for further evaluation and potential TCDD quantitative dose-response analyses and 2
identification of candidate PODs: 3
4
1. The lowest dose level tested is ≤ 1 μ g/kg-day for cancer studies and ≤ 30 ng/kg-day for 5 noncancer studies. 6 2. The study design consists of orally admi nistered TCDD-only doses, and specifies the 7 purity and matrix used to administer the doses. 8
9 Then, EPA evaluated the remaining in vivo animal studies based on the following four 10
considerations. 11
12 1. The study tests mammalian species, identifying the strain, gender, and age of the tested 13 animals. 14 2. The study clearly documents testing protoc ol, including dosing fre quency, duration, and 15 timing of dose administration rela tive to age of the animals. 16 3. The overall study design is consistent w ith standard toxicological principles and 17 practices. The control group or groups are appr opriate, given the tes ting protocol, and are 18 well characterized. Clinical and pathological examinati ons conducted during the study 19 are endpoint-appropriate, partic ularly for negative findings. 20 4. The magnitude of animal responses is outside the range of normal va riability exhibited by 21 control animals (e.g., greater than or le ss than one standard deviation). 22
23
Those studies that met the aforementioned c onsiderations and incl usion criteria (see 24 Sections 2.4.2 and 2.4.3) were then s ubjected to dose-response analysis. 25 The criteria for dose requirements, although somewhat arbitrary, are intended to be 26
reasonable cutoffs that restrict the number of studies that would need to be modeled while 27 ensuring that all study/data set combinations that could be candidates for the cancer slope factor 28 or RfD were modeled. Thus, the dose range under consideration allows for liberal ranges of 29
no-observed-adverse-effect levels (NOAELs), lowest-observed-adverse-effect levels (LOAELs), 30 and benchmark dose lower confidence bound (BMDLs) for assessment of both cancer and 31
noncancer effects. 32 For cancer studies, the dose requireme nts were selected based on an initial evaluation of 33 available average daily doses administered in TCDD animal bioassays in which adverse effects 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-10 were observed. For example, in cancer studies, a sample of the relatively low ranges of tested 1 average daily doses include 1-1000 ng/kg-day (Toth et al., 1979), 1

− 100 ng/kg-day (Kociba et 2 al., 1978), 1.43

− 286 ng/kg-day (NTP, 1982) and 2.14

− 71.4 ng/kg-day (NTP, 2006) with 3 statistically significant increases in tumor incidence via pair-wise or trend tests found in all of 4
these studies. The entire ra nge of each these studies is ≤ 1 μ g/kg-day. The linearized multi-stage 5 model used by EPA to estimate OSFs is most appr opriately applied to studies from which PODs 6 can be estimated as closely as possible to the e xperimental data. Thus, given the dose ranges in 7
these studies that are available for modeling, the restriction to ≤ 1 μ g/kg-day for cancer was 8
considered to be a reasonable cutoff. 9 For noncancer studies, dose ranges are more complex and vary according to study 10 endpoint. Examples of the lowest administered doses (not including developmental studies) that 11 might be considered as NOAELs or LOAELs in POD determin ations for noncancer endpoints 12 include 1 ng/kg-day (Toth et al., 1979), 1.43 ng/ kg-day (Cantoni et al., 1981), 1.07 ng/kg-day 13 (Smialowicz et al., 2008) 1.43 ng/kg-day (NTP, 1982) and 2.14 ng/kg-day (NTP, 2006). Most of 14
the lowest tested doses in the TCDD studies have been designated as LOAELs (see Section 4.1). 15 Given the available database, it is likely that the same composite uncertainty factor (e.g., of 300; 16
3 for UF A [interspecies], 10 for UF H [intraspecies], and 10 for UF L [LOAEL to NOAEL]) would 17 be applied to any animal noncancer LOAEL used to derive an RfD for TCDD. This implies that 18 any study that has a LOAEL of 30 ng/kg-day or mo re would result in a candidate RfD that is 19 more than an order of magnitude hi gher than the example doses of 1

− 2 ng/kg-day shown here. 20 BMDLs that might be derived from such data also would not be expected to be lower than these 21 example doses of 1

− 2 ng/kg-day. Thus, a tested dose ≤ 30 ng/kg-day is cons idered to be a 22
reasonable cutoff where the lowest tested dose woul d never be used as a POD to derive an RfD 23 given that much lower tested doses (associated with adverse effects) are available from other 24 studies of acceptable quality. 25
26
2.4. EVALUATION OF KEY STUDIES FOR TCDD DOSE RESPONSE 27
2.4.1. Evaluation of Epidemiological Co horts for Dose-Response Assessment 28
2.4.1.1. Cancer 29 In the 2003 Reassessm ent, EPA selected thr ee cohort studies from which to conduct a 30 quantitative dose-response analys is: the Nationa l Institute for Occupational Safety and Health 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-11 (NIOSH) cohort (Steenland et al., 2001), th e BASF cohort (Ott and Zober, 1996), and the 1 Hamburg cohort (Becher et al., 1998). Alt hough these studies were deemed suitable for 2 quantitative dose-response analysis, the criteria EP A used to reach this conclusion were unclear. 3 In this section, the study selec tion criteria and methodological considerations presented in 4 Section 2.3 are systematically applied to evaluate a number of studies to determine their 5 suitability for inclusion in dose-response modeli ng. In addition to the three cohorts used in 6 previous TCDD quantitative risk assessment, consid erations are applied to other relevant TCDD 7 epidemiological data sets that were identified through a literature revi ew for epidemiological 8 studies of TCDD and cancer. Study summaries and suitability for quantitative dose-response 9 analysis evaluations are discussed below. 10
11
2.4.1.1.1. Cancer cohorts. 12
2.4.1.1.1.1. The NIOSH cohort. 13 In 1978, the NIOSH undertook research that identified workers employed by U.S. 14 chemical companies that made products contaminated with TCDD between 1942 and 1982. 15 TCDD was generated in the produc tion of 2,4,5-trichlorophenol and subsequent processes. This 16 chemical was used to make 2,4,5-trichlorophe noxyacetic acid (2,4,5-T), which was a major 17 component of the widely-used defoliant, Agent Or ange. The NIOSH cohort is the largest cohort 18 of occupational workers studied to date and has been the subject of a series of investigations 19 spanning more than two decades. It is important to note that this cohort c onsists mostly of male 20 workers that were exposed to TCDD via daily oc cupational exposure, as compared to an acute 21 accidental exposure scenario seen with other cohort s. The investigations have progressed from a 22 comparison of the mortality patterns of th e cohort to the U.S. general population to 23 dose-response modeling using serum-derive d estimates of TCDD that have been 24 back-extrapolated several decades. Analyses of cancer data from the NIOSH cohort that are 25
addressed in this section incl ude Fingerhut et al. (1991), Steen land et al. (1999, 2001), Cheng et 26 al. (2006), and Collins et al. (2009). 27
28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-12 2.4.1.1.1.1.1. Fingerhut et al., 1991. 1
2.4.1.1.1.1.1.1. Study summary. 2 The investigation of Fingerhut and her co lleagues published nearly two decades ago 3 attracted widespread attention (Fingerhut et al., 1991). This retrospective study examined 4 patterns of cancer mortality for 5,172 workers who comprised the NIOSH cohort, which 5 combined workers from the company-specific c ohorts of Dow Chemical (Ott et al., 1987; Cook, 6 1981) and the Monsanto Company (Zack and Gaffey, 1983; Zack and Suskind, 1980). These 7 workers were employed at 12 plan ts producing chemicals contaminat ed with TCDD. Almost all 8 workers in the cohort (97%) had production or maintenance jobs with processes involving 9 TCDD contamination. On average, workers were employed for 2.7 years specifically in 10 processes that involved TCDD contamination, and overall, were employed for 12.6 years. The 11 mortality follow-up began in 1940 and extende d until the end of 1987. Vital status was 12 determined using records from the Social Secur ity Administration, the Inte rnal Revenue Service, 13
or the National Death Index. The ascertainment of vital status in the cohort was nearly complete, 14
with less than 1% of the cohort not followe d up until death or the end of the study period. 15 Comparisons of mortality were made relativ e to the U.S. male general population and 16 expressed using the standardized mortality ra tio (SMR) metric and 95% confidence intervals 17 (CIs). Life-table methods were used to genera te person-years of risk accrued by cohort members 18 at each plant. Person-years and corresponding deat hs were tabulated across age, race, and year 19 of death strata, which permitted the SMRs to be examined for potential confounding from these 20 three characteristics. No unadjus ted SMRs were presented in the paper. Cross-classification of 21 person-years and deaths was also done acro ss several exposure-related groupings, including 22 duration of employment, years since first exposur e, years since last exposure, and duration of 23 exposure. Employment durati on was categorized as <5, 5

− <10, 10

− <15, 15

− <20, 20

− <25, 24
25

− <30, and ≥ 30 years. The variable “year s since first exposure” (<10, 10

− <20, and ≥ 20 years) 25
was used to evaluate associations in relation to different latency periods. The analysis was 26 jointly stratified by duration of employment and for varying latency interval s to evaluate whether 27 cohort members with higher cumulative TCDD leve ls had higher cancer mortality rates than 28
those cohort mem bers with lower cumulative levels. 29 Overall, the cohort of workers had slightly elevated cancer morta lity than the general 30 population (SMR = 1.15, 95% CI = 1.02

− 1.30). Comparisons to the general population, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-13 however, yielded no statistically significant excess for any site-specific cancer. Cancer mortality 1
was examined for the subset of workers that wo rked for at least one year and had a latency 2 interval of at least 20 years ( n = 1,520). The 1-year cut-point wa s selected based on analyses of 3
serum levels in a subset of 253 workers which re vealed that every worker employed for at least 4 one year had a lipid-adjusted serum level that exceeded the mean (7 ppt). Relative to the 5 U.S. general population, statistica lly significant excesses in cancer mortality were observed for 6 all cancers (SMR = 1.46, 95% CI = 1.21

− 1.76), cancers of the respiratory system (SMR = 1.42, 7
95% CI = 1.03

− 1.92), and for soft tissue sarcoma (SMR = 9.22, 95% CI = 1.90

− 26.95) among 8 this subset of 1,520 male workers. The elevat ed SMR for soft tissue sarcoma, however, was 9 based on only three cases in this subset. 10 SMRs also were generated ac ross joint categories of duration of exposure and period of 11 latency for deaths from all cancer sites (combi ned), and cancer of the trachea, bronchus, and 12 lung. Increased SMRs were observed in strata defined by longer expos ure and latency, but no 13 statistically significant linear trends were found. 14
15
2.4.1.1.1.1.1.2. Study evaluation. 16 This cohort was the largest of four the International Agen cy for Research on Cancer 17 (IARC) considered in its 1997 classification of TCDD as a Group 1 human carcinogen (IARC, 18 1997). Duration of employment in processes th at involved TCDD contamination was used as a 19 surrogate measure of cumulative ex posure. In using this exposure metric, Fingerhut et al. (1991) 20 assumed that TCDD exposures were equivalent at all production plants. Doses for individual 21
cohort members were not reconstructed for thes e analyses, although they were in subsequent 22 analyses of this cohort. 23 Workers in this cohort also were exposed to other chemicals, which could lead to bias 24 due to confounding if these exposures were associ ated with both TCDD exposure and the health 25 outcomes being examined. At one plant, worker s were exposed to 4-am inobiphenyl. Previous 26 investigators also reported that workers at another plant were exposed to 2,4,5-T and 27 2,4-dichlorophenoxyacetic acid (2,4-D) (Bond et al ., 1988, 1989; Ott et al., 1987). Subsequent 28 analyses revealed only modest correlations between duration of employment (a surrogate 29 measure of exposure to other chemicals) and cu mulative TCDD exposure. This suggests that 30 confounding due to other coexposures is unlikely to have biased the results. In addition, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-14 subsequent analyses of this cohort indicat e that after excluding workers exposed to 1 pentachlorophenols from analyses, positive associations between cumulative TCDD and all 2 cancer mortality persisted (Steenla nd et al., 1999). Removal of workers who died from bladder 3
cancer also did not substantia lly change the dose-response as sociation between TCDD and 4 cancer mortality from all other sites combine d. This finding suggests that exposures to 5 4-aminobiphenyl did not confound the associat ion between cancer mortality and TCDD 6 exposure. Overall, there is little evidence of confounding by these co-exposures among this 7 cohort, however, exposure to other possible c onfounders, such as dioxin-like compounds, was 8
not examined. 9 The study collected no information on smoking be havior of the workers, and therefore, 10 the SMRs do not account for any differences in the prevalence of smoking that might have 11 existed between the workers and the general population. For severa l reasons, however, the 12 inability to take into account smoki ng is unlikely to have been an important source of bias. First, 13 mortality from other smoking-re lated causes of death such as nonmalignant respiratory disease 14
were not more common in the cohort th an in the general population (SMR = 0.96, 15
95% CI = 0.54

− 1.58). Second, stratified analyses of work ers with at least a 20-year latency 16 (assuming this subset shared similar smoking habits) revealed that excesses were apparent only 17 among those who were exposed for at least 1 year. Specifically, when compared to the general 18 population, the SMR among workers exposed for at le ast 1 year with a latency of 20 years was 19 1.46, (95% CI = 1.21

− 1.76) while those exposed for less th an 1 year had an SMR of 1.02 20 (95% CI = 0.76

− 1.36). Third, for comparisons of cancer mortality between blue-collar workers 21 and the general population, smoking is unlikely to explain cancer exce sses of greater than 22
10

− 20% (Siemiatycki et al., 1988). Finally, the investigators found no substantial changes in the 23 results for lung cancer when risks were adjusted for smoking histories obtained in 1987 from 24 223 workers employed at two plants. These data were used to adjust for th e expected number of 25 lung cancer deaths expected in the entire cohor t (Fingerhut et al., 1991). Following this 26 adjustment, the expected number of lung cancer deaths resulted in only a small change in the 27 SMR for lung cancer in the overall cohort from 1.11 (95% CI = 0.89

− 1.37) to 1.05 28 (95% CI = 0.85

− 1.30). Similarly, there was little change in the SMR for lung cancer in higher 29 exposure subcohort (SMR = 1.39, 95% CI = 0.99

− 1.89; to SM R = 1.37, 95% CI = 0.98

− 1.87). 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-15 The use of death certificate information from the National Death Inde x is appropriate for 1 identifying cancer mortality outcomes. For site-specific cancers such as soft tissue sarcoma, 2 however, the coding of this underlying cause of d eath is more prone to misclassification (Percy 3 et al., 1981). Indeed, a review of tissues from four men concluded to have died from soft-tissue 4
sarcoma determined that two deaths had been misc lassified (Fingerhut et al., 1991). A review of 5 hospital data revealed that two ot her individuals had soft tissue sarc omas that were not identified 6 by death certificate information. The use of d eath certificate information to derive SMRs for 7 cancer as a whole is likely not subject to significant bias; the same might not hold true, however, 8 for some site-specific cancers su ch as soft tissue sarcoma. 9 Using the SMR metric to compare an occupa tional cohort with the general population is 10 subject to what is commonly referred to as th e “healthy worker effect” (Li and Sung, 1999; Choi, 11 1992). The healthy worker effect is a bias th at arises because those healthy enough to be 12 employed have lower morbidity and mortality ra tes than the general population. The healthy 13 worker effect is likely to be larger for occupations that are more physically demanding 14 (Aittomaki et al., 2005; Checkoway et al., 1989), and the healthy worker effect is considered to 15 be of little or no consequence in the interp retation of cancer mortality (McMichael, 1976; 16 Monson, 1986). Few cancers are associated with a prolonged period of poor health that would 17 affect employability long before death. Also recognized is that, as the employed population 18 ages, the magnitude of the healthy worker effect decreases as the absolute reduction in mortality 19 becomes relatively smaller in older age groups (McMichael, 1976). The mo rtality follow-up of 20 occupational cohorts generally spans several d ecades, which should minimize the associated 21 healthy worker effect in such studies. Bias c ould also be introduced in that workers who are 22 healthier might be more likely to stay employed a nd therefore accrue higher levels of exposure. 23 In the NIOSH cohort, however, mortality was as certained for those who could have left the 24 workforce or retired by linking su bjects to the National Death Index. Although internal cohort 25 comparisons can minimize the potential for the healthy worker effect for the reasons presented 26 above, for cancer outcomes, the SMR statistic is a valuable tool for characterizing whether 27 occupational cohort are more likely to die of cancer than the general population. Moreover, 28 stratified analyses across categor ies of duration of exposure, or latency periods within a cohort 29 can yield important insights about which workers are at greatest risk. Perhaps most im portant, 30 subsequent analyses of the NIOSH cohort that pr esented risk estimates derived from external 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-16 comparisons using the SMR were remarkably consiste nt with rate ratios de rived using an internal 1 referent (Steenland et al., 1999). 2
3
2.4.1.1.1.1.1.3. Suitability of data for TCDD dose-response modeling. 4
This cohort meets most of the identified considerations for conducting a quantitative 5 dose-response analysis for mortality from all cancer sites combined. The NIOSH cohort is the 6 largest cohort of TCDD-exposed workers, exposur e characterization at an individual level is 7 possible but not available in this particular study, the follow- up period is long enough to evaluate 8 latent effects, and the study is not subject to impor tant sources of bias that would materially alter 9 findings. For the purpose of quantitative dose-response modeling, it is important to note that 10 subsequent studies of this cohort adopted methods that greatly improved the characterization of 11 TCDD exposure in this cohort and increased the follow-up inte rval (Steenland et al., 2001; 12 Cheng et al., 2006). As such, for all practical purposes, due consideration for dose-response 13 modeling should focus on the more recently developed data sets. 14 For quantitative dose-response modeling for indi vidual cancer sites, the data are much 15 more limited. A statistically significant positive a ssociation with TCDD was noted only for soft16 tissue sarcoma among those with more than 1 year of exposure and 20 years of latency 17 (SMR = 9.22, 95% CI = 1.90

− 26.95). However there were only three deaths from soft tissue 18 sarcoma among this exposed component of the cohor t, and four deaths in total in the overall 19 cohort. Also, misclassification of outcome for soft-tissue sarcom a through death registries is 20 well recognized and supported with additional review of tissue from two of the men. 21 Specifically, tissues from the four men who died of soft-tissue sarcoma rev ealed that only two of 22 these cases were coded correctly. 23 The design of this initial pub lication of the NIOSH cohort di d not allow for exposures to 24 other possible confounders, such as dioxin-like compounds and other occupational exposures, to 25 be examined and accounted for in the analyses . TCDD exposures were based on duration of 26 employment and effective doses could not be estimated based on the TCDD exposure measure 27 (i.e., duration of employment). Therefore, dos e-response modeling was not conducted for this 28 study. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-17 2.4.1.1.1.1.2. Steenland et al., 1999. 1
2.4.1.1.1.1.2.1. Study summary. 2 A subsequent analysis of the NIOSH cohort ex tended the follow-up interval of Fingerhut 3 et al. (1991) by 6 y ears (i.e., from 1940

− 1993) and improved characteri zation of TCDD exposure 4 (Steenland et al., 1999). A key distinction from the work of Fingerhut et al. (1991) was the 5
exclusion of several workers that had been incl uded in the previous mortality analyses. The 6
authors excluded 40 workers who were either female, had never worked in TCDD-exposed 7 departments, or had missing date of birth info rmation. An additional 238 workers were excluded 8 as occupational data for characte rizing duration of exposure were l acking, preventing their use in 9 a subcohort dose-response analysis . This subcohort was furthe r reduced by excluding workers 10 from four plants ( n = 591) because the information on th e degree of TCDD contamination in 11 work histories was limited, preventing the ch aracterization of TCDD levels by job type. 12 Thirty-eight additional workers were excluded from the eight remaining plants because TCDD 13 contamination could not be estimated. Finally, 727 workers were excluded because they had 14 been exposed to pentachlorophenol. In total, exposures were assigne d to 3,538 (69%) members 15 of the overall cohort, a cohort substantially reduced from th e 5,172 on which Fingerhut et al. 16 (1991) reported. Steenland et al. (1999) also ev aluated the mortality e xperience of a subcohort 17
of 608 workers with chloracne who had no exposure to pentachlorophenol. 18 For each worker, a quantitative exposure score for each day of work was calculated based 19 on the concentration of TCDD ( μ g/g) present in process materi als, the fraction of the day 20 worked, and a qualitative contact level based on estimates of the amount of TCDD exposure via 21 dermal absorption or inhalation. The authors derived a cumulative meas ure of TCDD exposure 22 by summing the exposure scores across the worki ng lifetime history for each worker. The 23 authors validated this cumulative exposure metric indirectly by comparing values obtained for 24
workers with and without chloracne. Such a va lidation is appropriate, gi ven that chloracne is 25 considered a clinical sign of exposure to dioxin. The median exposure score among those with 26 chloracne was 11,546 compared with 77 among thos e without (Steenland and Deddens, 2003). 27 Cancer mortality was compared using two a pproaches. As in Fi ngerhut et al. (1991), 28 external comparisons were made to the U.S. general population using the SMR statistic. The 29 authors adjusted the SMR statistics for race, age, and calendar time. They also applied life-table 30
methods to characterize risks across the s ubcohort of 3,538 workers with exposure data by 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-18 categorizing the workers into seven cumulativ e exposure groups. The cut-points for these 1 categories were selected so that the number of deaths in each category was nearly equal to 2 optimize study power. Life-table analyses were extended further to consider a 15-year lag 3 interval, which in a practical sense means that person-years at risk woul d not begin to accrue 4 until 15 years after the first exposure occurred. Th e person-years and deaths that occurred in the 5 first 15 years were included in the lowest exposure grouping. The Cox proportional hazards 6
model was used to characterize risk within the cohort. Cox regression was used to provide an 7
estimate of the hazard ratios and the 95% CIs for ischemic heart disease, all cancers combined, 8 lung cancer, smoking related cancers, and all othe r cancers. The authors also performed Cox 9 regression analyses using the seve n categories of exposure, adjust ing the regression coefficients 10 for year of birth and age. The regression models were run for both unlagged and lagged 11 (15 years) cumulative exposure scores. 12 Overall, when compared with the U.S. ge neral population, a sli ght excess of cancer 13 mortality (from all sites) was noted in the 5,132 cohort study population (SMR = 1.13, 14
95% CI = 1.02

− 1.25). This result did not substantially differ from the earlier finding that 15 Fingerhut et al. (1991) publis hed (SMR = 1.15, 95% CI = 1.03

− 1.30). Site-specific analyses 16 revealed statistically significant excesses relative to the U. S. general population for bladder 17 cancer (SMR = 1.99, 95% CI = 1.13

− 3.23) and for cancer of the larynx (SMR = 2.22, 18
95% CI = 1.06

− 4.08). In the chloracne subcohort ( n = 608), a borderline st atistically significant 19 excess relative to the general population was f ound for all cancer site s combined (SMR = 1.25, 20
95% CI = 0.98

− 1.57) and for lung cancer (SMR = 1.45, 95% CI = 0.98

− 2.07). The authors also 21 found statistically significant excesses for connective and soft tissue sarcomas (SMR = 11.32, 22
95% CI = 2.33

− 33.10) and for lymphatic and hemat opoietic malignancies (SMR = 3.01, 23
95% CI = 1.43

− 8.52). 24 External comparisons made by grouping workers into se ptiles of cumulative TCDD 25 exposure and generating an SMR for each septile using the U.S. population as the referent group 26 suggested a dose-response relationship. For all cancer sites combined, workers in the highest 27 exposure score category had an SMR of 1.60 (95% CI = 1.15

− 1.82); increases also were 28 observed in the sixth (SMR = 1.34) and fift h (SMR = 1.15) septiles. The two-sided p -value 29
associated with the test for trend for cumula tive TCDD exposure was statistically significant 30 ( p = 0.02). A similar approach for lung cancer revealed virtually the same pattern. The 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-19 incorporation of a 15-year latenc y for the analyses of all cancer deaths, in general, produced 1 slightly higher SMRs across the septiles, although a slight attenua tion of effect was noted in the 2 highest septile (SMR unlagged = 1.60 vs. SMR lagged = 1.54). For a 15-year lag, the lung cancer 3 SMRs were mixed compared to the unlagged resu lts with some septile exposure categories 4 increasing and others decreasing re lative to the lowest exposure group. 5 For the internal cohort comparisons using C ox regression analyses higher hazard ratios 6 were found among workers in the higher exposure ca tegories than in the lowest septile. The 7 linear test for trend, however, wa s not statistically significant ( p = 0.10). The associations across 8 the septiles for the unlagged e xposure for the internal cohort co mparisons were not as strong as 9 for the external cohort comparisons. The opposit e was true, however, for cumulative exposures 10 lagged 15 years. 11 Relative to the lowest septile, stratified anal yses revealed increased hazard ratios in the 12 upper septiles of the internal cohort compar isons for both smoking- and nonsmoking-related 13
forms of cancer. The test for linear trend was stat istically significant for all other cancers (after 14 smoking-related cancers were excluded). These analyses suggest that the overall cancer findings 15
were not limited to an interaction between TCDD and smoking. Additional sensitivity analyses 16 by the authors indicated the findings for smoking -related cancers were largely unaffected by the 17 exclusion of bladder cancer cases. This observation suggests that the exposure to 18 4-aminobiphenyl, which occurred at one plant an d might have contributed to an increased 19 number of bladder cancers, did not substantially bias the dose-response relationship between 20
TCDD and all cancers combined. 21 The investigators also evaluated the dose -response relationship with a Cox regression 22 model separately for each plant using internal cohort comparisons and found some heterogeneity. 23 This finding is not unexpected particularly given the relatively small number of cancer deaths at 24 each plant, and given that exposures were quite low for one plant at which no positive 25
association was found. The vari ability among plants was taken into account by modeling plant 26 as a random effect measure in the Cox model, which produced little change in the slope 27 coefficient ( β = 0.0422 vs. β = 0.0453). 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-20 2.4.1.1.1.1.2.2. Study evaluation. 1 This study represents a valuable extension of that by Fingerhut et al . (1991). Internal 2 comparisons were performed to help minimize potenti al biases associated with using an external 3 comparison group (e.g., healthy worker effect, and di fferences in other risk factors between the 4 cohort and the general population) . That similar dose-response relationships were found for 5 internal and external comparison populations suggests th at the bias due to the health worker 6 effect in the cohort might be minimal for cancer mortality. More importan tly, the construction of 7 the cumulative exposure scores provides an improved opportunity to evaluate dose-response 8 relationships compared with th e length of exposure and durati on of employment metrics that 9 Fingerhut et al. (1991) used. 10 A potential limitation of the NIOSH study wa s the inability to account for cigarette 11 smoking. If cigarette smoking did co ntribute to the increased cancer mortality rates in this and 12 other cohorts, increased cancer mortality from exposure to TCDD would be expected only for 13 smoking-attributable cancers. This study demonstrates associ ations with TCDD for both 14 smoking- and nonsmoking-related cancers, including a stronger association for 15 nonsmoking-related cancers. Therefore, the data provide evidence that associations between 16 TCDD and cancer mortality are not likely due to cigarette smoking. 17 The findings regarding latency s hould be interpreted cautiously as the statistical power in 18 the study to compare differences across latency intervals was limited. Caution also should be 19 heeded, given that latency intervals can vary on an individual basi s as they are often 20 dose-dependent (Guess and Hoel, 1977). The eval uation of whether TCDD acts as either an 21 initiating or promoting agent (or both) is severe ly constrained by the reliance on cancer mortality 22
data rather than incidence data. This constraint is due to the fact that survival time can be quite 23 lengthy and can vary substantially across individual and cancer subtype. For example, the 5-year 24 survival among U.S. males for al l cancer sites combin ed ranged between 45 and 60% (Clegg et 25 al., 2002). When only mortality data are available, evaluating the time between when individuals 26 are first exposed and when they are dia gnosed with cancer is nearly impossible. 27 Starr (2003) suggested that St eenland et al. (1999) focused too heavily on the exposures 28 that incorporated a 15-year pe riod of latency and that those who experienced high exposures 29 would inappropriately cont ribute person-years to the lowest exposure group “irrespective of how 30 great the workers’ actual cumulative exposure sc ores ma y have been.” Most cancer deaths 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-21 would, however, typically occur many years postemp loyment. Given that the follow-up interval 1 of the cohort was long and the average exposure duration was 2.7 years, at the time of death, 2 person-years for those with high cumulative e xposures would be capture d appropriately. The 3 median 5-year survival for all cancers is appr oximately 50% (Clegg et al., 2002), so applying a 4 minimum latency of 5 years when using cancer mo rtality rather than cancer incidence data is 5 needed to assure that the exposure metric is capturing exposures that occur before diagnoses. 6 Increasing this latency period, for example to 10 or 15 years, would eliminate consideration of 7 exposures that occur in the pe riod between tumor occurrence a nd tumor detection (diagnosis), 8 and allows for an appropriate focus on exposures that act either early or late in the pathogenic 9 process. The IARC recommendation also underscores the relevance of including a period of 10 latency when estimating cancer risks related to TCDD. Specifically, IARC indicated that if the 11 association of TCDD with cancer is causal, effects might become apparent only at high 12 exposures and with adequate latenc y; they suggested that a latenc y interval of 15 years could be 13 too short (IARC, 1997). EPA considers the Steenla nd et al. (1999) presentati on to be balanced in 14 that they provided the range in lifetime excess risk estimated across the various models used. 15 The authors’ finding that the models with a 15- year lag provided a sta tistically significant 16 improvement in fit based on the chi-square test statistic should not be readily dismissed. 17
18
2.4.1.1.1.1.2.3. Suitability of data for TCDD dose-response modeling. 19 This study meets most of the epidemiological considerations for conducting a 20 quantitative dose-response analysis for mortality from all cancer sites combined. This study 21 excludes a large number of workers who were e xposed to pentachlorophenol, thus eliminating 22 the potential for bias from this exposure and used an improved methodology for assigning TCDD 23 exposures to the workers. However, given that exposures to other di oxin-like compounds were 24 not described, it is unclear if the exposures among this cohort were primarily to TCDD. 25 Therefore, dose-response modeling was not pursued for this study, but was for the subsequent 26 NIOSH study by Steenland et al. (2001), which did examine exposure to dioxin-like compounds. 27
28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-22 2.4.1.1.1.1.3. Steenland et al., 2001. 1
2.4.1.1.1.1.3.1. Study summary. 2 In 2001, Steenland et al. publishe d a risk analysis using the NIOSH cohort that for the 3 first time incorporated serum measures in the derivation of TCDD exposures for individual 4 workers. The authors applied the same exclusion criteria to the entire cohort of workers across 5 the 12 plants in the Steenland et al. (1999) study, which left 3,538 workers for which risk 6
estimates could be calculated. Cumulative TCDD serum levels were estimated on an individual 7 basis for all 3,538 workers by developing predictive m odels that used a subset of 170 workers for 8 which both serum measures and TCDD exposures sc ores were available (S teenland et al., 2001). 9 Unlike previous analyses of the NIOSH cohort that considered several different mortality 10 outcomes, the analyses presented in Steenland et al. (2001) focused exclusively on mortality 11
from all cancers sites co mbined. The authors observed 256 ca ncer deaths in the cohort during 12 the follow-up interval that extended from 1942 un til the end of 1993. All ri sks estimated in the 13 Steenland et al. (2001) study were base d on internal cohort comparisons. 14 Characterization of TCDD exposure levels among the workers was based on serum 15
measures obtained in 1988 from 199 workers who we re employed in one of the eight plants. The 16 researchers restricted the development of th e model to include only those workers whose 17 measured serum levels were deemed to be gr eater than the upper range of background levels 18 (10 ppt), which resulted in 170 workers. 19 The authors developed a regression model that could estimate the level of TCDD at the 20 time of last exposure for the 170 workers. The model was developed based on the estimated 21 half-life of TCDD, the known wo rk history of each worker, a pharmacokinetic model for the 22 storage and excretion of TCDD, a nd exposure scores for each job held by each worker over time. 23 The resulting equation follows 24
25

y last exposure = y 1988 exp ( λ∆ t) (Eq. 2-1) 26 27 The first-order elimination rate constant ( λ ) was based on a half-life of 8.7 years 28 previously reported for the Ranch Hands cohort (M ichalek et al., 1996). The background rate of 29 TCDD exposure was assumed to be 6.1 parts per trillion (ppt), which was based on the median 30 level in a sample of 79 unexposed workers in the NIOSH cohort (Piacitelli et al., 1992). This 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-23 value was subtracted when TCDD values were ba ck-extrapolated, and then added again after the 1 back-extrapolation was completed. A background leve l of 5 ppt also was used in some of the 2 analyses with minimal demonstrable effects on th e results. Sensitivity analyses also were 3 incorporated to consider a 7.1-ye ar half-life estimate that had been developed for the earlier 4 Ranch Hands study (Pir kle et al., 1989). 5 After back-extrapolating to obtai n TCDD serums levels at the time of last exposure, the 6 investigators estimated cumulative (or “area unde r the curve”) TCDD serum levels for every 7 cohort member. This estimation procedure was the same method Flesch-Janys et al. (1998) 8 applied to the Hamburg cohort to derive a coe fficient for relating serum levels to exposure 9 scores. The “area under the curve” approach in tegrates time-specific serum levels over the 10 employment histories of the indi vidual workers. The slope co efficient was estimated using a 11 no-intercept linear regression model. This mode l is based on the assumption that a cumulative 12 score of zero is associated with no serum levels above background. 13 Cox regression was also used to model the c ontinuous measures of TCDD. A variety of 14 exposure metrics were considered that took into account different lags, nonlinear relationships 15 (e.g., log-transform and cubic spline), as well as threshold and nonthreshold exposure metrics. 16 Categorical analyses were used to evaluate risks across TCDD exposure groups, while different 17 shapes of dose-response curves were eval uated through the use of lagged and unlagged 18 continuous TCDD measures. Categorical analys es of TCDD exposure were conducted using the 19 Cox regression model to derive estimates of rela tive risk (RR) as descri bed by hazard ratios and 20 95% CIs. The reference group in this analys is was those workers in the lowest septile 21 cumulative exposure grouping (<335 ppt-years). The septiles were chosen based on cumulative 22
serum levels that considered no lag and also a 15-year lag. 23 The investigators also conducte d dose-response analyses usin g the toxicity equivalence 24 (TEQ) approach. The TEQ is calculated as the sum of all exposures to dioxins and furans 25 weighted by the potency of each specific co mpound. In this study, TCDD was assumed to be 26 account for all dioxin exposures in the workplace. For background TEQ levels, the investigators 27 used a value of 50 ppt in the dose-response m odeling. This is based on the assumption that 28 TCDD accounted for 10% of the toxicity of all dioxins and furans (WHO, 1988), and is 29 equivalent to using a background level of 5 ppt/yr that was used in the derivation of cumulative 30
serum TCDD levels. A statisti cally significant dose-re sponse pattern was observed for all cancer 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-24 mortality and TCDD exposure based on log of cumulative TEQs with a 15-year lag. A 1 comparison of the overall model chi-square values indicated that the fit of this model was not as 2 good as that for TCDD. 3 The hazard ratios among workers grouped by categories of cumulative TCDD exposure 4 (lagged 15 years) suggested a dose-response relationship. Steenla nd et al. (2001) found 5 statistically significant excesses in the higher exposu re categories compared to the lowest septile. 6 The RR was 1.82, 95% CI = 1.18

− 2.82 for the sixth septile (7,568

− 20,455 ppt-years) and 1.62, 7
95% CI = 1.03

− 2.56) for the seventh septile (>20,455 ppt-yea rs). Cox regression indicated that 8 log TCDD serum concentrations (lagged 15 year s) was positively asso ciated with cancer 9 mortality ( β = 0.097, standard error ( β ) = 0.032, p < 0.003). A statistically significant 10 improvement in fit was observed when a 15-year lag interval was incorporated into the model 11 compared to a model with no such lag [Model χ 2 with 4 degrees of freedom (df) = 7.5]. Results 12 were similar when using a half-life of 7.1 years rath er than 8.7 years. The excess lifetime risk of 13 death from cancer at age 75 for TCDD intake (per 1.0-picogram per k ilogram [pg/kg] of body 14 weight (BW) per day) was about 0.05

− 0.9% above a background lifetime risk of cancer death of 15 12.4%. The results from the best-fitting models pr ovide lifetime risk estima tes within the ranges 16 derived using data from the Ham burg cohort (Becher et al., 1998). 17 In both categorical and continuous analyses of TCDD based on a linear exposure metric, 18 the dose-response pattern tailed off at high e xposures suggesting nonlinear effects. This 19 phenomenon could be due to saturation effects (S tayner et al., 2003) or, alternatively, could have 20 resulted from increased exposure misclassificat ion of higher exposures (Steenland et al., 2001). 21 As the authors highlighted, some of the highest exposures might have been poorly estimated as 22 they occurred in workers exposed to short-term high exposures during the clean-up of a spill. 23 The choice of a linear model to develop data from a single time point can also result in exposure 24
misclassification in those individua ls that have differences in th e length of exposure (Emond et 25 al., 2005). Misclassification woul d be less likely at low concen trations where dose-dependent 26
elimination is minimal. 27
28
2.4.1.1.1.1.3.2. Study evaluation. 29 An important consideration in the Steenland et al. (2001) study was the use of a small 30 subset of workers ( n = 170) to infer exposures for the re mainder of the cohort. This subset 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-25 comprised surviving members of the cohort (in 1 988), and therefore, their age distribution would 1
have differed from the rest of the cohort. Furt hermore, these workers were employed at a single 2 plant, at which the work histories were less detail ed than at other plants; thus, the development of 3 the exposure scores differed between this plant and that of the others. Also, many of the workers 4 at this plant had the same job title and were employed during the same calendar period. The use 5
of serum data from this subset adds a level of uncertainty that is not readily characterized. 6 Despite this limitation, the use of these sera data to derive cumulative measures for all cohort 7 workers has merit given the strong correlation observed between the e xposure scores, and TCDD 8 serum levels estimates at the time of last exposure (Spearman r = 0.90). 9 The authors performed an extensive series of sensitivity analyses and considered several 10 alternative exposure metrics to the simple linear model. The lifetime excess risk above 11 background was nearly twice as high for the log cumulative serum measures with a 15-year lag 12 when compared to the piecewise linear models w ith no lag. An important observation was that 13 the exposure metric based on cumulative serum (la gged 15 years) did not fit the data as well as 14 the cumulative exposure score used in earlier an alyses (Steenland et al., 1999). A priori, one 15 would expect that a better fit would be obtai ned with serum-based measures because serum 16
levels are a better measure of re levant biological dose. As the authors noted, inaccuracies 17 introduced in estimating the external-based exposure scores could have contributed to a poorer 18 fit of the data. Alternatively, exposure miscla ssification error could be introduced if serum 19 samples based on the 170 workers were not repres entative of the entire cohort. Although the 20
serum-based measures did not fit the data as we ll as the exposures scores, the authors regarded 21 them as providing a reasonable fit. Moreove r, the serum-based measures enabled better 22 characterization of risk in units (pg/kg-day ) that can be used in regulation exposures. 23
24
2.4.1.1.1.1.3.3. Suitability of data for TCDD dose-response modeling. 25 This study meets all of the ep idemiological considerations for conducting a quantitative 26 dose-response analysis for mortality from all cancer sites combined. As mentioned previously, 27 the NIOSH cohort is the largest assembled to date for which TCDD-related risks of cancer 28 mortality can be estimated. The use of serum- based measures provides an objective measure of 29 TCDD exposure. Repeated measures in othe r study populations have provided reasonable 30 estimates of the half-life of TCDD, which permitted back-extrapolation of exposures. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-26 The authors have made extensive efforts to evaluate a wide variety of nonlinear and 1 linear models with varying lengths of latency and log transformati ons. The model chi-square test 2 statistics were fairly similar for the log cumulative serum (15-year lag) (Model 

### χ 2 (4df) = 11.3) 3 model and the piecewise linear model (no lag) (Model 

### χ 2 (5df) = 12.5). These models, however, 4 produced results with twofold differences in lifet ime excess risks. These differences underscore 5 the importance of characterizing uncertainty in modeling approaches when conducting 6 dose-response analysis. 7 The Steenland et al. (2001) st udy characterizes risk in term s of pg/kg of body weight per 8 day. Given that tolerable daily intake dioxin levels are typically expressed in pg/kg of body 9 weight (WHO, 1998), the presentation of risks in terms of these units is an important advance 10 from the earlier analyses that used exposure scores (Steenland et al., 1999). Many of the 11 Steenland et al. (2001) findings are consistent with earlier work from this cohort, which is not 12 surprising given that exposures scores were used to derive serum-based levels for the cohort. 13 The findings of excess lifetime risks obtained for th e best- fitting model are also consistent with 14 those derived from the Hamburg cohort (Bech er et al., 1998). This study meets the 15 epidemiological considerations noted previously as there is no evidence th at the study is subject 16 to bias from confounding due to cigarette smoking or other occupational exposures. Given the 17
considerable efforts to measur e effective dose to TCDD among th e study participants, this study 18 also meets the requisite dose-response modeli ng criteria and will be used in quantitative 19 dose-response analyses of cancer mortality. 20
21
2.4.1.1.1.1.4. Cheng et al., 2006. 22
2.4.1.1.1.1.4.1. Study summary. 23 Cheng et al. (2006) undertook a subsequent qua ntitative risk asse ssment of 3,538 workers 24 in the NIOSH cohort using serum-derived estimates of TCDD. This dose-response analysis was 25 published after the 2003 Reassessment document was released. The goal of this study was to 26 examine the relationship between TCDD and cancer mortality (all sites combined) using a new 27 estimate of dose that estimated TCDD as a function of both exposure inte nsity and age using a 28 kinetic model. This physiologically based pharmacokinetic model ha s been termed the 29 “concentration- and age-dependent elimination model” (CADM) and was developed by Aylward 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-27 et al. (2005b). This model de scribes the kinetics of TCDD following oral e xposure to humans by 1 accounting for key processes affecting kinetics by simulating the total concentration of TCDD 2 based on empirical consideration of hepatic proce sses (see Section 3.3). An important feature of 3 this kinetic model is that it incorporates con centration- and age-dependent elimination of TCDD 4 from the body; consequently, the effective ha lf-life of TCDD elimination varies based on 5 exposure history, body burden, and age of the ex posed individuals. The study was motivated by 6 the reasoning that back-calculations of TCDD using a first-order elimination model and a 7
constant half-life of 7

− 9 years underestimated exposures to TCDD among workers. This 8 underestimate, in turn, would re sult in overestimates of the ca rcinogenic potency of TCDD. 9
As with the earlier Steenla nd et al. (2001) analyses, th e cohort follow-up period was 10
extended from 1942 until the end of 1993 and work histories were linked to a job exposure 11
matrix to obtain cumulative TCDD scores. Tw o cumulative serum lipid exposure metrics (in 12 ppt-years) were constructed using the data obtained from the sample of 170 workers. The first 13 replicated the metric used in a previous analysis of the cohor t (Steenland et al., 2001) and was 14
based on a first-order elimination model with an 8.7-year half-life (Michalek et al., 1996). The 15
second metric was based on CADM and had two fi rst-order elimination processes (Aylward et 16 al., 2005a). This metric assumes that the eliminati on of TCDD in humans occurs at a faster rate 17 when body concentrations are high and at slower rates in older individu als (Aylward et al., 18 2005a, b). The model was optimized using indivi duals for which serial measures of serum 19
TCDD were available. These measures were obta ined from 39 adults with initial serum levels 20 between 130 and 144,000 ppt (Aylward et al., 20 05b). This group included 36 individuals who 21 had been exposed in the Seveso accident and 3 e xposed in Vienna, Austria. In practice, for 22 serum levels greater than 1,000 ppt , the effective half-life would be less than 3 years, and for 23 serum TCDD levels less than 50 ppt, the eff ective half-life would be more than 10 years 24 (Aylward et al., 2005b). Results from the model indicate that men eliminate TCDD faster than 25 women do as demonstrated previously by N eedham et al. (1994). These age- and 26 concentration-dependent processes were assume d to operate independently on TCDD in hepatic 27 and adipose tissues, and TCDD levels in liver an d adipose tissue were assumed to be a nonlinear 28 function of body concentration. Cheng et al. (2006) calibrated CADM using a dose of 156 ng 29 per unit of exposure score and assumed a bac kground exposure rate of 0.01 ng/kg-month. The 30 average TCDD ppt-years derived from CADM with a 15-year lag was 4.5

− 5.2 times higher than 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-28 with the first-order elimination model. The tw o metrics, however, were highly correlated based 1 on a Pearson correlation coefficient of 0.98 ( p < 0.001). Comparisons of fit between the CADM 2 and first-order elimination model were made using R 2 values and presented in Aylward et al. 3 (2005b). 4 Cheng et al. (2006) compared the mortality experience of NIOSH workers to the U.S. 5 general population using the SMR st atistic. SMR statistics also were generated separately for 6 each of the 8 plants and for all plants combined. Cox regression models were used to analyze 7 internal cohort dose-response. These models used age as the time variable, and penalized 8 smoothing spline functions of the CADM metr ic also were considered. The possible 9 confounding effects of other occupational exposures and other regional population differences 10 were assessed by repeating analyses after excl uding one plant at a time. Lagged and unlagged 11 TCDD exposures were analyzed se parately, and stratified analyses compared risk estimates for 12 smoking- and nonsmoking-related cancers. Cheng et al. (2006) adjusted the slope estimates 13 derived from the Cox model for potential conf ounding effects of race a nd year of birth. 14 Overall, a statistically significant excess in all cancer mortality in the cohort occurred 15 relative to the gene ral population (SMR = 1.17, 95% CI = 1.03

− 1.32). The plant-specific SMRs 16 ranged from 0.62

− 1.87, with a statistically significant excess eviden t only for plant 10 17 (SMR = 1.87, 95% CI = 1.35

− 2.52). For lung cancer mortality, the overall SMR was not 18 statistically significant (SMR = 1.11, 95% CI = 0.89

− 1.37). A statistically significant excess for 19 lung cancer also was found for pl ant 10 (SMR = 2.35, 95% CI = 1.44

− 3.64). The SMRs between 20 smoking- (SMR = 1.22, 95% CI = 1.01

− 1.45) and nonsmoking-related cancers (SMR = 1.12, 21
95% CI = 0.94

− 1.33) were comparable. 22 For the internal cohort analyses of serum-de rived measures, the authors were able to 23 replicate the one-compartmental model used prev iously (Steenland et al ., 2001). As had been 24 noted by Steenland et al. (2001), an inverse-dose-response pattern was seen for individuals with 25 high exposures (above 95 th percentile); this type of pattern is often seen in occupational studies 26 (Stayner et al., 2003). Excluding th ese data produced a stronger association between TCDD and 27 all-cause mortality. In fact, only when the uppe r 2.5% or 5% of observa tions was removed did a 28 statistically significant positive association b ecome evident with the untransformed data. 29 Similarly, when the model incorporated a lag of 15 years, a statistically significant association 30 was noted only for the untransformed TCDD ppt-years with the upper 5% of observations 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-29 removed. Stratified analyses revealed little difference between smoking- and 1 nonsmoking-related cancers, and the removal of one plant at a tim e from the analyses of TCDD 2 ppt-years changes did not substantially change the slope. 3
4
2.4.1.1.1.1.4.2. Study evaluation. 5 The authors reported that CADM provided an improved fit over the one-compartmental 6 model, but presented no evidence regarding any formal test of statistical significance. A 7 comparison of R 2 values presented in Aylward et al. (2005b), however, does reveal that the R 2 8 value increased from 0.27 (first-order compartm ental model with an 8.7-year half-life) to 0.40 9 for CADM. TCDD exposures estimated using CADM were approximately fivefold higher than 10 the one-compartmental model estimates among cohor t members with higher levels of exposure. 11 Differences in exposure estimates between the tw o metrics were less striking among individuals 12 with lower TCDD exposures. The net effect was that CADM produced a 6- to 10-fold decrease 13 in estimated risks compared to estimates pr eviously reported (St eenland et al., 2001). 14 Nonetheless, the estimates produced by CADM sp an more than two orders of magnitude under 15 various assumptions. Further uncertainties arise from betw een-worker variability of TCDD 16 elimination rates, possible resi dual confounding, and the va riability associated with the use of 17 data obtained from other cohorts. Nevertheless, the use of the CADM model to estimate TCDD 18 exposure is considered a signi ficant advantage over the pr evious first-order body burden 19
calculations. 20
21
2.4.1.1.1.1.4.3. Suitability of data for TCDD dose-response modeling. 22 The value of including the NIOSH cohort da ta has already been established based on 23 investigations published by Steenland et al. (199 9, 2001). The decision to include data from the 24 quantitative dose-response analysis that Cheng et al. (2006) conducted relates to the added value 25 that the CADM exposure estimates would provide. The earlier modeling wo rk of Aylward et al. 26 (2005b) provided some support for a modest improvement of the fit of CADM over the 27 first-order compartmental model, and they also confirmed previous studies that found that TCDD 28 elimination rates varied by age and sex. Recent wo rk by Kerger et al. (2006) also demonstrates 29
that the half-life for TCDD is shorter among Seveso children than the corresponding half-life for 30 adults, and that body burdens influence the elimin ation of TCDD in humans. That estimates of 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-30 half-lives among men have been remarkably consistent, with mean estimates ranging between 1 6.9 and 8.7 years (Flesch-Janys et al., 1996; Pirkle et al., 1989; Michalek et al., 2002; Needham 2 et al., 2005), however, is noteworthy. Based on the underlying strengths of the NIOSH cohort 3 data and efforts by Cheng et al. (2006) to improve estimates of effective dose, these data support 4 further dose-response modeling. 5
6
2.4.1.1.1.1.5. Collins et al., 2009. 7
2.4.1.1.1.1.5.1. Study summary. 8 In a recent study, Collins et al. (2009) inves tigated the relationship between serum TCDD 9 levels and mortality rates in a cohort of trichlorophenol work ers exposed to TCDD. These 10 workers were part of the NIOSH cohort ha ving accounted for approximately 45% of the 11 person-years in an earlier analysis (Bodner et al., 2003). The investigators completed an 12
extensive dioxin serum evaluation of workers em ployed by the Dow Chemical plant in Midland, 13 Michigan, that made 2,4,5-trichlorophenol (TCP) from 1942 to 1979 and 2,4,5-T from 1948 to 14 1982. Collins et al. (2009) developed historic al TCDD exposure estimates for all TCP and 15 2,4,5-T workers. This study represents the larg est group of workers from a single plant ever 16
studied for the health effects of TCDD. Little information on how vital status was ascertained, 17 either in this paper or in the Bodner et al. (2003) report of mortality in this cohort. Although the 18
authors indicate that death certif icates were obtained from the states in which the employees 19 died, whether vital status was ascertained from co mpany records or through record linkage to the 20
National Death Index is unclear. 21 The follow-up interval for these workers covered the period between 1942 and 2003. 22 Thus, the study included 10 more y ears of follow-up than earlier i nvestigations of the entire 23 NIOSH cohort. Serum samples were obtaine d from 280 former workers collected during 24
2004

− 2005. A simple one-compartment first-order pharmacokinetic model and elimination rates 25 as estimated from the BASF cohort were used (F lesch-Janys et al., 1996). The “area under the 26 curve” approach was used to characterize worker s’ exposures over the co urse of their working 27 careers and provided a cumulative measure of ex posure. Analyses were performed with and 28 without 165 of the 1,615 workers exposed to pentachlo rophenol to evaluate the impact of these 29

exposures. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-31 External comparisons of cancer mortality rate s to the general U.S. population were made 1 using SMRs. Internal cohort comparisons of e xposure-response relationships were made using 2 the Cox regression model. This model used age as the time variable, and was adjusted for year 3 of hire and birth year. Only those causes of death for which an excess was found based on the 4 external comparisons or for which previous studies had identified a positive association were 5 selected for dose-response analyses. 6
A total of 177 cancer deaths were observed in the cohort. For the external comparison 7 with the U.S. general population, overall, no statistically significant differences were observed in 8 all cancer mortality among all workers (SMR = 1.0, 95% CI = 0.8

− 1.1). Results obtained after 9 excluding workers exposed to pentachlorophe nol were similar (SMR = 0.9, 95% CI = 0.8

− 1.1). 10 Statistically significant ex cesses in the cohort were found for leukemia (SMR = 1.9, 11
95% CI = 1.0

− 3.2) and soft tissue sarcoma (SMR = 4.1, 95% CI = 1.1

− 10.5). Although not 12 statistically significant SMRs for other ly mphohemopoietic cancers included non-Hodgkin’s 13 lymphoma SMR = 1.3; 95%CI = 0.6, 2.5) and Hodgkin’s disease (SMR = 2.2; 95% CI = 0.2, 14 6.4). 15 Internal cohort comparisons using the C ox regression model were performed for all 16 cancers combined, lung cancer, prostate can cer, leukemia, non-Hodgki n’s lymphoma, and 17
soft-tissue sarcoma. Whether the internal comparisons excluded those workers exposed to 18 pentachlorophenol is not entirely clear from the text or accompanying table, but presumably they 19
do not. The RR was 1.002 (95% CI = 0.991

− 1.013) for all cancer mortality per 1 ppb-year 20
increase in cumulative TCDD exposure was not statistica lly significant. Except for soft tissue 21 sarcomas, no statistically signifi cant exposure-response trends were observed for any cancer site. 22 For soft tissue sarcoma, analyses were based on only four deaths. 23
24
2.4.1.1.1.1.5.2. Study evaluation. 25 A key limitation of this study is that SMRs were not derived for different periods of 26 latency for the external comparison group anal ysis. The original publication on the NIOSH 27 cohort found that SMRs increased when a 20-year latency period was incor porated (Fingerhut et 28 al., 1991), and similar patterns have been observe d in other occupational cohorts (Manz et al., 29 1991; Ott and Zober, 1996) and among Seveso re sidents (Consonni et al ., 2008). Additionally, 30 dose-response analyses showed marked increas es in slopes with a 15-year latency period 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-32 (Steenland and Deddens, 2003; Cheng et al., 2006). In this context, the ab sence of an elevated 1 SMR for cancer mortality is consistent with prev ious findings of the NIOSH cohort. While the 2 cohort did have sufficient follow-up, no evaluation of possible latent effects was presented and 3 this is a major limitation of this study. Furthe r, the evaluation of the exposure metrics should be 4 expanded from what was presented in Collins et al . (2009) due to the previous analyses of the 5 same workers finding positive associations be tween cancer mortality and TCDD (Steenland et 6 al., 2001). 7 Unfortunately, the Collins et al. (2009) study did not include a cate gorical analysis of 8 TCDD exposure and cancer mortality. This cat egorical analysis would have enabled an 9
evaluation of whether a nonlinear association exis ts between TCDD exposure and cancer risk. 10 The analyses of both Cheng et al. (2006) and Stee nland et al. (2001) sugg est an attenuation of 11 effects at higher doses, and several investigations have considered log-transformed associations 12 as a means to address nonlinearity. Also, the earlier plant- specific dose-response analyses of 13 Steenland et al. (2001) are not consistent with th e findings for the Midland plant that Collins et 14 al. (2009) presented. These differences could be due to differences in the construction of 15 exposure metrics, additional follow- up, or lagging of exposures. 16
17
2.4.1.1.1.1.5.3. Suitability of data for dose-response modeling. 18 The Collins et al. (2009) study uses serum leve ls to derive TCDD exposure estimates and 19 does not appear to be subject to important biases . The reliance on data from one plant offers 20 some advantages over the multiplant analyses, as heterogeneity in exposure to other occupational 21 agents would be lower. The number of individuals who provided serum samples ( n = 280) is 22 greater than the 170 individuals used to derive TCDD estimates for the NIOSH cohort. This 23 study’s main limitation is that it lacks rigor in sensitivity analys es to explore the impact that 24 other exposure metrics and model assumptions had on the study findings. The data used in 25 quantitative dose-response modeling should retain fl exibility to account for latency effects. The 26 data, as structured for this study, are inadequa te for quantitative dose-response analysis given 27 that no dose-response effects were detected. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-33 2.4.1.1.1.2. The BASF cohort. 1 In 1953, dioxin contamination occurred as a result of an autoclav e accident during the 2 production of trichlorophenol at the BASF plant in Ludwigshafen, Germany. A second dioxin 3
incident occurred in 1988 that was attributed to the blending of thermoplastic polyesters with 4
brominated flame retardants. Of the two ev ents, the one on November 13, 1953, was associated 5 with more severe acute health effects, incl uding chloracne that resulted in immediate 6 hospitalizations for seven work ers. These adverse events were not linked to TCDD until 1957 7 when TCDD was identified as a byproduct of the production of trichlor ophenol and was shown 8 to induce chloracne (Zober et al., 1994). Zober and colleagues (1998) noted that with the 1988 9 accident, affected individuals did not exhibit clin ical symptoms or chloracne, but rather were 10 identified through “analytical measures.” In bo th instances, efforts were made to limit the 11 potential for exposure to employees. 12
13
2.4.1.1.1.2.1. Thiess and Frentzel-Beyme, 1977 and Thiess et al., 1982. 14
2.4.1.1.1.2.1.1. Study summary. 15 A study of the mortality of workers employed at the BASF plant was first presented in 16 1977 (Thiess and Frentzel-Beyme, 1977) with subs equent updates in both 1982 (Thiess et al., 17 1982), and in 1990 (Zober et al., 1990). In the first published paper (Thiess et al., 1982), 18 74 employees involved in the 1953 accident were tr aced and their death ce rtificate information 19 extracted. Of these, 66 suffered chloracne or severe dermatitis. Observed deaths were compared 20 to the expected number using three external reference groups : the town of Ludwigshafen 21 ( n = 180,000), the district of Rhinehessia-Palatinate ( n = 1.8 million), and the Federal Republic 22 of Germany ( n = 60.5 million). Another comparison group was assembled by selecting 23 age-matched employees taken from other cohorts under study. This additional comparison was 24 aimed at avoiding potential biases associated with healthy worker effect when using an external 25
referent. 26 During a follow-up interval of up to 26 years (1953

− 1979), 21 individuals died. Of 27 these, seven deaths were from cancer. The e xpected number of cancer deaths derived for the 28 three external comparison groups ranged between 4.1 and 4.2, producing an SMR of 1.7 29 ( p -values ranged between 0.12 and 0.14). Excess mo rtality was found for stomach cancer based 30 on the external comparisons ( p < 0.05); however, this was base d on only three cases. No other 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-34 statistically significant excesses were found with the external comparisons made to the other 1
cohorts of workers. 2
3
2.4.1.1.1.2.1.2. Study evaluation. 4 In the Thiess et al. (1982) study, no TCDD expo sures were derived for the workers, thus 5 no dose-reconstruction was performe d. The findings from this study are limited by the small size 6
of the cohort. The 74 workers followed in this cohort represent the smallest number of workers 7 across the occupational cohorts (F ingerhut et al., 1991; Steenland et al., 2001; Becher et al., 8 1998; Hooiveld et al., 1998; Mi chalek and Pavuk, 2008; McBride et al., 2009a, b) that have 9 investigated TCDD exposures and cancer mortality. Mechanisms of follow-up were excellent as 10 all individuals were traced, and death certificates were obtained fr om all deceased workers. 11 Although the study does compare the mortality experience to other occupational cohorts, 12 the paper provides insufficient information to ade quately interpret the associated findings. For 13 example, a description of these occupations is lacking making it impossible to determine whether 14 these cohorts were exposed to other occupati onal carcinogens that might have confounded the 15
associations between TCDD e xposure and cancer mortality. 16
17
2.4.1.1.1.2.1.3. Suitability of data for TCDD dose-response modeling. 18 Subsequent data assembled for the BA SF cohort provide more detailed exposure 19
characterization and also include information for 243 male worker s employed at the plant. As 20 such, this study did not meet the considera tions for further dose-response analysis. 21
22
2.4.1.1.1.2.2. Zober et al., 1990. 23
2.4.1.1.1.2.2.1. Study summary. 24 Zober et al. (1990) also examined the mort ality patterns of 247 individuals involved in 25 the 1953 accident at the BASF plant. As detailed in their paper, the size of the original cohort 26 was expanded by efforts to locate all individuals who were exposed in the accident or during the 27 clean-up. Three approaches were followed in asse mbling the cohort. Sixty-nine cohort members 28 were identified from the company physician’s list of employees exposed as a result of the 29 accident (Subcohort C1). Sixty-six of these workers were included in the original study 30 population of workers Thiess et al. (1982) exam ined. Eighty-four other workers who were 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-35 potentially exposed to TCDD due to their involveme nt in demolitions or operations were added 1 to the cohort. This group included 43 firemen, 18 plant workers, 7 bricklayers, 5 whitewashers, 2 4 mechanics, 2 roofers, and 5 individuals in other occupations (Subcohort C2). The cohort was 3 further augmented through the Dioxin Investiga tion Program, which sought to locate those who 4
were involved in the 1953 accident and were stil l alive in 1986. Current and former workers 5 enrolled in the study were asked to identify other current or former coworkers (including 6 deceased or retired) who might have been exposed from the accident. This third component of 7 94 workers (Subcohort C3) included 27 plant workers, 16 plumbers, 10 scaffolders, 8 10 professionals, 7 mechanics, 6 transportation wo rkers, 5 bricklayers, 5 laboratory assistant, 9 3 insulators, and 5 individuals in other occupa tions. A medical examination was performed for 10 those identified through the Dioxin Investigation Program, and blood measures were obtained for 11
28 of these workers. 12 External comparisons of the workers’ mort ality experience to th e general population of 13 the Federal Republic of West Germany were ma de using SMRs. Person-years were tabulated 14 across strata defined by calendar period, sex, an d age group. Sixty-nine deaths including 15 twenty-three from cancer were detected among the workers during the 34-year follow-up period 16 (November 17, 1953 through December 31, 1987). Caus e-specific death rates for these same 17 strata were available for the Federal Republic of West Germany. Stra tified analyses were 18
conducted to examine variations in the SMRs according to years since first exposure (0

− 9, 19
10

− 19, and ≥ 20 years) for each of the three subcohorts, as well as 114 workers with chloracne. 20 Although it was consistent in magnitude w ith findings from the NIOSH cohort, a 21 statistically significant SMR for all can cer mortality was not observed (SMR = 1.17, 22
90% CI = 0.80

− 1.66). The SMRs for each of the three su bcohorts varied substantially. For 23 Subcohorts C1, C2, and C3, the SMRs were 1.30 (90% CI = 0.68

− 2.26), 1.71 24 (90% CI = 0.96

− 2.83), and 0.48 (90% CI = 0.13

− 1.23), respectively. The SMRs increased 25 dramatically when analyses were restricted to th ose with 20 or more years since first exposure in 26 Subcohort C1 (SMR = 1.67, 90% CI = 0.78

− 3.13) and Subcohort C2 (SMR = 2.38, 27
90% CI = 1.18

− 4.29). Meanwhile, in a subgroup analysis of those with chloracne, for the period 28 of 20 or more years after first exposure, a statis tically significant excess in cancer mortality was 29 noted (SMR = 2.01; 90% CI = 1.22

− 3.15). 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-36 2.4.1.1.1.2.2.2. Study evaluation. 1 An important limitation of the study is the ma nner in which the cohort was constructed. 2 Subcohort C3 was constructed by identifying indivi duals who were alive in 1986. This resulted 3 in 97 active and retired employees who participat ed in the program, with 94 included in the 4 analysis. Although these individu als did identify other workers w ho might have also retired or 5 died, inevitably, some individuals who had died were not included in th e cohort. This would 6 serve to underestimate the SMRs that were gene rated with external comparisons to the German 7 population. Indeed, cancer mortality rates in this subcohort were about half of what would have 8 been expected based on general popul ation rates (SMR = 0.48, 90% CI = 0.13

− 1.23). 9 Additionally, more than half of Subcohort C2 were firemen (43 of 84), who would likely have 10 been exposed to other carcinogens as a conseque nce of their employment. Quantitative analyses 11 of epidemiologic data for firefighters have de monstrated increased cancer risk for several 12 different forms of cancer (Youakim, 2006). Th erefore, potential confounding from other 13 occupational exposures of the firefighters could have contri buted to the higher SMR in 14
Subcohort C2 cohort and is a concern. Data on ci garette smoking were not available either. No 15 excess for nonmalignant respiratory disease was found, however, suggesting this might not be an 16 important source of bias. 17
18
2.4.1.1.1.2.2.3. Suitability of data for TCDD dose-response modeling. 19 As with the Thiess et al. (1982) publication, wo rker exposure was not estimated. Lack of 20 exposure estimates precludes a qua ntitative dose-response analysis using these data. Also, the 21 study design is not well suited to characterization of risk using the SMR statistic. Mortality is 22 also likely under-ascertained in the large component of the cohort that was constructed through 23 the identification of surviving members of the cohort. 24
25
2.4.1.1.1.2.3. Ott and Zober, 1996. 26
2.4.1.1.1.2.3.1. Study summary. 27 Ott and Zober (1996) extended the analyses of the BASF cohort to include estimates of 28
individual-level measures of TC DD. The researchers also inves tigated associations with cancer 29 mortality and identified incide nt cancer cases. The cohort follo w-up period of 39 years extended 30 until December 31, 1992, adding 5 years to a previ ous study (Zober et al., 1990). Ott and Zober 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-37 (1996) identified incident cases of cancer using occupational medical record s, death certificates, 1 doctor’s letters, necropsy reports, and informa tion from self-reported surveys sent to all 2 surviving cohort members. Self-reported can cer diagnoses were confirmed by contacting the 3 attending physician. 4 This study characterized exposur e by two methods: (1) determ ining chloracne status of 5 the cohort members and (2) estimating cumulative TCDD ( μ g/kg) levels. In 1989, serum 6 measures were sought for all surviving members of the 1953 accident, and serum TCDD levels 7 were quantified for 138 individuals. These seru m levels were used to estimate cumulative 8
TCDD concentrations for all 254 members of the acci dent cohort. Ott et al. (1993) published a 9 description of the exposure estimation procedure, which was a regression model that accounted 10
for the circumstances and duration of individual exposure. The average internal half-life of 11 TCDD was estimated to be 5.8 years based on rep eated serum sampling of 29 individuals. The 12 regression model allowed for this half-life to vary according to the percentage of body fat, and 13 yielded half-lives of 5.1 and 8.9 years among thos e with 20% and 30% body fat, respectively. 14 Previous analyses of this cohort had used a half-life of 7.0 years (Ott et al., 1993). 15 TCDD half-life has been reported to increa se with percentage of body fat in both 16 laboratory mammals (Geyer et al., 1990) and humans (Zober and Papke, 1993). Ott and Zober 17 (1996) contend that observed correlations with ch loracne severity and cu mulative estimates of 18 TCDD exposure indirectly validated this e xposure metric. Specifically, the mean TCDD 19
concentration for those without chloracne was 38.4 ppt; for thos e with moderate and severe 20 forms of chloracne, the mean wa s 420.8 ppt and 1,008 ppt, respectively. 21 Unlike for the NIOSH cohort, individual-level data were collected for other cancer risk 22
factors. These factors incl uded body mass index at time of first exposure, history of 23 occupational exposure to β -naphthylamine and asbestos, and history of smoking. Smoking data 24
were available for 86% of the cohort. SMRs we re based on the external referent population of 25 West Germany. For cancer incidence, Ott and Zober (1996) generated standardized incidence 26 ratios (SIRs) using incidence rate s for the state of Saarland (1970

− 1991) as the external referent. 27 They calculated SMRs (and SIRs) for three ca tegories of cumulative TCDD levels: <0.1 μ g/kg, 28
0.1

− 0.99 μ g/kg and ≥ 1 μ g/kg. The Cox regression model was used to characterize risk within 29 the cohort using a continuous m easure of TCDD. These analys es considered the potential 30 confounding influence of age, smoking, and body mass index using a stepwise regression 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-38 modeling approach. The Cox modeling employed a stratified approach usi ng the date of first 1 exposure to minimize possible confounding between calendar period and exposure. The three 2 first exposure groups were exposure within the first year of the accident, exposure between 3 1 year after the accident and before 1960, and exposure after 1959. The Cox regression 4 estimates were presented in terms of conditional risk ratios (i.e., hazard ratios adjusted for body 5 mass index, smoking and age). 6 Although no statistically significant excesses relative to the general population were 7 detected for all cancer mortality, there wa s some suggestion of an exposure-response 8 relationship. In the 0.1

− 0.99 μ g/kg and ≥ 1 μ g/kg exposure groups, the all cancer SMRs were 1.2 9 (95% CI = 0.5

− 2.3) and 1.6 (95% CI = 0.9

− 2.6), respectively. Higher SM Rs for cancer (all sites 10 combined) were also found with an increase d interval since expos ure first occurred. 11 Specifically, when observed versus expected counts of cancer were compared in the time interval 12 20 years after first exposure, the SMR in the highest exposure group ( ≥ 1 μ g/kg) was 1.97 13 (95% CI = 1.05

− 5.36). An excess in lung cancer also wa s noted with the same lag in this 14 exposure group (SMR = 3.06, 95% CI = 1.12

− 6.66). For cancer incidence, a statistically 15 significant increased SIR for lung cancer was observed in the highest exposure category 16 (SIR = 2.2, 95% CI = 1.0

− 4.3), but no other statistically signif icant associations were detected 17 for any other cancer site. No cases of soft- tissue sarcoma were found among the cohort members 18 in this analysis. 19 Based on internal cohort comparisons, Cox regres sion models also were used to generate 20
hazard ratios as measures of relative risk for TCDD exposures following adjustment for 21 smoking, age and body mass index. A statistically significant association between TCDD dose 22 (per μg/kg) and cancer mortality wa s detected (RR = 1.22, 95% CI = 1.00

− 1.50), but not for 23 cancer incidence (RR = 1.11, 95% CI = 0.91

− 1.35). Statistically si gnificant findings were 24 observed for stomach cancer mortality (RR = 1.46, 95% CI = 1.13

− 1.89) and incidence 25 (RR = 1.39, 95% CI = 1.07

− 1.69). 26 The Ott and Zober (1996) study also com par ed the relationship between TCDD exposure 27 categories and cancer mortality from all sites combined according to smoking status. 28
Associations were noted between increased e xposure to TCDD and mortality from cancer among 29 smokers, but not among nonsmokers or former smokers. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-39 2.4.1.1.1.2.3.2. Study evaluation. 1 The Ott and Zober (1996) study characterizes ex posure to TCDD at an individual level. 2 Therefore, unlike in past studies involving this cohort, these data can provide an opportunity for 3 conducting quantitative dose-response modeling. As with the more recent studies involving the 4 NIOSH cohort, serum samples were obtained from surviving cohort members and then used to 5 back-extrapolate TCDD values for all cohort members. In the BASF cohort, however, serum 6
data were available for a much higher percentage of cohort me mbers (54%) than in the NIOSH 7 cohort (5%). An additional study strength was the collection of questionnaire data, which 8 allowed for the potential confounding from cigarette smoking and body mass index to be 9
examined. 10 The Ott and Zober (1996) study also eval uates the relationship between TCDD and 11
cancer incidence. Most cohort studies of TCDD- exposed workers have re lied solely on mortality 12 outcomes. The availability of incidence data bett er allows for period of latency to be described, 13 and moreover, to characterize risks associated with cancers that typical ly have long survival 14 periods. The authors provide few details on th e expected completeness of ascertainment for 15 incident cancer cases, which makes determining a ny associated bias difficult. They do, however, 16 suggest that nonfatal cancers are more likely to have been missed in the earlier part of the 17 follow-up. The net result of differential cas e ascertainment over time makes evaluating 18
differences in risk estimates across di fferent periods of latency impossible. 19 The small sample size of the cohort ( n = 243 men) likely limited the statistical power to 20 detect small associations for some of the exposur e measures. This also effectively limited the 21 ability to analyze dose-response re lationships quantitatively, partic ularly across strata such as 22 time since exposure. For site-spe cific analyses, the cancer site w ith the most cancer deaths was 23 the respiratory system ( n = 11). Thus, quantitative dose-re sponse analysis using these cohort 24
data would be limited to the evalua tion of all cancer sites combined. 25 The most important limitation of this study is related to the cons truction of the third 26 component of the cohort. As mentioned earlier, this cohort wa s assembled by actively seeking 27 out surviving members of the cohort in the mid-1980s . The mortality experi ence of this cohort is 28 much lower than that of the gene ral population over the entire follow-up, a result that is expected 29 given that the individuals were known to be alive as of 1986. The ne t result is likely an 30
underestimate of the SMR. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-40 2.4.1.1.1.2.3.3. Suitability of data for TCDD dose-response modeling. 1 This study was included in the quantita tive dose-response modeling for the 2003 2 Reassessment (U.S. EPA, 2003). The characterizati on of exposure data and availability of other 3 risk factor data at an individual level are ap propriate for use in qua ntitative dose-response 4 analyses. 5
6
2.4.1.1.1.3. The Hamburg cohort. 7 The Hamburg cohort has been the subject of seve ral cancer risk assessments. As with the 8 NIOSH and BASF cohorts, analyses have progressed from basic comparisons of mortality 9 experience to general population rates to more sophisticated inte rnal cohort analyses involving 10 the reconstruction of TCDD exposures using se rum measures. This cohort consists of 11 approximately 1,600 workers who were employed in the production of herbicides at a plant in 12 Hamburg, Germany during 1950

− 1984 (Flesch-Janys et al., 1995; Becher et al., 1998). The 13 herbicides produced included 2,4,5-T, β -hexachlorocyclohexane and li ndane. The production of 14 TCP and 2,4,5-T was halted in 1954 following a chloracne outbreak. The plant ceased 15 operations in 1984. Approximately 20 different working areas were identified, which, in turn, 16 were grouped into five main areas based on put ative TCDD exposure levels. One working area 17 was deemed to be extremely contaminated, havi ng TCDD exposures at least 20-fold higher than 18 in other areas. In this section, the studies undert aken in this cohort that have examined cancer 19 mortality are summarized. 20
21
2.4.1.1.1.3.1. Manz et al., 1991. 22
2.4.1.1.1.3.1.1. Study summary. 23 Manz et al. (1991) investigated patterns of mortality in the Hamburg cohort. The study 24 population consisted of 1,583 workers (1,184 men, 399 women) who were employed for at least 25
three months between 1952 and 1989. Casual work ers were excluded as they lack sufficient 26 personal identifying information thereby not allo wing for associations with mortality outcomes 27
to be examined. Vital status was determined using community-based registries of inhabitants 28 throughout West Germany. Cause of death until the end of 1989 was determined from medical 29
records for all cancer deaths and classified ba sed on the ninth revision of the International 30 Classification of Diseases (WHO, 1978). Alt hough Manz et al. (1991) present some data on 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-41 cancer incidence for the cohort, the data are in complete as informati on was available on only 1 12 cases; 93 cancer deaths were observed in the cohort. 2 In this study, the authors used information on production processes to group workers into 3 categories of low, medium, or high exposure to TCDD. This information was based on TCDD 4 concentrations in precursor mate rials, products, waste, and soil from the plant grounds, measured 5 after the plant closed in 1984. The distributi on of workers into the low, medium, and high 6 exposure groups was 186, 901, and 496, respectively. The authors examined the validity of the 7 three exposure categories using a separate gr oup of 48 workers who provided adipose tissue 8 samples. The median exposure of the 37 vol unteers in the high group was 137 and 60 ng/kg in 9 the remaining 11. Information about chloracne in the cohort was incomplete, and, therefore, was 10 not used as a marker of TCDD exposure. Other surrogate measures of exposure were considered 11 in this study, including duration of exposure a nd year of first employment. For the latter 12 measure, employment that began after 1954 was assumed to result in much lower exposures 13 given that production of 2,4,5-T and TCP stopped in 1954. 14 External comparisons of cancer mortality were made by calculating SMRs using the 15 general population of West Germany as a referent . Comparisons of mortality in the cohort also 16 were made to a separate cohort of 3,417 gas supply workers to avoid bias from a healthy worker 17
effect. Vital status and cause of death in the gas supply workers were determined using the same 18 methods as used in the Hamburg cohort. SMRs were calculated relati ve to both referent 19 populations (West Germany and gas supply wo rkers) across low, medium, and high TCDD 20 exposure groups. The comparison of mortality to the gas supply workers, however, extended 21 only until the end of 1985, whereas, comparisons to the general population extended until 1989. 22 Stratified analyses were undertaken to calculate SMRs for each of the three exposure groups for 23 categories of duration of employment (<20 versus ≥ 20 years) and date of entry into the cohort 24 ( ≤ 1954 versus >1954). 25 When compared to the general population, overall cancer mortality was elevated in male 26 cohort members (SMR = 1.24, 95% CI = 1.00

− 1.52) but not in females (SMR = 0.80, 27
95% CI = 0.60

− 1.05). A two-fold increase in female br east cancer mortality was noted but was 28 of borderline statistical signi ficance (SMR = 2.15, 95% CI = 0.98

− 4.09). The SMR among men 29 was further increased when analyses were restri cted to workers who were employed for at least 30 20 years (SMR = 1.87, 95% CI = 1.11

− 2.95). Analyses restricted to those in the highest 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-42 exposure group produced an even higher SMR for those with at least 20 years of employment 1 (SMR = 2.54, 95% CI = 1.10

− 5.00). Statistically significant ex cesses in risk were detected 2 among those who first worked before 1954, but not afterward. Furthermore, a dose-response 3 trend was observed across increasing exposure categories in the subset of workers employed 4 before 1954. The SMRs using the cohort of gas s upply workers as the referent group for the low, 5 medium, and high groups in this subset were 1.41 (95% CI = 0.46

− 3.28), 1.61 6 (95% CI = 1.10

− 2.44), and 2.77 (95% CI = 1.59

− 4.53), respectively. This finding is consistent 7 with what was known about TCDD exposures levels at the plant, namely, that TCDD 8 concentrations were much higher between 1951 a nd 1954, with subsequent declining levels after 9
1954. 10 Generally speaking, patterns of excess morta lity were similar when the cohort of gas 11 workers was used as a reference group. The overall SMR for men was 1.39 12 (95% CI = 1.10

− 1.75); and was 1.82 (95% CI = 0.97

− 3.11) when analyses were restricted to 13 workers with 20 or more years of employment. A dose-response trend also was observed across 14 exposure categories when analyses were restricted to those employed for at least 20 years. In 15 particular, with these analyses, no cancer de aths were observed among those in the lowest 16 exposure group, while the SMRs in the middle and high exposure groups were 1.36 17 (95% CI = 0.50

− 2.96) and 3.07 (95% CI = 1.24

− 6.33). 18 SMRs also were generated for several site-sp ecific cancers relative to the West German 19 general population and the gas worker cohort. No statisti cally significant excesses were 20 observed using the general populatio n reference. In contrast, st atistically significant excesses 21 were observed for lung cancer (SMR = 1.67, 95% CI = 1.09

− 2.44) and hematopoietic system 22 cancer (SMR = 2.65, 95% CI = 1.21

− 5.03) relative to the gas workers cohort. 23
24
2.4.1.1.1.3.1.2. Study evaluation. 25 The Manz et al. (1991) findings indicate an excess of all cancer mortality among the 26 workers with the highest exposures, particularly those who worked for at least 20 years and were 27 employed before 1954. The findings across categories of exposure within the subsets of workers 28 employed for at least 20 years and before 1954, particularly us ing the cohort of gas supply 29 workers, are consistent with a dose-response rela tionship. These elevated cancer mortality rates 30 found among those employed before 1954 were li kely due to higher TCDD exposures. Other 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-43 carcinogenic coexposures, such as benzene, asbestos , and dimethyl sulfate, could have occurred 1 among this population. Given that no substantia l changes in the production processes at the 2 Hamburg plant occurred after 1954, comparable leve ls of these coexposures would be expected 3 before and after 1954. Therefore, confounding due to these coexposures is unlikely to explain 4 the dose-response relationship demonstrated between all cancer mortality and TCDD exposures. 5 No information, however, was presented on pot ential exposure to other dioxin-like compounds 6 which may confound the associations that were detected. 7 Detailed information on workers’ smoking behaviors was not collected. Limited 8 evidence indicated, however, that smoking preval ence between the Hamburg cohort and the gas 9 supply workers cohort was quite similar. A nonr epresentative sample of 361 workers in the 10 Hamburg cohort and the sample of 2,860 workers in the gas s upply cohort indicated that the 11 self-reported smoking prevalence was 73% and 76 %, respectively. This suggests that the two 12 cohorts are comprised predominantly of smokers. The similarity in overall smoking prevalence 13 indicates that comparisons of cancer mortality between the two groups are not unduly influenced 14 by an inability to adjust for smoking. 15
16
2.4.1.1.1.3.1.3. Suitability of data for TCDD dose-response modeling. 17 The data compiled for the Manz et al. (1991) study do satisfy many of the considerations 18 for conducting quantitative dose-response analysis; he alth outcomes appear to be ascertained in 19 an unbiased manner, and exposure was characterized on an individual-level basis. However, as 20 demonstrated in later studies, there was a la rge dioxin-like compound component that was not 21 quantified or assessed in this study. Dose-re sponse associations between TCDD and cancer 22 mortality were detected, with st ronger associations observed with increased periods of latency 23
and for those who first worked when TCDD was at higher levels. 24 The size of the cohort, although not as large as the NIOSH cohort, does offer sufficient 25 statistical power to evaluate TCDD-related risk for cancers from all cancer sites. The data are 26 limited, however, for characterizing cancer risks among women; only 20 cancer deaths occurred 27
in the 399 women included in the cohort. It is unlikely that the findings are biased by 28 confounding due to cigarette smoking since dose -response patterns were strengthened when 29 comparisons were made to the cohort of ga s supply workers rather the general population 30 referent where smoking rates were likely lower. The inability to account for other occupational 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-44 exposure when TCDD exposures were much higher (pre-1955) could result in confounding if 1 these other exposures were related to TCDD and the health outcomes under consideration. This 2 data set would be suitable for quantita tive dose-response modeling if the exposure 3 characterization of the cohort could be impr oved using biological measures of dose. 4
5
2.4.1.1.1.3.2. Flesch-Janys et al., 1995. 6
2.4.1.1.1.3.2.1. Study summary. 7 In 1995, Flesch-Janys et al. published an analysis of the male employees from the 8 Hamburg cohort that extended the follow-up to 40 years (1952

− 1992). Inclusion of these three 9 additional years of follow-up resulted in a sample size of 1,189 male workers. 10 The authors estimated a quantitative exposure variable for concentr ations of TCDD in 11 blood at the end of exposure (i.e ., when employment in a department ended) and above German 12 median background TCDD levels. The TCDD exposure assessment defined 14 production 13 departments according to TCDD levels in various pr oducts in the plant, in waste products, and in 14 various buildings. The time (in years) each worker spent in each department then was 15
calculated. Concentrations of TCDD were determined in 190 male workers using serum 16 ( n = 142) and adipose tissue samples ( n = 48). The authors used a first-order kinetic model to 17 calculate TCDD levels at the end of exposure fo r the 190 workers with av ailable polychlorinated 18
dibenzo- р -dioxin (PCDD) and -furan (PCDF) at various time points. Half-lives were calculated 19 from an elimination study of 48 workers from this cohort, and the median TCDD background 20 level was estimated at 3.4 ng/kg blood fat from the German population (Papke et al., 1994; 21 Flesch-Janys et al., 1994). Using the one-compartme nt, first-order kinetic m odel, the half-life of 22 TCDD was estimated to be 6.9 years (Flesch-Ja nys, 1997). Increased age and higher body fat 23 percentage were associated with increased TCDD half-life, while smoking was associated with a 24 higher decay rate for most of the congeners ex amined (Flesch-Janys et al., 1996). Cumulative 25 TCDD exposures were estimated by summing exposur es over the time spent in all production 26 departments and were expressed in terms of ng/ kg of blood fat. The authors also applied a 27 metric of total toxicity equivalence (TOTTEQ) as the weighted sum of all congeners where 28 weights were TEQs that denot ed the toxicity of each congener relative to TCDD. 29 Similar to previous analyses on this cohort, comparisons were made using an external 30 referent group of workers from a gas supply co mpany (Manz et al., 1991). In contrast to 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-45 previous analyses where SMR statistics were gene rated using this “externa l” reference, however, 1 Flesch-Janys et al. (1995) used Cox regressi on. The Cox regression models treated the gas 2
worker cohort as the refere nt group, and six exposure groups were defined by serum-derived 3 cumulative TCDD estimates. The groups were determ ined by using the first four quintiles with 4 the upper two exposure categories co rresponding to the ninth and te nth deciles of the cumulative 5 TCDD. Internal cohort comparis ons used those workers in the lo west quintile as the referent 6 group, as opposed to the cohort of gas workers. A similar approach was used to model TEQs. 7 No known TCDD exposures occurred in the gas workers, so they were assigned exposures based 8 on the median background levels in the gene ral population. RRs were calculated based on 9 exposure above background levels; in other word s, background levels were assumed to be 10 equivalent across all workers and also for those employed by the gas supply company. The RRs 11 derived using the Cox model were adjusted for total duration of em ployment, age, and year when 12 employment began. 13 The Cox regression with the cohort of gas wo rkers as the referent exposure group yielded 14 a linear dose-response relationship between cumu lative TCDD exposure a nd cancer mortality for 15 all sites combined ( p < 0.01). The RRs for all-cancer mortality were 1.59, 1.29, 1.66, 1.60, 1.70, 16 and 3.30. For four of the six categories (excluding the referent group), the RRs were statistically 17 significant ( p < 0.05); in the highest TC DD exposure category (344.7

− 3890.2 ng/kg) the RR was 18 3.30 (95% CI = 2.05

− 5.31). Similar findings were evident with TOTTEQ. A dose-response 19 pattern for all cancer mortality ( p < 0.01) based on the internal cohort comparisons was also 20
detected. 21 The authors performed an additional analysis to evaluate the potential confounding role 22 of dimethylsulfate. Although no direct measur es of dimethylsulfate were available, the 23 investigators repeated analyses by excluding 149 workers who were employed in the department 24 where dimethylsulfate was present. A dose-response pattern persisted for TCDD ( p < 0.01), and 25 those in the highest exposure group (344.7

− 3,890.2 ng/kg of blood fat) had a RR of 2.28 26 (95% CI = 1.14

− 4.59). 27
28
2.4.1.1.1.3.2.2. Study evaluation. 29 The Flesch-Janys et al. (1995) study used se rum-based measures to determine cumulative 30 exposure to TCDD at the end of employment for al l cohort members. They used the standard 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-46 one-compartment, first-order kinetic model and samples obtained from 190 male workers. This 1 quantitative measure of exposure permits an estimation of a dose-response relationship. 2 Confounding for other occupationa l exposures is unlikely to ha ve biased the results. A 3 dose-response relationship persis ted after excluding workers exposed to dimethylsulfate. Other 4 potential exposures of interest included ben zene and isomers of hexachlorocyclohexane. 5 Exposure to these agents, however, was highest in the hexachlorocy clohexane and lindane 6 department, where TCDD exposures were lower. As outlined earlier, the study findings are 7 unlikely to be biased for cigarette smoking as ci garette smoking in the cohort was similar to that 8 in the comparison population. Moreover, more recent analyses of serum-based TCDD exposure 9 measures found no correlation with smoking status in this cohort (Flesch-Janys et al., 1995)—a 10 necessary condition for confounding. 11 The authors used an exposure metric th at described cumulative TCDD exposure of 12 workers at the time they were last exposed. As a result, the authors were unable to characterize 13
risks associated with this metric for different periods of latency despite a sufficient follow-up 14 period. Subsequent analyses constructed time-dependent measures of cumulative TCDD and 15 accounted for excretion of TCDD during follow-up. 16
In contrast to most risk assessments of TCDD exposure, this study modeled the 17 relationship between other diox in-like compounds and the risk of cancer mortality using the 18 TOTTEQ metric. 19
20
2.4.1.1.1.3.2.3. Suitability of data for TCDD dose-response modeling. 21 The data used in this study satisfy most of the considerations developed for performing a 22 quantitative dose-response analysis. However, latency period was not examined in this study. 23 Dose-response analyses were, therefore, limited to a subsequent study of th is cohort (Becher et 24 al., 1998), which did examine latency. 25
26
2.4.1.1.1.3.3. Flesch-Janys et al., 1998. 27
2.4.1.1.1.3.3.1. Study summary. 28 Flesch-Janys et al. (1998) undertook another analysis on this cohort that incorporated 29 additional sera data for 275 workers (39 females and 236 males). The follow-up period was the 30 same as that used in the 1995 analyses, with mortality follow-up extending until December 31, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-47 1992. Analyses were based on 1,189 males who we re employed for at least 3 months from 1 January 1, 1952 onward. The authors continue d this dose-response analysis to address 2 limitations in their previous work. One limitati on was that the previous method did not account 3 for the elimination of TCDD while exposures were being accrued during follow-up. A second 4
limitation was that the amount of time worker s spent in different departments was not 5 considered. In the 1998 study, th e “area under the curve” approach was used because it accounts 6
for variations in concentrations over time and reflects cumulative exposure to TCDD. The 7
authors used a first-order kineti c model to link blood levels an d working histories to derive 8 department-specific dose rates for TCDD. The TCDD background level of 3.4 ng/kg blood fat 9 for the German population was used (Papke et al., 1994). The dose rates were applied to 10
estimate the concentration of TCDD at every point in time for al l cohort members. A cumulative 11 measure expressed as ng/kg blood fat multiplied by years was calculated and used in the SMR 12 analysis. SMRs were calculated using gene ral population mortality rates for the German 13 population between 1952 and 1992. No lag period wa s incorporated into the derivation of the 14 SMRs. The SMRs were estimated for the entire cohort and for exposure groups based on 15 quartiles obtained from the area under the curve. Linear trend tests were also performed. The 16 overall SMR for cancer mortality in the cohort was 1.41 (95% CI = 1.17

− 1.68). This SMR value 17 was higher than the SMR of 1.21 reported for this same cohort with 3 fewer years of follow-up 18 (Manz et al., 1991). In terms of site-speci fic cancer mortality, excesses were found for 19 respiratory cancer (SMR = 1.71, 95% CI = 1.24

− 2.29) and rectal cancer (SMR = 2.30, 20
95% CI = 1.05

− 2.47). Increased risk for lymphatic and hematopoietic cancer (SMR = 2.16, 21
95% CI = 1.11

− 3.17) were also noted largely attr ibutable (SMR = 3.73, 95% CI = 1.20

− 8.71) to 22 lymphosarcoma (i.e., non-Hodgkin’s lymphoma). A dose-response relationship was observed 23 across quartiles of cumulative TCDD for all-cancer mortality ( p < 0.01). The SMRs for these 24 quartiles were 1.24, 1.34, 1.34, and 1.73. Dose-respons e relationships were not observed for 25 lung cancer or hematopoietic cancers using this sa me metric. Dose-response relationships were 26 not observed with cumulative TEQ for any of th e cancer sites examined (i.e., all cancers, lung 27 cancer, hematopoietic cancer). 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-48 2.4.1.1.1.3.3.2. Study evaluation. 1 The approach used in the Flesch-Janys et al. (1998) study offers a di stinct advantage over 2 earlier analyses involving the sa me cohort. Three more years of follow-up were available, and 3 the characterization of exposure using the “area under the curv e” better captures changes in 4 cumulative exposure using a person-years approach rather than cumulative TCDD at the time of 5 last exposure. As noted prev iously, other occupational expo sures or cigarette smoking are 6 unlikely to have biased the study findings. A sufficient length of follow-up had accrued, and 7 dose-response associations were evident. Dioxin-like compounds were evaluated in this study. 8 For TCDD, the mean concentration was 101.3 ng/kg at the time of measurement. For other 9 higher chlorinated congeners, the corres ponding mean (without TCDD) was 89.3 ng/kg. 10
11 2.4.1.1.1.3.3.3. Suitability of data for TCDD dose-response modeling. 12 The data used in this study satisfy most of the considerations developed for performing a 13 quantitative dose-response analys is. However, latency was not examined in this study. 14 Dose-response analyses were, therefore, limited to a subsequent study of th is cohort (Becher et 15 al., 1998) which did examine latency and supers edes the Flesch-Janys et al. (1998) study. 16
17
2.4.1.1.1.3.4. Becher et al., 1998. 18
2.4.1.1.1.3.4.1. Study summary. 19 The Becher et al. (1998) quant itative cancer risk assessmen t for the Hamburg cohort was 20 highlighted in the 2003 Reassessment as bei ng appropriate for conducting dose-response 21 analysis. The integrated TCDD concentration over time, as estimated in the Flesch-Janys et al. 22 (1998) study, was used as the exposure variable. Estimates of the half-life of TCDD based on 23 the sample of 48 individuals with repeated measures were incorporated into the model that 24 back-calculated TCDD exposures to the end of the employment (Flesch-Janys et al., 1996). This 25 method took into account the age and body fat percen tage of the workers. In Becher et al. 26 (1998), the analysis used the estimate of cumu lative dose (integrated dose or area under the 27 curve) as a time-dependent variable. 28 Poisson and Cox regression models were used to characterize dose-response 29 relationships. Both models were applied to internal comparis ons where a person-years offset 30 was used and to an external comparison where an o ffset of expected number of deaths was used. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-49 The person-years offset was used to account for varying person-time accrued by workers across 1 exposure categories. The use of the expected number of deaths as an offset allows risks to be 2 described in relation to that expe cted in the general population. Within each classification cell of 3 deaths and person-years, a continuous value TC DD and TEQ levels based on the geometric mean 4
were entered into the Poisson model. For the Cox model, accumulated dose was estimated based 5 on area under the curve for TCDD, TEQ, TEQ without TCDD, and β -hexachlorocyclohexane. 6 These other coexposure metrics were adjusted for in the Cox regression analyses. Other 7
covariates considered included in the models were year of entry, year of birth, and age at entry 8 into the cohort. A background level of 3.4 ng/kg blood fat for the German population was used 9 (Papke et al., 1994). A variety of latencies was evaluated (0, 5, 10, 15, and 20 years), and 10
attributable risk and absolute risk were es timated. The unexposed cohort of gas workers was 11 used for most internal analyses. 12
Internal and external comparisons using the Poisson model found positive associations 13 with TCDD exposure and mortality from all cancers combined. The slope associated with the 14 continuous measure of TCDD ( μ g/kg blood fat × years) for th e internal comparison was 0.027 15 ( p < 0.001), which decreased to 0.0156 ( p = 0.07) after adjusting fo r age and calendar period. 16 The slope for the external comparison was 0.0163 ( p = 0.055); this estimate was not adjusted for 17 other covariates. For TEQ, the slopes ba sed on the internal comparisons were 0.0274 ( p < 0.001) 18 in the univariate model and 0.0107 ( p = 0.175) in the multivariate model after adjusting for age 19 and calendar period. The external estimate of slope for TEQ was 0.0109 ( p = 0.164). Cox 20 regression of TCDD across six exposure categories, with a lag of 0 year s, found a statistically 21 significant linear trend ( p = 0.03) and those in the upper exposure group had a RR of 2.19 22 (95% CI = 0.76

− 6.29). These estimates were adjusted for year of entry, age at entry, and 23 duration of employment. A similar pattern was observed with the Cox regression analysis of 24 TEQ; the linear test for trend, however, wa s marginally statis tically significant ( p = 0.06). 25 Cox regression models that included bot h TCDD and TEQ (excluding TCDD) were 26 applied. In this m odel, the slope parameter for TCDD was marginally statistically significant 27 ( β = 0.0089, p = 0.058), while the coefficient for TEQ (excluding TCDD) was not statistically 28 significant ( β = -0.024, p = 0.70). This suggests that confounding by other dioxin-like 29 compounds was unlikely and the increased risk of cancer was due to TCDD exposure. For all 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-50 TEQs combined, the parameter estimate was gene rated from another Cox regression model and 1 was borderline statistically significant ( β = 0.0078, p = 0.066). 2 The authors used multiple Cox models to evaluate the effect of latency. The slope 3 estimates for both TCDD and TEQ increased drama tically with increasing latency. The slope 4 estimates for TCDD increased from 0.0096 to 0.0160 ( p < 0.05) when latency was increased 5 from 0 to 20 years. Similar changes in the TEQ slopes were noted (0.0093 to 0.0157). 6 Evaluations of dose-response curves found that th e best-fitting curve was concave in shape, 7 thereby yielding higher risk at lo w exposure. Differences between the fit of the class of models 8 considered [i.e., RR(x, β ) = exp ( β log(kx = 1))], however, were small. 9 Attributable risks were generated only for TC DD, as the data suggested no effects with 10 other TEQs. The additional lifetime risk of cancer assuming a daily intake of 1 pg TCDD/kg 11 body weight/day was estimated to range between 0.001 and 0.01. 12
13
2.4.1.1.1.3.4.2. Study evaluation. 14 The Becher et al. (1998) study represent perhaps the most de tailed analyses performed on 15 any cohort to date. The findings were robust, as similar patter ns were found with and without 16 using the gas supply worker cohort as the re ferent group. Exposures to other potential 17 confounding coexposures, such as dioxin-like co mpounds, were taken into account, and workers 18 with exposure to other carcinogens (e.g., lindane ) were excluded. Furthermore, latency was 19 examined in this study, unlike earl ier studies of this cohort. 20
21
2.4.1.1.1.3.4.3. Suitability of data for TCDD dose-response modeling. 22 This study was included in the quantita tive dose-response modeling for the 2003 23 Reassessment (U.S. EPA, 2003). The data in th e Becher et al. (1998) study are suitable for 24 conducting quantitative dose-response modeling. The exposure data capture cumulative 25 exposure to TCDD as well as exposures to ot her dioxin-like compounds. The length of the 26 follow-up is sufficient, and the study appears to not be subject to confounding or other types of 27 biases. Therefore, this study is utiliz ed in quantitative dose-response analysis. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-51 2.4.1.1.1.4. The Seveso cohort. 1 Several studies have evaluated the morbidity a nd mortality effects of residents exposed to 2 TCDD following a July 10, 1976, accidental release th rough an exhaust pipe at a chemical plant 3 in the town of Meda near Seveso, Italy. The released fluid mixture contained 2,4,5-T, sodium 4 trichlorophenate, ethylene glyc ol, and sodium hydroxide. Ve getation in the area showed 5 immediate signs of damage, and in the days foll owing the accident, resi dents developed nausea, 6 headaches, eye irritation, and dermal lesions, particularly children. 7 This accident transported TCDD up to 6 km from the plant. Soil samples taken near the 8 plant revealed average levels of TCDD that ranged from 15.5 μ g/m 2 to 580.4 μ g/m 2 in the most 9 contaminated area near the plant (referred to as Z one A) (Bertazzi et al., 2001). Zone A covered 10 87 hectares and extended 2,200 m south from the pl ant. Another, more distant contaminated 11 zone (Zone B) covering 270 hectares also had contaminated soil levels, but the TCDD 12 concentration range was much lower (1.7

− 4.3 μ g/m 3 ). A reference zone (Zone R), which 13 surrounded the two contaminated areas, had lower TCDD soil levels (range: 0.9

− 1.4 μ g/m 3 ) and 14 included approximately 30,000 resident s. Following the accident, most residents in Zone A left 15 the area. Although residents in Zone B remaine d, they were under strict regulations to avoid 16 consuming homegrown products. In total, 736, 4,737, and 31,800 individuals lived in Zones A, 17 B, and R, respectively. Within days of th e accident, 3,300 animals (mostly poultry and rabbits) 18 were found dead. Emergency slaughtering was undertaken to prevent TCDD from entering the 19 food chain, and within 2 years more than 80,000 animals had been slaughtered. Mechanisms 20 were put into place for long-term follow-up of thes e residents. Unlike the other studies based on 21 occupational cohorts, the follow-up of this populati on allows for risks to be characterized for 22
females. 23 The mortality studies from Seveso published to date have not incorporated serum TCDD 24
levels that were measured in individuals. Needha m et al. (1997) describe the collection of serum 25 samples from a sample of the exposed populatio n and control subjects in 1976. In 1988, human 26 exposure to TCDD was assessed by measuring sma ll volumes of serum remaining from medical 27
examinations done in 1976. An examination of th ese data revealed some of the highest serum 28 TCDD levels ever reported, that the half-life of TCDD in this population was between 7 and 29 8 years, and that half-life varied between wome n and men. The half-life of TCDD in serum was 30 longer in women (~9 years) than in men (~7 year s) (Needham et al., 1994). In this report, the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-52 findings of studies that charac terized cancer risks in relation to exposure to TCDD from the 1976 1 accident are highlighted. These studies include comparisons of cancer mortality rates to the 2 general population based on zone of residence at the time of accident (Bertazzi et al., 2001; 3 Consonni et al., 2008). More recent work done by Warner et al. (2002) investigated the 4 relationship between serum-base d measures of TCDD and breast cancer among participants in 5 the Seveso Women’s Health Study (SWHS). 6
7
2.4.1.1.1.4.1. Bertazzi et al., 2001. 8
2.4.1.1.1.4.1.1. Study summary. 9 Several studies have reported on the mortality experience of Se veso residents. The more 10 recent publications having a longer follow-up of the cohort are evaluated here. In 2001, the 11 findings from a 20-year mortality study of Seveso residents was pub lished (Bertazzi et al., 2001). 12 The Bertazzi et al. (2001) study was an extension of the 10- and 15-year follow-ups for mortality 13 (Bertazzi et al., 1989, 1997; Pesato ri et al., 1998) and the 10-year follow-up for cancer incidence 14 (Bertazzi et al., 1993). 15 In this cohort, TCDD exposures were assi gned to the population using a three-level 16 categorical variable representative of the individual ’s place of residence (Zones A, B, or R) at the 17 time of the accident or when the person first becam e a resident of the z one, if that was after 18 1976. An external comparison to the province of Lombardy was made by ge nerating rate ratios 19 (RR) using Poisson regression techniques. Person-years of follow-up were tabulated across 20 strata defined by age, zone of residence, duration of reside nce, gender, calendar time, and 21 number of years that had elap sed since the time of exposure. Mortality rates during the 22 preaccident period also were compared to evaluate potential changes in rates due to the accident 23 and to evaluate whether patterns were cons istent before and after the accident. 24 No overall excess in mortality rates from all cancer sites combined was observed in 25 Zones A or B (combined) when compared to the reference population of Lombardy 26 ( n = 9 million residents) (RR = 1.0, 95% CI = 0.9

− 1.2). Analyses of site-specific cancer 27 mortality revealed statistically significant excesses among resident s in Zones A or B (combined) 28 for cancer of the rectum (RR = 1.8, 95% CI = 1.0

− 3.3) and lymphatic and hematopoietic 29 malignancies (RR = 1.7, 95% CI = 1.2

− 2.5). Lymphatic and hematopoietic malignancies were 30
elevated in women (RR = 1.8, 95% CI = 1.1

− 3.2) and in men (RR = 1.7, 95% CI = 1.0

− 2.8). 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-53 Analyses stratified by the num ber of years since first exposure (i.e., 1976) revealed 1 higher risk among men with an increased number of years elapsed. Similar to other studies, the 2 RR for all cancers (combined) was 1.3 (95% CI = 1.0-1.7) among men 15

− 20 years after first 3 exposure. No such increase after 15 year s postexposure, however, was noted in women 4 (RR = 0.8, 95% CI = 0.6

− 1.2). 5
6
2.4.1.1.1.4.1.2. Study evaluation. 7 Ascertainment of mortality appears to be ex cellent. Vital status was established using 8 similar methods for both the exposed and refe rence populations. No individual data were 9 collected and, therefore, the possibility that confounding by individual characteristics such as 10 cigarette smoking cannot be enti rely dismissed. Bertazzi et al. (2001) do note that the 11 sociodemographic characteristic s of residents in the three zones were similar based on 12 independently conducted surveys, and no differen ces in chronic respiratory disease were found 13
across the different zones. If excess mortalit y was attributable to cigarette smoking, such 14 excesses would be expected to be evident duri ng the entire study period. Latency analyses 15
revealed elevated risks 15

− 20 years postaccident. Finally, no excesses were observed for other 16 smoking-related cancers of the larynx, esophagus, pancreas, and bladder. The observed excesses 17 in all cancer mortality do not appear to be at tributed to differential smoking rates between the 18 two populations. 19 To examine potential for bias due to noncom parability in the tw o study populations, a 20 comparison of cancer mortality rates between the Seveso regions and the reference population of 21 Lombardy was conducted. Elevated rates for brai n cancer mortality we re noted in Seveso 22 relative to Lombardy, but the higher rates of leukemia mortality were found in Lombardy 23 relative to Seveso. That no excess was reported fo r all cancer sites combined lends credence to 24 the hypothesis that the exposure to TCDD from th e accident increased rate s of cancer after a 25 sufficient period of latency. 26 Stratified analyses were performed across se veral categorical vari ables including gender 27 and time since exposure. The numbers of cancer s ite-specific deaths are quite small in many of 28 the 5-year increments since firs t exposure. The study, therefore, has limited statistical power to 29 detect differences in mortality rates among the comparison groups for many cancer sites. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-54 Bertazzi et al. (2001) assigned exposures based on zone of residence. Soil sampling 1
within each zone revealed cons iderable variability in TCDD soil levels within each zone. 2 Moreover, some individuals would have left the area shortly afte r the accident, and determining 3
the extent to which individuals in Zone B who were subject to the recommendations near the 4
time of the accident adhered to them is difficult. As a result, exposure misclassification is 5 possible, and the use of individu al measures of TCDD level in serum is preferred over zone of 6 residence for determining exposure. As noted by th e authors, the study is be tter suited to “hazard 7 identification” than to quanti tative dose-response analysis. 8
9
2.4.1.1.1.4.1.3. Suitability of data for TCDD dose-response modeling. 10 Given the variability in soil TCDD levels with in each zone and the lack of individual 11 level, no effective dose can be es timated for quantitativ e dose-response analyses. Uncertainty in 12 identifying the critical exposure window for the Seveso cohort is a key limitation. The 13 evaluation of this study indicates that this st udy is not suitable for quantitative dose-response 14 analysis. 15
16
2.4.1.1.1.4.2. Pesatori et al., 2003. 17
2.4.1.1.1.4.2.1. Study summary. 18 Pesatori et al. (2003) publishe d a review of the short- and long-term studies of morbidity 19 and mortality outcomes in the Seveso cohort in 20 03. This paper presented external comparisons 20
of cancer incidence from 1977 to 1991 for Seveso males and females separately. As in the 21 Bertazzi et al. (2001) study, RRs were estimated using Poisson regression. For males who lived 22 in Zones A and B, the only statistically significantly elevated RR was for lymphatic and 23 hematopoietic cancers (RR = 1.9, 95% CI = 1.1

− 3.1). This excess was due primarily to 24 non-Hodgkin’s lymphoma, which accounted for 8 of the 15 incidence cases (RR = 2.6, 25
95% CI = 1.3

− 5.3). Among females living in Zones A a nd B, higher rates were observed for 26 multiple myeloma (RR = 4.9, 95% CI = 1.5

− 16.1), cancer of the vagina (RR = 5.5, 27
95% CI = 1.3

− 23.8), and cancer of the biliary tract (RR = 3.0, 95% CI = 1.1

− 8.2). 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-55 2.4.1.1.1.4.2.2. Study evaluation. 1 All RRs presented in the Pesatori et al. (2003) study were based on fewer than 2
five incident cases. 3
4
2.4.1.1.1.4.2.3. Suitability of data for TCDD dose-response modeling. 5 As with the studies of mortality among Seveso residents, the Pesato ri et al. (2003) study 6 does not capture TCDD exposure on an individual ba sis, and soil TCDD levels considerably vary 7 within each zone. Therefore, the quality of the exposure data is insufficient for estimating the 8 effective dose needed for quantitative dose-response analysis. 9
10
2.4.1.1.1.4.3. Consonni et al., 2008. 11
2.4.1.1.1.4.3.1. Study summary. 12 Similar analytic methods were applied with 25 years of follow-up of the Seveso cohort 13 (Consonni et al., 2008). An important addition in this paper was the presentation of RRs for 14 Zone R, which had the lowest TCDD levels. Po isson regression models were used to calculate 15 RRs of mortality using Seregno as the reference population. Cancer deaths observed in Zones A 16 and B were 42 and 244, respectively. 17 No statistically significant differences in all cancer mortality relative to the reference 18 population were noted in any of the zones (Zone A: RR = 1.03, 95% CI = 0.76

− 1.39; Zone B: 19 RR = 0.92, 95% CI = 0.81

− 1.05; Zone R: RR = 0.97, 95% CI = 0.92

− 1.02). Statistically 20 significant excesses in mortality fr om non-Hodgkin’s lymphoma (RR = 3.35, 21
95% CI = 1.07

− 10.46) and multiple myeloma (RR = 4.34, 95% CI = 1.07

− 17.52) were observed 22 in the area with the highest TCDD levels (Zone A). No other statistically significant increases in 23 cancer mortality relative to the reference populat ion were apparent. The absence of elevated 24 breast cancer mortality among women in this stu dy was noteworthy, as this finding differs from 25 the results of a study of Seveso women for wh ich TCDD exposures were estimated using serum 26 samples (Warner et al., 2002). A more detailed description of this study is provided later in this 27
section. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-56 2.4.1.1.1.4.3.2. Study evaluation. 1 Although no individual-level data on smoki ng were available, the potential for 2 confounding is likely minimal. Independent sm oking surveys found that the smoking prevalence 3 rates in Desio, one of cities affect ed by the accident, were similar to those in districts just outside 4 the study area (Cesana et al., 1995). As mentione d earlier, one would expect elevated RRs over 5 the entire study period if smoking had biasedthe study results, and not just after 15

− 20 years 6 since exposure to TCDD. 7
8
2.4.1.1.1.4.3.3. Suitability of data for TCDD dose-response modeling. 9 The lack of individual-level exposure data precludes quantitative dose-response modeling 10 using these data. 11
12
2.4.1.1.1.4.4. Baccarelli et al., 2006. 13
2.4.1.1.1.4.4.1. Study summary. 14 Given previous findings from Seveso, B accarelli et al. (2006) examined t(14;18) 15 translocations in the DNA of circulating lymphoc ytes of healthy dioxin-exposed individuals. 16
These translocations are associated with the development of cancer, namely follicular 17 lymphomas. The study included 211 healthy s ubjects of the Seveso area, and 101 who had 18 developed chloracne. The investigators analyzed data from 72 high-TCDD plasma level 19 individuals ( ≥ 10 ppt) and 72 low-TCDD plasma levels (<10 ppt). A three-level categorical 20 variable was used to evaluate dose-response. This variable was developed by dividing those 21 with exposures ≥ 10 ppt into two groups: 10– <50 ppt, and 50

− 475.0 ppt. Trained interviewers 22 administered a questionnaire that collected da ta on demographic charac teristics, diet, and 23 residential and occupational history. 24 The prevalence of t(14;18) was estimated as those individuals havi ng a t(14;18) positive 25 blood sample divided by the t(14;18) frequency (number of copies per million lymphocytes). 26 Baccarelli et al. (2006) found that the frequency of t(14;18) was a ssociated with plasma TCDD 27 levels, but no association between TCDD and the prevalence of t( 14;18) was detected. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-57 2.4.1.1.1.4.4.2. Study evaluation. 1 Whether the frequency of t(14;18) associated wi th plasma TCDD levels translates into an 2 increased risk of lymphoma is uncertain as prospective data of TCDD on those who developed 3 non-Hodgkin’s lymphoma are lacking. Moreover, the t(14;18) transloc ation could be an 4 important event in the pre-B stag e cell that contributes to tumo rigenicity, however subsequent 5 exposure to carcinogenic agents might be nece ssary for t(14;18) cells to develop into a 6 malignancy (Hoglund et al., 2004). 7
8
2.4.1.1.1.4.4.3. Suitability of data for TCDD dose-response modeling. 9 Given that current TCDD plasma levels were measured for this study, it is unclear if the 10 effects of lymphocyte translocati ons may be due to initial high exposure or are a function of the 11 cumulative exposure for a longer exposure window . Additionally, whether the frequency of 12 t(14;18) associated with plasma TCDD levels translates into an increased risk of lymphoma is 13 unknown. Dose-response analysis for this outcome, therefore, was not conducted. 14
15
2.4.1.1.1.4.5. Warner et al., 2002. 16
2.4.1.1.1.4.5.1. Study summary. 17 To date, Warner et al. (2002) is the only published investigation of the relationship 18
between serum-based measures of TCDD and cancer in Seveso. Eligible participants from the 19 SWHS (see Section 2.4.1.2.1.4 for details) were wome n who, at the time of the accident in 1976, 20 were 40 years of age or younger, had lived in one of the most highly contaminated zones (A or 21 B), and had adequate sera collected soon after the explosion. Enrollment in SWHS was begun in 22 March 1996 and lasted until July 1998. Of th e total 1,271 eligible women, 981 agreed to 23 participate in the study. Cancer cases were id entified during intervie w and confirmed through 24
review of medical records. Information on othe r risk factors including reproductive history and 25 cigarette smoking was obt ained through interview. 26 Serum volumes greater than 0.5 mL co llected between 1976 and 1981 volume were 27 analyzed. Most sera we re collected in 1976/77 ( n = 899); samples were collected in 1978

− 1981 28 for 54 women, and in 1996/97 for 28 women. Fo r most samples collected after 1977, serum 29 TCDD levels were back-extrapolated using a firs t-order kinetic model with a 9-year half-life 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-58 (Pirkle et al. 1989). For 96 wome n with undetectable values, a serum level that was equal to 1
one-half the detection level was used. 2 Analyses were based only on women who pr ovided serum samples; no extrapolation of 3 values to a larger population was done. Risks were therefore generated usi ng data collected at an 4 individual level. Serum TCDD was analyzed as both a continuous variable and a categorical 5
variable. The distribu tion of serum TCDD levels of the 15 cases of breast cancer was examined 6
in relation to the distribution of all women in the SWHS. Th e median exposure was slightly 7 higher among with the 15 cases of breast cancer (71.8 ppt) compared to th ose without (55.1 ppt), 8 and the exposure distribution among breast cancer cases appeared to be shifted to the right (i.e., 9 the exposures were higher but followed the same distribution); however, no formal test of 10 significance was conducted. 11 Warner et al. (2002) used Cox proportional haza rds modeling techniques to evaluate risk 12
of breast cancer in relation to TCDD serum levels while controlling for a vari ety of potential risk 13 factors. In all, 21 women had been diagnosed w ith cancer, and of these, 15 cases were cancer of 14 the breast. The analysis revealed that for ever y 10-fold increase in TCDD log-serum levels (e.g., 15 from 10 to 100 ppt) the risk of breast cancer in creased by 2.1 (95% CI = 1.0

− 4.6). Risk 16 estimates also were generated across four categories (<20, 20.1

− 44, 44.1

− 100, >100 ppt), with 17 the lowest category used as the reference. Th e RRs estimated in the third and fourth highest 18 exposure categories were 4.5 (95% CI = 0.6

− 36.8) and 3.3 (95% CI = 0.4

− 28.0). Although 19 statistical significance was not achieved for eith er category, likely because of the small number 20 of cases, the greater than threefol d risk evident in both categories is worth noting. Given that the 21 reference category had only one incident case un derscores the limited inferences that can be 22 drawn from these analyses. The authors adjusted for nume rous potential confounders, but 23
observed no differences between the crude and adjusted results; the authors, therefore, presented 24 unadjusted risks. 25
26
2.4.1.1.1.4.5.2. Study evaluation. 27 The findings from the Warner et al. (2002) st udy differ from reports in earlier studies in 28 which mortality outcomes noted the absence of an SMR association. The design of this study is 29 much stronger than earlier ones, given the impr oved characterization of exposure, the ability to 30 compare incidence rates within the cohort, the abili ty to control for potential confounding 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-59 variables at an individual level, and the availability of incident outcomes. The use of incident 1 cases (versus mortality data) should also help minimize potential bias due to disease survival. 2 Another important advantage was the ability to measure TCDD near the time of the accident, 3 thereby reducing the potential for exposure measurement error. 4 A potentially important limitation of the Warner et al. (2002) study wa s that information 5 was collected only from those who were alive as of March 1996. Therefore, TCDD and other 6
relevant risk factor data coul d not be collected for those who had previously died of breast 7 cancer. Thirty-three women could not participate b ecause they were either too ill or had died. 8 Of these, three died of breast cancer. Given th at there were only 15 breast cancer cases, the 9 exclusion of these 3 cases could have dramatically impacted the findings in either direction. 10 Another limitation was that, at the time of the follow-up, most women were still 11 premenopausal and therefore, most of the cohort ha d not yet attained the ag e of greater risk of 12 breast cancer. An ongoing follow-up of the cohort should be completed by 2010, which should 13
allow for increased number of incident breast cance rs to be identified. Given that the current 14 analyses were based only on 15 incident cases, this will substantially improve the statistical 15 power of the study. A secondary benefit is th at the increased follo w-up will allow for an 16 investigation of possible differen tial effects according to the age the women were at the time of 17

exposure. 18
19
2.4.1.1.1.4.5.3. Suitability of data for TCDD dose-response modeling. 20 Several aspects of the Warner et al. (2002) study are weakness es in the cons ideration of 21 this study for further dose-respon se modeling. Only 15 cases of breast cancer were available, 22
and no increases in risk were found with serum TCDD exposures between 20.1 and 44 ppt 23 ( n = 2) when compared to those with <20 ppt ( n = 1). The average age at the time of enrollment 24 was 40.8 years while the average age at diagnosi s among the cases was 45.2 years. As most 25 women had not yet reached the age when breast cancer cases are typically diagnosed, additional 26 follow-up of the cohort would improve the quan titative dose-response an alysis and strengthen 27 this study. A key strength of this study, however , is that Warner et al. (2002) includes an 28 investigation of the relationshi p between individual serum-based measures of TCDD and cancer 29 in Seveso. Despite the weaknesse s, this study meets the evaluati on considerations and criteria 30 for inclusion and will be analyzed for quantitative dose-response modeling. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-60 2.4.1.1.1.5. Chapaevsk study. 1
Industrial contamination of di oxin in the Chapaevsk region of Russia has been the focus 2 of research on the environmentally-induced cance r and other adverse health effects. The 3 Chapaevsk region is located in the Samara re gion of Russia and has a population of 83,000. The 4 region is home to a chemical plant that produ ced lindane and its deri vatives between 1967 and 5 1987, which are believed to be responsible for lo cal dioxin contamination. Soil sampling has 6 demonstrated a strong gradient of increased TC DD concentrations with decreased proximity to 7 the chemical plant (Revich et al., 2001). 8
9
2.4.1.1.1.5.1. Revich et al., 2001. 10
2.4.1.1.1.5.1.1. Study summary. 11 Revich et al. (2001) used a cr oss-sectional study to compare mortality rates of Chapaevsk 12 residents to two external populati ons of Russia and the region of Sa mara. Mortality rates for all 13 cancers combined among males in Chapaevsk were found to be 1.2 times higher when compared 14 to the Samara region as a whole and 1.3 times hi gher than Russia. Similar to other studies, 15 statistically significant excess was no ted in men (SMR = 1.8, 95% CI = 1.6

− 1.9) but not in 16 women (SMR = 0.9, 95% CI = 0.8

− 1.1). Among men, the excess was highest for the 17 smoking-related cancers of the lung (SMR = 3.1, 95% CI = 2.6

− 3.5) and larynx (SMR = 2.3, 18
95% CI = 1.2

− 3.8) and urinary organs (SMR = 2.6, 95% CI = 1.7

− 3.6). Among females, there 19
was no increased SMR for all cancer sites combin ed, but excesses for breast cancer (SMR = 2.1, 20
95% CI = 1.6

− 2.7) and cancer of the cer vix (SMR = 1.5, 95% CI = 1.0

− 3.1) were statistically 21 significant. 22 Revich et al. (2001) also comp ared age-standardized cancer incidence rates in Chapaevsk 23 to those in Samara. Although statistical tests examining these differences were not reported, 24 higher incidence rates were observed for all cance rs combined, cancer of the lip, cancer of the 25 oral cavity, and lung and bladde r cancer among males in Chapaevs k. Considerably lower cancer 26 incidence rates also were observed for pros tate cancer, cancer of the esophagus, and 27 leukemia/lymphoma among males from Chapaevs k. Among females, incidence rates were 28 higher in 1998 for all cancers in Chapaevsk when compared to Russia and the Samara region, an 29 observation that appears somewhat counter to the presented SMR of 0.9 for all cancer mortality 30
from 1995

− 1998. Like mortality, rates of breast can cer incidence among women in Chapaevsk 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-61 were higher than in Russia, as were rates of cervical cancer. Leukemia/lymphoma rates were 1 higher among women in Chapaevsk than in thos e who lived in the re ference populations of 2
Samara and Russia. This findi ng is contrary to the finding fo r males who had lower rates of 3 leukemia/lymphoma in Chapaevsk. 4
5
2.4.1.1.1.5.1.2. Study evaluation. 6 Although the Revich et al. (2001) findings suggest TCDD exposures in Chapaevsk are 7 quite high relative to other parts of the worl d (Akhmedkhanov et al., 2002), evaluation of health 8 outcomes to date have been based on ecological da ta only. This analysis did not adjust for the 9
influence of other risk factors (e.g., smoking, reproductive characteri stics) that could contribute 10 to increased cancer rates for lung cancer in men a nd breast cancer in women. Given that both the 11 SMRs and SIRs for cancer outcomes vary consid erably between men and women, this suggests 12 the possibility that occupational exposures might be a contributing factor in these adverse health 13

outcomes. 14 Future research in Chapaevsk includes pl ans to conduct a breast cancer case-control 15 study. Women who were born from 1940 onward a nd who have been diagnosed with breast 16 cancer before the age of 55 were included in th e study, although the plan to characterize TCDD 17 using serum is uncertain (Revich et al., 2005). 18
19
2.4.1.1.1.5.1.3. Suitability of data for TCDD dose-response modeling. 20 This study did not meet the considerations a nd criteria for inclusion in a quantitative 21 dose-response assessment. Given the lack of ex posure data on an individua l basis, no effective 22 dose can be estimated for this study populati on. As such, no dose-response modeling was 23
conducted. 24
25 2.4.1.1.1.6. The Air Force Health ( “ Ranch Hands ” cohort) study. 26 Between 1962 and 1971, the U.S. military spraye d herbicides over Vietnam to destroy 27 crops that opposition forces depended upon, to clear vegetation from the perimeter of U.S. bases, 28 and to reduce the ability of opposition forces to hide. These herbicides were predominantly a 29 mixture of 2,4-D, 2,4,5-T, picloram, and cacodylic acid (Institute of Medicine, 2006). A main 30 chemical sprayed was Agent Orange, which was a 50% mixture of 2,4-D and 2,4,5-T. TCDD 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-62 was produced as a contaminant of 2,4,5-T and had levels ranging from 0.05 to 50 ppm (Institute 1 of Medicine, 1994). A series of studies ha ve investigated cancer outcomes among Vietnam 2 veterans. A review of military records to char acterize exposure to Agent Orange led Stellman 3 and Stellman (1986) to conclude that assignment of herbicide levels should not be based solely 4 on self-reports or a crude measure such as military branch or area of service within Vietnam. 5 Investigations have been performed on the Ranc h Hands cohort, which consisted of those who 6 were involved in the aerial spraying of Agen t Orange between 1962 and 1971. More elaborate 7 methods were used to characterize exposures among these individuals, and these studies are 8
summarized below. 9
10
2.4.1.1.1.6.1. Akhtar et al., 2004. 11
2.4.1.1.1.6.1.1. Study summary. 12 Akhtar et al. (2004) investigated the incidence of cancer in the Ranch Hand cohort, which 13 was published after the release of the 2003 R eassessment document (U.S. EPA, 2003). The 14 Ranch Hand Unit was responsible fo r aerial spraying of herbicides , including Agent Orange, in 15
Vietnam from 1962 to 1971. Cancer incidence in the Ranch Hand cohort were compared to a 16
cohort that included other Air Fo rce personnel who served in Southeast Asia during the same 17 period but were not involved in the spraying of pesticides. He alth outcomes were identified 18 during the postservice period that extended from th e time each veteran left Southeast Asia until 19 December 31, 1999. In contrast to previous analys es of this cohort, th e Akhtar et al. (2004) 20 study took into account concerns that both the comparison and spraying cohorts had increased 21 risks of cancer, and addressed the possibility that workers with service in Vietnam or Southeast 22 Asia might have increased cancer risk. The auth ors addressed the latter c oncern by adjusting risk 23 estimates for the time spent in Southeast Asia and for the proportion of time spent in Vietnam. 24 The Ranch Hand cohort comprised 1,196 indi viduals, and the comparison cohort had 25 1,785 individuals. The comparison cohort was sel ected by matching date of birth, race, and 26 occupation (i.e., officer pilot, o fficer navigator, nonflying office r, enlisted flyer, or enlisted 27 ground personnel). TCDD levels were determined using serum levels collected from veterans 28 who completed a medical examination in 1987. For those who did not have a serum measure 29 taken in 1987, but provided one in subsequent y ears, TCDD levels were back-extrapolated to 30 1987 using a first-order kinetic model that assu med a half-life of 7.6 years. Those with 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-63 nonquantifiable levels were assigned a value of th e limit of detection divided by the square root 1 of 2. A total of 1,009 and 1,429 individuals in the Ranch Hand and comparison cohorts, 2 respectively, provided serum measures that were used in the risk assessment. Veterans also were 3 categorized according to the time their tours e nded. This date corresponded to changes in 4 herbicide use. These categorie s were before 1962 or after 1972 (no herbicides were used), 5
1962

− 1965 (before Agent Orange was used), 1966

− 1970 (when Agent Orange use was greatest), 6
and 1971

− 1972 (after Agent Orange was used). Inform ation on incident cases of cancer in the 7 cohort was determined from physical examinati ons and medical records. Some malignancies 8 were discovered at death and coded from the underlying causes of death as detailed on the death 9
certificate. A total of 134 a nd 163 incident cases of cancer we re identified in the Ranch Hand 10 and comparison cohort, respectivel y. Akhtar et al. (2004) descri be case ascertainment verified 11 by record review as being complete. 12 External comparisons were made based on the expected cancer experience derived from 13 U.S. national rates using SIRs and the correspond ing 95% confidence interv al. Person-years and 14 events were tabulated by 5-y ear calendar and age intervals. 15 When compared to the general population, no statistically signifi cant excesses in all 16
cancer incidence were observed for either the Ranch Hand (SIR = 1.09, 95% CI = 0.91

− 1.28) or 17 the comparison cohort (SIR = 0.94, 95% CI = 0.81

− 1.10). Statistically significant differences 18 were found for three site-specifi c cancers in the Ranch Hands c ohort relative to the general 19 population. Excesses were noted for mali gnant melanoma (SIR = 2.33, 95% CI = 1.40

− 3.65) 20 and prostate cancer (S IR = 1.46, 95% CI = 1.04

− 2.00). In contrast, a reduced SIR was found for 21 cancers of the digestive syst em (SIR = 0.61, 95% CI = 0.36

− 0.96). The excess in prostate cancer 22 was also noted in the comparis on cohort (SIR = 1.62, 95% CI = 1.23

− 2.10) relative to the 23 general population. External comp arisons were repeated by restri cting the cohorts to the period 24 when Agent Orange was used (1966

− 1970). Again, no statistically significant excesses in all 25
cancer incidence were noted in th e Ranch Hand (SIR = 1.14, 95% CI = 0.95

− 1.37) or 26 comparison cohort (S IR = 0.94, 95% CI = 0.80

− 1.11). Statistically significant excesses 27 continued to be observed for malignant melanoma (SIR = 2.57, 95% CI = 1.52

− 4.09) and 28 prostate cancer (SIR = 1.68, 95% CI = 1.19

− 2.33) in the Ranch Hand component of the cohort. 29 No other statistically significant differen ces were found among Ranch Hands personnel. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-64 For internal cohort analyses, ve terans were assigned to one of four exposure categories. 1 Those in the comparison cohort were assigne d to the “comparison category.” Ranch Hand 2 veterans that had TCDD serum levels <10 ppt were assigned to the “background” category. 3 Those with a TCDD levels >10 pp t had their TCDD level estimated at the end of their Vietnam 4
service with a first-order ki netic model that used a half -life of 7.6 years. These 5 back-extrapolated values that were less than 118 .5 ppt were assigned to a “low” exposure group, 6
while those with values above 118.5 ppt were classified as “high” exposure. Akhtar et al. (2004) 7 used Cox regression models to describe risks across the exposure groups using the comparison 8 category as the reference. Risks were adjust ed for age at tour, military occupation, smoking 9 history, skin reaction to sun exposur e, and eye color. Internal cohor t analyses were restricted to 10 those who spent no more than 2 years in Southeast Asia and Ranch Hand workers who served 11 exclusively in Vietnam, and the comparison cohort who served exclusively outside of Vietnam. 12 Statistically significant excesses of cancer incidence (all sites combined) were observed 13 in the highest two exposure groups. A statistically signifi cant trend test ( p = 0.04) was detected 14 based on the RRs for the background-, low-, and high- exposure groups: 1.44 15 (95% CI = 0.82

− 2.53); 2.23 (95% CI = 1.24

− 4.00), and 2.02 (95% CI = 1.03

− 3.95). For 16 malignant melanoma, the RRs across the three increasing exposure ca tegories were 2.99, 7.42, 17 and 7.51. The corresponding risk estimates for prostate cancer were 1.50, 2.17, and 6.04. 18
19
2.4.1.1.1.6.1.2. Study evaluation. 20 An important strength of this study is the manner in which TCDD exposure was 21
estimated. Serum data were available for most veterans, and therefor e, generalizing exposure 22 from a small sample of cohort members is not a concern as was the case with the NIOSH and 23 Hamburg cohorts. Back-extrapolating to derive past exposures was base d on a methodology that 24 has been applied in many of the cohorts, thereb y facilitating risk comparisons. An additional 25 strength of the study is the examination of incide nce as a measure of disease occurrence rather 26 than mortality. 27 In contrast to the previous an alysis (Ketchum et al., 1999) th e analysis by Akhtar et al. 28 (2004) was restricted to individua ls who spent no more than 2 year s in Southeast Asia. Previous 29
research had demonstrated that increased time spent in Southeas t Asia was associated with an 30 increased risk of cancer. Confounding might have been introduced given that the comparison 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-65 cohort spent much more time in Southeast Asia than the Ranch Hands. To illustrate, the median 1 number of days spent in Southeast Asia was 790 for comparison cohort members, and the 2 median days for the Ranch Hand cohort in th e background, low, and high exposure groups were 3 426, 457, and 397, respectively. After restricting to t hose who spent at most 2 years, statistically 4 significant associations were observed for all cancer sites combined, prostate cancer, and 5 malignant melanoma using the internal cohort comparisons. 6 An important issue in the study is the hi gh correlation between 2,4,5-T and 2,4-D, given 7 that both were used in equal concentrations in Agent Orange. As a result, distinguishing the 8 effects of each is impossible. This point is re levant, given that 2,4-D has been associated with 9 prostate cancer in several studies. As a result, the dose-response association with prostate cancer 10 might be due to 2,4-D exposure and not TCDD. This issue also has implications for the 11 interpretation of the dose-respon se pattern for all cancer sites combined, given that incident 12 prostate cancers accounted for 4 of the 12 incident cases in the high-exposure group. 13
14
2.4.1.1.1.6.1.3. Suitability of data for TCDD dose-response modeling. 15
The ascertainment of incident cases and ch aracterization of exposure to TCDD based on 16 serum measures are strong feat ures of the cohort. Confounding by 2,4-D is a major concern. 17 Since delineating the independent effects of other Agent Orange contaminants is not possible, 18 quantitative dose-response analysis was not conducted on this study. 19
20
2.4.1.1.1.6.2. Michalek and Pavuk, 2008. 21
2.4.1.1.1.6.2.1. Study summary. 22 Michalek and Pavuk (2008) recently published an updated analysis of the incidence of 23
cancer and diabetes in the cohor t of Ranch Hand veterans. As with the Akhtar et al. (2004) 24 analysis, the study included a comparison cohort of other Air Force vete rans who served in 25
Southeast Asia at the same time but were not in volved with the spraying of herbicides. This 26 study extended previous analyses (Henriksen et al., 1997; Ketchum et al., 1999) by addressing 27 the number of days of herbicide spraying, calend ar period of service, and the time spent in 28 Southeast Asia. Veterans who attended at least one of five examinations were eligible for 29
inclusion. Incident cancer cases also we re identified from me dical records. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-66 The methods used to determine TCDD exposures were as described above in the review 1 of the Akhtar et al. (2004) study. Blood measures also we re taken in 1992, 1997, and 2002 for 2 subjects with no quantifiable TCDD levels in 198 7, those who refused in 1987, and those new to 3 the study. TCDD dose at the end of service in Vietnam was assigned to Ranch Hands that had 4 TCDD levels above background using a constant half -life of 7.6 years. Each veteran was then 5 assigned to one of four dose categories: comparison, backgro und (i.e., Ranch Hands with 1987 6
TCDD ≤ 10 ppt), low (Ranch Hands with 1987 levels 10.1

− 91 ppt), and high (Ranch Hands with 7
1987 ≥ 91 ppt). Serum TCDD estimates are available for 1,597 veterans in the comparison 8 cohort, and 986 veterans in the Ranch Hand cohor t. The comparison cohort was selected by 9 matching on date of birth, race, a nd occupation of the Ranch Hands. 10 Michalek and Pavuk (2008) used Cox regression to characterize risks of cancer incidence 11 across the three upper expos ure categories using the comparis on category as the referent group. 12 Risk estimates were adjusted for year of birth, race, smoking, body mass index at the qualifying 13 tour, military occupation, and skin reaction to sun exposure. Tests for tre nd for increased risk of 14 cancer were conducted by testi ng the continuous covariate log 10 TCDD

. 15 Overall, no association between the TCDD expo sure categories and RR of all-site cancer 16 was observed. Those in the highest expos ure group had an RR of 0.9 (95% CI = 0.6

− 1.4). 17 Stratified analyses by calendar period of serv ice showed more pronounced risk for those who 18 served before 1986 (when higher amounts of Agen t Orange were used). A statistically 19 significant dose-response trend ( p < 0.01) was observed fo r cancer risk and log 10 TCDD 20 exposure. The RRs for the background, low, a nd high groups used in these comparisons were 21 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 22 significant increase, however, was noted when anal yses were restricted to those who had sprayed 23 for at least 30 days before 1967 and spent ti me in Southeast Asia (RR = 2.2, 95% CI = 1.1

− 4.4). 24
25
2.4.1.1.1.6.2.2. Study evaluation. 26 Michalek and Pavuk (2008) used the same st udy population that Akhtar et al. (2004), and 27 so it has the same strengths and limitations as noted above. The follow-up, however, extends an 28 additional 5 years (until the end of 2004). The fi ndings for the dose-response analyses were not 29 as compelling as the earlier Akhtar et al. (2004) findings. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-67 2.4.1.1.1.6.2.3. Suitability of data for TCDD dose-response modeling. 1 The key limitation precluding dose-response an alysis for the Michalek and Pavuk (2008) 2 study is the possible confounding from the inability to control for 2,4-D and other agents used in 3 Agent Orange. As such, quantitative dose-respon se analysis was not conducted on this study. 4
5
2.4.1.1.1.7. Other studies of potential rel evance to dose-response modeling. 6
2.4.1.1.1.7.1. t’ Mannetje et al., 2005—New Ze aland herbicide sprayers. 7
2.4.1.1.1.7.1.1. Study summary. 8 t’Mannetje et al. (2005) described the morta lity experience of a cohort of New Zealand 9 workers who were employed in a plant locate d in New Plymouth. The plant produced phenoxy 10 herbicides and pentachlorophenol between 1950 and the mid-1980s. This study population also 11
was included in the internationa l cohort of producers and spraye rs of herbicides that was 12 analyzed by IARC (Saracci et al., 1991; Kogevina s et al., 1997). In this 2005 study, analyses 13
were restricted to those who ha d worked at least 1 m onth; clerical, kitchen, and field research 14
staff were excluded. The authors followed up 1,025 herbicide producers and 703 sprayers from 15 1969 and 1973, respectively, until the end of 2000. 16 The cohort consisted of two components : those involved with the production of 17 herbicides and those who were sprayers. For the herbicide producers, exposures were 18 determined by consulting occupational history records; no direct measures of exposure were 19 available. Each department of employment wa s assigned to one of 21 codes as in the IARC 20 international cohort (Saracci et al., 1991). I ndustrial hygienists and factory personnel with 21 knowledge of potential exposures in this workforce classified each job according to potential to 22 be exposed to TCDD, other chlorinated dioxins , and phenoxy herbicides. Exposure was defined 23 as a dichotomous variable (i.e., exposed and unexposed). Among producers, 813 were classified 24 as exposed, with the remaining 212 considered unexposed. 25 The “sprayer” component of the cohort include s those who were registered in the national 26 registry of applicators at any time from Ja nuary 1973 until the end of 1984. For the sprayers, 27 detailed occupational information was lacking. Exposure was, therefore, based on an exposure 28 history questionnaire completed in a previous st udy of congenital malformations (Smith et al., 29 1982). This questionnaire, administered to 548 applicators in 1980 and 232 applicators in 1982, 30 achieved a high response rate (89% ). Participants were asked to provide information about 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-68 2,4,5-T-containing product use on an annual basis from 1969 up to the year the survey was 1 completed. As the use of 2,4,5-T ceased in the mid-1980s, data on occupational exposure to 2 TCDD among these workers are fairly complete. Virtually all sprayers (699 of 703) were 3 exposed to TCDD, higher chlorina ted dioxins, and phenoxy herbicides. 4 Deaths among workers were identified through r ecord linkage to death registrations in the 5
New Zealand Health Information Service. Electoral rolls, drivers’ licenses, and social security 6
records also were consulted to confirm identifie d deaths. External comparisons of mortality 7 were made to the New Zealand population using th e SMR statistic. The mortality follow-up for 8 the producers began on January 1, 1969 and extended until December 31, 2000. For the 9 sprayers, the follow-up period extended from January 1, 1973 until December 31, 2000. A total 10
of 43 cancer deaths occurred in th e producer group and 35 cancer de aths occurred in the sprayer 11 group in the cohort. Where pos sible, stratified analyses by duration of employment and 12 department were conducted. The departments examined for producers included synthesis, 13 formulation and lab, maintenance and waste, pa cking and transport, other, and unexposed. 14 SMRs were generated using the New Zealand population as an external referent. A linear test 15 for trend was applied to evaluate dose-response trends according to categories of duration of 16 employment. Stratified analyses also were also done for spraye rs who started working before 17 1973, as TCDD levels in 2,4,5-T produced at the Ne w Zealand plant dropped dramatically after 18 1973. Although an SMR was presented for female producers, given that only one cancer death 19 was observed, this study can provi de no insight on differentia l risks between the sexes. 20 Among TCDD-exposed producers, for all cancers combined, no statistically significant 21 excess mortality was found when compared to the general population (SMR = 1.24, 22
95% CI = 0.90

− 1.67). No dose-response trend in the SM Rs for all cancers was observed with 23 duration of employment ( p = 0.44). No statistica lly significant elevated SMR was observed in 24 any of the duration of employment categories for any of the six specific departments examined. 25 A statistically significant positive linear trend, however, was noted among synthesis workers 26 ( p = 0.04). There was some suggestion of reduced mortality in the upper exposure levels for 27 workers in the formulation and lab departments. For sprayers, the SMR for all cancer sites 28
combined was not elevated relative to th e New Zealand general population (SMR = 0.82, 29
95% CI = 0.57

− 1.14), nor was a dose-response pattern observed with increasing duration of 30 employment ( p = 0.86). Additionally, no statistically si gnificant excess in cancer mortality for 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-69 all sites combined was evident in workers who were first employed either before 1973 1 (SMR = 0.75, 95% CI = 0.50

− 1.07) or from 1973 on (SMR = 1.81, 95% CI = 0.59

− 4.22). For 2 site-specific analyses of cancer mortality, an excess of multiple myeloma was observed among 3 production workers relative to the gene ral population (SMR = 5.51, 95% CI = 1.14

− 16.1). This 4 SMR was based on three deaths. No statistically significant excess (or deficit) of mortality was 5 found for any other cancer site examined in either the sprayers or the producers. 6
7
2.4.1.1.1.7.1.2. Study evaluation. 8 The physical activity demands of spraying cont ribute to a healthy worker effect that 9
manifests itself in a lower SMR based on both exte rnal comparisons to th e general population as 10 a referent, and the SMR generated for the producers in the cohort. The an alyses conducted using 11 a simple dichotomy of exposure and duration of employment are limited, as nearly all of the 12 sprayers were unexposed. 13 The dose-response pattern with duration of employment coupled with the observation 14 that higher levels of exposure to TCDD occurre d among workers in the synthesis department is 15 an important finding. These workers were also exposed to several other contaminants, however, 16 that include processing chemical s, technical products, intermed iates, and byproducts (Kauppinen 17 et al., 1993). These included phenoxy herbicides and dioxin-like compounds such as chlorinated 18 dioxins. Since the dichotomous exposure measur e was based on exposure to TCDD, chlorinated 19 dioxins and phenoxy herbicides, th e associated dose-response anal yses presented in this study 20 should be interpreted cautiously in light of the inability to either characterize or control for these 21 potential confounders. As such, these co-exposures might have contribute d to the dose-response 22 pattern observed with increas ed duration of employment in the synthesis workers. 23
24
2.4.1.1.1.7.1.3. Suitability of data for TCDD dose-response modeling. 25 Although the study authors completed a subseq uent analysis of this cohort using 26 serum-derived TCDD (McBride et al., 2009a), the lack of in dividual-level TCDD exposures 27 precludes dose-res ponse modeling. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-70 2.4.1.1.1.7.2. McBride et al., 2009b—New Zeal and herbicide sprayers. 1
2.4.1.1.1.7.2.1. Study summary. 2 McBride et al. (2009b) publishe d an updated analysis of the mortality of the New 3 Zealand cohort. The follow-up period wa s from January 1, 1969 to December 31, 2004 4 extending the previous study by an additional 4 years. In contrast to the previous study where 5 the cohort comprised individuals employed fo r at least 1 month pr ior to 1982 (or 1984) 6 (t’Mannetje et al., 2005), the cohort in this study consisted of all those who worked at least one 7 day between January 1, 1969 and October 1, 2003. Th is resulted in a cohort of 1,754 workers, of 8 which 247 died in the follow-up interval. Sevent een percent of the cohort members were lost to 9 follow-up, which could be a source of selection bias if loss to follow-up was related to both the 10 exposure metrics and the health outcome of interest . Previous data from this cohort (t’Mannetje 11 et al., 2005), however, showed fairly comparable loss to follow-up rates among the unexposed 12 (23%) and the exposed populations (17%). 13 Comparisons to the New Zealand general popula tion were made using the SMR statistic. 14 Stratified analyses were conducted by duration of employment (<3 months, ≥ 3 months), sex, 15 latency (<15 years, ≥ 15 years), and period of hire (<1976, ≥ 1976). The authors defined latency 16 as the period between the day last worked and the earliest of date of death, date of emigration or 17 loss to follow-up, or December 31, 2004. 18 The overall SMR for mortality from all ca ncer sites combined relative to the New 19 Zealand population was 1.01 (95% CI = 0.85

− 1.10). Although not statis tically significant there 20 was suggestion of an increased risk of r ectal cancer (SMR = 2. 03; 95%CI = 0.88–4.01) among 21 the employees. SMRs for lymphatic and hematopoietic cancers (overall SMR = 1.21, 22 95% CI = 0.52–2.39) included 3.12 (95% CI = 0.08

− 17.37) for Hodgkin’s disease, 1.59 23 (95% CI = 0.43–4.07) for non-Hodgkin’ s lymphoma and 3.73, 95% CI = 1.20

− 8.71), and 1.66 24 (95% CI = 0.20–5.99) for multiple myeloma. No statistically significant excess of cancer 25 mortality was noted among workers em ployed for <3 months (SMR = 1.19, 26
95% CI = 0.65

− 2.00), or for ≥ 3 months (SMR = 0.98, 95% CI = 0.75

− 1.26). A statistically 27 significant excess of digestive cancers was found for those who worked fewer than 3 months 28 relative to the New Zealand popul ation (SMR = 2.52, 95% CI = 1.15

− 4.78). No excesses were 29 observed for any site-specific cancers when analys es were restricted to those who worked for 3 30 or more months. No statistically signifi cant elevated SMRs were found for all cancers 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-71 (combined) either for a latency period of fewer than 15 years (SMR = 1.14, 95% CI = 0.72

− 1.71) 1 or a latency period of ≥ 15 years (SMR = 0.96, 95% CI = 0.72

− 1.26). Similarly, no statistically 2 significant excess in cancer mortality was obser ved for all cancer sites combined, or any 3 site-specific cancer when analyses we re stratified by date of hire (<1976, ≥ 1976) or by sex. The 4 SMR among women who were employed at the site was 0.68 (95% CI = 0.45

− 1.00). 5
6
2.4.1.1.1.7.2.2. Study evaluation. 7 High rates of emigration in New Zealand (9 % among workers in the cohort) contributed 8 to a fairly high loss to follow-up (22% among workers) during the study period. The loss to 9 follow-up would reduce the overall mortality es timates among the workers, which could 10 underestimate the SMRs if loss to follow-up (a nd health status) was not comparable in the 11 general population. For example, it is uncl ear if workers and th e general population who 12 emigrated were sicker than those remaining in th e cohort. Previous data from the cohort workers 13 suggests that loss to follow-up rates were slightly higher among the low and unexposed 14 populations (t’Mannetje et al., 2005; McBride et al., 2009a) work er population, so presumably 15 the highly exposed workers were not lost to follow-up more so than other workers. 16
17 2.4.1.1.1.7.2.3. Suitability of data for TCDD dose-response modeling. 18 This study extended the mortality follow- up and included stratified analyses to 19 investigate effect modification by period of latency, sex, and date of hire. A key limitation was 20 the lack of direct measures of exposure fo r study participants whic h precluded estimating 21 effective dose needed for dose-response modeling. This study did not meet the considerations 22 and criteria for inclusion in qua ntitative dose-response analysis. 23
24
2.4.1.1.1.7.3. McBride et al., 2009a—New Zeal and herbicide sprayers. 25
2.4.1.1.1.7.3.1. Study summary. 26 McBride et al. (2009a) recently published the mortality experience of the New Zealand 27
cohort in relation to serum es timates of TCDD levels. This study included 1,599 workers who 28 were employed between 1969 and November 1, 1989, which was the date that 2,4,5-T was last 29 used. As in their study publis hed earlier in th e same year (McBride et al., 2009b), the follow-up 30 period extended from the first day of employment until December 31, 2004. Vital status was 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-72 ascertained through record linkage to the New Zealand Health Information Service Mortality 1 Collection and the Registrar General’s I ndex to Deaths for deaths up to 1990. 2
All current and former workers who lived w ithin 75 km of the pl ant were invited to 3 provide serum samples. A total of 346 of the eligible workers (68%) provided samples, which 4 represented 22% of the overall study population (346/1599). Base d on the serum measures, 70% 5 (241/346) had been exposed to TCDD. This per centage is similar to the estimated 71% of 6 workers who were deemed to have been exposed based on a review of occupational records. The 7 mean serum TCDD value was 9.9 ppt. The highest exposures were observed for those employed 8 in the trichlorophenol operati on (23.4 ppt). Values among unexposed workers averaged 4.9 ppt, 9 which is close to the background level of 3.9 ppt among individuals of similar age in the New 10 Zealand general population (Bates et al., 2004). Details on smoking hi stories of individuals were 11 also collected for the 346 individuals who provide d serum, allowing for an examination of the 12 potential confounding role that smoking might ha ve on derived risk estimates for TCDD. 13 Cumulative exposure to TCDD as a time-depe ndent metric was estimated for each 14 worker. A detailed description of the methods used to derive TCDD exposure was described in 15 Aylward et al. (2009). The quali tative TCDD scores available for those with serum measures 16 were used to estimate the cumulative exposures ba sed on a half-life of approximately 7 years. A 17 time-dependent estimate of TCDD exposure was de rived and the area under the curve was used 18 to obtain cumulative workplace TCDD exposure above background levels. Model performance 19 appears modest as the model explaine d only 30% of the variance (adjusted R 2 ) when these 20 TCDD exposure estimates were compared with actual serum levels (Aylward et al., 2009). 21 As with previous analyses of the cohort (t ’ Mannetje et al., 2005; McBride et al., 2009b), 22 external comparisons to the New Zealand general population were made using the SMR statistic. 23 The SMR statistic also was used to compare mort ality across four exposure groups relative to the 24 general population, as defined by the serum TCDD estimates: 0

− 68.3, 68.4

− 475.0, 25
475.1

− 2085.7, and ≥ 2085.8 ppt-month. The proportional hazards model also was used to 26 conduct internal cohort comparisons across these same four exposur e groups. In these analyses, 27 age was used as the time variable, and the covariat es of date of hire, se x, and birth year were 28 included in the proportional hazards model. Th e cut-points for these four exposure categories 29 were chosen so that approximately equal numbe rs of deaths were included in each category. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-73 Consistent with earlier SMR an alyses of the same cohort, no increased cancer mortality 1 was observed among “ever” exposed workers in this cohort when compared to the general 2 population (SMR = 1.1, 95% CI = 0.9

− 1.4). No statistically signifi cant excess was noted for any 3 of the site-specific cance rs, although there was some suggestion of increased risk of soft tissue 4 sarcoma (SMR = 3.4, 95% CI = 0.1–19.5), multiple myeloma (SMR = 2.2, 95% CI = 0.2

− 8.1), 5 non-Hodgkin’s lymphoma (SMR = 1.6, 95% CI = 0.3–4.7), and cancer of the rectum 6 (SMR = 2.0, 95% CI = 0.7

− 4.4). No statistically significant in creases in cancer mortality (all 7 sites combined) was found in any of the four exposure categories as measured by the SMR 8 statistic, nor was a dose-response trend noted with increasing exposure categories. No 9 dose-response trend (based on SM R analyses) was noted for five site-specific cancers examined 10 (i.e., digestive organs, bronchus, trachea and lung, soft tissue sarcomas, lymphatic and 11 hematopoietic tissue, and non-Hodgkin’s lym phoma), although SMRs for three of the 12 four exposure categories exceeded 2.0. 13 In contrast to the external cohort comparis ons, the RRs generated with the proportional 14 hazards model supported a dose-response trend, as rate ratios increase d across increasing TCDD 15 exposure categories. The RRs and their 95% conf idence intervals relative to the lowest of the 16 four groups were 1.05 (95% CI = 0.48

− 2.26), 1.38 (95% CI = 0.64

− 2.97) and 1.58 17 (95% CI = 0.71

− 3.52). Neither the linear ( p = 0.29) or quadratic ( p = 0.82) test for trend, 18 however, was statistically significant. An incr eased risk of lung cancer mortality was observed 19 in the highest TCDD exposure category relative to the lowest (RR = 5.75, 20
95% CI = 0.76

− 42.24). The tests for trend for lung cancer, however, also were not statistically 21 significant. 22 A smoking survey was administered to a sample of surviving workers of this cohort, and 23 smoking prevalence was found to be slightly hi gher among those with higher cumulative 24 exposure (61%) compared to lower exposures (51

− 56%). These minor differences in smoking 25 prevalence unlikely was a strong enough confounder to explain the fivefold increase in risk of 26 lung cancer found in the highest exposure category. Although the smoking data assessme nt was 27 a strength of the study, it was limited to only samp le of workers and was not available for those 28 who died of lung cancer. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-74 2.4.1.1.1.7.3.2. Study evaluation. 1 Given high rates of emigration, loss to follow- up (22%) was a potential concern in this 2 study. If comparable emigration rates did occu r among the general population then the SMRs 3
would be underestimated. It is unclear to wh at extent emigration o ccurred among the general 4 population and whether emigration in both the wo rker and general populations was dependent on 5 health status. If emigration rates were comp arable among these two populations, the associated 6
bias from the under-ascertainment of mortalit y in the lost to follow-up group would likely 7 attenuate a positive association between TC DD and cancer mortality. Among the worker 8 population, there was not much evidence of diffe rential loss to follow-up with respect to 9 exposure as average exposures were lower (3.2 ppt) among those loss to follow up compared to 10 those with complete follow-up (5.7 ppt). Pr evious studies among this population also found 11 slightly higher loss to follow-up rates among the unexposed (23%) compared to the exposed 12 (17%) workers (t’Mannetje et al., 2005). 13 McBride et al. (2009a) did not present resu lts using a continuous measure of TCDD 14 exposure (lagged or unlagged) as was done in mo st other occupational co horts. Additionally, the 15 modeling did not consider the use of different periods of latency. 16
17
2.4.1.1.1.7.3.3. Suitability of data for TCDD dose-response modeling. 18
There is no evidence that the authors considered exposure metrics that are consistent with 19 environmental cancer-causing agents such as expo sure modeling that takes latency into account. 20 Given that past occupational cohort studies of TCDD-exposed workers have consistently 21 demonstrated stronger association with lag interval of 15 years, such an approach should be 22 applied to this cohort. Th is precludes this study from consideration for quantitative 23 dose-response modeling. 24
25
2.4.1.1.1.7.4. Hooiveld et al., 1998—Neth erlands workers. 26
2.4.1.1.1.7.4.1. Study summary. 27 Hooiveld et al. (1998) re-analyzed the mo rtality experience of a cohort of workers 28 employed in two chemical plants in the Nether lands using 6 additional years of follow-up from 29 an earlier study (Bueno de Mesquita et al., 1993). The cohort consisted of those employed 30 between 1955 and June 30, 1985, and vital status was ascertained until December 31, 1991 (i.e., 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-75 36 years of follow-up). These cohort members were involved in the synthe sis and formulation of 1 phenoxy herbicides, of which the main produc t was 2,4,5-trichlorophenoxyacetic acid and 2
monochloroacetic acid. This cohor t, with a shorter follow-up in terval than the original study 3 (t’Mannetje et al., 2005), was incl uded in the IARC intern ational cohort. The cohort consisted of 4 1,167 workers, of which 906 were known to be aliv e at the end of the follow-up. The average 5 length of follow-up was 22.3 years, and only 10 individuals were lost to follow-up. 6 The authors used detailed occupational hist ories to assign exposures. Workers were 7 classified as exposed to phenoxy herbicides or ch lorophenols and contaminants if they worked in 8 selected departments (i.e., s ynthesis, finishing, formulati on, packing, maintenance/repair, 9 laboratory, chemical effluent wa ste, cleaning, shipping-transport, or plant supervision); were 10 exposed to the accident in 1963; or were exposed by proximity (i.e., if they entered an exposed 11 department at least once a week). The 1963 accident was the result of an uncontrolled reaction 12 in the autoclave in which 2,4,5-trichlorophenol wa s synthesized; an explosion resulted, with 13 subsequent release of PCDDs that included TCDD. Based on these methods of exposure 14 assignment, 562 workers were deemed to be expo sed to phenoxy herbicides or chlorophenols, 15 and 567 were unexposed. Due to limited informa tion, 27 workers were classified as having 16 unknown exposure. 17 TCDD exposures also were assigned using seru m measured on a sample of workers who 18 were employed for at least 1 year and first st arted working before 1975. Dioxin-like compounds 19 including PCDDs were also measured in the seru m samples, but not analyzed for this study. Of 20 the 144 subjects who were invited to provide samples, 94 agreed. TCDD levels were 21 back-extrapolated to the time of maximum exposur e using a one-compartment, first-order kinetic 22
model that used a half-life estimate of 7.1 years. The mathematical model used was 23 ln(TCDDmax) = ln(TCDD) + lag × ln(2)/7.1. The la g was defined as the nu mber of years since 24 last exposure for those exposed by virtue of th eir normal job duties. For those exposed as a 25 result of the accident in 1963, the lag was defi ned as the number of years since the accident 26
occurred. 27 The authors made external comparisons of cohort mortality to the Netherlands population 28 using the SMR statistics. Poisson regression wa s used to perform internal cohort comparisons 29 using unexposed workers as the referent. RRs (measured using rate ra tios) generated from the 30 Poisson model also were used to compare mortality based on low, medium, and high TCDD 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-76 serum-derived categories. The Poisson model in cluded the following covariates as adjustment 1 factors: age, calendar peri od at end of follow-up, and time since first exposure. 2 When compared to the general population, work ers had an excess mortality from cancer 3 (SMR = 1.5, 95% CI = 1.1

− 1.9), based on 51 cancer deaths. Generally, no excesses were 4 observed for site-specific cancers. The excepti on included eight deaths from cancers of the 5 urinary organs (SMR = 3.9, 95% CI = 1.7

− 7.6). Although not statistic ally significant, SMRs 6 comparable in magnitude to other studies were detected for non-Hodgkin’s lymphoma 7 (SMR = 3.8, 95% CI = 0.8

− 11.0 ) and Hodgkin's disease (SMR = 3.2, 95% CI = 0.1

− 17.6). A 8 statistically significant excess of cancer mortality ( n =20 deaths among occupational workers) 9 also was also observed relative to the general po pulation when analyses were restricted to those 10 exposed as a result of the 1963 accident (SMR = 1.7, 95% CI = 1.1

− 2.7). Three deaths from 11 prostate cancer were also noted amo ng these workers (SMR = 5.2, 95% CI = 1.1

− 15.3), but no 12
excess was observed with a ny other cancer site. 13 Internal cohort comparison also demonstrated an increased risk of all cancer mortality 14 among those exposed to phenoxy herbicides, chlor ophenols, and contaminants relative to those 15 unexposed (RR = 4.1, 95% CI = 1.8

− 9.0). A statistically signifi cant increased risk was also 16 noted for respiratory cancer mo rtality (RR = 7.5, 95% CI = 1.0

− 56.1). Analyses across 17 categories of TCDD exposure revealed excesses in cancer mortality for all cancer sites 18 combined; however, no dose-response trend was apparent. 19
20 2.4.1.1.1.7.4.2. Study evaluation. 21
Several other studies that have characterized cohorts by TCDD levels have used the area 22 under the curve approach and thus have derived an exposure metric that is time dependent. 23 Hooiveld et al. (1998) instead created an exposure metric to capture the maximum exposure 24 attained during the worker’s employment. Charact erizing risks using this metric assumes that 25 other TCDD exposures accrued during a workers’ lifetime are not relevant predictors of cancer 26
risk. 27
28
2.4.1.1.1.7.4.3. Suitability of data for TCDD dose-response modeling. 29 One study limitation is that although dioxin- like compounds were measured in the serum 30 samples, Hooiveld et al. (1998) reported associations with mortality for TCDD only. There is 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-77 some utility to examining dose-response analyses using alternative exposure metrics as those 1 constructed in this cohort. However, the small number of identified cancer deaths, limitations in 2 terms of the exposure assignment (based on nonrepresentative sample, and maximum exposure 3 level) and concern over potenti al confounding by co-exposures prec lude using these data for a 4 dose-response analysis. 5
6
2.4.1.1.2. Key characteristics of epidemiologic cancer studies 7 See Table 2-1 at the end of the chapter for a comparison of the length of follow-up, 8 latency period used, the half-life for TCDD us ed, and the fraction of TEQs accounted for by 9 TCDD (when applicable) for each study. 10
11
2.4.1.1.3. Feasibility of TCDD cancer dose-response modeling

— summary discussion by 12 cohort. 13
2.4.1.1.3.1. Using the NIOSH cohort in dose-response modeling. 14 It is important to evaluate the NIOSH cohor t in cancer dose-response modeling of TCDD. 15 This cohort is the largest assembled to date, direct measures of TCDD based on sampling are 16 available, and the lengthy follow-up interval allows for latent eff ects to be taken into account. 17 Further, although this cohor t consists mostly of ma le workers, these workers were occupationally 18 exposed to TCDD daily, as compared to the ac ute accidental exposures of other occupational 19 cohorts. Although the most recent analyses of a subset of the NIOSH cohort showed no 20
association between serum TCDD le vels and cancer mortality, the study authors did not examine 21 latency effects (Collins et al., 2009). Incorporation of latency inte rvals is important in light of 22 the stronger dose-response relationships that consistently have been observed with a 15

− 20 year 23 latency interval in previous investigations of the NIOSH and other coho rts (Steenland et al., 24 2001). 25 Most published studies of the NIOSH cohort did not evaluate expos ures to dioxin-like 26 compounds. An exception is the analysis by Stee nland et al. (2001). A lthough Steenland et al. 27 (2001) did not incorporate individual-level da ta on dioxin-like compounds, based on their 28 previous work (Piacitelli et al., 1992) they assu med that TEQ occupational exposures occurred as 29 a result of TCDD alone in this population. TCDD e xposures provided a better fit to the data than 30 the TEQ-based metric, and 15-y ear latencies improved the fit for both metrics (relative to 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-78 unlagged exposures). The lifetime risk estima tes for an increase in 10 TEQs (pg/kg of body 1 weight/day/sex) ranged from 0.05

− 0.18%. The value added for this measure is the incorporation 2 of the contribution of other dioxin-like compounds to the background rates. 3 Blue collar workers, such as those in the NIOSH cohort, typically have higher rates of 4 smoking than the general populati on (Lee et al., 2007; Bang and Kim, 2001). This potential 5 source of confounding would be expected to produce a higher SMR for lung cancer mortality, 6
and could contribute to the exce ss noted in the cohort with longe r lag intervals. This bias, 7 however, likely is not large as no statistically significant excess of nonmalignant respiratory 8 mortality was found in these workers. Any associ ated bias from smoking would be expected to 9 be smaller for comparisons conducted within the c ohort, as fellow workers would be expected to 10 be more homogeneous with respect to their risk factor profile than with an external general 11 population referent group. Stratified analyses using both internal and external comparison 12 groups also did not identify im portant differences in associations with TCDD exposure between 13 smoking and nonsmoking cancers. Thus, fatal cancer risk estimates reported for workers in the 14 NIOSH cohort appear to provide a reasonable estimate of the carcinogenic potency of TCDD. 15 Although the Steenland et al. (2001) study did not directly account for the possible 16 confounding effects of other occupational exposure, the authors did address this source of 17 potential bias. No known occupa tional exposures to carcinogens occurred, with the exception of 18 4-aminobiphenyl, which occurred at one plant. Tw o deaths from mesothelioma also occurred in 19 the cohort, so some exposure to asbestos might al so have occurred in the cohort (Fingerhut et al., 20 1991). The statistical analyses suggested that the inability to contro l for other occupational 21 exposures would not have unduly affected risk estimates gene rated from internal cohort 22 comparisons. For instance, the removal of one plant at a time from the analysis did not 23 materially change dose-response estimates gene rated from the Cox model (Cheng et al., 2006). 24 Moreover, adding a variable to re present plant in the Cox regressi on had little impact on the risk 25 estimates. Given that other occupational exposures varied by plant, a change in risk estimates 26 would be expected if such e xposures were strong confounders. 27 The Cheng et al. (2006) analysis provides important information about the impact of 28 applying kinetic models to the data. The C ADM TCDD kinetic model resulted in dramatic 29
decreases in the TCDD cancer m ortality risk estimates when compared to the one-stage 30 compartmental model that had been applied. A lthough Cheng et al. (2006) suggested that the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-79 CADM model provides a better fit to the data th an the typically used simple one-compartmental 1 model, statistical comparisons of model fit were not reported. Therefor e, there is value in 2 presenting the range in risk estimates across di fferent models when characterizing dose-response 3 relationships. 4 Finally, the half-life of TCDD is generall y recognized to vary according to body fat 5 percentage, data that were not available for the NIOSH workers. The inability to account for 6 between-worker variability in body fat would introduce exposure m easurement error. That body 7 fat percentage would not be expected to correl ate with cumulative exposure to TCDD exposure, 8 however, would limit the potential for misclassifi cation bias. The effect of any nondifferential 9 exposure measurement error likely would serve to attenuate the risk estimates of the study. 10
11
2.4.1.1.3.2. Using the BASF cohort in dose-response modeling. 12 The availability of blood lipid data for TCDD allows for characterization of cumulative 13 TCDD exposures in the BASF cohort. TCDD blood lipid data were collected for 90% of the 14 surviving members of the cohort (138 of 154) and these serum measures we re used to generate 15 TCDD exposure estimates for all 254 cohort members. Therefore, the potential for 16 misclassification from extrapolati ng these exposures to the entire cohort may not be as likely as 17
for the NIOSH cohort where sera data were availa ble for only a small fraction of workers. These 18 data were, however, collected long after the accide nt (36 years) and had to be back-extrapolated 19 to derive the initial exposures. 20
The data on this cohort included several risk factors such as ciga rette smoking and body 21 mass index. One advantage is that cumu lative TCDD levels by body mass index can be 22
estimates on an individual-level basis. As e xpected, the derived cumulative measures appear to 23 compare well with severity scores of chloracn e. The finding that more pronounced risks are 24
found 15

− 20 years after first exposure are also cons istent with findings from several other 25 cohorts (Fingerhut et al., 1991; Manz et al., 1991; Bertazzi et al., 2001). 26 One key limitation of the BASF cohort is its relatively small sample size ( n = 243), which 27 limits the ability to evaluate dose-response relationships for s ite-specific cancers. Also, the 28 quality of the ascertainment of cancer incidence cannot be readily evaluated as the geographic 29 area of the cohort is not covered by a tumor regist ry. Ott and Zober (1996 ) state that nonfatal 30 cancers could have been more likely to be missed in early years, which coul d partially contribute 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-80 to the larger standardized inci dence ratio found for cancer with longer latencies. Commenting on 1
risk differences derived from incident and deced ent cancer outcomes is difficult. Among those 2 comprising the cohort, the asce rtainment of incident outcome s was recognized to be less 3 complete in early years. Although the ascert ainment of mortality outcomes was generally 4 regarded to be good among the 243 workers, some workers who died or moved likely were 5
missed when the cohort was constructed. These d eaths would have been more likely to have 6 occurred several years before the second component of the cohort was assembled. 7
The use of the SMR statistic for this st udy population is associat ed with important 8 sources of uncertainties. Deaths were surely mi ssed, particularly for the third component of the 9 cohort that accounts for approximate ly 38% (94/247) of the entire c ohort; this factor would serve 10
to underestimate the overall SMR. As menti oned before, this component of the cohort was 11 assembled through the recruitment of workers know n to be alive in 1986. Despite this limitation, 12 the characterization of exposure da ta and availability of other risk factor data at an individual 13 level allow the development of quan titative dose-response analyses. 14
15
2.4.1.1.3.3. Using the Hamburg cohort in dose-response modeling. 16 The Hamburg cohort lacked data on cigarett e smoking, and, therefore, effect estimates 17 could not be adjusted for this covariate. Additi onal analyses that excl uded lung cancers resulted 18 in an even stronger dose-response relationship be tween all cancer mortality and TCDD. Serum 19
levels of TCDD also were also not associated wi th smoking status in a subgroup of these workers 20 (Flesch-Janys et al., 1995) suggesting that smoking is not likely a confounder of the association 21 between all cancer mortality and TCDD. 22 An important limitation of the cohort is the reliance on blood and tissu e measurements of 23 190 workers that likely represent a highly selectiv e component of the cohort. This subset of 24
workers was identified at the end of the obser vation period, and therefor e, excludes workers who 25
died or could not be traced. There are uncertain ties in deriving department- and period-specific 26 estimates for a period that extends over thr ee decades using this number of workers. 27 Additionally, the crite ria applied to the refere nce population could have introduced some bias. 28 Workers were included only in the reference gr oup if they had been employed for at least 29 10 years in a gas supply industry. The criteria were much different for the workers who were 30 exposed to TCDD (only 3 months of employment). As a result, the reference group likely would 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-81 be more susceptible to the healthy worker eff ect. Internal cohort comparisons, which should be 1 void of such bias, however, generally produced re sults similar to those based on the external 2 comparison population. Therefore, the Becher et al. (1998) study meet s the criteria and 3 additional epidemiological consider ations for dose-response modeling. 4
5
2.4.1.1.3.4. Using the Seveso cohort in dose-response modeling. 6 Unlike many of the occupationa l cohorts that were examined, data from the Seveso 7 cohort are representative of a residential population whose prim ary exposure was from a single 8 TCDD release. A notable exception is the BASF cohort where workers were exposed primarily 9 through two accidents that occurred in the plant. The Seveso data, therefore, might permit 10 cancer dose-response investiga tions in women and children. 11 Uncertainty in identifying the critical exposure window for mo st of the outcomes related 12 to the Seveso cohort is a key limitation. An impo rtant feature of the Seve so cohort, however, is 13 that TCDD levels were much lower among thos e in the highest exposure zones in Seveso 14 (medians range from 56

− 136 ng/kg) (Eskenazi et al., 2004) th an those in the occupational 15 cohorts who had TCDD exposures that were sometimes more than 1,000 ng/kg. Given these 16 dramatic differences in exposures, the standard ized mortality ratios (after incorporating a 17
15

− 20 year latency period) for all cancer sites combined are remarkably similar between the 18 Seveso and the occupational cohort analyses. Pe rhaps more importantly, the data from Seveso 19 might be more relevant for extrapolating to lower levels, given that exposures to TCDD are 20 two orders of magnitude higher than background levels (Smith and Lopipero, 2001). 21 The Warner et al. (2002) st udy found a positive association between serum levels of 22
TCDD and breast cancer. As noted previously, ascertainment of incident cases for all cancers 23 would allow for a dose-response relationship to be evaluated. Moreover, future breast cancer 24 analyses in this cohort should strengthen the quan titative dose response anal yses of this specific 25 cancer site. The strengths of the Warner et al . (2002) study outlined earlier suggest that this 26 study should be considered for cancer dose-response modeling. 27 Earlier Seveso studies likely are unsuitable fo r conducting quantitative risk assessment. 28 These previous studies used an indirect measure of TCDD exposure, namely, zone of residence. 29
Soil concentrations of TCDD varied wide ly in these three zones (Zone A: 15.5

− 580.4 ppt; 30
Zone B: 1.7

− 4.3 ppt; and Zone R: 0.9

− 1.4 ppt), which could have resulted in considerable 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-82 exposure misclassification. The Warner et al. (2002) study greatly improved the characterization 1 of TCDD exposure using serum measur es, and also allowed for control of salient risk factors that 2 may have resulted in bias due to confounding. 3 At this time it is unclear whether any study has examined the relationship between cancer 4 and serum estimates of TCDD among Seveso males exposed from the 1976 accident. 5
6
2.4.1.1.3.5. Using the Chapaevsk related data in dose-response modeling. 7 Currently, individual-level exposur e data are lacking for resident s of this area and there is 8 no established cohort for which cancer outcomes can be ascertained. These limitations, 9 therefore, preclude the inclusion of Chapaevsk da ta in a quantitative dose-response analysis. 10
11
2.4.1.1.3.6. Using the Ranch Hands cohort in dose-response modeling. 12 An important limitation of the Ranch Hands cohort for TCDD and cancer dose-response 13 modeling is an inability to isol ate TCDD effects from the effects of other agents found in the 14 associated herbicides. Exposure to other dioxin- like compounds was not estimated in this study 15 and could confound the previously reported associations. As such, dose-response analyses on 16 this population were not conducted. 17
18
2.4.1.1.4. Discussion of general issues re lated to dose-response modeling 19
2.4.1.1.4.1. Ascertainment of exposures. 20 Several series of epidemiological data have used serum measures to estimate TCDD 21 levels. Serum data offer a distinct advantag e in that they provide an objective means to 22 characterize TCDD exposure at th e individual level. The serum measures in the occupational 23 cohorts, however, are limited in two important ways . First, these samples are generally collected 24 from small subsets of the larger cohorts; therefor e, using these measures to extrapolate to the 25
remainder of the cohort could in troduce bias due to exposure misclassification. The second 26
limitation is related to estimati ng the half-life of TCDD. As noted previously, exposures to 27 TCDD were back-extrapolated several decades from serum samples collected among surviving 28
members of several cohorts. This approach was used in th e NIOSH, Ranch Hands, BASF, New 29 Zealand, and Hamburg cohorts. The reported ha lf-life of TCDD among these populations was 30 reported between 7.1 to 9.0 years and shown to va ry with several individual characteristics 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-83 including age, body fat composition, and smoking. The derivation of half-lives from a sample of 1 workers, and application of these estimates to retrospec tively characteri ze exposure can 2 introduce uncertainty into the life time exposure estimates. It is im portant to note, however, that 3 sensitivity analyses results in several studies have been fairly consistent when evaluating the 4 impact of half-life of TCDD (Steenland et al., 2001; Flesch-Janys et al., 1995). 5 A unique advantage of the Seveso study is that serum measures were taken shortly after 6 the accident, and therefore ch aracterization of TCDD exposure in this population does not 7 depend on assumptions needed to back-e xtrapolate exposures several decades. 8
9 2.4.1.1.4.2. Latency intervals. 10 Many of the epidemiological studies indica te stronger associations between TCDD and 11 cancer outcomes once a latency period has been c onsidered. Generally, risks are higher when a 12 lag period of 15

− 20 years is included. As noted previousl y, this observation is consistent with 13 many other environmental carcinogens such as radon, radiation, and ciga rette smoking. That 14 recent exposures do not contribute to increased cancer risk provides some support that the 15 initiation and promotion phases might occur many years before death making recent exposures 16 irrelevant for these analyses. The ability to discriminate between models of varying latency, 17 however, was limited in many studies. The appl ication of biologically based modeling could 18 provide additional important in sights on which phase(s) of carcinogenesis TCDD exerts an 19 influence. Such modeling, however, would necessita te having data on an individual-level basis. 20 Ideally, this modeling would use can cer incident data rather than mortality outcomes, given that 21 for many cancers, the median survival time exceeds 5 years. 22
23
2.4.1.1.4.3. Use of the SMR metric. 24 The occupational cohorts and the studies in Se veso and Chapaevsk have made inferences 25 regarding the effects of TCDD on mortality using the SMR. When compared to the general 26 population, the healthy worker effect may result in a downward bias in the SMR. This often can 27
manifest as SMRs less than 1 for several causes of mortality. The effect of this bias is, however, 28 generally lower for cancer outcomes. Cancer out comes, whether incidence or death, typically 29 occur later in life and do not generally affect an individual’s ability to wo rk at earlier ages. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-84 There are several approaches th at can be taken to minimize potential biases introduced by 1 the healthy worker effect, whic h would account for workers bei ng healthier than the general 2 population. Comparisons of morta lity (or cancer incidence) can be made to other cohorts of 3 similar workers. If done properl y, this can allow for some contro l of characteristics such as 4 sociodemographic characteristics and smoking as the two populations can be matched by these 5 factors. However, it may be the case that other working populations are exposed to other 6 harmful exposures, thereby making it difficult to estimate risk asso ciated with a specific agent 7 (such as TCDD) in the cohort of interest. A s econd and preferred approa ch to control for the 8 healthy worker effect, should it prove feasible, is to conduct comparisons of health outcomes in 9 relation to exposure within the c ohort. These comparisons are less likely to be influenced by 10 other potential confounding vari ables such as smoking, soci oeconomic status, and other 11 occupational exposures that ar e generally more homogeneous within the cohor t relative to 12 external populations. Moreover, the mechanisms used to identify health outcomes and follow 13 individuals over time are generally applied in the same manner to all cohort members. Taken 14 together, where different comparisons have been made to generate risk estimates, those that have 15 been conducted using internal cohor t comparisons are preferable. 16 In addition to potential bias from the hea lth worker effect, the comparison of SMRs 17 between studies is not always straightforward and is not recommended by some (Rothman, 1986; 18 Myers and Thompson, 1998). The SMR is the ratio of the observed number of deaths to the 19 expected number of deaths and is often referred to as the method of indirect standardization. The 20 expected number of deaths is estimated by multiplying the number of person-years tabulated 21 across individuals in the cohort, stratified by age, by rates from a reference population that are 22 available for the same strata. Therefore, each pop ulation cohort will have an estimated number 23 of cases derived using a different underlying ag e structure. As outlin ed by Rothman (1986), the 24 mortality rates might not be directly comparable to each other, although the impact of such bias 25 will be much less if the age-distribution of the cohor ts is similar. While it might be reasoned that 26 the TCDD exposed workers would have similar age distributions this is in fact not the case 27 (Becher et al., 1998; Ott et al., 1993 ; Thiess et al., 1982). This ma y be due to exposure occurring 28 both chronically, as well as from acute exposures due to accidental rel eases that happened at 29
various tim es at different plants. This is ev ident with the Hamburg and the BASF cohorts, as 30 most individuals comprising the BASF cohort we re employed at the time of the accident 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-85 (1953/1954), while most of the Hamburg cohor t (852/1048) was employed after 1954; the 1 follow-up of these cohorts ended at approximately the same time. 2
The method of direct standardization allo ws for a more meaningful comparison of 3 mortality rates to be made between cohorts. W ith this approach, weights (usually based on age 4 and sex) are drawn from a standa rd population and are, in turn, applied to disease rates for the 5
same strata observed in the cohort of interest. A comparison of weighted rates between different 6 cohorts would then be based on the same population standard. 7 Despite these limitations in comparing SMRs between studies, Armstrong (1995) argues 8 that the comparisons are valid if the underlyi ng stratum specific rates in each exposure grouping 9 are in constant proportion to exte rnal rates. Comparisons of the SMRs between studies will be 10 biased only if there is an interaction between age and TCDD (i.e., the RR of disease due to 11 exposure differs by age). For cancer outcomes, the finding that associations become stronger 12 after a period of latency is incorporated into th e analyses suggests that this assumption does not 13 hold true. That is, risk estimates would be lower among young workers. Similarly, for 14 noncancer outcomes, some of the data from th e Seveso cohort suggests differential effects 15 according to the age at exposure. 16 The use of the SMR might also be biased in that workers exposed to TCDD could be 17 subject to more intensive follow-up than the general population, and as a result, differential 18 coding biases with cause of death might occur. Moreover, some cohorts (e.g., the BASF cohort) 19 have been assembled, in part, by actively seeking out survivors exposed to accidental releases of 20 dioxins. As such, they would not include persons who have died or who were lost to follow-up. 21
This would result in underascertainment of deat hs and SMRs developed from these data. The 22 use of an internal cohort comparison offers dis tinct advantages to overcome potential sources of 23 selection bias. Given these uncertainty about comparability across the different studies, 24 conducting a meta-analysis of cancer outcomes for TCDD using the SM R statistic is not 25 warranted for this analysis. 26
27
2.4.1.1.4.4. All cancers versus site-specific. 28 An important consideration for quantitative dos e-response modeling is the application of 29 models for all cancers combined, or for site-specific cancers. C onsistency is often lacking for 30 site-specific cancers, which might be due in larg e part to the relatively small number of cases 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-86 identified for site-specific cancers in the cohor ts. Although the risk es timates produced for all 1 cancer sites have important limitations and uncerta inties, the data are far more consistent in 2 terms of the magnitude of an association and la tency intervals. The IARC evaluation has put 3 forth the possibility of a pleu ripotential mode of action between TCDD and the occurrence of 4 cancer. Despite the criticism of this asser tion by some (Cole et al., 2003), the general 5 consistency of an increased ri sk for all-cancer mortality acro ss the occupational cohorts when 6 latency intervals have been incorporated, prov ides adequate justification for dose-response 7 quantification of all cancer sites combined. 8
9
2.4.1.1.4.5. Summary of epidemiologic cancer study evaluations for dose-response modeling. 10 All epidemiologic cancer studies summarized above were evaluated for suitability of 11 quantitative dose-response assessment usi ng the TCDD-specific considerations and study 12
inclusion criteria. The results of this evaluation are summarized in a matrix style array (see 13 Table 2-2) at the end of this section, and descriptively in Appe ndix B. Table 2-4 summarizes the 14 key epidemiologic cancer studies suitable for further TCDD dose-response analyses. 15
16
2.4.1.2. Noncancer 17 In this section, the ava ilable epidemiological data that could be used in a dose-response 18 analysis for noncancer endpoints ar e evaluated. Because many of th e key studies also evaluated 19 cancer outcomes, the noncancer studies are presented in the same order as presented in 20 Section 2.4.1.1. Generally, the strengths and limitations of the cancer studies also apply to the 21
noncancer outcomes. In this secti on, key features of these studies that have direct relevance to 22 modeling of noncancer outcomes in particular are highlighted. To reduce redundancy, a detailed 23 overview of many of these studies is not provide d here. Instead, the reader should refer to 24
Section 2.4.1.1.1. 25
26This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-87 2.4.1.2.1. Noncancer cohorts. 1
2.4.1.2.1.1. The NIOSH cohort. 2
2.4.1.2.1.1.1. Steenland et al., 1999. 3
2.4.1.2.1.1.1.1. Study summary. 4 The 1999 published report of NIOSH workers exposed to TCDD also conducted external 5 cohort comparisons to the U.S. general population using SMRs for mortality outcomes other than 6 cancer (Steenland et al., 1999). Analyses are based on 3,538 worker s employed at 8 plants from 7 1942 to 1984. SMRs were based on a mortality follow-up that was extended until the end of 8 1993. Cox regression analyses were used to compare mortality risk in relation to TCDD 9 exposure within the cohort. 10
11
2.4.1.2.1.1.1.2. Study evaluation. 12 Overall, no statistically significant differe nces in all-cause mortality (SMR = 1.03, 13
95% CI = 0.97

− 1.08) were observed. Mortality from ischemic heart disease (SMR = 1.09, 14
95% CI = 1.00

− 1.20) and accidents (SMR = 1.25, 95% CI = 1.03

− 1.50) was slightly elevated. 15 The dose-response relationship for ischemic hear t disease observed with the SMRs calculated 16 across septile exposure categories, howeve r, was not statisti cally significant ( p = 0.14). Overall, 17 excess risk was not evident for diabetes, cerebr ovascular disease, or nonmalignant respiratory 18 disease using the external popula tion comparisons. Internal c ohort comparisons using the Cox 19 regression model were performed using 0 and 15-year lag intervals. A dose-response trend was 20
observed for the derived ratios across th e septiles for ischemic heart disease ( p = 0.05) and 21 diabetes ( p = 0.02). For ischemic heart disease mortalit y, those in the upper two septiles had rate 22 ratios of 1.57 (95% CI = 0.96

− 2.56) and 1.75 (95% CI = 1.07

− 2.87), respectively, relative to 23 those in the lowest septile. In contrast, an inverse dose-response relationship was observed for 24 diabetes mortality. The inverse association found for diabetes is inconsistent with the positive 25 association reported in the Ranch Hands study (Michalek and Pavuk, 2008). However, previous 26 reports have questioned the use of death certificates as the means to ascertain outcome as 27 diabetes may be under-reported especially among de scendents with diabetes who die from cancer 28 (McEwen et al., 2006). 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-88 2.4.1.2.1.1.1.3. Suitability of data for TCDD dose-response modeling. 1
The inverse association with di abetes precludes dose-response analysis for this outcome. 2 Although a dose-response pattern was observed fo r ischemic heart disease mortality, it was 3 borderline statistically significant, and this as sociation was not observed in other cohorts. 4 Furthermore, fatal outcomes are not a suitable basis for development of an RfD. For these 5 reasons, dose-response analysis fo r this outcome is precluded. 6
7
2.4.1.2.1.1.2. Collins et al., 2009. 8
2.4.1.2.1.1.2.1. Study summary. 9 Collins et al. (2009) recently described the mortality experience of Dow employees who 10 worked in Midland, Michigan. This plant produced 2,4,5-trichlorophenol between 1942 and 11 1979, and 2,4,5-T between 1948 and 1982. The cohort consisted of 1,615 workers exposed to 12 TCDD from as early as 1942; the follow -up of the cohort extended until 2003. 13 TCDD exposures were derived using seru m samples obtained from 280 surviving 14 individuals. A simple one-compartment, first- order pharmacokinetic model was used to estimate 15 time-dependent TCDD measures. The area under the curve approach was then applied to 16 estimate cumulative TCDD exposure above bac kground. A half-life of 7.2 years for TCDD 17 based on earlier work was incorp orated into the exposure estima tion (Flesch-Janys et al., 1996). 18 Collins et al. (2009) made an external compar ison of the mortality rates of the cohort to 19 the U.S. general population using the SMR statisti c. Noncancer causes of death included all 20 causes, diabetes, cerebrovascular disease, nonmaligna nt respiratory disease, cirrhosis of the liver, 21 and accidents. Overall, no stat istically significant difference in all-cause mortality of these 22 workers was detected when compared to the general population (SMR = 0.9, 95% CI = 0.9

− 1.0). 23 Except for cirrhosis of the liver (SMR = 0.4, 95% CI = 0.1

− 0.8), no differences were found for 24 any of the noncancer causes of deat h relative to the general population. 25 Internal cohort analyses based on cumula tive measures of TCDD were conducted for 26 mortality from diabetes, ischemic heart disease, and nonmalignant respir atory disease using the 27 Cox regression model. These models adjusted fo r possible confounders such as year of hire and 28 birth year. No statis tically significant asso ciation was found betwee n continuous measure of 29
TCDD and these causes of death. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-89 2.4.1.2.1.1.2.2. Study evaluation. 1 Given that the external comparisons may resu lt in bias from the healthy worker effect, 2 results from the internal cohort comparisons using the Cox regression model are preferred. 3 These analyses were performed for diabetes, ischemic heart disease, and nonmalignant 4 respiratory disease. TCDD levels for these workers were estimated using a simple 5 one-compartment pharmacokinetic model (Aylward et al., 2007). The hazard ratios generated 6 from the Cox regression model were not statistically significant for any of the three noncancer 7
outcomes modeled. 8
9
2.4.1.2.1.1.2.3. Suitability of data for TCDD dose-response modeling. 10
No association of an increased risk for an adverse effect was observed with any of the 11 noncancer outcomes. Therefore, dose-respons e modeling based on this population was not 12
conducted. 13
14
2.4.1.2.1.2. The BASF cohort. 15
2.4.1.2.1.2.1. Ott and Zober, 1996. 16
2.4.1.2.1.2.1.1. Study summary. 17 In 1996, Ott and Zober publishe d a report on the mortality ex perience of the cohort of 18 243 BASF male workers who were accidentally e xposed to 2,3,7,8-TCDD in 1954 or in the clean 19 up that followed. The mortality follow-up of this cohort extended until the end of 1992. 20 External comparisons of mortality were made to the German population using the SMR statistic. 21 Internal cohort comparisons were also ma de by estimating cumulative TCDD for the cohort 22 using serum measures that were obtained from 1 38 workers. Ott et al. (1993) provided a detailed 23 account of the methodology to estimate TCDD. Briefly, a cumulative measure of TCDD 24 expressed in μg/kg was derived, by first estimati ng the half-life of TCDD using individuals who 25 had repeated serum measures; the half-life was estimated to be 5.8 years. Individual-level data 26 on body fat were used to account for the influence of body fat on decay rates. Half-life estimates 27 of TCDD varied (range: 5.1

− 8.9 years) and were dependent on body fat composition (20% and 28 30%, respectively). This approach differed from previous analysis of th is cohort that used a 29 constant 7-year half-lif e (Ott et al., 1993). TCDD levels at the time of serum sampling were then 30 estimated as the product of TCDD concentration in blood lipid and th e total lipid weight for each 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-90 worker. Nonlinear models then were applied to estimate the contribution of duration of exposure 1 to TCDD dose extrapolated to the time of exposure. 2 External comparisons to the German populat ion using the SMR statistic also were 3 examined across dose categories. The noncance r causes of death examined by Ott and Zober 4 (1996) included all-cause mortality, diseases of the circulatory system, ischemic heart disease, 5 diseases of the digestive system, external causes, suicide, and residual cause s of death. Overall, 6 no statistically significant differences in the SM R with the general popula tion for all-causes of 7 death (SMR = 0.9, 95% CI = 0.7

− 1.1) were found. No statistically significant differences were 8 noted for any of the other causes of death examined. 9 Ott and Zober (1996) performed internal c ohort comparisons usi ng the Cox regression 10 model. These analyses found no dose-response patterns when cause-specific mortality was 11 examined across increasing cumulative TCDD ex posure categories. Although an inverse 12 association for diseases of the respir atory system (SMR = 0.1, 95% CI = 0.0

− 0.8) was detected, 13 it was based only on 1 reported case. Many of these comparisons are limited by small sample 14
sizes as 92 deaths occurred in the cohort, and of these, 31 were from cancer. Also, the third 15 component of the cohort was identified primarily from former employees who were alive in 16 1986. As a result, the SMR based on the gene ral population might be underestimated by the 17
exclusion of deceased workers. 18
19
2.4.1.2.1.2.1.2. Study evaluation. 20 As noted previously, caution should be exerci sed in the interpretation of SMR values of 21 noncancer outcomes as they could be influenced by the healthy worker effect. Although the 22 mechanism of identifying vital status appears to be excellent and unbiased, SMRs might be 23
underestimated for the cohort due to the manner in which they were constructed. Specifically, a 24 large component of the cohort wa s assembled by actively seeking out former workers who were 25
known to be alive in 1986. 26
27
2.4.1.2.1.2.1.3. Suitability of data for TCDD dose-response modeling. 28 No dose-response patterns were observed between TCDD and the noncancer outcomes in 29 the Ott and Zober (1996) study. Therefore, dose-response modeling was not conducted. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-91 2.4.1.2.1.3. The Hamburg cohort. 1
2.4.1.2.1.3.1. Flesch-Janys et al., 1995. 2
2.4.1.2.1.3.1.1. Study summary. 3 Flesch-Janys et al. (1995) reported on the mo rtality experience of a cohort of individuals 4 employed by an herbicide-producing plant in Hamburg, Germany, covering the period 1952 to 5
1992. As described in more detail in Secti on 2.4.1.1.1.3, the authors developed a cumulative 6 measure of TCDD using serum measures from 1 90 workers. This study also examined the 7 relationship between to tal TEQ and mortality. In the st udy population, the mean TEQ without 8 TCDD was 155 ng/kg, and for the mean TEQ including TCDD was 296.5 ng/kg. 9 Risks relative to the unexposed referent gr oup of gas workers were estimated using Cox 10 regression across six exposed TCDD groups (i.e., th e first four quintiles, and the ninth and tenth 11 deciles). A linear dose-response relationship was found with all causes of mortality and 12 cardiovascular mortality ( p < 0.01). The RR for all cardiovasc ular deaths in the upper exposure 13 category was 1.96 (95% CI = 1.15

− 3.34), although there was no evidence of a linear 14 dose-response trend ( p = 0.27). The dose-response relationship was most marked for ischemic 15 heart disease, with a RR of 2.48 (95% CI = 1.32

− 4.66) in the highest exposure group. A 16 dose-response relationship was also observed across TEQ groupi ngs for all cause mortality, 17 cardiovascular disease mortality, and ischemic heart disease mortality. The authors did not 18 perform joint modeling of TEQ (without TCDD) and TCDD, so determining the extent that 19 dioxin-like compounds contributed to an increased risk of mortality is not possible. 20
21
2.4.1.2.1.3.1.2. Study evaluation. 22 The Flesch-Janys et al. (1995) study lacks in formation on other potential risk factors for 23 cardiovascular disease, which coul d result in confounding if those ri sk factors are al so related to 24 TCDD exposure. Dose-response patterns were strong, however, and persisted across numerous 25 TCDD (and TEQ) exposure categories based on the use of an external reference group (i.e., gas 26 workers) or based on the internal comparison. The findings based on the internal comparison are 27 noteworthy in that these groups should be more homogenous with respect to confounding 28 factors. As noted previousl y, the poor correla tion between TCDD and smoking among workers 29 and similar smoking prevalence between the workers and the external gas company workers 30 suggest that smoking was not likely a confounder of the TCDD and cardiovascular disease 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-92 relationship. No other evaluati on of noncancer mortality outcomes has been undertaken in this 1
cohort since 1995. 2 A strength of the Flesch-Janys et al. (1995) study was that it incl uded the collection of 3 blood serum measures, which provided an objec tive measure of TCDD exposure. Blood serum 4 data, however, were obtained only for 16% of th e cohort. The assumption of the first-order 5 kinetic elimination model is critical, given that measures were taken at the end of follow-up. The 6
model also assumed the half-life of TCDD was 6.9 years. If the kine tics are not first order, or if 7
the half-life estimate is inaccura te, estimates of TCDD levels during exposure would be biased, 8 particularly for workers having longer periods between exposure and PCDD and PCDF assays. 9 Sensitivity analyses completed by the authors sugge st that such bias is not likely to present 10
because the results were unaffected when diffe rent model assumptions regarding kinetic and 11
half-lives were examined. The lack of an impact on RR estimates with varying half-life 12 estimates was similar to findings by Steenland et al. (2001). 13
14
2.4.1.2.1.3.1.3. Suitability of data for TCDD dose-response modeling. 15 Despite the aforementioned study strengths, th e study focused on fatal outcomes such as 16 all cause mortality, cardiovascular disease mortal ity, and ischemic heart disease mortality. As 17 such, dose-response analysis was not conducted since these outcomes are not suitable for 18 development of an RfD. 19
20
2.4.1.2.1.4. The Seveso Women ’ s Health Study (SWHS). 21 Eskenazi et al. (2000) presen ted an overview of the SWHS . The SWHS is the first 22 comprehensive epidemiologic study of the reproduc tive health of a female population exposed to 23 TCDD. The primary objective of the SWHS is to investigate the relationship of TCDD and 24 several reproductive endpoints, incl uding endometriosis, menstrual cycle characteristics, birth 25 outcomes, infertility, and age at menopause. A second phase of follow-up that focuses on 26 osteoporosis, thyroid hormone, breas t cancer, diabetes, and metabolic syndrome is expected to be 27 completed in 2010. 28 Women were eligible for participation in the SWHS if they resided in Zones A and B (the 29 most contaminated areas) at the time of the explosion, were 40 years of age or younger at the 30 time of the explosion in 1976, and samples of th eir blood were collected and stored between 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-93 1976 and 1980. The enrollment of women in the SWHS began in March 1996 and continued 1 until July 1998. Of the 1,271 eligible women, 17 could not be found, 21 had died, and 12 were 2 too ill to participate. Of the 96% of the remaining women, 80% ( n = 981) participated in the 3 study. Participation in the SWHS included a blood draw and an interview by a trained nurse who 4 was blind to subjects’ TCDD level and zones of residence at the time of the accident. The 5
interview included detailed information on potential confounders including occupational, 6 medical, and reproductive, and pregnancy history. Also, women who were premenopausal were 7 asked to undergo a vaginal ultrasound and pelv ic exam and to complete a daily diary on 8
menstruation. 9 Depending on the health outcome under study, TCDD exposures were characterized for 10 the women at different times. For example, TCDD exposure levels were estimated at the time of 11
the accident for some studies and at the time of conception for others. The SWHS study 12 population has been used to investigate associ ations between maternal TCDD levels and the 13 following health outcomes: me nstrual cycle characteristic s (Eskenazi et al., 2002a); 14 endometriosis (Eskenazi et al., 2002b); birth outcomes (Eskenazi et al., 2003); age at menarche 15 (Warner et al., 2004); age at menopause (Eskenazi et al., 2005); uter ine leiomyomas (Eskenazi et 16 al., 2007); and ovarian function (Warner et al., 2007). An evaluation of the studies in 17 chronological order is pr esented in this section. 18
19
2.4.1.2.1.4.1. Eskenazi et al., 2002a—Menstrual cycle characteristics. 20
2.4.1.2.1.4.1.1. Study summary. 21 Eskenazi et al. (2002a) evalua ted serum TCDD exposures in re lation to several menstrual 22 cycle characteristics in the SW HS. A total of 981 women who were 40 years of age or younger 23 at the time of the accident comprised the SWHS. The following exclusion criteria was applied 24 44 years of age or older, women with surgical or natural menopause, those with Turner’s 25 syndrome, and those who in the past year had been pregnant, breastfed, or used an intrauterine 26 device or oral contraceptives. 27
A trained interviewer collected data on menstrual cycle characteristics using a 28 questionnaire. Women were aske d to indicate how long their cy cles were, whether the cycles 29 were regular (e.g., irregular cycle defined as length varied by more than 4 days), how many days 30 the menstrual flow lasted, and whether this flowwas “scanty, moderate, or heavy.” Information 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-94 was also collected on obstetri c and gynecological conditions. TCDD exposures were derived 1 from serum samples collected in 1976

− 1985. The authors selected th e earliest available serum 2 sample, and back-extrapolated to 1976 values usi ng either the Filser mode l (Kreuzer et al., 1997) 3 for women aged 16 years or younger in 1976 ( n = 20) or the first-order kinetic model ( n = 6) 4 (Pirkle et al., 1989). 5
Serum TCDD levels were transformed usi ng the log10 scale, and the relationships 6 between these levels and length of menstrual cycl e and days of menstrual flow were examined 7 using linear regression. The author s applied logistic regression to characterize the risk between 8 log 10 TCDD and heaviness of flow or regularity of cy cle. In these analyses, moderate or heavy 9 flow and regular cycle were used as the referen ce categories. Stratified analysis was performed 10 by menarcheal status at the time of the accident. 11 Overall, the association with TCDD exposur e (per 10-fold incr ease) and length of 12 menstrual cycle was of marginal statis tical significance for premenarcheal ( β = 0.93, 13
95% CI =

− 0.01, 1.86) women, but not postmenarcheal women ( β = − 0.03, 95% CI =

− 0.61, 14 0.54). The corresponding estimates found for days of menstrual flow were β = 0.18 15 (95% CI =

− 0.15, 0.51) and β = 0.16 (95% CI =

− 0.18, 0.50), respectively. Reduced flow was 16 not associated with TCDD when compared to m oderate or heavy flow (odds ratio [OR] = 0.84, 17 95% CI = 0.44, 1.61); effect modification by me narcheal status, however, was evident ( p = 0.03). 18 Specifically, women exposed to TCDD who were premenarcheal had lower odds of reduced 19 flow, while those exposed to TCDD who were pos tmenarcheal did not. These findings counter 20 the hypothesis that TCDD exposure is related to ovarian dysfunction. Finally, statistically 21 significant ORs were found between serum TCDD levels (per 10-fold increase) and having an 22 irregular cycle (OR = 0.46, 95% CI = 0.23, 0.95). Th is inverse association was evident in both 23 premenarcheal women (OR = 0.50, 95% CI = 0.18, 1.38) and postmenarcheal women 24 (OR = 0.41, 95% CI = 0.15, 1.16). 25
26
2.4.1.2.1.4.1.2. Study evaluation. 27 Overall, the findings from th e Eskenazi et al. (2002a) st udy suggest that exposures to 28 TCDD can affect menstrual cycle characteri stics among women who were exposed before 29 menarche. Exposures to TCDD were well charact erized using serum samples available on an 30 individual-level basis, and the de sign allowed for the influence of other risk factor data to be 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-95 controlled for in regression analyses. Analysis of TCDD levels and the le ngth of menstrual cycle 1 in premenarcheal women produced associations that were of marginal sta tistical significance. 2 However, it is unclear whether the endpoints that were measured constitute adverse health 3 outcomes as they are not definitive markers of ovarian dysfunction. Another source of 4 uncertainty is measurement error due to the subj ective nature of menstrual flow reporting. Any 5 resulting misclassification of th e outcome should be nondifferential, as the measurement error is 6 unlikely to be dependent on TCDD exposure. 7
8
2.4.1.2.1.4.1.3. Suitability of data for TCDD dose-response modeling. 9
The lack of a clear adverse health outcome related to TCDD exposure is a weakness of 10 this study. Although it is difficult to define th e critical window of exposure for quantitative 11 exposure calculations, it can be estimated for th e women that were premenarcheal at the time of 12 the accident as 13 years. Therefore, this study is suitable for further consideration for 13 quantitative dose-response modeling. 14
15
2.4.1.2.1.4.2. Eskenazi et al., 2002b—Endometriosis. 16
2.4.1.2.1.4.2.1. Study summary. 17 The SWHS provided the opportunity to inves tigate the association between serum TCDD 18 levels and endometriosis (Eskenazi et al., 2002b ). The rationale the authors provided for 19 undertaking this study was the experimental animal studies that suggested an association, the 20 high prevalence of endometriosis among infertile women where breast milk concentrations of 21 dioxin are high, and the unknown etio logy of endometriosis. The study consisted of 601 women 22 who were younger than 30 years at the time of the Se veso accident. Stored sera that had been 23
collected between 1976 and 1980 were also available for these women. 24 Given that laparoscopy could not be performed on women unle ss clinically indicated, no 25 “gold” standard was available for endometriosis diagnosis. Based on the results of a validation 26 study they conducted in a clinic al population, the researchers classified women as having 27 endometriosis based on symptom report, gynecol ogic exam results, and vaginal ultrasound. 28
TCDD was measured in sera in 1976 for 93% of the women. Values for women whose 29
serum TCDD levels were collected after 1977 and had values exceeding 10 ppt were 30 back-extrapolated to 1976 using either the Filser model (<16 years of age) (Kreuzer et al., 1997) 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-96 or a first-order kinetic model ( ≥ 16 years) (Pirkle et al., 1989). These estimates of TCDD were 1 then modeled as both continuous (o n a log scale) and categorical ( ≤ 20, 20.1

− 100, and >100 ppt) 2

exposures. 3 Polytomous logistic regression was applied wi thin the cohort used to generate RRs. In 4
relation to women in the lowe st exposure category, the RR fo r endometriosis among women in 5 the middle and upper categories was 1.2 (95% CI = 0.3

− 4.5) and 2.1 (95% CI = 0.5

− 8.0), 6 respectively. The trend tests were not statistically significant for either the categorical ( p = 0.25) 7 and continuous measures of TCDD ( p = 0.84). 8
9
2.4.1.2.1.4.2.2. Study evaluation. 10 It is important to note that disease misclassi fication could have led to an underestimate of 11
the true risk of endometriosis if this misclassi fication was not differential with respect to TCDD 12 exposure. Also, younger women were likely to be under-represented as those who had never 13 been sexually active could not be examined due to cultural reasons . Other dioxin-like 14 compounds (PCDD, PCDFs, or polychlorinated biphenyls [PCBs]) were not considered because 15 of small serum volumes, but any potential TEQ exposures occurring in the population were 16 thought to be mostly attributable to TCDD in the exposed women. 17
18
2.4.1.2.1.4.2.3. Suitability of data for TCDD dose-response modeling. 19 Given that no statistically significant dose-re sponse patterns were observed with either 20 log-transformed or across TCDD exposure categori es, and that the elevated risks among those 21 with higher exposures had very wide confiden ce intervals (that incl uded unity) quantitative 22 dose-response analyses were not recommended for this outcome. 23
24
2.4.1.2.1.4.3. Eskenazi et al., 2003—Adverse birth outcomes. 25
2.4.1.2.1.4.3.1. Study summary. 26 Eskenazi et al. (2003) examined the relationship between serum TC DD levels and birth 27 outcome measures. Analyses were based on 745 of the 981 women enrolled in the SWHS who 28 reported having been pregnant ( n = 1,822). Most of these pre gnancies (888 pregnancies among 29 510 women) occurred after the accident. Analysis of spontaneous abortions was restricted to 30 769 pregnancies among 476 women th at did not end in abortion or in ectopic or molar 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-97 pregnancy. Congenital anomalies were evaluate d for the 672 pregnancies that did not end in 1 spontaneous abortion. For the birth outcomes of fe tal growth and gestatio nal age, analysis was 2 performed using 608 singleton births from wome n without hypertensive pregnancy disorders. 3 TCDD exposures were based on se rum measures, most of whic h were taken shortly after 4
the accident. Serum was collected in 1976

− 1977 for 413 women, between 1978 and 1981 for 5 12 women, and in 1996 for 19 women. TCDD exposur es based on serum samples collected from 6 1977 onward were back-extrapolated to 1976. 7 Statistical analyses were performed on pre gnancies that ended be tween 1976 and the time 8 of interview. A continuous measure of log 10 TCDD (base 10 scale) was used to investigate 9 associations with adverse birth outcomes. Logi stic regression was used to characterize the 10 relationship between TCDD exposure spontaneous abortions, small for gestational age, and 11 preterm birth (<37 weeks gestatio n). Linear regression was used to describe the relationship 12 between TCDD and birth weight (in gram s) and gestational age (in weeks). 13 The risk estimates were adjusted for a series of characteristics that in cluded sex of infant, 14 history of low birthweight child, maternal hei ght, maternal body mass index, maternal education, 15 maternal smoking during pregnancy, and parit y. No association was evident between TCDD 16 serum levels and spontaneous abortion for pregnancies between 1976 and 1998 (OR = 0.8, 17
95% CI = 0.6

− 1.2), or those between 1976 and 1984 (OR = 1.0, 95% CI = 0.6

− 1.6). No 18 statistically significant a ssociations were found for birth wei ght or small gestational age, though 19 the association with birth weight for pregna ncies between 1976 and 1984 associated with a 20 10-fold increase in TCDD was fairly large and marginally statistically significant ( β = − 92, 21
95% CI =

− 204 to 0). No association was noted fo r preterm delivery in relation to log 10 TCCD 22
levels. 23
24
2.4.1.2.1.4.3.2. Study evaluation. 25 This study was well-designed with well character ized exposures. Sta tistically significant 26 associations were not evident, although the birth-weight findings should be pursued with further 27 follow-up of the cohort. As the authors point out, those who were most vul nerable at the time of 28 the accident (the youngest) had no t yet completed their childbear ing years. While the study 29 lacked exposure data for the fathers, the auth ors indicated that only a small proportion were 30 believed to have high exposures to TCDD. Th e key limitation of the study was a reliance on 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-98 self-reported measures of pregnancy history, which may lead to some misclassification of the 1
birth outcomes. The observation that a la rge proportion of Seveso women had a voluntary 2 abortion because of fears of possi ble birth defects due to exposure s from the accident suggest an 3 awareness bias is possible as a result of diffe rential reporting of birth outcomes according to 4 exposure status. 5
6
2.4.1.2.1.4.3.3. Suitability of data for TCDD dose-response modeling. 7 No statistically signif icant associations were found in the study; in addition, possible 8
awareness bias could have influenced the self-re ported measures of birth outcomes. Therefore, 9 quantitative dose-response assessment was not considered for this study. 10
11
2.4.1.2.1.4.4. Warner et al., 2004—Age at menarche. 12
2.4.1.2.1.4.4.1. Study summary. 13 Warner et al. (2004) examined the relations hip between TCDD and age at menarche in 14
the SWHS cohort. As described earlier in this report, the SWHS comprised 981 participants. 15 This study was restricted only to those who were premenarcheal at the time of the accident 16 ( n = 282). The proportional hazards model was used to model TCDD exposures and age at 17 menarche. Age at menarche was determined by questionnaire administered by a trained 18 interviewer. Covariates examined as potenti al confounders included he ight, weight, body mass 19 index, athletic training at the time of interview, smoking, and alcohol consumption. 20 TCDD exposures were determined using seru m samples collected from 257 of these 21 women between 1976 and 1977. For the remaining women, TCDD levels were quantified from 22 measures collected between 1978 and 1981 ( n = 23) and in 1996 ( n = 2). TCDD levels were 23 back-extrapolated to the time of the explos ion in 1976. TCDD was modeled as both a 24 continuous variable (log 10 TCDD) and a categorical variable based on quartile values ( ≤ 55.9, 25
56

− 140.2, 140.3–300, >300 ppt). The lowest group was furt her subdivided into those with levels 26
≤ 20, and >20 ppt; this cut-point represented ba ckground levels found in a sample of women 27 living in an unexposed area. 28
No association was found between the continuous measure of TCDD and age at 29 menarche (hazard ration [H R] = 0.95, 95% CI = 0.83

− 1.09). Analyses restri cted to those who 30 were younger than 8 in 1976 produced si milar results (HR = 1.08, 95% CI = 0.89

− 1.30). 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-99 Additionally, no dose-res ponse trend was observed with categ orical measures of TCDD among 1 all women, as well as those under the age of 8. A marginally statistically significant association 2 with earlier menarche was found when analyses were limited to 84 women under the age of 5 at 3 the time of the accident (HR = 1.20, 95% CI = 0.98

− 1.60). 4
5
2.4.1.2.1.4.4.2. Study evaluation. 6 An important strength of the Warner et al. (2004) study is the ability to characterize 7 TCDD exposures using serum sample s that were collected shortly after the accident occurred. 8 The outcome of interest, age at menarche, wa s determined by asking women “At what age did 9 you get your first menstrual period?” Recent work suggests that self-reported measures of age at 10 menarche decades later have modest agreement with responses provided during adolescence with 11 recall varying by education and by history of an adverse birth out come (Cooper et al., 2005). In 12 the Seveso study, bias would be introduced if recall varied accordi ng to exposure levels. 13
14
2.4.1.2.1.4.4.3. Suitability of data for TCDD dose-response modeling. 15 Although the TCDD exposure characterizati on of study subjects was based on serum 16 data, and no major biases were introduced from the study design, the analyses produced largely 17 null associations. Therefore, quantitative dose-response assessment was not considered for this 18 study. 19
20
2.4.1.2.1.4.5. Eskenazi et al., 2005—Age at menopause. 21
2.4.1.2.1.4.5.1. Study summary. 22 Eskenazi et al. (2005) evalua ted the relationship between ag e at onset of menopause and 23 serum levels of TCDD among women in the SWHS. Of the 981 women who agreed to 24 participate in SWHS, this analysis was rest ricted to those who ha d not reached natural 25 menopause before the time of the accident and who were at least 35 years of age at the time of 26
the interview. The recruitment and interview of women occurred approximately 20 to 22 years 27 after the accident (March 1996

− July 1998). 28 The population was divided into quintiles of serum TCDD levels for the categorical 29 analysis. For most women ( n = 564), TCDD levels were estimated from samples provided in 30
1976

− 1977. For the remaining women included in th ese analyses, TCDD levels were estimated 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-100 from samples collected between 1978 and 1982 ( n = 28) and between 1996 and 1997 ( n = 24). 1 As noted previously, exposure le vels for women with post-1977 de tectable levels of TCDD were 2 back-extrapolated to 1976 using either the fi rst-order kinetic model (Pirkle et al., 1989) 3 (>16 years at time of accident) or the Filser model (<16 years at time of ac cident) (Kreuzer et al., 4 1997). Women were classified as premenopausal if they were still menstr uating or if they had 5 amenorrhea as a result of pregnancy or lactation (a t the time of interview) with an indication of 6 subsequent menstruation based on maintained diarie s or further examination. Subjects for which 7 amenorrhea had persisted for at least 1 year with no apparent medical explan ation were classified 8 into a natural menopause category. The category, surgical menopause, pertained to women with 9 a medically confirmed hysterectomy or an oophorectomy. Finally, impending menopause was 10 defined for subjects in which menstruation had been absent for 2 months, but who provided 11 evidence of subsequent menstruation, or had a secretory endometrial lin ing, or indicated less 12 predictable cycles in the previous 2

− 5 years. If participants’ me nopausal status could not be 13 determined, they were grouped into the “other ” category. This category included those for 14 whom status could not be determined due to current use of oral contraceptives, hormone 15 replacement therapy, or prev ious cancer chemotherapy. 16 Statistical analysis was based on both a con tinuous measure of log-transformed TCDD 17 exposures and categories base d on quintiles (<20.4 ppt; 20.4

− 34.2 ppt; 34.3

− 54.1 ppt; 18
54.2

− 118.0 ppt; >118.0 ppt). The Cox model was used to generate hazard ratios as estimates of 19
relative risks and their 95% confidence intervals examining natural menopause as the outcome. 20 Several covariates previously identified as asso ciated with menopausal status in the literature 21 were considered as potential confounders. These covariates included body mass index, physical 22 activity, premenopausal smoking, education, marital status, history of heart disease and other 23 medical conditions, and other re productive characteristics. 24
The RRs were found to increase across the se cond through fourth quintiles (RRs = 1.1, 25 1.4, and 1.6, respectively) of serum TCDD categories in relation to those in the lowest category, 26 but not in the upper quintile (RR = 1.0, 95% CI = 0.6

− 1.8). A statistically significant test of 27
trend was detected across the fi rst four quartiles (p = 0.04) bu t not across all five quintiles 28 (p = 0.44). A statistically signi ficant association with onset of menopause was not detected 29 (RR = 1.02, 95% CI = 0.8

− 1.3) based on the logTCDD continuous m easure. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-101 2.4.1.2.1.4.5.2. Study evaluation. 1 The categorical exposure results fr om this study support a non-monotonic 2 dose-related-association for earlier menopause with increased serum TCDD levels up to 3 approximately 100-ppt TCDD serum, but not above. Eskenazi et al. (2005 ) speculated that the 4 inverse “U” shape of the dose-re sponse relationship is explained by the mimicking of hormones 5 at lower doses of a chemical, while at higher leve ls the toxic effect of a chemical does not have 6 the capacity to either inhibit or stimulate hormonal effects. 7 A study limitation is the potenti al for residual confounding due to adjustment based on 8 current smoking status and not at the time of onset of menopause. It is unclear to what extent 9 smoking status may differ between these two ti me periods and whether smoking is related to 10 TCDD exposures in this cohort. Exposures to other dioxin-like compounds were not considered 11 in this study because of small serum volumes, bu t any potential TEQ exposures occurring in the 12 exposed population were thought to be mostly attributable to TCDD in the exposed women. 13
14
2.4.1.2.1.4.5.3. Suitability of data for TCDD dose-response modeling. 15 To date, this study is the only one that has examined the relationship between TCDD 16 levels and onset of menopause. Although the findings suggest the possi bility of a nonlinear 17 dose-response function, the log 10 TCDD exposure metric was not st atistically significant, nor 18 were any category-specific hazard ratios statistically significant relative to the lowest category. 19 Therefore, a quantitative dose-res ponse analysis was not undertaken. 20
21
2.4.1.2.1.4.6. Warner et al., 2007—Ovarian function. 22
2.4.1.2.1.4.6.1. Study summary. 23 Warner et al. (2007) investigated the a ssociation between serum TCDD levels and 24 ovarian function in subjects in the SWHS w ho were younger than 40 in 1976 and for whom sera 25
collected after the accident had been stored. These women were recruited from March 1996 until 26 July 1998. Ovarian function analysis was limite d to 363 women between 20 and 40 years of age 27 and who were not using oral co ntraceptives. Of these, 310 underwent transvaginal ultrasound 28 and were included in the functional ovarian cyst analysis. Ninety-six women were in the 29 preovulatory stage of their menstr ual cycles and were included in the follicle analysis. For the 30 hormone analysis, 126 women who were in the last 2 weeks of their cycle were included. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-102 The authors used logistic regression to ex amine the relationship between TCDD and the 1 prevalence of ovarian follicles greater than 10 mm. Linear regression models examined the 2
continuous outcome variables: number of ovarian follicles >10 mm and di ameter of dominant 3
ovarian follicle. Covariates considered for inclusion in the model were age at ultrasound, age at 4 accident, age at menarche, marital status, parit y, gravidity, lactation history, current body mass 5 index, age at last birth, and sm oking history. For the serum hor mone analyses, estradiol and 6 progesterone were measured in blood at the time of interview. Ovulation status was defined as a 7 dichotomous variable (yes/no) based on a serum progesterone cut-point value of 3 ng/mL. 8 The adjusted ORs across categories of TC DD exhibited no dose-response trend for the 9 presence of follicles in relation to TCDD in the fo llicular phase; also, no statistically significant 10 differences were noted in any of the upper exposure categories relati ve to those in the lowest. 11 The adjusted OR for the continuous measure of log 10 TCDD was 0.99 (95% CI = 0.4

− 2.2). A 12 similar nonstatistically significant finding was found for log 10 TCDD in relation to ovulation in 13 both the luteal (OR = 0.99, 95% CI = 0.5

− 1.9) and mid-luteal phases (OR = 1.03, 14
95% CI = 0.4

− 2.7). Analyses of progesterone and estr adiol also were not related to serum 15 TCDD levels for either the luteal or mid-luteal phases ( p = 0.51 and p = 0.47). 16
17
2.4.1.2.1.4.6.2. Study evaluation. 18 The investigators found no re lationship between serum TCDD levels and serum 19 progesterone and estradiol levels among women w ho were in the luteal phase at the time of 20
blood draw. No association with number of ovarian follicles detected from ultrasound. 21 Although no association was found, th e authors suggested that the lack of significant results 22
could be because the women in SWHS were all e xposed postnatally and the relevant and critical 23 time period for an effect might be in utero (a nimal studies support re levance of in utero 24 exposures). 25
26
2.4.1.2.1.4.6.3. Suitability of data for TCDD dose-response modeling. 27 One limitation of the study was the lack of examination of confounding by dioxin-like 28 compounds. The absence of associ ations between TCDD and adverse health effects in this study 29 precludes conducting quantitati ve dose-response analyses. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-103 2.4.1.2.1.4.7. Eskenazi et al., 2007—Uterine leiomyoma. 1
2.4.1.2.1.4.7.1. Study summary. 2 Associations between TCDD exposures and uter ine leiomyoma (i.e., fibroids) were also 3 examined among 956 women in the SWHS (Esken azi et al., 2007). The sample population was 4 based on the on the original 981 SWHS partic ipants excluding 25 women diagnosed with 5 fibroids before the date of the accident (Jul y 10, 1976). Women who previously had fibroids 6 were identified both through the admi nistered questionnaire and the review of medical records. 7 Transvaginal ultrasounds were perf ormed for 634 women to determine if they had fibroids at the 8 time of follow-up. Similar to other SWHS st udies, exposure to TCDD was estimated using 9 serum collected from women shortly after th e time of the accident, between 1978 and 1981 and 10
in 1996. TCDD levels were back -extrapolated to 1976 levels. 11 The study authors performed statistical analys es using two definiti ons of fibroids as 12
outcome measures. The first was fibroids detect ed before the study, and the second was fibroids 13 detected via ultrasound. A proportional odds me thod Dunson and Baird (2001) developed was 14
used to model the cumulative odds of onset of fi broids. This method combines historical and 15 current information of diagnoses of fibroids. Continuous and categoric al measures of TCDD 16 were modeled. Regression models were adjusted for known or suspected risk factors of fibroids 17 including parity, family history of fibroids , age at menarche, body mass index, smoking, alcohol 18 use, and education. 19
20
2.4.1.2.1.4.7.2. Study evaluation. 21 Categorical measures of TCDD suggested an inverse dose-re sponse relationship with the 22
onset of fibroids. Relative to those with TCDD levels less than 20 ppt, those having TCDD 23 exposures between 20.1 and 75.0 ppt and greater than 75.0 ppt had RRs of 0.58 24 (95% CI = 0.41

− 0.81), and 0.62 (95% CI = 0.44

− 0.89), respectively. The continuous measure of 25 log 10 TCDD produced a hazard rati o of 0.83 (95% CI = 0.65

− 1.07). 26
27
2.4.1.2.1.4.7.3. Suitability of data for TCDD dose-response modeling. 28
The inverse association between TCDD and uterine fibroids s upports the possibility of an 29 anti-estrogenic effect of TCDD. The observed direction of th e reported associations precludes 30 quantitative dose-response modeling. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-104 2.4.1.2.1.5. Other Seveso noncancer studies. 1
2.4.1.2.1.5.1. Mocarelli et al., 2008—Semen quality. 2
2.4.1.2.1.5.1.1. Study summary. 3 Mocarelli et al. (2008) examined the relationship between TCDD and endocrine 4 disruption and semen quality in a cohort of Seveso men. A total of 397 s ubjects of the eligible 5 417 males (<26 years old in 1976) from Zone A and nearby contamin ated areas were invited to 6 participate. Frozen serum samples were used to derive TCCD exposures. Also, 372 healthy 7 blood donors not living in the TCCD-contaminated area were invited to participate. The 8 researchers collected a health questionnaire a nd semen samples from participants. Analyses 9
were based on 257 individuals in the exposed group and 372 in the comparison group. 10 Semen samples were collected postmasturbat ory at home. Ejaculate volume, sperm 11 motility, and sperm concentration were measured on these samples. Fasting blood samples also 12 were collected from the subjects for reproductive hormone analyses, including 17 β -estradiol 13 (E 2 ), follicle stimulating hormone (FSH), inhibin B, luteinizing hormone (LH), and testosterone. 14
The researchers estimated serum concentra tions of TCDD from samples provided in 15
1976

− 1977, and also in 1997

− 1998 for individuals whose earlier samples had TCDD values that 16 exceeded 15 ppt. Serum concentrations for the comparison group were assumed to be less than 17 15 ppt in 1976 and 1977 and <6 ppt in 1998/2002 on the basis of serum results for residents in 18 uncontaminated areas. The exposed and compar ison groups were divided into three groups 19 based on their age in 1976: 1

− 9, 10

− 17, and 18

− 26 years. Mocarelli et al. (2008) applied a 20 general linear model to the sperm and hormone da ta and included exposure status, age, smoking 21 status, body mass index, and occupati onal exposures as covariates. 22 Men exposed between the ages of 1 and 9 ha d reduced semen quality 22 years later. 23 Reduced sperm quality included decreases in sperm count ( p = 0.025), progressive sperm 24 motility ( p = 0.001), and total number of motile sperm ( p = 0.01) relative to the comparison 25 group. The opposite pattern was observed for severa l indices of semen quality among those aged 26
10

− 17 at the time of the accident. For the hormone analyses, those in the exposed group had 27
lower serum E 2 levels, and higher follicle stimulati ng hormone concentrations. Neither 28
testosterone levels nor inhibi n B concentrations were asso ciated with TCDD exposure. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-105 2.4.1.2.1.5.1.2. Study evaluation. 1 The findings of the Mocarelli et al. (2008) study support the hypothesis that exposure to 2 TCDD in infancy/prepuberty reduces sperm qualit y and could contribute to reported decrease in 3 sperm quality in young men in the industrialized world. Although most semen analysis studies 4 have low compliance rates in general population samples (20

− 40%) (Jorgenson et al., 2001; 5 Muller et al., 2004), the complian ce rate in this study was much higher (60%). Given that the 6 compliance rates were similar between the exposed and comparison groups and the strong 7 differences detected across the two age groups, selection bias appears unlikely in this study. 8
9
2.4.1.2.1.5.1.3. Suitability of data for TCDD dose-response modeling. 10
Health outcomes are well defined in the Moca relli et al. (2008) study, and exposures are 11 well characterized using serum data. Because the men exposed to elevated TCDD levels 12 between the ages of 1 and 9 had reduced semen quali ty 22 years later, it is difficult to identify the 13
relevant time interval over which TCDD dose shoul d be considered. Specifically, it is difficult 14
to discern whether this effect is a consequence of the initial high exposure between 1 and 9 years 15 of age or a function of the cumulative exposure for this entire exposure window beginning at the 16 early age. However, the differences between th ese two dose estimates (the initial high exposure 17 versus the cumulative exposure for the 9 year window) are minimal (i.e. within an order of 18 magnitude). Despite the uncertainty in es timating the critical window of exposure, 19 dose-response analysis for this outcome was conducted. 20
21
2.4.1.2.1.5.2. Mocarelli et al., 1996, 2000—Sex ratio. 22
2.4.1.2.1.5.2.1. Study summary. 23 A letter to the editor was the first report of a possible change in the sex ratio from dioxin 24 among Seveso residents following the July 10, 1976 accident (Mocarelli et al., 1996). The 25 authors reported that 65% ( n = 48) of the 74 total births that had occurred from April 1977 to 26
December 1984 were females. This male to female ratio of 26:48 (35%) is significantly different 27
from the worldwide birth ratio of 106 males to 100 females (51%) (James, 1995). Between 1985 28 and 1994, the Seveso male to female ratio levele d out at 60:64 (48%). The authors suggested 29 that the finding supported the hypot hesis that dioxin might alter the sex ratio through several 30 possible mechanistic pathways. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-106 Mocarelli et al. (2000) late r reported on an investigation between serum-based TCDD 1 measures in parents and the sex ratio of offspring. In this study, serum samples were collected 2
from mothers and fathers who lived in the areas at the time of the explosion, were between the 3 ages of 3 and 45 at the time of the explosi on, and produced offspring between April 1, 1977 and 4 December 31, 1996. The study population included 452 families and 674 offspring, and serum 5
measures were available for 296 mothers and 239 fathers. An estimate of TCDD at the time of 6 conception was also examined in relation to ma le to female birth ra tios. TCDD exposure 7 estimates between the years of 1976 and 1996 were estimated using Filser’s model (Kreuzer et 8 al., 1997). 9 Mocarelli et al. (2000) used chi- square test statistics to compare observed sex ratio to an 10 expected value of 0.51 in this Seveso population. Concentrations of TCDD were modeled as 11 categorical variables in several ways. Firs t, a dichotomous variable was used whereby 12 unexposed parents were defined as those who lived outside Zones A, B, and R or had a serum 13
TCDD concentration of less than 15 ppt; parents with exposures of 15 ppt or higher were 14 considered exposed. Second, a tr ichotomous exposure variable was created that consisted of 15 parents who (1) lived outside Zones A, B, and R or had serum concentrations of less than 15 ppt, 16 (2) had serum concentrations of 15

− 80 ppt, and (3) had serum concentrations that exceeded 17 80 ppt. These cut-points were chosen as they represented tertiles based on the distribution of 18 TCDD among parents. Analyses were conducted separately fo r paternal and maternal TCDD 19
levels. 20 The overall proportion of 0.49 ma le births (based on male to female ratio of 328:346) was 21 not significantly different from the expected proportion of 0.51 ( p > 0.05). Statistically 22 significant differences were found, however, if both parents had TCDD levels >15 ppt (sex 23 ratio = 0.44) or just the father had serum TCDD levels >15 ppt (sex ratio = 0.44). No 24 statistically significant differen ces were found when the fathers had TCDD levels less than 25 15 ppt, irrespective of the maternal levels. A dose-response pattern in the sex ratio was found 26 across the paternal exposure categories. That is , the sex ratio decreased with increased paternal 27 TCDD levels (linear test for trend, p = 0.008). In the unexposed gr oup, the sex ratio (male to 28 female) was 0.56 (95% CI = 0.49

− 0.61), while in the highest exposure group 29 (281.0

− 26,400.0 ppt) the corresponding sex ratio was 0.38 (95% CI = 0.28

− 0.49). 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-107 Stratified analyses by age at paternal exposure revealed that the sex ratio was altered to a 1 greater degree among fathers who were younger than 19 at the time of the explosion. The male 2 to female ratio among the unexposed fathers was 0.56 (95% CI = 0.50

− 0.62), while it was 0.38 3 (95% CI = 0.30

− 0.47) for those younger than 19 when exposed and 0.47 (95% CI = 0.41

− 0.53) 4 for those exposed after 19. Regardless of the ag e at the time of exposure, however, fathers who 5 were exposed had a statistically si gnificantly different birth ratio (t hey were more likely to father 6 girls) than those who were unexposed ( p < 0.05). 7 Separate analysis of birth ra tios based on paternal TCDD e xposure estimated at the time 8 of conception did not show the same dose-response pattern but did show strong evidence of 9 consistently decreased male birt hs relative to females. More specifically, the male to female 10 birth ratios among the four successive qua rtiles (first through fourth) were 0.41, 0.33, 0.33, 11
and 0.46. 12
13
2.4.1.2.1.5.2.2. Study evaluation. 14 Mocarelli et al. (2000) base d the characterization of TC DD exposure on serum samples, 15 which is an objective method for characterizing dose. Unlike for the occupational cohorts, serum 16 measures for this study were taken close to the time of the accident, and therefore, 17 back-extrapolation of TCDD exposures is unnece ssary. Exposure received before the age of 19 18 at the time of the explosion were more strongly associated with a reduced male to female ratio 19 than those received after the age of 19. The cut off age of 19 s eems to be somewhat arbitrary, 20 resulting in a highly uncertain cr itical exposure window. TCDD leve ls at the time of conception 21 did not demonstrate a dose-response relationship, but paternal exposur es resulted in consistently 22 reduced male to female birth ratios (range: 0.33

− 0.46). 23 The study findings are unlikely to be influen ced by age at conception as these values 24 were found, on average, to be similar across calendar years. This suggests that age at conception 25 was not an important confounder and that the birt h ratio findings may be related to paternal 26

exposures. 27 The methods used to identify births app ear to be appropriate. Even if some 28
under-ascertainment of births occu rred, there is no reason to belie ve that ascertainment would be 29 related to TCDD exposure and the sex of the ba by. Therefore, no bias is suspected due to 30 incomplete birth ascertainment. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-108 2.4.1.2.1.5.2.3. Suitability of data for TCDD dose-response modeling. 1 TCDD exposures were well-char acterized, and internal cohort analyses demonstrate 2 association between paternal TCDD levels at the time of the accide nt and birth ra tio. However, 3 the change in sex ratio was only statistically significant when exposure occurred before 19 years 4 of age. It is impossible to identify the rele vant time interval over which TCDD dose should be 5 considered for dose-response analysis; specifically, it is difficult to discern whether the different 6 sex ratio is a consequence of the initial peak expo sure before 19 years of age or a function of the 7 average cumulative exposure over this entire exposure window. Assu ming the initial high 8 exposure is the correct exposure window, using th e initial exposures in a dose-response model 9 would yield LOAELs that are too high to be relevant to factor into the RfD calculation. The 10 differences between the two dose estimates are qu ite large. Dose-response analysis for this 11 outcome, therefore, was not conducted. 12
13
2.4.1.2.1.5.3. Baccarelli et al., 2008—Neonatal thyroid function. 14
2.4.1.2.1.5.3.1. Study summary. 15 Baccarelli et al. (2008) inves tigated the relationship betw een thyroid function and TCDD 16 among offspring of women of reproductive age w ho were exposed in the 1976 accident. This 17 health endpoint is relevant because thyroid function is important for energy metabolism and 18 nutrients and for stimulating growth and develo pment of tissues. Neonatal thyroid function at 19 birth is evaluated through blood thyroid-stimulating hormone (b -TSH). Apart from iodine 20 deficiency, no other environmental exposure has been associated consistently with reduced 21 neonatal thyroid functioning. 22 The study population was drawn from 1,772 wome n who were identifie d as having lived 23 in the highly contaminated areas (Zones A or B) at the time of the accident or between July 10, 24 1976 and December 31, 1947; were of fertile age (born after 1947); and were alive as of 25 January 1, 1994. A random sample of 1,772 unexposed women who lived in the reference area 26 was selected using frequency matching by year of birth to the exposed women, and residency in 27
the reference area at the time of the accident. Th e reference area represents the noncontaminated 28
areas that surround the three z ones of decreasing exposure (Zones A, B and R). In total, 29 55,576 women had lived in the reference ar ea. Population registry offices ( n = 472) were 30
contacted to detect children born to these women. Records co uld be traced for virtually all 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-109 subjects (1761/1772 exposed; 1762/1772 unexposed). Children born outsid e the Lombardy area 1
were excluded as b-TSH could not be obtai ned for them. This accounted for 156 of the 2 1,170 children identified. The analyses were based on the remaining 56, 425, and 533 singletons 3 born between January 1, 1994, and June 30, 2005 in Zone A, B, and from the reference area, 4 respectively. 5 Thyroid function is tested in all newborns by b-TSH measures in the region of Lombardy 6
where Seveso is located. These measures are obtained from blood samples taken 72 hours after 7 birth using a standardized protoc ol. The b-TSH levels were l og transformed to approximate a 8 normal distribution. Linear regression analysis was used to conduct test for trends in mean 9
b-TSH levels across different cova riates. Logistic regression wa s used to assess associations 10 between elevated b-TSH levels defined by the cutpoint of 5 μ U/mL and residence in particular 11 zones of contamination. Generalized estimating equations were used to adjust the standard 12 errors of the ORs for correlation between siblings. 13 The mean levels of b-TSH were positiv ely associated with average soil TCDD 14 concentrations in the three areas (Zone A: 1.66 μ U/mL; Zone B: 1.35 μ U/mL; and Zone R: 15
0.98 μ U/mL) ( p < 0.001). Plasma TCDD levels also were shown to be much higher in a group of 16
51 newborns that had b-TSH levels >5 μ U/mL. Compared to the re ference population, adjusted 17 ORs were elevated for Zone B (OR = 1.90, 95% CI = 0.94

− 3.86) and Zone A (OR = 6.63, 18
95% CI = 2.36

− 18.6). These ORs were adjusted for gender, birth weight, birt h order, maternal 19 age at delivery, hospital, and type of delivery. The adjusted ORs however differed only slightly 20 from those that were unadjusted (Zone B, OR = 1.79, 95% CI = 0.92

− 3.50; Zone A OR = 6.60, 21
95% CI = 2.45

− 17.8). Of the risk factors considere d, both gender and birth weights were 22
associated with neonatal b-TSH. 23 The paper also included an an alysis of children born to 109 women who were part of the 24 Seveso Chloracne Study (Baccarelli et al., 2005). A total of 51 children were born to 38 of these 25 women, of these 12 lived in Zone A, 10 in Zone B, 20 in Zone R, and 9 from the reference 26 population. Several congeners in cluding TCDD were measured in ma ternal plasma. TCDD 27 levels were extrapolated to the date of delivery using a fi rst-order pharmacokinetic model 28 (Michalek et al., 1996). The elimination rate used was 9.8 years based on the mean half-life 29 estimate from a previous study of women in th e Seveso region (Michale k et al., 2002). TEQs 30
were calculated for a mixture of dioxin-like compounds by multiplying the concentration of each 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-110 congener by its toxicity equivalence fact or. The maternal average TEQ was 44.8 ppt 1 (range: 11.6

− 330.4) among 51 mothers. The measurement of noncoplanar PCBs occurred only 2 later in the study (1996) and, th erefore, total mean TEQs (i.e ., including the sum of PCDDs, 3 PCDFs, coplanar PCBs, and noncoplanar PCBs) are available only on a subset ( n = 37) of the 4 population. Dioxin-like c ongeners were examined in this study as several studies suggest 5 associations between the sum of PCBs, or indivi dual congeners having decreased thyroxine (T4; 6 Longnecker et al., 2000; Sandau et al., 2002), and increased TSH (A lvarez-Pedrerol et al., 2008; 7 Chevrier et al., 2007). 8
The authors used a linear model to exam ine the association between maternal TCDD 9 levels and b-TSH. The standardized regressi on coefficient obtained from this model was 0.47 10 ( p < 0.001). For the evaluation of TEQs, a simila r association was noted for PCDDs, PCDFs, 11 and coplanar PCBs ( n = 51, β = 0.45, p = 0.005) but not with non-coplanar PCBs ( n = 37, 12 β = 0.16, p = 0.45). Multivariate regression models that were adjusted for several covariates 13 (i.e., gender, birth weight, birth or der, maternal age at delivery, hospital, and type of delivery) 14 found statistically signif icant associations with plasma TCDD, PCDDs, PCDFs, and coplanar 15 PCBs, but not with non-coplanar PCBs. The sum of all total TEQs from the measured 16 compounds was not statistically significant ( n = 37, β = 0.31, p = 0.14). 17
18
2.4.1.2.1.5.3.2. Study evaluation. 19 The Baccarelli et al. (2008) study satisfies the epidemiological considerations and criteria 20 for determining whether dose-response modeli ng should be pursued. The outcome is well 21 defined, and a dose-response patt ern was observed. The study al so contained a substudy that 22 characterized TCDD and exposures to other dioxin-like congeners and used serum measures for 23 a sample of mothers. Results were consistent among the zone of residence analysis and the 24 substudy based on serum measures. 25
26
2.4.1.2.1.5.3.3. Suitability of data for TCDD dose-response modeling. 27 Given the potential for exposure misclassificat ion due to variability in TCDD soil levels 28 within each zone, modeling should rely on indi vidual-level TCDD exposures derived from the 29 serum sampling substudy. The study data prov ide an opportunity for quantitative dose-response 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-111 analyses as the critical expos ure window of 9 months can be used for exposure assessment 1

purposes. 2
3
2.4.1.2.1.5.4. Alaluusua et al., 2004—Oral hygiene. 4
2.4.1.2.1.5.4.1. Study summary. 5 Alaluusua et al. (2004) examined the rela tionship between TCDD and dental defects, 6 dental caries, and periodontal disease among Seveso residents w ho were children at the time of 7 the accident. Subjects were randomly selected from those individuals who had previously 8 provided serum samples in 1976, which was shortly after the accident. A total of 65 subjects 9 who were less than 9.5 years of ag e at the time of the acc ident, and who lived in Zones A, B, or 10 R were invited to participate. Recruitment was initiated 25 years after the time of the Seveso 11 accident. An additional 130 subjects from the surrounding area (outside Z ones A, B, or R or 12 “non-ABR zone”) having the same age restrictio n were recruited. Subjects were frequency 13 matched for age, sex, and educati on. Questionnaires were administ ered to these individuals to 14
collect detailed information on dental and medical histories, education, and smoking behaviors. 15 Ten subjects who had completed at least high school were randomly excluded from the non-ABR 16 zone to create groups with similar educational profiles. Participation rates for the ABR and 17
non-ABR zones were 74% and 58%, respectively. 18 One dentist who was blind to the patients ’ TCDD exposure levels assessed dental 19
aberrations. Dental caries was assessed using recommendations of the World Health 20 Organization. Periodontal status was described fo llowing a detailed evaluation of the surfaces of 21 the teeth. A radiographic examination was done to identify missing teeth, alveolar bone loss, 22 deformities in the roots, and jaw cysts. 23 Comparisons of the presence of dental enamel defects according to exposure status were 24 performed using logistic regressi on. Chi-square test statistics were applied to compare the 25 distributions in the prevalence of dental defe cts across several categor ical covariates (i.e., 26 education, age, and serum TCDD level). For thos e who were younger than 5 at the time of the 27 accident, dental defects were more prevalent am ong patients in zone ABR (42%) than those in 28 the non-ABR zone (26%) ( p = 0.14). Zone ABR is characterized by higher levels of soil TCDD 29 levels relative to non-ABR. Serum levels permitte d an improved characterization of risk as they 30
were available at an individual le vel, rather than using a zone of residence. Defect prevalence 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-112 was highest among those in the upper serum TCDD category (700

− 26,000 ng/kg) with 60% of 1 subjects having dental defects. The continuous measure of se rum TCDD was associated with 2 developmental defects ( p = 0.007) and hypodontia ( p = 0.05). 3
4
2.4.1.2.1.5.4.2. Study evaluation. 5 Although the subjects with seru m measures were selected randomly, no direct measures 6 of TCDD were made in subjects from the unexpos ed area (i.e., non-ABR zones). That those who 7
resided in the non-ABR areas ha d lower TCDD exposures would be a reasonable assumption. 8 Alaluusua et al. (2004), however, provide few details about the sampling frame used to identify 9 these participants. Despite this fact, it is important to note that a dose-response pattern was 10 observed between TCDD exposure and presence of developmental defects in the ABR 11 population alone ( p = 0.016). This finding is based on 27 s ubjects with developmental defects. 12 This positive association provide s support for a quantitative dose -response modeling of dental 13 aberrations. The numbers of such subjects are sm all, however, with one, five, and nine subjects 14 having defects in the exposure groups of 31

− 226, 238

− 592, and 700

− 26,000 ng/kg TCDD, 15 respectively. 16 TCDD exposures were characteri zed using serum measures for those who resided in zone 17 ABR in 1976 (near the time of the accident). The authors could not account for additional 18 exposure to TCDD across subjects that might have occurred since the time of the accident, so 19 there is considerable uncertainty in delineati ng the critical exposure window for the reported 20 effects. In addition, the lack of exposure da ta for those in the non- ABR zone, however, makes 21 interpretation of the findings difficult. This di fficulty is particularly evident, given that the 22 prevalence of dental defects was less among thos e in the low exposure category of zone ABR 23 (31

− 226 ng/kg TCDD) (10%) when compared to those in the non-ABR zone (26%). 24
25
2.4.1.2.1.5.4.3. Suitability of data for TCDD dose-response modeling. 26
Most of the considerations for conducting a dose-response anal ysis have been satisfied 27 with the study population, although, exposure assessm ent uncertainties are a limitation of this 28 study. For example, it is difficult to discern whet her these health effects are a consequence of 29 the initial high exposure during ch ildhood or a function of the cumula tive exposure for this entire 30 exposure window beginning at the early age. If the latter is true, aver aging exposure over the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-113 critical window would add considerable uncertain ty to effective dose estimates given the large 1 difference between initial TCDD body burden a nd body burden at the end of the critical 2 exposure window. Despite the uncertainty in defining the critical window of exposure, 3 dose-response analysis was conducted for this outcome. 4
5
2.4.1.2.1.5.5. Bertazzi et al., 1989; Consonni et al., 2008—Mortality outcomes. 6
2.4.1.2.1.5.5.1. Study summary. 7
Several studies have evaluated the morta lity of Seveso residents exposed to TCDD 8 following the 1976 accident. The ear lier section of this report described the designs of these 9 studies and discussed their findings as they relate to cancer mortal ity. In this section, some of 10 the findings for other causes of death are descri bed. A key feature of these studies is that 11 patterns of mortality among Seveso residents were investigated accordi ng to their zone of 12 residence at the time of explosion re lative to general population rates. 13 A 10-year mortality follow-up of residents of Seveso was published in 1989 (Bertazzi et 14 al., 1989). Poisson regression was used to derive RRs for those who had lived in Zone A at the 15 time of explosion using a referent group cons isting of inhabitants who had lived in the 16 uncontaminated study area. Between 1976 and 1986, no statistically significant difference was 17 observed in all-cause mortality relative to th e general population among those who lived in the 18 most highly exposed area (Zone A) at the time of the accident. This finding was evident in both 19 males (RR = 0.86, 95% CI = 0.5

− 1.4) and females (RR = 1.14, 95% CI = 0.6

− 2.1). A 20 statistically significant excess in circulatory disease mortality wa s found among males relative to 21 those in the referent popul ation (RR = 1.75, 95% CI = 1.0

− 3.2); this increased risk was more 22 pronounced when the follow-up period was restricted to the first 5 years after the accident 23 (1976

− 1981) (RR = 2.04, 95% CI = 1.04

− 4.2). Between 1982 and 1986, the RR decreased 24 substantially and was not statistica lly significant (RR = 1.19, 95% CI = 0.4

− 3.5). Among 25 females, a risk similar in magnitude was dete cted for circulatory di sease mortality although it 26

was not statistically significant (RR = 1.89, 95% CI = 0.8

− 4.2). Contrary to the calendar 27 period-specific findings for males, the excess of circulatory mortality among females occurred 28 between 1982 and 1986 (RR = 2.91, 95% CI = 1.1

− 7.8) and not between 1976 and 1981 29 (RR = 1.12, 95% CI = 0.3

− 4.5). The number of deaths in this cohort with the 10 years of 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-114 follow-up was relatively small; in Zone A, 16 deaths were observed among males and 11 among 1
females. 2 The most recently published account of the mortality experience of Seveso residents 3 provides further information on follow-up of thes e residents until the end of 2001 (25 years after 4 the accident) (Consonni et al., 2008 ). Three exposure groups we re considered: Zone A (very 5 high contamination), Zone B (high contaminatio n), and Zone R (low contamination). The 6 reference population consisted of those reside nts who lived in unaffect ed surrounding areas, as 7 well as residents of five nearby towns. Th e authors used Poisson regression to compare 8 mortality rates for each zone rela tive to the reference population. 9 For all causes of death, no excess was found in Zone A, B, or R relative to the reference 10 population. Statistically significan t excesses were noted for those who lived in Zone A relative 11 to the reference population for chroni c rheumatic heart disease (RR = 5.74, 12
95% CI = 1.83

− 17.99) and chronic obstructive pulmonary disease (RR = 2.53, 13
95% CI = 1.20

− 5.32). These risks, however, were base d on only 3 and 7 deaths, respectively. 14 For those in Zone A, no statistically significant excesses in mortality were noted for diabetes, 15 accidents, digestive diseases, ischemic heart di sease, or stroke. Among Zone A residents, 16 stratified analysis by time since accident showed increased rates of circulatory disease 5

− 9 years 17 since the accident (RR = 1.84, 95% CI = 1.09

− 3.12). Increased mortalit y from diabetes relative 18 to the reference population was noted among females who lived in Zone B (RR = 1.78, 19
95% CI = 1.14

− 2.77). 20
21
2.4.1.2.1.5.5.2. Study evaluation. 22 The ascertainment of mortality in this cohort is nearly comp lete. Misclassification of 23 some health outcomes, such as diabetes, may occur due to use of d eath certificate data. 24 The characterization of exposur e is based on zone of reside nce. Soil sampling indicated 25 considerable variability in TCDD soil levels, and therefore, the generation of risks based on zone 26 of residence likely does not accurately reflect individual exposure. Exposure misclassification 27 might also occur because residency in the ar eas does not necessarily reflect whether the 28 individual would have been pres ent in the area at th e time the accident occurred. Any exposure 29 misclassification would likely be nondifferential which would tend to bias the risk estimates 30
towards the null. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-115 Although some excess of circulatory diseas e mortality was found, the finding was not 1 consistent between men and women. Moreover, excess circulatory disease mortality was more 2 pronounced among men within the first 5 years of exposure, while, for women, the excess was 3 more pronounced in years 5

− 10. Numerous other risk factors for circulatory disease were not 4 controlled for in these analyses and may be c onfounders if related to TCDD exposure. Taken 5 together, the possibility that TCDD increased circ ulatory disease mortality based on these data is 6
tenuous at best. 7
8
2.4.1.2.1.5.5.3. Suitability of data for TCDD dose-response modeling. 9 There is considerable uncertainty in these data due to the pote ntial for outcome and 10 exposure misclassification. The lack of the indi vidual-level TCDD levels and the examination of 11 fatal outcomes reported in this st udy are not a suitable basis for development of an RfD. For 12 these reasons, dose-response analysis for this outcome is not conducted. 13
14
2.4.1.2.1.5.6. Baccarelli et al., 2005—Chloracne. 15
2.4.1.2.1.5.6.1. Study summary. 16 Baccarelli et al. (2005) published findings from a case-control study of 110 chloracne 17
cases and 211 controls. The authors collected information on pigment characteristics and an 18 extensive list of diseases. This study was performed to yield info rmation about the health status 19 of chloracne cases, TCDD-chloracne exposure re sponse, and factors th at could modify TCDD 20 toxicity. TCDD was measured from plasma. Following adjustment for confounding, TCDD was 21 associated with chloracne (OR = 3.7, 95% CI = 1.5

− 8.8), and the risk of chloracne was 22 considerably higher in subjects younger than 8 at the time of the accidents (OR = 7.4, 23
95% CI = 1.8

− 30.3). Among individuals with lighter ha ir, the association between TCDD and 24 chloracne was stronger than among those with darker hair. 25
26
2.4.1.2.1.5.6.2. Study evaluation. 27 Although a dose-response associ ation was observed, chloracne is a rare health outcome 28 likely only to occur among those highly exposed. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-116 2.4.1.2.1.5.6.3. Suitability of data for TCDD dose-response modeling. 1 Given the very high TCDD levels needed to cause chloracne, quantitative dose-response 2 modeling to characterize risks for the genera l population with much lower TCDD exposures 3
would be of little value. Ther efore, quantitative dose-response assessment for the Baccarelli et 4 al. (2005) study was not conducted. 5
6
2.4.1.2.1.5.7. Baccarelli et al., 2002, 2004—Immunologic effects. 7
2.4.1.2.1.5.7.1. Study summary. 8 The relationship between TCDD and immunological effects was evaluated in a sample of 9 Seveso residents (Baccarelli et al., 2002, 2004). Both studies were based on findings from 10 62 individuals who were randomly selected from Zones A and B. An additional 59 subjects 11 were chosen from the surrounding noncontaminat ed areas. Residency was based on where 12 subjects lived at the time of the accident (J uly 10, 1976) (Landi et al., 1998). Frequency 13 matching ensured that the two groups of subjects were similar with respect to age, sex, and 14 cigarette smoking status. 15 TCDD levels were determined by mass spectr ometric analysis of plasma samples. 16 TCDD levels at the time of sampling were obtained, and estimates of levels at the time of the 17 accident also were estimated by assuming an 8.2-y ear half-life (Landi et al., 1998). The plasma 18
was also used to characterize levels of the immunoglobulins (Ig) IgG and IgM and the 19 complement components C3 and C4. One subject was excluded due to lack of an immunological 20 evaluation. Analyses are, therefore, based on 58 subjects in the noncontaminated areas and 21
62 individuals from the contaminated areas. 22 Nonparametric tests were applied to test for differences between the two groups. 23 Multiple regression also was used to describe the relationship between the variables. Adjustment 24 was made for several potentially confounding variable s that were collected via a questionnaire. 25
An inverse association was noted with incr easing TCDD levels and plasma IgG levels; 26 this result remained statistically significan t after adjusting for ot her potential confounding 27 variables in the regression models. Specifically, the slope coefficient and p -value for the 28 unadjusted model were

− 0.35 ( p = 0.0002) and for the adjusted model the p -value was 0.0004. 29 The authors did not present the sl ope coefficient for the adjusted model in either paper but noted 30 minimal differences between the adjusted and unadjusted results. In the 2004 analysis, the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-117 authors present IgG, IgM, IgA, C3, and C4 median and interquartile values across TCDD 1 exposure quintiles. Decreased levels of IgG were observed in the highest exposure groups. 2 Specifically, the median values across the five quintiles (for lowest to highest) were 1,526; 3 1,422; 1,363; 1,302; and 1,163. The Kruskal-Wallis test for differences across the TCDD 4 categories was statistically significant ( p = 0.002), which is consistent with the findings for the 5 continuous measures of TCDD. This finding pe rsisted after excluding those subjects with 6 inflammatory diseases and those who used antibi otics or nonsteroidal an ti-inflammatory drugs. 7 For the other plasma measures, no dose-response relationship was apparent based on median 8 values for IgM, IgA, C3, or C4 across TCDD qui ntiles. The authors hi ghlight the need for 9 additional research, particularly given the ex cess of lymphatic tumors noted in the area. 10 Exposure to other dioxin-like compounds for both the TCDD and nonexposed areas were 11 reported to be at background levels. 12
13
2.4.1.2.1.5.7.2. Study evaluation. 14 Both TCDD exposure and health outcome measures are well characterized. TCDD 15 exposures, in particular, are base d on current serum measures a nd, therefore, are not dependent 16 on assumptions needed to back-extrapolat e to earlier time pe riods of exposure. 17 A dose-response relationship between TC DD and IgG is well documented for the 18 unadjusted model, but no details are provided on the change in th e slope coefficient when other 19
covariates were added to the model. 20 Interpreting the invers e association between TCDD exposure and IgG in terms of clinical 21 significance is not possible. The IgG values reported are much higher than those subjects with 22 antibody immunodeficiency disorders. 23
24
2.4.1.2.1.5.7.3. Suitability of data for TCDD dose-response modeling. 25 Although the data support an inverse dose-response association between IgG and TCDD, 26 because the relationship cannot be described in te rms of clinical releva nce with respect to a 27 specific adverse health outcome, these data were not suitable for qunatitative dose-response 28 modeling. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-118 2.4.1.2.1.6. The Chapaevsk study. 1
2.4.1.2.1.6.1. Revich et al. (2001)—Mortalit y and reproductive health. 2
2.4.1.2.1.6.1.1. Study summary. 3 Revich et al. (2001) describe a series of investigations that have evaluated adverse health 4 outcomes among residents of Chapaevsk where eco logical measures of TCDD have been noted 5 to be higher than expected. In the earlier cancer section of this report, the cross-sectional 6 comparisons of mortality that the authors carried out between Chapaevsk residents and a general 7 population reference were described. Although the general focus of this paper is on cancer, the 8
authors examined other adverse health outcomes. 9 For all-cause mortality, rates were found to be higher in Chapaevsk relative to the Samara 10 region and other nearby towns. The magnitude of this increase, however , was not quantified in 11 the review by Revich. Cardiovascular mortality accounted for nearly two-thirds of women’s 12 deaths and almost half of those among men. The rates of cardiova scular mortality among 13 Chapaevsk men have been reported to be 1.14 times higher than those in Russia. 14 Revich et al. (2001) also re ported on the occurrence of adverse reproductive events. 15 Although the authors indicated that official medical information was used to make comparisons 16 between regions, no details were provided about data quality, completeness, or surveillance 17 differences across areas. The presented rate s for reproductive health outcomes should be 18 interpreted cautiously. A hi gher rate of spontaneous abor tions (24.4 per 100 pregnancies 19 finished by delivery) was found in Chapaevsk wo men relative to rates that ranged between 10.6 20 and 15.2 found in five other areas. The frequency of preeclampsia also was found to be higher in 21 Chapaevsk women (44.1/100) relative to other town s, as was the proportion of low birth-weight 22 babies and preterm births. The percentage of newborns with low birth weight was slightly larger 23 in Chapaevsk (7.1%) when compared to other towns in Samara (5.1

− 6.2%); observed 24 differences, however, were not stat istically significant. The aut hors also reported on the sex ratio 25
of newborns born between 1983 and 1997. These rati os (boys:girls) were highly variable and 26 ranged between 0.79 and 1.29. Given the annual variabil ity of this ratio on a year-to-year basis, 27 it is unclear if this is largely due to natural fluc tuations and to what extent this may result from 28 prior TCDD (or other contaminants) e xposure TCDD and other contaminants. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-119 2.4.1.2.1.6.1.2. Study evaluation. 1 The review by Revich et al. (2001) highlights analyses that have b een undertaken using 2 largely cross-sectional data. Although soil samp ling measures appear to demonstrate decreasing 3 levels of TCDD in the soil with increasing distance from the plant, at this time, no 4 individual-level TCDD exposure data are available. Increased rates of mortality relative to the 5 Samara region in Russia were observed among Chapaevsk men for all cancer sites combined; 6 this excess risk however, was not observe d among women. Although the authors provide 7 compelling evidence of increase d adverse events among resident s of Chapaevsk, the study lacks 8 a discussion about the validity of comparing health data across regions, and suffers from inherent 9 limitations from ecological studies su ch as exposure misclassification. 10
11
2.4.1.2.1.6.1.3. Suitability of data for TCDD dose-response modeling. 12 As with the cancer outcomes presented in this study, the data for noncancer outcomes are 13 limited by the absence of TCDD levels on an in dividual-level basis and information on other 14 potential confounding variables that could have bi ased the comparisons. Additional studies are 15 being undertaken to evaluate the relationshi p between TCDD and the sexual and physical 16 development of boys. The cross-sectional nature of the data that were presented does not 17 provide the necessary level of detail needed to estimate effective dose given the lack of 18 individual-level exposure data . Therefore, a quantitative dos e-response analysis was not 19
conducted. 20
21
2.4.1.2.1.7. The Air Force Health ( “ Ranch Hands ” cohort) study. 22
2.4.1.2.1.7.1. Michalek and Pavuk (2008)—Diabetes. 23
2.4.1.2.1.7.1.1. Study summary. 24 Michalek and Pavuk (2008) examined both the in cidence of cancer and the prevalence of 25
diabetes in the cohort of Ranch Hand workers ex posed to TCDD. As noted previously, these 26 veterans were responsible for aerial spraying of Agent Orange in Vietnam between 1962 and 27 1971. Exposure to TCDD was estimated using seru m collected from participants in 1987 and 28 assayed for TCDD. Exposure to TCDD was estim ated using a first-order pharmacokinetic model 29 with a half-life of 7.6 years and pr ovided an estimate of TCDD at the end of the tour of duty in 30 Vietnam. Veterans were grouped into four categories: comparison, background, low, and high. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-120 Diabetes was identified from diagnoses during the post-Vietnam era from medical records. 1 Overall, no differences were shown in the RR of diabetes between the Ranch Hand unit and the 2 reference group (RR = 1.21, p = 0.16). Stratified analyses by days of spraying (<90 days, 3
≥ 90 days), however, revealed a significant incr ease in risk of diabetes (RR = 1.32, p = 0.04) 4 among those who sprayed for at least 90 days. A dose-response relationship was also evident 5 when log10TCDD was modeled in the combined c ohort. Also, stratifi cation by calendar period 6 showed a dose-response relationshi p for those whose last year of service was during or before 7
1969. 8
9
2.4.1.2.1.7.1.2. Study evaluation. 10 The Michalek and Pavuk (2008) study provides an opportunity to characterize risks of 11 diabetes as the study is not subject to some of the potential bias of case ascertainment based on 12 death certificates (D’Amico et al., 1999). The quality of the TCDD exposure estimates is high, 13 given that serum data were available at an individual-level basis for all Ranch Hand and 14 comparison veterans used in the cohort. Alt hough disentangling the e ffects of 2,4-D and TCDD 15 is not possible because their concentrations in Agent Orange are equivalent, 2,4-D has not been 16
associated with diabetes. 17
18
2.4.1.2.1.7.1.3. Suitability of data for TCDD dose-response modeling. 19 The reported dose-response relationship be tween TCDD and diabetes is supported by 20 study strengths including the use of the indivi dual-level level TCDD se rum measures and the 21
identification of diabetes thr ough medical records are important strengths of the Michalek and 22 Pavuk (2008) study. Nonetheless, the possible c onfounding from the inability to control for 23 2,4-D and other agents used in Agent Orange pr ecludes a quantitative dos e-response analysis. 24
25
2.4.1.2.1.8. Other noncancer studies of TCDD. 26
2.4.1.2.1.8.1. McBride et al., 2009a—Noncancer mortality. 27
2.4.1.2.1.8.1.1. Study summary. 28 The McBride et al. (2009a) mortality st udy of New Zealand workers employed as 29 producer or sprayers with poten tial exposure to TCDD was descri bed earlier in this report. 30 These individuals were employe d at a plant that manufactur ed 2,4,-dichlorophenoxyacetic acid, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-121 and later 2,4,5-T and 4-chloro 2-methyphenoxyacet ic acid. In 1987, the plant closed and 2,4,5-T 1 production ceased in 1988. 2 The cohort consisted of 1,754 individuals who were employed for at least one day at the 3 New Plymouth site between January 1, 1969, and October 1, 2003. Vital status was determined 4 until the end of 2004. Comparisons of mortality were made to the New Zealand general 5 population using the SMR statistic. Exposure wa s characterized by duration of employment. 6 Person-years of follow-up were tabulated across strata defined by age, calendar period, duration 7 of employment, sex, latency, and pe riod of hire. Analyses were stratified to compare risks by 8 duration of employment (<3 or ≥ 3 months), latency (<15 or ≥ 15 years), and period of hire 9 (<1976, ≥ 1976). 10 Overall, no statistically significant differences in all-cause mortality relative to the 11 general population were found am ong those who worked for at least 3 months (SMR = 0.92, 12
95% CI = 0.80

− 1.06) or for less than 3 months (SMR = 1.23, 95% CI = 0.91

− 1.62). No 13 statistically significant excesses we re found for mortality from diab etes, cerebrovasc ular disease, 14 heart diseases, or accidents. The incorporat ion of a latency period of 15 years revealed no 15 statistically significant excesses for these same causes of death. Similarly, no excesses for any 16 cause of death were noted among those who we re hired either before or after 1976. 17 In subsequent analyses of the same cohort that used estimated TCDD levels from serum 18 samples, McBride et al. (2009b) found no excesses for all-cause mortality or mortality from 19
diabetes or heart disease. 20
21
2.4.1.2.1.8.1.2. Study evaluation. 22
For the McBride et al. (2009a) study, the size of the cohort is la rge enough to characterize 23 mortality risks relative to the general popula tion for most common causes of deaths. An 24 important limitation of this study is the loss to follow-up of a substantial percentage of workers 25 (22%). This would have imp acted statistical power by reduci ng the number of deaths among the 26 workers. If this incomplete ascertainment of mortality outcomes did not occur in a similar 27 fashion with the general population th en the SMR may also be biased. 28 For noncancer causes of death, the use of th e SMR statistic is more likely to be 29 influenced by the healthy-worker effect. Ther efore, the findings obtained for these outcomes 30 should be interpreted with caution. Subseque nt analyses published by the same authors 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-122 (McBride et al., 2009b) provide improved characterization of TCDD exposure using serum 1 samples. 2
3
2.4.1.2.1.8.1.3. Suitability of data for dose-response analysis. 4 Overall, no associations were evident between surrogate measures of TCDD (duration of 5 employment, year of hire) and noncancer mortality outcomes. Further, the use of mortality 6 endpoints is inconsistent with EPA RfD methodology. As such, thes e data do not support further 7 use in a quantitative dose-response analysis. 8
9
2.4.1.2.1.8.2. McBride et al., 2009b—Noncancer mortality. 10
2.4.1.2.1.8.2.1. Study summary. 11 McBride et al. (2009b) further analyzed the cohort of New Zeala nd workers to include 12 estimates of TCDD exposure based on serum samples. Current and former employees who were 13 still alive and living within 75 km of the site were asked to provide serum samples. Samples 14 were collected from 346 workers representing 22% (346/1599) of the en tire study population. 15
These serum measures were used to estimate cu mulative TCDD levels for all workers. The 16 exposure assessment approach by Flesch-Janys et al. (1996) was used to estimate time-dependent 17 exposures based on area under the curve mode ls. This was based on a one-compartment 18 first-order kinetic model with a half-life of 7.2 years. 19 Comparisons of mortality were made to the general population using the SMR statistic. 20 The Cox proportional hazards model was used to c onduct an internal cohort analysis across four 21 categories of cumulative TCDD levels for diabetes and ischemic heart disease mortality. The 22 RRs generated from these models were adjusted fo r sex, hire year, and birt h year. No diabetes 23 deaths were observed among women, and therefore, analysis of th is outcome was limited to men. 24 Relative to the general population, no differen ce in the all-cause mort ality experience was 25 observed in exposed cohort members (SMR = 1.0, 95% CI = 0.9

− 1.2). Similarly, no excess in 26 these workers was observed for heart disease (SMR = 1.1, 95% CI = 0.9

− 1.5); cerebrovascular 27 disease (SMR = 1.1, 95% CI = 0.6

− 1.9); diabetes (SMR = 0.7, 95% CI = 0.2

− 2.2); or 28 nonmalignant respiratory dis ease (SMR = 0.8, 95% CI = 0.4

− 1.4). For the internal cohort 29 analysis, the RR associated with cumulati ve categorical TCDD measure was 1.0 for both 30
diabetes and ischemic heart disease. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-123 2.4.1.2.1.8.2.2. Study evaluation. 1 The McBride et al. (2009b) st udy extends the earlier work the same authors completed in 2 two ways. First, serum measures were used to estimate cumulative TCDD with methodology 3 that has been applied to severa l other cohorts of workers exposed to TCDD. Second, the authors 4 used regression analyses that examined indivi dual-level TCDD exposures in relation to various 5 outcomes as part of the internal cohort compar isons. For noncancer outcomes, no dose-response 6
associations with TCDD were observed with th e internal comparisons. Also, as found with 7 earlier analyses of this same cohort, no excess noncancer mortality relative to the New Zealand 8 general population was observed. 9
Associations between TCDD and diabetes ha ve been found previously in TCDD-exposed 10 populations, most notably in the Ranch Hands cohort (Michalek and Pavuk, 2008). In this 11 cohort, only five deaths from diabetes were identified, and of these, only three occurred among 12 those who were exposed to TCDD. The study, th erefore, has limited statistical power to 13
characterize associations between TCDD and mortality from diabetes . Further, the identification 14 of diabetes deaths is subject to misclassification errors due to under-reporting (McEwen et al., 15 2006). 16
17
2.4.1.2.1.8.2.3. Suitability of data for TCDD dose-response modeling. 18 McBride et al. (2009b) found no statistically significant associati ons in any of the 19 noncancer causes of death. Furthermore, the use of mortality endpoints is inconsistent with EPA 20 RfD methodology. Therefore, the da ta were not suitable for quanti tative dose-response analysis 21
for these outcomes. 22
23
2.4.1.2.1.8.3. Ryan et al., 2002—Sex ratio. 24
2.4.1.2.1.8.3.1. Study summary. 25 Ryan et al. (2002) conducted an investigation on the sex ratio in offspring of children of 26 pesticide workers who were invo lved with the production of tr ichlorophenol and the herbicide 27 2,4,5-T in Ufa, Bashkortostan, Russia. Ufa was the site of a state agrochemical plant that has 28 been in operation since the 1940s. Between 1961 and 1988, the plant employed more than 29 600 workers, most in their early 20s. Females, however, accounted for about 15% of the 30 workforce that produced 2,4,5-T a nd 30% for 2,4,5-trichlorophenol. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-124 Serum samples previously taken in 1992 among 60 men, women, and children from the 1 factory and city of Ufa showed TCDD exposures that were approximately 30 times higher than 2 background levels (Ryan and Sch ecter, 2000). Blood data were subsequently measured on a 3 sample of 20 workers between 1997 and 2000, and on 23 2,4,5-trichlorophenol workers between 4 1997 and 2001. In all, 84 individuals who provi ded blood samples formed the basis of the 5 analysis in this study. Of these, 55 were exposed to 2,4,5-T and 29 were exposed to 6 2,4,5-trichlorophenol. 7 Ryan et al. (2002) reviewed company records for these workers to determine the number, 8 sex, and date of birth of any children; birth da ta were available for 198 workers. Awareness of 9 the study led other workers who ha d not provided serum to provide information on births that 10
occurred 9 months after the time of first employment in the factory. 11 The authors calculated descrip tive statistics for the 198 work ers and compared them to 12 values for the city of Ufa between 1959 and 1996. Tests of statistical significance were made 13 using the z-test, and the chi-squa re test. The observed proporti on of male births (0.40) among 14 the factory workers was much lower than that for the city of Ufa (0.51) ( p < 0.001). Stratified 15 analyses revealed that this lower ratio was observed only among those paternally exposed to 16 TCDD. Specifically, the propor tion of male births among exposed fathers was 0.38 and among 17 exposed mothers was 0.51. This pattern was obs erved in both the workers exposed to 2,4,5-T 18 (proportion of male births = 0.40) and 2,4,5-trichlorophenol (propor tion of male births = 0.35). 19
20
2.4.1.2.1.8.3.2. Study evaluation. 21 The Ryan et al. (2002) findings are consistent with earlier work completed for Seveso 22 residents (Mocarelli et al., 2000). Although serum measures were available for 84 individuals, 23 no dose-response of birth ratios was performed us ing exposure quantified at an individual-level 24 basis. This approach would have been preferred and consistent with that which Mocarelli et al. 25 (2000) used. All comparisons were made usi ng an external comparison group, namely the sex 26
ratio observed in Ufa between 1959 and 1996. 27 Although serum measures were used to desc ribe TCDD exposure for a sample of the 28 workers, individual-level dose estimates we re not calculated fo r the study population. 29 Specifically, exposures were char acterized many years after exposure, and no attempt was made 30 to back-extrapolate to the time of conception. The two groups of workers in the study also 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-125 reportedly had high exposure le vels of 1,2,3,7,8-pentachlorodibenzo- p- dioxin. So, the group 1 level exposure classification (by plant) did not allow considera tion of confounding due to other 2 dioxin-like compounds. Another li mitation of the study is that the study population is likely 3 nonrepresentative of all workers em ployed at the plant. Particip ants included onl y those willing 4 to provide serum samples and those who volunteer ed to participate in the study after learning 5 about it in a public forum. If particip ation was dependent on TCDD exposures and the 6 reproductive health of these subjects, then bias may have occurred. 7
8
2.4.1.2.1.8.3.3. Suitability of data for TCDD dose-response modeling. 9 The findings are notable in their consistenc y with those found in Seveso residents by 10 Mocarelli et al. (2000). For the Ryan et al. (2002) study, serum data were quantified at an 11 individual-level basis. Risk estimates, however , were not derived in re lation to these exposures 12 but instead in two separate subgroups (2,4,5- T and 2,4,5-trichlorophenol workers). This 13 important limitation precludes the use of thes e data for quantitative dose-response modeling. 14
15
2.4.1.2.2. Feasibility of dose-response modeling for noncancer. 16 Relatively few study populations permit quan titative dose-response mo deling to be 17 performed for noncancer outcomes. The se rum collected among Seveso men and women 18 provide an opportunity to characterize risks fo r several health conditions in relation to TCDD 19 exposure. The collection of thes e serum samples, shortly after th e accident does not require the 20 back-extrapolation of TCDD levels as in the occupational cohorts, which should reduce the 21 exposure assessment uncertainty and minimize the potential for exposure misclassification. 22
An added feature of the SWHS is the detailed collection of ot her risk factor data from 23
trained interviewers. These data allow for risk estimates to be adjusted for potential confounding 24 variables. For the evaluations of reproductive hea lth outcomes, this adjust ment is critical given 25
there are various documented risk factors for th e different outcomes that were examined. For 26 some health outcomes, continued follow-up of th e cohort is needed, give n that several of the 27 Seveso studies suggest that those exposed at a very young age might be more susceptible to 28 subsequent adverse health effects. 29 The findings of positive associations and dos e-response relationships with serum-based 30 measures of TCDD suggest several noncancer hea lth outcomes could be associated with TCDD 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-126 exposure. These health outcomes include neona tal thyroid function, sex ratio, diabetes, and 1 semen quality. Although findings have suggested an association between TCDD and age at 2 menopause, they were not statistically signi ficant and no dose-response trend was observed. 3 Weak or nonstatistically signi ficant associations have been noted for endometriosis and 4 menstrual cycle characteristics and do not s upport quantitative dose-response analyses. 5 Associations between TCDD exposure and cardi ovascular disease have been noted in 6 some, but not all, of the occupational cohorts, an d also shortly after the accident among Seveso 7 residents. Findings from the cohort studies based on external comparisons using the SMR 8 statistic should be interpreted cautiously due to potential bias from the healthy worker effect. 9 Because the magnitude of the healthy worker bias is recognized to be larger for cardiovascular 10 diseases than for cancer outcomes, risk estim ates in some occupational cohorts might be 11
underestimated for cardiovascular outcomes. Information on cardiovascular risk factors 12 generally was not captured in these studies, and se nsitivity analyses were generally designed to 13 examine risk estimates genera ted for cancer outcomes. 14
15
2.4.1.2.3. Summary of epidemiologic noncancer study evaluations for dose-respon se 16 modeling. 17 All epidemiologic noncancer studies summarized above were evaluated for suitability of 18 quantitative dose-response assessment usi ng the TCDD-specific considerations and study 19
inclusion criteria. The results of this evaluation are summarized in a matrix style array (see 20 Table 2-3) at the end of the chapter, and descriptively in Appendix B. The key epidemiologic 21
noncancer studies suitable for further TCDD dose-response assessment are presented in 22
Table 2-5. 23
24
2.4.2. Summary of Animal Bioassay Studies 25 This section summarizes studies that have m et the in vi vo animal bioassay TCDD study 26 inclusion criteria (see Section 2.3.2) and are considered in the dose-response modeling conducted 27 later in this document (see Sections 4 and 5) . The sections that follow summarize the 28 experimental protocol, the results, and the NOAELs and LOAELs identified in reproductive 29 studies, developmental studies, and general toxicity studies (subdivided by duration). 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-127 To evaluate and discuss studi es consistently, doses were converted to nanograms per 1 kilogram body weight per day (n g/kg-day) and were also adjust ed for continuous exposure. 2 Some doses were adjusted based on daily dietar y intake and body weight. For these studies, 3 EPA uses 10% of an animal’s body weight as th e daily feed rate. More commonly, doses were 4 adjusted from 5 days/week to a 7 days/week st andard adjustment, in which case administered 5 doses were multiplied by 5 and divided by 7 to obtain continuous doses. To adjust for weekly 6 dosing, the weekly administered doses were multip lied by the administration frequency per week 7 (in days) and divided by 7 to give continuous doses. 8 Other exposure protocols used a single lo ading dose followed by weekly maintenance 9 doses. To adjust these doses, the loading dose was added to the maintenance doses multiplied by 10 the administration frequency, and this sum wa s divided by the exposur e duration to give a 11 continuous dosing rate. The doses administered in single dose st udies were not averaged over 12 the observation period. 13
14
2.4.2.1. Reproductive Studies 15
2.4.2.1.1. Bowman et al., 1989a, b (and related Scha ntz and Bowman, 1989; Schantz et al., 16 1986). 17
Fema le rhesus monkeys (6 to 10 years old; 8 per treatment) were exposed to 0 or 5 ppt 18 (for 3.5 years), or 25 ppt (for 4 years) TCDD (purity not specified) (Bowman et al., 1989a, b; 19 Schantz and Bowman, 1989; Scha ntz et al., 1986). Female mo nkeys were mated to unexposed 20 males after 7 months (Cohort I) and 27 months (C ohort II) of exposure, then again 10 months 21 post-exposure (Cohort III). The average daily do ses to mothers were equivalent to 0, 0.15, and 22 0.67 ng/kg-day. The 0.67 ng/kg-day dose group had re duced reproductive rates in both Cohorts I 23 ( p < 0.001) and II ( p < 0.025; Bowman et al., 1989a). Th e mean number of days of offspring 24 survival ( p < 0.023) also decreased. No effects on birt h weight or growth, or physical evidence 25 of toxicity (Bowman et al., 1989b) were observed. Behavioral effects were observed in the 26 offspring (Cohort I: 7, 6, and 0 offspring, re spectively; Cohort II: 3, 5, and 0 offspring, 27 respectively; Cohort III: 6, 7, and 3, respectively). In the 0.67 ng/kg-day dose group, the number 28 of offspring was insufficient to form a group in either Cohorts I or II. Offspring in the 29 0.15 ng/kg-day dose group had alterati ons in social behavior of th e mother-infant pairs (mothers 30 had increased care giving, which appeared to be an effect of the infants and not due to the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-128 treatment of the mother) and peer group of the offspring after weaning (Cohort I offspring were 1 more dominant or aggressive and exhibited more self-directed behavior; Bowman et al., 1989b). 2 The performance of learning tasks was inversely related to the level of TCDD in the body fat. 3 Schantz and Bowman (1989) examined effects us ing discrimination-revers al learning (RL) and 4 delayed spatial alte ration (DSA). RL detected effects in the 0.15 ng/kg-day group as measured 5 by retarded learning of the shape reversal ( p < 0.05), but DSA did not. Schantz et al. (1986) 6 combined the cohorts and looked at 5, 5, a nd 3 mother-infant pairs in the 0, 0.15, and 7 0.67 ng/kg-day groups, respectively. They found th at TCDD-exposed mother-infant pairs spent 8 more time in close, social contact compared to the controls (m utual ventral contact, p < 0.025; 9 nipple contact, p < 0.01) and infants had reduced locomotor activity ( p < 0.05), but the 10 dose-effect was complex. Of note is that the co ntrol groups contained fewer males than did the 11 TCDD-exposed groups. 12 In a follow-up study, Rier et al. (2001) examin ed the DLC levels of sera collected from 13 some monkeys in this study. They reported that animals in this study ha d elevated serum PCB77 14
and PCB126 levels and an increa sed serum TEQ. In fact, the fractional contribution of serum 15 TCDD levels to total serum TEQ wa s 30% in treated animals. In this study, it is not possible to 16
determine the contribution of TCDD alone to the developmenta l effect due to the background 17 contamination; thus, EPA has not deve loped a TCDD LOAEL from the study. 18
19
2.4.2.1.2. Hochstein et al., 2001. 20
Adult femal e mink (12/treatment group) were administered dietary concentrations of 21 0.0006 (control), 0.016, 0.053, 0.180, or 1.40 ppb TCDD (purity >99.8%) for 132 days 22 (Hochstein et al., 2001). This dose is estimated to be equivalent to 0.03 (control), 0.8, 2.65, 9, 23 and 70 ng/kg-day assuming a food consumption of 5% of body weight per day. Females were 24 mated with unexposed males beginning on treatment day 35. Females were allowed to mate 25 every fourth day during a 29-day mating period or until a confirmed mating. Mated females 26 were presented with a second male either the day after initial mating or 8 days later. In the 27 70 ng/kg-day group, the treate d animals were lethargi c after 4 to 5 weeks, with several having 28 bloody (tarry) stools near the end of the trial. Two animals in the 70 ng/kg-day dose group died 29 prior to study termination. These animals had lost a large percentage of their body weight 30 (24

− 43%), and had pale yellow livers and inte stinal hemorrhages. Histopathology from both 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-129 mink indicated marked diffuse hepatocellular vac uolation. The mean body weight decreased in 1 all treatment groups including the control (losing an average of 3.29% of initial body weight), 2 compared to a dose-dependent loss of up to 26% in the 70 ng/kg-day group. Mating and 3 reproduction were considered subnormal in all groups. The number of females that gave birth in 4 the 0.03 (control), 0.8, 2.65, 9, and 70 ng/kg-da y dose groups were 5/ 12, 0/12, 3/12, 8/12, and 5 0/11, respectively. The study authors speculated that the subnormal breeding and reproductive 6 performances in the control females likely were due to the indoor environment in which the mink 7 were housed. In the three groups that gave bi rth, there was a dose-depe ndent decrease in kit 8 body weight at birth, which was significant ( p < 0.05) in the 9 mg/kg-day group compared to the 9 controls. The body weight in the kits was not sign ificantly different at 3 or 6 weeks after birth. 10 Three-week survival rates of 71, 47, and 11% we re recorded for kits in the 0.03 (control), 2.65, 11 and 9 ng/kg-day dose groups, respectively. Six-w eek kit survival rates were 62, 29, and 11% in 12 the 0.03 (control), 2.65, and 9 ng/kg- day dose groups, respectively. 13 In the adult females, clinical signs of toxicity were noted in the 70 ng/kg-day group near 14 the end of the study and included alopecia and notably thickened, defo rmed, and elongated 15 toenails. There was a dose-dependent decreas e in plasma total solids, total protein, and 16 osmolality that reached statistical significance ( p < 0.05) in the two highest exposure groups. 17 Anion gap was significantly decreased ( p < 0.05) and alanine aminot ranferase was significantly 18 increased in the 70 ng/kg-day group compared to the controls. At terminal sacrifice, there was a 19 dose-related decrease in body weight. There was a dose-related increase in liver weight that 20 reached statistical significance ( p < 0.05) in the 70 ng/kg-day dos e group. The brains of 42% of 21 the animals in the 70 ng/kg-day dose group had local ized accumulation of ly mphatic cells within 22 the meninges with mild extension into the adjace nt neuropil and mild g liosis. Of the 10 mink 23 surviving to study termination in the 70 ng/ kg-day group, 3 had periportal hepatocellular 24
vacuolation. These same brain and liver lesi ons were not observed in the control mink. 25 As there were no litters produced in the low-dose group and pregnancy outcomes were 26 not dose related, the 0.8 ng/kg-day exposure leve l does not inform the choice of NOAEL or 27 LOAEL. Thus, the LOAEL for this study is 2.65 ng/kg-day (132-day m aternal exposure 28 duration) based on reduced kit survival (47% of control at 6 weeks). A NOAEL cannot be 29 determined for this study. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-130 2.4.2.1.3. Ikeda et al., 2005. 1 Ikeda et al. (2005) studied the effect of repeated TCDD exposu re to F0 dams on the ma le 2 gonads of F1 generation and sex ratio in the F2 generation. Twelve female Holtzman rats were 3 treated with a single dose of 400 ng/kg TCDD ( ≥ 98% purity) orally, vi a gavage, followed by 4 weekly treatment doses of 80 ng/kg TCDD (16.5 ng/kg-day adjusted for continuous exposure of 5 10 weeks; specified 2 weeks premating, assume d 1 week for successful mating, 3 weeks of 6 gestation, and specified 4 weeks to weaning ) during mating, pregnancy, and lactational periods 7 (total exposure duration approxima tely 10 weeks). Corn oil serv ed as the control in another 8 group of 12 dams. Four dams were sacrificed on gestation day (GD) 20 to evaluate the in utero 9 toxicity of TCDD. Litter sizes from the remaining eight dams were examined on postnatal day 10 (PND) 2, and some of the F1 offspring were sacrif iced to estimate TCDD tissue concentrations. 11 The remaining offspring were weaned on PND 2 8. Some of the F1 (number not specified) 12 offspring were mated with untreated females on PND 98, following which, litter size, sex ratio, 13 weight, and anogenital distance of F2 pups were examined on PND 2. Mated and unmated F1 14 males were sacrificed and the testes, epididymis, seminal vesicle, and the ventral prostate were 15 weighed; the cauda epididymis was we ighed and examined for sperm count. 16 All fetuses in the control and TCDD group as a result of in utero exposure in the F0 17 generation survived. Litter size, sex ratio, a nd anogenital distance in the F1 generation on 18
PND 2 were not altered as a result of in uter o TCDD exposure. Pup weight was significantly 19 ( p < 0.05) lower in the TCDD-treated group than in controls. TCDD concentration in the 20 adipose tissue of the F0 dams on GD 20 was significantly ( p < 0.05) higher than in the liver. 21 Adipose TCDD was significantly ( p < 0.01) reduced at weaning, however, compared to 22 concentrations on GD 20. F1 pup liver TCDD concentration increased significantly ( p < 0.01) 23 and was higher on PND 28 than PND2. The liver weight in F1 males in creased by 14-fold at 24 PND 28 compared to PND 2, implying a transferof approximately 850 pg of TCDD from the 25 dam to the F1 pup livers during lactation. TCDD also was detected in pup adipose tissue on 26 PND 28. Body weight of TCDD-exposed F1 males was significantly ( p < 0.001) lower than 27 control males at weaning (PND 28). No significant differences in testis and cauda epididymis 28 weights were observed between the control and treat ed groups. Ventral prostate weight in the F1 29 males exposed to TCDD, however, was approximately 60% lower than controls. No change in 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-131 weight of the body, brain, testes, cauda epididymis , or seminal vesicle was observed at PND 120. 1 Ventral prostate weight, however, was 16% lower than that of the control group ( p < 0.001). 2 Sperm count in the cauda epididymis of the F1 males was not affected by TCDD exposure. 3 Examination of F2 generation litters indicated no significant differences in litter size, pup 4 body weight, and anogenital distance between TCDD- treated or vehicle control groups. The 5 percentage of male F2 pups born to materna lly and lactationally TCDD-exposed males was 6 significantly ( p < 0.05) lower (38%) than those sired by control group males (52%). Every 7 female mated with maternally TCDD-exposed F1 males delivered more female than male pups. 8 A LOAEL for TCDD of 16.5 ng/kg-day for an estimated 10 week exposure duration in 9 F0 rat dams is identified in this study for decreas ed development of the ventral prostate in the F1 10 generation (60% lower than cont rols) and for significantly ( p < 0.05) altered sex ratio (decreased 11 percentage of males) in the F2 generation. A NOAEL cannot be determined for this study. 12
13
2.4.2.1.4. Ishihara et al., 2007. 14 Ishihara et al. (2007) examined the effect of repeated TCDD exposure of F0 m ales on the 15 sex ratio of F1 offspring. Seven-week-old male ICR mice ( n = 127) were divided into three 16 groups and treated via gastric in tubation with an initial loadin g dose of either 2 or 2,000 ng 17 TCDD/kg BW or an equivalent volume of sesame oil (vehicle) as control, followed by a weekly 18 maintenance doses of 0, 0.4, or 400 ng/kg until the animals were 12 weeks old. One week after 19 the last exposure, the animals were mated with un treated female mice. On the day a vaginal plug 20 was identified, F0 male mice were sacrificed an d major organs including testes, epididymis, and 21 liver were removed and weighed. Organ tissues also were examined fo r histopathological and 22 immunohistochemical changes. Treatment levels, averaged over the 6 week period from start of 23 treatment to mating (five main tenance doses), were 0, 0.095, and 950 ng/kg-day for the control, 24 low dose and high dose groups, respectively. 25 All TCDD-treated males successfully impregna ted untreated females and yielded viable 26 offspring. Mortality, pup weight s, and mating and fertility indi ces were not affected by TCDD 27 exposure. There were no significant differences in body weights or in rela tive weights of testes, 28 epididymis, or livers in the TCDD-treated F0 male s compared to the control group. The livers of 29 some animals (number not specified) in the high-dose group, however, were larger and heavier 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-132 than in the controls or the low-dose group. He nce, tissues from the high-dose animals were 1
selected for detailed immunohistochemical examination. 2 General histopathological findi ngs in the TCDD-treated groups showed no changes in 3 cell morphology in germ, Sertoli, and Leydig cells of the testes. Arrangement of the germ cells 4 was normal and there was no difference in the epid idymis spermatozoon number in either of the 5 TCDD-treated groups compared to controls. Live rs of some of the animals in the high-dose 6 group however, showed enlarged an d vacuolated areas in the cen trilobular area when compared 7 to the low-dose group and the control gr oup. Immunohistochemical and quantitative 8 immunohistological findings show ed a marked increase in staining intensity for cytochrome 9 P450 (CYP)1A1 in the cytoplasm of the hepatocy tes in the centrilobular area of the high-dose 10 TCDD group compared to the cells in the low- dose and the control groups. In addition, 11 proportions of immunoreactive CYP1A1 areas in the liver sections of the high-dose group were 12 higher than in the low-dose and control groups . The proportions of immunoreactive CYP1A1 13 also varied across animals ( n = 33) in the high-dose group. 14 In addition to the above findings, there was a dose-related decrease in the male/female 15 sex ratio. The proportion of male offspri ng of the high-dose group was significantly lower 16 ( p < 0.05) than that observed in controls (46.2% versus 53.1%, respectively). Hepatic 17 immunoreactive CYP1A1 staining levels in indivi dual F0 males were strongly correlated with 18 the sex ratio of their offspring. 19 A LOAEL for TCDD of 950 ng/kg-day for a 6 w eek exposure duration of F0 male mice 20 is identified for significantly ( p < 0.05) decreased ma le/female sex ratio (i.e., higher proportion 21 of female offspring) in the F1 generation. The NOAEL is 0.095 ng/kg-day. 22
23
2.4.2.1.5. Latchoumycandane and Mathur, 2002 (and related: Latchoumycandane et al., 24 2002a, b, 2003). 25 Latchoumycandane and Mathur (2002) conducted a study to de termine whether treatment 26 with vitamin E protected rat te stes from TCDD-induced oxidative stress. Groups of albino male 27 Wistar rats ( n = 6) were administered an oral do se of 0 (vehicle alone) 1, 10, or 100 ng 28 TCDD/kg-day for 45 days, while another group of animals ( n = 6) was co-administered TCDD at 29 the same doses, along with vitamin E at a therap eutic dose of 20 mg/kg-day for 45 days. At 30 study termination, animals were fasted overnight, weighed, and sacrificed. Testis, epididymis, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-133 seminal vesicles, and ventral prostate were removed, weighed, and preserved for further 1
examination. The left testis was used to dete rmine daily sperm production, while the right testis 2 was used for biochemical studies. Superoxide dismutase, catalase, gl utathione reductase, and 3 glutathione peroxidase activity were measured in the testes, along with production of hydrogen 4 peroxide and lipid peroxidation. 5 Body weights of TCDD-treated ra ts did not differ significantly from the control group. 6 Testis, epididymis, seminal vesicle, and ventra l prostate weights in the TCDD-treated groups, 7 however, decreased significantly ( p < 0.05) when compared to controls. None of these changes 8 were observed in the TCDD-exposed groups recei ving vitamin E. There was a dose-related 9 decrease in daily sperm production ( p < 0.05) in all three TCDD-tr eated groups when compared 10 to the control group. In contrast, the TCDD treat ment groups that also received vitamin E did 11 not show any significant changes in daily sper m production compared to the controls. The 12 TCDD-treated groups also showed significantly ( p < 0.05) lower activitie s of the antioxidant 13 enzymes (superoxide dismutase, catalase, glutathi one reductase, and glutathione peroxidase) than 14 the control group. Levels of hydrogen peroxide and lipid peroxidation increased significantly 15 ( p < 0.05) in the testes of the rats treated with TCDD compared to the correspondi ng controls. 16 The TCDD-treated groups that had been co-a dministered vitamin E show no difference in 17 antioxidant enzyme activities or in reactive oxygen species production when compared with 18
controls. 19 A LOAEL for TCDD of 1.0 ng/kg-day for a 45-day exposure duration in ra ts is identified 20 in this study for significantly ( p < 0.05) reduced sperm production and significantly ( p < 0.05) 21 decreased reproductive organ weights. A NO AEL cannot be determined for this study. 22
23
2.4.2.1.6. Murray et al, 1979. 24 Male (10

− 16 per treatment) and female (20

− 32 p er treatment) Sprague-Dawley rats were 25 administered diets containing TC DD (purity >99%) to achieve da ily concentrations of 1, 10, or 26 100 ng/kg-day through three generati ons. After 90 days of treatment, F0 rats were mated to 27 produce F1a offspring. Thirty-three days after wean ing of the last F1a litter, the F0 rats were 28 mated again to produce F1b offspring. Some F0 rats were mated a third time for a cross-mating 29 study. The F1b and F2 rats were mated at a bout 130 days of age to produce the F2 and F3 30 generations. No clinical signs of toxicity or changes in body we ight and food consumption were 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-134 observed in F0 rats during the 90 days of treat ment before mating. The 100 ng/kg-day group was 1 discontinued due to the lack of offspring. In th e three surviving offspring (all males), no changes 2 in appearance, body weight, or food consumption occurred. A dose of 10 ng/kg-day caused a 3 consistent decreased body weight in both sexes of F1 and F2 ra ts, which was associated with 4 decreased food consump tion. A significant ( p < 0.05) decrease in fertil ity in F1 and F2 rats 5 occurred, but not in F0 rats, administered 10 ng/ kg-day. The number of live pups and gestational 6 survival index were significantly ( p < 0.05) decreased in the 100 ng/ kg-day F0 rats and in the 7 10 ng/kg-day F1 and F2 rats. The gestationa l survival index also was significantly ( p < 0.05) 8 decreased in F2 rats administered 1 ng/kg- day. Postnatal survival was significantly ( p < 0.05) 9 reduced only in F2 rats administered 10 ng/ kg-day. Growth (as measured by body weight) was 10 affected at 10 ng/kg-day only in the third gene ration. In the 10 ng/kg-day group, a significant 11 ( p < 0.05) decrease in relative t hymus weight and increase in li ver weight also occurred in F 3 rats 12 (weights were not measured in F2 rats). Additionally, mating 100 ng/kg-day TCDD-treated 13 females with untreated males increased the percen t of implants resorbed as assessed by uterine 14 histopathology. 15 The reproductive LOAEL is 10 ng/kg-day, based on a significant ( p < 0.05) decrease in 16 fertility (33

− 37% lower than controls); decrease in the number of live pups (18

− 27% lower than 17 controls); decrease in ge stational survival (10

− 11% lower than controls); decrease in postnatal 18 survival (32% lower than controls); and decreased postnatal body weight (14

− 19% lower than 19 controls at weaning) in one or more genera tions. The reproductive NOAEL is 1 ng/kg-day. 20
21
2.4.2.1.7. Rier et al., 1993, 1995. 22 Reir et al. (1993, 1995) examined the impact of c hronic TCDD exposure on 23 endometriosis in monkeys. Female rhesus monkeys (8 animals per treatment group) were 24 exposed to 0, 5, or 25 ppt TCDD (purity not specifi ed) in feed for 4 years. Previously, Bowman 25 et al. (1989b) determined that these dietary concentrations were equivalent to 0, 0.15, and 26 0.67 ng/kg-day, respectively. Ten years after term ination of TCDD treatment, the presence of 27 endometriosis was determined via laparoscopic su rgical procedure, and the severity of the 28 disease was assessed. The study authors report ed that three monkeys in the 0.67 ng/kg-day 29 exposure group died at 7, 9, and 10 years after te rmination of TCDD treatment. Autopsy results 30 attributed the deaths to widespread and severe peritoneal en dometriosis (all three monkeys) 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-135 along with obstruction of the colon (one monkey) and blockage of the jejunum (one monkey). 1
Other deaths also occurred in th e control group (1 death from bi rthing complications and another 2 from an unknown cause); in the 0.15 ng/kg-day dose group (1 death due to natural causes with 3 no endometriosis), and in the 0.67 ng/kg-day dose group (1 death due to a breeding fight with no 4 incidence of endometriosis). At study terminatio n, 17 live animals plus the 3 that had previously 5 died of endometriosis were evaluated (total n = 20). 6
Incidence of endometrios is was significantly ( p < 0.05) higher than in the control group 7
with 71 and 86 % incidence rates in the 0.15 and 0.67 ng/kg-day dose groups, respectively, 8 compared to 33% in the contro l group. Severity of endome triosis was also significantly 9 ( p < 0.001) correlated with TCDD dose. Stagin g by rAFS indicated that untreated control 10
animals had either minimal or no incidence of e ndometriosis. In comparison, endometriosis was 11 absent in 2 of the 7 monkeys in the 0.15 ng/kg- day dose group, while only 1 of the 7 animals in 12 the high dose group was disease free. Moderate-t o-severe disease was observed in 3 of the 13 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 14 group. Moderate-to-severe diseas e was not observed in the cont rol group. The authors also 15 compared the incidence and severity of e ndometriosis in TCDD-exposed animals with 16 304 normal, non-neutered females with no dioxin e xposure and reported that the disease was not 17 present in monkeys that were less than 13 years of age, while the disease rate was 30% among 18 animals 13 years of age or older. The study auth ors report that these fi ndings are in agreement 19 with human and rhesus studies demonstrating that the prevalence of detectable endometriosis can 20 increase with advanced age. 21 As noted previously, in a follow-up study, Rier et al. (2001) examined the DLC levels of 22 sera collected from some monkeys in this study. They reported that animals in this study had 23
elevated serum PCB77 and P CB126 levels and an increase d serum TEQ; the fractional 24
contribution of serum TCDD levels to total serum TEQ was 30% in treated animals. They also 25 reported that the severity of the endometriosis corresponded to the serum PCB77 concentrations 26 rather than total TCDD. In this study, it is not possible to determine the contribution of TCDD 27 alone to the edometriosis due to the background contamination; thus, EPA has not developed a 28 TCDD LOAEL from the study. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-136 2.4.2.1.8. Shi et al., 2007. 1 Pregnant Sprague-Dawley rat dams (3 per tr eatment group) were adm inistered 0, 1, 5, 50, 2 or 200 ng/kg TCDD (purity >99%) in corn oi l by gavage on GD 14 and GD 21 and on PND 7 3 and PND 14 for lactational exposure to pups (Shi et al., 2007). Ten female pups per treatment 4 were selected and administered TCDD weekly at the same dose levels through their reproductive 5 lifespan (approximately 11 months). The co rresponding equivalent daily TCDD doses are 0, 6 0.14, 0.71, 7.14, and 28.6 ng/kg-day. Vaginal open ing was slightly but significantly ( p < 0.05) 7 delayed in 28.6 ng/kg-day females. Vaginal openi ng was also delayed, but not significantly, in 8 the 0.14 and 7.14 ng/kg-day groups. Reproductive senescence with normal cyclicity was 9 significantly ( p < 0.05) accelerated beginning at 9 mont hs in 7.14 and 28.6 ng/kg-day females. 10
Serum estradiol concentrations were decreased at all time points across the estrous cycle in a 11 dose-dependent manner with a statistically significant decrease ( p < 0.05) in all but the lowest 12 dose group. TCDD exposure, however, did not affect the number or size distribution of ovarian 13 follicles; responsiveness of the pituitary gla nd to gonadotropin-releasing hormone, or serum 14 profiles of FSH, LH, or progesterone. 15 A LOAEL for TCDD of 0.71 ng/kg-day for an 11-month exposure duration was 16 identified in this study based on significantly ( p < 0.05) decreased estradiol levels in offspring. 17 The NOAEL for this study is 0.14 ng/kg-day. 18
19
2.4.2.1.9. Yang et al., 2000. 20 Yang et al. (2000) studied the impact of TC DD exposure on the incidence and severity of 21
endom etriosis in female rhesus monkeys. Groups of 7- to 10-year old nulliparous cynomolgus 22 monkeys were treated with 0 ( n = 5), 1, 5, or 25 ( n = 6 per group) ng/kg BW TCDD 5 days per 23 week via gelatin capsules for 12 months. Because the monkeys received one capsule 5 days per 24 week, the doses adjusted for continuous exposure were 0, 0.71, 3.57, and 17.86 ng/kg-day. Prior 25 to TCDD administration, all animals had e ndometriosis induced during days 12–14 of the 26 menstrual cycle by auto-transplantation of endometrial-strips in multiple abdominal sites. All 27 TCDD-treated and control groups were laparoscopi cally examined during months 1, 3, and 6 to 28 monitor the survival of endometrial implantations and to obtain peritoneal fluid to determine the 29 concentration and immunotype of endometrial gr owth regulator cytokines interleukin-6 (IL-6) 30 and interleukin-6 soluble receptor (IL-6sR). Becau se insufficient peritoneal fluids were present 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-137 in the treated and control monke ys, however, the study authors collected blood samples at 6 and 1 12 months during laparoscopy for routine hematology and to assess the circul ating levels of IL-6 2
and IL-6sR. All animals were s acrificed at 12 months, and circul ating levels of gonadal steroids 3 also were measured at the time of necropsy. 4 No changes were observed among treatment le vels in general toxicological endpoints 5 such as body weight changes, food consump tion, hematological endpoints, general activity 6 levels, and caretaker interaction. In addition, TC DD did not impact circul ating levels of gonadal 7 steroids measured during necr opsy. Similarly, there were no differences in the number of 8 menstrual cycles, the length of the menstrual cycle, and bleeding intervals. Endometrial implants 9
were found in at least one si te in all TCDD-treated and c ontrol monkeys during the first 10 laparoscopic examination. Follow-up laparoscopies revealed that there wa s a continuous loss of 11 endometrial implants over time in each dose grou p. At the 1-, 3-, and 6-month examination, the 12 number of endometrial losses was not significan tly different among different dose groups. At 13 the 12-month examination, however, a significantly ( p < 0.05) higher rate of survival of 14 endometrial implants was observed in the 3.57 and 17.86 ng/kg-day dose groups compared to the 15 control group. The high est rate of endometrial implant su rvival was observed in the ovaries 16 regardless of the dose group. In c ontrast, all lesions disappeared from the left broad ligament, 17 whereas two on the right broad li gament and one on the uterine fundus survived. There was a 18 dose-dependent divergence in th e growth response of endometrial implants following TCDD 19 exposure. Both the maximum and minimum implant diameters in the 17.86 ng/kg-day dose 20 group were significantly ( p < 0.05) larger compared to contro ls. In contrast, the maximum and 21 minimum implant diameters in the 0.71 ng/ kg-day dose group were significantly ( p < 0.05) 22 smaller compared to controls. TCDD did not impact implant diameters in the 3.57 ng/kg-day 23 dose group when compared to cont rols. Histological examinati ons revealed that endometrial 24 glands and stromal cells were present in all su rviving implants. Sections examined in the 25 17.86 ng/kg-day of TCDD possessed cystic endometr ial glands that were more frequently 26 observed in this dose group compared to other gr oups including controls. In addition, circulating 27 levels of IL-6 were significantly ( p < 0.05) lower in monkeys exposed to 17.86 ng/kg-day TCDD 28
both at 6 and 12 months com pared to the control gr oup. In contrast, circulat ing levels of IL-6sR 29 were significantly ( p < 0.05) higher in animals treated with 3.57 and 17.86 ng/kg-day TCDD at 30 6 months, while the levels were higher only in the 17.86 ng/kg-day TCDD group at 12 months. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-138 A LOAEL for TCDD of 17.86 ng/kg-day for a 1 y ear exposure duration was identified in 1 this study for significantly ( p < 0.05) increased endometriosis induced by endometrial implant 2 survival, significantly ( p < 0.05) increased maximum and mi nimum implant diameters, and 3 growth regulatory cytokine dys regulation (as assessed by signifi cantly decreased IL-6 levels, 4

p < 0.05). A NOAEL of 3.57 ng/kg-day is identified in this study. 5
6
2.4.2.2. Developmental Studies 7
2.4.2.2.1. Amin et al., 2000. 8 Amin et al. (2000) studied the impact of in-utero TCDD exposure on the reproductive 9
behavior in m ale pups. Groups of pr egnant Harlan Sprague-Dawley rats ( n = 108 divided into 10 4 cohorts; number of animals in the TCDD treatme nt group is ~3 per dose group) were dosed via 11 gavage with 0, 25, or 100 ng/kg-day TCDD (purity >98%) in corn oil on GDs 10

− 16. On the 12 day of birth (PND 0), pups were examined for gross abnormalities and the number of live pups, 13 their weights, and sex were recorded from each litter. Litters consisting of more than eight pups 14 were reduced to eight, comprised of four ma les and four females when possible. Litters 15 consisting of fewer than five pups were exclude d from the study to mi nimize between-litter 16 differences in growth rate, maternal behavior, and lactational exposure. After this exclusion, 17 approximately 10 to 11 litters per exposure gr oup remained. All pups were weaned on day 21 18
and one male and one female were retained to assess reproductive develo pment, play behavior, 19 reproductive behavior, and saccharin preference be havior. Both male and female pups were 20 tested for saccharin preference between 189 and 234 days of age. A saccharin preference test 21 was conducted for 8 days. For the first 4 days, ra ts were provided bottles containing tap water, 22 and on days 5 and 6 the animals were provided a bottle containing water and a bottle containing 23 0.25% saccharin solution. On days 7 and 8, the animals were provided water and a bottle 24 containing 0.50% of saccharin solution. A 0.50% saccharin solution was used because previous 25 studies have reported that male rats exhibited a greater reduction in preference for this saccharin 26 concentration compared to females, hence the sex di fference in preference is more marked at this 27
saccharine dose. 28 None of the treated dams exhibited any signs of toxicity as a result of exposure to TCDD. 29 Gestational body weight, liver weight, litter size and per cent live births were all comparable to 30 the corresponding control group. Birth rate and weaning weight of the pups also were not 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-139 affected by TCDD exposure. Sex-related wa ter consumption, however, was significantly 1 ( p < 0.001) affected during the first 4 days with female pups drinking more water per 100 g of 2 body weight compared to the respective male counterparts. Saccharin consumption was 3 significantly ( p < 0.001) affected, with females consumi ng greater amounts of saccharin solution 4 per 100 g body weight compared to the corresponding males. Additionally, both male and 5 female pups drank significantly ( p < 0.001) more of the 0.25% saccharin solution compared to 6 the 0.50% saccharin solution. Females of all exposure groups consumed less of both the 0.25 7 and 0.50% saccharin solution compared to the same-sex control group. Comparisons of each 8 exposure group to the control group indica ted that only the high TCDD exposure group 9 (100 ng/kg-day) different significantly ( p < 0.05) compared to contro l in the consumption of 10 0.25% saccharin solution. In contrast, for the 0.50% saccharin solution, both the low and high 11 TCDD dose groups differed significantly ( p < 0.05 and p < 0.01, respectively) compared to the 12 control group. The saccharin pref erence of TCDD-exposed male rats did not differ from that of 13 the male control group. The TCDD-exposed fe males’ preference for saccharin solution, 14 however, was significantly reduced in both the 25 ( p < 0.05) and the 100 ng/kg-day ( p < 0.005) 15 dose group compared to that of the female controls . The study authors stat e that the reduction in 16 saccharin consumption and preference in females could be due to the anti-estrogenic action of 17 TCDD and that recent research reports suggest that TCDD can decrease the level of estrogen 18 receptor (ER) mRNA by blocking the ability of ER to transactivate from the estrogen response 19
element. 20 A LOAEL for TCDD of 25 ng/kg-day for 7 days of gestational exposure is identified for 21 significantly ( p < 0.05) decreased preference in the consumption of 0.25% saccharin solution. A 22 NOAEL cannot be determined for this study. 23
24
2.4.2.2.2. Bell et al., 2007a. 25 Bell et al. (2007a) examined the reproductive effects of TCDD in rats exposed during 26 developm ent. Female CRL:WI (Han) rats were treated with TCDD (99% purity; dissolved in 27 acetone) in the diet at concen trations of 0 (acetone alone; n = 75), 28, 93, or 530 28 ( n = 65/group) ng TCDD/kg diet, which provide d average doses of 0, 2.4, 8, or 46 ng/kg-day, 29 respectively. Rats were exposed to TCDD 12 weeks prior to mating, during mating, and through 30 pregnancy. Dams were switched to the control diet after parturition. Litt ers from pregnant dams 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-140 were reduced to a maximum size of eight on PND 4 and to five males (if possible) on PND 21. 1
These males were left untreated until sacr ificed (25/group, one/litte r) on PND 70, while all 2 remaining animals were sacrificed on PND 120. All sacrificed animals were necropsied and 3 received a seminology examination. Prior to sacr ifice, during weeks 12 and 13, 20 animals from 4 each dose group were tested for learning ability a nd motor activity, and were also administered a 5 functional observation battery. During postnatal week 16, groups of 20 male F1 rats from each 6 treatment group were paired with untreated virg in females for 7 days, and mated females were 7 killed on GD 16 and examined for terminal body weights, pregnancy status, number of corpora 8 lutea, and number of intr auterine implantations. 9 The study authors found no evidence of direct ma ternal toxicity from exposure to TCDD. 10 In the high-dose groups, 8 of 27 dams suffered comp lete litter loss compared to 3 dams in the 11 control group, but the difference was not statistically significant. Pup survival at PND 4 was also 12 lower in the high-dose group, but the differen ce again was not statistically significant. 13 A dose-related decrease in mean pup body we ight was observed on PND 1, and this trend 14 continued throughout the lacta tion period. High-dose male pups had lower body weights when 15 compared to controls at PND 21, with this tr end continuing over the course of the study. 16 Balanopreputial separation (B PS) was significantly ( p < 0.05) delayed compared to controls in 17 all three treatment groups by 1.8, 1.9, and 4.4 days in the low-, medium-, and high-dose groups, 18 respectively. The study authors reported that adjustment fo r lower body weights observed at 19 PND 21 and PND 42 did not affect the estimate of delay in BPS. No adverse effects from 20 maternal treatment were observed on learning or in functional observational battery performance. 21 Offspring in the high-dose group exhibited less activity when compared to controls ( p < 0.05) 22 when they were subjected to a te st of motor activity for 30 minutes. 23 The median precoital time was 2

− 3 days for all 20 F1 males that were mated during 24 postnatal week 16. The uterine and implantation data were similar in all dose groups and there 25 were no significant differences in the proportion of male offspr ing between groups. Epididymal 26 sperm counts and sperm motility did not differ significantly between dose groups in animals 27 sacrificed during postnatal week 10. The mean number of spermatids was significantly lower 28 (14%; p < 0.05) and the proportion of a bnorma l sperm was significantly ( p < 0.05) higher in the 29 high-dose group when compared to controls on PND 70. These effects, however, were not seen 30
in animals sacrificed on PND 120. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-141 Terminal body weights were significantly ( p < 0.05) decreased in the high-dose group 1 (6.9 %) compared to controls on PND 120, while the depression in body weight in the 2 medium-dose group (5.5%) was not statistically significant. At PND 70, the relative and 3 absolute testis weight of the high-dose group was less than the controls (12 and 18%, 4 respectively). Absolute spleen weight in th e high-dose group was significantly higher (8%) on 5 PND 70, and increased significantly ( p < 0.05) by 1

− 3% on PND 120 in all dose groups 6 compared to controls. Kidney weight in th e low and medium-dose groups was significantly 7 ( p < 0.05) greater than in contro ls (~2%) at PND 120. In addition to these organs, ventral 8 prostate (9.4%) and relative liver (~4.5%) weights were significantly ( p < 0.05) higher than 9
controls on PND 120 in the medium- and lo w- and high-dose groups , respectively. On 10 PND 120, absolute brain weight was significantly ( p < 0.05) less than th e control in the 11 medium-dose group, while relative br ain weight was significantly ( p < 0.05) higher than the 12 control in the low- and high-dose group. Histol ogical examination reve aled no unusual findings. 13 A LOAEL for TCDD of 2.4 ng/kg-day following an estimated 17 week exposure duration 14 of dams was identified in this study for significantly ( p < 0.05) delayed BPS. A NOAEL was not 15 identified in this study. 16
17
2.4.2.2.3. Franczak et al., 2006. 18 Franczak et al. (2006) examined the impact of chronic TCDD exposure on the onset of 19 reproductive senescence in fem ale rats . Pregnant Sprague-Dawley rats ( n = 2–3/dose group) 20 were fed 50 or 200 ng/kg TCDD (>99% purity) or corn oil vehicle (4 mL/kg) orally on GD 14 21 and 21 and PND 7 and 14 to provide in utero an d lactational exposure to TCDD. On PND 21, 22 female pups ( n = 7/dose group) were weaned and were s ubsequently given weekly doses of 50 or 23 200 ng/kg-week TCDD by gavage (7.14 or 28.6 ng/kg-day adjusted for continuous exposure; 24 administered doses divided by 7) or corn oil ve hicle. Exposure continued for up to 8 months, 25 and animals were observed for changes in estrus cycle at 4, 6, and 8 months. Rats were 26 sacrificed at 8 months of ag e when the TCDD-treated animals had entered the transition to 27 reproductive senescence. Followi ng sacrifice, diestrus concen trations of serum LH, FSH, 28 progesterone, and estradiol were measured, a nd the ovaries were collected for examination. 29 Estrus cycles at 4 months exhibited normal cyclicity in both TCDD-exposed groups and 30 did not differ significantly from the control group . At 6 months, however, there was a tendency 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-142 ( p < 0.1) toward loss of normal estrus cyclicity in animals treated with TCDD. At the 8 month 1 observation, estrus cyclic ity was significantly ( p < 0.05) different in bot h dioxin-exposed groups 2 compared to controls (cumulative TC DD exposure is reported as 1.7 and 8 μ g/kg for the 50 and 3 200 ng/kg dose groups, respectively). The study authors noted that although the low-dose 4 animals showed an increased prevalence of prolo nged cycles, persistent es trus or diestrus was 5 observed in only 10% of the rats . Conversely, approximately 50% of the rats exhibited loss of 6 cyclicity in the high-dose group. There were no changes in the number and size distribution of 7
ovarian follicles or the number of corpora lutea at either dose. Progesterone levels at 8 months 8 tended to be higher ( p < 0.08) in animals receiving e ither 7.14 or 28.6 ng/kg-day TCDD 9 compared to controls, while serum estradiol concentrations were significantly ( p < 0.03) lower at 10
diestrus. Serum LH levels in TCDD-treated anim als were comparable to those in the control 11 group, while FSH levels were elevated in rats receiving 7.14 ng/kg-day TCDD, but not in the 12 28.6 ng/kg-day dose group. 13 A LOAEL for TCDD of 7.14 ng/kg-day for an 8-month exposure duration was identified 14 for significantly ( p < 0.03) decreased serum estradiol le vels. A NOAEL cannot be determined 15 for this study. 16
17
2.4.2.2.4. Hojo et al., 2002 (and related: Zareba et al., 2002). 18 Hojo et al. (2002) studied the impact of prenatal exposure to TCDD on sexually 19
dim orphic behavior in rats. Thirty-six pregnant Sprague-Dawley rats were assigned according to 20 a randomized block design to groups receiv ing 0, 20, 60, or 180 ng/kg TCDD (98% purity) on 21 GD 8. Litters from pregnant dams were culled to 5 females and 5 males on PND 4 and allowed 22 to wean normally, at which time 5, 5, 6, and 5 litters from the 0, 20, 60, and 180 ng/kg TCDD 23 treatment groups, respectively, were maintained for examination of behavioral response. 24 Offspring were exposed to TCDD (from a single maternal exposure) for about 35 days through 25 gestation and lactation. After w eaning at PND 21, offspring were fed ad libitum until PND 80, at 26 which time a fixed amount of food was supplied da ily to maintain constant body weights. At 27 90 days old, the rats in these tr eatment groups were trained to pr ess a lever to obtain food pellets 28 using two operant behavior procedures. Initially, each lever press was reinforced. The 29 fixed-ratio (FR) requirement was then increased ev ery fourth session from th e initial setting of 1 30 to values between 6 and 71. The responses for 30 days were studied under a multiple schedule 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-143 combining FR 11 and another schedule requiring a pause of at least 10 sec between responses 1 (differential reinforcement of low rate, or DRL 10-sec) 2 Pup and dam body weights were not affected by TCDD exposure, and all pups were 3 successfully trained in th e lever-press response within 3–4 days . Analyses of the FR procedure 4 data indicated that the male pups responded at a lower rate at all TCDD doses when compared to 5 the control group. In case of female pups, all TCDD-treated groups responded at a higher rate 6 than controls. None of these results were, by themselves, however, statistically significant. 7
Examination of the FR 11 and DRL 10-second da ta indicated that when considering the FR 8 component of this multiple procedure, males from all three treatment groups responded at lower 9 rates when compared to the contro ls. Conversely, all female pups responded at a higher rate than 10 controls. In addition, the treatmen t-by-sex interaction was significant ( p = 0.036), with the 11 60 ng/kg female pups responding at a higher rate th an the 60-ng/kg male pups. Examination of 12 the delayed response component in the multiple FR 11 and DRL 10-sec procedures indicated that 13 almost all TCDD treatment groups were affected. Like the FR component, male pups at all 14 TCDD dose groups responded at a lower rate compared to controls, while female pups at all dose 15 groups responded at a higher rate than cont rols. There was al so a significant ( p = 0.001) 16 sex-by-treatment interaction for the DRL 10-sec similar to the FR component. Following 17 behavioral testing, the animals were sacrificed and cortical depth measurements were taken in 18 selected right and left brain re gions. Reduced cortical thickne ss and altered brain morphometry 19
were observed in both male and female o ffspring in the 180-ng/kg exposure group when 20 compared to controls (reported in a se parate article; Zareba et al., 2002). 21 A nominal LOAEL for TCDD of 20 ng/kg for a single exposure on GD 8 is established 22 for this study based on abrogation of sexually dimorphic neurobehavioral responses. A NOAEL 23 cannot be derived for this study. 24
25
2.4.2.2.5. Kattainen et al., 2001. 26 Pregnant Line A, B, and C rats derive d from Han/Wi star and Long-Evans rats 27 (4

− 8 pregnant dams/strain/treatment group) were administered a single gavage dose of 0, 30, 28 100, 300, or 1,000 ng/kg TCDD (purity >99%) in corn oil on GD 15 (Kattainen et al., 2001). On 29 PND 1, the litters were culled to three males and three females. Offspring were weaned on 30 PND 28. Female pups were sacrificed on PND 35 and male pups were sacrificed on PND 70. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-144 TCDD treatment did not affect body we ight or cause clinical signs of toxicity in the dams. In 1 Line B offspring, body weights in the 1,000 ng/ kg group were slightly decreased during 2
PND 1

− 7, while Line C offspring had slightly decreased body weights throughout the study 3 period (data were not provided). The developmen t of the third molar was affected the most in 4 Line C offspring. In 5 of 10 Line C females and 6 of 10 Line C males treated with 1,000 ng/kg 5 TCDD, the lower third molar did not develop. In comparison, 1 of 19 Line A females and 1 of 6 18 Line B females administered 1,000 ng/kg TCDD l acked the third molar at sacrifice. Third 7 molars were present in all th e controls and all male Line A and B offspring administered 8 1,000 ng/kg. Due to the lack of eruption of the th ird molar in the majority of Line B and C 9 control females (only 30% erupted ), however, the effects of TC DD on third molar eruption could 10 only be evaluated in Line A female offspring (with 94% eruption). Th ere was a dose-dependent 11 decrease in the eruption of the lower third molar in Line A fema le offspring with a significant 12 ( p < 0.05) decrease observed in the 300 and 1,000 ng/kg dose groups. In the male offspring, any 13 third molar that developed erupted by PND 70. The mesiodistal length of the existing lower 14 third molar was reduced in a dose-dependent manner in both genders of all three rat lines. In 15 Line A and C females, the decrease was significant ( p < 0.05) at all doses. The size of the 16 second molars was also signifi cantly decreased with 1,000 ng/kg ( p < 0.05) in all but Line C 17
males. 18 A developmental LOAEL for TCDD of 30 ng/ kg for maternal exposure on GD 15 is 19 established for this study, based on impaired tooth development (significantly reduced 20 mesiodistal length of the lower third molar by approximately 12% to 38% [ p < 0.05]). A 21
NOAEL could not be determined. 22
23
2.4.2.2.6. Keller et al., 2007, 2008a, b. 24 Keller et al. (2007, 2008a, b) conducted three se parate experiments to assess the impact 25 of TCDD on molar tooth developmen t using different mouse strains. In Experiment 1, Keller et 26 al. (2007) used six inbred mouse strains (C57B L/6J, BALB/cByJ, A/J, CBA/J, C3H/HeJ, and 27 C57BL/10J) known to possess high affinity liga nd-binding aryl hydrocar bon receptor alleles ( b ), 28
two with b1 alleles (C57BL/6J and CBA/J), and four with b2 alleles (BALB/cByJ, A/J, 29 C3H/HeJ, and CBA/J). Females (number not specified) from each strain were mated with males 30 of the same strain. On GD 13, each pregnant fema le was assigned to one of the four dose groups 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-145 and treated with 0, 10, 100, or 1,000 ng TCDD/kg BW via oral gavage. The control group 1
received corn oil. GD 13 was chosen for dosi ng because the first mor phological signs of tooth 2 development occur on GD 11. The first visible signs of the M1 (molar) occur on GDs 13–14 3 followed by final cuspal morphology, which is determined on GD 15. The F1 offspring of 4 females from each strain were weaned and sepa rated by sex at PND 28 and were euthanized at 5
PND 70. Each F1 mouse was examined for th e presence or absence of both maxillary (M 3 ) and 6 mandibular third molars (M 3 ) on both the left and right sides. In addition, all mice were scored 7
as either normal or variant in M 1 morphology for both molar rows. 8 In Experiment 2 (Keller et al., 2008a), dams from six inbred m ouse strains (C57BL/6J, 9 BALB/cByJ, A/J, CBA/J, C3H/HeJ, and C57BL/10J) were orally dosed on GD 13 with 0, 10, 10 100, or 1,000 ng TCDD/kg BW in corn oil. GD 13 was used as the dosing day because it 11
coincided with the formation of Meckel’s cartil age (a major signal center) in the mouse mandible 12 that is followed shortly by intramembranous bone formation on GD 15. The A/J mouse strain 13 was abandoned because the authors had difficulty r earing the offspring from this strain. All 14 offspring ( n = 4 or 5 per treatment group) from the rema ining strains were euthanized at 70 days 15 of age. Mandible size and shape from all selected offspring were examined using geometric 16 morphometric methods to assess the impact of TCDD exposure. 17 In Experiment 3 (Keller et al., 2008b), dams from six inbred m ouse strains (C57BL/6J, 18 BALB/cByJ, A/J, C3H/HeJ, CBA/J, and C57BL/10J) were treated with a si ngle oral dose of 0, 19 10, 100, or 1,000 ng TCDD/kg-BW in corn oil. GD 13 was chosen as the dosing day because the 20 first visible signs of the first molar (M1) o ccurs on GDs 13–14 and the final cuspal morphology 21 (the pattern of projections on th e chewing surface of the tooth) is not determined until after 22 GD 15. Similar to Experiment 2, the A/J mouse strain was abandoned due to difficulty in rearing 23 offspring. All offspring ( n = 107–110 in each of the five stra ins for all treatment groups) were 24 euthanized at 70 days of age and their molar si ze, shape, and asymmetry traits were examined 25 using geometric morphometric methods. 26 In Experiment 1, all four M 3 s were present in all dose gr oups in mice from C57BL/6J, 27 BALB/cByJ, and C57BL/10J strains. A similar response was observed in the A/J strain mice 28 with only 3 of 51 F1 mice exhibiting missing third molars. Approximately one-third of the mice 29 from the CBA/J and C3H/HeJ strains, however, were missing at least one M 3 or M 3 molar. The 30 numbers of CBA/J mice missing one or both M 3 or M 3 molars were 0/29, 2/21, 6/29, and 30/30 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-146 in the 0, 10, 100, and 1,000 ng/kg groups, respectivel y. In the C3H/Hej animals, the numbers 1 missing one or both molars were 1/ 24, 3/28, 1/26, and 30/36, respectively. 2 Maternal TCDD exposure was also f ound to affect the frequency of M 1 variants, but only 3 in the C57BL/10J strain, and the dose-response relationship was nonmonot onic. The proportions 4 of variants observed in the 0, 10, 100, a nd 1,000 ng/kg dose groups were 33, 68, 59, and 58%, 5 respectively. 6 A LOAEL for TCDD of 10 ng/kg maternal expo sure on GD 13 is identified for this study 7
for increased incide nce (33%) of the M 1 variant in the C57BL/10J mouse strain. A NOAEL 8 cannot be determined in this study. 9 In Experiment 2 TCDD exposure of dams did not affect offspring survival or 10-week 10 body weight in any of the inbred mouse strains us ed. Analysis of vari ance (ANOVA) indicated 11 that although mandible size in both male a nd female offspring varied significantly ( p < 0.0001) 12 among strains, it was not affected by TCDD exposu re. In contrast, analysis of covariance 13
indicated that TCDD ex posure significantly ( p = 0.0033) decreased the mandible size in male 14 offspring in the C3H/HeJ strain at all treatm ent groups. The mean mandible size was similar 15 across all treatment groups in bot h sexes in all strains with male offspring exhibiting larger 16 mandibles compared to females. Males in the C3H/HeJ strain exhibited a significant (level not 17 reported) downward trend in mandible size throug hout all treatment groups. Females in the C3H 18
strain also showed a similar trend in mandible size, but the trend was not significant. ANOVA 19 on mandible shape indicated th at males had significantly ( p < 0.0001) different mandible shape 20 in strain × treatment groups. In contrast, in female offspring, although the mandible shape was 21 significantly ( p < 0.0001) different due to strains, trea tment groups, and litter, the strain 22 × treatment interaction was not significant. Male offspring from th e C3H/HeJ and C57BL/6J 23 mouse strains appear to be more sensitive to TCDD than BALB/cByJ or CBA/J mice, with the 24 C57BL/10J strain exhibiting interm ediate sensitivity. In addition to these analyses, Procrustes 25 distance analysis also indicated that C3H/HeJ mice had the greates t response to the highest dose 26 of TCDD, followed by the C57BL/6J strain. Fe male offspring in the C3H/HeJ and C57BL/6J 27
strains also exhibited th e largest change in Procrustes distan ce with TCDD exposure. This trend, 28 however, was not statistically significant ( p = 0.29). 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-147 A LOAEL for TCDD of 10 ng/kg maternal exposure on GD 13 was identified for this 1 study for significantly ( p = 0.0033) decreased mandible shape and size in male C3H/HeJ mice. 2 A NOAEL cannot be determined in this study. 3 In Experiment 3, effect of TCDD exposure on offspring survival or body weight was not 4 reported. Three-way ANOVA re sults showed significant ( p < 0.0001) differences in molar size 5 among strains, sexes, and litters, but not between treatment groups. Molar size difference in sex 6 × strain interaction was significant ( p = 0.03), whereas differences in sex × treatment and sex 7
× strain × treatment were not significant. Additionally, mola r size in treatment × strain 8 interaction also was not statistically significant. Based on these results, th e authors reported that 9 molar size varied significantly ( p < 0.0001) among all five strains tested, with all strains 10 exhibiting similar trends in all f our treatment groups. Strain diffe rences in molar size were more 11 apparent in male offspring. A hormesis-like tr end in molar size was obs erved in all strains 12 (except in BALBc/ByJ) and sexes with an incr ease at the 100 ng/kg dose and a decrease in the 13 1,000 ng/kg dose. In addition to lack of differen ce in molar size for all treatment groups in all 14 strains, fluctuating asymmetry in molar size al so did not increase w ith increasing doses of 15
TCDD. 16 In contrast to these results on molar size, the Procrustes ANOVA indicated that molar 17 shape was significantly ( p < 0.0001) affected by strain, sex, treat ment, and litter size. Molar 18 shape in sex × strain and sex × strain × treatm ent interactions was also highly significant 19 ( p < 0.0001). Based on these results, the authors conc luded that differences between males and 20
females varied based on the stra in, and that the effect of TC DD exposure on each strain also 21 differed for male and female offspring. Because molar shape in treatment × strain interaction 22 was significant ( p < 0.0001), differences in molar shape be tween the three treatment groups and 23 the control group were analyzed for each strain using nonorthogonal contrasts. In male 24 offspring, contrasts between the control group and 1,000 ng/kg were statistically significant only 25 in the C3H/HeJ ( p < 0.0001) and CBA/J ( p < 0.03) strains. These resu lts suggest that these two 26 strains are most susceptible to TCDD effect on mo lar shape, and sim ilar results were observed in 27 female offspring of these two strains. The cont rast in molar shape between the control and the 28 100 ng/kg treatment group for the female C57BL/ 6J mice also was statistically significant 29 ( p = 0.0096). On the whole, when considering Procru stes distance results for molar shape, the 30 C3H/HeJ male offspring had the largest respons e at the low and high doses, while the female 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-148 offspring had the largest response at low and mi d doses. This observation in male C3H/HeJ 1
mice is consistent with that of TCDD-induced changes in mandible size from Keller et al. 2 (2008a). 3 A LOAEL for TCDD of 10 ng/kg maternal expo sure on GD 13 is identified for this study 4 for significant ( p < 0.0001) differences in molar shape in male C3H/HeJ mice. A NOAEL 5 cannot be determined in this study. 6
7
2.4.2.2.7. Kuchiiwa et al., 2002. 8 Kuchiiwa et al. (2002) studied the impact of in utero and lacta tional TCDD exposure on 9
serotonin-immunoreactiv e neurons in raphae nuclei on F1 male mouse offspring. Twenty-one 10 adult female ddY mice (seven per treatment group) were administered TCDD (99.1% purity) by 11 oral gavage once a week for 8 weeks at dos es of 0, 4.9, or 490 ng/kg (0, 0.7, or 70 ng/kg-day 12 average daily dose; administered doses divided by 7) or an equivalent volume of olive oil vehicle 13 (6.7 mL/kg) by gavage. Immedi ately following the final treatment, the mice were housed with 14 untreated male mice for mating. At approximate ly 20–21 days after mating, 3 female mice from 15 each dose group, including the control group gave birth to 10–12 offspring. One day after birth, 16 each litter was culled to 10 offspring to accomm odate similar lactational TCDD exposure. On 17 PND 28, the offspring were weaned, and three offspring from each TCDD exposed group and 18 the control group were selected for an immunocyt ochemical examination at 42 days of age. 19 Following sacrifice of these offspring, the brai n of each animal was removed and every second 20 serial section of the brain was processed for i mmunocytochemistry. In addition to the serial 21 sections of the brain, cells from 18 offspring (6 males per treatment group) were used to assess 22
the number of cells in the dorsa l and median raphe nucleus, the supralemniscal area, and the 23 Nucleus raphe magnus. 24 Examination of external morphology, birth, a nd postnatal body weights indicated that 25
there were no differences between the male TCDD-exposed offspring and the control male 26 offspring. TCDD-exposed males, however, were aggressive toward other normal mice and were 27 also hypersensitive to soft touch. 28
Serotonin-immunoreactive neurons were found to be distributed throughout the entire 29 brainstem in 42-day-old males, and the gene ral pattern in the TCDD-exposed animals was 30
consistent with those observed in control male offspring. Serotonergic neurons were identified 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-149 and counted in the caudal linear nucleus, the me dian and dorsal raphe nucleus, Nucleus raphe 1 pontis, interpeduncular nucleus, supralemniscal area, peduncul opontine segmental nuclei, deep 2 mensencephalic nucleus, Nucleus raphe magnus, pall idus, and obscurus, dorsal and medial to the 3
facial nucleus and the ventro lateral medulla. Results from computerized cell counts ( n = 6) 4 showed an average of 1,573.3 immunoreactive neur ons in the raphe nuclei from the control 5 group versus 716.3 and 419.8 neurons in the low- and high-dose offspring, respectively. The 6
numbers of immunoreactive neurons in the individual raphe nuclei (dorsalis, medianus, magnus, 7 and B9) from the TCDD-exposed offspring were significantly ( p < 0.01) lower than control 8 values, with the degree of re duction being dose-related. 9
In the absence of other relevant neurotoxic ity endpoints, reduced serotonin is not an 10 adverse endpoint of toxicologi cal significance in and of itsel f, thus, neither a NOAEL nor a 11 LOAEL can be established for this study. A lowest-observed-effect level (LOEL) of 12 0.7 ng/kg-day for an 8-week exposure duration is identified in this study for a significantly 13 ( p < 0.01) lower number of serotonin-immunoreac tive neurons in the raphe nuclei of male 14 offspring. A no-observed-eff ect level (NOEL) cannot be determined for this study. 15
16
2.4.2.2.8. Li et al., 2006. 17 Pregnant and pseudopregnant (obtained by mating normal estrous fem ale mice with 18 vasectomized male mice) NIH mice (10 per tr eatment group) were exposed to 0, 2, 50, or 19 100 ng/kg-day of TCDD (purity 99%) during earl y gestation (GDs 1–8), preimplantation 20 (GDs 1

− 3), or peri-implantation to postimplanta tion (GDs 4–8) (Li et al., 2006). On GD 9, 21 animals were evaluated. The two highes t TCDD doses (50 and 100 ng/kg-day) caused 22 significant ( p < 0.05) early embryo loss independent of gestational exposure time. At 23 100 ng/kg-day, however, the embryo loss was great er when administer ed during GDs 1–8 or 24 GDs 1–3 compared to GDs 4–8 ( p < 0.01). Uterine weight was significantly decreased in the 25 pseudopregnant mice when administered 50 or 100 ng/kg-day TCDD during GDs 1–8 26 ( p < 0.001) or 1–3 ( p < 0.01), but was only decreased at 100- ng/kg-day in pseudopregnant mice 27 when administered during GDs 4–8 ( p < 0.01). Estradiol levels were increased at all TCDD 28 treatment levels (100% at the lo west dose), but statistical signif icance was not indicated. All 29
doses at all treatment times re sulted in a significant reduction ( p < 0.01) in serum progesterone 30 levels, with a 45% decrease at the lowest dose. Because the hormone effects were observed 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-150 following 4 days of treatment, the nominal doses were averaged over the entire test period of 1 8 days prior to measurement. The resulting average daily doses of TCDD were 0, 1, 25, and 2 50 ng/kg-day. 3 A LOAEL of 2 ng/kg-day administered for 4 to 8 days is establishe d in this study for a 4 significant ( p < 0.01) decrease in progesterone (45% above control) and an approximate 2-fold 5 increase in estradiol levels (significance not indicated). A NOAEL cannot be determined. 6
7
2.4.2.2.9. Markowski et al., 2001. 8 Pregnant Holtzman rats (4–7 per treatm ent group) were administered a single gavage 9 dose of 0, 20, 60, or 180 ng/kg TCDD (purity not spec ified) in olive oil on GD 18 (Markowski et 10 al., 2001). One female rat from each liter (4–7 per treatment group) was assigned to training on 11 a wheel apparatus to respond on a lever for br ief opportunities to run. Once animals responded 12 to an FR1 schedule of reinforcement, the requ irement for lever pressing was increased to FR2, 13 FR5, FR10, FR20, and FR30 schedules. After each training session, the estrous cycle stage was 14 determined. Maternal body weight, length of gestation, number of pups per litter, and sex 15
distribution within litters were unaffected by treatment. For each of the FR schedules, there was 16 a significant dose-related ( p = 0.0001) decrease in the number of earned run opportunities, lever 17 response rate, and total number of revolutions in the wheel in the adult female offspring. There 18 was no correlation between estrous cycle a nd responding for access to wheel running. 19 The developmental LOAEL for this study is a single dose of 20 ng/kg administered on 20
GD 18 for neurobehavioral eff ects. A NOAEL cannot be determined for this study. 21
22
2.4.2.2.10. Miettinen et al., 2006 23 Miettinen et al. (2006) ad ministered a single oral dose of 0, 30, 100, 300, or 1,000 ng/kg 24 TCDD (purity >99%) in corn oil on GD 15 to pre gnant Line C rats. The offspring (24–32 per 25

treatm ent group) were assigned to a sugar-rich cariogenic diet (via feed and drinking water) and 26 were orally inoculated three separa te times with fresh cultures of Streptococcus mutans . Three 27 control groups varied with rega rd to TCDD exposure and admini stration of a cariogenic diet. 28 Two of the control groups received no TCDD, an d the offspring were either maintained on a 29
normal diet without inoculation with S. mutans (C1; n = 48) or were given the cariogenic diet 30
with S. mutans inoculation (C2; n = 42). The final control gr oup was maternally exposed to 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-151 1,000 ng/kg TCDD with offspring fed a normal diet without S. mutans inoculation (C3; n = 12). 1 TCDD did not affect the maternal or offspri ng body weight. Survival of the offspring was 2 reduced in the 1,000 ng/kg dose group (50–58% surv ival compared to 83–95% in C1 and C2, 3 respectively). All offspring administered 1,000 ng/kg were missing all lower third molars. 4 Two animals (8%) in the 100 ng/kg group were missi ng one of their lower third molars. All 5 doses, except the 100 ng/kg dos e, caused a significant ( p < 0.05) increase in the number of caries 6 lesions compared to group C2 (60, 79, 76, 83, and 91% in the C2, 30, 100, 300, and 1,000 ng/kg 7 groups, respectively). Group C3 (1,000 ng/kg TCDD exposure, normal diet) animals also had 8 increased caries lesions compared to C1 (8% ve rsus 0%, respectively). There were no changes 9 in tooth mineral composition that could expl ain the increase in caries susceptibility. 10 The developmental LOAEL from this study is a single dose of 30 ng/kg administered on 11 GD 15 based on the significant ( p < 0.05) increase in dental carie s in pups (30% above control). 12 A NOAEL cannot be determined from this study. 13
14
2.4.2.2.11. Nohara et al., 2000. 15 Pregnant Holtzman rats were adm inister ed 0, 12.5, 50, 200, or 800 ng/kg TCDD in corn 16 oil by gavage on GD 15 (Nohara et al., 2000). On PND 2, five males were randomly selected 17 from each litter and dose group. TCDD was detect ed in the thymus, spleen, and bone marrow of 18 the male pups on PND 21 and PND 49. TCDD was s till detected in the thymus and spleen on 19
PND 120 but the levels decreased over time. The TCDD concentration was highest in the 20 thymus at all time points. There were no change s in the body, thymus, or spleen weights of the 21 male offspring on PND 5, PND 21, PND 49, or PND 120. On PND 5, there was a 200-fold 22 increase in CYP1A1 in the thymus of the high-dose male pups. CYP1A1 was only slightly 23 increased in the spleen. This induction decr eased through PND 49. There was a slight (not 24 statistically significant) dose-depe ndent decrease in thymus cellular ity in the male offspring at 25 PND 120. Spleen cellularity at PND 49 decrea sed in a dose-dependent manner (15–50% of the 26 control), with a statistically significant ( p < 0.05) decrease observed in the high-dose group. A 27 slight but not significant reducti on in spleen cellularity was not ed in the high-dose group at 28
PND 21. The same effect was not observed at PND 120, nor was there any change in the percent 29 of B or T cells in the spleen. No changes in cytokine levels were observed in the 800-ng/kg 30

group. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-152 Although a change in spleen cellularity on PND 49 (puberty) was observed, this effect 1 was transient and there were no coexisting change s in the percentage of splenic lymphocytes, 2 spleen weight, and cytokine levels. Theref ore, a developmental NOAEL of a single dose of 3 800 ng/kg administered on GD 15 is identified for this study. A LOAEL is not established. 4
5
2.4.2.2.12. Ohsako et al., 2001. 6 Pregnant Holtzman rats (6 per treatm ent group) were administered 0, 12.5, 50, 200, or 7 800 ng/kg TCDD (purity >99.5%) in corn oil by ga vage on GD 15 (Ohsako et al., 2001). On 8 PND 2, five males were randomly selected from each litter. Two male offs pring from each litter 9
were sacrificed on PND 49 and PND 120. Neither maternal nor male offspring body weight was 10 affected by TCDD treatment. TCDD was detected in both fat and testes at all dose levels 11 (including controls) with highest levels found in fat. There we re no apparent treatment-related 12 effects on testicular weight, epididymal weight , daily sperm production, ca uda epididymal sperm 13 reserves, luteinizing hormone, follicle stimulating hormone, or testosterone levels. There was, 14 however, a clear dose-dependent decrease in urogenital complex weight and ventral prostate 15 weight at both PND 49 and PND 120. For male offspring, statis tically-significant ( p < 0.05) 16 decreases were noted in urogenital complex we ight at PND 120 in the 200 and 800 ng/kg groups, 17 in ventral prostate weight at PND 49 in 800 ng/kg group, and at PND 120 in the 200 and 18 800 ng/kg groups. There was also a dose-dependent decrease in anogenita l distance (the length 19 between the base of the genital t ubercle and the anterior edge of the anus); the decrease was not 20 statistically significant at PND 49. At PND 120, however, male of fspring in all but the lowest 21 dose group had significantly ( p < 0.05) reduced anogenital dist ance compared to the control 22 animals. There was also a dose-dependent increase in 5 α R-II mRNA expressi on in the ventral 23 prostate on PND 49 with significant increases ( p < 0.05) in the 200 and 800 ng/kg animals. 24 There was a significant ( p < 0.01) decrease in the androgen receptor mRNA in the ventral 25 prostate on PND 49 at all doses te sted. Similar effects were not observed on PND 120 or in the 26 caput epididymis on PND 49. 27 The developmental LOAEL for this study is a single dose of 50 ng/kg administered on 28 GD 15 for significantly ( p < 0.01) reduced anogenital distance in male offspring (approximately 29 14%). The NOAEL for this study is 12.5 ng/kg. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-153 2.4.2.2.13. Schantz et al., 1996. 1 Schantz et al. (1996) studied the impact of in utero TCDD exposure on spatial learning in 2

m ale and female pups. Groups of pre gnant Harlan Sprague-Dawley rats ( n = 108, divided into 3 4 cohorts; number of animals in each TCDD grou p approximately 4 per treatment group) were 4 dosed via gavage with 0, 25, or 100 ng/kg-da y TCDD (purity >98%) in corn oil on GDs 10

− 16. 5 On the day of birth (post natal day [PND] 0), the pups were examined for gross abnormalities 6 and the number of live pups, weight, and sex were recorded for each litter. On PND 2, litters 7 were culled to eight animals and were balanced to include four males and four females whenever 8 possible. To minimize litter-size effects, litters with fewer than five pups were excluded from 9 the study. The exclusion of th ese litters resulted in 10

− 11 litters per treatment group. Pups were 10
weaned on PND 21 and one male and one female pup from each litter were maintained for the 11 learning tests. Pups were tested 5 days per week for spatial learning and memory in a radial arm 12 maze and a T-maze. A radial arm maze working me mory test and a T-maze DSA task were used 13 a part of the testing process. 14
TCDD treatment did not affect dam gestational weight gain, dam liver weight, gestation 15 length, litter size, percentage of live births, birth weight, or postnatal growth of the pups 16 observed during the course of the study. Exposed pups, however, exhibited some signs of 17 toxicity in all exposure groups. Thymus weight was decreased and liver weight was increased in 18 the 100 ng/kg-day TCDD dose group. Also, liver microsomal 7-ethoxyresorufin-O-deethylase 19 (EROD) activity was markedly induced in pups from both the 25 and 100 ng/kg-day dose 20 groups. In the radial maze test, rats from a ll TCDD exposure groups displayed a significant 21 ( p < 0.01) learning behavior as shown by progressi vely fewer errors from the first block of 22 sessions through the fourth session. The tr eatment by sex and treatment by session block 23 interactions were not significan t. Comparisons between the aver age number of errors per session 24 block in the TCDD-exposed and control group indicated that both the 25 and the 100 ng/kg-day 25 dose groups made significantly ( p < 0.05 and p < 0.001, respectively) fewer errors compared to 26 the control group. TCDD did not significantly a ffect adjacent arm selection behavior as 27 measured by C statistic; hence th e reduction in errors observed di d not appear to be accounted 28 for by an increased tendency to run into ad jacent arms. Female pups had a significant ( p < 0.05) 29
shorter radial arm maze latency, however, compared to the male pups. In the T-maze test, 30 TCDD did not significantly affect the percent of correct performance. All exposure groups 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-154 performed best at the shortest delay, which showed a decline as the length of the inter-trial delay 1
interval was increased. Add itionally, all treated groups impr oved their performance over a 2 three-block session period. This finding indicated that animals in all groups could learn the task. 3 These observations were confirmed by a hi ghly significant main effect of delay ( p < 0.001) and 4 highly significant main effect of session blocks ( p < 0.001). At the shortest 15-second delay, 5 average percent correct performance increased from 75 to 92%, while at the longest 40-second 6 delay, the average percent correct performance increased from 62 to 82%. A significant 7 ( p < 0.05) main effect of exposure was eviden t in latency to respond in the T-maze. 8 Comparisons of the exposed group to contro l group, however, indicated that none of the 9 individual exposure groups diffe red significantly from the contro ls. Because no clear pattern 10 was observed in the various exposure groups, diff erences in latency to respond had no impact on 11 learning of the task. 12 Based on these results, the st udy authors state that the fa ct TCDD seems to have a 13 facilitatory effect on radial ar m maze learning in rats should be interpreted with caution and 14 needs further evaluation using different and more varied learning tasks. No toxicologically 15 adverse endpoints were concurrently exam ined. Thus, a LOAEL and a NOAEL cannot be 16 determined for this study. 17
18
2.4.2.2.14. Seo et al., 1995. 19 To study developmental effects of TCDD on th yroid hormone levels, time-m ated female 20 Sprague-Dawley rat dams ( n = 10

− 14/treatment group) were administered 25 or 100 ng/kg-day 21 of TCDD (>98% pure) in corn oil via gavage from GDs 10–16. Vehicl e controls received 22 equivalent amounts of corn oil. The study also investigated PCB treatment outcomes. At birth, 23 pups were weighed and grossly examined for abnormalities. At 2 days of age, litters with fewer 24 than 5 pups were excluded from the analysis and the remaining litters were culled to 4 males and 25 4 females. Each treatment group contained 10 or 11 litters. Pups remain ed with the dams until 26 weaning. At weaning, 4

− 6 pups were retained for neurobeha vioral tests (which were not 27 reported as part of this study). The remain ing offspring were sacr ificed, which provided 28
5

− 9 litters per treatment group. Data were collected from one male and one female where 29 possible. No signs of toxicity were eviden t in the dams; measurements on dams included 30 gestational weight gain, liver we ight, litter size, and live births. Pup birth weight and weaning 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-155 weight were unaffected by treatment. In pups sa crificed at weaning (21 days old), a significant 1 ( p < 0.05) decrease occurred in t hymus weight for the high-dose group, but not in thyroid, liver, 2 or brain weight. A significant ( p < 0.05) decrease (20.4%) was obs erved in T4 in high-dose 3 females. Thyroid stimulating hormone and T 3 were unaffected by treatment. Uridine 4 diphosphate (UDP)-glucuronosyl transferase activity toward s 4-nitrophenol significantly 5 ( p < 0.05) increased in both treatment groups ove r control values, and the increase in the 6 high-dose group was significantly ( p < 0.05) greater than in the low-dose group. Liver 7 microsomal EROD activity was significantly ( p < 0.05) increased in bot h treatment groups, but 8 is considered to be an adaptiv e response and not adverse. 9 A LOAEL of 100 ng/kg-day for decreased thym us weights and decreased thyroxine is 10 identified for this study. A NO AEL of 25 ng/kg-day is established. 11
12
2.4.2.2.15. Simanainen et al, 2004. 13 Simanainen et al. (2004) studi ed the impact of in utero and lactational TC DD exposure 14 on the male reproductive system in three rat line s that are differentially sensitive to TCDD. 15 Groups of 5 to 8 pregnant Line A, B, and C C57BL/6N CYP1A2 dams were given a single dose 16 of 0, 30, 100, 300, or 1,000 ng/kg of TCDD (purity >99 %) in corn oil on GD 15 via oral gavage. 17 Control animals were similarly dosed with a corn oil vehicle. One day after birth, litters were 18 randomly culled to include three males and three females to allow uniform postnatal exposure. 19 Offspring were weaned on PND 28. Dam and p up viabilities were m onitored throughout the 20 study. Pup body weights were determined on PNDs 1, 4, 7, 14, and 28. Anogenital distance and 21 crown-rump length were measured on PNDs 1 an d 4. On day 70, pups were sacrificed and trunk 22
blood was collected. Serum was collected for test osterone analysis. The testes, cauda of the 23 right epididymis, ventral prostrate, seminal ve sicles, and thymus was dissected and weighed. 24 Absolute and relative organ weights were determin ed, and cauda epididymis and testes were also 25 preserved for sperm count analysis. 26 TCDD caused no mortality or overt signs of toxicity to the dams. Pup survival from 27 implantation to the day after birth also was not affected by TCDD exposure. Survival from the 28 day of implantation to the day after birth, howev er, was uncharacteristically lower in control 29 Line B rats (41%), resulting in a significant difference compared with the two lowest doses (30 30 and 100 ng/mg TCDD). The average survival percen tage in the controls for Line A, B, and C 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-156 rats was 85% (range 80

− 86%); 64% (41

− 86%); and 74% (63

− 85%); respectively. Percentage of 1 male pup survival in each line between PND 1 and PND 28 was 99% except for Line B males 2 exposed to 30 ng/kg TCDD and Line C males expos ed to 30 or 100 ng/kg, where male survival 3 rate averaged 81% (range 81

− 83%). On PND 70, a significant ( p < 0.05) reduction in body 4 weight was observed only in Line B and C rats at 1,000 ng/kg. In pups exposed to 1,000 ng/kg 5 TCDD, both absolute and relative weight of the ventral, anterior, and dorsolateral prostrate 6 decreased in all three lines at most postnatal time points measured. The change was most 7 consistent and significant ( p < 0.05) in the ventral lobe. Animals exposed to 1,000 ng/kg TCDD 8 had an average decrease in absolute weight of the anterior prostrate of 37, 32, and 34% in 9 Lines A, B and C, respectively. Additionally, the average dorsolateral prostrate weight was also 10 decreased by 34, 28, and 39% in Lines A, B, and C, respectively. The effect on the ventral 11 prostrate was reversible w ith the only significant ( p < 0.05) decrease in weight observed in 12 Line B rats at PND 70 in the 1,000 ng/kg TCDD dose group. The authors reported that TCDD 13 had no consistent effects on the weight of seminal vesicles. The absolute weights of the testis 14 and epididymis showed a significant ( p < 0.05) increase on PNDs 28

− 49, but the relative testis, 15 epididymis, and cauda epididymis weights rema ined unchanged. In pups exposed to 1,000 ng/kg 16 TCDD, severe malformation, including small ca put and cauda and degeneration of corpus 17 epididymis, was observed. Malformations in the epididymis were observed in 6 of 44 Line C 18 male rat offspring and 3 of 47 Line A male rat o ffspring. In Line A, B, and C rats at PND 70in 19 the 1,000 ng/kg TCDD dose group, daily sperm production was reduced by 9, 25, and 36% and 20 cauda epididymal sperm reserv es were reduced by 18, 42, and 49%, respectively. Daily sperm 21 reduction (17%) was significant ( p < 0.05) in Line C rats at a TCDD dose of 300 ng/kg and in 22 Line B and C rats at 1,000 ng/kg. A reduction in cauda epididymal sperm reserves (25%) was 23 significant ( p < 0.05) in Line C rats at 300 and 1,000 ng/kg TCDD. 24 A LOAEL for TCDD of 300 ng/kg is identifiedfor reduction in daily sperm production 25 and cauda epididymal sperm reserves in Line C rats. A NOAEL of 100 ng/kg is identified for 26 this study. 27
28
2.4.2.2.16. Sugita-Konishi et al., 2003. 29 Sugita-Konishi et al. (2003) ex amined the immu notoxic effect s of lactational exposure to 30 TCDD in newborn mice. Eight pregnant female C57BL/6NCji mice were administered 0, 1.8, or 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-157 18 ng/L of TCDD via drinking water fr om parturition to weaning of the offspring (for a total of 1 17 days). Based on an average water intake of 14–16 mL/day, the average daily intake of TCDD 2 for the dams was 1.14 and 11.3 ng/kg-day in the lo w- and high-dose groups, respectively. In 3 male offspring sacrificed at weaning (21 days after birth), there was a statistically-significant 4 ( p < 0.05) decrease in relati ve spleen weight and a statistically-significant ( p < 0.005) increase in 5 thymic CD4+ cells in the high-dose group. The ch anges in relative sple en weight and thymic 6 CD4+ cells were dose related, bu t effects in the low-dose grou p did not achieve statistical 7 significance. Changes in spleen weight and CD4+ cell numbers we re not observed in the female 8 offspring. In a separate experi ment, offspring infected with Listeria monocytogenes following 9 lactational TCDD exposure exhibite d a statistically sign ificant increase in serum tumor necrosis 10 factor alpha (TNF- α ) 2 days after infection in both sexes in the low- ( p < 0.05) and high-dose 11 ( p < 0.005) groups. There was also a statistically significant increa se in serum interferon gamma 12
in Listeria -infected high-dose females ( p < 0.05). The number of bacteria in the spleen was also 13 significantly increased ( p < 0.05) 2 days after infection in th e high-dose females compared to the 14 controls, but not in males. Listeria levels in the spleen returned to control levels by 4 days after 15
infection in both sexes. 16 Based on these results, a LOAEL for TC DD of 11.3 ng/kg-day following a 17 day 17 exposure to dams was identified for significantly ( p < 0.05) decreased spleen weight (in male 18 pups), a significant ( p < 0.005) increase in thymic CD4+ cel ls (in male pups), and for increased 19 susceptibility to Listeria monocytogenes (in male and female pups). The NOAEL for this study 20 is 1.14 ng/kg-day. 21
22
2.4.2.3. Acute Studies 23
2.4.2.3.1. Burleson et al., 1996. 24 Burleson et al. (1996) studied the impact of TCDD exposure on mice that were 25 challenged with the influenza vi rus 7 days after treatm ent with TCDD. Groups of 8-week-old 26 female B6C3F1 mice ( n = 20, 2 replicate groups) were treated one time with 0, 1, 5, 10, 50, 100, 27 or 6,000 ng/kg TCDD (purity >99%, disso lved in corn oil) via oral gavage. In addition to the 28 treated groups, randomly selected animals were assigned as a sentinel group and screened for 29 numerous pathogens. Results of all tests performe d on this sentinel group were negative. Seven 30 days after TCDD treatment, all animals were lightly anesthetized and infected intranasally with a 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-158 highly lethal influenza A/Hong Kong/8/68 virus (H3N 1; passage 14). The animals were infected 1
with sufficient H3N1 virus to achieve a 30% mortal ity rate in the control animals. Animals were 2 observed for mortality and morbid ity for 21 days following viral infection. Six mice from each 3 treatment group were sacrificed on days 3, 9, and 12 post-infection, and body, thymus, and wet 4 lung weights were recorded. Infl uenza viral titers were examin ed by sacrificing eight mice each 5 at 2 hours and at 1, 4, 6, 7, 8, 9, 10, and 11 days post infection. 6 Exposure to TCDD resulted in significantly ( p < 0.05) increased mortality in the 10, 50, 7 and 100 ng/kg dose groups. No stat istically significant difference in the percentage alive was 8 observed between these dose groups. TCDD doses of 1 and 5 ng/kg did not alter mortality in 9
influenza infected animals. A time-related increas e in the wet weights of the lungs in infected 10
mice as a result of increased edema also was re flected in an increase in the lung weight-to-body 11 weight ratio. The study authors stated that this ratio was not altered as a result of TCDD 12 exposure. TCDD-only exposures at 1, 10, or 100 ng/kg did not affect thymus weight. Similarly, 13
animals infected with the in fluenza virus following TCDD exposure also showed no loss in 14 thymic weight. Enhanced mort ality in TCDD-treated animals was not correlated with an 15
increase in influenza virus titers. Additio nally, animals treated with 1, 10, 100, or 1,000 ng/kg 16 did not affect pulmonary viral ti ter assays on days 6, 7, and 8 pos t-infection. The authors also 17 concluded that TCDD did not alter Hong K ong virus replication or clearance. 18 Although these results support immunotoxic effects induced by TCDD, the findings were 19 not reproduced by Nohara et al. (200 2) using the identical study design, and the translation of 20 these findings to humans is dubious. Thus, no LOAEL/NOAEL was established. A LOEL for 21 TCDD of 10 ng/kg for a single exposure is identified for significantly ( p < 0.05) increased 22 mortality in mice infected 7 days later with the infl uenza virus. The NOEL for this study is 23 5 ng/kg. 24
25
2.4.2.3.2. Crofton et al., 2005. 26 Crofton et al. (2005) studied the impact of TCDD exposure in addition to the im pact of 27 mixtures of thyroid disrupting chemicals and PCBs on serum total thyroxine (TT4) 28 concentration. Groups of female Long-Evans rats were dosed via oral gavage with 0, 0.1, 3, 10, 29 30, 100, 300, 1,000, 3,000, or 10,000 ng/kg-day TCDD (purity >99%) in corn oil ( n = 14, 6, 12, 30 6, 6, 6, 6, 6, 6, and 4, respectively) for 4 consecutive days. On the day following the last dose, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-159 animals were sacrificed, trunk blood was collect ed, and serum obtained via centrifugation was 1 assayed for TT4 concentration using standard radioimmunoassay methods. 2 No visible signs of toxicity or changes in animal body weight as a result of TCDD 3 exposure were observed. Serum T4 levels show ed a dose-dependent decr ease, with the levels 4 dropping sharply beginning at 100 ng/kg-day dose. Percent se rum T4 levels were 96.3, 98.6, 5 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 6 10,000 ng /kg-day groups, respectively. 7 A LOAEL for TCDD of 100 ng/kg-day for 4 consecutive days of exposure is identified in 8 this study for a reduction in serum T4 levels (70.9% compared to 100% in controls). The 9 NOAEL for this study is 30 ng/kg-day. 10
11
2.4.2.3.3. Kitchin and Woods, 1979. 12
Fema le Sprague-Dawley rats (nine per cont rol and four per treatment group) were 13 administered a single dose of 0, 0.6, 2, 4, 20, 60, 200, 600, 2,000, 5,000, or 20,000 ng/kg TCDD 14 (purity >99%) in corn oil. Animals were sacr ificed 3 days after treat ment and CYP level and 15 benzo(a)pyrene hydroxylase activity in the liver were measured. A significant ( p < 0.05) 16 increase in cytochrome P450 levels occurred with doses of 600 ng/ kg or greater and in 17 benzo(a)pyrene hydroxylase activity with doses of 2 ng/kg or greater. Cytochrome P450 was 18 significantly ( p < 0.05) higher 1 month after a single exposure of 2,000 ng/kg (the only dose 19 measured), but not after 3 or 6 months. Aryl hydrocarbon hydralase (AHH; p < 0.05) and EROD 20 ( p < 0.01) were both significantly increased th rough 3 months after treatment, and although 21 elevated at 6 months, the resu lts were not significant. 22 CYP induction alone is not considered a signi ficant toxicologically adverse effect given 23
that CYPs are induced as a mean s of hepatic processi ng of xenobiotic agents. Thus, no LOAEL 24 or NOAEL was established for this study becau se adverse endpoints (e.g., indicators of 25 hepatotoxicity) were not measured. The acute LOEL, however, is 2 ng/k g based on a significant 26 ( p < 0.05) increase in benzo(a)pyrene hydroxylase activity (37% above c ontrol). The NOEL is 27 0.6 ng/kg. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-160 2.4.2.3.4. Li et al., 1997. 1
Fema le Sprague-Dawley rats (22 days old; 10 per treatment) were administered a single 2 oral dose of TCDD (>98% pure) in corn oil via gavage at doses of 3, 10, 30, 100, 300, 1,000, 3 3,000, 10,000, or 30,000 ng/kg. Vehicle controls received equivalent amounts of corn oil, while 4 naïve controls were sham-treated only. In a preliminary time-course study, animals received a 5 single dose of 10,000 ng/kg and were sacrifi ced at 1, 2, 4, 8, 16, 24, 48, and 72 hours. The 6 time-course study showed two peaks in LH and FSH levels at 1 hour and 24 hours, with a 7 decrease to control values by 48 hours. T hus, in the dose-response study, animals were 8
sacrificed at 1 or 24 hours afte r treatment, blood was collecte d, and serum FSH and LH were 9 measured. The dose-response study demonstrated that the peak at 1 hour was related to the 10 vehicle as the peak also occurred in the vehicle controls, but did not occur in the naïve controls. 11 At 24 hours, FSH was increased at 10 ng/kg and hi gher (>4-fold increase at 10 ng/kg). Doses of 12 10 to 1,000 ng/kg showed similar increases (not all reached statis tical significance; p < 0.05). A 13 dose-dependent increase occurred for doses ≥ 3000 ( p < 0.05) with a maximum increase of 14
20-fold over the vehicle control. At 24 hours, the LH response significantly ( p < 0.05) increased 15 only for doses ≥ 300 ng/kg with a maximum increase of 15-fo ld above the vehicle control. The 16 study authors calculated an ED 50 of 500 ng/kg for gonadotropin in crease. The dose-dependent 17 release of LH was confirmed in in vitro studies, but did not occur with th e same magnitude. The 18
increase did not occur in calcium-free medium and was unrelated to gonadotropin releasing 19
hormone. 20 Based on the increase in serum FSH, the LOAEL was 10 ng/kg and the NOAEL was 21 3 ng/kg. 22
23
2.4.2.3.5. Lucier et al., 1986. 24
Adult femal e Sprague-Dawley rats (six per treatment) were administered a single gavage 25 dose of TCDD (purity not specified) in either corn oil or contaminated soil at doses of 15, 40, 26 100, 200, 500, 1,000, 2,000, 5,000 (corn oil), or 5,500 (contaminated soil) ng/kg. Animals were 27 sacrificed 6 days later an d livers were removed for analysis. No clinical signs of acute toxicity 28 or changes in body weight were observed at any dose. AHH in creased in a dose-dependent 29 manner with significant ( p < 0.05) increases observed at 15 ng/kg or greater in corn oil or 30 40 ng/kg or greater in contaminated soil. Cytochrome P450 was significantly ( p < 0.05) 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-161 increased with doses of 1,000 ng/kg or greater in co rn oil or 500 ng/kg or greater in contaminated 1 soil. A dose-dependent increase was observed fo r UDP glucoronyltransferase (significance of 2 individual doses not reported), with the resu lts twice as high with corn oil than with 3
contaminated soil. The authors state that the resu lts indicate bioavailabilit y from soils is 50%. 4
Because the association between AHH activit y and TCDD-mediated hepatotoxicity is 5 unknown and no adverse endpoints were measure d, a LOAEL or NOAEL was not determined 6 for this study. The acute LOEL for this study is 15 ng/kg, based on the significant ( p < 0.05) 7 increase (80% above control) in AHH. No NOEL is established. 8
9
2.4.2.3.6. Nohara et al., 2002. 10 Male and female B6C3F1 (C57BL/6 x C3 H), BALB/c, C57BL/6N, and DBA2 m ice 11 (10

− 40 per treatment group) were administer ed a single dose of 0, 5, 20, 100, or 500 ng/kg 12 TCDD in corn oil via gavage. Seven days follo wing TCDD treatment, mice were infected with a 13 mouse-adapted strain of influenza (A/PR/34/8; H1N1) at a plaque forming unit dose designed to 14 target approximately 30% mortality in each st rain. TCDD did not affect the body weight or 15 survival in any of the infected mouse strains at any dose. 16 Therefore, no LOAEL is established in this study. The NOAEL is 500 ng/kg. 17
18
2.4.2.3.7. Simanainen et al., 2003. 19 Simanainen et al. (2003) studi ed the short-term effects of TCDD exposure to determine 20 the efficacy and potency relationships among three di fferentially susceptible rat lines. The three 21 rat lines used were A, B, and C, which were selectively bred from TCDD-resistant Han/Wistar 22 and TCDD-sensitive Long-Evans rats. The study au thors reported that Li ne A rats were most 23 resistant to TCDD acute lethality followed by Line B and C. Groups of five or six randomly 24 selected rats (sex not specified) were treated with a single oral dose of TCDD (purity >99%) in 25 corn oil by oral gavage. The dose of TC DD was reported to range between 30 ng/kg and 26 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 27 Line C. Control animals were similarly dosed with a corn oil vehicle. Rats were sacrificed on 28 day 8 post-exposure, and trunk blood was collected and serum separated. Liver and thymus were 29 removed and weighed, and liver samples were collected and preserved. Liver EROD activity, 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-162 serum aspartate aminotransferase (ASAT) activit y, free fatty acid (FFA) concentration, and total 1
bilirubin concentration we re determined. Teeth we re also examined. 2 Relative thymus weights were reduced 25% at 300 ng/kg relative to controls in Line B 3 rats. Liver enzyme (CYP1A1) induction, as measured by EROD activity, was evident at all 4 exposure levels; CYP induction is co nsidered to be an adaptive eff ect and not adverse in itself. 5 No other endpoints were affected below 1 μg /kg in any of the three rat lines. 6 A LOAEL for TCDD of 300 ng/kg is identified for decreased relative thymus weight in 7 Line B rats. A NOAEL of 100 ng/ kg is identified for this study. 8
9
2.4.2.3.8. Simanainen et al., 2002. 10 To study the short-term effects of TCDD on hormone levels, adult fem ale Long-Evans 11 (TCDD-sensitive) and Han/Wistar (TCDD-resistant) rats ( n = 9

− 11/treatment) were administered 12 a single dose of TCDD (>99% pure) in corn oil via gavage at doses ranging from 30 ng/kg to 13 100 μg/kg. Vehicle controls received an equi valent amount of corn oil. The study also 14 examined other polychlorinated dibenzo-p-dioxins outcomes. Ra ts were sacrificed on day 8 15 post-exposure, and trunk blood was collected a nd serum separated. Liver and thymus were 16 removed and weighed, and liver samples were collected and preserved. Liver EROD activity, 17 serum ASAT activity, FFA concentr ation, and total bilirubin con centration were determined. 18
Teeth were also examined. 19
Neither FFA or ASAT levels in Han/Wistar ra ts showed a dose-response relationship. In 20 Long-Evans rats, however, a significant ( p < 0.05) dose-dependent increase in FFA occurred at 21 300 ng/kg TCDD. Serum ASAT sh arply increased in Long-Evans rats between 3,000 and 22 10,000 ng/kg. Body weight change and relative thym us weights were significantly decreased 23 ( p < 0.05) in Han/Wistar rats with doses ≥ 10,000 ng/kg and in Long-Evans rats with doses 24
≥ 1,000 ng/kg. Liver EROD activ ity was significantly ( p < 0.05) increased with all doses in both 25 strains. Serum T4 was significantly ( p < 0.05) decreased in Long-Evan s rats at concentrations 26
≥ 300 ng/kg, but were not significantly affected in Han/Wistar rats. Serum bilirubin was 27 significantly ( p < 0.05) increased with doses ≥ 10,000 ng/kg in Long-Evans rats and 28
≥ 30,000 ng/kg in Hans/Wistar rats. Both strains of rat showed a dose-dependent increase in 29 mean severity of incisor tooth defects. The results indicate that TCDD was the most potent 30 congener tested in both rat strains. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-163 A LOAEL of 300 ng/kg for decreased T4 in th e Long-Evans rat is identified for this 1 study. A NOAEL of 100 ng/kg is is established. 2
3
2.4.2.3.9. Smialowicz et al., 2004. 4 Smialowicz et al. (2004) examined the im pact of TCDD exposure on immunosuppression 5
in m ice. Groups of female (number not spec ified) C57BL/6N CYP1A2 (+/+) wild-type mice 6 were administered a single dose of 0, 30, 100, 300, 1,000, 3,000, or 10,000 ng/kg TCDD (purity 7 >99%) in corn oil via oral gavage . Control animals were similarly dosed with a corn oil vehicle. 8 To assess immune function, 7 days after TCDD administration, all mice were immunized with 9 sheep red blood cells (SRBCs) via injection into the lateral tail vein. Five days after 10 immunization, mice were sacrificed, blood was collected, and enzyme-linked immunosorbant 11 assays were performed. Additiona lly, spleen, thymus, and liver we ights also were measured. 12 Body and spleen weights of the wild-type mice were unaffected by the TCDD exposure. 13 A decrease in thymus weights of the mice appeared to be dose related. Only mice treated with 14 10,000 ng/kg TCDD, however, showed a statistically significant ( p < 0.05) decrease in thymus 15 weights compared to corresponding controls. Live r weights also showed a dose-related increase 16 with only animals treated with 3,000 and 10,000 ng/ kg TCDD showing statistical significance 17 ( p < 0.05) compared to the control group. The antibody response to SRBCs indicated a 18 dose-related suppression in the wild-type mice, with animals treated with 1,000, 3,000, and 19 10,000 ng/kg TCDD showing statistically significant ( p < 0.05) suppression compared to the 20
controls. 21 A LOAEL for TCDD of 1,000 ng/kg is identified in female C57BL/6N CYP1A2 (+/+) 22 wild-type mice for significant ( p < 0.05) suppression of SRBCs. The NOAEL for this study is 23 300 ng/kg. 24
25
2.4.2.3.10. Vanden Heuvel et al., 1994. 26 Vanden Heuvel et al. (1994) examined th e dose-response relationship between TCDD 27 exposure and induction of hepatic mRNA. Groups of 10-week-old fema le Sprague-Dawley rats 28 were administered TCDD (purity ~99%) in corn oil once at 0, 0.1, 0.05, 1, 10, 100, 1,000, or 29 10,000 ng/kg-BW. Four days after TCDD treatment, animals were sacrificed and livers were 30 excised and preserved. Total hepatic RNA was extracted usi ng guanidine thiocyanate and DNA 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-164 was removed using standard phenol-chloroform- isoamyl alcohol partitioning procedures. 1 Quantitative competitive RNA-PCR met hod was used to analyze CYP1A1, 2 UDP-glucuronosyltransferase I (UGT1), pl asminogen activator i nhibitor 2 (PAI2), β -actin, and 3 transforming growth factor α (TGF α ). In addition to hepatic mR NA levels, microsomal protein 4 was assayed for EROD activity and livers were tested for TCDD concentration. 5
CYP1A1 mRNA induction levels in the TCDD-tr eated groups were low in the low-dose 6 region and sharply increased to plateaus at higher doses. Th e lowest dose that showed a 7 statistically significant ( p < 0.05) difference compared to co ntrols was the 1 ng/kg dose, which 8
showed a three-fold increase in CYP1A1 mR NA levels. In contrast, a 130-fold increase 9 occurred at 100 ng/kg and a 4,000- and 7,000-fold increase occurred at 1,000 and 10,000 ng/kg, 10 respectively. A slight increase in the CYP1A1/ β -actin levels was observed in the 0.1 ng/kg 11 group, but this increase was not significant. EROD activity exhibited a pattern similar to 12 CYP1A1 activity. EROD activity, however, was approximately 100-fold less sensitive 13 compared to mRNA levels in TCDD-trea ted groups. Statistical significance ( p -value not 14 provided) in CYP1A1 level was observed at the 100 ng/kg dose compared to the 1 ng/kg dose. 15 The study authors reported that, despite this difference in CYP1A1 and EROD activity, the 16 correlation between CYP1A1 enzyme activity and mRNA levels was good. Dose-response 17 relationships for the induc tion of UGT1, PAI2, and TGF α mRNA differed from what had been 18
observed for CYP1A1 mRNA. UGT1 mRNA was induced, but at the much higher dose of 19 1,000 ng/kg. Additionally, the five-fold maximu m induction of UGT1 mRNA was much less 20 than the 7,000-fold induction observed for CYP1A1 mRNA at the 10,000 ng /kg dose. The 21 authors state that this could be a result of the constitutive level of UGT1, which is much higher 22 than CYP1A1, which makes detecting induction of UGT1 in the low dose regions more difficult. 23
PAI2 and TGF α mRNA were not affected by TCDD in rat liver in the dose range tested. These 24
results indicate that dioxin-i nducible genes have a quite dissi milar dose-response relationship. 25 Induction of CYP1A1 expression is not consid ered an adverse effect, as the role of 26 CYP1A1 in TCDD-mediated hepatotoxicity is un settled. Therefore, in the absence of other 27 indicators of hepatotoxicity, a NOAEL/LOAEL ca nnot be determined for this study. A LOEL 28 for TCDD of 1 ng/kg for a single exposure wa s identified for statistically significant ( p < 0.05) 29
increase in CYP1A1 mRNA levels. The NOEL for this study is 0.1 ng/kg. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-165 2.4.2.4. Subchronic Studies 1
2.4.2.4.1. Chu et al., 2001. 2
Adult femal e Sprague-Dawley rats (five pe r treatment group) were administered TCDD 3 (purity >99%) in corn oil by gavage at dos es of 0, 2.5, 25, 250, or 1,000 ng/kg-day for 28 days 4 (Chu et al., 2001). The 1,000 ng/kg-day dos e of TCDD caused a significant ( p ≤ 0.05) decrease 5 in body weight gain (36% lower than the control) , increase in relative liver weight (40% greater 6 than the control), and decrease in relative thymus weight (50% lower than the control). There 7 was a significant ( p ≤ 0.05) increase in EROD activity, me thoxy resoufin-O-deethylase (MROD) 8 activity, and UDP-glucuronosyl transferase (UDP GT) activity in the liver of female rats 9 receiving 250 or 1,000 ng/kg-day TCDD. In addition, significant ( p ≤ 0.05) increases in serum 10 cholesterol were observed in the 250 and 1,000 ng/ kg-day dose groups, and liver ascorbic acid 11 (AA) also was significantly increased in the 1,000 ng/kg-day dose group. There was ~1.5-fold 12 increase in liver glutathione- S -transferase (GST), which was not statistically significant. Other 13 significant ( p ≤ 0.05) findings for the 1,000 ng/kg-day group included a decrease in liver 14 vitamin A (51% lower than the control), an in crease in kidney vitamin A (15.5-fold increase 15 above the control), an increase in liver benz yloxy resoufin-O-deethylase (BROD, 30-fold 16 increase above control), a decrease in liver pentoxyresoufin-O-deethyl ase (PROD, 37% lower 17 than the control), increase in serum albumin (18% above the control), and a decrease in mean 18 corpuscular hemoglobin (MCH, 7% below the cont rol) and mean corpuscular volume (MCV, 7% 19 below the control). 20 Based on the numerous significant ( p ≤ 0.05) liver-related biochemical changes and 21 significant ( p ≤ 0.05) increased relative liver weight, as well as significantly decreased body 22 weight and relative thymus weight, the LOAEL for 28 days of exposure in this study is 23 1,000 ng/kg-day and the NOAEL is 250 ng/kg-day. 24
25
2.4.2.4.2. Chu et al., 2007. 26 Chu et al. (2007) examined the potential impact of TCDD on various organs and the 27
toxico logical impacts as a result of interactions between TCDD and PCBs in rats. Groups of 28 female Sprague-Dawley rats ( n = 5 per treatment group) were treated daily for 28 days via 29 gavage with 0, 2.5, 25, 250, or 1,000 ng /kg-day TCDD ( purity not specified) dissolved in corn 30 oil. Body weights were determined three times per week, and clinical observations were made 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-166 daily. At study termination, all animals were sacrificed and blood was analyzed for various 1 biochemical and hematological parameters. Live r, spleen, heart, thymus, brain, and kidneys 2 were removed and weighed. A small portion of the liver was homogenized and assayed for 3 BROD; EROD; MROD; and PROD. UDPGT, GST, and ascorbic acid levels also were 4 measured. Vitamin A levels in the liver, ki dney, and lungs were analyzed as free retinol 5 (vitamin A), and histopat hological analysis was conduc ted on various tissues. 6 Growth rate and thymic weights in rats treated with 1,000 ng/kg-day TCDD were 7 significantly ( p ≤ 0.05) inhibited compared to the control group. Enzyme analysis indicated that 8 measured levels of TCDD in the liver correlated with hepatic microsomal enzyme activity. The 9 authors reported that liver microsomal EROD and MROD activities were significantly ( p < 0.05 10 for EROD activity, significance level for MROD not reported) increased in the 250 and 11 1,000 ng/kg-day TCDD dose groups compared to the control group. UDPGT levels were 12 significantly (significance level not reporte d) increased in the 250 and 1,000 ng/kg-day TCDD 13 dose groups compared to the controls. Se rum albumin levels were significantly ( p < 0.05) 14 increased in the 1,000 ng/kg-day TCDD dose group compared to the control group. Serum 15 cholesterol levels were significan tly (level not reported) increased compared to the control group 16 at 250 ng/kg-day TCDD dose, while liver ascorbic acid concentrati ons were significantly (level 17 not reported) increased in the 1,000 ng/kg-day dose group. Hematologi cal analysis indicated that 18 hemoglobin, packed cell volume, MCH, MCV, an d platelet values were decreased in the 19 1,000 ng/kg-day TCDD dose group. Significant ( p ≤ 0.05) differences were observed only in 20 MCH and MCV levels compared to the control. Vitamin A levels in the liver and kidney were 21 significantly ( p < 0.05) lower in the 1,000 ng/kg-day TC DD group compared to the control 22 group. Histopathological evaluation of various tissues indicated th at liver, thyroid, and thymus 23 were the target organs. No TCDD-related affect s were found in other tissues. A dose-dependent 24 alteration in the thymus consisted of reduced th ymic cortex and increased medullar volume with 25 more animals exhibiting these changes at the 250 and 1,000 ng/kg-day dose level compared to 26 the control group. Alterations in thyroid included reduced follicles, reduced colloid density, and 27 increased ep ithelial height. A dose-dependent change in the thyroid was observed, with the 28 highest impact evident in reduced follicles a nd reduced colloid density beginning at a dose of 29 25 ng/kg-day TCDD. Changes in liver were characterized by accentuated hepatic zones, 30 anisokaryosis of hepatocytes, increased cytopl asmic density, and vacuolation. These changes 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-167 were also dose dependent, with more animals exhibiting these histopathological changes with 1 increasing TCDD dose. Based on these results, th e study authors concluded that exposure to 2 TCDD resulted in a wide range of adverse effects with the thyroid proving to be most sensitive. 3 A LOAEL for TCDD of 25 ng/kg for a 28-day e xposure is identified for alterations in 4 thyroid, thymus, and liver histopathology. The NOAEL for this study is 2.5 ng/kg-day. 5
6
2.4.2.4.3. DeCaprio et al., 1986. 7 Hartley guinea pigs (10 per se x per dose) were administered TCDD (purity not specified) 8 in the diet for 90 days at concentrations of 0, 2, 10, 76, or 430 ppt (equivalent to 0, 0.12, 0.61, 9 4.9, and 26 ng/kg-day in m ales and 0, 0.12, 0.68, 4.86, and 31 ng/kg-day in females calculated by 10 the study authors using food consumption and body weights). Other animal s were administered 11 the high-dose diet (i.e., 430 ppt) for 11, 21, or 35 days and then administered the control diet 12 (i.e., no exposure) for the remainder of the 90 days for recovery analysis. Four high-dose males 13 died and two were sacrificed moribund by day 45; th e remaining four animals were sacrificed on 14 day 46 for necropsy. Four high-dose females also died and two were sacrificed moribund by day 15 55 with the remaining females sacrificed on da y 60 for necropsy. Animals in the 76- and 16 430-ppt groups had significantly ( p < 0.05) reduced body weights. Organ weights were not 17 obtained in the 430-ppt group due to the early sa crifice, but in the 76-ppt group a significant 18 decrease in relative thymus weight ( p < 0.05) was observed, and relative liver ( p < 0.01) and 19 brain ( p < 0.05) weights in males increased. Alth ough a similar trend occurred in the females, 20 the results were not statistically significant. Ma les administered 76 ppt in the diet also had a 21 53% increase in triglycerides ( p < 0.05). The same increase was observed in females, but was 22 not statistically significant. In the recovery groups, mortality during the recovery period after 11 23 or 21 days of treatment was 10% and after 35 days of treatment was 70%. Animals lost weight 24 during the treatment period. Although the body wei ght increased during the recovery period, the 25 body weight remained low compared to the control for the study duration. 26 The LOAEL from this study is 4.9 ng/kg-day for 90 days of exposure, based on 27 decreased body weight (12

− 15%; p < 0.05) and changes in organ weights (10

− 30%, significant 28 only in the males). The NOAEL is 0.61 ng/kg-day. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-168 2.4.2.4.4. Devito et al., 1994. 1
Fema le B6C3F1 mice (5 per treatment) were administered 0, 1.5, 4.5, 15, 45, or 2 150 ng/kg TCDD (98% pure) in corn oil via gavage, 5 days a week for 13 weeks. This dose is 3 equivalent to 0, 1.07, 3.21, 10.7, 32.1, 107 ng/kg-day (adjusted for continuous exposure, 4 administered dose multiplied by 5 and divided by 7). Body weight was recorded weekly and 5 animals were sacrificed 3 days after the last treatment. Exam inations were performed on the 6 lung, skin, uterus, and liver. No differences were observed in the li ver or uterus weights or in the 7 estrogen receptor levels in th ese two tissues. A dose-dependen t increase in EROD activity (an 8 indicator of CYP1A1 [CYP] induc tion) in the lung, skin, and liver was observed, with significant 9 ( p < 0.05) increases even at the lowest dose. The TCDD doses used did not achieve maximal 10 EROD induction. A significant ( p < 0.05) increase in liver ac etanilide-4-hydroxylase (ACOH; 11
an indicator of CYP1A2 inducti on) also was observed with all doses. A maximum induction of 12 ACOH occurred with doses of 3.21 ng/kg-day an d greater. A dose-dependent increase in 13 specific phosphotyrosyl pr otein (pp) levels also was obser ved. Levels of pp34 and pp38 were 14 significantly ( p < 0.05) increased even at the lowest dose, while pp32 reached statistical 15 significance ( p < 0.05) with doses of 4.5 ng/kg-day and above. 16 The role of CYPs and phosphorylated pp32, pp34, and pp38 in TCDD-mediated toxicity 17 is unknown, and changes in the activ ity or function of these protei ns are not considered adverse 18 Therefore, no LOAEL or NOAEL is establis hed. The 13-week LOEL is 1.07 ng/kg-day, based 19 on a significant ( p < 0.05) increase in EROD, ACOH, pp34, and pp38 levels (all increased by at 20 least 2-fold). No NOEL is established for this study. 21
22
2.4.2.4.5. Fattore et al., 2000. 23 Fattore et al. (2000) examined TCDD-induced reduction of hepatic vitami n A levels in a 24 subchronic rat bioassay on Sprague-Dawley ra ts. Four experiments were conducted; 25 Experiments 1, 2, and 3 were conducted in both ma le and female rats, while Experiment 4 was 26 conducted only in female rats. The dosing regimens for each experiment were as follows 27
28 Experiment 1 : Groups of six Iva:SIV 50 rats (male a nd female) were maintained on a diet 29 consisting of 0, 200, 2,000, or 20,000 ng TCDD/kg diet and 3- μ g vitamin A/kg diet for 30 13 weeks. Assuming food consumption of 10% of body weight per day, the average daily 31 doses are 0, 20, 200, and 2,000 ng/kg-day TCDD. 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-169 Experiment 2 : Groups of six male and female rats were treated with 0 or 200 ng 1 TCDD/kg-day and 3 μ g vitamin A/kg diet for 13 weeks. 2
Experiment 3 : Groups of six male and female rats were fed 0, 200, or 1,000 ng 3 TCDD/kg-day and 3 μ g vitamin A/kg diet for 13 weeks. 4
Experiment 4 : Groups of female rats (number not specified; IVA;SIV 50 Sprague-Dawley 5 strain) were treated with TCDD for 26 and 39 weeks in addition to a 13-week dietary 6 treatment with 0 or 100 ng TCDD/kg-day and 3 μ g vitamin A/kg diet for 13 weeks. 7 8 For a 13-week exposure duration employed in all four experiments, male and female rats 9 were treated at 0, 20, 100 (females only), 200, 1,000, or 2,000 ng/kg-day. In all four 10 experiments, liver from control and treated an imals was analyzed at termination for free 11 retinol content to determine hepatic vitamin A levels. 12 13
Results : 14 Experiment 1 : Liver and body weights in both treated males and females were significantly 15 affected at all but the lowest dos e tested (20 ng/kg-day). Liver injury was severe, particularly 16 in female rats treated with 2,000 ng TCDD/kg-day. Dietary intake of vita min A in male rats 17 was comparable to intake in controls, ex cept in the 2,000 ng/kg-day group, which showed a 18 reduction of 16% in the dietary intake of v itamin A compared to controls. There was no 19 effect of TCDD on vitamin A intake in female rats. Hepatic vitamin A levels showed a 20 dose-dependent reduction with levels dropping sharply in the 200 and 2,000 ng/kg-day dose 21 groups, particularly in treated females. The reduction was significant at 200 ng/kg-day 22 ( p < 0.05) and 2,000 ng/kg-day ( p < 0.01) in males, and at 200 ng/kg-day ( p < 0.5) and 23 2,000 ng/kg-day ( p < 0.001) in females. The reducti ons ranged from 68–99% in males and 24 72–99% in females when compared to corresponding controls. 25
Experiment 2 : Changes in liver and body weights we re not reported. Hepatic vitamin A 26 level in males and females were reduced by 70% and 99%, respectively, compared to 27 controls, in rats receiving 20 ng/kg- day (significance level in females: p < 0.01). 28
Experiment 3 : Similar to the resu lts of Experime nts 1 and 2, a dose-related trend of 29 significantly ( p < 0.001) reduced hepatic vitamin A le vel was observed in both males and 30 females, with males exhibiting a partic ularly sharp drop at the 1,000 ng/kg-day dose 31 compared to controls. 32
Experiment 4 : Females treated with 100 ng/kg-day show ed significant reductions in hepatic 33 vitamin A levels ( p < 0.05–0.001) at all three treatment durations (13, 26, and 39 weeks). 34 35 A LOAEL for TCDD of 20 ng/kg-day for a 13-week subchronic exposure was identified 36 in this study for decreased hepatic vitamin A levels (27 and 24 % lower than the corresponding 37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-170 control in female and male rats, respectively). This LOAEL is determined using data from 1 Experiment 1. A NOAEL was not identified in this study. 2
3
2.4.2.4.6. Fox et al., 1993. 4 Sprague-Dawley rats (6 per sex per dose) were gavaged with TCDD (purity not 5 specified) in corn oil using a dos e-loading regime to achieve and maintain steady-sta te levels of 6 0.03, 30, or 150 ng/g in the liver. The regime cons isted of an initial lo ading dose of 5, 2,500, or 7 12,000 ng/kg followed every 4 days with a maintenance dose of 0.9, 600, or 3,500 ng/kg. 8 Averaging the doses over the 14 days pr ovides average daily doses of 0.55, 307, and 9 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 10 + 0.9 + 0.9/14 = 0.55 ng/kg-day). Body weight, liver weight, and liver gene expression were 11 measured at 7 and 14 days. A significant ( p < 0.05) decrease in body weight occurred in 12 high-dose males (at 14 weeks only) and female s (at 7 and 14 days). A significant ( p < 0.05) 13 increase in absolute and relative liver weight s was observed in mid- and high-dose males and 14 females at both 7 and 14 days. Although the liver of treated animals indicated moderate 15 vacuolization and swelling, there wa s no indication of necrosis. An increase in gene expression 16 (clone 1, CYP1A1, CYP1A2, and albumin) was obs erved in the mid- and high-dose groups. A 17 significant ( p < 0.05) decrease in labeling i ndex (indication of cell proliferation) occurred in both 18 females (all doses) and males (high-dose only) during week 1, but not during week 2. 19 The 14-day LOAEL is 307 ng/kg-day for significant ( p < 0.05) increases in absolute and 20 relative liver weights (25

─ 34%). The NOAEL is 0.55 ng/kg-day. 21
22 2.4.2.4.7. Hassoun et al., 1998. 23
Female B6C3F1 mi ce (number not specified) received TCDD (>98% pure) in corn oil 24 5 days per week for 13 weeks via gavage at doses of 0, 0.45, 1.5, 15, or 150 ng/kg (equivalent to 25 0, 0.321, 1.07, 10.7, and 107 ng/kg-day adjusted for c ontinuous exposure; administered dose 26 multiplied by 5 and divided by 7). Three days after the final dose, animals were sacrificed and 27
brains were removed for oxida tive stress testing. Biomarke rs for oxidative stress included 28 production of superoxide anion, li pid peroxidation, and DNA single- strand breaks. A significant 29 ( p < 0.05) increase was observed insuperoxide anion production, lip id peroxidation as measured 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-171 by thiobarbituric acid-reactive substances (T BARS), and DNA single-strand breaks with all 1
doses tested. 2 No other indicators of brain pathology were assessed, and it is unfeasible to link the 3 markers of oxidative stress to a TCDD-induced toxicological outcome in the brain. Thus, no 4
LOAEL/NOAEL was established. The subchron ic (13-week) LOEL is 0.32 ng/kg-day, based on 5 significant ( p < 0.05) increases in supe roxide anion production ( 80% above control); lipid 6 peroxide production (25% above the control); and DNA single-st rand breaks (2-fold over the 7 control). No NOEL is established. 8
9
2.4.2.4.8. Hassoun et al., 2000. 10 Hassoun et al. (2000) examined the effect of subchronic TCDD exposure on oxidative 11 stress in hepatic and brain tissu es. Groups of 8-week-old female Harlan Sprague-Daw ley rats 12 (6 rats/group) were administered TCDD (98% purit y, dissolved in 1% acetone in corn oil) via 13 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, 14 or 71.4 ng/kg-day adjusted for continuous exposure; administered doses were multiplied by 5 15 and divided by 7 days/week). Animals were sacr ificed at the end of the study period, and brain 16
and liver tissues were collected and used to determine the production of reactive oxygen species, 17 lipid peroxidation, and DNA si ngle-strand breaks (SSBs). 18 A dose-dependent effect was obs erved in both the liver and br ain tissue as a result of 19
TCDD treatment. Based on the maximal induction of superoxide anion by various doses, more 20 production of superoxide anion was observed in the liver tissue when compared to the brain 21
tissue with an observed increase of 3.1- and 2.2-fo ld respectively, when compared to the control 22 group. A similar dose-dependent effect was obser ved in the induction of lipid peroxidation in 23 TCDD-treated animals with an approximately 1.8- fold increase in lipid peroxidation in both 24 tissues relative to the corresponding controls. A dose-dependent relationship was also observed 25 for DNA SSBs in both the hepatic and brain tissu es at all TCDD-treated doses compared to 26 controls. Increases were statistically significant ( p ≤ 0.05) beginning at the lowest administered 27
dose. 28 Similar to the statement above, because no adverse endpoints were measured, no 29 LOAEL/NOAEL was established. However, a LOEL for TCDD of 2.14 ng/kg-day for a 30 13-week exposure duration was identified in this study for significant increases ( p ≤ 0.05) in 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-172 superoxide anion, lipid peroxi dation, and DNA SSBs in the liver and brain tissues. A NOEL 1 cannot be determined for this study. 2
3
2.4.2.4.9. Hassoun et al., 2003. 4 Hassoun et al. (2003) examined the role of antioxidant enzyme s in TCDD-induced 5 oxidative stress in various regi ons of the rat brain after s ubchronic exposure. Groups of 6 8-week-old female Harlan Sprague-Dawley rats (12 rats/group) were administered TCDD (98% 7 purity, dissolved in 1% acetone in corn oil) via gavage at 0, 10, 22, or 46 ng/kg-day (0, 7.14, 8 15.7, or 32.9 ng/kg-day adjusted for continuous expos ure; administered doses were multiplied by 9 5 and divided by 7) daily for 13 weeks. Animals were sacrificed at the end of the study period 10 and the brain was immediately removed and dissect ed to the following regions: cerebral cortex 11 (Cc), hippocampus (H), cerebellu m (C), and brain stem including midbrain, pons, and medulla. 12 Four pooled samples from each region per dose (i.e ., 3 animals/pooled sample) were used in the 13 study. Dissected regions were s ubsequently assayed for lipid pe roxidation (thiobarbituric acid 14 reactive substances, or TBARS), superoxide dism utase, catalase, and gl utathione peroxidase. 15 Because the cytochrome c reduction method was used to determine superoxide anion (SA) 16 production in brain tissues, supero xide dismutase (SOD) was added to some of the brain tissue 17 samples that had the highest SA production (tissu e homogenates from Cc and H from rats treated 18 with 46 ng/kg-day TCDD). 19 A dose-dependent increase in the production of SA was observed in the Cc and H, but 20 significant changes in SA production were not observ ed in either the C or the mid-brain, pons, or 21 medulla brain stem cells. Similar to SA produc tion, there was a dose-dependent increase in the 22 production of TBARS in the Cc and H regions of the brain, but no significant changes were 23
observed in either the C or the B sections of the brain. The study authors also measured the 24
activities of various en zymes as a result of TCDD treatm ent and reported a dose-dependent 25 increase in SOD activity in the C and B secti ons, while there was dose-dependent suppression in 26 SOD activity in Cc and H. In contra st, catalase activity was significantly ( p < 0.05) increased in 27 H and Cc at the 10 ng/kg-day TCDD dose level comp ared to controls and the mid- and high-dose 28 animals. Catalase activity also was increased in a dose-dependent manner in the C section, but 29 no significant changes in the activity of this enzy me were observed in the B section at any of the 30
three TCDD tested doses. The effects of subc hronic exposure to diffe rent doses of TCDD on 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-173 glutathione stimulating hormone peroxidase (GSH-Px) showed a different response compared to 1 other enzymes. There was a dose-dependent increase in the activity of this enzyme in the C and 2 B regions of the brain, while a si gnificant increase in the activity of GSH-Px occurred in Cc and 3 H only at the 10 ng/kg-day TCDD dose. In additi on, the activity of this enzyme was suppressed 4 in a dose-dependent manner in the Cc and H at 22 and 46 ng/kg-day TCDD doses. Based on 5 these results, the study authors concluded that induction of oxidative st ress by TCDD in the rat 6 brain occurs mainly in the Cc and H regions. 7 Similar to the statement above, because no adverse endpoints were measured, no 8 LOAEL/NOAEL was established. However, a LOEL for TCDD of 7.14 ng/kg-day for a 9 13-week exposure duration was iden tified for this study for increas es in superoxide anion and 10 lipid peroxidation production, as well as incr eased activity in SOD, catalase, and GSH-Px. 11
12
2.4.2.4.10. Kociba et al., 1976. 13 Adult Sprague-Dawley rats (12 per sex per treatment group) were admi nistered TCDD 14 (purity not reported) in corn oil via gavage 5 days per week at doses of 0, 1, 10, 100, or 15 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 16 dose). Five animals per group were sacrificed at the end of trea tment, and the remaining animals 17 were observed over 13 weeks post treatment (only initial results for the post-treatment period 18 were provided in the report). Body weights a nd food consumption were measured semi-weekly. 19 Hematology and clinical chemistry were measured after 36

− 37 or 85

− 86 days of treatment and 20
59

− 60 days after termination of tr eatment. Forty-eight hour urine samples were collected from 21
select rats from 85

− 89 days of treatment and 52

− 56 days after cessation of treatment. Gross and 22 histopathological exams we re conducted on the tissues. 23 Four high-dose females died during tr eatment. Two high-dose females and two 24 high-dose males died during the post-treatment period. Animals treated with 714 ng/kg-day 25 were less active during the treatment pe riod, which became less evident during the 26 post-treatment period. Yellow discoloration of th e external pinnae also was noted in this group, 27 both during treatment and during the pos t-treatment period. A significant ( p < 0.05) reduction in 28 body weight and food consumption was observed in the 71.4 and 714 ng/kg-day groups. The 29 following significant ( p < 0.05) hematology changes were observed in the high-dose 30 (714 ng/kg-day) males at all measured time points: decreased packed cell volume, decreased red 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-174 blood cells, decreased hemoglobin, increased reticulocytes, a nd decreased thrombocytes. 1 Significant ( p < 0.05) changes also occurred in the hi gh-dose females, but the only consistent 2 observation was a decrease in thrombocytes and increased leukocytes. Significant changes in 3 clinical chemistry ( p < 0.05) and urinalysis ( p < 0.05) were more consiste nt between th e sexes in 4 the high-dose group and included increases in total and direct serum bilirubin; increase in serum 5 alkaline phosphatase; decreased urinary creatinine; and in creased urinary coproporphyrin, 6 uroporphyrin, and delta-amino-levulinic . The following significant ( p < 0.05) changes were 7 observed in the 71.4 ng/kg-day group: decreased packed cell volume (4

− 9%) in males; decreased 8 red blood cells (2

− 10%) in males; decreased hemoglobin (2

− 13%) in males; increased urinary 9 coproporyphin (2.2-fold increase during trea tment) in females; increased urinary 10 delta-amino-levulinic (47% increase during treatmen t) in females; increased total and direct 11 serum bilirubin (48

− 61%) in females; and increased seru m alkaline phosphatase (2-fold) in 12 females. The following significant ( p < 0.05) changes in relative or gan weights were observed 13 increased brain weight in 714 ng/kg-day males a nd females; increased li ver weight in males 14 (71.4 and 714 ng/kg-day) and females (7.14, 71.4, and 714 ng/kg-day); increased spleen weight 15 in 714-ng/kg-day males and females; decreased thymus weight in 71.4 and 714 ng/kg males and 16 females; and increased testes weight in 714 ng/kg-day males. Microscopic changes were 17 observed in the thymus, and in other lymphoid ti ssues, and in the liver in rats treated with 18 71.4 ng/kg-day or greater. 19 The subchronic (13-week) LOAEL is 71.4 ng/kg-day, based on the numerous changes 20 noted in body weight, hematology, clinical ch emistry, urinalysis, and histopathology. The 21 NOAEL is 7.14 ng/kg-day. 22
23
2.4.2.4.11. Mally and Chipman, 2002. 24
Fema le F344 rats (3 per treatment group) were administered TCDD at concentrations of 25 0, 2.5, 25, or 250 ng/kg in corn oil via gavage for e ither 3 consecutive days or 2 days per week 26 for 28 days (Mally and Chipman, 2002). The av erage daily doses for the 28-day study when 27 adjusted for 7 days a week were 0, 0.71, 7.1, and 71 ng/kg-day (i.e., 2/7 of administered dose). 28 No clinical signs of toxicity were observed. Histological examination of the liver revealed no 29 abnormalities. All doses of TCDD reduced the number of connexin (Cx) 32 plaques and Cx32 30 plaque area in the liver, which was considered the target tissue. Th e reductions were not 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-175 statistically significant after the 3-day treatment, but were significant after the 28-day treatment 1 ( p < 0.05). TCDD also caused a reduction in th e Cx32 plaque number and area in the thyroid 2 after 28 days, but the results we re not statistic ally significant. Alt hough the reduction in Cx32 3 plaque number and plaque area in the liver and thyroid occurred at all dose levels, there was no 4
relation to dose. TCDD did not i nduce hepatocyte proliferation. 5
In the absence of additional indicators of he patotoxicity, changes in Cx32 plaques are not 6 clearly linked to TCDD-mediated hepatotoxicity, nor are they considered an adverse effect. 7 Additionally, no toxicologically-relevant endpoints were examined. Therefore, a NOAEL or 8 LOAEL cannot be determined. A 28-day LO EL at the lowest dose of 0.71 ng/kg-day for 9 significantly ( p < 0.05) decreased Cx32 plaque area is evident (approximately 70% of the 10 controls). 11
12
2.4.2.4.12. Slezak et al., 2000. 13 Slezak et al. (2000) studied the impact of subchronic TCDD exposure on oxidative stress 14 in various organs of B6C3F1 female mice. Grou ps of 8- to 10-week-old female B6C3F1 mice 15 (number not specified) were admini stered TCDD (purity >98%, dissolv ed in corn oil) via gavage 16 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 17 for continuous exposure) 5 days per week for 13 w eeks. Three days after the last treatment, the 18 animals were sacrificed and organs were re moved for the measurement of oxidative stress 19 indicators including SA, lipid peroxida tion (TBARS), and GSH-Px. Tissue TCDD 20
concentrations also were measured. 21 The study authors reported that TCDD dose range resulted in overlapping tissue 22 concentrations for liver, lung, kidney and splee n. Liver had the highe st TCDD concentration, 23 with each tissue demonstrating a dose-dependent increase in TCDD concentration. Compared to 24 controls, SA production was significantly ( p < 0.05) lower at the 0.15 ng/kg-day TCDD dose, 25 while it was significantly ( p < 0.05) higher at 15 and 150 ng/kg-day. A dose-dependent increase 26 in hepatic TBARS production was observed, a lthough the rate of production was significant 27 ( p < 0.05) only at the highest TC DD administered dose (150 ng/kg-da y) compared to controls. 28 AA also followed the same pattern observe d for SA and TBARS with AA production 29 significantly ( p < 0.05) increased at the 15 and 150 ng/kg- day TCDD doses. Contrary to the SA, 30 TBARS, and AA responses, GSH levels were de creased at 0.15 ng/kg-day, were increased at 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-176 0.45 and 150 ng/kg-day, and did not ch ange at 1.5 or 15 ng/kg-day when compared to the control 1 group. Unlike the liver, there was no significant increase in SA production in the lung at any of 2 the TCDD tested doses; a dose dependent re duction, however, was observed at 0.45, 15, and 3 150 ng/kg-day compared to controls. GSH and AA production was decreased at 0.15 ng/kg-day, 4 while AA production was significantly ( p < 0.05) increased at 15 and 150 ng/kg-day. Kidney 5 SA production showed a st atistically significant ( p < 0.05) increase onl y at the 15 and 6 150 ng/kg-day doses. GSH, like the liver and the lung, exhibited a decrease in production 7 following treatment at 0.15 ng/kg-day with this trend continuing at 0.45 and 1.5 ng/kg-day. AA 8 levels were significantly ( p < 0.05) lower at all subchronic dose s, except at 1.5 ng/kg-day dose. 9 SA levels in the spleen differe d little from the control group at any of the TCDD doses. Total 10 GSH was higher only at the 150 ng/kg-day dose level, while the AA levels were significantly 11 ( p < 0.05) decreased at 0.15, 1.5, and 150 ng/kg-day. 12 Similar to the statements regarding the Hassoun et al. studies above, because no adverse 13 endpoints were measured, no LOAEL/NOAEL wa s established. Therefore, a NOAEL or 14 LOAEL cannot be determined. However, a NOEL and LOEL of 1.07 and 10.7 ng/kg-day, 15 respectively, are identified in this study for in creases in superoxide anion in the liver. 16
17
2.4.2.4.13. Smialowicz et al., 2008. 18
Fema le B6C3F1 mice (8–15 per treatment group) were administered TCDD (purity 19 >98%) in corn oil by gavage at doses of 0, 1.5, 15, 150, or 450 ng/kg-day, 5 days a week for 20 13 weeks (1.07, 10.7, 107, or 321 ng/kg-day, adjusted for continuous exposure; i.e., 5/7 of the 21 dose) (Smialowicz et al., 2008). Mice were i mmunized 3 days after the final TCDD exposure 22 with an intravenous injection of an optimal concentration of 4 × 10 7 SRBCs and sacrificed 4 days 23 later. No TCDD-related effects on body weight were observed. There was a dose-related 24 decrease in relative spleen wei ght (9–19% lower than control valu es) with statistically significant 25 ( p < 0.05) decreases at all but the lowest dose. Additionally, there was a st atistically significant 26 ( p < 0.05) increase in relative liver weight (5–21%) in all treatment groups compared to controls. 27 Statistically significant dose-dep endent decreases were observe d in the antibody response to 28 SRBCs (24–89% lower than control values), as measured by both the number of plaque forming 29 cells per 10 6 cells and plaque forming cells per spleen. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-177 The 13-week LOAEL for th is study is 1.07 ng/kg-day based on a significant ( p < 0.05) 1
increase in relative liver we ight (10%) and a significant ( p < 0.05) decrease in antibody response 2 to SRBCs (24%). A NOAEL cannot be determined for this study. 3
4
2.4.2.4.14. Van Birgelen et al. 1995a, b. 5 Van Birgelen et al. (1995) studied the imp act of TCDD exposure on various biochemi cal 6 endpoints in rats. Group s of 7-week-old female Sprague-Dawley rats ( n = 8 per treatment group) 7 were treated with 0, 200, 400, 700, 5,000, or 20,000 ng/kg TCDD (purity >99%) in diet for 8 13 weeks. Daily TCDD intake based on food cons umption, diet level, and mean weight was 9 estimated to be 0, 14, 26, 47, 320, or 1,024 ng/kg-day. Blood samples were collected from 10
treated animals and assa yed for retinol (vitamin A), triiodot hyronine, and total (TT4) and free 11 (FT4) thyroxine. At study termination, the animal s were sacrificed and the liver, thymus, spleen, 12 and kidneys were removed and weighed. Parts of the liver were homogenized and assayed to 13 determine EROD; CYP1A1; CYP1A2; and UDPGT ac tivity. Liver samples also were analyzed 14
for retinol content. 15
TCDD-treated animals showed a dose-relate d decrease in food consumption. Animals 16 treated with 1,024 ng/kg-day TCDD consumed 32% le ss food compared to controls. Similarly, a 17 dose-related decrease in body weight gain was ob served in all animals treated with TCDD. 18
Animals treated with ≥ 47 ng/kg-day of TCDD showed a statistically significant ( p < 0.05) 19 decrease in body weight gain. Relati ve liver weights were significantly ( p < 0.05) increased in 20 the 320 and 1,024 ng/kg-day TCDD dose groups compared to the controls. Absolute and relative 21 thymus weights were significantly ( p < 0.05) decreased at all TCDD dose groups compared to 22 the control group. Relative kidney and spleen weights were significantly ( p < 0.05) higher in 23
animals dosed with ≥ 47 ng/kg-day of TCDD compared to the control group, with the greatest 24 increase occurring in animals treated with 1,024 ng/kg-day TCDD (121 and 173% higher than 25 controls for kidney and spleen, respectively). Cytochrome P450 enzymes, including EROD, 26 CYP1A2, CYP1A1, and UDPGT, exhibi ted statistically significant ( p < 0.05) increases in 27 activity at all TCDD dose groups compared to the control group. TT4 and FT4 thyroid hormone 28 concentrations were statistically significantly ( p < 0.05) decreased only at TCDD doses 29
≥ 47 ng/kg-day. A dose-dependent in crease was observed in the plasma retinol concentrations 30 with significant ( p < 0.05) increases occurring at ≥ 47 ng/kg-day TCDD after a 13-week 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-178 exposure. A dose-dependent reduction in liver re tinoid levels also was observed after 13 weeks 1 of TCDD exposure with the le vels dropping significantly ( p < 0.05) at all TCDD-treated doses 2 compared to the control group. 3 A LOAEL for TCDD of 14 ng/kg for a 13-week exposure is identified for significantly 4 ( p < 0.05) decreased absolute and relativ e thymus weights and significantly ( p < 0.05) decreased 5 liver retinoid levels. A NOAEL cannot be determined for this study. 6
7
2.4.2.4.15. Vos et al., 1973. 8 Vos et al. (1973) conducted a study to exam ine the immune re sponse in laboratory 9 animals treated with TCDD. In one experiment, 10 female Hartley strain gui nea pigs were orally 10 treated with 8 weekly doses of 0, 8, 40, 200, a nd 1,000 ng/kg TCDD in corn oil (purity of TCDD 11 not specified) (0, 1.14, 5.71, 28.6, and 143 ng/kg-da y adjusted for continuous exposure; 12 administered dose divided by 7). At study termin ation, the animals were sacrificed, and heart 13 blood was used to determine total leukocyte a nd differential leukocyte counts. In another 14 experiment, the effect of TCDD on humoral imm unity was determined by injecting 0.1 mL of 15 tetanus toxoid into the right hind-foot pad on day 28 (1 left foot tetanus toxoid, aluminum 16 phosphate-adsorbed) and again on day 42 (1 left foot tetanus toxoid, unadsorbed). Blood was 17 collected ( n = 10) on days 35 and 49, and the serum tetanus-antitoxin concentrations were 18 determined using a modified single radial immunodi ffusion technique. 19 All guinea pigs receiving 1,000 ng/kg-day TCDD either died or were killed when 20 moribund between 24 and 32 days. These animals s howed severe weight loss, lymphopenia, and 21 depletion of the lymphoid organs, especially th e thymus. Microscopic observations revealed 22 severe atrophy of the thymic cortex with substan tial destruction of lymphocytes, with the nuclear 23 debris being engulfed by macrophages. La rge cystic Hassall bodies, filled with 24 polymorphonuclear leukocytes were observed in the medulla. All animals treated with 0, 8, 40, 25 or 200 ng/kg-day TCDD survived until study termin ation. Body weight gain was significantly 26 ( p < 0.01) lower in the 200 ng/kg-day group. Absolu te thymus weight was significantly reduced 27 in the 40 and 200 ng/kg-day treatment groups ( p < 0.01 and p < 0.05, respectively). In contrast, 28 relative thymus weight was significantly ( p < 0.01) reduced only in the 200 ng/kg-day dose 29 group. The absolute weight of the superfic ial cervical lymph nodes was significantly ( p < 0.05) 30 decreased in the 200 ng/kg-day group, while the relative adrenal weight was significantly 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-179 ( p < 0.05) increased in the 200 ng/kg-day dose gro up. Total leukocyte count was significantly 1 ( p < 0.05) decreased in the 40 ng/kg-day dos e group and total lymphocyte count was 2 significantly decreased at 8, 40, and 200 ng/kg-day ( p < 0.01, p < 0.05, and p < 0.05, 3 respectively). A significant ( p- values not provided) monotonic dose-response relationship was 4 determined for body weight (decrease), relative thymus weight (decrease), relative adrenal 5 weight (increase), and total leukocyte and lymphocyte count (decrease). Microscopic 6 examination of the lymphoid organs and adrenals showed no effects, while slight cortical atrophy 7 of the thymus was observed at the 200 ng/kg-day dose. 8 Animals receiving the tetanus toxoid injection showed a small but significant increase in 9
serum tetanus antitoxin concentratio ns at the 8 and 40 ng/kg-day dose ( p < 0.05 and p < 0.01, 10 respectively). Measurement at days 49 and 56 indicated that serum antitoxin levels had 11 decreased sharply a nd the significant ( p < 0.05 on day 49 and p < 0.01 on day 56) effect was 12 seen only at the 200 ng/kg-day dose level. 13 A LOAEL for TCDD of 5.71 ng/kg-day for an 8-w eek exposure is identified in this study 14 for significantly ( p < 0.01) reduced absolute th ymus weight, significantly ( p < 0.05) reduced 15 leukocyte and lymphocyte c ount, and significantly ( p < 0.01) increased serum tetanus antitoxin 16
concentration. The NOAEL for this study is 1.14 ng/kg-day. 17
18
2.4.2.4.16. White et al., 1986. 19 White et al. (1986) studied the impact of TCDD exposure on serum com plement levels. 20 Groups of female (C57BL/6 × C3H)F1(B6C3F1) mi ce were treated for 14 consecutive days with 21 TCDD in corn oil (purity of TCDD not sp ecified) at doses of 0, 10, 50, 100, 500, 1,000 or 22 2,000 ng/kg-day via gastric intubation ( n = 6

− 8). At study termination, blood was collected from 23 anesthetized animals and assaye d for serum complement activity and complement component C3 24
levels. 25 Serum complement activity between the 10 and 100 ng/kg-day doses was between 69 and 26 59% compared to the vehicle control group, w ith all treatment groups being significantly 27 ( p < 0.05) low compared to the vehicle control. In contrast, C3 levels were comparable to the 28
vehicle control with levels ra nging between 98 and 94% of the control group. The higher doses 29 of 500, 1,000, and 2,000 ng/kg-day, however, produced a marked decrease of the component 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-180 hemolytic activity (45, 35, and 19% of the vehicle control) and of C3 levels (91, 81, and 74 % of 1 the vehicle control, respectiv ely; significance level at p < 0.05). 2 A LOAEL for TCDD of 10 ng/kg-day for a 14-day exposure is identified in this study for 3 significantly ( p < 0.05) lower serum complement activity. A NOAEL cannot be determined for 4 this study. 5
6 2.4.2.5. Chronic Studies (Noncancer Endpoints) 7
2.4.2.5.1. Cantoni et al., 1981. 8 CD-COBS rats (4 per trea tment) were orally administered TCDD (purity not specified) 9 dissolved in acetone:corn oil (1:6) at doses of 0 (vehicle alone), 10, 100, or 1,000 ng/kg per week 10 (equivalent to 1.43, 14.3, and 143 ng/kg-day adjusted for continuous exposure, administered 11 dose by dividing the dose by 7) for 45 weeks. Urine was collected several times during 12 treatment and tested for porphyrin excretion. Twenty-four hours after the final dose, animals 13 were sacrificed and their livers, spleens, a nd kidneys were removed for analysis of total 14 porphyrins. All treatment groups had a significant ( p < 0.05) increase in coproporphyrin 15 excretion beginning at 6, 3, or 2 months, respec tively. Uroporphyrin excretion was significantly 16 ( p < 0.05) increased in the 14.3 ng/kg-day group at 10 months and in the 143 ng/kg-day group 17 beginning at 6 months. The high-do se group also had a significant ( p < 0.05) increase in 18 excretion of heptacarboxylic me thyl ester beginning at 6 mont hs. The high-dose group had a 19 marked porphyric state beginning at 8 months as indicated by a 70- fold increase above controls 20 in total urinary porphyrin excretion. This group also had a significant ( p < 0.05) increase in total 21 porphyrins in the liver, kidneys, and spleen. 22
The 45-week LOAEL for this st udy is 1.43 ng/kg-day, based on a 2- to 3-fold increase in 23 urinary coproporphyrin excretion. No NOAEL was established for this study. 24
25
2.4.2.5.2. Croutch et al., 2005. 26 Croutch et al. (2005) examined the im pact of TCDD exposure on body weight via 27 insulin-like growth fact or (IGF) signaling. Fema le Sprague -Dawley rats were randomly assigned 28 in groups of five to initial lo ading doses of TCDD ( purity >98.5%, dissolved in corn oil) at 0, 29 12.5, 50, 200, 800, or 3,200 ng/kg-day, followed by treatmen t with maintenance doses equivalent 30 to 10% of the initial loading dose every third day to maintain a pharmacokinetic steady state 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-181 throughout the entire study (equi valent to: 14-day average = 0, 1.25, 5, 20, 80, or 320 ng/kg-day; 1 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, 2 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). 3 Following 2, 4, 8, 16, 32, 64, or 128 days of initia l dosing, the animals were sacrificed, livers 4 were removed and weighed, and trunk blood was coll ected to analyze glucose content. Rat liver 5 phosphoenolpyruvate carboxykinase (PEPCK) mRNA and pr otein levels also were analyzed, and 6 PEPCK activity was measured. 7 Body weights of TCDD-treated animals de creased after the s econd week of the 8 3,200 ng/kg-day TCDD loading dose, with significan t differences beginnin g at week 9. There 9 was also a statistically significant ( p ≤ 0.05) difference in body we ights at weeks 10, 11, 13, 18, 10 and 19 at the highest loading dose (3,200 ng/kg-da y). PEPCK activity in the liver was also 11 decreased in a dose-dependent manner follo wing TCDD administration at approximately 12 16 days. PEPCK inhibition wa s statistically significant ( p ≤ 0.05) on day 4 in rats treated with 13 either 800 or 3,200 ng/kg-day TCDD when compared to animals treated wi th a loading dose of 14 200 ng/kg-day. A similar statistically significant change was observed in animals treated with 15 3,200 ng/kg-day on day 16 when compared to the 200 ng/kg-day treatment group. In contrast, 16 differences in PEPCK activity at other doses or ti me points were not statistically significant. In 17 TCDD-treated animals, there was also a dose- dependent decrease in PEPCK mRNA expression 18 along with a decrease in PEPCK protein levels in the liver. In a ddition to body weight and 19 PEPCK activity changes, animals treated with 3,200 ng/kg-day TCDD showed a sharp decline in 20 circulating IGF-I levels on day 8 compared to the control group (corn oil) and TCDD-treated 21 animals at lower doses. In the highest dose animal s, IGF-I levels continue d to decline to 42% of 22 the control group by day 16 of the st udy. The IGF-I levels at the highest dose plateaued at an 23 average decrease of 66% through day 128 when comp ared to controls. Beginning at day 8, the 24
decrease in IGF-I was statistica lly significant at every time point through day 128 compared to 25 the control group, as well as groups treated w ith either 12.5 or 50 ng/kg-day TCDD. Similar 26 statistically significant decreases also were observed for the 800 ng/kg-day TCDD-treated groups 27 with an initial decrease of 37% on day 16 follo wed by a further decline to approximately 45% 28 thereafter compared to controls and the 12.5, 50, and 200 ng/kg-day dose groups. In contrast to 29 these results, circulating levels of insulin a nd glucose were unaffected by TCDD treatm ent, while 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-182 the active or phosphorylated form of AMPK- α protein increased with dose as a result of TCDD 1

treatment. 2 A LOAEL for TCDD of 217 ng/kg-day for a 28- day exposure duration (because this 3 represented the most sensitive time for elicitation of effects) was identified in this study for 4 decreased body weight, significant ( p ≤ 0.05) inhibition of PEPCK activity, and reduced IGF-I 5 levels (42% lower than the c ontrol group). A NOAEL of 54.3 ng/ kg-day was identified in this 6 study. 7
8
2.4.2.5.3. Hassoun et al., 2002. 9 Hassoun et al. (2002) examined the potential of TCDD and ot her dioxin-like chemicals to 10 induce oxidative stress in a chronic rat bioassay. Groups of six Harlan Sprague-Dawley female 11 rats were treated with 0, 3, 10, 22, 46, or 100 ng/kg -day TCDD (98% purity), 5 days a week via 12 gavage for 30 weeks. The administered dos es adjusted for continuous exposure were 0, 2.14, 13 7.14, 15.7, 32.9, and 71.4 ng/kg-day, respectively (administered doses were multiplied by 5 and 14 divided by 7). At study terminati on, hepatic and brain tissues from all treated rats were divided 15 into two portions and examined for the production of reactive oxygen species and SSBs in DNA. 16 When compared to controls, there was a dos e-dependent increase in the production of 17 superoxide anion in TCDD-treated animals ra nging from 21–998% and 66–257% in hepatic and 18 brain tissues, respectively. Hepatic tissues had statistically significant ( p < 0.05) increases in 19 superoxide anion production at doses ≥ 7.14 ng/kg-day, while the brain tissue had a statistically 20 significant ( p < 0.05) increase over controls at all doses. Simila rly, increases in lipid 21 peroxidation were observed in hepatic a nd brain tissues with a 481% increase ( p < 0.05) at 22 71.4 ng/kg-day in the hepatic tissue when compared to controls. The incr ease in lipid oxidation 23 in brain tissue ranged from 33–188% ( p < 0.05) in the 2.14–71.4 ng/kg-day dose groups. DNA 24
SSBs were also observed in both he patic and brain tissue in all tr eated groups. When compared 25 to the control group, there was a dose- dependent statistically significant ( p < 0.05) increase in 26 DNA SSBs ranging from 58–322% and 29–137% in hepatic and brai n tissues, respectively. 27 Nonmonotonic dose-response relationships were observed for superoxide production and lipid 28 peroxidation in liver tissues, w ith greater-than-linear increases in effect between the two highest 29
dose levels. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-183 As stated above, because no adverse e ndpoints were measured, no LOAEL/NOAEL was 1 established. However, a LOEL for TCDD of 2.1 4 ng/kg-day for a 30-week exposure duration is 2 identified in this study for significant ( p < 0.05) increases in superoxi de anion, lipid peroxidation 3 production, and DNA SSBs in the liver and brain ti ssues. A NOEL cannot be determined for this 4 study. 5
6
2.4.2.5.4. Kociba et al., 1978. 7 Sprague-Dawley rats (50 per sex per treatm ent group) were admini stered TCDD (purity 8 >99%) in the diet at doses of 0, 1, 10, or 100 ng/kg-day for 2 years. Body weights and food 9 consumption were routinely measured. Hemato logy, clinical chemistry, and urinalysis were 10 measured after 3, 12, or 23 months of treatment. Animals were routinely palpitated for tumors. 11 Gross and histopathological exams were conducted on the tissues of dead or dying animals or at 12 terminal sacrifice. Specific organs also were weighed. 13 The high-dose females had a statistically significant ( p < 0.05) increase in mortality 14 compared to the controls during the second half of the study. Mo rtality changes in males were 15 variable and of questionable toxico logical significance. A significant ( p < 0.05) reduction in 16 body weight occurred in the 100 ng/kg-day males and females beginning at 6 months. Mid-dose 17 females also had reduced body weight, but to a le sser degree during the same time frame. There 18 were no consistent changes in food consumption. The following significant ( p < 0.05) 19 hematology changes were observed in the high-dos e animals: decreased packed cell volume in 20 males after 3 months and in females after 1 ye ar, decreased red blood cells in females after 21 1 year and in males at terminal sacrifice, decr eased hemoglobin in males after 3 months and in 22 females after 1 year, and decreased total white bl ood cell count in females after 1 year. Changes 23 in clinical chemistry ( p < 0.05) occurred only in high-dose fema les and consisted of an increase 24 in serum alkaline phosphatase and gamma glutam yl transferase. Significant changes in 25 urinalysis occurred only in females and include d increased urinary copr oporphyrin in the mid26 and high-dose groups, increased urinary uroporp hyrin in the mid- and high-dose groups, and 27 increased urinary delta-amino-levulinic ac id in the high-dose group. Significant ( p < 0.05) 28 changes in relative organ weight s were observed, including increa sed liver weight in mid- and 29 high-dose females and decreased thymus weight in high-dose females. Mid- and high-dose rats 30 showed hepatocellular degeneration and inflammatory and necrotic changes in the liver. Thymic 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-184 and splenic atrophy were noted in high-dose females. An increas e in non-neoplas tic lung lesions 1 was noted in mid-dose females and high-dose male s and females. High-dose females had an 2 increase in uterine changes. Hi gh-dose males had a significant ( p < 0.05) increase in the 3
incidence of stratified squamous cell carcinomas of the t ongue. High-dose males and females 4 had a significant ( p < 0.05) increase in the in cidence of squamous cell carcinomas of the hard 5 palate/turbinates. 6 The chronic (2-year) LOAEL is 10 ng/kg-da y, based on the numerous significant 7 ( p < 0.05) changes noted in coproporphyrin excr etion (67% increase above control) and an 8 increase in liver and lung lesions in female rats. The NOAEL is 1 ng/kg-day. 9
10
2.4.2.5.5. Maronpot et al., 1993. 11 An initiation-promotion study was perfor me d in female Sprague-Dawley rats (8

− 10 rats 12 per group). Rats were initiated with saline or diethyl nitrosamine (DEN), fo llowed 2 weeks later 13 by promotion with biweekly administration of TCDD (purity not specified) in corn oil via 14 gavage for 30 weeks. The doses were stated to be equivalent to 3.5, 10.7, 35.7, or 15 125 ng/kg-day. Rats were sacrificed 7 days after the final treatment. A significant ( p < 0.05) 16 decrease in body weight occurred in th e 125 ng/kg-day group. A significant ( p < 0.05) increase 17 in relative liver weight occurred in the 35.7 and 125 ng/kg-day groups. There was a significant 18 ( p < 0.05) increase in the labeling index in the 125 ng/kg-day group, but only with DEN 19 initiation. In the TCDD-alone group, a 2-fold increase in labeling in dex occurred in the 20 125 ng/kg-day group that did not reach statistical signifi cance. A significant ( p < 0.05) trend for 21 increased alkaline phosphatase le vels was observed in TCDD-treat ed animals, but despite a 50% 22 increase in the highest dose group the increase was not statistically signifi cant. Total cholesterol 23 and triglycerides were significantly ( p < 0.05) higher in the 125 ng/kg-day TCDD-alone group. 24 A significant ( p < 0.05) increase in 5’-nucleotidase occurred in the 35.7 and 125 ng/kg-day 25 TCDD-alone groups. A dose-dependent increase in the incidence and severity of liver toxicity as 26 measured by microscopic lesions was observed. 27 The 30-week LOAEL is 35.7 ng/kg-day, based on a significant ( p < 0.05) increase in 28 relative liver weight (12%, accompanied by increases in incidence and severity of liver lesions). 29 The 30-week NOAEL is 10.7 ng/kg-day. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-185 2.4.2.5.6. National Toxicology Program, 1982. 1 National Toxicology Program (NTP, 1982) c onducted a carcinogenic bioassay of TCDD 2
on rats and mi ce. Fifty male and female Osbor ne-Mendel rats and male and female B6C3F1 3 mice were treated twice per week wi th TCDD (purity not specified) in corn oil via oral gavage at 4 doses of 0, 5, 25, or 250 ng/kg for rats and male mice (1.4, 7.1, 71 ng/kg-day adjusted for 5 continuous exposure; administered doses multip lied by 2 and divided by 7) and 0, 20, 100, or 6 1,000 ng/kg for female mice (5.7, 28.6, or 286 ng/kg-day adjusted for continuous dosing; 7 administered doses multiplied by 2 and divided by 7) for 104 weeks. Seventy-five rats and mice 8
of each sex served as vehicle controls. One unt reated control group of 25 rats and mice of each 9 sex was present in the TCDD treatment room and one untreated control group consisting of 10 25 rats and mice of each sex were present in the vehicle-control room. Animals surviving until 11 study termination were sacrificed at 105 or 108 weeks. A complete histopathological evaluation 12
was conducted on all animals. 13
Survival rates were not affect ed by TCDD exposure in rats or mice of either sex. Male 14 rats exhibited a dose-related depression in mean body weight after week 55, while the females 15 exhibited a dose-related body-weig ht depression after 45 weeks of TCDD exposure. However, 16 the magnitude of the body weight response is no t indicated. Mean body weights in male and 17 female mice were comparable to the vehicle control group throughout the bioassay. Noncancer 18 histopathologic findings included increased incidences of liver lesions (termed toxic hepatitis) 19 from TCDD exposure, and were detected in the high-dose rats and high-dose mice of each sex. 20 A LOAEL for TCDD of 1.4 ng/kg-day for a 104-w eek exposure duration is identified for 21
increased incidences of liver lesions in mice of both sexes. A NOAEL cannot be determined for 22 this study. 23
24
2.4.2.5.7. National Toxicology Program, 2006. 25
Fema le Sprague-Dawley rats (81 control; 82 treatment group) were administered TCDD 26 (purity >98%) in corn oil:acetone (99:1) via gavage at doses of 0, 3, 10, 22, 46, or 27 100 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, 28 adjusted for continuous exposure) (NTP, 2006). In addition to this primary group, a stop group 29 of 50 animals was administered 100 ng/kg-day TCDD in corn oil:acetone (99:1) via gavage for 30 30 weeks and then just the vehicle for the remainder of the study. Up to 10 rats per dose group 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-186 from the primary study were sacrificed and evaluated at 14, 31, or 53 ( n = 8) weeks for 1 biologically noteworthy changes in the incidences of neoplasms or non-neoplastic lesions in the 2 liver, lung, oral mucosa, uterus, pancreas, thymus , adrenal cortex, heart, clitoral gland, ovary, 3 kidney, forestomach, bone marrow, mesentery gla nd, and pituitary gland. All animals also 4 received a complete necropsy and microscopic examination, and the following organs were 5 weighed: the left kidney, liver , lung, left ovary, spleen, thymus (14 weeks only), and thyroid 6 gland. At study termination, the number of surviv ing animals had declined to 25 in the control 7 group and to 21, 23, 19, 22, and 21 in five treatm ent groups, respectively, due to accidental 8 deaths, moribund animals, or death due to natural causes. 9 Survival rate was not affected by TCDD treat ment. Mean body weights in the high dose 10 primary study group and the 100 ng/kg stop group we re less than the vehicl e control group after 11 week 13 of the study. The mean body weights of animals in the 46 ng/kg-day group were less 12 than in the vehicle control at study termination (2 years), whereas animals in the 22 ng/kg-day 13 had lower mean body weights compared to cont rols during the last 10 weeks of study. In 14 addition to body weight changes, liver weights were also impacted as a result of TCDD 15 exposure. Absolute and relative liver weights were significantly (either p ≤ 0.01 or p ≤ 0.05) 16 higher in all dose groups compared to controls at the 14- and 31 -week evaluation period, whereas 17 the relative liver weights were significantly (either p ≤ 0.01 or p ≤ 0.05) higher only at 18
≥ 10 ng/kg-day at 53 weeks. 19 No clinical findings associated with TCDD treatment were observed. TCDD caused 20 changes in thyroid hormone levels at 14, 31, and 53 weeks. The following changes were 21 statistically significant ( p ≤ 0.05) compared to the vehicle cont rol: decrease in TT4 at doses 22
≥ 22 ng/kg-day at 14 and 31 weeks and at doses ≥ 46 ng/kg-day at 53 weeks; decrease in FT4 at 23
doses ≥ 22 ng/kg-day at 14 and 31 w eeks; increase in total T 3 at doses ≥ 46 ng/kg-day at 14 and 24
31 weeks and at doses ≥ 10 ng/kg-day at 53 weeks; and increase in TSH at doses ≥ 46 ng/kg-day 25 at 14 weeks. There was a statistically-significant ( p ≤ 0.05) increase in hepa tocyte proliferation 26 at 14 weeks (22 ng/kg-day group only); 31 weeks (all doses); and 53 weeks ( ≥ 46 ng/kg-day). 27 There were statistically significant ( p ≤ 0.01) dose-dependent increas es in liver (includes EROD 28 [CYP1A1-associated] activity; 7-pentoxyresoruf in-O-deethylase [PROD; CYP2B-associated] 29 activity; and acetanilide-4-hydroxylase [CYP1A 2-associated] activity) and lung (EROD) 30 cytochrome P450 enzyme activit ies in all treatment groups at all three evaluation periods 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-187 compared to the vehicle control group. The largest effect was an 82-fold induction of hepatic 1 EROD activity in the 46 ng/kg-day group at 31 weeks. 2 TCDD was detected at the greate st concentration in the liver , followed by fat tissue, with 3 tissue concentration increasing in both of these tissues in a dose-dependent manner. TCDD 4 tissue levels generally remained constant after the first measur ement at week 14. Pathological 5
examination at week 14 revealed increased inci dences of hepatocellular hypertrophy in animals 6
administered ≥ 10 ng/kg-day TCDD. Examinations at w eeks 31 and 53 indicated that incidence 7 and or severity of hepatocellular hypertrophy was increased at all treatment doses although 8 incidences were statistically significant ( p ≤ 0.05) only at ≥ 10 ng/kg-day doses. The incidence of 9 non-neoplastic hepatic lesions (including inflammation, necrosis, multiple eosinophilic focus, 10 diffuse fatty change, pigmentation, toxic hepa topathy) in the liver increased at doses 11
≥ 22 ng/kg-day beginning at 14 weeks. Severity of the lesions increased at 14 weeks at doses 12
≥ 46 ng/kg-day and were also observe d at lower dose levels during later evaluation periods (31 13 and 53 weeks). By terminal sacrifice, nume rous non-neoplastic changes were noted in TCDD 14 treated rats, even at the lowest dose tested. 15 Noncancer cardiovascular and pulmonary eff ects were evident afte r 2 years of TCDD 16 exposure. Significantly increased incidences of minimal to mild cardiomyopathy were seen in 17
male and female rats at ≥ 10 ng/kg-day. In the lung, th ere was a significant ( p ≤ 0.01) 18 dose-dependent increase, when compared to the ve hicle control, in the incidence of bronchiolar 19 metaplasia of the alveolar epithelium at all dose groups in the primary study. 20 A LOAEL for TCDD of 2.14 ng/kg-day adjusted dose for a 105-week exposure duration 21 is identified in this study for significantly (either p ≤ 0.01 or p ≤ 0.05) increased absolute and 22 relative liver weights, increased incidence of hepatocellula r hypertrophy, and increased incidence 23
of alveolar to bronchiol ar epithelial metaplasia . A NOAEL cannot be determined for this study. 24
25
2.4.2.5.8. Rier et al., 2001a, b. 26
Fema le rhesus monkeys (8 per treatment gr oup) were administered 0, 5, or 25 ppt TCDD 27 (purity not specified) in the diet for 4 years. Previously, Bowman et al. (1989b) determined that 28 these dietary concentrations were equivalent to 0, 0.15, and 0.67 ng/kg-day, respectively. 29 Thirteen years after termination of TCDD tr eatment, serum concentrations of TCDD and 30 dioxin-like polyhalogenated arom atic hydrocarbons (PHAH) were measured in six control 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-188 monkeys, six monkeys treated with 0.15 ng/kg-day, and three monkeys treated with 1 0.67 ng/kg-day (Rier et al., 2001a). Even afte r 13 years without treatment, there was 2 significantly ( p < 0.05) elevated serum levels of TC DD and other dioxin-like compounds in 3 treated monkeys. There was a significant increase in triglycerides and total lipids in the serum of 4 monkeys treated with either 0.15 or 0.67 ng/kg-day, but not in cholesterol or phospholipids. In 5 addition to these 15 animals, 8 other female mo nkeys (4 treated with 0.67 ng/kg-day TCDD that 6 died 7 to 11 years after treatment and 4 lead-tre ated animals with no history of PHAH exposure) 7
were evaluated for endometriosis. Elevated seru m concentrations of TCDD were not correlated 8
with endometriosis. Increased serum levels of 3,3’,4,4’-tetrachlorobi phenyl (TCB), however, 9 were associated with the presence and severity of endometriosis ( p < 0.05). TCB was found in 10 none of the animals without endometriosis, incl uding TCDD-treated animals, nor was it found in 11
control animals with endometriosis. Anim als with elevated serum levels of TCB, 12 pentachlorobiphenyl, and total serum analyte TCDD equivalent s (TEQ) had an increased 13 incidence of endometriosis, but severity was associ ated only with increased levels of TCB. EPA 14 did not develop a LOAEL for TCDD for this study, because of DLC contamination. 15 In a separate study that evaluated the same 15 monkeys 13 years after exposure, Rier et 16 al. (2001b) examined effects on systemic i mmunity. Peripheral blood mononuclear cells 17 (PBMC) obtained from untreated monkeys secreted no detectable levels of TNF- α in response to 18 T-cell mitogen exposure. Ther e was, however, a significant ( p < 0.05) dose-dependent increase 19
in TNF- α production in PBMC from the TCDD-treated monkeys. Although PBMC from treated 20 monkeys with endometriosis produced more TNF- α than cells from unexp osed controls without 21 the disease (median 128 pg/mL compared to not detected; p < 0.01), PBMC from TCDD-treated 22
animals without endometriosis also produced more TNF- α than controls (median 425 pg/mL, 23

p < 0.067). TNF- α production from the animals without endometriosis, however, was much 24
more variable and was not statis tically significant compared to c ontrols. In addition, there was a 25
dose-related but statistically insignificant de crease in PBMC cytotoxicity against natural 26
killer-sensitive RAJI cells in TCDD-treated an imals compared to the unexposed controls. The 27 results were again related to TCDD exposure an d not the presence of endometriosis. TCDD 28 alone was not associated with changes in PBMC surface antigen expression, but increased serum 29 levels of TCDD. 1,2,3,6,7,8-Hexach lorodibenzofuran and 3,3’,4,4’ ,5-pentachlorobiphenyl were 30
correlated with increased numb ers of CD3+/CD25- and CD3-/C D25+ leukocytes, as well as 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-189 increased secretion of TNF- α in response to T-cell mitogen exposure. Although TNF- α 1 production is considered to be a general indicato r of inflammation, relative adversity of increased 2
TNF- α secreted by PBMCs in and of itself cannot be substantiated in the absence of concurrent 3 physiological measurements of an inflammatory response. Therefore, neither a LOAEL nor 4 NOAEL can be determined for this study. 5
6 2.4.2.5.9. Sewall et al., 1993. 7 Sewall et al. (1993) examined the impact of TCDD exposure on the hepatic epidermal 8 growth factor receptor (EGFR) as a critical effect in hepatoca rcinogenicity. In two separate 9 experim ents, groups of 6- to 8-week-old female Sprague-Dawley rats were randomly assigned to 10 the following groups: control group, receiving sa line and corn oil; a promoted group that 11 received four different doses of TCDD along wi th saline; a DEN-only in itiated control group; 12 and a DEN and TCDD initiated and promoted group th at received four different doses of TCDD. 13 DEN was administered via intraper itoneal injection at a dose of 175 mg/kg [saline (S) vehicle] as 14 the initiating agent to animals that were 70 days old. The control animals received saline only. 15 In the first experiment, each treatment gr oup (S/TCDD and DEN/TCDD) that included 16 sham-operated or ovariectomized and intact animals were treated with TCDD (purity >98%) at 17 125 ng/kg-day. In the second dose-response expe riment, DEN-initiated and saline control 18 treatment groups (intact animals, 84 days old) were administered TCDD (purity >98%) in corn 19 oil via oral gavage once every 2 weeks for 30 weeks at doses equivalent to 0, 3.5, 10.7, 35.7, or 20 125 ng/kg-day ( n = 9). A week after the last treatment, all animals were sacrificed and livers 21
were harvested and fixed for imm unohistochemistry. Sections of the fixed liver were tested for 22 EGFR binding, EGFR autophosphorylation, immunol ocalization of EGFR, and hepatic cell 23 proliferation. 24 In the first experiment, intact animals tr eated with 125 ng/kg-day TCDD exhibited a 65% 25 reduction in EGFR binding capacity. In cont rast, the EGFR equilib rium maximum binding 26 capacity (B max ) of the ovariectomized rats was not sta tistically different from the ovariectomized 27 control rats, and no changes in the K d were detected in any treatment group. In the 28 dose-response experiment with intact animals, a significant ( p < 0.05) TCDD dose-dependent 29
decrease in the B max of EGFR was shown. A two-factor , five-level ANOVA i ndicated that the 30 effect of TCDD exposure on EGFR B max was significant ( p = 0.0001), whereas, the effect of 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-190 DEN treatment on EGFR B max was not significant. Compara tive analysis using Fisher’s 1 protected least significant difference indicate d that the lowest TC DD dose resulting in a 2 statistically significant ( p < 0.05) decrease in the EGFR B max was 10.7 ng/kg-day S/TCDD 3 group. At the highest TCDD dos e of 125 ng/kg-day, the EGFR B max was reduced by 38% 4 compared to controls in both the DEN initiated and noninitiated groups. A two-factor, five-level 5 ANOVA showed no significan t effect on EGFR K d in either the DEN- or the TCDD-treated 6 groups. The EGFR autophosphorylation assay indi cated that, with increasing TCDD dose, the 7 amount of EGFR autophosphoryla tion in DEN/TCDD-treated animals decreased. The study 8
authors state that this decrease is similar to the dose-response a lterations observed for the EGFR 9
B max . Additionally, EGFR autophosphorylation in control and 125 ng/kg-day noninitiated 10 animals was similar to the corresponding dose le vels for the DEN-treated animals, suggesting 11
that DEN treatment did not affect the EG FR or the EGFR response to TCDD under the 12 experimental conditions. The immunolocalization assay indicat ed that staining was more 13 apparent in the centrilobular and midzonal regions of the liver in the DEN initiated control 14 animals, whereas, the amount of hepatocyte pl asma membrane staini ng in DEN/TCDD treated 15 animals substantially decreased. The cell prolif eration assay showed a decrease in the cell 16 labeling index in the 3.5 ng/kg-day DEN/TCDD dose group that was st atistically less ( p ≤ 0.05) 17 than the labeling index for the control group. In contrast, the labeling index for the 18 125 ng/kg-day DEN/TCDD treatment group was significantly ( p ≤ 0.05) higher compared to 19 controls. Except for the low-dose (3.5 ng/kg- day) group, a clear dose-response trend (two 20
mid-level doses were not statis tically significant) was observed in the other three TCDD treated 21

groups. 22 The role of EGFR in TCDD-mediated hepa totoxicity is unknown, and as such, this 23 endpoint cannot be unequivocally linked to TC DD-induced hepatotoxicity nor labeled as 24 adverse. Thus, no LOAEL/NOAEL was establis hed. A LOEL for TCDD of 3.5 ng/kg-day for a 25 30-week exposure duration was identifie d in this study for a significant ( p = 0.0001 using 26 ANOVA) decrease in EGFR B max levels. A NOEL cannot be determined for this study. 27
28
2.4.2.5.10. Sewall et al., 1995. 29 Sewall et al. (1995) studied th e dose-response relationship for thyroid function alterations 30 in female rats as a result of TCDD exposure. Groups of fe male Sprague-Dawley rats were 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-191 initiated with DEN at 70 days of age at a dos e of 175 mg/kg in a sali ne vehicle via an i.p. 1 injection. DEN was administered as a liver-initiating agent for a concurrent study to determine 2 TCDD promotion of hepatic preneopl astic foci. Saline-treated animals served as controls. At 3 84 days of age, both the DEN-initiated and th e saline-noninitiated groups of animals were 4 administered TCDD (purity >98%) or corn oil ve hicle via oral gavage once every 2 weeks for 5 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 6 per group). One week after the la st TCDD treatment, the animals we re sacrificed and the thyroid 7 was removed and fixed for further analysis. Bl ood was drawn from the abdominal aortic vein, 8 and the serum was isolated and preserved for hor mone analysis. Liver was also removed and 9 prepped for further analysis. Thyroid hormone an alysis was performed to determine serum TSH, 10 T3, and T4 levels using radioimmunoassay ki ts. Histological examination was conducted on 11 eosin-stained sections of the thyroid tissue. RNA level in the hepatic tissue was determined 12 using a reverse transcription polymeras e chain reaction (RT- PCR) technique. 13
TCDD treatment did not affect thyroid weight. A dose-dependent decrease in serum T4 14
levels was observed in both noninitiated and DE N-initiated animals with T4 levels dropping 15 significantly ( p < 0.05) at the 35 and 125 ng/kg-day TCDD doses in the noninitiated group. 16 Compared to the noninitiated c ontrol group, DEN alone did not signi ficantly affect T4 levels. 17 Serum T3 level in the 125 ng/kg-day treatmen t group was slightly elevated but was not 18 significantly different from levels in the control group. TSH levels in DEN initiated rats were 19 increased at a dose of 3.5 ng/kg-day. In the noninitiated group, TSH level in the 125 ng 20 TCDD/kg-day group was 3.27 ± 0.34 ng/mL ( n = 9) compared to 1.3 ± 0.18 ng/mL in the corn 21 oil control group ( n = 7). This result, in conjunction with the T4 data, demonstrates that TCDD 22 had a similar effect on thyroid hormone levels in both the noninitiated and DEN initiated groups. 23 Histological sections examined for nodular lesions or neoplasms exhibited thyroid follicular 24
adenoma in one DEN/corn oil control animal . The DEN/TCDD-treated animals exhibited 25 diffuse follicular hyperplasia, with the size of colloidal follicles decreasing with TCDD 26 treatment. Other qualitative DEN/TCDD-relate d changes included increased frequency of 27 abnormally shaped follicles. The study authors reported that im age analysis demonstrated a 28 significant ( p = 0.013) TCDD dose-related decrease in mean follicle size along with a significant 29 ( p = 0.001) TCDD dose-related increase in parenc hymal area. Additionally, like T4 and TSH 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-192 levels, DEN treatment alone or in combination w ith TCDD did not influence thyroid follicular or 1 C-cell morphology. 2
RT-PCR results for UGT1 and CYP1A1 mRNA levels indicated that the amount of 3 UGT1 mRNA at the 125 ng/kg-day dose was approxi mately 2.5-fold higher compared to the 4 concurrent controls. The study authors also stated that the maximal response for the UGT1 5 mRNA levels was reached at a dose between 1.0 and 3.5 ng TCDD/kg-day. In contrast, the 6
maximum induction of CYP1A1 mRNA was 260-fold higher at th e 125 ng/kg-day compared to 7
the concurrent controls. 8 A LOAEL for TCDD of 35 ng/kg-day for a 30-w eek exposure duration was identified in 9 this study for a significant ( p < 0.05) decrease in T4 levels. The NOAEL for this study is 10 10.7 ng/kg-day. 11
12 2.4.2.5.11. Toth et al., 1979. 13 Toth et al. (1979) examined the impact of TCDD exposure on the form ation of liver 14 tumors in male mice. Ten-week-old, outbred Swiss/H/Riop male mice were administered 15 sunflower oil or TCDD (purity no t specified; in sunflower oil) at 0, 7, 700 or 7,000 ng/kg (0, 1, 16 100, or 1,000 ng/kg-day adjusted for continuous dos ing; administered dose divided by 7; n = 38, 17 44, 44, and 43, respectively) once per week via ga stric tube for 1 year. Once exposure had 18 ceased, animals were followed for the rest of thei r lives. After spontaneous death or when mice 19 were moribund, autopsies were performed and a ll organs were examin ed histologically. 20 Average life span in the 1,000 ng/kg-day dose group decreased consid erably (72%) when 21 compared to the control group. TCDD also cau sed dose-dependent, severe chronic and ulcerous 22 skin lesions (12, 30, and 58% in the 1, 100, a nd 1,000 ng/kg-day dose groups, respectively) that 23 was followed by generalized lethal amyloi dosis (12, 23, and 40% in the 1, 100, and 24 1,000 ng/kg-day dose groups, respectively). 25 A LOAEL for TCDD of 1 ng/kg-day for 1-year exposure duration was identified in this 26 study for severe chronic and ulcerous skin lesion s (12% higher than controls), and generalized 27 lethal amyloidosis (12% higher than controls). A NOAEL cannot be determined for this study. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-193 2.4.2.6. Chronic Studies (Cancer Endpoints) 1
2.4.2.6.1. Kociba et al., 1978. 2 As discussed above, Kociba et al. (1978) conducted a lifetime (2-year) feeding study of 3

ma le and female Sprague-Dawley rats using doses of 0, 1, 10, and 100 ng/kg-day. There were 4 50 males and 50 females in each group. 5 With respect to the cancer endpoints exam ined, the most significant finding was an 6 increase in hepatocellular hyperplastic nodules and hepatocellular carcinomas in female rats. 7 The incidence of hepatocellular carcinomas was significantly elevated above the control 8 incidence at the 100 ng/kg-day dose, whereas in creased incidence of hyperplastic nodules was 9 evident in the 10 ng/kg-day dose group. 10
There have been two reevaluations of slides of liver sections from the Kociba et al. study 11 (Squire, 1980; Sauer, 1990; Goodman and Sauer, 1992). The Squire Review was requested by 12 EPA as an independent review of the slides. The Sauer Review was carried out using refined 13 criteria for the diagnosis of proliferative hepatocellular lesi ons (Maronpot et al., 1986, 1989). 14
Liver tumor incidences for the three evaluations are compared in Appendix F. Although there 15 are some quantitative differences between the eval uations, the lowest detect able effect for liver 16 tumor incidence is consistently observed at 10 ng/kg-day. 17 In the 10 ng/kg-day dose group, significant incr eases in the incidence of hyperplastic 18
nodules of the liver were observed in female rats (18/50 in the Kociba evaluation, 27/50 in the 19 Squire evaluation). Two females (2/50) had hepa tocellular carcinomas. In the 1990 reevaluation 20 (Sauer, 1990; Goodman and Sauer, 1992), nine females (9/50) were identified with 21 hepatocellular adenomas and none with carcinom as; thus only one-third of the previously 22
observed “tumors” were identifi ed when using the refined dia gnostic criteria. As discussed 23 below, the tumor reclassification of Goodman a nd Sauer (1992) was used in the dose-response 24 modeling for the Kociba et al. (1978) data set. 25 In addition to nodules in the liver, increas ed incidence of stratified squamous cell 26 carcinoma of the tongue and nasal turbinates/h ard palate, and kerati nizing squamous cell 27 carcinoma of the lung were also observed in fe male rats in the 100 ng/kg-day dose group. One 28 possible cause for the induction of lung tumors in the Kociba f eeding study may have been the 29 aspiration of dosed feed into the lungs. Ho wever the promotion of lung tumors has been 30 observed in mice treated systemically by intraperit oneal (i.p.) injections of TCDD (Beebe et al., 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-194 1995). In addition the induction of hyperplastic a nd metaplastic lesions in rats has been observed 1 following chronic oral gavage treatment with TCDD (Tritscher et al., 2000). More recently, 2 chronic oral exposure to HCDD re sulted in the induction of lung tu mors in treated female rats 3 (Rozman, 2000). These data indicate that the in duction of lung tumors in the Kociba was most 4 likely primarily the result of systemic chronic dietary exposure to TCDD rather than due to a 5 localized exposure to aspired dosed feed. 6
There was no detectable increase in liver tumo r incidences in male rats in any of the dose 7 groups. The mechanism responsible for dioxin-mediated sex specificity for 8 hepatocarcinogenesis in rats is not clear, but ma y involve ovarian hormones (Lucier et al., 1991). 9 Although there was no increase in liver tumors in male rats in this study, in the 10 100 ng/kg-day group, there was an increased inciden ce of stratified squam ous cell carcinoma of 11 the hard palate/nasal turbinate, stratified squamous cell carcino ma of the tongue, and adenoma of 12
the adrenal cortex. 13 Kociba et al. (1978) ha d reported that chemically relate d increases in preneoplastic or 14 neoplastic lesions were not found in the 1 ng/kg-da y dose group. However, Squire identified two 15 male rats in the 1 ng/kg-day dose group w ith squamous cell carcinoma of the nasal 16 turbinates/hard palate, and one of these male rats had a squamous cell carcinoma of the tongue. 17 These are both rare tumors in Sprague-Dawley rats, and these sites are targets for TCDD, 18 implying that 1 ng/kg-day may not represent a NO EL. However, no dose-response relationships 19 were evident for tumors at these sites (Huff et al., 1991) 20 There is considerable controversy concerni ng the possibility that TCDD-induced liver 21 tumors are a consequence of cytotoxicity. Goodman and Sauer (1992) have extended the 22
reevaluation of the Kociba slides to include liver toxicity data and have reported a correlation 23 between the presence of overt he patotoxicity and the development of hepatocellular neoplasms in 24 female rats. With the exception of two tumors in controls and one each in the low- and mid-dose 25 groups, all liver tumors occurred in livers showin g clear signs of toxicity. However, male rat 26 livers exhibit cytotoxicity in response to high TC DD doses, yet they do not develop liver tumors. 27 Moreover, both intact and ovariectomized female ra ts exhibit liver toxicity in response to TCDD, 28 yet TCDD is a more potent promoter in intact bu t not ovariectomized rats (Lucier et al., 1991). 29 Therefore, if cytotoxicity is playing a role in liver tumorigenesis, othe r factors must also be 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-195 involved. Also, there is little information on th e role of cytotoxicity in TCDD-mediated cancer 1 at other sites such as the lung and thyroid. 2
3
2.4.2.6.2. Toth et al., 1979. 4 In a study of 10-week-old outbred ma le Swiss/H/Riop m ice, Toth et al

. (1979) 5 administered oral gavage TCDD doses of 0, 7, 700, and 7,000 ng/kg-day in sunflower oil weekly 6 for 1 year (0, 1, 100, or 1,000 ng/kg-day adjusted fo r continuous dosing; see details above). All 7 mice (100/group) were followed for their entire live s. The study authors identified the effective 8 number of mice in each group to be the num ber of surviving animals when the first 9 tumor-bearing animal was identified. The averag e lifespan of the control, low, mid and high 10 dose groups was 588, 649, 633, and 424 days, respectively. 11 In the 100 ng/kg-day dose group, liver tumor incidence was twice that of the control 12 group and was statistic ally significant ( p < 0.01%). A dose-related increase in liver tumor 13 incidence was observed (18, 29, 48, and 30% in the control and three TCDD-treated groups, 14 respectively) in all treated mice. Increases were not statistica lly significant, however, at 1 and 15 1,000 ng/kg-day. The study authors also stated that spontaneous and induced liver tumors were 16 not histologically different. Additionally, the ratio of benign hepatomas to hepatocellular 17 carcinomas in the control group was not affected by treatment and an increase was observed only 18
in the absolute number of liver tumors. Ci rrhosis was not observed with the tumors. 19
20
2.4.2.6.3. NTP, 1982. 21 As discussed above, the NTP (1982) study was conducted using Osborne-Mendel rats 22 and B6C3F1 mice (NTP, 1982). Groups of 50 ma le rats, 50 f emale rats, and 50 male mice 23 received TCDD as a suspension in corn oil:acteon e (9:1) by gavage twice each week at doses of 24 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 25 mice and doses of 0, 5.7, 28.6, or 286 ng/kg-day for female mice. 26 There were no statistically significant dose- related decreases in survival in any 27 sex-species group. TCDD-induced ma lignant liver tumors occurred in the high-dose female rats 28
and in male and female mice. These can be considered to result from TCDD exposure because 29 they are relatively uncommon le sions in control Osborne-Mende l rats (male, 1/208; female, 30 3/208), are seen in female rats and mice of bot h sexes, and their increasing incidence with 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-196 increasing dose is statistically signi ficant (Cochran-Armitage trend test, p = 0.004). Because 1
liver tumors were increased in both sexes of mi ce, this effect is not female-specific as was 2 observed in rats. Interestingly, liv er tumor incidences were decrea sed in female rats in both the 3 NTP and Kociba low doses (not st atistically significant compared with controls). For example, 4
the combined control incidence data were 11/161 (7%) compared with 4/99 (4%) in the low-dose 5

group. 6 The incidences of thyroid gland (follicular ce ll) tumors were increased in all three dose 7 groups in male rats. Because the responses in the two highest dose groups are highly significant, 8 the statistically significant elevation of inci dence in the lowest dose group (Fisher exact 9

p -value = 0.042) is considered to be caused by exposure to TCDD, suggesting that thyroid tumor 10 incidence may be the most sensitive site for TCDD-mediated carcinogenesis. Because 11 71 ng/kg-day is above the maximu m tolerated dose (MTD) (Huff et al., 1991), thyroid tumors 12
occur at doses more than 50 times lower than the MTD. 13 TCDD-induced neoplasms of the adrenal gla nd were observed in the 7.1 ng/kg-day/dose 14 group in male rats and in high-dose female rats. Fibrosarcomas of the subcutaneous tissue were 15 significantly elevated in high-dose female mice and female rats. One additional tumor type, 16 lymphoma, was seen in high-dose female mice. L ung tumors were elevated in high-dose female 17 mice; the increase was not statistically significant when compared with concurrent controls, but 18 the increase was dose related (C ochran-Armitage trend test, p = 0.004). 19 Huff (1992) concluded, based on the NTP bioa ssay results, that TCDD was a complete 20 carcinogen and induced neoplasms in rats and mi ce of both sexes. As was observed in the 21 Kociba study (Kociba et al., 1978), liver tumors we re observed with greater frequency in treated 22 female rats, but in male rats the thyroid appe ars to be the most sensitive (increased tumor 23 incidence at doses as low as 1.4 ng/kg-day). 24
25
2.4.2.6.4. NTP, 2006. 26 As discussed above, female Sprague-Dawley rats (81 control; 82 treatment group) were 27 administered TCDD (purity >98%) in corn oil:ace tone (99:1) via gavage at doses of 0, 3, 10, 22, 28 46, or 100 ng/kg-day, 5 days per week for 105 weeks (0, 2.14, 7.14, 15.7, 32.9, or 29 71.4 ng/kg-day, adjusted for conti nuous exposure) (NTP, 2006). In addition to this primary 30 group, a stop-dose group of 50 animals was administered 100 ng/kg-day TCDD in corn 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-197 oil:acetone (99:1) via gavage for 30 weeks and th en just the vehicle for the remainder of the 1 study. At study termination, the number of surviving animals had declined to 25 in the control 2 group and to 21, 23, 19, 22, and 21 in five treatm ent groups, respectivel y, due to accidental 3 deaths, moribund animals, or death due to natural causes. 4 Incidence of hepatocellular adenomas was significantly ( p < 0.001) increased in the 5 100 ng/kg-day dose group in the primary study and exceeded incidences seen in historical 6 vehicle control range at study termination. A dose-related in crease in the incidence of 7 cholangiosarcoma was seen in the primary study group in animals receiving 22 ng/kg-day or 8 higher doses of TCDD. The high dose group of 100 ng/kg-day had the highest incidence of 9 cholangiosarcoma with a significant ( p < 0.001) number of animals exhibiting multiple 10 cholangiosarcomas. Such an incidence was not seen in historical vehicle controls. In contrast, 11 only two cholangiosarcomas and hepatocellular adenomas were seen in the 100 ng/kg-day group 12 in the stop-exposure study. 13 In the lung, at 2 years, there was a significantly ( p = 0.002) increased incidence of cystic 14 keratinizing epithelioma in the 100 ng/kg-day do se group of the primary study, while there were 15 no epitheliomas in the 100 ng/kg-day group of th e stop-exposure study. There was also a 16 significant ( p ≤ 0.01) dose-dependent increase, when comp ared to the vehicle control, in the 17 incidence of bronchiolar metaplas ia of the alveolar epithelium at all dose groups in the primary 18 study. Squamous metaplasia was also present in the 46 and 100 ng/kg-day dose groups in the 19 primary study, and was also observed in the 100 ng/kg-day dose group in the stop-exposure 20 study. 21 A positive trend in the incidence of gingival squamous cell carcinoma of the oral cavity 22 was seen at all doses (except 22 ng/kg-day ), with the incidence significantly ( p = 0.007) high in 23 the 100 ng/kg-day dose group. In addition, the occurrence of this lesion in the 46 and 24 100 ng/kg-day group of the primary study and 10 0 ng/kg-day group of the stop-exposure study 25
exceeded the historical contro l range. The incidence of gi ngival squamous hyperplasia was 26 significantly (either p ≤ 0.01 or p ≤ 0.05) increased in all dose groups of the primary study as 27 well as the 100 ng/kg-day group of the stop-exposure study. 28
In the ut erus, at 2 years, there was a significantly ( p = 0.032) higher rate of squamous cell 29 carcinoma in the 46 ng/kg-day group compared to ve hicle controls. In ad dition there were two 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-198 squamous cell carcinomas in the 100 ng/kg-day group of the stop-exposure study. No squamous 1 cell carcinomas have been reported in historical vehicle controls. 2
These results indicate that TCDD is carci nogenic to female Sprague-Dawley rats and 3 causes tumors at multiple sites. 4
5
2.4.3. Summary of Key Data Set Selection for TCDD Dose-Response Modeling 6 To meet the NAS concerns regarding transpar ency and clarity in the identification of 7 TCDD studies for dose-response assessment, EPA ha s, in this section, developed and applied 8 two sets of criteria, for animal bioassays a nd epidemiologic studies. EPA has collected and 9 evaluated these studies, including studies fr om the 2003 Reassessment and newer studies found 10
via literature search es and through public submissions. Tabl es 2-4 and 2-5 contain the final lists 11 of key cancer and noncancer studie s, respectively, that have me t EPA’s inclusion criteria for 12 epidemiologic data. Tables 2-6 a nd 2-7 provide the final lists of key studies that have met EPA’s 13 inclusion criteria for animal bioassay data for cancer and noncancer st udies, respectively. 14 Collectively, these four tables contain the final set of key studies that EPA has used to develop 15 noncancer and cancer dose-respons e assessments for TCDD in Sections 4 and 5 of this 16 document. In Sections 4 and 5, additional evaluations are made to determine which 17 study/endpoint data sets are th e most appropriate for develo pment of the RfD and OSF for 18 TCDD, using statistical criter ia, dose-response modeling resu lts and decisions regarding 19 toxicological relevance of the e ndpoints. The approaches taken to select the final candidate 20 study/endpoint data sets are discu ssed in Sections 4 and 5 and are illustrated in Figures 4-1 and 21
5-1 of those sections. 22This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-199 Table 2-1. Summary of epidemiologic cancer studies (key characteristics) 1 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 describedThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 2-200 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 studiesRevich 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/AThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-201 DRAFT: DO NOT CITE OR QUOTETable 2-2. Epidemiology cancer study selection considerations and criteriaMethodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimates arenotsusceptibleto biasesfromconfoundingexposures orfrom studydesign orstatisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertaintiesin exposureassessmentconsidered.Study sizeand follow-up largeenough toyield preciseestimates ofrisk andensureadequatestatisticalpower.Publishedin peer-reviewedliteraturewithappropriatediscussionofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipcan beassessed.Effective dose& oralexposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined.Pass fordose-responseanalyses?CancerConsiderationsCriteria Y/NNIOSH Cohort StudiesFingerhut et al., 1991all cancer sites, site-specific analyses√X X X√√X√NSteenland et al., 1999all cancer sites combined, site-specific analyses√√√√√√√√NaSteenland et al., 2001all cancer sites combined√√√√√√√√YCheng et al., 2006all cancer sites combined√√√√√√√√YCollins et al., 2009all cancer sites combined, site-specific analyses√√X√√√√X NBASF Cohort StudiesZober et al., 1990all cancer sites combined, site-specific analyses√√X X X√X X NOtt and Zober, 1996all cancer sites combined√√√√√√√√YThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-202 DRAFT: DO NOT CITE OR QUOTETable 2-2. Epidemiology cancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimates arenotsusceptibleto biasesfromconfoundingexposures orfrom studydesign orstatisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertaintiesin exposureassessmentconsidered.Study sizeand follow-up largeenough toyield preciseestimates ofrisk andensureadequatestatisticalpower.Publishedin peer-reviewedliteraturewithappropriatediscussionofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipcan beassessed.Effective dose& oralexposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined.Pass fordose-responseanalyses?CancerConsiderationsCriteria Y/NHamburg CohortManz et al., 1991all cancer sites combines, site-specific analyses√√√√√√X√NFlesh-Janys et al., 1995all cancer sites combined√√√√√√√X NFlesh-Janys et al., 1998all cancer sites combined, site-specific analyses√√√√√√√√NbBecher et al., 1998all cancer sites combined√√√√√√√√YSeveso CohortBertazzi et al., 2001all cancer sites combined, site-specific analyses√√√X√√X X NPesatori et al., 2003all cancer sites combined, site-specific analyses√√X X√√X X NConsonni et al., 2008all cancer sites combined, site-specific analyses√√√X√√X X NThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-203 DRAFT: DO NOT CITE OR QUOTETable 2-2. Epidemiology cancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimates arenotsusceptibleto biasesfromconfoundingexposures orfrom studydesign orstatisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertaintiesin exposureassessmentconsidered.Study sizeand follow-up largeenough toyield preciseestimates ofrisk andensureadequatestatisticalpower.Publishedin peer-reviewedliteraturewithappropriatediscussionofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipcan beassessed.Effective dose& oralexposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined.Pass fordose-responseanalyses?CancerConsiderationsCriteria Y/NBaccarelli et al., 2006site specific analysis√√X√√√√√NcWarner et al., 2002breast cancer incidence√√√√√√√√YChapaevsk StudyRevich et al., 2001all cancer sites combined, site-specific analyses X X X X√X X X NRanch Hands CohortAkhtar et al., 2004all cancer sites combined, site-specific analyses√X√√√√X√NMichalek and Pavuk, 2008all cancer sites combined√X√√√√X√NOtherstMannetje et al., 2005all cancer sites combined, site-specific analyses√X√√√X X X NMcBride et al., 2009ball cancer sites combined, site-specific analyses√X X√X√X X NThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-204 DRAFT: DO NOT CITE OR QUOTETable 2-2. Epidemiology cancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimates arenotsusceptibleto biasesfromconfoundingexposures orfrom studydesign orstatisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertaintiesin exposureassessmentconsidered.Study sizeand follow-up largeenough toyield preciseestimates ofrisk andensureadequatestatisticalpower.Publishedin peer-reviewedliteraturewithappropriatediscussionofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipcan beassessed.Effective dose& oralexposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined.Pass fordose-responseanalyses?CancerConsiderationsCriteria Y/NMcBride et al., 2009aall cancer sites combined, site-specific analyses√√X√X√√√NdHooiveld et al., 1998all cancer sites combined, site-specific analyses√√√√X√√X NaThis study has been superseded and updated by Steenland et al. (2001).bBecher et al. (1998) assessed this samecohort 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’s lymphoma. Given thislack 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 purpos es only and does not constitute Agency policy 1/15/10 2-205 DRAFT: DO NOT CITE OR QUOTETable 2-3. Epidemiology noncancer study selection considerations and criteriaMethodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimatesare notsusceptibleto biasesfromconfoundingexposuresor fromstudy designor statisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertainties inexposureassessmentconsidered.Study sizeandfollow-uplargeenough toyieldpreciseestimatesof risk andensureadequatestatisticalpower.Published inpeer-reviewedliteraturewithappropriatediscussion ofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipscan beassessed.Effective dose& oral exposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined for aNon-Fatalendpoint.Pass fordose-responseanalyses?NoncancerConsiderationsCriteria Y/NNIOSH CohortSteenland et al., 1999mortality (noncancer) -ischemic heart disease√X√√√√X X NCollins et al., 2009mortality (noncancer)√√X√√√√X NBASF CohortOtt and Zober, 1996mortality (noncancer)√√X√√√√X NHamburg CohortFlesch-Janys et al., 1995mortality (noncancer)√√√√√√√X NSeveso CohortEskenazi et al., 2002amenstrual cycle characteristics√√√√√√√√YEskenazi et al., 2002bendometriosisX X X√X√√X NThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-206 DRAFT: DO NOT CITE OR QUOTETable 2-3. Epidemiology noncancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimatesare notsusceptibleto biasesfromconfoundingexposuresor fromstudy designor statisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertainties inexposureassessmentconsidered.Study sizeandfollow-uplargeenough toyieldpreciseestimatesof risk andensureadequatestatisticalpower.Published inpeer-reviewedliteraturewithappropriatediscussion ofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipscan beassessed.Effective dose& oral exposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined for aNon-Fatalendpoint.Pass fordose-responseanalyses?NoncancerConsiderationsCriteria Y/NEskenazi et al., 2003birth outcomesX X X√√√√X NWarner et al., 2004age at menarch√√X√√√√X NEskenazi et al., 2005age at menopause√√X√√√√X NWarner et al., 2007ovarian function√√X√√√√X NEskenazi et al., 2007uterine leiomyoma√√√√√√√X NaMocarelli et al., 2008semen quality√√√√√√√√YMocarelli et al., 2000sex ratio√√√√√X√X NbBaccarelli et al., 2008neonatal thyroid function√√√X√√√√YThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-207 DRAFT: DO NOT CITE OR QUOTETable 2-3. Epidemiology noncancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimatesare notsusceptibleto biasesfromconfoundingexposuresor fromstudy designor statisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertainties inexposureassessmentconsidered.Study sizeandfollow-uplargeenough toyieldpreciseestimatesof risk andensureadequatestatisticalpower.Published inpeer-reviewedliteraturewithappropriatediscussion ofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipscan beassessed.Effective dose& oral exposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined for aNon-Fatalendpoint.Pass fordose-responseanalyses?Alaluusua et al., 2004oral hygiene√√√√√√√√YBertazzi et al., 2001mortality (noncancer)√√X X√√X X NConsonni et al., 2008mortality (noncancer)√√X X√√X X NBaccarelli et al., 2005chloracne√√√√√√√√NcBaccarelli et al., 2002, 2004immunological effects√√√√√√√X NChapaevsk StudyRevich et al., 2001mortality (noncancer) and reproductive health√X X X√√X X NRanch Hands CohortMichalek and Pavuk, 2008diabetes√X√√√√X√NOtherMcBride et al., 2009aThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-208 DRAFT: DO NOT CITE OR QUOTETable 2-3. Epidemiology noncancer study selection considerations and criteria (continued)Methodsuse toascertainhealthoutcomeswereunbiased,highlysensitiveandspecific.Riskestimatesare notsusceptibleto biasesfromconfoundingexposuresor fromstudy designor statisticalanalysis.AssociationbetweenTCDD andadversehealth effect,withexposure-responserelationship.Exposureassessmentmethodologyclear andadequatelycharacterizesindividual-levelexposures.Limitationsanduncertainties inexposureassessmentconsidered.Study sizeandfollow-uplargeenough toyieldpreciseestimatesof risk andensureadequatestatisticalpower.Published inpeer-reviewedliteraturewithappropriatediscussion ofstrengths,limitations.ExposureprimarilyTCDD andquantified sothat dose-responserelationshipscan beassessed.Effective dose& oral exposureestimable &consistent w/currentbiologicalunderstanding.Latency andappropriatewindow(s) ofexposureexamined for aNon-Fatalendpoint.Pass fordose-responseanalyses?mortality (noncancer)X X X√X√√X NMcBride et al., 2009bmortality (noncancer)X√X√X√X X NRyan et al., 2002sex ratioX X XX√√X X NaCategorical measures of TCDD suggest an inverse association between TCDD exposure and uterine fibroids. The observed directionof the reportedassociations precluded quantitative dose-response modeling.bThe somewhat arbitrary cut off age of 19 for statistically significant exposure associations results in a highly uncertain critical exposure window. It is difficultto determine whether effects are a consequence of the initial highexposure during childhood or a function of the cumulative exposure for this entire exposurewindow. 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 development, as exposure levelsobserved in the general population are much lower.√= Consideration/criteria satisfied. X=Consideration/criteria not satisfied.This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-209 DRAFT: DO NOT CITE OR QUOTETable 2-4. Epidemiology studies selectedfor TCDD cancer dose-response modelingHealthoutcomeLocation,timeperiodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceMortalityfrom allcancersUSA,1942–1993NIOSH cohortincluding 3,538occupationallyexposed maleworkers at 8plants in theUnited States;256 cancerdeathsCumulativeserum lipidTCDDconcentrations(CSLC) basedon workhistories, job-exposurematrix, andconcentrationand age-dependent two-compartmentmodel ofeliminationkineticsNo exposurecategoriesprovided256cancerdeathsThe slope (β) was3.3 × 10−6for lagof 15 yearsexcluding upper5% of TCDDexposures.The slopes rangedtwo orders ofmagnitudedepending onmodelingassumptionAvailable: age,year of birth, andraceRisks adjusted for:year of birth, age,and raceConfounding bysmoking wasconsidered indirectlyby analysis ofsmoking-related andsmoking-unrelatedcancers.Other occupationalexposures wereconsidered indirectlyby repeated analysesremoving one plantat a time.Based on indirectevaluation, therewas no clearevidence ofconfounding.Cheng etal., 2006Mortalityfrom allcancersUSA,1942–1993NIOSH cohortincluding 3,538male workers,256 cancerdeathsCSLC based onwork histories,job-exposurematrix, and asimple one-compartmentfirst-orderpharmacokinetic eliminationmodel with 8.7-year half-lifeCSLC(ppt-years)<335335−520520−1,2121,212−2,8962,896−7,5687,568−20,455≥20,455642922303132481.001.26 (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 ofbirth and ageAdjusted for: dateof birth, and agewas used as timescale in CoxmodelIncluded inU.S. EPA (2003)Steenland etal., 2001This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-210 DRAFT: DO NOT CITE OR QUOTETable 2-4.Epidemiology studies selected for TCDDcancer dose-response modeling (continued)HealthoutcomeLocation,timeperiodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceMortalityfrom allcancerscombinedHamburg,Germany,productionperiod was1950−1984andmortalityfollow-upextendedthrough1992Boehringercohort includingapproximately1,189 workersemployed in theproduction ofherbicidesCumulativeTCDD serumlipidconcentrationsbased on areaunder curve (inμg/kg years);backextrapolation todate of lastemploymenttook intoaccount age andpercent bodyfat; half-lifevalue was 7.2yearsCategoricalexposures(Cox model)0−<11−<44−<88−<1616−<6464+ContinuousexposureTCDD (μg/kgyears)1241241.01.12 (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= 0.0047Available: year ofentry, age ofentry, duration ofemployment, birthcohort,β-HCH;TEQ other thanTCDDAvailable: year ofentry, age ofentry, duration ofemployment, birthcohort,β-HCH;TEQ otherthanTCDDIncluded inU.S. EPA (2003)A large number ofmodels were fitted.These includedmodels for 5different latencyintervals (0, 5, 10,15, and 20 years), aswell asmultiplicative,additive and powermodels, anddifferent offsetvariables (personyears and expecteddeaths)Becher etal., 1998This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-211 DRAFT: DO NOT CITE OR QUOTETable 2-4.Epidemiology studies selected for TCDDcancer dose-response modeling (continued)HealthoutcomeLocation,timeperiodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceMortalityandincidencefor allcancerscombined,as well asfor specificcancer sitesLudwig-shafen,Germany,1954−1992BASF cohort,243 menexposed fromaccidentalrelease thatoccurred in1953 duringproduction oftrichlorophenol,or who wereinvolved inclean-upactivitiesCumulativeTCDD serumlipidconcentrationsexpressed inμg/kg based onTCDD half-lifeof 5.1-8.9 years,Cox regressionmodelInternalcomparisonsbased oncontinuousmeasure ofTCDD.Externalcomparisonsexposurecategories:<0.1,0.1−0.99,1.0−1.99>2 μg/kgInternalcohortanalysis31cancerdeaths47incidentcancersExternalcohortanalysesDeaths8887Date of 1stTCDDexposure1.22 (95% CI:1.00−1.50)1.11 (95% CI:0.91−1.35)SMRs0.8 (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, smokingstatus and historyof occupationalexposure toaromaticamines andasbestosIncluded inU.S. EPA (2003)Positive associationsnoted for digestivecancer, but not forrespiratory cancerAssociated betweenTCDD andincreased SMRsfound only amongcurrent smokersLast publishedaccount of thiscohortOtt andZober, 1996This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-212 DRAFT: DO NOT CITE OR QUOTETable 2-4.Epidemiology studies selected for TCDDcancer dose-response modeling (continued)HealthoutcomeLocation,timeperiodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceBreastcancerincidenceItaly1976–1998981 womenfrom zones Aand B withavailablearchive serumsamples, 15breast cancercasesTCDD serumlipidconcentrations(ppt) collectedbetween 1976and 1981. Formost samplescollected after1977, serumTCDD levelswere back-extrapolatedusing a first-order kineticmodel with a 9-year half-life.<20 ppt20.1−44 ppt44.1−100 ppt>100 pptLog10TCDDalso modeledas continuousvariableCases1275151.01.0 (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 firstpregnancy, age atlast pregnancy,lactation, familyhistory of breastcancer, age atmenarche, currentbody mass index,oral contraceptiveuse, menarchealstatus atexplosion,menopause statusat diagnosis,height, smoking,alcoholconsumption.Adjusted for age,which was used astime scale in Coxmodel; othercovariates wereevaluated butwere notidentified asconfounders.Included inU.S. EPA (2003)Warner etal., 2002This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-213 DRAFT: DO NOT CITE OR QUOTETable 2-5. Epidemiology studies selectedfor TCDD noncancer dose-response modelingHealthoutcomeLocation,time periodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments Referenceb-TSHmeasured 72hours afterbirth from aheel pick(routinescreening forall newborns inthe regionItaly, 1976;children,1994–2005Population-based study:1,041singletons(56 fromzone A, 425from zone Band 533 fromreference)born betweenJan. 1, 1994-June 30,2005.Plasmadioxin study:51 childrenborn to 38women offertile agewho werepart of theSevesoChloracneStudy.Based on zoneof residence,estimated meanvalues from aprevious study.Maternalplasma TCDDlevels estimatedat the date ofdelivery using afirst-orderpharmacokinetic model andelimination rateestimated inSeveso women(half-life =9.8years).Population-based study:ReferenceZone BZone APlasmadioxinstudy:ContinuousmaternalplasmaTCDD533births425births56birthsPopulation-basedstudyMean b-TSHReference: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)Associationbetween neonatalb-TSH withplasma TCDD:adjustedβ= 0.75(p< 0.001)Available: gender,birth weight, birthorder, maternal ageat delivery,hospital, type ofdelivery.There was limitedevidence ofconfounding, somean TSH resultspresented here areunadjusted.An association withserum TCDD levelsof mothers wasfound with b-TSHamong the 51 birthsin the plasma dioxinstudy.Baccarelliet al., 2008This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-214 DRAFT: DO NOT CITE OR QUOTETable 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued)HealthoutcomeLocation,time periodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceSperm conc.(million/ mL)Progressivemotility (%)Serum E2(pmol/L)Italy, 1976,1998135 exposed(from zoneA) and 184non-exposedmen aged1−26 in 1976wereincluded.Thesesubjects wereselected fromthe cohort of257 exposedand 372unexposedpeople.Serum TCDD(in ppt) from1976-1977samples (forexposed men);backgroundvalues wereassumed forunexposed menbased on serumanalysis ofresidents inuncontaminatedareas.TCDDquartilesMean valueswere comparedbetween theexposed andcomparisongroups for spermconcentration,volume, motilityand count, FSH,E2, LH, andInhibin B.Available: age,abstinence time,smoking status,education, alcoholuse, maternalsmoking duringpregnancy,employment status,BMI, chronicexposure tosolvents and othertoxic substances.Adjusted forsmoking status,organic solvents,age at time of tests,BMI, alcohol use,education,employment statusand abstinence(days) for spermdata.Hormone data notadjusted foreducation level,employment status,and abstinencetime.Results stratified bytiming of exposure(1−9 yrs old vs.10−17 yrs old in1976).Mocarelliet al., 2008This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-215 DRAFT: DO NOT CITE OR QUOTETable 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued)HealthoutcomeLocation,time periodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceDental defects Seveso, Italy,Dentalexamsadministeredin 2001among thoseexposed toTCDD in197665 subjects<9.5 yearsold at time ofSevesoexplosionand residingin zonesABR; 130subjectsrecruitedfrom thenon-ABRregion(unexposed)Serum TCDD(ng/kg) from1976 samplesfor those whoresided in ZoneABR; no serumlevels for non-ABR residents(unexposed).TCDDexposurerepresent levelsas of 1976(after accident)Non-ABRZone31−226ng/kg238−592ng/kg700−26000ng/kg<5 years ofage at timeof accidentNon-ABRZone or31−226ng/kg serumTCDD238-26,000ng/kg serumTCDD10/391/105/119/1525/75Dental defect %26%10%45%60%Odds Ratios(among those <5years of age attime of accident)1.02.4 (1.3−4.5)Available: medicalhistory, age, sex,education, smokingDose-responsepattern observedwith dental defectsin the ABR zone;however, the controlpopulation had amuch higherprevalence of dentaldefects (26%) thanthose in the lowestexposure group(10%).Also assessedhypodontia andother dental and oralaberrations, butthese were too rareto allow modelingby ABR zone.Alaluusuaet al., 2004This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-216 DRAFT: DO NOT CITE OR QUOTETable 2-5. Epidemiology studies selected for TCDD noncancer dose-response modeling (continued)HealthoutcomeLocation,time periodCohortdescriptionExposureassessmentExposuremeasuresNo. ofcases/deathsEffect Measure/RR (95% CI)Risk factors Comments ReferenceMenstrualcyclecharacteristics:menstrual cyclelength.Seveso, Italy,follow-upinterviewconducted in1996-1997 ofwomenexposed toTCDD in the1976accidentWomen whowere <40years fromzones A or Bin 1976,A positiveassociationfound amongwomen whowere pre-menarchealat the time ofaccident(n=134)Serum TCDD(ng/kg) from1976 samples.TCDDexposure levelwas back-extrapolated to1976 using theFilser or thefirst-orderkinetic models.Interquartilerange was64-322 pptTCDDexamined ascontinuousmeasure(per 10-foldincrease inserumlevels).Lengthening ofthe menstrualcycle by 0.93days (95% CI: -0.01, 1.86)Interview data:medical history,personal habits,work history,reproductivehistory, age,smoking, bodymass index, alcoholand coffeeconsumption,exercise, illness,abdominalsurgeries.Eskenazietal., 2002aThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-217 DRAFT: DO NOT CITE OR QUOTETable 2-6. Animal bioassays selected for cancer dose-response modelingSpecies/strainSexexposureroute/durationnAverage dailydose levels(ng/kg-day)Cancer typesStatistical significant tumors(pairwise with controls or trend tests) ReferenceRat/Sprague-DawleyMale/femaleOral-lifetimefeeding; 2 years50 each(86 each invehicle controlgroup)0, 1, 10, or 100 Females: liver, lung,oral cavityMales: adrenal, oralcavity, tongueAdrenal cortex: adenomaLiver: hepatocellular adenoma(s) orcarcinoma(s); hyperplastic nodulesLung: keratinizing squamous cell carcinomaOral cavity: stratified squamous cellcarcinoma of hard palate or nasal turbinatesTongue: stratified squamous cell carcinomaKociba et al.,1978; Femaleliver tumorsanalysisupdated inGoodman andSauer, 1992Mouse/B6C3F1Male/femaleOral-gavage twiceper week; 104weeks50 each(75 each invehicle controlgroup)0, 1.4, 7.1, or 71for males;0, 5.7, 28.6, or286 for femalesFemales:hematopoietic system,liver, subcutaneoustissue, thyroidMales: liver, lungHematopoietic system: lymphoma or leukemiaLiver: hepatocellular adenoma or carcinomaLung: alveolar/bronchiolar adenoma orcarcinomaSubcutaneous tissue: fibrosarcomaThyroid: follicular-cell adenomaNTP, 1982aRat/Osborne-MendelMale/femaleOral-gavage twiceper week; 104weeks50 each(75 each invehicle controlgroup)0, 1.4, 7.1, or 71 Females: adrenal, liver,subcutaneous tissue,thyroidMales: adrenal, liver,thyroidAdrenal: cortical adenoma, or carcinoma oradenoma, NOSLiver: neoplastic nodule or hepatocellularcarcinomaSubcutaneous tissue: fibrosarcomaLiver: neoplastic nodule or hepatocellularcarcinomaThyroid: follicular-cell adenoma or carcinomaNTP, 1982aRat/HarlanSprague-DawleyFemaleOral-gavage5 days per week;2 years53 or 54 0, 2.14, 7.14,15.7, 32.9, or71.4LiverLungOral mucosaPancreasLiver: hepatocellular adenomaLiver: cholangiocarcinomaLung: cystic keratinizing epitheliomaOral mucosa: squamous cell carcinomaPancreas: adenoma or carcinomaNTP, 2006Mouse/OutbredSwiss/H/RiopMaleGastric intubationonce per week; 1year43 or 44(vehicle controlgroup = 38)0, 1, 100, or1,000LiverLiver: tumorsToth et al.,1979This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-218 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modelingSpecies/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceReproductive toxicity studiesMonkey/RhesusDaily dietaryexposure infemalemonkeys(3.5−4 years)F (F0, F1,F2, F3)3 to 7 (F1) 0, 0.15, or 0.67 0.15 0.67 ReproductiveanddevelopmentaleffectsNeurobehavioral effects(e.g., discriminationreversal learningaffected)Bowman etal., 1989a, b(and relatedSchantz andBowman,1989; Schantzet al., 1986)Mink Daily dietaryexposure(132 days)F 12 0.03 (control),0.8, 2.65, 9, or70None 2.65 ReproductiveeffectsReduced kit survival Hochstein etal., 2001Rat/Holtzman Corn oilgavage (initialloading dosefollowed byweekly doseduring mating,pregnancy, andlactation–about10 weeks)F (F0)F and M(F1 andF2)12 (F0)Not specified(F1 and F2)0 or 16.5 None 16.5(maternalexposure)ReproductiveanddevelopmentaleffectsDecreased developmentof the ventral prostrate(F1), decreased sex ratio(percentage of males)(F2)Ikeda et al.,2005aMouse/ICR Sesame oilgavage (initialloading dosefollowed byweekly dosesfor 5 weeks)M (F0)42 or 43 0, 0.095, or9500.1 100 ReproductiveeffectsDecreased male/femalesex ratio (percentage ofmales) (F1)Ishihara et al.,2007This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-219 DRAFT: DO NOT CITE OR QUOTE1Table 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceReproductive toxicity studies (continued)Rat/WistaralbinoOlive oilgavage (dailyfor 45 days)M 60, 1, 10, or 100 None 1 ReproductiveeffectsReduced spermproduction, decreasedreproductive organweightsLatchoumy-candane andMathur, 2002(and relatedLatchoumy-candane et al.,2002a, b,2003)Rat/Sprague-DawleyDaily dietaryexposure(3 generations)F and M,(F0)F and M,(F1 andF2)10–32 (F0)22 (F1)28 (F2)0, 1, 10, or 100 1 10 ReproductiveanddevelopmentaleffectsDecrease in fertility,decrease in the numberof live pups, decrease ingestational survival;decrease in postnatalsurvival, decreasedpostnatal body weight inone or more generationsMurray et al.,1979Monkey/RhesusDaily dietaryexposure(4 years)F 80, 0.15, or 0.67 None 0.15 ReproductiveeffectsIncreased incidence ofendometriosis (diseaseranged from moderate tosevere)Rier et al.,1993, 1995Rat/Sprague-DawleyMaternal cornoil gavage(weekly onGD 14 and 21;PND 7 and 14)Offspring cornoil gavage(weekly for11 months)F (F0)F (F1)3 (F0)10 (F1)0, 0.14, 0.71,7.14, or 28.60.14 0.71 ReproductiveeffectsDecrease serumestradiol levels (F1)Shi et al.,2007This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-220 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceReproductive toxicity studies (continued)Rhesusmonkey/CynomolgusFed gelatincapsules(5 days/weekfor 12 months)F 6 (treatment)5 (controls)0, 0.71, 3.57,or 17.8617.86None EndometriosiseffectsIncreased endometrialimplant survival,increased maximum andminimum implantdiameters, growthregulatory cytokinedysregulationYang et al.,2000Developmental toxicity studiesRat/HarlanSprague-DawleyCorn oilgavage (GD10−16F (F0) 80−88 (F1)0, 25, or 100None 25 DevelopmentaleffectsDecreased preference inthe consumption of0.25% saccharinsolution (F1)Amin et al.,2000Rat/CRL:WI(Han)Maternal dailydietaryexposure for anestimated20 weeks(12 weeksprior to matingthroughparturition)F (F0)M (F1)65 (F0treatments)75 (F0controls) atstudyinitiation;followinginterimsacrifice~30 animalswere allowedto litter; F1on PND 21was ~70, 2.4, 8, or 46 None 2.4(maternalexposure)ReproductiveanddevelopmentaleffectsDelayed BPS (F1) Bell et al.,2007aThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-221 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceDevelopmental toxicity studies (continued)Rat/Sprague-DawleyMaternal cornoil gavage(GD 14 and21; PND 7 and14)Offspring cornoil gavage(weekly for8 months)F (F0 andF1)2 or 3 (F0)7 (F1)0, 7.14, or 28.6 None 7.14 DevelopmentaleffectsDecreased serumestradiol levels (F1)Franczak etal., 2006Rat/Sprague-DawleyMaternalsingle corn oilgavage (GD 8)Offspringexposed duringgestation andlactation(35 days)F (F0)F and M(F1)12 (F0)50 or 60 (F1)0, 20, 60, or180None 20(maternalexposure)DevelopmentaleffectsAbrogation of sexuallydimorphic neuro-behavioral responses(F1)Hojo et al.,2002 (andrelated Zarebaet al., 2002)Rat/Han/Wistarand Long-EvansMaternalsingle corn oilgavage(GD 15)F (F0)F and M(F1)4 to 8 (F0)3F/3M pertreatmentgroup (F1)0, 30, 100,300, or 1,000None 30(maternalexposure)DevelopmentaleffectsReduced mesiodistallength of the lower thirdmolar (F1)Kattainen etal., 2001This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-222 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceDevelopmental toxicity studies (continued)Mouse/C57BL/6J,BALB/cByJ,A/J, CBA/J,C3H/HeJ, andC57BL/10JMaternalsingle corn oilgavage(GD 13)F (F0)F and M(F1a, b, c)Dams notspecified(F0);23−36 (F1a);4−5 (F1b);107−110(F1c)0, 10, 100, or1,000None 10(maternalexposure)DevelopmentaleffectsVariation in M1morphology inC57BL/10J males andfemales (F1a);decreased mandibleshape and size inC3H/HeJ males (F1b);variation in molar shapein C3H/HeJ males (F1c)Keller et al.,2007, 2008a, bMouse/ddY Maternal oliveoil gavage(weekly for8 weeks priorto mating)F (F0)M (F1)7 (F0)3 (F1immunocytochemicalanalysis)6 (F1 cellnumbercount)0, 0.7, or 70 None 0.7(LOEL)(maternalexposure)Neurotoxicity Decreased serotoninimmunoreactive neuronsin raphe nuclei of maleoffspring (F1)Kuchiiwa etal., 2002Mouse/NIH(pregnant andpseudopregnant)Maternalsesame oilgavage dailyfor 8 days(GD 1−8)F 10 0, 2, 50, or 100 None 2 DevelopmentaleffectsDecreased progesteroneand increased serumestradiol levelsLi et al., 2006Rat/Holtzman Maternalsingle olive oilgavage(GD 18)F (F0 andF1)4−7 (F0 andF1)0, 20, 60, or180None 20(maternalexposure)BehavioraleffectsDecreased trainingresponses (F1)Markowski etal., 2001Rat/Line C Maternalsingle corn oilgavage(GD 15)F (F0)F and M(F1)24−32(treatment)12−48(controls)0, 30, 100,300, or 1,000None 30(maternalexposure)DevelopmentaleffectsIncrease in dental caries(F1)Miettinen etal., 2006This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-223 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceDevelopmental toxicity studies (continued)Rat/Holtzman Maternalsingle corn oilgavage(GD 15)F (F0)M (F1)Not specified(F0)5 males and3 females(F1)0, 12.5, 50,200, or 800800(maternalexposure)None Immunotoxicity Decreased spleencellularity (F1)Nohara et al.,2000Rat/Holtzman Maternalsingle corn oilgavage(GD 15)F (F0)M (F1)6 (F0)5 males and3 females(F1)0, 12.5, 50,200, or 80012.5(maternalexposure)50(maternalexposure)DevelopmentaleffectsDecreased anogenitaldistance (F1)Ohsako et al.,2001Rat/HarlanSprague-DawleyMaternal cornoil gavage(GD 10−16F(F0) ~4 (F0);80−88 (F1)0, 25, or 100 NotdeterminedNotdeterminedDevelopmentaleffectsFacilitatory effect onradial arm maze learning(F1)Schantz et al.,1996Rat/SpragueDawleyMaternal cornoil gavage(GD 10−16)F and M(F1)~15 (F0);5−9 (F1)0, 25, or 100 25 100 DevelopmentaleffectsDecreased thymusweightSeo et al.,1995Rat/TCDDresistantHan/Wistarbred withTCDDsensitiveLong-EvansMaternal cornoil gavage(GD 15)F (F0)M (F1)5−8 (F0) 0, 30, 100,300, or 1,000100 300 ReproductiveeffectsReduction in dailysperm production andcauda epididymal spermreservesSimanainen etal., 2004Mouse/C57/6NCjiMaternaldrinking waterexposure(daily for17-daylactationalperiod)F (F0)F and M(F1)8 (F0)Not specified(F1)0, 1.14, or 11.3 1.14(NOEL)(maternalexposure)11.3(LOEL)(maternalexposure)Immunotoxicity Increased susceptibilitytoListeria(F1 malesand females); increase inthymic CD4+ cells(F1 males);decreasedspleen weight(F1 males)SugitaKonishi et al.,2003This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-224 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceAcute toxicity studiesMouse/B6C3F1Corn oilgavage (singleexposure)F200, 1, 5, 10, 50,100, or 6,0005 10 Immunotoxicity Increased mortality frominfluenza infection7 days after a singleTCDD exposureBurleson etal., 1996Rat/Long-EvansCorn oilgavage(4 consecutivedays)F 14, 6, 12, 6,6, 6, 6, 6, 6,and 4,respectivelyin controland treatedgroups0, 0.1, 3, 10,30, 100, 300,1,000, 3,000,or 10,00030 100 Thyroid effects Reduction in serum T4levelsCrofton et al.,2005Rat/Sprague-DawleyCorn oilgavage (singledose)F 4 (treated);9 (control)0, 0.6, 2, 4, 20,60, 200, 600,2,000, 5,000,or 20,0000.6(NOEL)2(LOEL)EnzymeinductionIncreasedbenzo(a)pyrenehydroxylase (BPH)Kitchin andWoods, 1979Rat/Sprague-DawleyCorn oil dosevia oral gastricintubation(single dose)F 10 0, 3, 10, 30,100, 300,1,000, 3,000,10,000, or30,0003 10 HormonaleffectsIncreased serum FSH Li et al., 1997This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-225 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceAcute toxicity studies (continued)Rat/Sprague-DawleyCorn oilgavage orTCDD-contaminatedsoil (singledose)F 60, 15, 40, 100,200, 500,1,000, 2,000,or 5,000 incorn oil0, 15, 44, 100,220, 500,1,100, 2,000,or 5,500 incontaminatedsoilNone 15(LOEL)EnzymeinductionInduction ofarylhyrocarbonhydroxylase (at lowdose in both treatmentprotocols)Lucier et al.,1986Mouse/B6C3F1(BALB/c(C57BL/6N(and DBA2Corn oilgavage (singledose)M, F 10−40 0, 5, 20, 100,or 500500 None Mortality andbody weightchangesNo increased mortalityof virus-infected mice ortreatment-relatedchanges in body weightNohara et al.,2002Rat/TCDDresistantHan/Wistarbred; TCDDsensitiveLong-EvansCorn oilgavage (singledose)M, F 9−11 30–100,000 100 300 Generaltoxicologicalendpoints, organweights, dentaldefectsReduction in serum T4levelsSimanainen etal., 2002Rat/TCDDresistantHan/Wistarbred withTCDDsensitiveLong-EvansCorn oilgavage (singledose)M, F 5−6 Line A:30−3,000,000Line B:30−1,000,000Line C:30−100,000100 300 Generaltoxicologicalendpoints, organweights, dentaldefectsDecreased thymusweightSimanainen etal., 2003This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-226 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceAcute toxicity studies (continued)Mouse/C57BL/6NCYP1A2 (+/+)wild-typeCorn oilgavage (singledose)F Not specified 0, 30, 100,300, 1000,3000, or10,000300 1,000 Immunotoxicity Decreased antibodyresponse to SRBCsSmialowicz etal., 2004Rat/Sprague-DawleyCorn oilgavage (singledose)F 5−15 0, 0.05, 0.1, 1,10, 100, 1,000,or 10,0000.1(NOEL)1(LOEL)Liver effects Increase in hepaticEROD activity andCYP1A1 mRNA levelsVandenHeuvel et al.,1994Subchronic toxicity studiesRat/SpragueDawleyCorn oilgavage (dailyfor 28 days)F 50, 2.5, 25, 250,or 1,000250 1,000 Body and organweight changesDecreased body weight,increased relative liverweight and relatedbiochemical changes,decreased relativethymus weightChu et al.,2001Rat/SpragueDawleyCorn oilgavage (dailyfor 28 days)F 50, 2.5, 25, 250,or 1,0002.5 25 Liver effects Alterations in thyroid,thymus, and liverhistopathologyChu et al.,2007Guinea pig/HartleyDaily dietaryexposure(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 organweight changesDecreased body weight(male and females);increased relative liverweights (males);decreased relativethymus weight (males)DeCaprio etal., 1986Mice/B6C3F1 Corn oilgavage(5 days/weekfor 13 weeks)F 50, 1.07, 3.21,10.7, 32.1, or107None 1.07(LOEL)Body and organweight changes;enzymeinductionIncreased EROD,ACOH andphosphotyrosyl proteinsat all dosesDevito et al.,1994This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-227 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceSubchronic toxicity studies (continued)Daily dietaryexposure(13 weeks)M, F 60, 20, 200, or2,000Daily dietaryexposure(13 weeks)M, F 60 or 200Daily dietaryexposure(13 weeks)M, F 60, 200, or1,000Rat/Iva:SIV50-Sprague-DawleyDaily dietaryexposure(13 weeks, 26,and 39 weeks)F 60 or 100None 20 Liver effects Reduced hepaticvitamin A levelsFattore et al.,2000Rat/Sprague-DawleyGavageloading/maintenancedoses (every4 days for14 days)M, F 60, 0.55, 307, or1,6070.57 327 Body and liverweight changes;hepatic cellproliferationIncreased absolute andrelative liver weightFox et al.,1993Mouse/B6C3F1Corn oilgavage(5 days/weekfor 13 weeks)F Notspecified0, 0.32, 1.07,10.7, or 107None 0.32(LOEL)Brain effects Induction of biomarkersof oxidative stress at alldosesHassoun et al.,1998Rat/HarlanSprague-DawleyCorn oilgavage(5 days/weekfor 13weeks)F 60, 2.14, 7.14,15.7, 32.9, or71.4None 2.14(LOEL)Liver and braineffectsInduction of biomarkersof oxidative stress at alldoses in liver and brainHassoun et al.,2000This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-228 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceSubchronic toxicity studies (continued)Rat/HarlanSprague-DawleyCorn oilgavage(5 days/weekfor 13 weeks)F 12 0, 7.14, 15.7,or 32.9None 7.14(LOEL)Brain effects Induction of biomarkersof oxidative stress at alldosesHassoun et al.,2003Rat/Sprague-DawleyCorn oilgavage(5 days/weekfor 13 weeks)M, F 12 0, 0.71, 7.14,71.4, or 7147.14 71.4 Liver effects,body weightchanges, andhematologic andclinical effectsReduced body weightand food consumption,slight liver degeneration,lymphoid depletion,increased urinaryporphyrins and deltaaminolevulinic acid,increased serum alkalinephosphatase andbilirubinKociba et al.,1976Rat/F344 Corn oilgavage(2 days/weekfor 28 days)F 30, 0.71, 7.14,or 71.4None 0.71(LOEL)Clinical signsandhistopathologyDecreased Cx32 plaquenumber and area in theliverMally andChipman,2002Mouse/B6C3F1Corn oilgavage(5 days/weekfor 13 weeks)F Not specified 0, 0.11, 0.32,1.07, 10.7, or107.141.07(NOEL)10.7(LOEL)Liver, lung,kidney, andspleen effectsIncreased hepaticsuperoxide anionSlezak et al.,2000Mouse/B6C3F1Corn oilgavage(5 days/weekfor 13 weeks)F 8−15 0, 1.07, 10.7,107, or 321None 1.07 Immunotoxicityand organweightReduced antibodyresponse to SRBC,increased relative liverweightSmialowicz etal., 2008Rat/SpragueDawleyTCDD in diet(13 weeks)F 8 0, 14, 26, 47,320, or 1,024None 14 Multiple end-pointsDecreased absolute andrelative thymus weights,decreased liver retinoidlevelsVan Birgelen1995a, bThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-229 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceSubchronic toxicity studies (continued)Guinea pig/HartleyCorn oilgavage(weekly for8 weeks)F 10 0, 1.14, 5.71,28.6, or 1431.14 5.71 Immunotoxicity Decreased totalleukocytes andlymphocyte count,decreased absolutethymus and weight,increase in primaryserum tetanus antitoxinVos et al.,1973Mouse/B6C3F1Corn oilgavage (dailyfor 14 days)F 6−8 0, 10, 50, 100,500, 1,000, or2,000None 10 Immunotoxicity Reduction of serumcomplement activityWhite et al.,1986Chronic toxicity studiesRat/CDCOBSCorn oilgavage(weekly for45 weeks)F 40, 1.43, 14.3,or 143None 1.43 HepaticporphyriaIncreased urinaryporphyrin excretionCantoni et al.,1981Rat/SpragueDawleyLoading/maintenancedose (every3 days fordifferentdurations up to128 days)F 50, 0.85, 3.4,13.6, 54.3, or217(28-dayduration)54.3(28-dayduration)217(28-dayduration)Body weightchanges andchanges inPEPCK activityand IGF-I levelsDecreased body weight,decreased PEPCKactivity, and reducedIGF-I levelsCroutch et al.,2005Rat/SpragueDawleyCorn oilgavage(5 days/weekfor 30 weeks)F 60, 2.14, 7.14,15.7, 32.9, or71.4None 2.14(LOEL)Brain effects Induction of biomarkersof oxidative stress at alldosesHassoun et al.,2002This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-230 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceChronic toxicity studies (continued)Rat/Sprague-DawleyDaily dietaryexposure(2 years)M, F 50 0, 1, 10, or 100 1 10 MultipleendpointsmeasuredIncreased urinaryporphyrins,hepatocellular nodules,and focal alveolarhyperplasiaKociba et al.,1978Rat/Sprague-DawleyBiweeklygavage(30 weeks)F 9 0, 3.5, 10.7,35, or 12510.7 35 Body and organweight changes,clinicalchemistry,hepatocellularproliferationIncreased relative liverweightMaronpot etal., 1993Mouse/B6C3F1;Rat/OsborneMendelCorn oilgavage(2 days/weekfor 104 weeks)M, F 50 0, 1.4, 7.1, or71 for rats andmale mice; 0,5.7, 28.6, or286 for femalemiceNone 1.4 Liver and bodyweight changesIncreased incidences ofliver lesions in mice(males and females)NTP, 1982Rat/Sprague-DawleyCorn oilgavage(5 days/weekfor 105 weeks)F 53 0, 2.14, 7.14,15.7, 32.9, or71.4None 2.14 Liver and lungeffectsIncreased absolute andrelative liver weights,increased incidence ofhepatocellularhypertrophy, increasedincidence of alveolar tobronchiolar epithelialmetaplasiaNTP, 2006Monkey/RhesusDaily dietaryexposure(4 years)F 80, 0.15, or 0.67 None 0.15 Generaltoxicologicalendpoints andreproductiveeffectsElevated serumtriglycerides and totallipidsRier et al.,2001a, bThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 2-231 DRAFT: DO NOT CITE OR QUOTETable 2-7. Animal bioassay studies selected for noncancer dose-response modeling (continued)Species/strainExposureprotocolSex(exposuregroup)nAverage dailydose levels(ng/kg-day)NOAEL(ng/kg-day)LOAEL(ng/kg-day)Endpoint(s)examinedLOAEL/NOAELEndpoint(s) ReferenceChronic toxicity studies (continued)Rat/Sprague-DawleyBiweeklygavage(30 weeks)F 9 0, 3.5, 10.7,35, or 125None 3.5(LOEL)EGFR kineticsand auto-phosphorylation,hepatocellularproliferationDecrease in EGFRmaximum bindingcapacitySewall et al.,1993Rat/Sprague-DawleyBiweeklygavage(30 weeks)F 90, 0.1, 0.35, 1,3.5, 10.7, 35,or 12510.7 35 ThyroidfunctionDecreased serum T4levelsSewall et al.,1995Mouse/Swiss/H/RiopSunflower oilgavage(weekly for1 year)M 38−44 0, 1, 100, or1,000None 1 Skin effects Dermal amyloidosis andskin lesionsToth et al.,1979ND = not determined.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 2-232 1 Figure 2-1. EPA’s process to select and identify in vivo mammalian and 2 epidemiologic studies for use in the dose-response analysis of TCDD. EPA 3 first conducted a literature search to identify studies published since the 2003 4 Reassessment. Results were published and additional study submissions were accepted 5 from the public. Next EPA developed TCDD-sp ecific study inclusion criteria for in vivo 6 mammalian studies and held a Dioxin Workshop where these criteria were discussed and 7 refined. Third, EPA developed two final sets of study inclusion criteria, one for in vivo 8 mammalian studies and another for epidemiol ogic studies. Finally, EPA applied these 9 two sets of criteria to all studies from the literature search, public submissions, 2003 10 Reassessment, and additional studies iden tified by EPA after the Dioxin Workshop 11 through October 2009. The studies that met these criteria formed a list of key studies for 12 EPA’s consideration in TCDD dose-response assessment. 13 Studies Included in final list of key cancer and noncancerstudies 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 noncancerstudies 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 studiesThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 2-233 1 Figure 2-2. EPA’s process to evaluate available epidemiologic studies using 2 study inclusion criteria for use in th e dose-response analysis of TCDD. EPA 3 applied its TCDD-specific epidemiologic study inclusion criteria to all studies published 4 on TCDD and DLCs. The studies were initia lly evaluated using five considerations 5 regarded as providing the most relevant kind of information needed for quantitative 6 human health risk analyses. For each study that satisfied most of these considerations 7 and was published in the peer-reviewed lite rature, EPA then examined whether the 8 exposures were primarily to TCDD and if the TCDD exposures could be quantified so 9 that dose-response analyses could be conducte d. Finally, EPA require d that the effective 10 dose and oral exposure be estimable: (1) for cancer, information is required on long-term 11 exposures, (2) for noncancer, information is required regarding the appropriate time 12 window of exposure that is relevant for a sp ecific, nonfatal health endpoint, and (3) for 13 all endpoints, the latency period between TCDD exposure and the onset of the effect is 14 needed. Only studies meeting these cr iteria were included in EPA’s TCDD 15 dose-response analysis. 16 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 YesThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

― DO NOT CITE OR QUOTE 2-234 1 Figure 2-3. EPA’s process to evaluate ava ilable animal bioassay studies using study 2 inclusion criteria for use in the dose-response analysis of TCDD . EPA evaluated all 3 available in vivo mammalian bi oassay studies on TCDD. St udies had to be published in 4 the peer-reviewed literature. Next, to ensu re working in the lo w-dose range for TCDD 5 dose-response analysis, EPA applied dose require ments to the lowest tested average daily 6 doses in each study, with specific requirements for cancer ( ≤ 1 μ g/kg-day) and noncancer 7 ( ≤ 30 ng/kg-day) studies. Third, EPA required th at the animals were exposed via the oral 8 route to only TCDD and that the purity of th e TCDD was specified. Finally, the studies 9 were evaluated using four considerations rega rded as providing the most relevant kind of 10 information needed for quantitative human health risk analyses from animal bioassay 11 data. Only studies meeting all of these cr iteria and considerations were included in 12 EPA’s TCDD dose-response analysis. 13 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

NoThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-1 3. THE USE OF TOXICOKINETICS IN THE DOSE-RESPONS E MO DELING FOR 1 CANCER AND NONCANCER ENDPOINTS 2
3 4 A key recommendation from the National Academy of Sciences (NAS) for improving the 5
2003 Reassessment was that U.S. Environmental Protection Agency (EPA) should justify its 6 approaches to dose-response modeling for cancer and noncancer endpoints. Further, the NAS 7 suggested that EPA incorporate the most up-to-date and releva nt state of the science for 8 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) dose-response assessment. 9
While EPA believes that at the time of its release, the 2003 Reassessment offered a 10 substantial improvement over the general state-of -the-science regarding dose-response modeling, 11 EPA agrees that the justification of the appr oaches to dose-response modeling can be improved 12 and the methodologies updated to reflect the most current EPA practices and science. In 13 Section 3, EPA describes the use of toxicokine tic (TK) information in the dose-response 14 modeling assessment of TCDD. Section 3.1 summarizes the NAS comments regarding the use 15 of TK in the dose-response a pproaches for TCDD. Section 3.2 overviews EPA’s responses to 16
the NAS comments. Section 3.3 discusses TCDD kinetics, including TK models developed to 17 simulate disposition of this compound in r odents and humans (see Se ction 3.3.4), alternative 18
measures of dose that could be used in a TC DD dose-response analysis and uncertainties in the 19 TCDD dose estimates (see Section 3 .3.5). Sections 4 and 5 of this document incorporate the TK 20 information into noncancer and cancer dose response modeling, respectively. 21
22
3.1. SUMMARY OF NAS COMMENTS ON TH E USE OF TOXICOKINETICS IN 23 DOSE-RES PONSE MODELING APPROACHES FOR TCDD 24 The NAS commented on the appropriate use of TK models in dose-response modeling 25 for TCDD. Specifically, the committee requested that EPA consider using such models to 26 provide refined estimates of dose, for exampl e, as the underlying science and predictive 27 capabilities of these models improved. 28
29 [Discussing Kinetic models]...the committee encourages further development and 30 use of these models as data become avai lable to validate and further develop them 31 (NAS, 2006a, p. 59). 32 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-2 Although the NAS basically agr eed with EPA’s use of body burden as a dose metric in 1 the 2003 Reassessment (e.g., see NAS, 2006a, p. 7), the NAS was concerned about the 2 limitations of first order kinetic models, such as the one used in the 2003 Reassessment, to 3 estimate TCDD body burdens. 4
5 TCDD, other dioxins, and DLCs act as pot ent inducers of CYP, a property that 6 can affect both the hepatic sequestration of these compounds and their half-lives. 7 Hepatic sequestration of dioxin may influe nce the quantitative ex trapolation of the 8 rodent liver tumor results because the body-burden distribution pattern in highly 9 dosed rats would differ from the corres ponding distribution in humans subject to 10 background levels of exposure. EPA shoul d consider the possible quantitative 11 influence of dose-dependent toxicokine tics on the interpretation of animal 12 toxicological data (NAS, 2006a, p. 129). 13 14 The NAS also asked EPA to evaluate the imp act of kinetic uncertain ty and variability on 15 dose-response assessment. The NAS committee asked EPA to use TK models to examine both 16 interspecies and human interindi vidual differences in the di sposition of TCDD, which would 17 better justify EPA dose-response modeling choices. 18
19 The Reassessment does not adequately c onsider the use of a PBPK model to 20 define species differences in tissue distri bution in relation to total body burden for 21 either cancer or noncancer end points (NAS, 2006a, p. 62). 22 23 EPA ...should consider physiologically based pharmacokinetic modeling as a 24 means to adjust for differences in body fat composition and for other differences 25 between rodents and humans (NAS, 2006a, p. 10). 26 27 The Reassessment does not provide detail s about the magnitudes of the various 28 uncertainties surrounding the decisions EPA makes in relation to dose metrics 29 (e.g., the impact of species differences in percentage of body fat on the steady30 state concentrations present in nonadipose tissues). The committee recommends 31 that EPA use simple PBPK models to de fine the magnitude of any differences 32 between humans and rodents in the re lationship between total body burden at 33 steady-state concentrations (as calcul ated from the intake, half-life, 34 bioavailability) and tissue concentrati ons. The same model could be used to 35 explore human variability in kinetics in relation to elimination half-life. EPA 36 should modify the estimated human equivalent intakes when necessary (NAS, 37 2006a, p. 73). 38 39This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-3 Finally, the NAS asked EPA to use TK considera tions to better justif y its choice of dose 1
metric. 2
3 EPA makes a number of assumptions about the appropriate dose metric and 4 mathematical functions to use in the Reassessment’s dose-response analysis ... 5 but does not adequately comment on the extent to which each of these 6 assumptions could affect th e resulting risk estimates...E PA did not quantitatively 7 describe how this particul ar selection affected its estimates of exposure and 8 therefore provided no overall quantitative perspective on the relative importance 9 of the selection (NAS, 2006a, p. 51). 10 11
3.2. OVERVIEW OF EPA’S RESPONSE TO THE NAS COMMENTS ON THE USE OF 12 TOXICOKENTICS IN DOSE-RESPONS E M ODELING APPROACHES FOR 13 TCDD 14 In response to the NAS recommendations regarding TCDD kinetics and choice of dose 15 metrics, this document presents an in depth evaluation of TCDD TK models, exploring their 16
differences and commonalities and their possible application for the derivation of dose metrics 17 relevant to TCDD. Initially, EPA discusses the application of first order kinetics to estimate 18 body burden as a dose metric for TCDD. This firs t order kinetic model is used to predict TCDD 19 body burden for all of the studies identified as Key Studies (see Section 2.4); this model uses a 20
constant half-life to simulate the elimina tion of TCDD from the body. However, given the 21 observed data indicating early influence of cytochrome P 450 1A2 (CYP1A2) induction and 22 binding to TCDD in the liver and later redistribution of TCDD to fat tissue, the use of a constant 23
half-life for TCDD clearance follo wing long term or chronic TCDD exposure is not biologically 24 supported. Therefore, using half- life estimates based on observed terminal steady state levels of 25 TCDD will not account for the possibility of an accelerated dose -dependent clearance of the 26 chemical during early stages following elevated TCDD exposures. Th e biological processes 27 leading to dose-dependent TCDD excretion ar e better described usi ng physiologically based 28 pharmacokinetic (PBPK) models than by simple firs t order kinetic models. Additionally, as part 29 of its preparation for developing this document, EPA evaluated recent TCDD kinetic studies as 30 NAS advocated. Although the NAS agreed with continued use of body burden metric as the 31 dose metric of choice, EPA believes that the stat e-of-the-practice has a dvanced sufficiently to 32 justify the consideration of alternative dose metr ics (other than administered dose) based on an 33 application of a physiologi cally-based TK model. 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-4 EPA identified a number of advances in the overall scient ific understanding of TCDD 1 disposition; many of these are documented in a summary disc ussion introducing the section on 2 TCDD kinetics (see Section 3.3). The increased understanding warranted an evaluation of 3 current kinetic modeling of TCDD to determine if the use of such models would improve the 4 dose-response assessment for TCDD. Justificati on of the final PBPK mode l choice is detailed in 5 Section 3.3. Through the choice of a published PBPK model to estimate dose metrics for dioxin, 6
EPA has addressed several of the NAS concerns. The PBPK model can be applied to estimate 7 dose metrics other than body burden that may be mo re directly related to response, e.g., tissue 8 levels, serum levels, blood concentrations, or dose metrics related to TCDD-protein receptor 9 binding. The selected PBPK model included explicit descri ption of physiological and 10 biochemical parameters, therefore, it can also provide an excellent tool for investigating 11 differences in species uptake and disposition of TCDD. One of the criteria used to select a 12 PBPK model for TCDD kinetics was the availability of both human and animal models so that 13 differences in species uptake and disposition of TCDD can be investigated. Additionally, the 14 PBPK model includes quantitative information that is suitable for addressing the impact of 15 physiological (e.g., body weight [BW] or fat tissu e volume), or biochemical (e.g., induction of 16 CYP1A2) variability on overall ri sk of TCDD between species, in response to another area of 17 concern in the NAS report. The sensitivity analys is and uncertainty in dose metrics derived for 18 the risk assessment of TCDD are also presented in Section 3.3. Detailed discussion on the 19 uncertainty in choice of PBPK model-driven dose metrics is also provided in Section 3.3. 20
21
3.3. PHARMACOKINETICS (PK) AND PK MODELING 22
3.3.1. PK Data and Models in TCDD Dose-R esponse Modeling: Overview and Scope 23 In general, the use of measures of internal dose in dose-response m odeling is considered 24 to be superior to that of administered dose (or uptake) because the former is more closely related 25 to the response. The evaluation of internal dos e, or dose metric, in exposed humans and other 26 animals is facilitated by an understanding of pharmacokinetics (i.e., absorption, distribution, 27 metabolism, and excretion). When measuremen ts of internal dose (e.g., blood concentration, 28 tissue concentration) are not available in anim als and humans, pharmacokinetic models can be 29
used to estimate them. The available data on the pharmacokinetics of TCDD in animals and 30 humans have been reviewed (Van Birgelen and Van den Berg, 2000; U.S. EPA, 2003; NRC, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-5 2006). It is evident based on thes e reviews and other analyses that three distinctive features of 1 TCDD play important roles in determining its pharmacokinetic behavior, as discussed below: 2
3 TCDD is very highly lipophilic and thus is more soluble in fat or other relatively nonpolar 4 organic media than in water. The n -octanol/water partition coefficient is a commonly5 used measure of lipophilicity equal to the equi librium ratio of a substance’s concentration 6 in n − octanol (a surrogate for biotic lipid) to the substance’s concentration in water (Leo 7 et al., 1971). For TCDD, this coefficien t is on the order of 10,000,000 or more (ATSDR, 8 1998). It follows that the solubility of TC DD in the body’s lipid fraction, i.e., the fatty 9 portions of various tissues, including adipose, organs, and blood, is extremely high. 10 TCDD is very slowly metabolized compared to many other organic compounds, with an 11 elimination half life in humans on the orde r of years following an initial period of 12 distribution in the body (Carrier et al., 1995a; Michalek et al., 2002). Most laboratory 13 animals used for toxicologic testing tend to eliminate TCDD much more quickly than 14 people, although even in animals TCDD is elimin ated much more slowly than most other 15 chemicals. 16

 TCDD induces binding proteins in the liver that have the effect of sequestering some 17 of the TCDD. The ability of TCDD to alter gene expressi on and the demonstration that 18 the induction of CYP1A2 is responsible fo r hepatic TCDD sequestration suggest that 19 both pharmacokinetic and pharmacodynamic ev ents must be incorporated for a 20 quantitative description of TC DD disposition (Santostefano et al., 1998). The induction 21 of these proteins implies that TCDD tends to be eliminated more rapidly in the early 22 years following short-term, high-level exposures than it is after those initial levels have 23 declined. Recent efforts of pharmacokineti c modeling have supported the concentration24 dependent elimination of TCDD in animals and humans (Aylward et al., 2005b; Emond 25 et al., 2006). 26
27 Sections 3.3.2 and 3.3.3 of this section pr esent the salient features of TCDD 28 pharmacokinetics in animals and hum ans, with pa rticular focus on mechanisms and data of 29 relevance to interspecies and intraspecies va riability. Section 3.3.4 de scribes the various dose 30 metrics for the dose-response modeling of TCDD and the characteristics of pharmacokinetic 31 models potentially useful for estimating thes e metrics. Finally, Sections 3.3.5 and 3.3.6 32 summarize the results of application of pharmacokine tic models to derive dose metrics as well as 33 the uncertainty associated with the predictions of dose metrics used in dose-response modeling. 34
Dose metrics derived via PBPK mo deling approaches are utilized in Sections 4 and 5 of this 35
document for noncancer and cancer TCDD dos e-response modeling, respectively. 36This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-6 3.3.2. PK of TCDD in Animals and Humans 1
3.3.2.1. Absorption and Bioavailability 2 When administered via the oral rou te in th e dissolved form, TCDD appears to be well 3
absorbed. Animal studies indicate that oral exposure to TCDD in the diet or in an oil vehicle 4 results in the absorption of >50% of the admini stered dose (Nolan et al., 1979; Olson et al., 5 1980). Human data from Poiger a nd Schlatter (1986) indi cate that >87% of th e oral dose (after 6 ingestion of 105 ng [ 3 H]

− 2,3,7,8

− TCDD [1.14 ng/kg BW] in 6 mL corn oil) was absorbed from 7 the gastrointestinal tract. Laks hmanan et al. (1986), investiga ting the oral absorption of TCDD, 8 suggested that it is absorbed primarily by the lymphatic route and transported predominantly by 9 chylomicrons. 10 Oral absorption is generally less efficient when TCDD is more tightly bound in soil 11 matrices. Based on experiments in miniature swine, Wittsiepe et al. (2007) reported an 12 approximately 70% reduction in bioavailability when TCDD was administered in the form of 13 contaminated soil, relative to TCDD after extraction from the same soil matrix with solvents. 14 Working with soil from the prominent contaminat ion site at Times Beach, Missouri, Shu et al. 15 (1988) reported an oral bioavailability of a pproximately 43% based on experiments in rats. 16 Percent dose absorbed by the dermal route is repor ted to be less than the oral route, whereas 17 absorption of TCDD by the transpulmonary route appears to be efficient (see, for example, 18 Banks et al., 1990; Banks and Birnbaum, 1991; Nessel et al., 1992; Diliberto et al., 1996; 19 U.S. EPA, 2003; Roy et al., 2008). 20
21
3.3.2.2. Distribution 22 TCDD in systemic circul ation equilibrates a nd moves into the tissues where it is then 23 accumulated, bound, or eliminated. Whereas the bulk of the body tissues are expected to 24 equilibrate in a matter of hours, the adipose tissu e will approach equilibrium concentrations with 25 blood much more slowly. Consis tent with these assertions, a number of experimental and 26 modeling studies in rats and hum ans have shown that TCDD has a large volume of distribution 27 (Vd), i.e., the apparent volume in which it is di stributed. The Vd corresponds to the volume of 28 blood plus the product of internal tissue vol umes and the corresponding tissue:blood partition 29 coefficients. This parameter is a key determin ant of the elimination rate of TCDD in exposed 30 organisms. The tissue:blood partition coefficients of TCDD, in turn, are determined by the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-7 relative solubility of TCDD in tissue and blood components (including neutral lipids, 1 phospholipids, and water). 2 Column 1 in Table 3-1 presents the tissu e:blood partition coeffici ents for TCDD (Wang 3 et al., 1997; Emond et al., 2005). Column 3 of this table lists the physical volume of each tissue, 4 scaled to a person weighing 60 kg. The last column shows the imp lications of the tissue volumes 5 and tissue:blood partition coefficients for the effective volumes of distribution for each tissue 6 and for the body as a whole. It can be seen that , purely on the basis of so lubility space, the fat 7 should be expected to contain about 94% of the TCDD in the body, and that the body as a whole 8 behaves as if it is about 1,200 liters in terms of blood-equiva lents (i.e., approximately 22

− fold 9 larger than its physical volume). 10 Maruyama et al. (2002) have published another set of tissue/ blood partition coefficients 11 for TCDD and other dioxin congeners based in part on observations of tissue concentrations 12 measured in autopsy specimens from eight Japa nese people without known unusual exposures to 13 TCDD. Their estimates of TCDD pa rtition coefficients seem to be rather large and variable, 14 with a fat:blood value of 247 ± 78 (standard deviation [SD]), a liver:blood value of 9.8 ± 5.7 and 15 a muscle:blood value of 18 ± 10.6. Depending on tim e of autopsy, tissue samples may not be an 16 accurate source of information on observed, in vivo partition coefficients because weight loss is 17 likely to occur pre and post mortem. In particular , a decline in fat stores volume could lead to an 18
increased concentration of dioxin in fat in autopsy specimens rela tive to what would be observed 19
in vivo. 20
The calculations shown in Table 3-1 do not include the additional amount that will be 21 bound to induced proteins in the liver. That induction and binding will tend to increase the 22
contribution of the liver on the effectiv e volume of distribution (Birnbaum, 1986). 23 It is also of interest to point out some basi c implications of the data in Table 3-1 for the 24 expected rates of perfusion-mediated tran sfer of TCDD between blood and each of the 25 organ/tissues. The rate of loss from a tissu e (occurring primarily via blood flow) and the 26 corresponding half-life can be calculated using the following equations: 27
28 Blood flow (liters / hour) -1 Rate constant for loss (hour ) = Tissue volume (liters) × Tissue / Blood Partition Coefficent (Eq. 3-1) 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-8 

### ( )

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) × × (Eq. 3-2) 1

2 Because TCDD is highly lipophilic, its concentr ation in the aqueous portion of the blood 3 is very small, and TCDD tends to partition fr om blood components into cellular membranes and 4 tissues, probably in large part via diffusion. As a result, full equilibrium concentrations of 5 TCDD are not attained by the end of the transit time through organs from the arterial to venous 6 blood. For organs in which this occurs, diffusion coefficients or “permeability factors” have 7 been estimated to assess the fractional attainment of equilibrium concentration that occurs by the 8 time the blood leaving each or gan reaches the venous circula tion. Table 3-2 presents the 9 permeability factors and implications for perfus ion half-lives for TCDD, per Emond et al. (2005, 10 2006). 11 Despite the high lipid bioconcentration potenti al of TCDD, it does not always occur at 12 the highest concentration in th e adipose tissue (Poiger and Schl atter, 1986; Geyer et al., 1986; 13 Abraham et al., 1988). Further, the ratios of ti ssue:tissue concentrations of TCDD and related 14 compounds (e.g., the liver:adipose ratio) may not remain cons tant during nonsteady-state 15
conditions. TCDD concentrations ha ve been observed to decrease mo re rapidly in the liver than 16 in adipose tissue. For example, Abraham et al. (1988) found that the liver:adipose tissue 17
concentration ratio in female Wistar rats e xposed to a subcutaneous TCDD dose of 300 ng/kg 18 decreased from 10.3 at 1 day postexposure to 0.5 at 91 days postexposure. It should be noted 19 that even at a ratio of 0.5, the amount of TCDD in the liver is gr eater than that based on lipid 20 content of the tissue alone, cons istent with the presence of hepatic TCDD binding proteins. The 21 liver/adipose tissue ratio also was dose-dependent, such that the liver TCDD burden increased 22 from ~11% of the administered dose at low doses (i.e., 1

− 10 ng/kg) to ~37% of the dose at an 23 exposure level of 300 ng/kg. The increase in TCDD levels in liver, accompanied by a decrease 24 in concentration in the adipose tissue, is a partic ular behavior to be considered in high dose to 25 low dose extrapolations. This behavior is essentially a result of dose-dependent hepatic 26 processes, as described below. 27This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-9 3.3.2.3. Metabolism and Protein Binding 1 The metabolism of TCDD is slow, particular ly in humans, and it is thought to be 2 mediated by the CYP1A2 enzyme that is induc ible by TCDD (Ramsey et al., 1982; Wendling et 3 al., 1990; Olson et al., 1994; Weber et al., 1997). The low rate of metabolism in combination 4 with sequestration appear to account for the re tention of TCDD in liv er, and these processes 5 collectively contribute to th e long half-life for elimin ation of TCDD from the body. 6 Dynamic changes in TCDD binding in liver a nd partitioning to fat have been studied 7 extensively in rats and mice (Diliberto et al., 1995, 2001). Figure 3-1 shows observations by 8 Diliberto et al. (1995) of the ratio of liver con centrations to adipose tissue concentrations for 9 mice given doses spread over a 100

− fold range and studied at f our different times following 10 exposure. It can be seen that even for the lowest dose studied the liver:fat concentration ratio is 11 higher than would be expected based on the lipid contents of th e tissues (i.e., 0.06:1, 12 corresponding to the ratio of human liver:b lood and fat:blood partition coefficients; see 13 Table 3-1). Moreover, the relativ e concentration in the liver cons istently rises with dose, with 14 the steepest rise observe d during the first two weeks after dos ing. If the distribution of TCDD 15 were governed solely by passive pa rtitioning into fat, there should be no such change in relative 16 concentrations with dose. However, data presente d in Figure 3-1 illustrate that at longer time 17 points, the ratio of TCDD in the liver to TCDD in fat decreases, indicating that a redistribution of 18 the chemical occurs as time goes on for each ap plied dose. The redistribution of TCDD tissue 19 levels from liver to fat with increasing time s uggests that binding of the chemical in the liver 20 (including via induction of CYP1A2 ) is an important kinetic cons ideration at early exposure 21 points with relatively high applied doses. At steady state levels (longer than 35 days, and low 22 applied doses), there seems to be a tendenc y for TCDD to redistribute to fat tissue. 23 Experiments with CYP1A2 “knock-out” mice (i.e., congenic strain s differing in only a 24 single gene that is “knocked out” in one of the strains) indicate that the inducible binding of 25 TCDD is attributable to CYP1A2 (Diliberto et al., 1997, 1999). As noted previously, this 26 enzyme is believed to make an important contribution to metabolism of TCDD. Given the 27
critical role of CYP1A2 induc tion in the kinetics of TCDD, do se-and time-dependent induction 28 of this protein in rats has b een examined and modeled (Wang et al., 1997; Santostefano et al., 29 1998; Emond et al., 2004, 2006). Accordingly, th e amount of CYP1A2 in the liver can be 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-10 computed as the time-integrated product of i nducible production and a simple first-order loss 1 process (Wang et al., 1997): 2
3

0 2 2A1 A 2t dCYP S(t)K K C dt = − (Eq. 3-3) 4

5
where CYP 2A1 is the concentration of the enzyme, K 2 is the rate constant for the first order loss, 6
C A2t is the concentration of CYP1A2 in the liver, K 0 is the basal rate of production of CYP1A2 in 7 the liver, and S ( t ) is a multiplicative stimula tion factor for CYP1A2 production in the form of a 8 Hill-type function: 9
10

h A2 ( ) ( ) = 1 + (IC ) ( ) h A2 Ah-TCDD h Ah-TCDD In C S t C + (Eq. 3-4) 11

12
where IC A2 corresponds to the concentration of th e aryl hydrocarbon (Ah)-TCDD complex at 13
which half of the maximum fold stimulat ion of CYP2A production is reached, and h , the Hill 14 exponent, determines the curvature of the stim ulation in relation to concentration of the 15 Ah-TCDD complex at relatively low doses. A value of 0.6 as the Hill exponent has been used by 16 Wang et al. (1997, 2000) and Em ond et al. (2004, 2005, 2006), indicative of a negative 17 cooperation, i.e., the curve is convex-upward (supra linear), depicting a fast er increase in the low18 dose region compared to a straight line. Add itional parameters in this expression include In A2 , 19 the maximum fold increase in the CYP1A2 synthesi s rate over the basal rate that can occur at 20 high levels of TCDD, and ( C Ah-TCDD ), the concentration of TCDD bound to the aryl hydrocarbon 21 receptor (AhR). This concentration in turn de pends on the concentration of TCDD in the liver 22 ( C Lif

), the concentration of the AhR ( Ah Li ) in liver, and the dissociation constant for the 23 Ah-TCDD receptor complex, K DAh : 24
25

Li Lif Ah-TCDD D Ah Lif Ah C C K C ×

= + (Eq. 3-5) 26

27This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-11 3.3.2.4. Elimination 1 Elimination half-lives (i.e., the time taken fo r the concentration to be reduced to one-half 2 of its initial level) of TCDD range from 11 days in the hamster to 2,120 days in humans 3 (U.S. EPA, 2003). Hepatic metabolism and binding processes, fecal excretion, and accumulation 4 in adipose tissue collectively determine the dos e-dependent elimination half-lives in various 5 species. Aylward et al. (2005a) depicted the relationship between the elimination rate versus 6 initial level of lipid-c orrected TCDD in serum for 36 people (see Figure 3-2). Even though this 7 analysis was done using the initial TCDD level, ra ther than the geometric mean or midpoint level 8 in the decline for each person, it indicated a concentration-dependency of the half-life and 9 elimination of TCDD in exposed individuals. 10
11
3.3.2.5. Interspecies Differences and Similarities 12 Among the pharmacokinetic determinants of TCDD, some are known to vary markedly 13 between species whereas others are not characteri zed sufficiently in this regard. Overall, the 14 qualitative determinants of the body burden and elim ination half-lives appear to be similar across 15 species. Based on empirical observations for TCDD as well as with other PCDFs, Carrier et al. 16 (1995a,b) argued that in rats, monkeys, and human s, the dose-dependent changes in the fraction 17 contained in liver and adipose tissue follow a similar pattern across species. The authors 18 suggested that the half-satur ation body burden is around 100 ng/kg and the plateau of liver dose 19 (as fraction of body burden) appear s to occur around 1,000 ng/kg. L iterature also indicates that 20 AhR is conserved phylogenetically (Nebert et al., 1991; Fujii-Kuriyama et al., 1995; Harper 21 et al., 2002) and is present in mammalian species , including experimental animals and humans 22 (Roberts et al., 1985, 1986; Manche ster et al., 1987; Lorenzen and Okey, 1991; Okey et al., 23 1994). These qualitative similarities in pharm acokinetic determinants and outcome support the 24 use of animal data to infer general patterns of the pharmacokinetic behavior of TCDD in humans. 25 However, quantitative differences in determin ants, including physiological, physicochemical, 26 and biochemical, need to be taken into accoun t. Even though species -specific physiological 27 parameters can be obtained from the literatur e, key data on species-specific biochemical 28 parameters (particularly binding constants, maximal capacity, induction rates, and other 29 parameters) are not available for humans at this time. However, these can be inferred by using a 30 pharmacokinetic model fit to in vivo data on the rate of TCDD elimination from specific 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-12 compartments in humans (Carrier et al., 1995a, b; Emond et al., 2004, 2005, 2006; Aylward 1 et al., 2005b). 2
3
3.3.3. PK of TCDD in Humans: Interindividual Variability 4 The pharmacokinetics and tissue dose of TCDD in humans can exhibit variability within 5 a population as a function of the in terindividual variability in the ke y determinants. If a chronic, 6 lifetime exposure is considered to be the relevant scenario for developing guidance for human 7 exposure standards, the key determinants of concern are clearance, binding, and temporal 8 changes in volume of distribution. When c onsidering the interindivi dual variability in 9 pharmacokinetics and dose metrics of TCDD, it is important to recogn ize that higher lipid10 corrected serum concentrations in highly exposed persons are associated w ith greater elimination 11 rates, probably due to greater degrees of induc tion of CYP1A2 in the liver and possibly other 12 related metabolic enzymes (Grassman et al., 200 0; Abraham et al., 2002 ; Aylward et al., 2005b; 13 Emond et al., 2006). 14 The interindividual variability in fat c ontent is a critical parameter given the 15 pharmacokinetic characteristics of TCDD (see Sec tion 3.3.2). Both metabolic elimination and 16 elimination via the GI tract depend on the fractio n of TCDD in the body that is available outside 17 of adipose tissue. As body fat content rises, a smaller portion of the total body TCDD will be 18 contained in the relatively available fraction out side of fat. Because elimination of TCDD by 19 both metabolism and fecal excretion depends on the small proportion of TCDD that exists 20 outside of fat tissue, people with larger proportio ns of body fat—including many older people— 21 will tend to require longer times to reduce TCDD le vels by a given proportion than leaner people 22 (Van der Molen et al., 1996, 1998; Rohde et al., 1999; Emond et al., 2006). 23 The sections that follow highlight key asp ects of interindividual variability in TCDD 24 pharmacokinetics, with an emphasis on the availabl e data related to elimination half-lives and 25
volume of distribution. 26
27
3.3.3.1. Life Stage and Gender 28 The influence of the variability of fat cont ent in human population on the distribution and 29
clearance of TCDD has been evalua ted by several investigators. Figure 3-3 shows the results of 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-13 a rare experiment in which TCDD elimination via feces was measured in six people in relation to 1 their body fat content (Rohde et al., 1999). 2 There are data which suggestive an inverse de pendency of half life on percent body fat. 3
Observations of TCDD elimination rates in a small number of men and women in the Seveso 4 cohort provide a modest opportunity to compare TC DD elimination rates on this basis. Based on 5 the partition coefficients reporte d by Emond et al. (2006), the elimin ation rates for the men in the 6 sampled group are expected to be greater than the elimination rates in the women. Based on 7
calculations similar to those shown in Table 3- 2, and fat proportions inferred from body mass 8 indices using the equations of Lean et al. (1996), the Seveso me n studied are expected to have an 9 overall average of about 3.92% of their TCDD body burden outside of fa t, whereas the women 10 are expected to have an average of only 2.36% outside of fat. On this basis, the TCDD 11 elimination rates in the men are expected to be 3.92/2.36 = 1.66 times faster than the elimination 12 rates in the women. By comparison, Michalek et al. (2002) reported obs erved elimination rates 13
in men and women that result in a slightly lower ratio: 14
15

1

1 men:0 111 year 0 010 (std.error) 1 56 women:0 071 year 0 010 (std.error) . .

.

. . −

− ±

=

± (Eq. 3-6) 16

17
The central estimates for the elim ination rates correspond to half lives of 6.5 and 9.6 years for 18 men and women, respectively. 19 A further point of comparison can be deri ved using the observed body mass index (BMI) 8 20
and TCDD elimination rate of each of the ma le Ranch Hand military veterans, whose TCDD 21 elimination rates were observed between 9 and 33 y ears after their time in Vietnam. The average 22 BMI over that time was 29.44 (based on 287 measurem ents for the 97 veterans, tabulated in three 23 periods by Michalek et al., 2002), and their average age was about 44.5 for the measurements. 24 Based on these data, the corresponding average estimated percent body fat is 29.7% using the 25 Lean et al. (1996) formula for men. The obser ved average TCDD elimination rate constant for 26 these men for the period was 0.092 year − 1 ± 0.004 (standard error), co rresponding to a half life of 27 7.5 years. This half life is slightly longer thanthe central estimate of the half life of 6.2 years 28

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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-14 (i.e., ln(2)/0.111) for the smaller group of Seveso males with th eir slightly smaller estimated 1 percent body fat. Figure 3-4 show s a simple plot of these data and a fitted unweighted regression 2 line characterizing the relationship between estimated fat content and TCDD elimination rates. 3 Variation in metabolic enzyme activities and other routes of loss is also likely to be important, 4 but there is little human quantitative information available on these issues. 5 More recently, Kerger et al. (2006) estimated the slope of the relationship between half6 life and age to be 0.12 years (95% confidence interval, 0.10

− 0.14), which corresponds to the rate 7
of increase in TCDD half-life fo r each year of age. The auth ors speculated that although age 8 explained most of the variance in the individual half-life trends, it was also correlated with 9 TCDD concentration, BMI, and body fat mass. The regression model developed by these 10 authors discriminated between the high and low TC DD exposures or concentrations. Thus, after 11 accounting for the TCDD (concentration × age) term’s effect on the slope of age, the final model 12
for TCDD concentration ≤ 700 ppt was 13
14

1 2 0 35 0 12 / t . . Age = + × (Eq. 3-7) 15 For TCDD concentration >700 ppt, the final model was: 16
17

1 2 0 35 0 088 / t . . Age = + × (Eq. 3-8) 18 19
where t 1/2 is the half-life and Age is the age at time of subsequent sampling. Pharmacokinetic 20 information relevant to specific age groups is presented in the sections that follow. 21
22
3.3.3.1.1. Prenatal period. 23
Data to estimate TCDD elimination rates for fetuses are not available. Levels of TCDD 24 in fetal tissues for rats were experimentally esti mated at different gestati onal periods and utilized 25 in a developmental model by Emond et al. ( 2004). There is information on body composition 26 that is relevant to prediction of TCDD dose to fetus. These data, summarized as part of the 27 radiation dosimetry model of the International Commission on Radiological Protection, are 28 consistent with the idea that early fetuses are ne arly all water and less than 1% lipid, and lipid 29 levels rise toward parity with prot ein near the time of normal delivery. 30This document is a draft for review purposes only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-15 Bell et al. (2007b) reported th at the disposition of TCDD into the fetus shows dose 1 dependency, with a greater proportion of the dose reaching the fetus at lower doses of TCDD. 2 Further, both CYP1A1 and CYP1 A2 are highly inducible (~103

− fold) in fetal liver, whereas 3 CYP1A2 shows much lower induction (10

− fold) in maternal liver. It has been speculated that 4
this is due to the lower basal levels of CYP1A2 in fetal liver, as compared to maternal liver 5 (Bell et al., 2007b). 6
7
3.3.3.1.2. Infancy and childhood. 8 Hattis et al. (2003) describe th e general pattern of change of body fat content with age in 9 children. Central tendency values for percent body fat begin at about 12% at birth and rise 10 steeply to reach about 26% near the middle of the first year of life. Fat content then falls to reach 11 a minimum of approximately 15% at 5

− 8 years of age, followed by a sex-dependent “adiposity 12
rebound” that takes females to about 26% body fat while the males remain near 16

− 17% on 13 average by age 20. The interindivi dual variability dist ributions about these central values are 14 complex, as some children experience the “ad iposity rebound” earlier than others, and this 15 creates patterns that are not simply interpretable as unimodal normal distributions. Hattis et al. 16 (2003) did find it possible to fit distributions of body fat content inferred from NHANES skin 17
fold measures to mixtures of two normal di stributions for children between age 5 and 18. 18 At least two groups of authors have p ublished PBPK modeling results indicating 19 generally more rapid clearance of TCDD in children th an in adults, a trend that is consistent with 20 the generally lower fat content of children (Kre uzer et al., 1997; Van der Molen et al., 2000; 21 Leung et al., 2006). The rapid expansion of the adipose tissue compartment can contribute, in 22 part, to the reduced apparent ha lf-life in children (Clewell et al., 2004). This reduction may also 23 be due to varying rates of metabolism and/or fecal lipid excretion (Abraham et al., 1996; 24 Kerger et al., 2007). 25 Furthermore, very young children have di fferent modes and qua ntities of exposure 26 compared to adults. Lakind et al. (2000) charact erize distributions of m ilk intake for nursing 27
infants to characterize distributions of TCDD ex posure. This is also a corresponding route of 28
loss of TCDD stores for lact ating women, as described in Section 3.3.3.2 below. 29
30This document is a draft for review purposes only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-16 3.3.3.1.3. Adulthood and old age. 1 The fraction of fat in relation to body weight in adulthood and old age can be computed 2 as a function of the BMI and age (e.g., Lean et al., 1996): 3
4 (males) 1 33 0 236 20 2 % Body Fat . BMI . Age . = × + ×− (Eq. 3-9) 5 (females) 1 21 0 262 6 7 % Body Fat . BMI . Age . = × + ×− (Eq. 3-10) 6 7 The above equations are the re sult of analysis of data ba sed on underwater weighing of 8 63 men and 84 women (age range 16.8

− 65.4). The salient observati on with respect to TCDD for 9 these data is that age and BMI-dependent variability in fat content have implications for the 10 variability in TCDD elimination rate s and internal dose among adults. 11
12
3.3.3.2. Physiological States: Pregnancy and Lactation 13 Data on body fat content in pre gnant women at various stages of gestation (Pipe et al., 14 1979) have potential implications for TCDD elimination rates dur ing pregnancy, even though the 15 relationship between these parameters has not been formally analyzed. 16
Lactation is viewed as an a dditional route of elimination for some chemicals such as 17 TCDD. According to a recent study, a breast-feeding woman expels through lactation an 18 estimated 8.76 kg fat per year [ q f (kg/day), 0.8 kg milk/day with an average 3% lipid], and the 19 partition coefficient between blood lipid and milk fat ( K

BM ) for TCDD is 0.92 (Wittsiepe et al., 20 2007; Milbrath et al., 2009). The estimated rate of elimination of TCDD due to breast-feeding 21 (k bfed ) can then be computed as follows (Milbrath et al., 2009): 22
23

100 f bfed bfed i BM i q Δ t k pbf K BW ×

=
× × (Eq. 3-11) 24

25
where 26
Δ t bfed (unitless) = the fraction of the yearduring which the woman was actively breast27 feeding; 28 pbf i = woman’s percent body fat; and 29
BW = woman’s body weight in kg. 30This document is a draft for review purposes only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-17 Assuming no interaction between breast-feed ing and other half-life determinants 1 Milbrath et al. (2009), the author s predicted a half-life of 4.3 years for TCDD in a 30-year-old, 2 nonsmoking woman with 30% body fat if she did not breast-feed that year , and a half-life of 3 1.8 years if she breast-fed for 6 months. 4
5
3.3.3.3. Lifestyle and Habits 6 One of the factors related to lifestyle and habits that could influence TCDD kinetics is 7 smoking. Smoking has been reported to enha nce the elimination of dioxin and dioxin-like 8 compounds (Flesch-Janys et al., 1996 ; Ferriby et al., 2007). Milbrath et al. (2009) accounted for 9 interindividual variation in body composition as well as smoking habits in an empirical model. 10 The predicted half-life (y ears) for an individual i as a function of age, smoking status, and 11 percent body fat i was as follows 12
13

1/2 (0 ) ( ) ( ) ( , , )[ ]

i i i age age i i ref age pbf t age smoke pbf age SF pbf β β = + × ×× (Eq. 3-12) 14

15
where 16

)0( age β = intercept constant deri ved from regressed data; 17

)( age β = slope constant derived from regressed data; 18

i age = specific age i (years); 19

i pbf = individual percent body fat; 20

)( i ageref pbf = reference percent body fat; and 21 SF i = the unitless, multiplicative smoking factor. 22 23
3.3.3.4. Genetic Traits and Polymorphism 24 One particular genetic locus that is pote ntially related to TCDD pharmacokinetics and 25 tissue dose is the gene for the AhR. Eight can didate AhR polymorphisms have been identified to 26 date (Harper et al., 2002; Connor and Aylward, 2006) . Given the role of AhR in regulating the 27 induction of CYP1 isozymes (Baron et al., 1998 ; Toide et al., 2003; Connor and Aylward, 2006), 28 the polymorphism might lead to interindividu al differences in metabolic clearance, the 29 significance of which would depend upon the dose, fat content, and exposure scenario. In this 30 regard, it should be noted that the inducibility of aromatic hydrocarbon hydroxylase in human 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-18 tissues has been reported to be highly variable, up to 100

− fold (Wong et al., 1986; Smart and 1 Daly, 2000; Connor and Aylward, 2006). 2 Finally, the scientific literature contains values of K d (the dissociation constant of the 3

TCDD

− AhR complex) ranging from a bout 1 to much higher valu es (corresponding to lower 4 binding affinity) (reviewed in Connor and Aylw ard, 2006). This provides suggestive evidence 5 for a heterogeneous human AhR, with functi onally important polymorphisms (Roberts et al., 6 1986; Micka et al., 1997), even though some of th e range may be attributed to experimental 7 procedural differences and to other factors (Manches ter et al., 1987; Lorenzen and Okey, 1991; 8 Harper et al., 2002; Connor and Aylmard, 2006). 9 The various pharmacokinetic processes and dete rminants (see Sections 3.3.2 and 3.3.3), 10 individually or together, might influence the dose metrics of relevance to the dose-response 11 modeling of TCDD. 12
13
3.3.4. Dose Metrics and Pharmacokinetic Models for TCDD 14
3.3.4.1. Alternative Dose Metrics for Dose-Response Modeling 15 The dose metric related to a toxicologic endpoint can range from the maximal 16 concentration, the area under a time-course curve (AUC), or the time-averaged concentration of 17 the toxic moiety in the body, blood, or target tissue, to an appr opriate measure of the resulting 18 interactions in the target tissue (e.g., receptor occupancy or functional biomarkers related to 19 specific effects). A single dose metric, however, is unlikely to be suffici ent for all endpoints and 20 exposure durations. Further, the ideal dose metric chosen on the basis of the mode of action 21 (MOA) may not be the dose metric for which mo del predictions can be obtained with a high 22
level of confidence. Consideration of these issues is critical to the selection of the dose metrics 23 of relevance to dose-response modeling of TCDD. 24 Figure 3-5 lists a range of alternative dose me trics for TCDD in terms of their relevance 25 based on considerations of pharmacokinetic mechanisms and MOA. The administered dose or 26 daily intake (ng/kg-day) is the least relevant dose metric for dose-response modeling of TCDD. 27 This dose adjusts only for body weight differenc es between species. When used with an 28 additional uncertainty factor for kinetics (or BW 3/4 ) and for dynamics, it can also account for 29 allometrically-predicted pharmacokinetic (cl earance) and pharmacodynamic differences between 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-19 species in deriving the human equiva lent dose (HED). In effect, it facilitates the computation of 1
administered dose associated with the same stea dy-state blood concentration of parent chemical 2 in humans and rats by accounting for differences in metabolic clearance (assumed to be related 3 to body surface area, with no corresponding temporal changes in the volume of distribution; see, 4 for example, Krishnan and Andersen, 1991). Su ch calculations of HE D for TCDD may not be 5 appropriate given that (1) steady-st ate was not attained in all critical toxicological studies chosen 6 for the assessment, (2) the clearance is mainly due to enzyme(s) and processes whose levels/rates 7 do not necessarily vary across species or life st ages as a function of body surface differences, and 8 (3) there is a likelihood of change in volume of di stribution over time. Furthermore, the use of 9 administered dose does not explicitly account fo r the dose-dependent elim ination of TCDD from 10 tissues as demonstrated in multiple studies (revie wed in Sections 3.3.2 and 3.3.4). The use of 11 administered dose in TCDD dose-response modeling is unlikely to facilitate the characterization 12 of the true relationship between th e response and the relevant measur es of internal dose that are 13 influenced by dose-dependent elimination and binding processes. Additionally, the use of 14 administered dose to extrapolate across species or life stages would not effectively take into 15
account the differences in fat content or the de monstrated dose-dependent and species-dependent 16
differences in elimination half-life of TCDD. 17 The other alternative dose metrics for TCDD include absorbed dose, body burden, serum 18 or whole blood concentration, ti ssue concentration, and functional-related metrics of relevance to 19 the MOA (e.g., receptor occupancy, change in prot ein levels). These measures can be calculated 20 as a current (terminal), average ( over a defined period), or integral quantity. The applicability of 21 the integral measures, such as the AUC (i.e., the area under the curve of a plot of blood or 22 plasma concentration vs. time), traditionally us ed for analyzing chronic toxicity data, is 23 questionable in the case of TCDD because of differences in lifes pan and uncertainties regarding 24 the appropriateness of the duration to be specified for averaging the AUC in experimental 25 animals and humans for certain critical effects (NRC, 2006). 26 Among the alternative dose metrics, the absorbed dose accounts for differences in body 27 weight as well as species-specific differences in bioavailability. Howeve r, this dose measure, 28 while more closely related to the endpoint th an administered dose, does not account for the 29 physiological and biochemical mechanisms responsib le for interspecies differences in internal 30
dose. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-20 Body burden , or more appropriately the body concen tration, represen ts the amount of 1 TCDD per kg body weight. TCDD body burdens, like other dose measures, can be determined 2 as the peak, the average over th e period of the bioassays, or the level at the end of the 3 experiments. Thus, the terminal or average body burdens can be obtained either using data or 4 pharmacokinetic models and used in dose-respon se modeling. The body burden is a measure of 5
TCDD dose that reflects the net im pact of bioavailability, uptake, distribution, and elimination 6 processes in the organism. It is essentially a function of the volume of distribution and clearance 7 processes, and as such it does take into account the temporal change s in volume of distribution as 8 well as the concentration-dependent clearance. These are phenom ena that are critical to the 9 understanding of TCDD dose to the target. Howe ver, the body burden may not accurately reflect 10 the tissue dose (NRC, 2006), and as such does not allow for analysis of species-specific 11 differences in target organ sensitivity to TCDD. In essence, the body burden represents only an 12 “overall average” of TCDD concentration in th e body, without regard to the differential 13 partitioning and accumulation in specific tissues, including the target tissue(s). 14 Serum (or blood) concentration of TCDD is a dose metric that reflects both the body 15 burden and the dose to target tissu es. Serum or blood concentrati on, at steady-state, would be 16 reflective of the impact of clearance processes, and expected to be dire ctly proportional to the 17 tissue concentrations of TCDD (N RC, 2006). This dose metric for lipophilic chemicals such as 18 TCDD is often expressed as a lipid-normalized valu e, to adjust for varying serum lipid content 19 (e.g., DeKoning and Karamus, 2000; Niskar et al., 2009), particularly in human biomonitoring 20 studies, thus of relevance to dose-response modeling; however , the serum lipid-normalized 21
concentrations of TCDD are not r outinely collected and reported in animal toxicologic studies. 22 The lipid-adjusted serum concentration, however , would be reflective of the lipid-adjusted 23 concentration of TCDD in other organs (review ed in Aylward et al., 2008) depending upon the 24 extent of steady-state attained and the simila rity of lipid compositi on across tissues in each 25 species. In essence, the serum lipid-normalized measure is representative of the amount of 26 TCDD per specified volume of total lipids, whereas the whole blood measure will be reflective 27 of the ensemble of free, lipi d-bound and protein-bound TCDD in pl asma and erythrocytes, which 28 may be species-specific. Even though these dos e metrics are thought to be more closely and 29 directly related to the tissue concentrations associ ated with an effect, a less direct association 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-21 might occur at increasing doses when nonlinear processes dominate the kinetics and distribution 1 of TCDD into organs such as the liver. 2
Tissue concentration of TCDD, as free, bound, or total TCDD, is a more relevant 3 pharmacokinetic measure of dose, given that it provi des a measure of exposure of the target cells 4 to the chemical. In this regard, the CYP1A2- bound fraction may be cons idered as a relevant 5
dose metric for certain toxic eff ects; however, the available data contain mixed results regarding 6 the mechanistic linkage of this dose metric to to xicity and carcinogenicity (reviewed in Budinsky 7 et al., 2006). In such cases, the use of altern ative dose metrics (e.g., bound concentration as well 8 as the serum concentration) in dose-response modeling could be considered. Other function9
related biomarkers and dose metrics could facilitate the additional consideration of 10 pharmacodynamic aspects reflecting tissue- and sp ecies-specific sensitivity. These metrics 11 represent the most relevant measures of tissue exposure and sensitivity to TCDD. 12 Empirical time-course data on the alternativ e dose metrics of TCDD associated with 13 epidemiologic and experimental (animal) studi es are not available, requiring the use of 14 pharmacokinetic models to obtain estimates of thes e dose metrics. These models may be simple, 15 based on first order kinetics (see Section 3.3.4. 2), or more complex based on physiochemical, 16 biochemical, and physiological parameters fo r simulating uptake, distribution (including 17 sequestration to proteins), and clear ance of TCDD (see Section 3.3.4.3). 18
19
3.3.4.2. First-Order Kinetic Modeling 20 Figure 3-6 illustrates the process of estima ting a human-equivalent TCDD oral exposure 21 from an experimental animal-administered dose , based on the assumption that body burden is the 22 effective dose metric for TK equivalence acro ss species. The primary assumption is that the 23 time-weighted average (TWA) TCDD body burde n over some critical time period is the 24 proximate toxicokinetically-effective dose eliciting a toxicologic effect. 9 The process consists of 25 estimating the effective average body burden in the experimental animal over some time t A 26 (generally the experimental dura tion) using a TK model, then “back-calculating” a daily human 27 exposure level that would result in that average body burden over some time t H

(the human 28 equivalent to t A ). 29

9 The conversion depicted in Figure 3-6 does not account for toxicodynamic differences between species.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-22 The following closed-form equation is the general formula used to calculate a TCDD 1 terminal body burden in an experimental animal or human at time ( t ). 2
3 (1 ) ( ) (0) kt d e fa BB t BB k −

− = + (Eq. 3-13) 4

5 where 6 BB ( t ) = the body burden at time t (ng/kg); 7 BB (0) = the initial body burden (ng/kg); 8 d = the daily dose (ng/kg-day); 9 k = the whole-body elim ination rate (days − 1 ); 10 t = the time at which the body burden is determined (days); and 11 fa = the fraction of oral dose absorbed (unitless). 12 13

For the experimental animal, BB(t) is A A tk A tk A A k faed eBBtBB AA AA )1( )0()( −

− − + = , and for 14

humans, this parameter is H H tk H tk H H k faed eBBtBB HH HH ) 1( )0()( −

− − + =
. 15

16 Setting BB H ( t ) = BB A ( t ) obtains the following expression: 17
18

A A tk A tk A H H tk H tk H k faed eBB k faed eBB AA AA HH HH )1( )0( ) 1( )0( −

− −

− − + = − + (Eq. 3-14) 19

20 Rearranging yields th e general solution for d H . 21
22

HH AA 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( (Eq. 3-15) 23

24 Assuming that initial body burdens are very small compared to BB(t) and that the fraction of 25 TCDD absorbed is the same for humans and expe rimental animals, and using the relationship 26

2/1 )2ln(

t k = , where t 1⁄2 is the whole-body half-lif e, a simplified solution for d H

is obtained. 27
28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-23 1/2

1/2 (1 ) (1 ) A A

H H k t A H A k t H t e d d t e −

− − =

− (Eq. 3-16) 1

2 The term 1

− e − kt is the daily fraction eliminated. Therefore, d H

can be seen to be the 3 average daily administered dose to the experiment al animal times the ratio of the animal:human 4
half-life times the ratio of the animal:human dail y fraction eliminated over the respective times, 5

t A and t H . For both species at (t heoretical) steady state ( t → ∞ ; daily fraction eliminated → 1), 6 the latter ratio approaches unity, reducing the animal:human conversi on factor to the ratio of the 7 half-lives. The latter approach was used in the 2003 Reassess ment for conversion of animal 8 cancer slope factors to the human equivalent , where only lifetime e xposures are relevant. 10 9 However, for less-than-lifetime exposures eliciting noncancer effects, specific values for 10

t A and t H

must be considered. Furthermore, Eq. 3-16 computes d H

on the basis of terminal body 11
burdens at times t A and t H . The more representative metric for toxicokinetic equivalence based 12 on average body burden over the respective time periods is given in Eq. 3-17. 13
14 

# ( ) ( ) ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− −+ − =− + = − −

− − 

## ∫∫ kt e k fa d kt e BBde tk fa dde t BBtBB kt kt t k t k 1 1 1 )0()1( 1 1 )0()(

0 0 τ τ τ τ (Eq. 3-17) 15

16 On the basis of average body burden as given in Eq. 3-17, is transformed again assuming 17 minimal initial body burden (BB(0) ~ 0), as follows: 18
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 (Eq. 3-18) 20

21
where t H0 is the initial human exposure time. 22
The value of t A is the duration of the experimental exposure period. For some gestational 23 exposures, if a critical ex posure window is defined, t A will be the duration of the critical 24

10 No conversions to human-equivalent exposures were attempted for other effects in the 2003 Reassessment.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-24 exposure window. The value of t H

is the human-equivalent duration corresponding to t A . 1 However, for less than lifetime exposure in humans, t H

does not begin at 0, but must end at 2 25,550 days (70 years) to include the terminal (pseudo) steady-state le vel, at which the BB H (t): 3
d H

ratio is highest. The average is determined from the terminal end of the human exposure 4 period because the daily exposure achieving the target blood concentr ation is smaller than for the 5 same exposure period beginning at birth and is health protectiv e for effects occurring after 6 shorter-term exposure. 11 Figure 3-7 depicts the relations hip of daily dose to TWA body burden 7 graphically for several exposure duration scenarios. For shorter durations o ccurring later in life, 8 the average body burden over the exposure period does not differ substantially from the steady9 state value. Even for half-lifetime exposures, th e deviation of the averag e from steady state is 10 minimal. Only for lifetime exposures does th e difference become more marked, but only by 11 about 15%. Note that in the 2003 Reassessment, a constant value of 3,000 was used for BB H (t): 12
d H

, based on the relationship of continuous e xposure to theoretical steady-state body burden 13 ( t = lifetime, t 1⁄2 = 2,593 days); this approach, while c onservative, does not account for exposure 14
scenarios of different durations and does not strict ly reflect the average body burden dose metric. 15 The simulation in Figure 3-7 is based on a un it daily exposure to humans, such that the 16 target body burden represents BB H ( t

H

): d H

as a general scalar for calculating d H

from any given 17
d A . Table 3-3 shows the resulting TK conversi on factors for the rodent species and strains 18 comprising the bulk of the experimental animals in TCDD studies. Monkey and mink values are 19 not shown in this table because, for the former, only chronic exposures were evaluated and, for 20 the latter, no TCDD half -life information is available. Monkey (Rhesus) half-life estimates 21 range from about 200

− 500 days. A representative value of 365 days is used for this TCDD 22
assessment. The d A to d H

conversion factor for the chronic monkey exposures (3.5

− 4 years) in 23
TCDD studies is 9.2

− 9.7 ( BB A : d A = 279

− 263). 24 Application of first order kine tics for the risk assessment of TCDD can only be used to 25 estimate total body burdens or back-calculate administered dose from experimental data. Body 26 burden calculations using first or der kinetics is based on the assu mption of a first order decrease 27
in the levels of administered dos e as function of time. In that sense, any loss of TCDD from the 28 body is described by using a rate cons tant that is not spec ific to any biological process. This 29

11 See the following section (3.3.4.3) for a more detailed discussion of this concept.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-25 constant is usually estimated from estimates of half-life of TCDD. Assu ming a constant half-life 1 value for the clearance for long-term or chr onic TCDD exposure is not biologically supported 2 given the observed data indicating early infl uence of CYP1A2 induc tion and binding to TCDD 3
and later redistribution of TCDD to fat tissue. Abraham et al. (1988) found that the liver:adipose 4
tissue concentration ratio in female Wistar rats exposed to a subcutaneous TCDD dose of 5 300 ng/kg decreased from 10.3 at 1 day postexp osure to 0.5 at 91 days postexposure. 6 Consequently, using half-life estimates based on observed steady-state levels of TCDD will not 7 account for the possibility of accelerated dose-depe ndent clearance of the chemical at the early 8 stages and thus would result in estimation of lower administered levels of the chemical. The 9 dynamic change in half-life due to dose-depende nt elimination at the early stages of TCDD 10 exposure and its later redistributi on to fat tissues for steady-state levels is better described using 11 biologically-based models, such as the PBPK models and concentration- and age-dependent 12 elimination (CADM) models (Car rier et al., 1995a, b; Emond et al., 2004, 2005, 2006; Aylward 13 et al., 2005b). Additionally, these models provide estimates for other dose metrics (e.g., serum 14 or tissue levels) that are more bi ologically relevant to response than administered dose or total 15 body burden (see Section 3.3.4.3). 16
17
3.3.4.3. Biologically-Based Kinetic Models 18 The development and evolution of biologica lly-based kinetic m odels for TCDD have 19 been reviewed by EPA (2003) and Reddy et al. ( 2005). The initial PBPK model of Leung et al. 20 (1988) was developed with the consideration of TCDD binding to CYP1A2 in the liver. The 21 next level of PBPK models by Andersen et al . (1993) and Wang et al. (1997) used diffusion22 limited uptake and described protei n induction by interaction of DN A binding sites. The models 23 of Kohn et al. (1993) and Ande rsen et al. (1997) further in corporated extensive hepatic 24 biochemistry and described zonal induction of CYP by TCDD. TCDD PBPK models have 25
evolved to include detailed desc riptions of gastrointestinal up take, lipoprotein transport, and 26 mobilization of fat, as well as biochemical intera ctions of relevance to organ-level effects (Kohn 27 et al., 1996; Roth et al., 1994). Subsequently, de veloped PBPK models either used constant 28 hepatic clearance rate (Wang et al., 1997, 2000; Maruyama et al., 2002) or implemented varying 29 elimination rates as an empirical function of body composition or dose (Andersen et al., 1993, 30 1997; Kohn et al., 1996; Van der Molen et al., 1998, 2000). The more recent pharmacokinetic 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-26 models explicitly characterize th e concentration-dependent elimin ation of TCDD (Carrier et al., 1 1995a, b; Emond et al., 2004, 2005, 2006; Aylward et al., 2005b). The biologically-based 2 pharmacokinetic models describing the concen tration-dependent el imination (i.e., the 3 pharmacokinetic models of Aylward et al. [2005b ] and Emond et al. [2005, 2006]) are relevant 4 for application to simulate the TCDD dose metric s in humans and animals exposed via the oral 5
route. The rationale for consid ering the application of Aylwar d et al. (2005b) and Emond et al. 6 (2004, 2005, 2006) models for estimating dose metr ics for possible application to TCDD risk 7 assessment is based on the following considerations. 8
9

• Both models represent research results from the more recent peer-reviewed publications. 10

• Both models are relatively simple and less parameterized than earlier kinetic models for 11 TCDD. The Aylward et al . (2005b) model is based on two-time scale TCDD kinetics 12 described by Carrier et al. (1995a), an d the Emond et al. (2004, 2005, 2006) PBPK 13 models are reduced versions of earlier complex PBPK models. Although simple, both 14 the Aylward et al. (2005b) a nd Emond et al. (2004, 2005, 2006) models are still inclusive 15 of important kinetic determinants of TCDD disposition. 16

• Both models are uniquely formulated with dos e-dependent hepatic elimination consistent 17 with the physiological interpretations co mmonly accepted by the sc ientific community. 18

• Both models and extrapolated human versions were tested against human data collected 19 in a variety of human exposur e scenarios (Alyward et al ., 2005b; Emond et al., 2005). 20

• Both models are capable of deriving one or mo re of the candidate dose-metrics that are of 21 interest to EPA’s dose-response assessment of TCDD. 22
23
3.3.4.3.1. CADM model. 24
3.3.4.3.1.1. Model structure. 25 The pharmacokinetic model of Aylward et al. (2005b), referred to as the CADM model in 26 this report, is based on an earlier model devel oped by Carrier et al. (1995a ,b) that describes the 27 dose-dependent elimination and half-l ives of polychlorinated dibenzo- p -dioxins and furans. This 28
model describes the TCDD levels in blood (body), liver, and adipos e tissue. Blood itself is not 29 characterized physically as a se parate compartment within the model, and the distribution of 30 TCDD to tissues other than adipose tissue and live r (usually less than 4%) is not accounted for 31 by the model. The original structure of the Ca rrier et al. (1995a, b) model was modified by 32 Aylward et al. (2005b) to include TCDD elimination through partitioning from circulating lipids 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-27 across the lumen of the large inte stine into the fecal content (see Figure 3-8). The most recent 1 version of the Carrier model (Aylward et al ., 2005b, 2008) includes f ecal excretion of TCDD 2 from two routes: (1) elimination from circul ating blood lipid through partitioning into the 3 intestinal lumen; and (2) elimination of unabsorbed TCDD from dietary intake. 4 A basic assumption of this model is that me tabolic elimination of TCDD is a function of 5
its current concentration in the liver. The current concentration of TCDD in the liver increases 6 with increasing body burden in a non linear fashion as a result of the induction of (and binding of 7 TCDD to) specific proteins (i.e., CYP1A2). Consequently, the fraction of TCDD body burden 8 contained in the liver increases nonlinearly (with a corresponding decrease in the fraction 9 contained in adipose tissues) with increasing body burden of TCDD (Carrier et al., 2005a). 10 Of particular note is that the adipose tissue compartment of the model is considered to 11 represent the lipid contai ned throughout the body. It then assumes that the concentrations of 12 TCDD in lipids of plasma and various organs is e ssentially equivalent to that of adipose tissue, 13
and as such these concentrations are included in the adipose compartment of the model. Even 14 though this approximation is fairly reasonable give n the available data, there is some concern 15 that the adipose compartment of this model also includes the lipid content of the liver to some 16 unknown extent. Removal of lipid volume from the liver would mathem atically alter total 17 hepatic concentration and therefor e would affect the estimated levels of the chemical available 18 for binding to proteins. 19 Distribution in the body is modeled to o ccur between hepatic and adipose/lipid 20 compartments, with the fraction of body burden in liver increasing according to a function that 21 parallels the induction of the bi nding protein CYP1A2. Elimina tion is modeled to occur through 22 hepatic metabolism (represented as a firs t-order process with rate constant K that decreases with 23 age) and through lipid-based pa rtitioning of unmetabolized TC DD across the intestinal lumen 24 into the gut, which is also modeled as a first-or der process. As the body burden increases, the 25
amount of TCDD in the liver incr eases nonlinearly, result ing in an increased overall elimination 26

rate. 27
28
3.3.4.3.1.2. Mathematical representation. 29
The CADM model describes the distribution to tissues (including liver and adipose 30 tissue) based on exchange from blood at time in tervals of one month. The model is based on 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-28 quasi-steady-state-approximation, and thus it is also based on the c onsideration that the 1 intertissue processes reach their equilibrium valu es “quasi-instantaneously.” In this regard, 2 absorption and internal distribution reflective of kinetics at the cellular level (e.g., diffusion, 3 receptor binding, and enzyme induction) likely o ccur on a relatively fast time scale (a few hours 4 to a few days). However, the overall body concen tration (i.e., body burden) varies slowly with 5 time such that it remains virtually unchanged during short time intervals. 6
The CADM model does not differentiate be tween binding to AhR and CYP1A2, and it 7 lacks explicit descriptions of CYP1A2 induc tion, a key determinant of TCDD kinetics. 8 However, the empirical equation in the CADM model is based on five parameters (i.e., f min , f max , 9 K, W a , and W l ; see Tables 3-4 and 3-5) that allow the successful description of the behavior of 10 TCDD in liver and adipose tissue (i.e., TCDD half -lives in each comp artment increase with 11 decreasing body burden). This observation implie s that the model adequately accounts for the 12 ensemble of the processes. E ssentially, the CADM model describes the rate of change in tissue 13
concentrations of TCDD as a func tion of total body burden such th at the global elimination rate 14 decreases with decreasing body bur den or administered dose. 15
16
3.3.4.3.1.3. Parameter estimation. 17 The CADM model is characterized by its si mplicity and fewer parameters compared to 18 physiologically-based models. Reflecting this si mplicity, hepatic extraction is computed with a 19 unified empirical equation that accounts for al l relevant processes (i .e., protein induction and 20 binding). 21 The key parameters (f min , f max , K, and k e ) were all obtained by fitting to species-specific 22 pharmacokinetic data. The physiological parameters (such as tissue weights) used in the model 23 are within ranges documented in the literature. Th e fat content is described to vary as a function 24 of age, sex, and BMI. However, the BMI of the model is not allowed to change during an 25 individual simulation (which can range from 20 years to 70+ years) when in reality the 26 percentage of fat in humans changes over time . None of the TCDD-specific parameters were 27
estimated a priori or independent of the data set simulated by the model. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-29 3.3.4.3.1.4. Model performance and degree of evaluation

. 1 The CADM model was not evaluated for its capabilities in predicting data sets not used 2 in its parameterization. In other words, one or more of the key input parameters (f hmin , f hmax , k e , 3 K) was or were obtained essentially by fitting to the species-specific pharmacokinetic data, such 4
that there was no “external” validation data set to which the model was applied. Despite the lack 5 of emphasis on the “external” validation aspect, the authors have demonstrated the ability of the 6 model to describe multiple data sets covering a range of doses and species. 7 The visual comparison of the simulated data to experimental values suggests that the 8 model could, to an approximate degree, corr ectly reproduce the whole set of data (e.g., 9 pharmacokinetic [PK] profile over a range of dose a nd time) and not just part of the PK curve, 10 essentially with the use of a single set of equations and parameters. 11 The pharmacokinetic data sets for TCDD that were used to calibrate/evaluate the CADM 12 model included the following: 13
14

• Adipose tissue and liver concentrations of TCDD following a single oral dose of 1 μg/kg 15 in monkeys (McNulty et al., 1982); 16

• Percent dose retained in liver for a total dose of 14 ng in hamsters (Van den Berg et al., 17 1986); 18

• Elimination kinetics of TCDD in female Wist ar rats following a single subcutaneous dose 19 of 300 ng/kg (data from Abraham et al., 1988); 20

• Liver and adipose tissue concentrations (terminal measurements) in Sprague

− Dawley rats 21 given 1, 10 or 100 ng TCDD/kg bw during 2 years (Kociba et al., 1978); and 22

• Serum lipid concentrations of TCDD over a period of severa l years in 54 adults (29 men 23 and 25 women) from Seveso and in three Austrian patients (Ayl ward et al., 2005a). 24
25 For illustration purposes, Figure 3-9 shows model simulations of rat data from Carrier et 26 al. (1995a). Figure 3-2 (see Sect ion 3.3.2.4) depicts the human data that were used by the 27 authors to support th e concentration-dependent elim ination concept; the model was 28 parameterized to fit approximately to these data (Aylward et al., 2005a). 29 The authors did not report any specialized analyses that quantitatively evaluated the 30 uncertainty, sensitivity, and/ or variability of CADM mode l parameters and structure. 31
32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-30 3.3.4.3.1.5. Confidence in CADM model predictions of dose metrics. 1 The level of confidence associated with the pr edictability and reliability of absorbed dose 2 and body burden for oral exposures in humans (as well as several animal species) by this model 3 can be ranked as high (see Table 3-6). This m odel, however, does not acc ount for the differential 4 solubility of TCDD in serum lipids and adipos e tissue lipids, nor doe s it account for the 5 diffusion-limited uptake by adipose tissue. Due to these limitations, the confidence associated 6 with the predictions of the serum lipid concentration of TCDD is considered medium, 7 particularly when it is not documented that steady- state is reached during th e critical toxicologic 8 studies and human exposures. Furthermore, the CADM mo del does not f acilitate the 9 computation of TCDD concentrations in specific internal organs (other than liver and adipose 10 tissue). The reliability of this model for simulati ng the liver concentration (free, bound, or total) 11
of TCDD at low doses is considered to be low. This low confidence level is a result of the 12 uncertainty associated with the key parameter ƒ hmin . This parameter needs to be re-calibrated for 13 each study / species/population to effectively represent th e free fraction of TCDD in liver and the 14 amount of TCDD contained in the hepatic lipids and bound to the liver proteins (whose levels 15 might be reflective of background exposures of vari ous sources; see Carrier et al., 1995a). The 16 uncertainty related to the numerical value of th is parameter in animals and humans—particularly 17 at very low exposures—raises concern regard ing the use of this model to predict TCDD 18 concentration (free, bound, or to tal) in liver as the dose metr ic for dose-response modeling. 19 Although the use of the parameter ƒ hmax permits the prediction of the dose to liver at high doses, 20 it does not specifically f acilitate the simulation of the amount bound to the protein or level of 21
induction in liver. Because the CADM model is not capable of simulating enzyme induction 22 based on biologically-relevant para meters, its reliability for pr edicting the concentration of 23 TCDD bound specifically to the AhR is not known. Finally, due to the lack of parameterization 24 or verification with kinetic data in pregnant, lactating, or developing animals or humans, the 25 CADM model is unlikely to be reliable in the current form for use in predicting potential dose 26 metrics in these subpopulations or study groups that might form the basis of points of departure 27 (PODs) for the assessment. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-31 3.3.4.3.2. PBPK model. 1
3.3.4.3.2.1. Model structure. 2 Emond et al. (2004, 2006) simplified the eigh t-compartment rat model of Wang et al. 3 (1997) to a four-compartmental model (liver, fat, rest of body and placenta with fetal transfer) 4 (Emond et al., 2004), and later to a three-compartment adult model (liver, fat, rest of the body) 5 (Emond et al., 2006) (see Figures 3-10 and 3-11). Their rationale for simplification of the model 6 was based on evaluating, critiquing, and impr oving all earlier PBPK models by Wang et al. 7 (1997). In general, the main reason for the si mplification was that ex trapolation of a PBPK 8 model to humans with these many (i.e., ei ght compartments) compartments would be 9 problematic due to the limited availability of relevant human data for validation (Emond et al., 10 2004). One major difference from earlier models , repeatedly emphasized by Emond et al. (2005, 11 2006), was their description (included in their simplified PBPK models) of the dose-dependent, 12
inducible elimination of TCDD. The rationa le for including TCDD bi nding and induction of 13
CYP1A2 into the model was earlier desc ribed by Santostefano et al. (1998). 14
The most recent version of the rat and huma n PBPK models developed by Emond et al. 15 (2006) describes the organism as a set of three compartments corresponding to real physical 16 locations—liver, fat, and rest of the body— interconnected by system ic circulation (see 17 Figure 3-10). The liver compartment includes descriptions of CYP1A2 induction, which is 18 critical for simulating TCDD sequestration in liver and dose-dependent elimination of TCDD. In 19 this model, the oral absorption of TCDD from the GI tract accounts for both the lymphatic (70%) 20 and portal (30%) systems. 21 The biological relationship between TCDD “s equestration” by liver protein and its 22 “elimination” by the liver is not entirely clear. TCDD is metabolized slowly by unidentified 23 enzymes. CYP1A2 is known to metabolize TCDD based on studies in CYP1A2 KO mice 24 (Diliberto et al., 1997, 1999), in which the metabolic profile is different compared to wild-type 25 mice. However, since several metabolites appear in the feces of CYP1A2 knock out mice, it is 26 assumed that there are other enzymes involved in TCDD metabolism. TCDD binds to the AhR 27 and induces not only CYP1A2, but also CYP1A1 , CYP1B1, and several UGTs and transporters 28 (Gasiwicz et al., 2008). Both hydroxylated and glucuronidated hydroxyl metabolites are found 29 in the feces of animals treated with TCDD (H akk et al., 2009). Because the exact enzymes 30 involved with TCDD are unknown and yet the metabolism is induced by TCDD, an assumption 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-32 of increased the elimination rate of TCDD in pr oportion to the induction of CYP1A2 is made. In 1 the PBPK model, CYP1A2 is needed because TCDD binds to rat, mouse, and human CYP1A2 2 (Staskal et al., 2005; Diliberto et al., 1999). Thus CYP1A2 induc tion is necessary to describe 3 TCDD pharmacokinetics due to TCDD binding. Hen ce, CYP1A2 can be used as a marker of 4 Ah-receptor induction of “TCDD metabolizing enzymes.” Other models use AhR occupancy as 5
a marker of induction of “TC DD metabolizing enzymes” (Ander sen et al., 1997; Kohn et al., 6 2001). 7 Figure 3-11 depicts the structure of the PBPK model developing rat (Emond et al., 2004). 8
9
3.3.4.3.2.2. Mathematical representation. 10 The key equations of the PBPK model of Emond et al. (2004) are reproduced in 11 Text Boxes 3-1 and 3-2, whereas those from Emond et al. (2005, 2006) are listed in Table 3-7. 12 The rate of change of TCDD in the various ti ssue compartments is modeled on the basis of 13 diffusion limitation considerations. Accordingly, mass balance equations are used to compute 14 the rate of change in the tissue (i.e., intr acellular compartment) and tissue blood (i.e., 15 extracellular compartment). The membrane tran sfer of TCDD is computed using a permeation 16 coefficient-surface area cross product (PA) for each tissue. Metabolism and binding of TCDD to 17 the AhR and inducible hepatic protein (CYP1A2) are described in the liv er. The total mass in 18 the liver was then apportioned between free dioxin (C lf ) and bound forms of TCDD (see Figure 19 3-12). The dose- and time-depende nt induction of hepatic CYP1A2 in the liver is described per 20 Wang et al. (1997) and Sant ostefano et al. (1998). Accordi ngly, the amount of CYP1A2 in the 21 liver was computed as the time-integrated produc t of inducible production and a simple first22 order loss process (Wang et al., 1997): 23
24

0 2 () 1A2 A 2t dCYP StK KC dt = − (Eq. 3-19) 25

26 In this expression, CYP 1A2 is the concentration of the enzyme (nmol/g), K 2 is the rate constant for 27 the first order loss (liter/hour), C A2t is the concentration of CYP1A2 in the liver (nM/hour), K 0 is 28 the basal rate of production of CYP1A2 in the liver, and S ( t ) (unitless) is a multiplicative 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-33 stimulation factor for CYP1A2 production in the form of a Hill-type function (see 1 Section 3.3.2.3): 2
3 

### ( ) ( ) = 1 + ( )( ) h A2 Ah-TCDD

h h A2 Ah-TCDD 

### In C S t IC C + (Eq. 3-20) 4

5 where, S ( t

) is the stimulation function, In A2 is the maximum fold of CY P1A2 synthesis rate over 6 the basal rate, C Ah-TCDD is the amount of AhR occupied by TCDD, and IC A2 is the Michaelis7 Menten constant of CYP1A2 induction (nM). The dose-dependent or variable elimination of 8
TCDD was described usi ng the relationship: 9
10

12 12 1 2 ⎡ ⎤

− = ×

⎢ ⎥ ⎣ ⎦ induced basal

basal CYP A CYP A KBILE LI Kelv CYP A (Eq. 3-21) 11

12
where CYP1A2 induced is the concentration of induced CYP1A2 (nmol/mL), CYP1A2 basal is the 13 basal concentration of CYP1A2 (nmol/mL), and Kelv is the interspecies constant adjustment for 14 the elimination rate (hour − 1 ). 15 There are various ways of formulating the dose-dependent elimination as a function of 16 the level of CYP1A2, and the a bove equation (used by the authors) can be viewed as one means 17 of describing this behavior quantitatively. Th e numerator in the equation above will always be 18 greater than zero when there is TCDD in the system (including TCDD derived from either 19 background exposures or defined external sources) . Consequently, the ra te of elimination will 20 correspond to a nonzero value for situations invo lving TCDD exposures. The above equation, 21 however, does not describe changes in elimina tion rate in direct pr oportionality with the 22 CYP1A2 levels; also, the Kelv value by itself does not reflect a scalable basal metabolic rate. 23 Rather, these two terms collectively describe the outcome related to the TCDD elimination 24 processes, based on fitting to observations in ra ts (Santostefano et al., 1998). The impact of 25 CYP1A2 induction and sequestration on binding and elimination of TCDD is simulated using the 26 Emond et al. (2004) model. 27This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-34 The gestational model consisted of a fetal compartment, and the transfer of TCDD 1 between the placental and fetal compartments was described as a diffusion-limited (rather than a 2 perfusion-limited) process (see Text Boxes 3-1 and 3-2). 3
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 =
× + × + × + × + −×This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-35 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 fet dAPla fet nmol h Cl Cpla dt = ×

Dioxin Transfer from Fetuses to Placenta

_ _ ( /) Pla fet dAfet 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 =This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-36 3.3.4.3.2.3. Parameter estimation. 1
Table 3-8 lists the numerical values of the adult rat and human PBPK models of Emond 2 et al. (2005, 2006). The values for key input parameters of the rat gestational model are 3
summarized in Table 3-8 as well as Figure 3-13. 4 The parameters for the rat model were obtaine d primarily from Wang et al. (1997) except 5 that the value of affinity constant for CYP1 A2 was changed from 0.03 to 0.04 nmol/mL to get 6 better fit to experimental data (Emond et al., 2004) and the variable elimination parameter ( Kelv ) 7 was obtained by optimization of model fit to kinetic data from Santostefa no et al. (1998) and 8 Wang et al. (1997) (Emond et al., 2005, 2006). Wang et al. (1997) used measured tissue weights 9
whereas the tissue blood flows a nd tissue blood weights were obtai ned from International Life 10 Sciences Institute (ILSI, 1994). The partition coe fficients (which were similar to those of Leung 11 et al. [1988, 1990]), the permeability x area (PA) va lue for tissues, the dissociation constant for 12 binding to CYP1A2 (IC A2 ) and the Hill coefficient ( h ) were estimated using a two-stage process 13 of fitting to dose-response and time-course data on TCDD tissue distribution (Wang et al., 1997). 14 In the initial stage, the experime ntal data of arterial blood concen trations were used as input to 15 the individual compartment to estimate the parameters; then, with the values obtained during 16 stage one as initial estimates, those unknown parameters were re-e stimated by solving the entire 17 model at once using an optimization route (Wang et al., 1997). The receptor concentrations and 18
dissociation constant of TCDD bound to AhR we re obtained by fitting the model to TCDD tissue 19 concentration combining with enzyme data re ported by Santostefano et al. (1998) whereas the 20
basal CYP1A2 in liver was based on literature data (Wang et al., 1997). 21 The parameters for the human PBPK model were primarily based on the rat model (Wang 22 et al., 1997; Emond et al., 2005, 2006). Specifica lly, the blood fraction in the tissues, the 23 tissue:blood partition coefficients, tissue permeab ility coefficient, the binding affinity of TCDD 24 to Ah and CYP, and the maximum binding capacity in the liver for AhR were all set equal to the 25 values used in the rat model. The species-specific Kelv was estimated by fitting to human data 26 (Emond et al., 2005). 27 For the gestational rat model, the parameters describing the growth of the placental and 28 fetal compartments as well as temporal change in blood flow during gesta tion were incorporated 29 based on existing data. Exponential equations fo r the growing compartments were used (see 30 Figure 3-13), except for adipose tissue for which a linear increment based on literature data was 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-37 specified. While physiological parameters for the pregnant rat were obtaine d from the literature, 1 all other input parameters were set equal to th at of nonpregnant rat (obt ained from Wang et al. 2 [1997]), see Tables 3-7 and 3-8. The current version of the rat gest ational model contains 3 parameters for variable elimination from Emond et al. (2006; Table 3-8), and still provides the 4 essentially the same predictions as the original publication (Emond et al., 2004). 5
6 3.3.4.3.2.4. Model performance and degree of evaluation

. 7
The PBPK model of Emond et al. (2004, 2005, 2006) had parameters estimated by fitting 8 to kinetic data, such that the resulting model cons istently reproduced the kinetic data. The same 9 model structure with a single se t of species-specific parameters could reproduce the kinetics of 10 TCDD following various doses and exposure scenario s not only in the rat but also in humans. 11
The simulations of the PBPK mode l of Emond et al. (2006) have been compared with two sets of 12 previously published rat data: blood pharmacoki netics following a single dose of 10 μg/kg (the 13 dose corresponding to the mean effective dose for induction of CYP1A2) (Santostefano et al., 14 1998) (see Figure 3-14); and hepa tic TCDD concentrations during chronic exposure to 50, 100, 15 500, or 1,750 ng/kg (Walker et al., 1999) (see Figure 3- 15). It is relevant to note that the PBPK 16 model of Emond et al. (2004, 2006) is essentially a reduced version of the Wang et al. (1997) 17 model, and it therefore provide s simulations of liver and fat concentrations of TCDD that 18 deviated by not more than 10

− 15% of those of Wang et al. (1 997). The nongestational model of 19 Emond et al. (2004) simulated the ki netic data in liver, fat, bl ood and rest of body of female 20 Sprague-Dawley rats given a single dose of 10 μg TCDD/kg (data from Santostefano et al., 21 1996) and in liver and fat of male Wistar rats treated with a loading dose of 25 ng ⁄

kg followed by 22 a weekly maintenance dose of 5 ng TCDD ⁄

kg by gavage (data from Krowke et al., 1989). 23 The gestational rat PB PK model simulated the following PK data sets (Emond et al., 24 2004): 25
26

• TCDD concentration in blood, fat, liver, placenta, and fetus of female Long

− Evans rats 27 given 1, 10, or 30 ng ⁄

kg, 5 days ⁄

week, for 13 weeks prior to mating followed by daily 28 exposure through parturiti on (Hurst et al., 2000a); 29

• TCDD concentration in tissues (liver, fat) , blood, placenta and fetus determined on 30 gestation day (GD) 16 and GD 21 followi ng a single dose of 0.05, 0.8, or 1 μ g/kg given 31 on GD 15 to pregnant Long Evan s rat (Hurst et al., 2000b); 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-38 • Maternal and fetal tissue concentrations on GD 9, GD 16 and GD 21 after a single dose 1 of 1.15 μ g TCDD/kg given to Long

− Evans rats on GD 9 or GD 15 (Hurst et al., 1998); 2 and 3

• Fetal TCDD concentrations determined on GD 19 and GD 21 in rats exposed to 5.6 μ g 4 TCDD/kg on GD 18 (Li et al., 2006) 5
6 Furthermore, the scaled rat model was shown to be capable of simulating human data 7 from the Austrian and Seveso subjects (see Figures 3-16 and 3-17). In this regard, it is useful to 8 note that the computational version of the PBPK model of Emond et al. (2005, 2006) also 9 contained the necessary equation to transform the model output of bl ood concentration into 10 serum lipid adjusted concentration of TCDD. 11 The human model of Emond et al. (2005) has advantages for improving the TCDD 12 dosimetry used in existing human epidemiological studies because its PBPK structure naturally 13 develops expectations for the redistribution of TCDD within the body (to stores in fat and liver) 14 relative to metabolism. However, because the do se-dependency of metabo lic elimination in the 15 Emond et al. (2005) human model was not calibrated to human data, it is im portant to review the 16 expectations of this model usi ng a database of human observati ons that is as extensive as 17 possible and a spread of internal TCDD concentrat ions that is as wide as possible. Thus, 18 presented below is a juxtaposition of expectati ons of elimination rates for the Emond model with 19 observations for two highly expos ed Austrian patients and ni ne of 10 Ranch Hand veterans 12 20 used for the original “validation” co mparisons in the Emond et al. (2005). 21 Figure 3-18 shows the time course of the dec lines in TCDD serum concentrations in two 22 highly-exposed Austrian subjects compared with expectations from the Emond et al. (2005) 23 model. The comparison in Figures 3-17 and 3- 18 indicates that the Em ond model adequately 24
describes the rate of TCDD elimination for th e more highly exposed Au strian patients, but 25 predicts a somewhat faster rate of decline than that observed for the less heavily exposed patient. 26 Figure 3-19 shows the results of combining th e simulated and observed rates of loss for a 27 group of Austrian and Ranch Hand subjects eval uated by Emond et al. (2005), counting only one 28 data point per person. The X-axis in this figure is the TCDD serum concentration at the 29

12 In preliminary comparisons, the simulation run for the 10 th Ranch Hand veteran appeared anomalous and was therefore excluded from this summary.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-39 midpoint of the observations for each subject. The error bars in the figure represent ±1 standard 1

error. 2 Table 3-9 presents the results of regression an alyses of the observed rates of decline in 3
relation to the estimated TCDD seru m levels at the midpoint of the observations for each subject 4 in the Ranch Hand study (see Figure 3-19). Thes e results indicate that some appreciable dose 5 dependency of TCDD elimination is unequivocally supported. Howe ver, the central estimate of 6 the slope of the relationship be tween the log of the TCDD elimination rate and the log of the 7 TCDD level is only about 75% of that e xpected under the Emond et al. PBPK model 8 (i.e., 0.092 ÷ 0.123 = 0.748). 9 Based on Table 3-9 and Figure 3-19, the expected slope from the Emond model 10 (slope = 0.123) is about 2.8 standard errors above the regr ession line (slope = 0.092 with 11 standard errors value of 0.011) indicated by the observations, s uggesting that the departure is 12 statistically significant. It can be seen th at some appreciable dose dependency of TCDD 13 elimination is unequivocally supported, but it is indicated that the dependency is somewhat less 14 than presently incorporated into the Emond model. The difference might be slightly enhanced if 15 the modest effects of the special measures taken to accelerate removal of TCDD from the 16 Austrian subjects were subtracted from the observe d elimination rates (i.e., oral administration of 17 Olestra for both patients and LDL-apheresi s for the more highly exposed patient). 18
It would be desirable in the future to extend this juxtapos ition to data for other intensively 19 studied subjects. Figure 4 in Ay lward et al. (2005a) (r eproduced here as Figur e 3-2) shows a plot 20 of elimination rate versus initial level of lipid-corrected TCDD in serum for 36 people. This is 21 not the most desirable comparison for characteriz ing the relationship, how ever, because the rate 22 constant for loss should be related to the geomet ric mean or midpoint level in the decline for 23 each person (rather than to the in itial level) in order to be mo st accurate in relating current 24 TCDD levels to elimination rates and to avoid po ssible “regression to the mean” type statistical 25 errors due to measurement imperfections. Overall, the conclusion from this analysis is that it is 26
not unreasonable to use the Emond model as it stands, but the model might be modestly 27 improved by adapting it to (1) include the two nondose-dependent pathways of elimination 28 documented in the Geusau papers (GI elimination via the feces and loss via the sloughing of skin 29 cells), and (2) reducing the extent of loss via the dose-dependent metabolism pathway from the 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-40 liver (Geusau et al., 2002; Ha rrad et al., 2003) so that overa ll loss rates for the average 1
elimination rates from the Ranch Hand veterans is maintained. 2 A sensitivity analysis of inputs used to estim ate inducible elimination rate for a single 3 oral dose of 0.001 to 10 μg/kg in the rat indicated that the number of key parameters ranged from 4 seven at the low dose region to 12 at the high dose (see Figure 3- 20) (Emond et al., 2006). The 5 sensitive parameters identified included the oral absorption parameters (KABS), volumes of liver 6 and adipose tissue (WLIO, WFO), adipose tissue: blood partition coefficient (PF), and the basal 7 CYP1A2 level (CYP1A2 1A2). At high doses, the most sensitive parameters also included those 8
related to the maximal induc tion of CYP1A2 and AhR bindi ng capacity (see Figure 3-20) 9 (Emond et al., 2006). 10 The gestational rat model de scribed in Emond et al. (2004) , upon reparameterization, 11 could simulate the kinetics of TCDD in mice. The initial changes to the rat model parameters 12 included: rest of the body:blood partition coeffi cient (PRE), basal conc entration (CYP1A2_1A2), 13 delay in induction time (CYP1A2_1TAU) and adi pose tissue permeability coefficient (PAFF), in 14 accordance with Wang et al. (2000) (see Table 3-8) . Subsequently, four parameters (adipose 15 tissue:blood partition coefficient, CYP1A2 affinity parameter, GI tract elimination transit 16 constant (hour − 1 ) and the interspecies metabolic parameter Kelv (hour − 1 ) were re-estimated based 17 on visually fit of model simulations to the PK da ta from Deliberto et al . (2001), following an oral 18 dose 150 ng TCDD/kg/day, 5 days/week for 17 week s (see Table 3-7). The resulting mouse 19 model is capable of reproducing the kinetics of TCDD in the adult (see Figures 3-21 through 20 3-27), as well as, to a very limited extent, th e kinetics during gestati on (see Figure 3-28). 21
22
3.3.4.3.2.5. Confidence in PBPK model predictions of dose metrics

. 23
The PBPK model facilitate s prediction of absorbed dose, body burden, and blood 24 concentration of TCDD for oral exposures in ad ult humans and rats (adul t and developing) with 25 high confidence (see Table 3-10). The model output of blood concentration can be normalized to 26 lipid content representati ve of the study group (species, sex, ag e, lifestage, and diet). However, 27
the PBPK model of Emond et al . (2004, 2005, 2006) does not simu late plasma and erythrocyte 28 TCDD concentrations separately , and it predicts tissue conc entrations on the basis of 29 tissue:whole blood partition coeffi cients and not on the basis of serum lipid-normalized values. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-41 The reliability of this model for simulating th e liver concentration of TCDD in rats is 1 considered to be high but it is considered to be medium for humans. Although empirical data on 2
bound or free concentrations were not used to evaluate model performance in humans, the 3 biological phenomena (consistent with availabl e data) related to the hepatic sequestration, 4 enzyme induction, and dose-dependent elimination are described in the model. This is one of the 5 situations where PBPK models are uniquely useful; that is, they permit the prediction of system 6 behavior based on understanding of the mechanistic determinants, even tho ugh the required data 7 cannot be directly obtained in the system (e.g ., bound concentrations in the liver of exposed 8 humans). For these dose measures (i.e., bound c oncentration and total li ver concentration), the 9 level of confidence can be further improved or dimi nished by the outcome of sensitivity analysis. 10 In this regard, the results of a focused sensit ivity analysis indicate that the most sensitive 11 parameters of the human model are among the most uncertain (i.e., those parameters for which 12 estimates were not obtained in humans) with respect to prediction of liver TCDD concentration, 13 contrary to the animal mode l (see Section 3.3.6). 14 With respect to the mouse model, however, the level of confidence is medium to low, 15 given that it has not been veri fied extensively with blood, body burden, or tissue concentration 16 time-course or dose-response data. However, the mouse PBPK model, based on the rat model 17 that has been evaluated with several PK data sets, has been shown to reproduce limited liver 18 kinetic data (see Figures 3-21 th rough 3-28; Boverhoff et al., 2005). The same model structure 19 has been used for simulating kinetics of TCDD in humans successfully. Ov erall, the adult mouse 20 model, given its biological basis combined with its ability to simulate TCDD kinetics in multiple 21 species, is considered to exhibi t a medium level of confidence for simulating dose metrics for use 22 in high to low dose extrapolation and interspecies (mouse to human) extrapolation. Even though 23 similar considerations are appl icable to gestational model in mice, the confidence level is 24 considered to be low since very limited comparis on with empirical data has been conducted (see 25 Figure 3-28). Despite the uncertainty in these pr edictions, the scaled rat gestational model, given 26 its biological and mechanistic basis, might be of use in predicting dose metrics in these groups 27 that might form the basis of PODs in certain key studies. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-42 3.3.4.4. Applicability of PK Models to Derive Dose Metrics for Dose-Response Modeling of 1 TCDD: Confidence and Limitations 2 Both the CADM and PBPK models descri be the kinetics of TCDD following oral 3 exposure to adult animals and humans by accounti ng for the key processes affecting kinetics, 4 including hepatic sequestration phenomena, induction, and nonlinearity in elimination, and 5 distribution in adipose tissue and liver. Both models can be used for estimating body burdens 6 and serum lipid adjusted concentrations of TC DD. However, there are several differences 7
between these two models. The PBPK model cal culates the free and bound concentrations of 8 TCDD in the intracellular subcompartment of tissues. The total or receptor-bound 9 concentrations in liver are unambiguous and more easily interpretable with the PBPK model than 10 with the CADM model. In addition, the PBPK model computes bound and total concentrations 11
as a function of the free concentration in the intrac ellular compartment of the tissue. By contrast, 12
the CADM model simulates the total concentrati on based on empirical cons ideration of hepatic 13 processes. Consequently, the amount of TC DD bound to AhR or CYP1A2 cannot be simulated 14 with the CADM model. The CADM model comp utes only the total TCDD concentration in 15 liver, and describes TCDD elimination through pa rtitioning from circulating lipids across the 16 lumen of the large intestine in to the feces, while the PBPK m odel accounts for this process 17 empirically within its hepatic elimination cons tant. Elimination of TCDD via skin, a minor 18 process, is not described by either model. Thus, dose-response modeling based on body burden 19
of TCDD in adult animals and human s can be conducted with either of the models, provided the 20 duration of the experiment is at least one month, due to limitations in the CADM model. As 21 shown in Figure 3-29, the predicted slope a nd body burden over a large dose range are quite 22 comparable (generally w ithin a factor of two). 23 Results of simulations of serum lipid concentr ations or liver concentrations vary for the 24 two models to a larger extent (up to a factor of 7), particularly fo r simulations of short duration. 25
These differences reflect two characteristics of the PBPK model: first, quasi-steady-state is not 26 assumed in the PBPK model; second, the serum lipid composition used in the model is not the 27 same as the adipose tissue lipids. The CADM model does not account for differential solubility 28 of TCDD in serum lipids and adipose tissue lipid s, nor does it account for the diffusion-limited 29 uptake by adipose tissue. Therefore, the PBPK m odel would appear to be superior to the CADM 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-43 model with respect to the abil ity to simulate serum lipid and tissue concentrations during 1 exposures that do not lead to the onset of steady-state conditi on in the exposed organism. 2 The CADM model is simple and based on fe wer parameters than the PBPK model. 3
Because the CADM model is cons tructed by fitting to data, its performance is likely to be 4 reliable for the range of exposure doses, spec ies, and life stages fr om which the parameter 5 estimates were obtained. On the other hand, the PBPK model structur e and parameters are 6 biologically-based and can be adopted for each sp ecies and life stage. Accordingly, the PBPK 7 model has been adopted to simulate the kinetics of TCDD in the fetus and in pregnant rats, as 8
well as in adult humans and rats (Emond et al., 2004, 2005, 2006). The time step for calculation 9 and dosing in the CADM model corresponds to 1 month. This requirement represents a 10
constraint in terms of the use of this model to simulate a variety of dosing protocols used in 11 animal toxicity studies. This requirement, howev er, is not a constraint with the PBPK models. 12 So, simulating the body burden and serum lipid con centrations for a longer duration of exposure, 13 either model would appear to be useful; but th e PBPK model would be the tool of choice for 14 simulating alternative dose metrics of TCDD (e.g., blood concentration, total tissue 15 concentration, bound concentration) for vari ous exposure scenarios (including single dose 16 studies), routes and life stages in the species of relevance, to TCDD dose-response assessment, 17 particularly, mice, rats, and humans. 18 Two minor modifications, to enhance the biologi cal basis, were made to the PBPK model 19 of Emond et al. (2006), before its use in the co mputation of dose metrics for TCDD. The first 20
one involved the recalculation of the volume of the rest of the body as follows: 21
22 0 (0.91 ( 0 0 0 0 0 0) (1 0)) WRE WLIB WLI WFB WF WLI WF WREB = − × + ×+ + + (3-22) 23

24
where 25 WRE 0 = weight of cellular co mponent of rest of body co mpartment (as fraction of 26 body weight); 27
WLI 0 = weight of cellular component of liver compartment (as fraction of body 28 weight); 29
WF 0 = weight of cellular component of fat compartment (as fraction of body 30 weight); 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-44 WREB 0 = weight of the tissue blood componen t of the rest of body compartment (as 1 fraction of body weight); 2
WLIB 0 = weight of the tissue blood componen t of the liver compartment (as fraction 3 of body weight); and 4
WFB 0 = weight of the tissue blood component of the fat compartment (as fraction of 5 body weight). 6
7 In the original code, the weight of the rest of body compartment was calculated as the 8 difference between 91% of body weight and the su m total of the fractional volumes of blood, 9 liver tissue (intracellular component), and adi pose tissue (intracellular component). The blood 10 compartment in the PBPK model is not explicitly characterized with a volume; as a result, the 11 total volume of the compartments is less than 91 %. The recalculations shown above were used 12 to address this problem. Given the very low affinity of TCDD for blood and rest of the body, 13 reparameterizing the model resulted in less th an a 1% change in output compared to the 14 published version of the PBPK model for chronic exposure scen arios (Emond et al., 2006). 15
The second minor modification related to the calculation of the rate of TCDD excreted 16 via urine. The original model code computed the rate of excretion by multiplying the urinary 17 clearance parameter with the con centration in the rest of the body compartment. Instead, the 18
code was modified to use the blood concentratio n in this equation. This resulted in the 19 re-estimation of the urinary clearance value in the rat and human models but it did not result in 20 any significant change in the fit and performance of the original model. 21 The revised parameter estimates of the rat, mouse, and human models are captured in 22
Table 3-8 with a footnote. 23
24
3.3.4.5. Recommended Dose Metrics for Key Studies 25 The selection of dose metrics for the dose-res ponse modeling of key studies is largely the 26 result of (1) the relevance of a dose metric on the basis of current knowledge of TCDD’s 27 mechanism of action for critical endpoints and (2 ) the feasibility and reliability of obtaining the 28
dose metric with available PK models. Sec ondarily, the goodness-of-f it of the dose-response 29 models (which reflects the relationship of the selected internal dose measures to the response) 30
can be used to inform selection of the most appropriate dose metric fo r use in deriving TCDD 31 toxicity values. 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-45 Body burden—even though this metric is based on mechanistic considerations—is a 1 somewhat distant measure of dose with respect to target tissue dose, and this metric represents 2 the “overall” average concentration of TCDD in the body. However, a benefit of body burden is 3 that this metric represents a dose measure for which the available PK models can provide highly 4 certain estimates. Thus, the overall confidence associated with the use of body burden in TCDD 5 assessment is categorized as medium. 6 The confidence in the ability of PK models to simulate blood concentration as a dose 7 metric is high, given that the models have b een shown to consistently reproduce whole blood (or 8 serum lipid-normalized) TCDD concentration profiles in both humans and rats. Considering the 9
facts that the PBPK models simulate whole blood rather than the serum lipid-normalized 10 concentrations of TCDD and that the study-specif ic values of serum lipid content are not known 11 with certainty, it is pr eferable to rely on TCDD blood concen trations as the dose metric. The 12 blood concentrations, if intended, can be normalized on the basis of appropriate total lipid levels. 13 However, based on mechanistic considerations, th e confidence in their use would be somewhat 14 lower for hepatic effects. This conclusion re flects the concern regarding the inconsistent 15 relationship between the two variables with in creasing dose levels and the fraction of steady16
state attained at the time of observation. For other systemic effects related to tissue 17 concentrations, the confidence in the use of TCDD serum or blood concentration is high, 18 particularly for chronic exposures, given the ab sence of organ-specific nonlinear mechanisms. 19 The tissue concentration typically cannot be calculated as a reliabl e dose metric with either PK 20 model. One exception to this conclusion is the use of PBPK models to estimate levels in liver, a 21 metric that is highly relevant based on MOA considerations. Finally, the bound concentration 22 may be evaluated for receptor-mediated effects. This dose metric, of medium-low confidence, 23 can be obtained with PBPK models for high dose- low dose and interspecies extrapolations. The 24 alternative dose metrics for dose-response modeli ng of TCDD selected on the basis of MOA and 25 PK modeling considerations are summar ized in Tables 3-11 and 3-12. 26
These measures of internal dose can be obtai ned as peak, average, integral (AUC), or 27 terminal values. For chronic exposures (e.g., exposures reflected by th e results of a cancer 28 bioassay) in rodents, the terminal and average va lues would be fairly comparable. For less-than 29 lifetime exposures, however, the terminal and aver age values will differ, and therefore an overall 30 average or integrated value (AUC) would be more appropriate. Similarly, for developmental 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-46 exposures, these alternative dose metrics can be obtained with refe rence to the known or 1 hypothesized exposure window of susceptibility. 2
3
3.3.5. Uncertainty in Dose Estimates 4
3.3.5.1. Sources of Uncertainty in Dose Metric Predictions 5
3.3.5.1.1. Limitations of available PK data. 6
3.3.5.1.1.1. Animal data. 7 The available animal data relate to blood, liver, and adipose tissu e concentrations for 8 certain exposure doses and scenarios. Although these data are informative regarding the dose9 and time-dependency of TCDD kinetics for the range covered by the specific studies (see 10 Section 3.3.2), they do not provide the peak, aver age, terminal, or lipid-normalized values of 11 dose metrics associated with the key studies sele cted for this assessment. The limited available 12 animal PK data are useful, however, in the evaluation of the pharmacokinetic models (see 13 Section 3.3.4). 14
15
3.3.5.1.1.2. Human data. 16 The human data on potential dose metrics are restricted to the serum lipid-adjusted 17
TCDD concentrations associated with mostly uncharacterized e xposures (see Sections 3.3.2 and 18 3.3.3). While these data are useful in estimati ng half-lives in exposed human individuals, they 19 do not provide estimates of hepatic clearance or reflect target organ e xposure. Some autopsy 20 data have been used to infer the partition co efficients; however, these data were collected 21 without quantification of the te mporal nature of TCDD uptake (s ee Section 3.2). Despite the 22
limitations associated with the av ailable human data, there has been some success in using these 23
data to infer the half-lives and elimination rates in humans using pharmacokinetic models 24 (Aylward et al., 2005b; Carrier et al., 2005a ; Emond et al., 2006). 25
26
3.3.5.1.2. Uncertainties associated with model specification. 27 Uncertainty associated with model specifica tion should be viewed as a function of the 28 specific application, such as interspecies extrap olation, intraspecies variability, or high dose to 29 low dose extrapolation. Because the use of pharmacokinetic models in this assessment is limited 30 to interspecies extrapolation and high dose to lo w dose extrapolation, it is essential to evaluate 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-47 the confidence in predicted dose metrics for these specific purposes. For interspecies 1 extrapolation, the PBPK and CADM models calcu late differences in dose metric between an 2 average adult animal and an average adult hum an. Both models have a biologically and 3 mechanistically-relevant structur e along with a set of parameters with reasonable biological 4 basis, and reproduce a variety of pharmacokinetic data on TCDD in both rodents and humans. 5 These models possess low uncertainty with respect to body burden, blood, and TCDD/serum 6 (lipid) concentration for the purpose of conducting rat to human extrapolation. However, for 7 other dose metrics, such as free, total, or bound he patic concentrations, the uncertainty is higher 8 in the CADM model compared to the PBPK model due to model specification differences related 9 to the mechanisms of seques tration and induction in th e liver (see Section 3.3.3). 10 For the purpose of high dose to low dose ex trapolation in experimental animals, 11 confidence in both models is high with respec t to a variety of dose metrics (see previous 12 discussion). The high confidence results from the use of the PBPK m odels to reproduce a 13 number of data sets covering a wi de range of dose levels in rodent s (rats, mice). This dose range 14 likely covers that of most of the key toxicologi cal studies. Given that the TCDD levels during 15 and at the end of exposures were not measured in most of the key studies, use of the PBPK 16 models is preferred because these models accoun t for dose-dependent elimination, induction, and 17 sequestration. Despite the empirical nature of the specification of these key processes in PBPK 18 models, they essentially reprodu ce the dose-dependent behavior in rodents, supporting their use 19 in deriving dose metrics for dose-response m odeling of TCDD. Overall, the uncertainty 20 associated with the use of the dose metrics (identified in Table 3-10) is less than the uncertainty 21
associated with the use of administered dose of TCDD, for relating to the concentration within 22 tissues to produce an effect. The administered dose does not take into account interspecies 23
differences in the volume of distribution and clearance or the comple x nonlinear processes 24 determining the internal dose. 25
The PBPK model of Emond et al. (2006) could benefit from further refinement and 26 validation, including a more exp licit consideration of nondose-dep endent elimination pathways. 27 As indicated in Section 4, there is some uncerta inty associated with the way the elimination of 28 TCDD is described in the existing human PBPK model. The current model essentially treats all 29
TCDD elimination as related to dose dependent metabolism in the liver. In this regard, the 30 classical and more recent PK data on TCDD may be useful in further improving the confidence 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-48 in their predictions. However, it is likely th at there is nondose-dependent elimination of TCDD 1 via feces and, to a lesser extent skin; juxtaposit ion of available elimina tion rate data with the 2 PBPK model predictions suggests that the current PBPK model modestly overestimates the dose 3 dependency of overall TCDD elimination. (The centr al estimate of the slope of the relationship 4 between the log of the TCDD elimination rate and the log of the TCDD level is only about 5
three

− fourths of that expected using the unmodifi ed PBPK model). Emond et al. (2005) 6 acknowledge that the model did no t describe the elimination of TCDD from the blood into the 7 intestines, but it indirectly accounted for this phenomenon with the use of the optimized 8
elimination rate. 9
10
3.3.5.1.3. Impact of human interindividual variability. 11 The sources and extent of human variability suggested by the available data are presented 12 in Section 3.3.3, although there is some discussion of the impact of individual differences in 13 body fat content. The CADM model facilitates the simulation of body burden and serum lipid 14
concentrations on the basis of BMI and tissue we ights of people, and th e PBPK model simulates 15 alternative dose metrics in the fetus and in pregnant animals in addition to adult animals and 16 humans. However, neither of these models has been paramete rized for simulation of population 17
kinetics and distribution of TCDD dose metrics. Therefore, at the present time, a quantitative 18 evaluation of the impact of human variability on the dose metrics of TCDD is not feasible, and 19 dose metric-based replacement of the default inte rindividual factor has not been attempted. 20
21
3.3.5.2. Potential Magnitude and Sources of Uncertainty in Dose Metrics 22
3.3.5.2.1. Magnitude of uncertainty. 23
The usefulness of the CADM and PBPK m odels for conducting high dose to low dose 24 and interspecies extrapolations is determined by their reliability in predicting the desired dose 25 metrics. The confidence, or conversely the magnit ude of uncertainty, in th e model predictions of 26 dose metrics is dictated by the extent to which the model has been verified with empirical data 27 relevant to the dose metric, supplemented by sensitivity and uncertainty analyses. Analysis of 28 sensitivity or uncertainty has not been conducted with the CADM model. For the PBPK model, 29 Emond et al. (2006) published the initial results from sensitivity analyses of acute exposure 30 modeling (see Section 3.3.3). One of the objectives of a sensitivity analysis that is of highest 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-49 relevance to this assessment is to identify the most critical model parameters with respect to the 1 model output (i.e., dose metric). 2 If the model simulations have only been comp ared to entities th at do not correspond to 3 the moiety representing the dose metric, or if th e comparisons have only been done for some but 4 not all relevant dose levels, routes, and species, then the reliability in the predictions of dose 5
metric can be an issue. The extent to which model results are uncertain will depend largely upon 6 the extent to which the dose metric is measurable (e.g., serum concentrations of TCDD) or 7 inferred (e.g., receptor-bound TCDD concentration). 8 With respect to body burden and blood concentr ation, extrapolation uncertainty is low, 9 and therefore the need for sensitivity and uncertain ty analysis is less critical. For serum lipid10 based metric, the lipid content used for normaliz ing the animal and human blood concentrations 11 will have a direct impact on the outcome. Becau se the PBPK models have directly evaluated 12
blood or tissue concentration data of relevance to dose metric calculations in the species and life 13 stage of interest to th e assessment, extrapolation uncertainty is low and therefore confidence in 14 these simulated dose metrics is high. For those dose metrics that are not directly measurable or 15 are less easily determined by available calibra tion methods (e.g., free liver concentration, 16 receptor bound concentration), sensitivity and uncer tainty analyses are crucial to inform the 17 reliability of the PBPK model predictions 18 Sensitivity analysis for the PBPK model pr edictions of liver concentration of TCDD 19 indicated hepatic CYP1A2 concentration is the most sensitive parameter in the rat model 20 (Emond et al., 2006). In addition, the absorption parameters, basal concentration of CYP1A2, 21 and adipose tissue:blood partition coefficients were identified as highly sensitive model 22 parameters for simulations of human kinetics. These results indicate that the confidence in the 23 use of the rat PBPK model for high dose to low do se extrapolation is high . Confidence is low for 24 the purpose of rat to human extrapolation give n that the values of these key human model 25 parameters are uncertain. A similar inference can be made with respect to the concentration of 26 bound TCDD in liver, suggesting that the use of bound concentration as a dose metric in the rat 27 is reliable for high dose to low dose extrapolation, and less reliable for extrapolating from rat to 28 human due to model sensitivity to parameters with high uncertainty. 29 With regard to the predictability of body bur den, the absorption and excretion parameters 30 were among the sensitive parameters in the rat. Several other parameters were also identified as 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-50 being sensitive in humans. Despite the sensitivity to these parameters and the uncertainty 1 associated with individual parameter estimates, the overall confidence in the model predictions 2 of body burden appears to be high given the repr oducibility of empirical data on tissue burdens 3
and blood concentrations of TCDD in various ex periments by both models. Similar conclusions 4 can be drawn for serum lipid c oncentration of TCDD predicted by the PBPK model, except that 5 the assigned value of lipid content will have a dditional, direct impact on this dose metric. 6 Therefore, knowledge of the statistical representativeness of the lipid levels for rodents and 7 humans, intended for use in normalization, will be cruc ial. In this regard, variability of the total 8 lipid levels and the variability of the contribution of phospholipids and neutral lipid s to the total 9 lipid pool across spec ies, lifestage and study groups is to be expected (Poulin and Theil, 2001; 10 Bernert et al., 2007). 11 Both conceptual uncertainty and prediction uncer tainty are relevant to dose metrics used 12 in the dose-response modeling. In the current context, conceptual uncertainty arises from the 13
assumed relevance of the dose metrics to the MOA and target organ toxicity of TCDD. 14 Prediction uncertainty reflects the lack of confidence in the model predictions of dose metrics. 15 Tables 3-13 and 3-14 provide a qualitative ranking of the importance and magnitude of each 16 dose metric with respect to these two sources of uncertainty. Overall, un certainty is high for the 17
use of administered dose and absorbed dose at one end of the spectrum and receptor occupancy 18 at the other end of the spectrum. Based on the in formation presented in Table 3-13, for instances 19 involving the use of rat and huma n PBPK models, the overall uncerta inty associated with the use 20 of body burden and serum concentrations are medi um, whereas for hepatic effects, the use of 21
liver concentration would be considered to be medium/low. While using mouse PBPK model 22 along with the human model (see Table 3-14), the contri bution of the predic tion uncertainty to 23 the overall uncertainty increases due to the lim ited comparison of the mouse model simulations 24 with empirical data. 25
26
3.3.6. Use of the Emond PBPK Models for Dose Extrapolation from Rodents to Humans 27 EPA has selected the Emond et al. (2004, 2005, 2006) PBPK models, as modified by 28 EPA for this assessment, for establishing toxico kinetically-equivalent exposures in rodents and 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-51 humans. 13 The 2003 Reassessment (U.S. EPA, 2003) presented a strong argument for using the 1
relevant tissue concentration as the effectiv e dose metric. However, no models exist for 2
estimation of all relevant tissue concentrati ons. Therefore, EPA has decided to use the 3 concentration of TCDD in blood as a surrogate for tissue concentrations , assuming that tissue 4 concentrations are proportional to blood concentrations. Furthermore, because the RfD and 5 cancer slope factor are necessarily expressed in terms of average daily exposure, the blood 6 concentrations are expressed as averages over the relevant peri od of exposure for each endpoint. 7 Specifically, blood concentrations in the model si mulations are averaged from the administration 8
of the first dose to the administration of the last dose plus one dosing interval unit in order to 9 capture the peaks and valleys for each administered dose. That is, for daily dosing, 24 hours of 10 TCDD elimination following the last dose is include d in the average (the modeling time interval 11 is one hour); for a weekly dosing protocol, a full week is included. In addition, because of the 12 accumulation of TCDD in fat and the large differe nces in elimination kinetics between rodent 13 species and humans, exposure durat ion plays a much larger role in TK extrapolation across 14 species than for rapidly-eliminated compounds. B ecause of these factors, EPA is using discrete 15 exposure scenarios that relate human and rode nt exposure durations. The use of discrete 16 exposure scenarios was introduced previously in Section 3.4.4.2 describing first-order kinetic 17 modeling and is further described in the following paragraphs. This section concludes with a 18 quantitative evaluation of the impact of exposure duration on the rodent-to-human TK 19 extrapolation from both the human and rodent “ends” of the process. 20 Figure 3-30 shows the TCDD blood concentratio n-time profile for continuous exposure 21 at 0.01 ng/kg-day, as predicted by the Emond human PBPK model, and the target TCDD 22 concentrations corresponding to the three discrete exposure sc enarios used by EPA in this 23 document. The target concentrations are those th at would be identified in the animal bioassay 24 studies that correspond to a part icular POD (no-observed-adverse- effect level, lowest-observed25 adverse-effect level, or benchmark dose lower co nfidence bound) established for that bioassay. 26 That is, the target concentrations represent the toxicokinetically-equivalent internal exposure to 27 be translated into an equivalent human intake (or HED). 28

13 The models will be referred to hereafter as the “E mond human PBPK model” and the “Emond rodent PBPK model,” with variations when referring to individual species or components (e.g., gestational).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-52 For the lifetime exposure scenario, the HED is “matched” to the lifetime average TCDD 1
blood concentration from a lifetime animal bioass ay result by determinin g the continuous daily 2 intake that would result in that average bl ood concentration for humans over 70 years. 3 For the gestational exposure scenario, the effective TCDD blood concentration (usually 4 the peak) determined for the par ticular POD in a particular developmental study is matched to 5 the average TCDD blood concentr ation over the gestational por tion of the human gestational 6 exposure scenario. The HED is determined as the continuous daily inta ke, starting from birth 7 that would result in that average blood concen tration over the 9-month gestational period for a 8 pregnancy beginning at 45 years of age. The choice of 45 years as the beginning age of 9 pregnancy is health protective of the population in that the daily exposure achieving the target 10
blood concentration is smaller th an for earlier pregnancies. 11 For a less-than-lifetime exposure, the average TCDD blood concentration over the 12 exposure period in the animal bioassay associated with the POD is matched to the average over 13 the 5-year period that includes th e peak concentration (58 years fo r an intake of 0.01 ng/kg-day). 14 The HED is determined as the continuous daily intake that would result in the target 15 concentration over peak 5-year period. The use of the peak is analogous to the approach in the 16 2003 Reassessment, where the terminal steady-st ate body burden played the same role. The 17 5-year average over the peak is taken to smooth out sharp peaks and more closely approximate a 18 plateau. The choice of peak is health protectiv e because humans of any age must be protected 19 for short-term exposures, and the daily intake achieving a given TCDD blood concentration is 20 smallest when matched to the peak exposure as opposed to an average over shorter durations. 21 Thus, target concentrations for any exposure dur ation of less-than-lifet ime must be averaged 22
backwards from the end of the lifetime scenario , rather than from the beginning. The only 23 exception would be if the short-te rm endpoints evaluated in the an imal bioassay were associated 24 with a specific life stage (such as for the gestational scenario). No te that this scenario lumps all 25 exposures from 1 day to over 1 year in rodents into the same less-than -lifetime category. 26 Conceptually, duration-specific scenarios could be constructed by defining equivalent rodent and 27 human exposure durations. However, for the mo st part, defining dura tion equivalents across 28 species is a somewhat arbitrar y exercise, not generally based on physiologic or toxicologic 29 processes, but relying primarily on fraction-of -lifetime conversions. EPA defines “lifetime” 30 exposure as 2 years and 70 years for rodents and hum ans, respec tively. S o , a half-lifetime 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-53 equivalence of 1 year in rodent s and 35 years in humans is defi ned easily. Also, considering a 1 subchronic exposure to be 10–15% of lifetime, leads to an equivalence of 90 days in rodents and 2 7–10 years in humans. However, in the practica l sense with respect to the Emond human PBPK 3 model predictions, the difference in the dose-to-t arget-concentration rati os are not significantly 4 different from the peak 5-year averag e scenario, differing by less than 5%. 5 The net effect of using three different s cenarios for estimating the HED from rodent 6 exposures is that, for the same ta rget concentration, the ratio of administered dose (to the rodent) 7 to HED will be larger for short-term exposures than for chronic exposures. Figure 3-31 is 8 similar to Figure 3-30, except that it shows the relationship of daily intake to a fixed target 9 TCDD blood concentration level. Figure 3-31 sh ows that, for human intakes of approximately 10 0.01 ng/kg-day, the difference in th e defined scenarios is 40% or less, with a lifetime-scenario 11 daily intake of 0.014 ng/kg-day requ ired to reach the same target concentration for a shorter-term 12 exposure of 0.01 ng/kg-day. The corresponding daily intake for the gestational scenario is 13 0.011 ng/kg-day. Because of the nonlinearit ies in the Emond human PBPK model, the 14 magnitude of the difference between the life time and less-than-lifetime exposure scenarios 15 increases at lower intake levels, but not to a substantial degree. 16 The differential effect of short- and long-term exposures is much more accentuated at the 17 rodent end of the exposure kinetic modeling. Analogous to the processes described in the 18 previous section for first-orde r body burden (see Section 3.4.2. 2), the TCDD blood concentration 19 for single exposures is essentia lly the immediate absorbed frac tion of the administered dose, 20
which will be somewhat lower than the admini stered dose, while for chronic exposure, the 21
TCDD blood concentration will re flect the long-term accumulation from daily exposure, which 22 will be very much larger than the administered dose (expressed as a daily intake). Table 3-15 23 shows the overall impact of TK modeling on the extrapolation of administered dose to HED, 24 comparing the Emond PBPK and first-order body burden models. For comparison purposes, the 25 administered dose is fixed at 1 ng/kg-day fo r all model runs. Large animal-to-human TK 26 extrapolation factors (TK EF ) are evident for short-term mous e studies, decreasing in magnitude 27 with increasing exposure duration. The only exception is the slightly lower extrapolation factor 28 for the mouse 1-day exposure, which is the result of the relatively short TCDD half-life (10 days) 29 in mice and the use of the peak TCDD blood concen tration as representati ve of single exposures, 30 compared to the average TCDD blood concentrat ion over the exposure period used for multiple 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-54 exposures. The TK EF s are lower for rats because of the slower elimination of TCDD in rats 1 compared to mice. Also, because of the nonlinea r kinetics inherent in the Emond PBPK model, 2 the span of the HED (13-fold for mice) across th ese exposure durations is greater than the span 3 of the lipid-adjusted serum concentration (LAS C; 4-fold for mice). Because of the dose4 dependence of TCDD elimination in the Emond model, the TK EF becomes smaller with 5 decreasing intake. The result of this nonlinearity is that, although Table 3-15 shows much lower 6
TK EF s for the Emond PBPK model than for the firs t-order body burden metric, at much lower 7 HED levels the two models give much closer predictions. 8This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-55 Table 3-1. Partition coefficients, ti ssue volumes, and volume of distribution 1 for TCDD in humans 2 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 *The total tissue volume presented here represents only 91% of body weight because some of the weight and 5 volume of the body is occupied by bone and other structures where TCDD uptake and accumulation do not occur to 6 a significant extent. 7 8 Source: Wang et al. (1997), Emond et al. (2005, 2006). 9 10 11 Table 3-2. Blood flows, permeability fa ctors and resulting half lives (t1⁄2) for 12 perfusion losses for humans as repres ented by the TCDD PBPK model of 13 Emond et al. (2005, 2006) 14 15

Tissue Permeability (fraction of compartment blood flow) Rate constant for compartmental elimination (hour − 1 ) t 1⁄2 (hrs)
Fat 0.12 0.0049 143
Liver 0.03 0.77 0.90 Rest of the body 0.35 3.84 0.18This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-56 Table 3-3. Toxicokinetic conversion fa ctors for calculating human equivalent 1 doses from rodent bioassays 2 3 Mouse Rat (Wistar) Rat (other) Guinea pig Half-life (days) a 10 20 25 40 Exposure duration (days) Conversion factor (CF) b BB A ( t A ): d A 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 Half-life for humans = 2,593 days (7.1 years). 5 b d H

= d A /CF; BB H ( t

H

): d H

= 2,185 (1

− 180 days), 2,202 (365 days), 2,555 (730 days). 6This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-57 Table 3-4. Equations used in the CADM PBPK model* 1 2

Parameter Equation
Hepatic Concentration (ng/kg) body body

l body hepatic CK Cff f W Q C + ∗

− + = )( (* min max min

Fat Concentration (ng/kg) )) )( (1(* min max min body body body adipose CK Cff f Wa Q C + ∗

− +− =

Hepatic Elimination )) )( (1(*

_ min max min body body body e CK Cff f Qkhepatic Exr + ∗

− +−∗=

Excretion via gut of Unchanged TCDD (Exsorption) aa QkgutExr *_ =

Change of TCDD due to bodyweight change )( ))()(( *

_ tBW tBWdttBW QBW ChangeTCDD body − +

=

Amount in body as a function of time BW ChangeTCDD gutExr hepatic ExrtQdttQ body body

_ _ _)()( + + = −+

Adipose tissue growth 100 *8.10)*23.0(*2.1 sex Age BMI W

a − +

=

Change of hepatic elimination constant with age Agekkk eslope ee * 0 −=

*For abbreviations and parameters, see Table 3-5. 3 Source: Aylward et al. (2005b). 4This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-58 Table 3-5. Parameters of the CADM model 1 2
Parameter Value Units Comments/sources
f hmin 0.01 unitless Minimum body burden fraction in liver
f hmax 0.7 unitless Maximum body burden fraction in liver
K 100 ng/kg Body burden at half-maximum of fraction liver
k e Calculated per year k e = k e0

− k e_slope *(age) with enforced minimum of k e_min k e0 0.85 per year CADM-mean hepatic elimination base rate at age 0
k e_slope 0.011 per year Change in k e per year of age
k e_min 0.2 per year Minimum hepatic elimination rate

w a (adipose weight fraction) Calculated unitless w a = [(1.2*BMI)+0.23*Age-10.8*sex]/100

w h (liver body weight fraction) 0.03 unitless Assumed constant
k a (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 unitle ss 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 Note: The values of fhmin, fhmax, and K were obtained by best fit of the model simulations to the experimental data 4 with the method of least squares (Carrier et al., 2005a). 5 6 Source: Aylward et al. (2005b). 7This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-59 Table 3-6. Confidence in the CADM model simulations of TCDD dose 1 metrics 2 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 H = high, M = medium, L = low, NA = not applicable. 5This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-60 Table 3-7. Equations used in the TCDD PBPK model of Emond et al. (2006) 1 2 Aspect Equation Body weight growth with age 0.41 () _ 0 1402.5 time time 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 2 LIBMAX 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-61 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 Note: Key parameters and abbreviations are defined in Table 3-10. 3This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-62 DRAFT: DO NOT CITE OR QUOTETable 3-8. Parameters ofthe PBPK model for TCDDParameter valuesParameterDescriptionSymbolHumannongestationalaHumangestationalaMousenongestationalMousegestationalRatnongestationalRatgestationalBody weight (g)BW Calculated Calculated 23-28b23-28 125-250b85-190bCardiac output (mL/hour/kg)QCCAR 15.36c,dCalculated 275c275c311.4e311.4eTissue (intracellular) volumes (fraction of BW)Liver WLI0CalculatedCalculated0.0549f0.0549f0.036e0.036eFat WF0CalculatedCalculated0.069eCalculated 0.069eCalculatedTissue blood volumesLiver (fraction of WLI0)WLIB0 0.266e0.266e0.266e0.266e0.266e0.266eFat (fraction of WF0)WFB00.05e0.05e0.05e0.05e0.05e0.05eRest of body (fraction of WRE0)WREB0 0.03e0.03e0.03e0.03e0.03e0.03ePlacenta tissue fraction of tissue bloodweight (unitless)WPLAB0 N/A 0.5gN/A 0.5eN/A 0.5eTissue blood flow (fraction of cardiac output)Liver QLIF0.26c0.26c0.161f0.161f0.183e0.183eFat QFF0.05c0.05c0.07h0.07h0.069e0.069ePlacenta QPLAFN/ACalculatedN/A Calculated N/A CalculatedTissue permeability (fraction of tissue blood flow)Liver PALIF0.35e0.35e0.35e0.35e0.35e0.35eFat PAFF0.12i0.12i0.12i0.12i0.091e0.091ePlacenta diffusionalpermeability fraction(unitless)PAPLAF N/A 0.3gN/A 0.03gN/A 0.3gRest of bodyPAREF0.03e0.03e0.03e0.03e0.0298e0.0298eThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-63 DRAFT: DO NOT CITE OR QUOTETable 3-8. Parameters of thePBPK model for TCDD (continued)Parameter valuesParameterDescriptionSymbolHumannongestationalaHumangestationalaMousenongestationalMousegestationalRatnongestationalRatgestationalPartition coefficientLiver PLI6e6e6e6e6e6eFetus/blood partition coefficient (unitless) PFETUS N/A 4jN/A 4jN/A 4jPlacenta/blood partition coefficient (unitless) PPLAN/A 1.5jN/A 3gN/A 1.5jFat PF100e100e400i400i100e100eRest of bodyPRE1.5e1.5e3k3k1.5e1.5eMetabolism constantsUrinary clearance elimination (mL/hour) CLURI 4.17E-08l4.17E-08l0.09i0.09i0.01j0.01jClearance - transfer from mother to fetus(mL/hour)CLPLA_FET N/A 16eN/A 0.17iN/A 0.17iLiver (biliary elimination and metabolism;hour-1)KBILE_LI Inducible Inducible Inducible Inducible Inducible InducibleInterspecies constant (hour-1) Kelv 0.0011i0.0011i0.4i0.4i0.15e0.15eAhRAffinity constant in liver (nmol/mL)KDLI0.1e0.1e0.0001e0.0001e0.0001e0.0001eBinding capacity in liver (nmol/mL)LIBMAX 0.35e0.35e0.00035e0.00035e0.00035e0.00035ePlacenta binding capacity (nmol/mL) PLABMAX N/A 0.2jN/A 0.0002jN/A 0.0002jAffinity constant protein (AhR) in placenta(nmol/mL)KDPLA N/A 0.1jN/A 0.0001jN/A 0.0001jThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-64 DRAFT: DO NOT CITE OR QUOTETable 3-8. Parameters of thePBPK model for TCDD (continued)Parameter valuesParameterDescriptionSymbolHumannongestationalaHumangestationalaMousenongestationalMousegestationalRatnongestationalRatgestationalCYP1A2 induction parametersDissociation constant CYP1A2 (nmol/mL) KDLI240j40j0.02i0.02i0.04j0.04jDegradation process CYP1A2 (nmol/mL) CYP1A2_1OUTZ 1,600e1,600e1.6e1.6e1.6e1.6eDissociation constant during induction(nmol/mL)CYP1A2_1EC50 130e130e0.13e0.13e0.13e0.13eBasal concentration of CYP1A2 (nmol/mL) CYP1A2_1A2 1,600e1,600e1.5k1.5k1.6e1.6eFirst-order rate of degradation (hour-1) CYP1A2_1KOUT 0.1e0.1e0.1e0.1e0.1e0.1eTime delay before induction process (hour) CYP1A2_1TAU 0.25e0.25e1.5k1.5k0.25e0.25eMaximal induction of CYP1A2(unitless) CYP1A2_1EMAX 9,300i9,300i600e600e600e600eOther constantsOral absorption constant (hour-1) KABS 0.06i0.06i0.48i0.48i0.48e0.48eGastric nonabsorption constant (hour-1) KST 0.01m0.01m0.30i0.30i0.36e0.36eaUnits for human nongestational parameters are L ratherthan mL and kg ratherthan 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).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-65 Table 3-9. Regression analysis resu lts for the relationship between log 10 1 serum TCDD at the midpoint of observations and the log 10 of the rate 2 constant for decline of TCDD levels using Ranch Hand data 3 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 Table 3-10. Confidence in the PBPK model simulations of TCDD dose 7 metrics 8 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 H = high, M = medium, L = low. 11This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-66 Table 3-11. Overall confidence associated with alternative dose metrics for 1 cancer and noncancer dose-response modeling for TCDD using rat PBPK 2 model 3 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 H = high, M = medium, L = low. 6 7 8 Table 3-12. Overall confidence associated with alternative dose metrics for 9 cancer and noncancer dose-response modeling for TCDD using mouse PBPK 10 model 11 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 H = high, M = medium, L = low. 14 15 16 Table 3-13. Contributors to the overall uncertainty in the selection and use 17 of dose metrics in the dose-respons e modeling of TCDD based on rat and 18 human PBPK models 19 20 Dose metric Conceptual uncert ainty 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 H = high, M = medium, L = low, NA = not applicable, ? = if relevant to MOA of response. 22This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-67 Table 3-14. Contributors to the overall uncertainty in the selection and use 1 of dose metrics in the dose-response modeling of TCDD based on mouse and 2 human PBPK models 3 4 Dose metric Conceptual uncert ainty 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 H = high, M = medium, L = low, NA = not applicable, ? = if relevant to MOA of response. 6 7 8 Table 3-15. Impact of toxicokineti c modeling on the extrapolation of 9 administered dose to HED, comparin g the Emond PBPK and first-order 10 body burden models 11 12 1 st -order BB Emond PBPK Exposure duration (days) HED (ng/kg

− day) TK EF LASC (ng/kg) HED (ng/kg

− day) TK EF 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 13This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-68 1 2

3 Figure 3-1. Liver/fat concentration ra tios in relation to TCDD dose at 4 various times after oral admi nistration of TCDD to mice. 5 6 Source: Dilberto et al. (1995). 7 10 1 .1 0 1 2 3 4

7 Day Liver/Fat 14 Day Liver/Fat 21 Day Liver/Fat 35 Day Liver/Fat



### Dose μg/KgRatio of Concentrations in Liver to FatThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-69 1 Figure 3-2. First-order el imination rate fits to 36 sets of serial TCDD 2 sampling data from Seveso patients as function of initial serum lipid TCDD. 3 4 Source: Aylward et al. (2005). 5This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-70 1

2 Figure 3-3. Observed relationship of fecal 2,3,7,8-TCDD clearance and 3 estimated percent body fat. 4 5 Source: Rohde et al. (1999). 6 26 25 24 23 22 21 20 0.02 0.03 0.04 0.05

% Body FatFract Fecal Elim/Year y = 0.09735 - 0.00282x R^2 = 0.752This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-71 1

2 Figure 3-4. Unweighted empirical rel ationship between percent body fat 3 estimated from body mass index and TCDD elimination half-life—combined 4 Ranch Hand and Seveso observation. 5 38 36 34 32 30 28 26 5.0 6.0 7.0 8.0 9.0 10.0 11.0



### % Body FatTCDD 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 errorThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-72 1 Figure 3-5. Relevance of candidate dose metrics for dose-response modeling, 2 based on mode of action and target organ toxicity of TCDD. 3 4This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-73 DRAFT: DO NOT CITE OR QUOTEFigure 3-6. Process of estimating a human-equivalent TCDD lifetimeaverage daily oral exposure (dH) from anexperimental animal average daily oral exposure (dA) based on the body-burden dose metric.The arrowsrepresent mathematical conversionsbased on toxicokinetic modeling.BBA(TWA animal body burden) andBBH(TWAhuman body burden) are assumed to betoxicokinetically equivalent.See text for further explanation.Experimental Applied DosekfaedeBBtBurdenBodyktktRat)1()0()(−−−+=)(tBurdenBodyRat)(tBurdenBodyHumanHumanEstimatedExposure1/21/2(1 )(1 )A AH Hk tAH Ak tHted dt e−−−=−Experimental Applied DosekfaedeBBtBurdenBodyktktRat)1()0()(−−−+=)(tBurdenBodyRat)(tBurdenBodyHumanHumanEstimatedExposure1/21/2(1 )(1 )A AH Hk tAH Ak tHted dt e−−−=−This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-74 Exposure daysBody burden

0 5000 10000 15000 20000 250000 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 Figure 3-7. Human body burden time pr ofiles for achieving a target body 3 burden for different exposure duration scenarios. BB:d is BB H ( t

H

): d H

in 4 Figure 3-6. The curve depicted using the solid line illustrates the increase in the 5 human body burden over time for a hypotheti cal human administered a daily 6 TCDD dose where the time-weighted average human body burden estimate over 7 the lifetime is equal to the target body burde n attained in a rodent bioassay. When 8 compared to shorter durations (dashed lines), a higher average daily TCDD dose 9 is required to yield a time-weighted average human body burden over a lifetime 10 that is equal to the target body burden attained in a r odent bioassay. The half11 chronic exposure scenario (depicted using a dashed line) is equi valent to a 1-year 12 exposure in rodents. When compared to a chronic BB H , a lower value of d H is 13 needed to attain the target body burden in a rodent bioassay when the time14 weighted average is over the last 35 years of life; the dose to plateau ratio is also 15 smaller (i.e., d H,C < d H,SC to attain the target body burde n in a rodent bioassay). 16 The shorter exposure scenario is equivalent to most other shorter rodent exposure 17 durations, from 1 day to subchronic, whic h are indistinguishabl e with respect to 18 the BB:d ratio (subchronic shown). 19This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-75 1 2 Figure 3-8. Schematic of the CADM structure. 3 4 Source: Aylward et al. (2005). 5 TCDD 

### ABSORPTION Liver Burden 

### () () ( ) h b hb 

### Qt Qt fC = ∗

Blood

Adipose Burden 

## ( ) () () 1 a b hb 

### Qt Qt fC ⎡ ⎤ = ∗− ⎣ ⎦ Tissue-Specific Concentration-Dependant
Hepatic metabolism with first-order rate constant k e

Fecal excretion with the first-order

rate constant k a 

### DISTRIBUTION ELIMINATION

TCDD 

### ABSORPTION Liver Burden 

### () () ( ) h b hb 

### Qt Qt fC = ∗

Blood

Adipose Burden 

## ( ) () () 1 a b hb 

### Qt Qt fC ⎡ ⎤ = ∗− ⎣ ⎦ Tissue-Specific Concentration-Dependant
Hepatic metabolism with first-order rate constant k e

Fecal excretion with the first-order

rate constant k a 

### DISTRIBUTION ELIMINATIONThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-76 Figure 3-9. Comparison of observed and simulated fractions of the body 1 burden contained in the liver and adipose tissues in rats. f h , fraction contained 2 in liver (observation) ( □ ); f h - sim , fraction contained in liv er (simulation) (—); f at , 3 fraction contained in the ad ipose tissue (observation) ( ◊ ); f at - sim , fraction contained 4 in the adipose tissue (simulation) (---); and C b, body concentration in ng TCDD/kg 5 body wt. 6 7 Source: Carrier et al. (1995a ); data from Abraham et al . (1988) measured 7 days 8 after dosing. 9This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-77 1 2 Figure 3-10. Conceptual representation of PBPK model for rat exposed to 3 TCDD. 4 5 Source: Emond et al. (2006). 6 

### 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 circulationThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-78 1 Figure 3-11. Conceptual representation of PBPK model for rat 2 developmental exposure to TCDD. 3 4 Source: Emond et al. (2004). 5 

### Oral absorption

Liver (AhR and CYP1A2 induction)

Fat

Rest of body

Placenta (AhR)

Fetus 

### Cipla_fet Elimination GI tractVenous bloodArterial blood Portal vein Urinary excretion


### Oral absorption

Liver (AhR and CYP1A2 induction)

Fat

Rest of body

Placenta (AhR)

Fetus 

### Cipla_fet Elimination GI tractVenous bloodArterial blood Portal vein Urinary excretionThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-79 1 2 Figure 3-12. TCDD distribut ion in the liver tissue. 3 4 Source: Wang et al. (1997). 5 

### Lipid fraction
C Lip 

### Plasma C Bb 

### proteins

C Tnb 

### Nonspecific bound Tissue blood

PA Water fraction

Tissue CYP1A2-TCDD TCDD-Ah C Bf



### C Tr 

### Ks

Kn Q C B 

### Q C TB 

### K AB



### Lipid fraction
C Lip 

### Plasma C Bb 

### proteins

C Tnb 

### Nonspecific bound Tissue blood

PA Water fraction

Tissue CYP1A2-TCDD TCDD-Ah C Bf



### C Tr 

### Ks

Kn Q C B 

### Q C TB 

### K ABThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-80 1

2 Figure 3-13. Growth rates for phy siological changes occurring during 3 gestation. (a) Placental growth during gestation (calculated for n = 10 placenta). 4 Experimental data from Sikov (1970). (b) Blood flow rate in Placental 5 compartment during gestation. Experime ntal data from Buelke-Sam et al. 6 (1982a, b). (c) Fat fraction of body weight during gestation. Experimental data 7 came from Fisher et al. (1989), and (d) Fetal growth during gestation. 8 Experimental data obtained from Sikov (1970). 9 10 11 A a b

c dThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-81 DRAFT: DO NOT CITE OR QUOTEFigure 3-14. Comparisons of model predictions to experimental data usinga fixed elimination rate model withhepatic sequestration (A) and an inducible elimination rate model with (B) and without (C) hepaticsequestration.EXBL, experimental blood levels.Model predictions were compared with the data of Santostefano etal. (1998), where female rats were exposed to a single oral dose of 10μg of TCDD/kg BW. Error bars are ± SD.Source: Edmond et al. (2006).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-82 1 2

Figure 3-15. PBPK model simulation of hepatic TCDD concentration (ppb) 3 during chronic exposure to TCDD at 50, 150, 500, 1,750 ng TCDD/BW using 4 the inducible elimination rate model co mpared with the experimental data 5 measured at the end of exposure. 6 7 Source: Emond et al. (2006). 8This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-83 1

Figure 3-16. Model predictions of TCDD blood concentration in 10 veterans 2 (A-J) from Ranch Hand Cohort. 3 Source: Emond et al. (2005). 4 1 10 100 1000 10000

0 1 0 2 0 3 0 4 0 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 JThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-84 1

2 3

4 5 Figure 3-17. Time course of TCDD in blood (pg/g lipid adjusted) for two 6 highly exposed Austrian women (patients 1 and 2). Symbols represent 7 measured concentrations, and lines represent model predictions. These data were 8 used as part of the model evaluation (Geusau et al., 2002). 9 10 Source: Emond et al. (2005). 11This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-85 1 2

3 Figure 3-18. Observed vs. Emond et al. (2005) model simulated serum 4 TCDD concentrations (pg/g lipid) ov er time (ln = natural log) in two 5 Austrian women. Data from Geusau et al. (2002). 6 4 3 2 1 0 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 ExposureLn(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 (p t2 Obs pg / g TCDD )This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-86 1 Figure 3-19. Comparison of the dose dependency of TCDD elimination in the 2 Emond model vs. observations of nine Ranch Hand veterans and two highly 3 exposed Austrian patients. Circles are observed data. 4 5 4 3 2 1 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 = 0.884This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-87 1

2

3 Figure 3-20. Sensitivity analysis was performed on the inducible elimination 4 rate. The analysis was performed at 0. 001 μg/kg (A) and at 10 μg/kg (B). 5 6 Source: Emond et al. (2006). 7 A

BThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-88 1 Figure 3-21

. Experimental data (symbols) and model simulations (solid lines) 2 of (A) blood, (B) liver and (C) adipose ti ssue concentrations of TCDD after 3 oral exposure to 150 ng/kg-day, 5 days/week for 17 weeks in mice. Y-axis 4 represents concentration in pg/g and X-axis represents time in days. 5 6 Source: Experimental data were obta ined form Diliberto et al (2001). 7 C B AThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-89 1 Figure 3-22. Comparison of PBPK mo del simulations with experimental 2 data on liver concentrations in mice administered a single oral dose of 3 0.001

− 300 μ g TCDD/kg. The simulations and experime ntal data were obtained 4 24 hour post-exposure. 5 6 Source: Data obtained from Boverhoff et al. (2005). 7 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 SimulatedThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-90 1 Figure 3-23. Comparison of mode l simulations (solid lines) with 2 experimental data (symbols) on the effect of dose on blood (cb), liver (cli) and 3 fat (cf) concentrations followi ng repetitive exposure to 0.1

− 450 ng TCDD/kg, 4 5 days/week for 13 weeks in mice. 5 6 Source: Data obtained from Diliberto et al. (2001). 7 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 measuredThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-91 1 Figure 3-24. Comparison of experi mental data (symbols) and model 2 predictions (solid lines) of (A) bl ood, (B) liver and (C) adipose tissue 3 concentrations of TCDD after oral exposure to 1.5 ng/kg-day, 5 days/week 4 for 17 weeks in mice. Y-axis represents concentration in pg/g and X-axis 5 represents time in days. 6 7 Source: Experimental data were obtained form Diliberto et al. (2001). 8 A

B

CThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-92 1 Figure 3-25

. Comparison of experimental data (symbols) and model 2 predictions (solid lines) of (A) blood concentration, (B) liver concentration, 3 (C) adipose tissue concentration (D) fece s excretion (% dose) and (E) urinary 4 elimination (% dose) of TCDD after oral exposure to 1.5 ng/kg-day, 5 5 days/week for 13 weeks in mice. Y-axis represents concentration in pg/g and 6 X-axis represents time in days. 7 Source: Experimental data were obtained form Diliberto et al. (2001). 8 A

B

C

D

EThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-93 1 Figure 3-26. Comparison of experi mental data (symbols) and model 2 predictions (solid lines) of (A) blood concentration, (B) liver concentration, 3 (C) adipose tissue concentration (D) fece s excretion (% dose) and (E) urinary 4 elimination (% dose) of TCDD after oral exposure to 150 ng/kg-day, 5 5 days/week for 13 weeks in mice. Y-axis represents concentration in pg/g and 6 X-axis represents time in days. 7 Source: Experimental data were obtained form Diliberto et al. (2001). 8 A

B

C

D

EThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-94 1 Figure 3-27

. PBPK model simulations (solid lines) vs. experimental data 2 (symbols) on the distribution of TCDD a fter a single acute oral exposure to 3 A

− B) 0.1, C

− D) 1.0 and E

− F) 10 μ g of TCDD/kg of body weight in mice. 4 Liver and adipose concentration for each dose was measured after 72 hours. 5 Y-axis represents the concentration in tissues (ng/g); insets A, C, and E represent 6 liver tissue, whereas B, D, and F correspond to adipose tissue. X-axis represents 7 the time in hours. 8 Source: experimental data were obta ined from Santostefano et al. (1996). 9 A

B

C

D

E

FThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-95 1 Figure 3-28

. PBPK model simulation (solid lines) vs. experimental data 2 (symbols) on the distribution of TCDD after a single dose of 24 μ g/kgBW on 3 GD 12 in mice. Concentrations expressed as ng TCDD/g tissue. (A) maternal 4 blood, (B) maternal liver and (C) maternal adipose tissue

. Y-axis represents the 5 tissue concentration whereas X-ax is represents the time in hours. 6

. 7 Source: Experimental data were ob tained from (Abbott et al., 1996). 8 A

B

CThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 3-96 1 Figure 3-29. Comparison of the near -steady-state body burden simulated 2 with CADM and Emond models for a daily dose ranging from 1 to 3 10,000 ng/kg-day in rats and humans . The rat model was run for 13 weeks and 4 the human model was run from age 20 to 30. The time-averaged concentration 5 was used for each. 6 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 purpos es only and does not constitute Agency policy 1/15/10 3-97 DRAFT: DO NOT CITE OR QUOTEYear

TCDD blood concentration (ng/kg)0 20 40 60

0 50 100 150 200peak 5-year average concentrationgestational average concentrationlifetime average concentrationConcentration-time profileLess-than-lifetime scenarioGestational scenarioLifetime scenario1Figure 3-30. TCDD serum concentration-time profile for lifetime, less-than-lifetime and gestational exposure2scenarios, with target concentrations shown for each; profiles generated with Emond human PBPK model.3This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 3-98 DRAFT: DO NOT CITE OR QUOTEYear

TCDD blood concentration (ng/kg)0 20 40 60

0 50 100 150 200 250 0 . 0 1 4 n g / kg - d a y

0 . 0 1 1 n g / k g -d a y

0 . 0 1 n g / k g - d a yLifetime scenarioLess-than-lifetime scenarioGestational scenarioTarget concentrationFigure 3-31. TCDD serum concentration-time profile for lifetime, less-than-lifetime and gestational exposurescenarios, showing continuous intake levels to fixed target concentration; profilesgenerated with Emond humanPBPK model.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-1 4. CHRONIC ORAL REFERENCE DOSE 1
2 3 This section presents U.S. Environmental Protection Agency (EPA)’s response to the 4 National Academy of Sciences (NAS) recommenda tions that EPA more explicitly discuss the 5 modeling of noncancer endpoints and develop a reference dose (RfD) to address noncancer 6
effects associated with oral 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) exposures. Section 2 7
details the selection of the animal studies w ith the lowest TCDD doses associated with the 8 development of adverse noncancer e ffects and the selection of rele vant epidemiologic studies of 9
adverse noncancer health effects. Section 3 di scusses the kinetic modeling and estimation of 10 human equivalent daily or al doses used in TCDD RfD development in this section. This section 11 discusses the modeling of noncancer health effect data associated with TCDD exposure and the 12 derivation of an RfD. Specifically, Secti on 4.1 summarizes the NAS comments on TCDD dose13 response modeling and EPA’s response, including ju stification of selected noncancer effects and 14
statistical characteriza tion of modeling results. Section 4.2 presents the TCDD dose-response 15 modeling undertaken for identification of candidate points of de parture (PODs) for derivation of 16 an RfD. Finally, in Section 4.3, EPA develops an RfD for TCDD. 17
18
4.1. NAS COMMENTS AND EPA’S RESPON SE ON IDENTIFYING NONCANCER 19 EFFECTS OBSERVED AT LOWEST DOSES 20 The NAS recommended that EPA identify the no ncancer effects associated with low dose 21 TCDD exposures and discuss its strategy for identifying and selecting PODs for noncancer 22 endpoints, including bi ological significance of the effects. 23
24 With respect to noncancer end points, th e committee notes that EPA does not use 25 a rigorous approach for eval uating evidence from studies. . . (NAS, 2006a, p. 47). 26 27 The Reassessment should describe clearly the following aspects: 28 1. The effects seen at the lowest body bur dens that are the primary focus for any 29 risk assessment—the “critical effects.” 30 2. The modeling strategy used for eac h noncancer effect, paying particular 31 attention to the critical effects, and th e selection of a point of comparison based 32 on the biological significance of the effect; if the ED 01 is retained, then the 33 biological significance of the response s hould be defined and the precision of 34 the estimate given... (NAS, 2006a, p. 187). 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-2 In this document, EPA has developed a stra tegy for identifying th e noncancer data sets 1 and PODs that represent the most sensitive and bi ologically relevant endpoints for derivation of 2 an RfD for TCDD. EPA began this proce ss by using the animal bioassays and human 3 epidemiologic studies that met its study inclusion criteria as sources of these data sets. For all 4 epidemiologic studies that were identified as suitable for fu rther quantitative dose-response 5 analyses in Section 2.4.3, EPA has chosen to identify PODs (i.e., estimates of a no-observed6 adverse-effect level [NOAEL], lowest-observed- adverse-effect level [LOAEL], or benchmark 7 dose lower confidence bound [BMDL] when possible). Figure 4-1 shows EPA’s process to 8 select and identify candidate PODs from these key epidemiologic studies. EPA first evaluated 9 the dose-response information in the study to de termine whether it provided an estimate of 10
TCDD dose and an observed noncancer effect that was relevant for RfD derivation. If such data 11 were available, then EPA identified a NOAEL or LOAEL as a candidate POD. For each of 12 these, EPA applied a human kinetic model to estimate the continuous oral daily intake 13 (ng/kg-day) associated with the POD that could be used in the derivation of an RfD (see Section 14 4.2). If all of this information was available, then the result was included as a candidate POD. 15 Figure 4-2 summarizes the st rategy employed for identifyi ng and selecting candidate 16 PODs from the key animal bioassays identifiedin Section 2.4.3 for use in noncancer dose17 response analysis of TCDD. For each noncancer endpoint, EPA first evaluated the toxicologic 18 relevance of each endpoint, rejecting those judged not to be relevant for RfD derivation. Next, 19 initial PODs (NOAELs, LOAELs, and BMDLs) ba sed on the first-order body burden metric (see 20 Section 3.3.4.2) and expressed as human-equiva lent doses (HEDs) were determined for all 21 relevant endpoints (summarized in Table 4-3) . Because there were very few NOAELs and 22 BMDL modeling was largely unsuccessful due to da ta limitations, the next stage of evaluation 23 was carried out using LOAELs only. Endpoints not observed at the LOAEL (i.e., reported at 24 higher doses) with BMDLs greater than the LOAEL were eliminated from further analysis, as 25 they would not be considered as candidates for the fina l POD on either a BMDL or 26 NOAEL/LOAEL basis (i.e. the POD would be hi gher than the PODs of other relevant 27 endpoints). In addition, a ll endpoints with HED estimat es based on LOAELs (LOAEL HED s) 28 beyond a 100-fold range of th e lowest identified LOAEL HED were eliminated from further 29 consideration, as they would not be potential POD candidates either (i.e., the POD would be 30 higher than the PODs of other relevant endpoi nts). For the remaining endpoints, EPA then 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-3 determined final potential PODs (NOAELs, LOAELs and BMDLs) based on TCDD blood 1
concentrations obtained from the Emond rodent physiologically based pharmacokinetic (PBPK) 2
models. HEDs were then estimated for each of these PODs using the Emond human PBPK 3 model. From these HEDs, a POD HED was selected 14 for each study as the basis for the candidate 4 RfD, to which appropriate uncertainty factor s (UFs) were applied following EPA guidelines. 5 The resulting candidate RfDs were then considered in the final selection process for the RfD. 6 Other endpoints occurring at slightly higher doses representing additional e ffects associated with 7 TCDD exposure (beyond the 100-fold LOAEL range) were evaluated, modeled, and included in 8 the final candidate RfD array 15 to examine endpoints not evaluated by studies with lower PODs. 9 In addition, BMD modeling based on administer ed dose was performed on all endpoints for 10 comparison purposes. The final array of selected endpoints is shown in Table 4-4 (summary of 11 BMD analysis) and Table 4-5 (candidate RfDs). 12 The NAS recommended that EPA better justif y the selection of response levels for 13 endpoints used to develop risk estimates. Th e NAS commented on EPA’s decision to estimate 14
an ED 01 (effective dose eliciting x percent re sponse) for noncancer bioassay/data set 15 combinations as a comparative tool across studies , suggesting that EPA identify and evaluate the 16 levels of change associated with adverse eff ects to define the benchmark response (BMR) level 17 for continuous noncancer endpoints. 18
19
The committee notes that the choice of the 1% response level as the POD 20 substantially affects ... the noncancer analyses.... The committee recommends 21 that the Reassessment use levels of change that represent clinical adverse effects 22 to define the BMR level for noncancer c ontinuous end points as the basis for an 23 appropriate POD in the assessment of noncancer effects (NAS, 2006a, p. 72). 24 25 The committee concludes that EPA did not adequately justify the use of the 1% 26 response level (the ED 01 ) as the POD for analyzing epidemiological or animal 27 bioassay data for ... noncancer effects (NAS, 2006a, p. 18). 28 29 In the 2003 Reassessment (U.S. EPA, 2003), EP A was not attempting to derive an RfD 30 when it conducted TCDD dose-response mode ling. The 2003 Reassessment developed ED 01 31

14 In the standard order of consideration: BMDL, NOAEL, and LOAEL. 15 However, 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-4 estimates for noncancer effects in an attempt to compare disparate endpoints on a consistent 1 response scale. Importantly, th e 2003 Reassessment defined the ED 01 as 1% of the maximal 2 response for a given endpoint, not as a 1% change from control. Because RfD derivation is one 3 goal of this document, the noncancer modeling effo rt undertaken here differs substantially from 4 the modeling in the 2003 Reassessment. 5
The NAS committee was concerned with the stat istical power to determine the shape of 6 the dose-response curve at doses far below obser ved dose-response information. EPA agrees 7 that the shape of the dose-re sponse curve in the low-dose region cannot be determined 8 confidently when based on higher-dose informa tion. An observed response above background 9 near (or below) the BMR level is needed for di scrimination of the shape of the curve and for 10
accurate estimation of an ED x or BMDL. Although many of the ED 01 s presented in the 2003 11 Reassessment were near the lowest dose tested, responses at the lowest doses were often high 12 and much greater than a 1% response (i.e., 1% of the maximum response). The lack of an 13 observed response near the BMR level is often a problem in interpretation of BMD modeling 14
results. 15 In this document, EPA has used a 10% BM R for dichotomous data for all endpoints 16 except for developmental study designs that incor porate litter effects, fo r which a 5% BMR is 17 used (U.S. EPA, 2000a). For continuous endpoint s in this document, EP A has used a BMR of 18
1 standard deviation from the control mean wh enever a specific toxicologically-relevant BMR 19 could not be defined. For the vast majority of continuous endpoints, EPA could not establish 20 unambiguous levels of change representative of adversity, which EPA defines as “a biochemical 21 change, functional impairment, or pathologic lesi on that affects the performance of the whole 22 organism, or reduces an organism's ability to respond to an additional environmental challenge” 23 (U.S. EPA, 2009b). For body and organ weight ch ange, EPA has previously established a BMR 24 of 10% change, which also is used in this document. 25 The NAS commented on EPA’s development of ED 01 estimates for numerous study/data 26 set combinations in the 2003 Reassessment, suggesting that EPA ha d not appropriately 27
characterized the statistical c onfidence around such model predicti ons in the low-response region 28
of the model. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-5 It is critical that the model used for determining a POD fits the data well, 1 especially at the lower end of the observed responses. Whenever feasible, 2 mechanistic and statistical information shoul d be used to estimate the shape of the 3 dose-response curve at lower doses. At a minimum, EPA should use rigorous 4 statistical methods to assess model fit and to control and reduce the uncertainty of 5 the POD caused by a poorly fitted model. The overall quality of the study design 6 is also a critical element in deciding which data sets to use for quantitative 7 modeling (NAS, 2006a, p. 18). 8 9 EPA should ... assess goodness-of-fit of dos e-response models for data sets and 10 provide both upper and lower bounds on cen tral estimates fo r all statistical 11 estimates. When quantitation is not possi ble, EPA should clearly state it and 12 explain what would be required to achieve quantitation (NAS, 2006a, p. 10). 13 14 The NAS also commented that EPA report in formation describing the adequacy of dose15 response model fits, particularly in the low response region. For those cases where biostatistical 16 modeling was not possible, NAS recomme nded that EPA identify the reasons. 17
18 The Reassessment should also explicitly address the importance of statistical 19 assessment of model fit at the lower end and the difficulties in such assessments, 20 particularly when using summary data from the literature instead of the raw data, 21 although estimates of the impacts of different choices of models would provide 22 valuable information about the role of this uncertainty in drivi ng the risk estimates 23 (NAS, 2006a, p. 73). 24 25 To address this concern, in this docume nt EPA has reported the standard suite of 26 goodness-of-fit measures from the benchmark dose modeling software (BMDS 2.1). These 27 include chi-square p -values, Akaike’s Information Criterion (AIC), scaled residuals at each dose 28 level and plots of the fitted models. In some cases, when restricted pa rameters hit a bound, EPA 29
used likelihood ratio tests to ev aluate whether the improvement in fit afforded by estimating 30 additional parameters could be justified. Goodne ss-of-fit measures are reported for all key data 31 sets in Appendix E. 32
33
4.2. NONCANCER DOSE-RESPONSE ASSESS MENT OF TCDD 34 This section describes EPA’s current effo rt to conduct an evaluation of TCDD dose35 response for the noncancer endpoints from studies that met the study in clusion criteria. 36
Discussions include benchmark dose modeli ng procedures, kinetic modeling, and POD 37
candidates for derivation of the RfD. Section 4.2.1 describes how EPA ha s used physiologically- 38This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-6 based pharmacokinetic (PBPK) modeling to estim ate effective internal exposures as an 1 alternative to using administered doses or body burdens based on first-order kinetics. 2
Section 4.2.2 details the dose-re sponse analysis of the epid emiologic data, with supporting 3 information on kinetic modeling in Appendix D. S ection 4.2.3 details the dose-response analysis 4 for the animal bioassay data; Appendix E provide s the BMDS input tables (see Section E.1) and 5 output for all modeling, including blood concentra tions (see Section E.2) and administered dose 6 (see Section E.3). 7
8
4.2.1.1. Determination of Toxicologi cally Relevant End points 9 The NAS committee commented on the low dos e model predictions and the need to 10 discuss the biological significance of the non cancer health effects modeled in the 2003 11 Reassessment. In selecting POD candidates from the animal bioassays for derivation of the 12 candidate RfDs, EPA had to consider the toxico logical relevance of th e identified endpoint(s) 13 from any given study. Some e ndpoints/effects may be sensitive, but lack general toxicological 14 significance due to not being clearly adverse (def ined in EPA’s Integrated Risk Information 15 System glossary as “a biochemical change, f unctional impairment, or pathologic lesion that 16 affects the performance of the whole organism, or reduces an organism’s ab ility to respond to an 17 additional environmental challeng e” (U.S. EPA, 2009)), being an adaptive response or not being 18 clearly linked to downstr eam functional or pathological altera tions. It is standard EPA RfD 19 derivation practice not to base a reference va lue on endpoints that ar e not adverse or not 20 precursors to an adverse effect. Studies meeting the study selection criteria with endpoints that 21
were not considered for derivation of a candidate RfD (because they were not considered to be 22 toxicologically relevant noncancer effects) are: Kitchin and Woods (1979) , Hassoun et al. (1998, 23 2000, 2002, 2003), Burleson et al. (1996), Kuchiiwa et al. (2002), Mall y and Chipman (2002), 24 Vanden Heuvel et al. (1994), Dev ito et al. (1994), Luci er et al. (1986), Su gita-Konishi et al. 25 (2003), and Sewall et al. (1993). Appendix G identifies the e ndpoints from these studies that 26
were not considered to be toxi cologically relevant (e.g., cytochrome P450 induction, oxidative 27 stress measures, gap junction di sruption, mRNA induction, brain serotonin levels) and provides 28 the rationales for the toxicological relevance deci sions on the endpoints. Note that for many of 29 these studies, other endpoints were examined that are toxicologically relevant and were 30 considered in the RfD derivation process. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-7 1
4.2.2. Use of Toxicokinetic Modeling fo r TCDD Dose-Response Assessment 2 Given that TCDD accumulates in fat with cont inuous exposure and is eliminated slowly 3 from the body, but at very different rates across species, EPA has determined that the standard 4 UF approach or allometric scaling of body we ight for interspecies extrapolation is not 5 appropriate. Therefore, EPA has decided to use toxicokinetic m odeling to estimate an effective 6 internal dose for equivalence across species. Th e toxicokinetic models chosen by EPA are the 7
rodent and human PBPK models described by Emond et al. (2004, 2006) and modified by EPA 8
for this assessment as described in Section 3.3.4 (hereafter referred to as the “Emond [rodent or 9 human] PBPK model”). Both the rodent and human models have a gestational component, 10 which allow for more relevant exposure compar isons between general adult exposures and the 11 numerous gestational exposure stud ies. Ideally, a relevant tissue concentration for each effect 12 would be estimated. However, no models exist for estimation of all relevant tissue 13 concentrations. Therefore, EPA has decided to use the concentration of TCDD in blood as a 14 surrogate for tissue concentrati ons, assuming that tissue concentr ations are proportional to blood 15
concentrations. 16 Furthermore, because the RfD is nece ssarily expressed in terms of average 16 daily exposure, the blood concentrations are expr essed as averages over the relevant period of 17 exposure for each endpoint. For the animal bioass ay studies, the relevant period of exposure is 18 the duration of dosing, starting at the age of the animals at the beginning of the study. For 19 humans, the relevant period of exposure is gene rally lifetime, which is defined as 70 years by 20 convention. However, EPA varied the av eraging time for the equivalent human blood 21 concentrations to correspond to the test-animal exposure duration in the following manner. 22
23

• For correspondence with an imal chronic exposures, 17 the human-equivalent 24 TCDD blood concentration is assume d to be the 70-year average. 25

• For correspondence with animal gesta tional exposures, the human-equivalent 26 TCDD blood concentration is assumed to be the average over 45 years for a 27 female, beginning at birth, plus 9 months of gestational exposur e. The choice of 28 45 years to beginning of pregnancy is heal th protective of the population in that 29

16 As 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 fa t fraction of each tissue and protein bi nding; EPA has not found sufficient data to implement this approach. 17 Assumed to be ≥ 75% of nominal lifetime, or about 550 days in rodents.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-8 the daily exposure achieving the target blood concentra tion is smaller than for 1 shorter averaging times. 18 2

• For correspondence with any other animal exposure duration, the human3 equivalent TCDD blood concentration is assumed to be the average over the 4 equivalent human exposure duration calculated backward from the peak exposure 5 plateau at or near the end of the 70-year scenario. The average is determined 6 from the terminal end of the human e xposure period because the daily exposure 7 achieving the target blood concentration is smaller than for the same exposure 8 period beginning at birth a nd is health protective fo r effects occurring after 9 shorter-term exposure. The determination of equivalent exposure durations across 10 species is problematic and somewhat arb itrary, so EPA uses the average peak 11 blood concentration as the human equivale nt for all less-than-chronic animal 12 exposures (other than gestational). 19 For the first-order kinetics model, the 13 average peak exposure is close to the theoretical steady-state asymptote (see 14 Section 3.3.4.2). However, for the Em ond human PBPK model used by EPA in 15 this assessment, the timing of the peak exposure is dose-dependent and tends to 16 decline after 60 years in some cases. Therefore, the 5-year average TCDD blood 17 concentration that includes the peak (“5-ye ar peak”) is used as the relevant dose18 metric for the PBPK model applications. 19
20
4.2.3. Noncancer Dose-Response Assessment of Epidemiological Data 21 The following four epidemiologic studies desc ribing noncancer endpoints were identified 22
in Section 2.4.3 as studies to be evaluated for development of PODs for derivation of candidate 23 RfDs: Baccarelli et al. (2008), Mocarelli et al. (2008) , Alaluusua et al. (2004) and Eskenazi et al. 24 (2002). Each of these studies described e ffects observed in the Seveso cohort (see study 25 summaries in Section 2.4.1 and Table 2-5). Each study provided individual-level human 26 exposure measures and an exposure window over whic h kinetic models could be used to quantify 27 TCDD exposures for dose-response assessment. EPA used kinetic information to estimate 28 group-mean daily TCDD intake rates for the expo sure groups presented in these studies (see 29 Appendix D for details). EPA focused on identifying NOAELs and LOAELs for these studies; 30 EPA did not conduct Benchmark Dose modeling b ecause the covariates identified by the study 31 authors could not be incorporated by modeli ng the grouped response data. EPA’s development 32
of PODs for these studies is described in this section and shown in Table 4-1. 33

18 See Section 3.3.4.2 for a discussion of this issue. 19 By comparison to a half-lifetime equiva lent (1 year in rodents, 35 years in humans), the ratio of body burden (1 st -order kinetic model) to oral intake does not differ sign ificantly from the average-peak scenario; all shorter-term scenarios differ even less (see Section 3.3.4.2). These re lationships, with respect to the 5-year peak, hold for the PBPK model results, as well (see Section 3).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-9 4.2.3.1. Baccarelli et al. (2008) 1 For Baccarelli et al. (2008), EPA was able to define a LOAEL as the group mean of 2 39 ppt TCDD in neonatal plasma for thyroid stim ulating hormone (TSH) va lues above 5 μU/mL. 3 Baccarelli et al. (2008) did not estimate the equiva lent oral intake associated with TCDD serum 4 concentrations and gave only neonatal serum TCDD concentrations for the groups above and 5 below the 5 μU/mL standard. The study aut hors, however, developed a regression model 6 relating the level of TSH in 3-da y-old neonates to TCDD concentra tions in maternal plasma at 7 birth (given as lipid-adjusted serum concentratio ns, LASC). The authors extrapolated maternal 8 plasma concentrations from previous measurem ents using simple first-order pharmacokinetic 9
model. Because there is limited information re garding the relationship between maternal and 10 neonatal serum TCDD levels, EPA determined that there was too much uncertainty in estimating 11
maternal intake from neonatal TCDD serum co ncentrations, directly. Therefore, EPA 12
determined the maternal intake at the LOA EL from the maternal se rum-TCDD/TSH regression 13 model by finding the maternal TCDD LASC at which neonatal TSH exceeded 5 μU/mL. EPA 14
then used the Emond PBPK model under the human gestational scen ario (see Section 4.2.1) to 15 estimate the continuous daily TCDD intake that would result in a TCDD LASC corresponding to 16 a neonatal TSH of 5 μU/mL at the en d of gestation, with the resulting maternal intake established 17 as the LOAEL (0.024 ng/kg-day), shown in Tabl e 4-1 as a candidate POD for derivation of 18
candidate RfDs. The results of the PBPK modeling are shown in Appendix D. 19
20
4.2.3.2. Mocarelli et al. (2008) 21 Mocarelli et al. (2008) reported decreased sp erm concentrations (20%), decreased motile 22 sperm counts (11%), and decreased serum estrad iol (23%) in men who were 1–9 years old in 23
1976 at the time of the accide nt (initial TCDD exposure event) . Men who were 10–17 years old 24 in 1976 were not affected, with the possible ex ception of reduced serum estradiol. Serum 25 (LASC) TCDD levels were measured within one year of the initial exposure. Serum TCDD 26 levels and corresponding responses were repor ted by quartile, with a reference group of less27 exposed individuals assigned a TC DD LASC value of 15 ppt (which was the mean of individuals 28 outside the contaminated area). The lowest exposed group mean was 68 ppt (1 st quartile). 29 Because effects were detected only among boys under the age of 10, EPA assumes there is a 30 maximum 10-year critical exposur e window for elicitation of these effects. However, for the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-10 exposure profile, with a high ini tial pulse followed by an extende d period of elimination with 1 only background exposure, the estimation of an av erage exposure resultin g in the effect is 2 problematic. Therefore, EPA implemented a pr ocedure for the estimation of the continuous 3 daily TCDD intake associated with the LOAEL in the Mocarelli et al . (2008) study using a 4 5-step process. 5
6
1. Using the Emond human PBPK model, the in itial (peak) blood TCDD concentrations 7 associated with the accident were back-cal culated based on the time that had elapsed 8 between the explosion and the serum collection. As serum measurements were taken 9 within 1 year after the event, a lag time of 0.5 years was assumed. 10
2. The oral exposure associated with the peak blood TCDD c oncentration (peak exposure) 11 was calculated using the Emond PBPK model. 12
3. Starting with the peak exposure and acc ounting for background TCDD intake, the 13 average daily blood TCDD concentration experi enced by a representative individual in 14 the susceptible population (boys under 10 years old) was estimated using the Emond 15 PBPK model. Assuming a ra ndom distribution of ages at the time of the event, the 16 average age of the exposed male children w ould be 5 years. Cons equently, a critical 17 exposure window for the cohort was estimated to be, on average, 5 years (i.e., a boy aged 18 5 years would remain in this exposure window for 5 more years until he was 10 years of 19 age). 20
4. Using the Emond PBPK model, the average dail y TCDD intake rate needed to attain the 21 5-year average blood TCDD concentration in a boy 10 years old was calculated. 22
5. The LOAEL POD was calculated as the averag e of the peak exposure (0.032 ng/kg-day) 23 and the 5-year average exposure (0.0 080 ng/kg-day), resulting in LOAEL of 24 0.020 ng/kg-day, shown in Table 4-1 as a can didate POD for deriva tion of a candidate 25 RfD. However, neither of the extremes wa s used because (1) the peak exposure does not 26 account for the continuing internal exposure from TCDD given its sl ow elimination, and 27 (2) the 5-year average does not reflect the influence of the much higher peak exposure, 28 which may be a significan t factor in TCDD toxicity (Kim et al., 2003). 29
30 The results of the modeling are shown in Appendix D. 31
32
4.2.3.3. Alaluusua et al. (2004) 33 For Alaluusua et al. (2004), the approach for estimation of daily TCDD intake is virtually 34 identical to the approach used for the Mocarelli et al. (2008) data. Alal uusua et al. (2004) 35 reported dental effects in male a nd female adults who were less than 5 years of age at the time of 36 the initial exposure (1976) . For the 75 boys and girls who were less than 5 year s old at the time 37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-11 of the accident, 25 (33%) were subsequently diagnosed with some form of dental enamel defect. 1 For the 38 individuals who were older than 5, only 2 (5.3%) suffered dental enamel defects at a 2 later date. A window of suscep tibility of approximately 5 y ears is established. Serum 3
measurements for this cohort were taken within a year of the acci dent. Serum TCDD levels and 4 corresponding responses were reported by tert ile, with a reference group of less-exposed 5 individuals assigned a TCDD LASC value of 15 ppt (ng/kg); th e tertile group means were 130, 6 383, and 1830 ppt. The incidence of dental effects for the refe rence group was 26% (10/39). 7
The incidence of dental effects in the 1 st , 2 nd and 3 rd tertile exposure grou ps was 10% (1/10), 8 45% (5/11) and 60% (9/15), respectively. EPA judged that the NOAEL and LOAEL were 130 9 and 383 ppt TCDD in serum. Following the same pr ocedure used for the Mo carelli et al. (2008) 10 study (see Section 4.2.3.2), EPA estimated the continuous daily human TCDD intake associated 11 with each of the tertiles for both peak and aver age exposure across the cr itical exposure window, 12 assuming that the average age of the susceptible cohort at the time of the accident was 2.5 years. 13 Separate estimates for boys and girls were developed based on bot h the peak intake and average 14
intake across the critical e xposure window (see Appendix D for details of the PBPK modeling 15 are shown in Appendix D). The estimated averaged daily intakes for the tertiles, averaged for 16 boys and girls, are 0.20, 1.7 and 30 ng/kg-da y for the peak exposure and 0.033, 0.15 and 17 1.5 ng/kg-day for the critical expos ure window average. A study NO AEL at the second tertile of 18 0.12 ng/kg-day was identified as a candidate POD fo r derivation of a candida te RfD in Table 4-1. 19
20
4.2.3.4. Eskenazi et al. (2002) 21 The approach used to estimate daily TCDD inta ke in Eskenazi et al. (2002) combines the 22 approaches EPA used for Baccarel li et al (2008), Mocar elli et al. (2008) an d Alaluusua et al. 23 (2004). Eskenazi et al. (2002) reported menstrual effects in female adults who were 24 premenarcheal at the time of th e initial exposure (1976). In Ri gon et al. (2009), the median age 25
at menarche was shown to be 12.4 in Italian fema les with intergenerationa l decreases in age at 26 menarche. Thus, EPA established a window of sus ceptibility of approximately 13 years for this 27 analysis. The average ag e of the premenarcheal girls at th e time of the initial exposure in 1976 28 was 6.8 years, establishing an average critical-window exposure duration of 6.2 years for this 29 cohort. Serum samples were collected within a year of the accident from this cohort. However, 30 serum TCDD levels and corresponding responses were not reported by per centile and no internal 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-12 reference group was identified. As for Baccarelli et al (2008), Eskenazi et al. (2002) developed a 1 regression model relating menstr ual cycle length to plasma TCDD concentrations (LASC) 2
measured in 1976. The model estimated that mens trual cycle length was increased 0.93 days for 3 each 10-fold increase in TCDD LASC, w ith a 95% confidence interval of

− 0.01 to 1.86 days. 4 EPA judged a 1-day increase in menstrual cycle le ngth to be adverse; a normal menstrual cycle 5 length is 28 days. EPA then determined th e 1976 TCDD serum level corresponding to a 29-day 6 menstrual cycle length in the exposed cohort from the regression model developed by Eskenazi 7 et al. (2002). Using this serum level, the peak initial exposur e and average exposure over the 8 6.2 year window were calculate d using the Emond human PBPK m odel, in the same manner as 9 for Mocarelli et al. (2008) and Alaluusua et al . (2004). The resulting peak TCDD intake is 10 3.2 ng/kg-day. The average exposure experience d by this cohort over the critical exposure 11 window is estimated to be 0.12 ng/kg-day. The av erage of these two estimates is 1.64 ng/kg-day, 12 which is designated as a LOAEL and shown in Table 4-1. Because the LOAEL is almost 13 2 orders of magnitude higher than the LOAELs fo r Baccarelli et al (2008) and Mocarelli et al. 14 (2008), it was not considered furt her as a candidate POD for deriva tion of the RfD. The results 15 of the PBPK modeling are shown in Appendix D. 16
17
4.2.4. Noncancer Dose-Response Assessment of Animal Bioassay Data 18 EPA followed the strategy illustrated in Figure 4-2 to evaluate the animal bioassay data 19 for TCDD dose-response. For the administered average daily doses (ng/kg-day) in each animal 20 bioassay, EPA identified NOAELs and/or LOAELs based on the original data presented by the 21 study author. Section 2.4.2 identifies these valu es in the study summaries and in Table 2-7. 22
These became candidate PODs for consideration in the derivation of an RfD for TCDD. The 23
candidate RfD values associated with these ca ndidate PODs are presented in Table 4-5. 24
Additional PODs were identifie d using BMD modeling. All P ODs were converted to HEDs 25 using the Emond PBPK models. The remainder of th is Section describes the steps in this process 26
and concludes with the POD candidates from the an imal bioassay data that were considered for 27
derivation of the RfD. 28
29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-13 4.2.4.1. Use of Kinetic Modeling for Animal Bioassay Data 1 Blood concentrations corresponding to the ad ministered doses in each mouse or rat 2 bioassay qualifying as a final RfD POD candidate were estimat ed using the appropriate Emond 3 rodent PBPK model. In each case, the si mulation was performed using the exposure and 4 observation durations, body weights, and average daily doses from the original studies. For all 5 multiple exposure protocols, the time-weighted average blood TCDD concentrations over the 6 exposure period were used as the relevant dose metric. For si ngle (gestational and 7 nongestational) exposures, the init ial peak blood TCDD concentrations were considered to be the 8 most relevant exposure metric. Gestational exposures were mode led using the species-specific 9 gestational component of the Emond rodent PBPK model. Bioassays employing exposure 10 protocols spanning gestationa l and postpartum life stages were modeled by sequential 11 application of the gestational and nongestationa l models. 12
The Emond PBPK models do not contain a lactation compon ent, so exposure during 13 lactation was not modeled explicit ly. Only one bioassay (Shi et al., 2007) considered as a POD 14 candidate for RfD derivation included exposure duri ng lactation. In Shi et al. (2007) pregnant 15 animals were exposed weekly to TCDD throughout gestation and lactation. Exposure was 16 continued in the offspring following weaning for 10 months. For assessment of maternal effects, 17 the Emond gestational model was used, terminating at parturition. For assessment of long-term 18 exposure in the offspring, the Emond nongestational model was us ed, ignoring prio r gestational 19 and lactational exposure, with the assumption th at the total exposure dur ing these periods was 20 small relative to exposure in the following 10 mo nths. The assumption is conservative in that 21 effects observed in the offspring would be attrib uted entirely to adult exposures, which is 22
somewhat less than the ac tual total exposure. 23 The model code, input files and PBPK modeling results for each bioassay are reported in 24 Appendix C. Note that the modeled output is given in terms of LA SC. The corresponding 25 TCDD whole blood concentrations, which are used directly as the toxiocologically-equivalent 26 dose metric, are derived by multiplying the LASC by the lipid fraction in rodent serum (0.0033) 27
and the fraction of blood that is serum (0.55). For the rat gest ational model, only, the lipid 28 fraction in serum is 0.0023, rather than 0.0033. 29 These predicted TCDD blood conc entrations were used for benchmark dose modeling of 30 bioassay response data and determination of NOAELs and L OAELs. BMD m odeling was 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-14 performed, as described in Section 3.5.2.2.1, by substituting the modeled blood concentrations 1 for the administered doses and calculating the corresponding BMDL. For each of these LOAEL, 2 NOAEL, or BMDL blood-concentrat ion equivalents, corresponding HEDs were calculated using 3 the Emond human PBPK model for the appropriate gestational or nongestational scenario as 4 described previously (s ee Section 4.2.1). 5
6
4.2.4.2. Benchmark Dose Modeling of the Animal Bioassay Data 7 Benchmark dose modeling was performed usi ng BMDS 2.1, Build 06/16/09 to estimate 8 BMDs and BMDLs for each study/endpoint combinat ion. The input data tables for these 9
noncancer studies are shown in Appendix E, Section E1, incl uding both administered doses 10 (ng/kg-day) and blood concentratio ns (ng/kg) and either incide nce data for the dichotomous 11 endpoints or mean and standard deviations fo r the continuous endpoints. (See Section 4.3.1 12 [below] and Sections 3.3.4 and 3.3.5 for a de scription of the development of TCDD blood 13 concentrations using kinetic modeling.) For the continuous endpoints, all available models were 14 run separately using both the assumption of co nstant variance and the assumption of modeled 15 variance. Saturated (0 degrees of freedom) mode l fits were rejected from consideration. 16 Parameters in models with power or slope para meters were constrained to prevent supralinear 17 fits, which EPA considers not to be biological ly plausible and which often have undesirable 18 statistical properties (i.e., the BMDL diverges towards zero). However, if the constrained 19 parameters were estimated at their lower bounds , the unrestricted model was fit to the data, 20 primarily for elucidation of the degree of supralinearity present in the data. Depending on the 21 latter and the magnitude of the BMDL relativ e to the BMD, unrestricted model fits were 22 occasionally deemed acceptable. Table 4-2 shows each model and any restri ctions imposed. For 23 the quantal/dichotomous endpoints, all primary BMDS dichotomous models were run. The 24
alternative dichotomous models were fit to several data sets, but the results were very sensitive to 25 the assumed independent background response and the fits were not acce pted. The confidence 26 level was set to 95% and all initial parameter values were set to their defaults in BMDS. For the 27 continuous endpoints, one standard deviation was chosen as the default for the BMR when a 28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-15 specific toxicologically-relevant BMR could not be defi ned. For the dichotomous endpoints, a 1 BMR of 10% extra risk was used for all endpoints. 20 2 The model output tables in Appendix E show all of the models that were run, both 3 restricted and nonrestricted, goodness-of-fit stat istics, BMD and BMDL estimates, and whether 4 bounds were hit for constrained parameters. After all models were run, the one giving the best 5 fit was selected using the sele ction criteria in the current BMDS guidance (U.S. EPA, 2000a) 6 where possible. Acceptable model fits we re those with chi- square goodness-of-fit p -values 7 greater than 0.1. For th e continuous endpoints, the p -value for the homogenous variance test 8 (Test 2) was used to determine whether the constant variance ( p > 0.1) model or modeled 9 (nonconstant) variance ( p < 0.1) model should be used. As BMDS offers only one variance 10 model, model fits for nonconstant variance models were not necessarily re jected if the variance 11 model did not fit well (Test 3 p -value < 0.05). Within the group of models with acceptable fits, 12 the nominal best fitting model was selected by de termining the model with the lowest AIC value 13 if the BMDLs were within a factor of 3; otherwise, the model with the lowest BMDL was chosen 14 (U.S. EPA, 2000). However, particularly for co ntinuous models, the fit of the model to the 15
control mean and standard devia tion and in the lower response range was assessed. Models with 16 higher AICs but much better fit to the lower resp onse data were often chosen over the nominally 17 best-fitting model. Judgment was also used to assess the plausibility of BMDLs far below the 18 BMD. In most of these cases, deficiencies in the response data resulted in rejection of the 19 modeling results. 20 For many data sets, no models satisfied the acceptance criteria and no clear BMD/BMDL 21 selection could be made. In theses cases, mode l fits were examined on an individual basis to 22 determine the reasons for the poor fits. On o ccasion, high doses were dropped and the models 23
were refit. If the fit was s till not acceptable, the NOAEL/LOAEL approach was applied to the 24 study/data set combination. 25
26
4.2.4.3. POD Candidates from Anima l Bioassays Based on HED 27 Table 4-3 summaries the PODs that EPA es timated for each key animal study included 28 for TCDD dose-response modeling. After estimating the blood TC DD concentration associated 29

20 There were no developmental studies that accounted fo r litter effects, for which a 5% BMR would be used.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-16 with a particular toxicity measure (NOAEL, LOAEL, or BMDL) obtained from a rodent 1 bioassay, EPA estimated a corresponding HED us ing the Emond human PBPK model (described 2 in Section 3). Table 4-3 summarizes the NOA EL, LOAEL, or BMDL (ng/kg) based on the 3 administered animal doses for each key bioassa y/data set combination. Table 4-3 also 4 summarizes the continuous daily HED corresponding to these administered doses. The doses in 5
Table 4-3 are defined as follows: 6
7

• Administered Dose NOAEL: Average da ily dose (ng/kg-day) from the study 8

• Administered Dose LOAEL: Average da ily dose (ng/kd-day) from the study 9

• Administered Dose BMDL: Dose from BM D modeling of the administered doses 10 converted to a blood concen tration (ng/kg) using the Emond rodent PBPK models 11

• Emond Model NOAEL: Administer ed Dose NOAEL converted to an HED (ng/kg-day) 12 using the Emond human PBPK model 13

• Emond Model LOAEL: Administ ered Dose LOAEL converted to an HED (ng/kg-day) 14 using the Emond human PBPK model 15

• Emond Model BMDL: Dose from BMD modeling of the blood concentrations (ng/kg) 16 converted to an HED (ng/kg-day) using the Emond human PBPK model 17
18 Tables showing the best model fit for each study/endpoint combination and the associated 19 BMD/BMDL are shown in Appendix E. 20 An evaluation of key BMD analyses is pres ented in Table 4-4. The BMD modeling was 21 largely unsuccessful, primarily because of a lack of response data near the BMR, poor modeled 22 representation of control values, or nonmonotoni c responses yielding poor fits. BMDLs were 23 often implausibly low (orders of magnitude be low the LOAEL) or im plausibly high. The 24
comments column in Table 4-4 lists reasons for poor or implausible results. 25
26
4.3. RfD DERIVATION 27 Table 4-5 lists all the studies and endpoints considered for derivation of the RfD. These 28
studies were chosen from the entire list of candidate study/ data set combinations (see 29 Section 4.2) based on the toxi cologic relevance of the endpoi nts and covering a range that 30
includes three of the four human studies 21 . Figure 4-3 (exposure-respons e array) shows all of the 31

21 The RfD derived from the study of Eskenazi et al. (2002) was outside the RfD range presented in Table 4-5.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-17 endpoints listed in Table 4-5 gra phically in terms of PODs in human-equivalent intake units 1 (ng/kg-day). The human study endpo ints are shown at the far left of the figure and the rodent 2 endpoints are arranged by categor y to the right. Figure 4-4 de monstrates the same endpoints 3 arrayed by RfD value, showing the POD, applicable UFs and candidate RfD. 4 Table 4-5 illustrates the study, species, strain and sex, study protocol, an d toxicologic 5 endpoints observed at the lowest TCDD doses. Th e table also identifies the human-equivalent 6 BMDLs (when applicable), NOAELs and LOAELs, as well as the composite UF that applies to 7 the specific endpoint, and finally , the corresponding candidate RfD. 22 The NOAELS, LOAELs, 8 and BMDLs are presented as HEDs, based on the assumption that blood concentration is the 9 toxicokinetically-equivalent TC DD dose metric across species and serves as a surrogate for 10
tissue concentration. 23 For rats and mice, these estimates relied on the two Emond PBPK 11
models—one for the relevant r odent species and one for the hu man—as described previously 12 (see Sections 3.3.4.3). The two guinea pig studies that are included in Table 4-5 are given in 13 HED units based on the first-order body burden m odel described in Sectio n 3.3.4.2; there is 14 currently no TCDD PBPK model for the guinea pig. The values listed for guinea pigs are not 15 directly comparable to those for rats and mice but are probably biased low, as first-order body 16 burden HED estimates for rats and mice are genera lly 2- to 5-fold lowe r than the corresponding 17
PBPK model estimates. The LOAELs for th e human studies also rely on the Emond PBPK 18 model, as described in Sections 4.2.2 and 4.2.3. 19 As is evident from the table, very few NOAELs and even fewer BMDLs have been 20
established for low-dose TCDD studies. BMD modeling was unsuccessf ul for all of the 21 endpoints without a NOAEL, primarily because of th e lack of dose-response data near the BMR 22 (see discussion in Section 4.2). Therefore, the RfD assessment rests largely on evaluation of 23
LOAELs to determine the POD. 24 The rows in Table 4-5 are arranged in or der of increasing candidate RfD magnitude. 25 Endpoints projected to occur at hi gher exposure levels are still c onsidered for qualitative support 26
of the effects shown in Table 4-5. 27
28

22 Extra significant digits are retained fo r comparison prior to rounding to one significant digit for the final RfD. 23 The procedures for estimating HEDs based on TCDD blood concentration are described in the preceding section.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-18 4.3.1. Toxicological Endpoints 1 As can be seen in Table 4-5, a wide arra y of toxicological endpoints has been observed 2 following TCDD exposure, ranging from subtle de velopmental effects to overt chronic liver 3 toxicity. Developmental effects in rodents include dental defect s, delayed puberty in males, and 4
several neurobehavioral effects. Reproductive effects reported in rodents include altered 5 hormone levels in females and decreased sper m production in males. Immunotoxicity endpoints 6 such as decreased response to SRBC cha llenge in mice and decreased delayed-type 7 hypersensitivity response in guinea pigs are also observed. L onger durations of TCDD exposure 8
in rodents elicit results such as organ and body weight changes, renal toxicity, and liver and lung 9
lesions. Adverse effects in human studies are also observed, which include male reproductive 10 effects, increased TSH in neonates, and dental defects in children . Analogous results have been 11 observed in animal bioassays for each of these human endpoints. 12 All but two of the study/endpoint combinations from animal bioassays listed in Table 4-5 13 are on TCDD-induced toxicity observed in mice and rats; the ot her two study/endpoint 14 combinations are effects in guinea pigs. Although the effects of TC DD have been investigated in 15 several other species (i.e., hams ters, monkeys, and mink), those st udies were not included for 16
final POD consideration because the effect levels were greater than those in Table 4-5, or 17
because the effects could not be attributed solely to TCDD exposure (i.e., confounding by 18 dioxin-like compounds [DLCs]). 19
Three human studies were also included for final POD consideration in the derivation of 20 an RfD and are presented in Table 4-5 as can didate RfDs. All three human study/endpoint 21
combinations are from studies on the Seveso c ohort. The developmental effects observed in 22 these studies were associated with TCDD exposures either in utero or in early childhood between 23 1 and 10 years of age. Baccarelli et al. (2008) reported increased levels of TSH in newborns 24 exposed to TCDD in utero , indicating a possible dysregulation of thyroid hormone metabolism. 25 Mocarelli et al. (2008) reported decreased sperm concentrations, decreased motile sperm counts, 26
and decreased serum estradiol in men who were 1–9 years old in 1976 at the time of the Seveso 27 accident (initial TCDD exposure event). Alaluusua et al. (2004) reported dental effects in adults 28 who were less than 9.5 years of age at th e time of the initial exposure (1976). 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-19 4.3.2. Exposure Protocols of Candidate PODs 1 The studies in Table 4-5 re present a wide variety of exposure protocols, involving 2
different methods of administrati on and exposure patterns across vi rtually all expo sure durations 3 and life stages. Both dietary and gavage administ ration have been used in rodent studies, with 4 gavage being the predominant method. Gavage dosing protocols vary quite widely and include 5 single gestational exposures, multiple daily exposures (for up to 2 weeks, intermittent schedules 6 that include 5 days/week, once weekly, or once every 2 weeks), and loading/maintenance dose 7 protocols, in which a relatively high dose is in itially given followed by much smaller weekly 8 doses. The intermittent dosing schedules requir e dose-averaging over time periods as long as 9 2 weeks, which introduces uncertain ty in the effective exposures. In other words, the high unit 10 dose may be more of a factor in eliciting the effect than the average TCDD tissue levels over 11 time. Although the loading/maintenance dose prot ocols are designed to maintain a constant 12 internal exposure, these protocols are somewhat inconsistent with the constant daily TCDD 13 dietary exposures associated with human ingestion patterns. 14 The epidemiologic studies conducted in th e Seveso cohort represent exposures over 15 different life stages including gestation, childhood, and young adulthood. The Seveso exposure 16 profile is essentially a high initia l pulse TCDD exposure followed by a 20

− 30 year period of 17
elimination. Effects are realized, or measured, 10

− 20 years following the initial exposure; the 18 critical exposure window for sus ceptibility varies with effect and is often unknown. Therefore, 19 the effective exposure profiles for the Seveso cohort studies vary c onsiderably. For the 20 Mocarelli et al. (2008) and Alaluusua et al. (2004) studies where ear ly childhood exposures 21 proximate to the initial event are associated with the outcomes, there is some uncertainty as to 22 the magnitude of the effective doses. Although th e effects are associated with TCDD exposure 23 in the first 10 years of life, it is not clear to what extent the initial peak exposure is primarily 24 responsible for the effects. It is also not clear if averaging ex posure over the critical window is 25 appropriate given the large di fference between initial TCDD body burden and body burden at the 26 end of the critical exposure window. The LOAELs for both Mocarelli et al. (2008) and 27 Alaluusua et al. (2004) ar e calculated as the average of th e peak exposure and average exposure 28 across the critical exposu re window (see Section 4.2 for detail s). For the gest ational exposure 29 study (Baccarelli et al., 2008), the critical exposure window is st rictly defined and relatively 30 short (9 months) and occurs l ong after the init ial exposure (15

− 20 years). In addition, the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-20 maternal serum TCDD meas urements were taken 10

− 15 years after the initial exposure and are 1 proximate to the actual pregnancies, allowing for less uncertainty in the kinetic extrapolation to 2
time of birth. The narrow critical exposure window at a much late r time than the initial exposure 3 (where the TCDD elimination curve is flattening) is assumed to lead to a relatively steady-state 4 exposure over the critical time period with much less uncertain ty in the magnitude of the 5 effective dose. With the exception of Eskenazi et al (2002) (see section 4. 2), the effective doses 6 for other effects reported for the Seveso cohort (s ee Section 2.4.1.1.1.4) have not been quantified 7 and are not represented in Table 4-5 because no critical exposure windows can be identified or 8 individual exposure estimates were not reported. 9
10
4.3.3. Uncertainty Factors (UFs) 11 The UF column in Table 4-5 shows the composite (total) UF that woul d be applied to the 12 POD for each endpoint. For the animal bioassays , a UF of 3 for the toxicodynamic component 13 of the interspecies ex trapolation factor (UF A ) was applied to all PODs. For both animal and 14 human studies, when a NOAEL was used as the POD, a factor of 10 was applied for human 15 interindividual variability (UF H ). For all of the animal bioa ssay endpoints lacking a NOAEL, a 16 UF of 10 for the LOAEL-to-NOAEL UF (UF L ) was included. For the human LOAELs, a UF L of 17 3 was applied because sensitive populations were identified. A subchronic-to-chronic UF (UF S ) 18 of 1 and a database factor (UF D ) of 1 are applied to all endpoints. A rati onale for each UF is 19 provided for the derivation of the RfD below. 20
21
4.3.4. Human Studies 22 For selection of the POD, the human studies are given the highest consideration, as 23 quality human data are always preferred. Although the lower end of the candidate RfD 24 distribution is dominated by mous e studies (comprising 6 of the 7 lowest rodent-based RfDs), 25
EPA considers these candidate RfD estimates to be much more uncertain than human candidate 26
RfD estimates. The LOAEL HED s identified in mouse bioassays are low primarily because of the 27 large toxicokinetic interspecies extrapolation fact or used for mice. The ratio of administered 28 dose to HED (D a :HED) ranges from 65 to 1227 depending on the duration of exposure. The 29
D a :HED for mice is, on average, about 4 times larger than that used for rats. In addition, each 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-21 one of the mouse studies has othe r qualitative limitations and uncer tainties (discussed above and 1 in Table 4-4) that make them poor ca ndidates as the basis for the RfD. 2
Most of the other rodent studies between the first six mouse studies and the human 3 studies of Mocarelli et al (2008) and Baccarelli et al. (2008) are of small size, using 10 or fewer 4 animals per dose group and are considered too uncerta in on which to base the final RfD. Two of 5 the rat bioassays–Bell et al ( 2007) and NTP (2006)--however, are of particular note. Both of 6 these studies were very well de signed and conducted, using 30 or more animals per dose group 7 (see Table 4-6). Bell et al (2007) evaluated several reproduc tive and developmental endpoints 8 starting exposure well before mating and continui ng through gestation. NTP (2006) is the most 9 comprehensive evaluation of TCDD chronic toxicity in rodents to date, evaluating dozens of 10 endpoints at several time points in all major tissues. The toxicoki netic extrapolation to humans, 11 however, is still very uncertain. Despite the overall strength of the Bell and NTP studies, EPA 12
still considers the human data to be a better basis for the RfD. 13 Baccarelli et al. (2008) reported increased leve ls of TSH in newborns exposed to TCDD 14
in utero , indicating a possible dysregu lation of thyroid hormone metabolism. The study authors 15 related TCDD concentrations in neonatal blood to TSH levels, reporting group mean TCDD 16
concentrations associated with TSH levels a bove or below 5 μ-Units TSH per mL of serum 17 (5 μU/mL). The 5 μU/mL standard was establ ished by the World Heal th Organization (WHO, 18 1994) as an indicator of potential iodine deficiency (and potential thyroid problems) in neonates. 19 Baccarelli et al. (2008) also showed , in graphical form, how the TSH distribution in each of three 20 categorical exposure groups (reference, zone A, and zone B—represen ting increasing TCDD 21 exposure) shifted to higher TS H values with increasing exposur e. The individuals comprising 22 the above 5 μU/mL group were fr om all three categorical exposu re groups, not just from the 23 highest exposure group. Theref ore, EPA was able to designate a LOAEL independently of the 24 nominal categorical exposure groups; the LOAEL is designated as the group mean of 39 ppt 25 TCDD in neonatal plasma as a LOAEL for TSH values above 5 μU/mL. The daily oral intake at 26 the LOAEL is estimated to be 0.024 ng/kg-day (s ee Section 4.2.3.1). A NOAEL is not defined 27
because it is not clear what maternal intake s hould be assigned to the group below 5 μU/mL. 28 Mocarelli et al. (2008) reported decreased sp erm concentrations (20%), decreased motile 29 sperm counts (11%), and decreased serum estrad iol (23%) in m en who were 1–9 years old in 30 1976 at the time of the Seveso accident (i nitial TCDD exposure event). The sperm 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-22 concentrations and motile sperm counts in men who were 10–17 years old in 1976 were not 1 affected; marginal serum estradio l reductions were repo rted in this group. Serum (LASC) TCDD 2 levels were measured within one year of th e initial exposure. Serum TCDD levels and 3 corresponding responses were reported by quartil e, with a reference group of less-exposed 4 individuals assigned a TCDD LASC value of 15 ppt (which was the mean of the TCDD LASC 5 reported in individuals outside the contaminated area). The lowest exposed group mean was 6 68 ppt (1 st -quartile). Mean sperm concentrations and motile sperm counts were reduced about 7 20% from the reference group. Further decreas e in these values was slight and reached a 8 maximum of about 33%. Although a decrease in sperm production of 20% would not have 9 clinical significance for an indivi dual, EPA considers a 20% shift in the population mean to be of 10 biological significance. Therefor e, EPA has designated the lowest exposure group (68 ppt) as a 11 LOAEL, which translates to a continuous daily oral in take of 0.020 ng/kg-day (see 12 Section 4.2.3.2). The reference group is not designated as a NOAEL becau se there is no clear 13 zero-exposure measurement for any of these endpoi nts, particularly cons idering the contribution 14 of background exposure to DLCs, which futher co mplicates the interpretation of the reference 15 group response as a true “control” response (see discussion in Sec tion 4.4). However, males less 16 than 10 years old can be designated as a sensi tive population by comparis on to older males who 17
were not affected. 18 Alaluusua et al. (2004) reported dental effects in male and fe male adults were were less 19 than 9.5 years of age, but not older, at the tim e of the initial exposure (1976) in Seveso. EPA 20 used the same approach to estimate daily TC DD intake as was used for the Mocarelli et al. 21 (2008) data; a window of susceptibility of about 5 years was established. Serum measurements 22
for this cohort were taken with in a year of the accident. Serum TCDD levels and corresponding 23 responses were reported by tertile, with a refe rence group of less-exposed individuals assigned a 24 TCDD LASC value of 15 ppt (ng/ kg); the tertile group means were 130, 383, and 1,830 ppt. 25
Both a NOAEL and LOAEL can be defined for this study. The NOAEL is 0.12 ng/kg-day, 26 corresponding to the TCDD LASC of 130 ppt at th e first tertile. The LOAEL is 0.93 ng/kg-day 27
at the second tertile. The children in this c ohort less than 5 years ol d can be designated as a 28 sensitive population by comparison to older individuals who were not affected relative to the 29 reference group. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-23 4.3.5. Derivation of the RfD 1 The two human studies, Baccarelli et al. (2008) and Mocarelli et al. (2008), have similar 2 LOAELs of 0.024 and 0.020 ng/kg-day, respectively. Together, these two studies constitute the 3 best foundation for establishing a POD for the RfD, and are designated as co-principal studies. 4 Therefore, increased TSH in neonates (Baccarell i et al., 2008) and male reproductive effects 5 (decreased sperm count and motility, increased estrad iol) are designated as cocritical effects. 6 Although the exposure estimate used in determination of the LOA EL for Mocarelli et al. (2008) 7
is more uncertain than the Baccar elli et al. (2008) e xposure estimate, the slightly lower LOAEL 8 of 0.020 ng/kg-day from Mocarelli et al. is designated as the PO D. A composite UF of 30 is 9 applied to account for lack of a NOAEL (UF L = 10) and human interindividual variability 10 (UF H = 3); the resulting RfD in standard units is 7 × 10 − 10 mg/kg-day. Table 4-7 presents the 11
details of the RfD derivation. 12
13
4.4. UNCERTAINTY IN THE RfD 14 Exposure assessment is a key limitation of th e epidemiologic studies (of the Seveso 15 cohort) used to derive the RfD. The Seveso c ohort exposure profile cons ists of an initial high 16 dose followed by a drop in body burden to background levels over a period of about 20 years, at 17 which time the effects were observed. This expos ure scenario is a mismat ch with the constant 18 daily intake scenario addressed by the RfD me thodology. The determination of an effective 19 average daily dose from the Seveso exposure scen ario requires an understa nding of the critical 20 time-window of susceptibility and the influence of the peak e xposure on the occurrence of the 21 observed effects, particularly when the peak e xposure is high relative to the average exposure 22 over the critical exposure window. For one of th e principal studies (Mocarelli et al., 2008), a 23 maximum susceptibility exposure window can be id entified based on the age of the population at 24 risk. However, the influence of the peak e xposure on the effects obser ved 20 years later is 25 unknown and the biological significance of averag ing the exposure over several years, with 26 internal exposure measures spanning a 4.5-fold range, is unknown. EPA, in this assessment, has 27 averaged intermittent exposures for rodent bioass ays over weekly dosing intervals, but the peak 28 and average body burdens varied by less than 50%. EPA has not developed guidance for larger29 interval averaging. Furthermore, because there is an assumption of a threshold level of exposure 30 below which the effects are not expected to occu r, averaging over large intervals could include 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-24 below-threshold exposures. The process used by EPA to estimate the LOAEL exposure for the 1 Mocarelli study is a compromise between the extrem es; as such, there is some uncertainty in the 2 estimate, perhaps in the range of 3- to 10-fold in either direction. This uncertainty also holds for 3
the LOAEL determined for the dental effects reported in Alaluusua et al. (2004) and the 4 increased menstrual cycle length re ported in Eskenazi et al. (2002a ; see Section 4.2.3.4); in both 5 of those studies, the uncertainty is greater, as the difference between peak and average internal 6 exposures is an order of magnitude or more . The LOAEL for increased TSH in neonates 7 (Baccarelli et al., 2008), however, is less uncertain because the cri tical exposure window is much 8 narrower (9 months) and the developmental exposur es occurred 10 to 15 y ears after the initial 9 exposure, when internal TCDD conc entrations for the pregnant wome n were leveling off; that is, 10 exposure over the critical window was more constant. However, there is uncertainty in the 11 magnitude of the exposures because they were extrapolated from serum measurements taken 12 several years earlier. 13 Another source of uncertainty us ing human epidemiologic data is the lack of completely 14 unexposed populations. The available TCDD epid emiologic data were obtained by comparing 15 populations that experienced elev ated TCDD exposures to populat ions that experienced lower 16 exposures, rather than to a population with no TCDD exposure. An additional complicating 17 factor is coexposure to DLCs, which can beha ve in the same way as TCDD. Although the 18 accidental exposure to the Seveso women’ s cohort was virtually 100% TCDD, background 19 exposure was largely to DLCs. Eskenazi et al. (2004) reported that TCDD comprised only 20% 20 of the total toxicity equivalen ce (TEQ) in the serum of the refe rence group that was not exposed 21 as a result of the factory explosion, which im plies that the effective background exposure could 22 have been as much as 5-fold higher. The hi gher background exposure could be significant at the 23 lower TCDD exposure levels, with the effect di minishing as TCDD exposure increased. For 24 dose-response modeling, the effect of a higher background dose (i.e., total TEQ), if included, 25 would be to shift the response cu rve to the right (responses asso ciated with higher exposures) 26 but, primarily, would reduce the sp read of the exposures, which w ould tend to alte r the shape of 27 the dose response towards sublinear. Both the right shift and the more sublinear shape would 28 result in higher ED x estimates, such as BMDs and BMDLs, from fitting dose-response models. 29 However, for determination of a LOAEL, which is the case for all the human studies in 30 Table 4-5, the impact may be minimal, as the LOAEL depends only on establishing that an effect 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-25 of sufficient magnitude was observed at some TCDD exposure level. In this case, the effect of 1 the increased effective background exposure woul d be to inflate the “control” (zero-TEQ) 2 response, providing the threshold for the response had been exceeded. The potential impact of 3 an inflated control response would be to mask a significant effect of the added TCDD exposure, 4 when the latter effect is determined by comp arison to the reference group response. To 5 compensate for this, EPA has been somewhat c onservative in interpre ting the magnitude of 6 responses defining LOAELs for the Seveso cohort studies. The actual magnitude of the impact 7 of the DLC background exposure is impossible to assess without knowing the true (TEQ-free) 8 background response. 9 A primary strength of the TCDD database is th at analogous effects have been observed in 10 animal bioassays for most of the human endpoi nts, increasing the ove rall confidence in the 11
relevance to humans of the e ffects reported in rodents and th e association of TCDD exposure 12 with the effects reported in humans. Table 4-5 shows that low dose TCDD exposures are 13 associated with a wide array of toxicologica l endpoints in rodents including developmental 14 effects, reproductive effects, immu notoxicity and chroni c toxicity. Effects reported in human 15 studies are similar, including ma le reproductive effects, increa sed TSH in neonates and dental 16 defects in children; other human health effects such as female reproductiv e effects and chloracne 17 have been observed at higher exposures (see Sect ion 2.4.1). Other effects reported in rodent 18 studies such as liver toxicity and overt immunological endpoints have not been reported in 19 human studies. However, with respect to immu nological effects, Bacca relli et al (2002, 2004) 20 evaluated immunoglobin and complement levels in the sera of TCDD-expos ed individuals from 21 the Seveso cohort and found slightly reduced i mmunoglobulin in the highest exposure groups but 22 no effect on other immunoglobulins or on C3 or C4 complement levels. The latter finding 23
indicates that at least one immunological measure in humans is not a sensitive endpoint, as it is 24 for mice, with large reductions in serum complement at low exposure levels (White et al., 1986). 25 Although there is a substantia l amount of qualitative concordance of effects between 26 rodents and humans, quantitative concordance is not evident in Table 4-5. The differential 27 sensitivity of mice and humans for the serum complement endpoint is one example. Other 28 examples of differential sensitivity are deve lopm ental den tal effect s and thyroid hormonal 29 dysregulation. Developmental dent al defects are relatively sensitiv e effects in rodents, appearing 30 at exposure levels in mice (Kelle r et al., 2007, 2008a,b) more than an order of magnitude lower 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-26 than effect levels in humans (Alaluusua et al., 2004). In contrast, thyr oid hormone effects are 1 seen in rats (Crofton et al., 2005) at 30-fold hi gher exposures than for humans (Baccarelli et al., 2 2008). Male reproductive effects (sperm produc tion) occur in rats (Latchoumycandane and 3 Mathur, 2002) and humans (Mocarelli et al., 2008) at about the same dose. To what extent these 4 differential sensitivities depend on specifics of th e comparison, such as species (mouse vs. rat), 5 life-stage (e.g., fetal vs. adult), endpoint measure (e.g., thyroxine [T4] vs. TSH) or magnitude of 6 the lowest dose tested, cannot be determin ed, so strong conclusions about quantitative 7
concordance cannot be made. 8 Considering the issue of lowest tested dose, the general lack of NOAELs and acceptable 9 BMDLs is a primary weakness of the rodent bioa ssay database. None of the 6 most sensitive 10 rodent studies in Table 4-5, spanning a 30- fold range of LOAELs, had defined NOAELs or 11 BMDLs. For only 4 of the next 10 rodent stud ies were NOAELs or BMDLs established. In 12 addition, many of these LOAELs are characterize d by relatively high respons es with respect to 13 the control population, so it is not certain that a 10-fold lower dose (based on the application of 14
UF L of 10) would be approximately equivalent to a NOAEL. A ma jor reason for the failure of 15 BMD modeling was that the responses were not “anc hored” at the low end; first response levels 16 were far from the BMR (see Table 4-4). A nother major problem with the data was non17 monotone and flat response profiles. The small dose-group sizes and large dose intervals 18 probably contributed to many of these response characteristics th at prevented successful BMD 19 modeling. Larger study sizes wi th narrower dose intervals at lo wer doses are still needed to 20 clarify rodent response to TCDD. 21
Lower TCDD doses have been tested in rode nts but almost entirely for investigation of 22 specialized biochemical endpoints 24 that would not be considered to be adverse health effects 23 (see Appendix G). There is, however, a fundamental limit to the lowest dose of TCDD that can 24 be tested meaningfully, as TCDD is present in feed stock and accumulates in unexposed animals 25 prior to the start of a study. This issue is illustrated by the presence of TCDD in tissues of 26 unexposed control animals, often at significant levels relative to the lowest tested dose in low 27 dose studies (Vanden Heuvel et al., 1994; Ohsako et al., 2001; Bell et al., 2007a; see 28

24 Enzyme induction, oxidative stress indicators, mRNA levels, etc.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-27 Text Box 4-1). Some DLCs have been measur ed in animal feeds a nd are anticipated to 1 accumulate in unexposed test animals further complic ating the interpretation of low dose studies. 2
3

4 5 6 Table 4-6 compares the qualitative strengths a nd limitations/uncertainties associated with 7 the top animal bioassays listed in Table 4-5. 8 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 (d 0 ) can be calculated as equal to 0.878 × (0.1 + d 0 ) , assuming proportionality of body burden to administered dose and that al l animals started with the same TCDD body burdens. The calculation yields a value of 0.72 ng/kg for d 0 , 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 week s 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 av erage of 0.35 ppt is re presentative 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-28 Table 4-1. POD candidates for ep idemiologic studies of TCDD 1 2 Study POD (ng/kg-day) Critical effects Alaluusua et al., 2004 7.20E-02 a (NOAEL) Dental effects in adults exposed to TCDD in childhood Baccarelli et al., 2008 2.40E-2 b (LOAEL) Elevated TSH in neonates Eskenazi et al., 2002 1.66E+0 c (LOAEL) Increased length of menstrual cycle in women exposed to TCDD in childhood Mocarelli et al., 2008 2.00E-2 d (LOAEL) Decreased sperm count and motility and increased estradiol in men exposed to TCDD in childhood 3

a Mean of peak exposure (0.15 ng/kg-day) and average exposure over 10-year critical window (0.0093 ng/kg-day). 4 b Maternal exposure corresponding to neonatal TSH concentration exceeding 5 μU/mL. 5

c Mean of peak exposure (3.2 ng/kg-day) and average exposure over 10-year critical window (0.12 ng/kg-day). 6
d Mean of peak exposure (0.035 ng/kg-day) and average exposure over 10-year critical window (0.0078 ng/kg-day). 7 8 9 Table 4-2. Models run for each study/endpoint combination in the animal 10 bioassay benchmark dose modeling 11 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, 2 nd degree polynomial
Probit None
Weibull Power ≥ 1 13This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-29 DRAFT: DO NOT CITE OR QUOTETable 4-3. Summary of key animal study NOAELs, LOAELs, and BMDLs for different dose metrics (ng/kg-day)Administered dosea1st-order body burden HEDbBlood concentration HEDcStudyEndpointNOAELLOAELBMDLdNOAELLOAELBMDLdNOAELLOAELBMDLdAmin et al., 2000Saccharin preference ratio,female-2.50E+015.15E+01-2.51E+045.17E+04-1.71E-013.19E-01Bell et al., 2007aBalano-preputial separationin male pups-2.40E+001.25E-01-1.41E+027.36E+00-1.10E-017.40E-03Cantoni et al., 1981Urinary coproporhyrins-1.43E+002.82E-01-1.65E+023.25E+01-6.51E-021.60E-03Chu et al., 2001Tissue weight changes 2.50E+02 1.00E+03-8.28E+043.31E+05----Chu et al., 2007Liver lesions2.50E+00 2.50E+01-8.28E+028.28E+03-3.56E-025.76E-01-Crofton et al., 2005Serum T43.00E+01 1.00E+023.01E+014.69E+041.56E+054.70E+041.73E-017.62E-011.41E-01Croutch et al., 2005Decreased body weight 5.43E+01 2.17E+02-1.33E+045.30E+04----DeCaprio et al.,1986Decreased body weight 6.10E-01 4.90E+00-9.05E+017.27E+02----Fattore et al., 2000Decreased hepatic retinol - 2.00E+01--3.25E+03--8.01E-01-Fox et al., 1993Increased liver weight 5.73E-01 3.27E+02-2.31E+021.32E+05----Franczak et al.,2006Abnormal estrous cycle - 7.14E+00--8.57E+02--0.326716-Hojo et al., 2002DRL response per min - 2.00E+011.10E+02-7.60E+044.18E+05-5.51E-022.59E-05Ikeda et al., 2005aSex ratio- 1.65E+01--2.60E+03--2.7500259-Ishihara et al., 2007Sex ratio1.00E-01 1.00E+02-3.15E+013.15E+04----Kattainen et al.,20013rd molar length-3.00E+012.14E+00-1.14E+058.15E+03-9.00E-021.71E-03Keller et al., 2007a,2008a, 2008bMissing mandibular molars-1.00E+019.17E+00-3.88E+043.56E+04-9.81E-031.70E-02Kociba et al., 1976Liver and hematologiceffects and body weightchanges7.14E+007.14E+01-1.13E+031.13E+04-1.60E-011.71E+00-This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-30 DRAFT: DO NOT CITE OR QUOTETable 4-3. Summary of key studyNOAELs, LOAELs, and BMDLs for different dose metrics (ng/kg-day)(continued)Administered dosea1st-order body burden HEDbBlood concentration HEDcStudyEndpointNOAELLOAELBMDLdNOAELLOAELBMDLdNOAELLOAELBMDLdKociba et al., 1978Liver and lung lesions,increased urinaryporphyrins1.00E+001.00E+017.30E-019.31E+019.31E+026.80E+016.46E-026.46E-012.00E-02Latchoumycandaneand Mathur., 2002Sperm production-1.00E+001.56E-02-2.59E+024.03E+00-1.70E-023.90E-05Li et al., 1997Increased serum FSH3.00E+001.00E+01-1.14E+043.80E+04-2.97E-031.72E-02-Li et al., 2006Hormone levels (serumestradiol)-2.00E+001.11E+02-4.06E+032.27E+05-1.57E-033.46E-01Markowski et al.,2001FR2 revolutions- 2.00E+015.81E+01-6.40E+041.86E+05-5.17E-021.34E-01Maronpot et al.,1993Increased relative liverweight1.07E+01 3.50E+01-1.28E+034.18E+03----Miettinen et al.,2006Cariogenic lesions in pups - 3.00E+014.99E+00-1.14E+051.90E+04-8.94E-029.32E-03Murray et al., 1979Fertility index in f2generation1.00E+00 1.00E+01-1.06E+021.06E+03- 2.96E-02 3.88E-01-NTP, 1982Liver lesions- 1.39E+001.38E+01-2.98E+022.97E+03-2.19E-021.78E-02NTP, 2006Liver and lung lesions - 2.14E+001.40E+00-1.96E+021.28E+02-1.39E-018.76E-02Nohara et al., 2000Decreased spleencellularity8.00E+02--3.04E+06--5.34E+00--Ohsako et al., 2001Anogenital distance inpups1.25E+015.00E+011.22E+014.75E+041.90E+054.62E+042.87E-021.80E-012.67E-02Seo et al., 1995Decreased thymus weight2.50E+011.00E+02-2.51E+041.00E+05-1.69E-019.31E-01-Sewall et al., 1995Serum T41.07E+013.50E+01 5.20E+001.28E+034.18E+036.20E+025.15E-011.76E+00 7.25E-02Shi et al., 2007Serum estradiol in femalepups1.43E-017.14E-012.24E-011.67E+018.32E+012.61E+014.71E-032.75E-024.95E-03This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-31 DRAFT: DO NOT CITE OR QUOTETable 4-3. Summary of key studyNOAELs, LOAELs, and BMDLs for different dose metrics (ng/kg-day)(continued)Administered dosea1st-order body burden HEDbBlood concentration HEDcStudyEndpointNOAELLOAELBMDLdNOAELLOAELBMDLdNOAELLOAELBMDLdSimanainen et al.,2002Decreased serum T41.00E+023.00E+02-3.80E+051.14E+06----Simanainen et al.,2003Decreased thymus weightand change in ERODactivity1.00E+023.00E+02-3.80E+051.14E+06----Simanainen et al.,2004Decreased daily spermproduction1.00E+023.00E+02-3.80E+051.14E+06----Smialowicz et al.,2004Decreased antibodyresponse to SRBCs3.00E+021.00E+03-1.16E+063.88E+06----Smialowicz et al.,2008PFC per 10^6 cells-1.07E+007.19E+01-2.29E+021.54E+04-6.38E-032.00E-03Toth et al., 1979Skin lesions-1.00E+006.85E-02-2.70E+021.85E+01-1.00E-028.61E-01VanBirgelen et al.,1995a,bDecreased liver retinylpalmitate-1.40E+019.89E+02-2.27E+031.61E+05-5.25E-015.00E+00Vos et al., 1973Decreased delayed-typehypersensitivity responseto tuberculin1.14E+005.71E+00-2.02E+021.01E+03----White et al., 1986Decreased serumcomplement-1.00E+012.89E+01-4.49E+031.30E+04-2.83E-024.65E-02Yang et al., 2000Increased endometrialimplant survival1.79E+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, where BMR = 10%relative deviation from control mean.- = value not established or not modeled.This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-32 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa)StudyNOAEL/LOAELEndpointControlresponseFirstresponsebMaxresponsecModel fit detailBMD/BMDLCommentsPFC per spleen(n =15)─24%↓(0.5 SD)89%↓Continuous power,unrestricted(p =0.27)6.54E+32.07E+3BMDL > LOAEL; fit at control andlow dose inconsistent with data;constrained parameters in other modelshit lower boundsSmialowicz etal., 2008(mouse)None/2.41E+2PFC per 10^6 cells(n =8−15)─24%↓(0.5 SD)9.3-fold↓Continuous powerunrestricted, constantvariance(p =0.48)1.05E+31.19E+2Constant variance test failed; observedcontrol variance underestimated by35%; poor fits for all non-constantvariance modelsserum estradiol(n =10)─2.0-fold↑(0.8 SD)2.4-fold↑Continuous linear(p =0.16)8.85E+32.96E+3BMDL > LOAEL; high control CV(1.25); near maximal response at lowdose; nonmonotonic response; othermodel fits are step-function-likeLi et al., 2006(mouse)None/8.75E+1serum progesterone(n =10)─33%↓(2.0 SD)61%↓Continuous Hill(p =0.44)8.95E-16.31E-3No response data near BMR; large CVs(>1) for treatment groups; poor fit forvariance model; Hill coefficient atlower bound (step-function);implausible BMDLskin lesions(n =38−44)0/38 5/44 25/43 Dichotomous log-logistic(p= 0.67)1.1E-24.7E-4Constrained parameter lower bound hit;implausible BMDLDichotomouslog-logistic, unrestricted(p =0.98)2.60E+23.18E+0Supralinear fit (slope = 0.48);implausible BMDLdermal amyloidosis(n =38−44)0/38 5/44 17/43 Dichotomous log-logistic(p =0.02)8.3E+34.8E+3Poor fit; constrained parameter lowerbound hit; BMDL > LOAELToth et al.,1979(mouse)None/3.15E+2Dichotomouslog-logistic, unrestricted(p =0.90)2.67E+22.93E+0Supralinear fit (slope = 0.33);implausible BMDLThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-33 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsdaily spermproduction(n =6)─29%↓(1.0 SD)41%↓Continuous Hill,restricted(p =0.98)3.39E+21.51E-2Near maximal response at LOAEL;constrained parameter bound hit;implausible BMDL; standarddeviations given in paper interpreted asstandard errorsLatchoumycandane andMathur, 2002(rat)None/4.37E+2ContinuousHill,unrestricted(p =0.96)3.32E+28.77E-3Slightly supralinear fit (n=0.91);implausible BMDLNTP, 1982(mouse)None/4.20E+2Toxic hepatitis;males(n =50)1/73 5/49 44/50 Dichotomous multistage(p =0.01)8.3E+23.7E+2No acceptable model fits; lowestBMDL shownTotal hemolyticcomplement activity(CH50)(n =8)─41%↓(2.6 SD)81%↓ContinuousHill, restricted(p =0.002)4.76E+38.56E+2Poor fit; no response near BMR;constrained parameter bound hit;BMDL > LOAELWhite et al.,1986(mouse)-6.03E+2ContinuousHill, unrestricted(p =0.07)8.17E+17.55E+1Supralinear fit (n=0.25); implausibleBMDLKeller et al.,2007, 2008a, b(mouse)None/2.96E+2Missing molars(n =23−36)0/29 2/23 30/30 Dichotomous 1° multistage(p =0.26)6.01E+24.20E+2Poor fit at first response level; not mostsensitive endpoint; other endpoints notamenable to BMD modelingShi et al., 2007(rat)1.88E+25.92E+2Serum estradiol infemale pups(n =10)─38%↓(0.4 SD)62%↓Continuous exponential(M4)(p =0.69)4.45E+21.95E+2Adequate fit; selectedCantoni et al.,1981(rat)None/1.02E+3Urinary uroporhyrins(n =4)─2.4-fold↑(5.7 SD)87-fold↑Continuous exponential(M2)(p =0.0003)2.07E+31.52E+3No response near BMR; poor fits for allnon-constant variance models; constantvariance poor representation of controlSD; BMDL > LOAELThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-34 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsUrinary copro-porhyrins(n =4)─2.4-fold↑(3.1 SD)4.0-fold↑Continuous exponential(M4)(p =0.49)2.94E+29.93E+1No response near BMRCantoni et al.,1981(rat, continued)None/1.02E+3(cont.)Continuous power,unrestricted(p =0.61)1.52E+12.31E-6Supralinear fit (n =0.30); implausibleBMDL; poor model choice for plateaueffectHepatocytehypertrophy(n =53−54)0/53 19/54 52/53 Dichotomous multistage(p =0.03)5.01E+24.33E+2Poor fits for all modelsAlveolar metaplasia(n =52−54)2/53 19/54 46/52 Dichotomous log-logistic(p =0.72)3.58E+22.07E+2No response near BMROval cell hyperplasia(n =53−54)0/53 4/54 53/53 Dichotomous probit(p =0.23)3.13E+32.64E+3Relatively poor fit for control and lowdose groups; negative responseintercept (same for logistic);implausible model; BMDL > LOAELDichotomousWeibull(p =0.08)3.15E+32.25E+3Marginal fit; BMDL > LOAELGingival hyperplasia(n =53−54)1/53 7/54 16/53 Dichotomous log-logistic(p =0.06)3.22E+32.25E+3Poor fit; constrained parameter boundhit; BMDL > LOAELDichotomousloglogistic, unrestricted(p =0.66)3.88E+26.95E-3Supralinear fit (slope = 0.33);implausible BMDLEosinophilic focus,multiple(n =53−54)3/53 8/54 42/53 Dichotomous probit(p =0.46)3.08E+32.68E+3Relatively poor fit to control response;BMDL > LOAELNTP, 2006(rat)None/1.41E+3Liver fatty change,diffuse(n =53−54)0/53 2/54 48/53 Dichotomous Weibull(p =0.72)2.16E+31.57E+3BMDL > LOAEL; otherwise adequatefitThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-35 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsLiver necrosis(n =53−54)1/53 4/54 17/53 Dichotomous log-probit,unrestricted(p =0.80)4.13E+31.93E+3Adequate fit; slightly supralinear;BMDL > LOAELLiver pigmentation(n =53−54)4/53 9/54 53/53 Dichotomous log-probit(p =0.96)1.36E+31.04E+3Adequate fitNTP, 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+31.70E+3BMDL > LOAEL; otherwise adequatefitDichotomous loglogistic, restricted(p =0.79)1.96E+31.22E+3Adequate fit; constrained parameterbound hit; not litter basedBell et al.,2007a(rat)None/2.00E+3Balano-preputialseparation in malepups(n =30 [dams])1/30 5/30 15/30Dichotomous loglogistic, unrestricted(p =0.51)1.81E+32.64E+2Adequate fit; slightly supralinear(slope = 0.95); selectedUroporphyrin percreatinine, females(n =5)─15%↑(0.48 SD)89%↑Continuous linear(p =0.79)7.20E+35.12E+3BMDL > LOAEL; otherwise adequatefitUrinarycoproporphyrins,females(n =5)─67%↑(5.1 SD)78%↑Continuous exponential(M4, non-constant var)(p =0.01)8.6E+24.0E+2Poor fit; no response near BMR;BMDL > LOAELLiver lesions(n =50)No data presentedKociba et al.,1978(rat)8.53E+23.94E+3Lung lesions(n =50)No data presentedMarkowski etal., 2001(rat)None/1.23E+3FR5 runopportunities(n =4−7)─10%↓(0.21 SD)51%↓Continuous Hill(p =0.94)1.37E+37.21E+2Constrained parameter upper bound hit;This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-36 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsContinuouspower,unrestricted(p =0.13)2.11E+38.15E-12Saturated model; supralinear fit (power= 0.39); BMD/BMDL ratio >> 100FR2 revolutions(n =4−7)─9%↓(0.15 SD)43%↓Continuous Hill(p =0.65)1.46E+34.76E+2Constrained parameter bound hit (upperbound)Continuouspower,unrestricted(p =0.16)4.54E+38.15E-12Supralinear fit (power = 0.32);implausible BMDLMarkowski etal., 2001(rat, continued)None/1.23E+3(cont.)FR10 runopportunities(n =4−7)─15%↓(0.24 SD)57%↓Continuous exponential(M2)(p =0.30)6.77E+32.28E+3BMDL > LOAELDRL reinforce permin(n =12)─55%↑(1.0 SD)80%↑Continuous exponential(M4)(p =0.054)1.04E+34.94E+0Poor fit; near maximal response atlowest dose, BMD/BMDL ratio >> 100Hojo et al.,2002(rat)None/1.29E+3DRL response permin(n =12)─105%↓(2.4 SD)105%↓Continuous exponential(M4)(p =0.48)3.02E+27.55E+0No response data near BMR; maximalresponse at lowest dose, BMD/BMDLratio >> 20Murray et al.,1979(rat)6.19E+2/3.24E+3fertility in f2 gen.(no litters)(n =20)4/32 0/20 9/20 Dichotomous multistage(p =0.08)1.50E+37.50E+2Poor fit; non-monotonic response; noresponse data near BMR3rdmolar length inpups(n =4−8)─15%↓(4.2 SD)27%↓Continuous Hill,restricted(p <0.01)2.48E+21.33E+2No response data near BMR;Constrained parameter lower bound hitKattainen etal., 2001(rat)None/1.76E+3Continuous Hill,unrestricted(p <0.01)2.01E+5-BMDL could not be calculatedThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-37 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLComments3rdmolar eruption inpups(n =4−8)1/16 3/17 13/19 Dichotomous loglogistic, restricted(p =0.98)1.90E+31.05E+3Constrained parameter lower bound hitKattainen etal., 2001(rat, continued)None/1.76E+3(cont.)Dichotomousloglogistic, unrestricted(p =0.95)1.53E+31.46E+2Supralinear fit (slope = 0.91)Cariogenic lesionsin pups(n =4−8)25/42 23/29 29/32 Dichotomous loglogistic, restricted(p =0.60)1.13E+34.09E+2Constrained parameter lower bound hit;near maximal response at LOAEL; highcontrol responseMiettinen etal., 2006(rat)None/1.76E+3Dichotomousloglogistic, unrestricted(p =0.73)3.91E+1-Supralinear fit (slope = 0.47); BMDLcould not be calculatedAno-genital distancein male pups(n =5)─12%↓(1.0 SD)17%↓Continuous Hill,restricted(p =0.26)3.63E+38.05E+2Constrained parameter lower bound hit;near maximal response at LOAELOhsako et al.,2001(rat)8.45E+2/2.76E+3Continuous Hill,unrestricted(p =0.11)4.74E+34.52E+2Supralinear fit (n= 0.62)Saccharinconsumed, female,(0.25%) (n =10)─22%↓(0.3 SD)66%↓Continuous linear(p =0.55)7.22E+34.81E+3BMDL > LOAEL; restricted powermodel, constrained parameter hit lowerboundContinuouspower,unrestricted(p =NA)6.61E+32.70E+3Saturated model; supralinear fit (power= 0.74)Amin et al.,2000(rat)None/2.67E+3Saccharinconsumed, female(0.50%) (n =10)─49%↓(0.7 SD)80%↓Continuous linear(p =0.06)8.02E+35.18E+3Restricted power model, constrainedparameter hit lower boundThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-38 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsContinuouspower,unrestricted(p =NA)5.19E+39.41E+2Saturated model; supralinear fit (power= 0.40)Saccharinpreference ratio,female (0.25%)(n =10)─29%↓(1.8 SD)33%↓Continuous linear(p =0.002)9.17E+34.39E+3BMDL > LOAEL; no response nearBMR; near maximal response atLOAELSaccharinpreference ratio,female (0.50%)(n =10)─39%↓(1.1 SD)54%↓Continuous linear(p =0.14)6.43E+34.03E+3BMDL > LOAEL; near maximalresponse at LOAEL; restricted powermodel, constrained parameter hit lowerboundAmin et al.,2000(continued)None/2.67E+3(cont.)Continuous power,unrestricted(p =NA)2.05E+31.26E-5Saturated model; supralinear fit (power= 0.28)Crofton et al.,2005(rat)1.91E+3/5.10E+3Serum T4,(n =4−14)─29%↓(1.9 SD)51%↓Continuous exponential(M4)(p =0.94)2.86E+31.67E+3No response near BMR─9.1%↓(0.6 SD)40%↓Continuous Hill(p =0.90)5.68E+31.98E+3Constrained parameter hit lower bound;otherwise acceptable fitSewall et al.,1995(rat)3.92E+39.16E+3Serum T4(n= 9)Continuous Hill,unrestricted (p =0.90)5.35E+31.09E+3Supralinear fit (power = 0.57);otherwise acceptable fitMaze errors perblock, female(n =10)─22%↓(0.2 SD)34%↓Continuous linear(p =0.16)5.53E+33.63E+3BMDL > LOAEL; near maximalresponse at LOAEL; restricted powermodel, constrained parameter hit lowerboundSchantz et al.,1996(rat)None/NoneContinuous power,unrestricted(p =NA)2.02E+38.11E-6Saturated model; supralinear fit (power= 0.37); implausible BMDLLi et al., 1997(rat)1.46E+2/4.40E+2FSH in female rats(n =10)─3.6-fold↑(2.0 SD)19-fold↑Continuous power,restricted(p< 0.01)1.11E+57.50E+4Power hit lower boundThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-39 DRAFT: DO NOT CITE OR QUOTETable 4-4. TCDD BMDL analysis (NOAEL, LOAEL, BMD, and BMDL values given as LASCa) (continued)StudyNOAEL/LOAELEndpointControlresponseFirstresponseMaxresponseModel fit detailBMD/BMDLCommentsHepatitic retinol(n =8)─44%↓(0.74 SD)96%↓Continuous exponential(M4) (p< 0.01)1.37E+41.85E+3Poor fitContinuous power,unrestricted (p= 0.01)1.03E+57.92E+4Poor fit; supralinear fit (power = 0.14),Hepatitic retinylpalmitate (n =8)─80%↓(1.4 SD)99%↓Continuous exponential(M4) (p< 0.01)7.79E+42.01E+4Poor fit; no response near BMRVan Birgelenet al., 1995ab(rat)None/3.97E+3Continuous power,unrestricted (p= 0.24)2.90E+13.25E–2Supralinear fit (power = 0.06);implausible BMDLaConverted 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 fromcontrol value (in the adverse direction); continuous response magnitudes given as relative tocontrol; 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 purpos es only and does not constitute Agency policy 1/15/10 4-40 DRAFT: DO NOT CITE OR QUOTETable 4-5. Candidate points of departure forthe TCDD RfD using blood-concentration-based humanequivalent dosesStudySpecies, strain(sex, if notboth) Protocol EndpointNOAELHED(N) orBMDLHED(B)(ng/kg-day)LOAELHED(ng/kg-day)UFaRfD(mg/kg-day)Li et al., 2006 Mouse, NIH (F) Gavage GD 1–3;n =10Hormone levels in pregnant dams (decreasedprogesterone, increased estradiol)- 1.6E-03 300 5.3E-12Smialowicz etal., 2008Mouse, B6C3F1(F)90-day gavage;n =8–15Decreased SRBC response- 6.4E-03 300 2.1E-11Keller et al.,2007, 2008a, bbMouse, CBA/Jand C3H/HeJGavage GD 13;n =23–36 (pups)Missing molars, mandibular shape changes inpups- 9.8E-03 300 3.3E-11Toth et al.,1979Mouse, Swiss/H/Riop (M)1-year gavage;n =38–44Dermal amyloidosis, skin lesions-1.0E-02 300 3.3E-11Latchoumy-candane andMathur, 2002Rat, Wistar (M) 45-day oralpipetting;n =6Decreased sperm production- 1.7E-02 300 5.7E-11NTP, 1982 Mouse, B6C3F1(M)2-year gavage;n =50Liver lesions- 2.2E-02 300 7.3E-11White et al.,1986Mouse, B6C3F1(F)14-day gavage;n =6–8Decreased serum complement- 2.8E-02 300 9.4E-11Li et al., 1997 Rat, S-D(F, 22 day-old)Single gavage;n = 10Increased serum FSH 3.0E-03 (N) 1.7E-02 30c1.0E-10DeCaprio et al.,1986Guinea pig,Hartley90-day dietary;n =10Decreased body weight, organ weightchanges (liver, kidney, thymus, brain)4.1E-03d(N) 3.3E-02d30c1.4E-10Shi et al., 2007 Rat, S-D (F) 11-month gavage;n =10Decreased serum estradiol4.7E-03 (N)5.0E-03 (B)2.8E-02 30c1.6E-10Markowski etal., 2001Rat, Holtzman Gavage GD 18;n =4–7Neurobehavioral effects in pups (running,lever press, wheel spinning)- 5.2E-02 300 1.7E-10Hojo et al.,2002Rat, S-D Gavage GD 8;n =12Food-reinforced operant behavior in pups-5.5E-02 300 1.8E-10Vos et al., 1973 Guinea pig,Hartley (F)8-week gavage;n =10Decreased delayed-type hypersensitivityresponse to tuberculin6.4E-03d(N) 3.2E-02d30c2.1E-10This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-41 DRAFT: DO NOT CITE OR QUOTE1Table 4-5. Candidate points of departure forthe TCDD RfD using blood-concentration-based humanequivalent doses (continued)StudySpecies, strain(sex, if notboth)Protocol EndpointNOAELHED(N) orBMDLHED(B)(ng/kg-day)LOAELHED(ng/kg-day)UFaRfD(mg/kg-day)Cantoni et al.,1981Rat, CD-COBS(F)45-week gavage;n =4Increased urinary porhyrins- 6.5E-02 300 2.2E-10Bell et al., 2007 Rat, CRL:WI(Han) (M)17 week dietary;n =30Delay in onset of puberty7.4E-03 (B) 1.1E-01 30c2.5E-10Miettinen et al.,2006Rat, Line C Gavage GD 15;n =3–10Cariogenic lesions in pups-8.9E-02 300 3.0E-10Kattainen et al.,2001Rat, Line C Gavage GD 15;n =4–8Inhibited molar development in pups-9.0E-02 300 3.0E-10NTP, 2006 Rat, S-D (F) 2-year gavage;n =53Liver and lung lesions-1.4E-01 300 4.7E-10Amin et al.,2000Rat, S-D Gavage GD 10–16;n =10Reduced saccharin consumption andpreference- 1.7E-01 300 5.7E-10Schantz et al.,1996Rat, S-D (F) Gavage GD 10–16;n =10Altered maze performance- 1.7E-01 300 5.7E-10Mocarelli etal., 2008Human (M)Childhoodexposure;n =157Decreased sperm conc, sperm motility andincreased estradiol, as adults-2.0E-02e30f6.7E-10Baccarelli etal., 2008Human infantsGestationalexposure;n =51Increased TSH in newborn infants-2.4E-02g30f8.2E-10Ohsako et al.,2001Rat, Holtzman Gavage GD 15;n =5Decreased ano-genital distance in male pups 2.9E-02 (N) 1.8E-01 30c9.6E-10Murray et al.,1979Rat, S-D 3-generation dietary Reduced fertility and neonatal survival (f 0and f 1)3.0E-02 (N) 3.9E-01 30c1.0E-09Chu et al., 2007 Rat, S-D (F) 28-day gavage,n =5Liver lesions3.6E-02 (N) 5.8E-01 30c1.2E-09Van Birgelen etal., 1995Rat, S-D (F) 13-week dietary;n =8Decreased liver retinyl palmitate-5.3E-01 300 1.8E-09This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-42 DRAFT: DO NOT CITE OR QUOTETable 4-5. Candidate points of departure forthe TCDD RfD using blood-concentration-based humanequivalent doses (continued)StudySpecies, strain(sex, if notboth)Protocol EndpointNOAELHED(N) orBMDLHED(B)(ng/kg-day)LOAELHED(ng/kg-day)UFaRfD(mg/kg-day)Kociba et al.,1978Rat, S-D (F) 2-year dietary;n= 50Liver and lung lesions, increased urinaryporhyrins6.5E-02 (N) 6.5E-01 30c2.2E-09Sewall et al.,1995Rat, S-D (F) 30-week gavage;n =9Decreased serum T45.2E-01 (N)7.3E-02 (B)1.8E+00 30c2.4E-09Fattore et al.,2000Rat, S-D 13-week dietary;n= 6Decreased hepatic retinol - 8.01E-1 300 2.7E-09Seo et al., 1995 Rat, S-D Gavage GD 10–16;n =10Decreased serum T4 and thymus weight 1.7E-01 (N) 9.3E-01 30c5.6E-09Crofton et al.,2005Rat, Long-Evans(F)4-day gavage;n =4−14Decreased serum T41.7E-01 (N) 7.6E-01 30c5.7E-09Alaluusua et al.,2004Human Childhood exposure;n =48Dental defects1.2E-01h(N) 9.3E-01i3j3.9E-08aExcept where indicated, UFA= 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.eMean 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).iMean 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 purpos es only and does not constitute Agency policy 1/15/10 4-43 DRAFT: DO NOT CITE OR QUOTETable 4-6. Qualitative analysis of the strengths and limitations/uncertaintiesassociated with animal bioassayspossessing candidate points-of-departure for the TCDD RfDStudy Strengths Limitations RemarksLi et al., 2006•Female reproductive effects (i.e., early embryo lossand changes in serum progesterone and estradiol)were tested at multiple exposure times—earlygestation, preimplantation, and peri- topostimplantation.•Relatively small sample size of mouse dams/doseemployed (n= 10)Study may have humanrelevance based on perceivedTCDD-induced femalereproductive effects; uterinesequestration of TCDDobserved.Smialowicz etal., 2008•Sheep red blood cell (SRBC) plaque forming cellassay is highly sensitive and reproducible acrosslaboratories when examining TCDD•Small sample size of mice/dose employed (n= 8)•Only female mice were tested•Thymus and spleen weights were only otherimmune response-related endpoints testedStudy adds to a substantialdatabase on theimmunotoxicity of TCDD inlaboratory animalsToth et al., 1979•Large sample size of mice/dose employed•One-year exposure duration•Reporting of findings is terse and lacks sufficientdetail (e.g., materials and methods, thoroughdescription of pathological findings, etc.)•Only male mice were tested for amyloidosis andskin lesionsStudy has human relevancebased on similarity of ulcerousskin lesions and amyloidosis inmice to chloracne observed inhumansLatchoumycandane andMathur, 2002•Compared to epididymal sperm counts, thetesticular spermatid head count provides betterquantitation of acute changes in sperm productionand can indicate pathology•Small sample size of rats/dose employed (n= 6)•Oral pipette administration of TCDD may be a lessefficient dosing method than gavageStudy has human relevancebased on observed TCDDinduced male reproductiveeffectsThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-44 DRAFT: DO NOT CITE OR QUOTE1Table 4-6. Qualitative analysis of the strengths and limitations/uncertaintiesassociated with animal bioassayspossessing candidate points-of-departure for the TCDDRfD (continued)Study Strengths Limitations RemarksKeller et al.2007, 2008a,b•Six different inbred mouse strains were utilized•Large sample size of mouse offspring/dose/strainevaluated•Low TCDD dose levels used compared to typicalmouse studies allowed for identification of subtlesensitivity differences in presence of absence ofthird molars, variant molar morphology, andmandible structure in offspring•Unknown sample size of mouse dams/dose/strainemployed•All inbred strains possessed sensitiveballele attheAhrlocus (i.e., a potentially resistant subpopulation was not evaluated for comparisonpurposes)•Morphological dental and mandibular changesinduced by TCDD at earlier or later gestationaldosing dates are unknown because of singlegavage administration on GD 13•Difficulties breeding A/J mice led to abandonmentof that strain in the analysis (Keller et al.,2008a, b)Studies have human relevancebased on observed TCDDinduced developmental dentaleffects.NTP, 1982•Large sample size of mice and rats/dose employed•Comprehensive 2-year bioassay that assessed bodyweights, clinical signs, and pathological changes inmultiple tissues and organs•Elevated background levels of hepatocellulartumors in untreated male mice•Gavage exposure was only 2 days/weekOne of two comprehensivechronic toxicity evaluations ofTCDD in rodentsWhite et al., 1986•Total hemolytic complement (CH50) isrepresentative functional assay of the completecomplement sequence•Small sample size of rats/dose employed (n= 6−8)•Individual complement factors may besignificantly depleted without affecting CH50activity (only C3 is measured)•TCDD used for dosing was of unknown purityFirst report on TCDD-inducedeffects on serum complementShi et al., 2007•Study design evaluated TCDD effects on agingfemale reproductive system (i.e., exposure beganinuteroand spanned across reproductive lifespan)•Several female reproductive endpoints wereevaluated, including cyclicity, endocrinology,serum hormone levels, and follicular reserves•Relatively small sample size of rats/doseemployed (n= 10)Study may have humanrelevance based on perceivedTCDD-induced femalereproductive effectsThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-45 DRAFT: DO NOT CITE OR QUOTETable 4-6. Qualitative analysis of the strengths and limitations/uncertaintiesassociated with animal bioassayspossessing candidate points-of-departure for the TCDDRfD (continued)Study Strengths Limitations RemarksMarkowski et al.,2001•Low TCDD dose levels used allowed for subtlebehavioral deficits to be identified in rat offspring.•Several training sessions on wheel apparatuseswere extensive.•Neurobehavioral effects are exposure-related andcannot 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 andorigin.•Neurobehavioral effects induced by TCDD atearlier or later gestational dosing dates areunknown because of single gavage administrationon GD 18.•Although BMD analysis was conducted, the modelparameters were not constrained according to EPAguidance, so the results cannot be used.Study informs a dearth ofinformation onneurobehavioral toxicity ofTCDD; observedneurobehavioral changessignify meaningfuldevelopmental outcome.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/doseemployed (n= 10)•No histopathological analyses performed•TCDD used for dosing was of unknown purityStandard subchronic oral studyVos et al., 1973•Three different animal species tested (guinea pigs,mice, rats)•Effects of TCDD tested onboth cell-mediated andhumoral immunity•Small sample size of animals/dose employed ineach experiment (n= 5−10)•Only female guinea pigs and rats were tested, andonly male mice were tested•Only one experimental assay was utilized to assesscell-mediated and humoral immunity in eachanimal species; humoral immunity was onlyinvestigated in guinea pigs•TCDD used for dosing was of unknown purityEarly study on theimmunotoxicity of TCDD inlaboratory animalsCantoni et al.,1981•Experiments were designed to test qualitative andquantitative composition and the course of urinaryexcretion in TCDD-induced porphyria•Small sample size of rats/dose employed (n= 4)•Concurrent histological changes with tissueporphyrin levels were not examined•TCDD used for dosing was of unknown purityEarly study on porphyrogeniceffects of TCDDThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-46 DRAFT: DO NOT CITE OR QUOTETable 4-6. Qualitative analysis of the strengths and limitations/uncertaintiesassociated with animal bioassayspossessing candidate points-of-departure for the TCDDRfD (continued)Study Strengths Limitations RemarksBell et al., 2007•Large sample size of both rat dams andoffspring/dose employed•Several developmental effects tested•Batch-to-batch variation of up to 30% in TCDDconcentration in the diet•Chronic dosing of dams does not accurately definegestational period when fetus is especiallysensitive to TCDD-induced toxicityStudy adds to a substantialdatabase on the developmentaltoxicity of TCDD in laboratoryanimalsHojo et al., 2002•Low TCDD dose levels used allowed for subtlebehavioral deficits to be identified in rat offspring.•Preliminary training sessions in operant chamberapparatuses were extensive.•Neurobehavioral effects are exposure-related andcannot be attributed to presence of learning ordiscrimination deficits.•Relatively small sample size of rat dams/doseemployed (n= 12).•Small sample size of rat offspring/dose evaluated(n= 5-6).•Neurobehavioral effects induced by TCDD atearlier or later gestational dosing dates areunknown because of single gavage administrationon GD 8.•Although BMD analysis was conducted, themodel parameters were not constrained accordingto EPA guidance, so the results cannot be used.Study informs a dearth ofinformation onneurobehavioral toxicity ofTCDD; observedneurobehavioral changessignify meaningfuldevelopmental outcome.NTP, 2006•Chronic exposure duration with several interimsacrifices•Large number of dose groups with close spacing•Large number of animals per dose group•Comprehensive suite of endpoints evaluated•Comprehensive biochemical, clinical andhistopathological tests and measures•Detailed reporting of results, with individualanimal data presented as well as group summaries•Evaluation of background exposure to TCDD andDLCs in feed•Single species, strain and sex•Lowest dose tested too high for establishingNOAELStudy is the mostcomprehensive chronic TCDDtoxicity evaluation in rats todateThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-47 Table 4-7. Basis and derivation of the TCDD reference dose 1 2 Principal study detail Study POD (ng/kg-day) Critical effects Mocarelli et al., 2008 0.020 (L OAEL) 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) Elevat ed TSH (> 5 μU/mL) in neonates
RfD derivation POD 0.020 ng/kg-day (2.0E-8 mg/kg-day)

UF 30 (UF L = 10, UF H = 3)
RfD 7 × 10 -10 (7E-10) mg/kg-day (2.0E-8 ÷ 30) Uncertainty factors
LOAEL-to-NOAEL (UF L ) 10 No NOAEL established; ca nnot quantify lower exposure group in Baccarelli et al. (2008) ; magnitude of effects at LOAEL sufficient to require a 10-fold factor.
Human interindividual variability (UF H ) 3 A factor of 3 (10 0.5 ) 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 (UF A ) 1 Human study.

Subchronic-to-chronic (UF S ) 1 Chronic effect levels are not well defined for humans; however, animal bioassays i ndicate 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 (UF D ) 1 The database for TCDD cont ains an extensive range of human and animal studies that examine a comprehensive set of endpoint s. There is no evidence to suggest that additional data would result in a lower reference dose. 3This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-48 1 Figure 4-1. EPA’s process to select a nd identify candidate PODs from key 2 epidemiologic studies for use in the noncancer risk assessment of TCDD. For 3 each noncancer study that qualified fo r TCDD dose-response assessment using 4 the study inclusion criteria, EPA first ev aluated the dose-response information 5 developed by the study authors for whet her the study provided noncancer effects 6 and TCDD dose data for a toxicologically re levant endpoint. If such data were 7 available, then EPA identified a NOAEL or LOAEL as a candidate POD. Then, 8 EPA used a human kinetic model to estim ate the continuous oral daily intake 9 (ng/kg-day) for the candidate POD that could be used in the derivation of an RfD 10 based on the study data. If all of this in formation was available, then the result 11 was included as a candidate POD. 12 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 populationThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 4-49 1 Figure 4-2. EPA’s process to select a nd identify candidate PODs from key 2 animal bioassays for use in noncancer dose-response analysis of TCDD. For 3 each noncancer endpoint found in the studies th at qualified for TCDD dose-response assessment 4 using the study inclusion criteria, EPA attempted to estimate six candidate PODs, i.e., estimates of 5 a NOAEL, LOAEL, and BMDL using administered average daily doses (ADD) and using blood 6 concentrations from the animal kinetic model. Benchmark dose modeling was not always possible 7 due to poor model fits. Then for all of the candidate PODs that were estimated, HEDs were 8 estimated using the human kinetic model. Next , the toxicological relevance of each candidate 9 POD was evaluated relative to its usefulness in human health risk assessment. Finally, the lowest 10 group of the toxicologically relevant candidate PODs are selected for final use in derivation of an 11 RfD. 12 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 1 st -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 than the LOAEL? No

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 1 st -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 than the LOAEL? No

Yes

Yes NoThis document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-50 DRAFT: DO NOT CITE OR QUOTE1.0E-041.0E-031.0E-021.0E-011.0E+001.0E+011.0E+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) Ohsako et al., 2001 (R) Amin et al., 2000 (R) Schantz et al., 1996 (R) Li et al., 2006 (M) Bell et al., 2007a (R)

2,3,7,8-TCDD Ingestion Rate (Human-equivalent dose, ng/kg-d)LOAELNOAELBMDLDosing RangeLess than1 yearGreater than1 yearDevelopmentalHumanReproductiveFigure 4-3. Exposure-response arrayfor ingestion exposures to TCDD.This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 4-51 DRAFT: DO NOT CITE OR QUOTE1.0E-121.0E-111.0E-101.0E-091.0E-081.0E-071.0E-06 Moccarelli et a l . 2 008

B a cc a rell i et al. 2008

A l aluu s ua e t a l . 2 004 L i et a l . 2006 Smialowicz et al. 2008 Keller et al. 2 0 07 , 20 0 8a , b

T o th et a l . 197 9

L at c h o umyc a nd a ne & Math u r 2 002 NT P 1 9 82
W hite e t a l. 1 9 86 L i e t a l . 1 9 9 7

D e C a p r i o et a l . 19 8 6 Shi et al. 2007

M a r kowski et al. 2001
H o j o e t a l. 20 0 2 Vos et al. 1973

C a n ton i et a l. 198 1 Be l l e t a l. 2 007 Mie t tine n e t al. 2 0 06 Katt a inen et a l . 2 0 01 N TP 2 00 6 Am in e t a l . 2 00 0 Schantz et al. 1 995

O h sa ko e t a l . 2 00 1

M u rray et a l . 19 7 9 Chu et a l. 20 0 7

Va n Bir g elen e t al. 19 9 5a

K oci b a e t a l . 1 978 Se w a l l e t a l . 1 995

F a tt ore e t al. 2 000
S eo e t a l . 1 99 5

C r o ft on et a l. 20 0 5

Oral Exposure (mg/kg-day)UFL (LOAEL-to-NOAEL)UfH (human variability)UFA (animal-to-human)RfDPoint of DepartureAnimal BioassaysHumanFigure 4-4. Candidate RfD array.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-1 5. CANCER ASSESSMENT 1
2 3 5.1. QUALITATIVE WEIGHT-OF-EVIDEN CE CARCINOGEN CLASSIFICATI ON 4 FOR 2,3,7,8-TETRACHLORODIBENZO- p

-DIOXIN (TCDD) 5
5.1.1. Summary of National Academy of Scien ces (NAS) Comments on the Qualitat ive 6 Weight-of-Evidence Carcinogen Classifi cation for 2,3,7,8-Tetrachlorodibenzo7 p -Dioxin (TCDD) 8 In its charge, the National Academy of Scinces (NAS) was requested to comment 9 specifically on U.S. Environmental Prot ection Agency (EPA)’s conclusion that 10 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) is best characteri zed as “carcinogenic to humans.” 11 While indicating that distinction between the cate gories of “carcinogenic to humans” and “likely 12 to be carcinogenic to humans” is “...ba sed more on semantics than on science...” 13 (NAS, 2006a, p. 141) and recomm ending that EPA “...spend its en ergies and resources on more 14 carefully delineating the assumptions used in quantitative risk estimates for TCDD...” 15 (NAS, 2006a, p. 141) rather than on the quali tative cancer descriptor for TCDD, the NAS 16 provided the following comments: 17
18 ...the classification of dioxin as “carcinoge nic to humans” versus “likely to be 19 carcinogenic to humans” depends greatly on the definition and interpretation of 20 the specific criteria used for classification, with the explicit recognition that the 21 true weight of evidence lies on a con tinuum with no bright line that easily 22 distinguishes between thes e two categories. The comm ittee agreed that, although 23 the weight of epidemiological evidence that dioxin is a human carcinogen is not 24 strong, the human data available from o ccupational cohorts are consistent with a 25 modest positive association between re latively high body burdens of dioxin and 26 increased mortality from all cancers. Pos itive animal studies and mechanistic data 27 provide additional support for classifica tion of dioxin as a human carcinogen. 28 However, the committee was split on whether the weight of evidence met all the 29 necessary criteria described in the cancer guidelines for classification of dioxin as 30 “carcinogenic to humans.” EPA should su mmarize its rationale for concluding 31 that dioxin satisfies the criteria set out in the most recent cancer guidelines for 32 designation as either “carci nogenic to humans” or “lik ely to be carcinogenic to 33 humans (NAS, 2006a, p. 140). 34 35 If EPA continues to designate dioxin as “carcinogenic to humans,” it should 36 explain whether this conclusion reflects a finding that there is a strong association 37 between dioxin exposure and human cancer or between dioxin exposure and a key 38 precursor event of dioxin’s mode of ac tion (presumably AhR binding). If EPA’s 39This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-2 finding reflects the latter association, EPA should explain why that end point 1 (e.g., AhR binding) represents a “key precursor event (NAS, 2006a, p. 141). 2 3 5.1.2. EPA’s Response to the NAS Comments on the Qualitative Weight-of-Evidence 4 Carcinogen Classification for TCDD 5 A cancer descriptor is used to express the c onclusion of the weight of evidence regarding 6 the carcinogenic hazard potential of a com pound. EPA agrees with the NAS committee that 7 cancer descriptors represent points along a c ontinuum of evidence. Relatedly, EPA 8 acknowledges that there are grad ations and borderline situations that can not be communicated 9 through a descriptor and are be st clarified by a full weight of evidence narrative. 10
The 2003 Reassessment contains a detailed disc ussion of TCDD carcinogenicity in both 11 humans (Part II, Chapter 7a; 8) a nd animals (Part II, Chapter 6; 8) as well as an overall summary 12 of TCDD carcinogenicity (Part III, Chapter 2.2.1) . Since the release of the 2003 Reassessment, 13 the database pertaining to TCDD carcinogenic ity has been strengthened and expanded by 14 numerous publications (see U.S. EPA, 2008b), incl uding a new chronic bioassay in female rats 15 (NTP, 2006) and several new follow-up epidemiologi cal investigations (s ee Section 2.4.1 and 16 references therein). Many of these studies have been published subsequent to the NAS review. 17
These new data are summarized and evalua ted in Section 2.4 of this document. 18 As noted by the NAS, the 2003 Reassessment was released prior to EPA’s publication of 19
the U.S. EPA Guidelines for Carcinogen Risk Assessment (“2005 Cancer Guidelines”; U.S. EPA, 20 2005). Using EPA’s guidance at the time of its release (U.S. EPA, 1996a), the 2003 21
Reassessment determined that the available evid ence was sufficient to classify TCDD as a 22 “human carcinogen.” The 1996 guidance suggested “human carcinogen” to be an appropriate 23 descriptor of carcinogenic potenti al when there is an absence of conclusive epidemiologic 24 evidence to clearly es tablish a cause-and-effect relati onship between human exposure and 25 cancer, but there are compelling ca rcinogenicity data in animals and mechanistic information in 26 animals and humans demonstrating similar modes of carcinogenic action. 27 The 2005 Cancer Guidelines (U.S. EPA, 2005) ar e intended to promote greater use of the 28 increasing scientific understanding of the mechanis ms that underlie the carcinogenic process. 29 The 2005 Cancer Guidelines expand upon earlier guidance applied in th e 2003 Reassessment and 30 encourage the use of chemical- a nd site-specific data versus defa ult options, the consideration of 31 mode of action information and understanding of biological changes, fuller characterization of 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-3 carcinogenic potential, and consideration of differences in su sceptibility. The 2005 Cancer 1 Guidelines also emphasize the importance of we ighing all of available evidence in reaching 2
conclusions about the human carci nogenic potential of an agent. As noted above, additional 3 information on TCDD carcinogenicity has been published since th e release of the 2003 4 Reassessment. This information has expande d the TCDD database and provided additional 5 support for conclusions made in the 2003 Reassessm ent regarding the carcinogenic potential of 6
TCDD. 7 Under the 2005 Cancer Guidelines (U.S. EPA, 2005), TCDD is characterized as 8 carcinogenic to humans , based on the available data as of 2009. The 2005 Cancer Guidelines 9 indicate that this descriptor is appropriate wh en there is convincing ep idemiologic evidence of a 10 causal association between human exposure and ca ncer or when all of the following conditions 11 are met (a) there is strong evidence of an asso ciation between human e xposure and either cancer 12 or the key precursor events of the agent’ s mode of action, but not enough for a causal 13 association, and (b) there is extensive evidence of carcinogenicity in animals, and (c) the mode(s) 14 of carcinogenic action and associated key precursor events have b een identified in animals, and 15 (d) there is strong evidence that the key precurs or events that precede the cancer response in 16 animals are anticipated to occur in humans and progress to tumors, base d on available biological 17
information. 18 As noted above, the NAS commented that EPA should “...explain whether this 19 conclusion reflects a finding that there is a strong association between dioxin exposure and 20 human cancer or between dioxin exposure and a ke y precursor event of dioxin’s mode of action 21 (presumably AhR binding)” (NAS, 2006a). When evaluating the carcinogenic potential of a 22 compound, EPA employs a weight of evidence appr oach in which all available information is 23
evaluated and considered in re aching a conclusion. The followi ng sections provide a summary 24 of EPA’s weight of evidence evaluation for TCDD. 25
26
5.1.2.1. Summary Evaluation of Epidemiologic Evidence of TCDD and Cancer 27 The available occupational epidemiologic studies provide convin cing evidence of an 28 association between TCDD exposur e and all cancer mortality. Am ong the strongest of these are 29 the studies of over 5,000 U.S. chemical manuf acturing workers (the National Institute for 30 Occupational Safety and Health [NIOSH] cohort) (Fingerhut et al., 1991; Steenland et al., 1999, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-4 2001; Aylward et al., 1996; Cheng et al., 2006; Collins et al., 2009); a study of nearly 1 2,500 German workers involved in the production of phenoxy herbicides and chlorophenols (the 2 Hamburg cohort) (Becher et al., 1996, 1998; Manz et al., 1991; Na gel et al., 1994; Flesch-Janys 3 et al., 1995, 1998); a study of more than 2,000 Dutch workers in two plants involved in the 4 synthesis and formulation of phenoxy herbicides and chlorophenols (the Dutch cohort) (Bueno 5 de Mesquita et al., 1993; Hooiveld et al., 1998) ; a smaller study of roughly 250 workers involved 6 in a chemical accident cleanup (the BASF cohort) (Thiess et al., 1982; Zobe r et al., 1990; Ott and 7 Zober, 1996); and an international study of more than 18,000 workers exposed to phenoxy 8 herbicides and chlorophenols (Saracci et al., 1991; Kogevi nas et al., 1997) including newer 9 studies of smaller subsets of these workers (t’M annetje et al., 2005; McBr ide et al., 2009a, b). 10 The findings from these studies have been thor oughly described either in the 2003 Reassessment 11
or in Section 2.4.1 of this document. 12 As noted in Section 2.4, there are considerable challenges inherent in addressing potential 13 sources of confounding from smoking and co-exposure to other carcinogens, (which could 14 produce inflated or spurious associations), the healthy worker effect, (which could result in 15 attenuated effects through comp arison with a referent backgro und with an inappropriately high 16 background risk), and quantifyi ng exposure to the populations included in many of these 17 retrospective studies. The more recent studies of these cohorts have made significant advances 18 in reducing the potential for bias from the hea lthy worker effect through use of internal cohort 19 analyses and/or controlling for potential confounders through statistical ad justment, restriction, 20 and use of internal comparisons. Although so me exposure assessment uncertainties remain, 21
some of these studies have also collected individual-level TC DD exposure estimates that allow 22 quantification of effectiv e dose necessary for dose-response m odeling. Overall, the occupational 23 data provide consistent support for an associ ation between exposure to TCDD and increased 24 cancer mortality. 25 Additional epidemiologic evidence supporti ng an association be tween TCDD exposure 26
and cancer comes from studies inve stigating the morbidity and mort ality of residents exposed to 27 TCDD following an accidental release from a chem ical plant near Seveso, Italy (the Seveso 28 cohort) (Bertazzi et al., 1989, 1993, 1997, 2001; Pesatori et al., 1998, 2003; Consonni et al., 29 2008; Warner et al., 2002). Pesato ri et al. (2003) and Consonni et al. (2008) were not available 30
at the time the 2003 Reassessm ent was released . Am ong individuals with relatively high 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-5 exposure at Seveso (Zones A a nd B combined), all-cancer mortal ity in the 20-year post-accident 1 period and all-cancer incidence in the 15-year post-accident period failed to exhibit significant 2 departures from the expected (Ber tazzi et al., 2001; Pesatori et al., 2003; Consonni et al., 2008). 3 However, an increased risk of all- cancer mortality wa s noted among men 15

− 20 years after first 4 exposure; not only is the associ ation similar in magnitude to other studies (relative risk 5 [RR] = 1.3; 95% confidence interval [CI] = 1.0

− 1.7) but also emphasizes the importance of 6
consideration of latenc y (Bertazzi et al., 2001). Furthermor e, associations between TCDD and 7 some specific cancer sites were detected in this cohort, including increas ed incidence (based on 8 15 years of follow-up) and mortality (based on 20 years follow-up) from lymphatic and 9 hematopoietic neoplasms in both males and female s from Zones A and B (Consonni et al., 2008). 10 This excess was primarily due to non-Hodgkin’s ly mphoma. Additionally, there was an increase 11 in lung and rectal cancer mortal ity in men (Bertazzi et al., 2001) and limited evidence of 12
increased liver cancer incidence in women based on the 15-year follow-up study (Bertazzi et al., 13 1993). In a separate analysis of 981 wome n in Zone A, breast cancer incidence ( n = 15) was 14 associated (a 2-fold increase for a 10-fold in crease in serum TCDD) w ith TCDD measurements 15 first collected in 1976 and 1977 (Warner et al ., 2002). The authors also reported a 2

− 3-fold 16 increase in all cancer incidence ( n = 21) for the two upper quartiles of TCDD exposure. 17 Overall, the newer studies of the Seveso c ohort have reported si gnificant increases in 18
cancer incidence and elevations in cancer mortality that were not ev ident in earlier studies of this 19
cohort. While these studies demonstrate an association between TCDD exposure and different 20 types of cancer, one of the main limitations is the small number of cancer cases to assess 21 site-specific associations with TCDD exposure. Ongoing studies in th at cohort should help 22 further elucidate potential risk for specific can cer types (and other endpo ints) associated with 23 TCDD exposures among this population. 24
25
5.1.2.1.1. Evidence for causality. 26 The evidence for causality for cancer from the human studies is briefly summarized in the 27 paragraphs that follow and is based on recomme ndations from the 2005 Cancer Guidelines. It 28 should be noted that there are methodological limitations of the epidemiologic studies that may 29 temper some of the conclusions regarding causality . These limitations include limited statistical 30 power, exposure assessment uncertainty, and lack of control of confounders (e.g., dioxin-like 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-6 compounds and smoking) in some studies. There is additional uncertainty regarding lack of 1 organ specificity in TCDD associated cancers, as the most consistent results occurred for 2 all-cancer mortality. Despite these uncertainties , many of the more recent studies have greatly 3 improved exposure assessments co mpared to earlier studies of the same cohorts and have 4 addressed the potential for conf ounding and other types of biases. 5 Temporality —exposure must precede the effect for causal inference. Given the long 6 induction period for many types of cancers, expos ure must precede the effect with a sufficient 7 latency (i.e., typically 15

− 20 years for environmental carcinog ens). In all the occupational 8 studies reviewed (with the exception of McBr ide et al., 2009a), TCDD exposure has preceded 9 the effect with sufficient latency to be consider ed causally associated. In the studies of the 10 Seveso cohort, the follow-up exposure period has now reached 20 years, a latency sufficient to 11 address some carcinogenic endpoints. Since mo st of the studies are based on occupational 12 exposures or accidental releases into the enviro nment, temporality is more readily established 13 due to the obvious determination of the specif ic exposure windows prior to disease onset. 14 Strength of Association —refers to the magnitude of measur es of association such as the 15 ratio of incidence or mortality (e.g., standardized mortality ratio [SMRs], standardized incidence 16 ratios, RRs, or odds ratios) irrespective of statistical significance. Effect estimates that are large 17 in magnitude are less likely to be due to chance, bias, or confoundi ng. Reports of modest risk, 18 however, do not preclude a causal association and may reflect an agent of lower potency, lower 19 levels of exposure or attenuati on due to nondifferential exposure mi sclassification. The four 20 occupational cohorts with the highest expos ures (NIOSH, Hamburg, Dutch, and BASF) 21 consistently showed statistically significant, al though moderate, elevations in cancer mortality. 22 When the data were combined, the SMR for all four subcohorts was 1.4 [95% CI = 1.2

− 1.6] 23 (IARC, 1997). Based on findings from the Interna tional Agency for Research on Cancer (IARC) 24 Working Group, increases in all cancer (combine d) mortality of the magnitude reported for 25 TCDD have rarely been found in occupationa l cohort studie s (IARC, 1997). Although these 26 estimates are higher than the all-cancer mo rtality results among Seveso men (RR = 1.1; 27
95% CI = 1.0

− 1.3), they are comparable to the risk estimated in this population (RR = 1.3; 28 95% CI = 1.0-1.7) 15

− 20 years after first exposure. Thes e consistent results comparable in 29 magnitude from the occupational cohorts and Seve so population are not likely due to chance. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-7 The occupational cohort studies also show an increased risk for lung cancer in the 1 previously mentioned four subcohorts. The rela tive risk for lung cancer in the combined highly 2 exposed subcohorts was estimated to be 1.4 (95% CI = 1.1

− 1.7) (IARC, 1997). This is 3 consistent with the lung cancer mortality findi ngs for the highest exposed group of men in 4 Seveso (RR = 1.3; 95% CI = 1.0

− 1.7). Additionally, there was an increase in rectal cancer 5 mortality in the Seveso cohor t (RR = 2.4; 95% CI = 1.2

− 4.6) (Bertazzi et al., 2001) with a 6 corresponding increase in incidence. Consistent relative risks of more than two were also 7
detected for rectal cancer in th e Hamburg and New Zealand cohorts , but increased risks were not 8
found in the other cohorts. A lthough there was limited eviden ce of increased incidence or 9 morality from hepatobiliary cancers across the cohor ts, liver cancer incidence was elevated in the 10 15-year postaccident period among women in th e Seveso cohort (RR = 2.4; 95% CI = 1.1

− 5.1, 11 Warner et al., 2002). An association in this population was also detected for between breast 12 cancer incidence (RR = 2.1; 95% CI = 1.0–4.6) and serum TCDD leve ls (per a 10-fold increase 13 in serum TCDD). Although findings were base d on small numbers, three- and four-fold 14
increased risks of soft tissue sarcoma were de tected among the NIOSH (Collins et al., 2009) and 15 New Zealand cohorts (McBride et al., 2009a). No other cases of this very rare cancer were 16 detected in the exposed populati ons from the other cohorts. 17 Elevated risk of lymphohem opoietic cancer mortality was noted among the Seveso cohort 18 (RR = 1.7; 95% CI = 1.2, 2.5) (Consonni et al ., 2008). Increased SMRs for lymphohemopoietic 19 cancer comparable in magnitude (range: 1.6

− 2.2) were also detected among the Hamburg and 20 New Zealand occupational cohorts, but limited evidence (range: 1.0 to 1.2) of increased 21 mortality was found in the BASF, NIOSH and Ranch Hands employees (Ott and Zober, 1996; 22 Steenland et al., 1999; Akhtar et al., 2004). Most of the lym phohemopoietic cancer mortality 23 risk was reportedly due to non-Hodgkin’s lymphoma in most of the cohorts. Relative risks for 24 non-Hodgkin’s lymphoma among TCDD exposed populat ions from the NIOSH, Hamburg, New 25 Zealand, Dutch, and Seveso cohorts ranged from 1.2 to 3.8. Although statistical power was 26 limited in most of these studies, relative ri sks exceeded 3.0 for non-Hodgkin’s lymphoma in 27 three of these cohorts (Flesch-Janys et al., 1998 ; Consonni et al., 2008; H ooiveld et al., 1998). 28 Consistency —the observation of the same site-speci fic effect across se veral independent 29 study populations strengthens an in ference of causality. Despite differences across occupational 30 cohorts, most studies have consistently reporte d increases in all-cancer mortality with TCDD 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-8 exposure. Several of these studies have also re ported increases in lung cancer related to TCDD 1 exposure. As noted above, there is also suggest ive evidence of an incr eased risk in all-cancer 2 and lung cancer mortality among the Seveso cohort consistent in magnitude to the occupational 3 cohorts. Elevated risk of lymphohemopoietic cancer mortality consistent in magnitude 4 (range: 1.6

− 2.2) was also detected among the Seveso , Hamburg and New Zealand cohorts. An 5 increased risk for non-Hodgkin’s lymphoma was found in two of the occupational cohorts as 6 well as in the Seveso cohort, although the relative risks larg ely did not achieve statistical 7 significance. Among those studies detecting an association, cons istent two-fold relative risks 8
were found for rectal cancer (McBride et al., 2009a ; Flesch-Janys et al., 1998; Bertazzi et al., 9 2001) and relative risks in excess of three were detected for soft tissue sa rcoma (McBride et al., 10 2009a; Collins et al., 2009). 11 Biological Gradient —refers to the presence of a dose- response and/or duration-response 12 between a health outcome and expo sure of interest. Several of the occupational cohort studies 13 (Ott and Zober, 1996; Flesch-Janys et al., 1998 ; Manz et al., 1991; St eenland et al., 1999; 14 Michalek and Pavuk, 2008) found evidence of a dos e-response relationship for all cancers and 15 various TCDD exposure measures. The SMR analys es based on internal co mparisons within the 16 occupational cohorts show a biol ogical gradient by comparing hi ghly TCDD exposed workers to 17 low or unexposed workers. A bi ological gradient was also dem onstrated in the Seveso cohort by 18 comparing highly exposed individuals (Zones A a nd B) to individuals in lower exposure zones 19 (Zones C and R). Warner et al. (2002) also reported evidence of a dose-response trend for breast 20 cancer and increasing TCDD exposures. 21 Biological Plausibility —refers to the observed effect ha ving some biological link to the 22 exposure. Most evidence suggests that toxic eff ects of TCDD are mediat ed by interaction with 23 the aryl hydrocarbon receptor (AhR). AhR is a highly conserved pr otein among mammals, 24 including humans (Fujii-Kuriyama et al., 1995; Harp er et al., 2002; Nebert et al., 1991). Several 25 hypothesized modes of action have been presented for TCDD-indu ced tumors in rodents, all 26 involving AhR activation. The available eviden ce does not preclude the relevance of these 27 hypothesized modes of ac tion to humans. 28 Specificity —as originally intended, this refers to increased inference of causation if a 29 single site effect, as opposed to multiple effects, is observed and associated with exposure. 30 Based on current biological understanding, this is now considered one of the weaker guidelines 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-9 for causality. As stated in the 2005 Cancer Gu idelines, given the current understanding that 1 many agents cause cancer at multiple sites, and cancers have multiple causes, the absence of 2 specificity does not detract from evidence for a ca usal effect. Given that the most consistent 3 findings associating TCDD and cancer are for all-cause cancer mortality, epidemiological 4 evidence suggests that TCDD lacks specificity fo r particular tumor sites. A key event in 5 TCDD’s mechanism of action is binding to a nd activating AhR; however, downstream events 6 leading to tumor formation are uncertain and may lik ely be tissue specific. Given that the AhR is 7 highly conserved within species a nd is expressed in various human tissues, the lack of tumor site 8 specificity does not preclude a determination of causality. 9 In summary, EPA finds the available epid emiological information provides strong 10
evidence of an association betw een TCDD exposure and human cancer that cannot be reasonably 11
attributed to chance or conf ounding and other types of bias, and with a dem onstration of 12 temporality, strength of association, consiste ncy, biological plausibility, and a biological 13 gradient. Additional evidence from animal studi es and from mechanistic studies (described 14 below) provides additional support for the clas sification of TCDD as car cinogenic to humans. 15
16
5.1.2.2. Summary of Evidence for TCDD Carcinoge nicity in Experimental Animals 17 An extensive database on the carcinogenicity of TCDD in experimental animals is 18 described in detail in Part II, Chapter 6 of the 2003 Reassessment. There is substantial evidence 19 that TCDD is carcinogenic in experimental animal s based on long-term bioassays conducted in 20 both sexes of rats and mice (Kociba et al., 1978; NTP, 1982, 2006) and in male hamsters (Rao et 21 al., 1988). Additionally, National Toxicology Program (NTP, 2006) has completed a new 22 chronic bioassay in female Sprague Dawley rats. These studies are summarized in Section 2.4.2 23 of this document. All studies have produced positive results, with TCDD increasing the 24
incidence of tumors at sites distant from the site of treatment and at doses well below the 25 maximum tolerated dose. In both sexes of rodents, when administered by di fferent routes and at 26 low doses, TCDD caused tumors at multiple sites; tumors were observed in liver, lung, 27 lymphatic system, soft tissue, nasal turbinates, hard palate, thyroid, adrenal, pancreas, and 28 tongue. The most consistent and best characte rized carcinogenic response s to TCDD are in the 29 rodent liver, lung, and th yroid (discussed below in Section 5.1.2.3). 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-10 5.1.2.3. TCDD Mode of Action 1 The 2005 Cancer Guidelines defines the term “mode of action” as “a sequence of key 2 events and processes, starting with interacti on of an agent with a cell, proceeding through 3 operational and anatomical changes, and resulting in cancer formation.” A “key event” is an 4 empirically observable precursor step that is itself a necessary element of the mode of action or is 5 a biologically based marker for such an element. Mode of action is contrasted with “mechanism 6 of action,” which implies a more detailed unders tanding and description of events, often at the 7 molecular level. In the case of TCDD, the term s ‘mechanism of action’ and ‘mode of action’ are 8 often used interchangeably in the scientific litera ture in reference to TCDD’s interaction with the 9 AhR. A thorough discussion of TCDD’s intera ction with the AhR can be found in the 2003 10 Reassessment (Part II, Chapter 2; Part III, Chapter 3), and is summarized below (see 11 Section 5.1.2.3.1). 12 Most evidence suggests that the majority of toxic effects of TCDD are mediated by 13
interaction with the AhR. EPA considers interaction with the AhR to be a necessary, but not 14 sufficient, event in TCDD carcinogenesis. The sequence of key events following binding of 15 TCDD to the AhR and that ultimately leads to the development of cancer is unknown. 16 Therefore, in the strictest sense, TCDD’s interaction with the AhR does not constitute a “mode 17 of action” as defined by the 2005 Cancer Guide lines because information about the progression 18 of necessary events is lacking. However, Ah R binding and activation by TC DD is considered to 19 be a key event in TCDD carcinogenesis. 20
21
5.1.2.3.1. The aryl hydrocarbon receptor (AhR). 22 While substantial evidence suggests that most toxic effects of TCDD are mediated by 23
interaction with the Ah R, less is known about the comple x responses that result in tumor 24 formation. Nonetheless, a picture is emerging wherein TCDD is considered a 25 “receptor-mediated carcinogen” in laboratory animals (see Figure 5-1), acting in a manner 26 similar to peroxisome proliferators, phor bol esters, or estrogen (Woods et al., 2007). 27
The mechanism of action of TCDD has been ex tensively studied. TCDD activates the 28 AhR, a member of the basic helix-loop-helix, Pe r-Arnt-Sim (bHLH-PAS) family of transcription 29 factors. AhR is present in most cell types and in the unactivated state is cy tosolic and exists in a 30 complex with chaperone proteins, such as heat shock protein 90 (Hsp90) . Binding of TCDD to 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-11 AhR leads to nuclear translocation and heterodime rization with its partne r protein Arnt, another 1 bHLH-PAS family member. The AhR:Arnt he terodimer binds to sp ecific cognate DNA 2 sequence elements known as dioxin/xenobiotic re sponse elements (DRE/XRE) present in the 3 regulatory region of specific genes. Binding of the AhR:Arnt hete rodimer to these elements, and 4 subsequent recruitment of transcri ption coactivator complexes, lead s to increased transcription of 5 specific genes, known as “target genes.” There is a battery of genes affected in this manner and 6 targets include certain xenobiotic-metabolizing enzymes, such as cyto chrome P450 (CYP)1A1, 7 CYP1A2, CYP2B1, and UDP-glucuronosyltransfer ase (UGT)1A6 (reviewed in Schwartz and 8 Appel [2005]). In addition, genes affected by the TCDD/AhR-complex code for both inhibitory 9 and stimulatory growth factors and their gene pr oducts affect cellular growth, differentiation and 10
homeostasis and have been shown to contribute to carcinogenicity as well as other forms of 11 toxicity (reviewed in Popp et al. [2006]). 12 Detailed molecular biology research has been performed to identity the extent of the 13 genes regulated by AhR (Woods et al., 2007); howe ver a complex and still ill-defined profile 14 remains. Additionally, it is important to note that the extent of the response of individual genes 15 may not correlate with site-specific tumorigenicity. For example, while TCDD is ineffective as a 16 tumor promoter in ovariectomized rats and does not stimulate liver cell proliferation in these 17 animals, it is still capab le of inducing CYP1A2 in roughly the same magnitude as in the intact 18 female rats (Lucier et al., 1991) . Similarly, CYP1A1 induction by TCDD is very similar in male 19 and female rats even though males are almost completely resistant to TCDD carcinogenicity 20 (Wyde et al., 2002). 21 Some of AhR’s effects on gene expression may be the result of interaction with other 22 transcription factors (such as the retinoblastoma protein [Ge and Elferink, 1998], NF- κ B [Tian et 23 al., 1999] or with the tyrosine ki nase c-Src (Blankenship and Ma tsumura, 1997]) rather than via 24 direct interaction with DNA. By far the most extensive studies involving cross talk between 25 AhR and another transcription factor are those involving the estrogen receptor alpha (ER α ). The 26 anti-estrogenic propert ies of TCDD have been well- documented, beginning with the 27 observations that TCDD repressed estradiol function in rat uterus and liver. The AhR-ER α 28
cross-talk can be manifested at sever a l levels including direct interaction, association of the 29 receptors with the other’s response element and altered metabolism of estradiol by AhR ligand 30 (Takemoto et al., 2004). The interactions betw een AhR/Arnt- and estrogen receptor-dependent 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-12 signaling pathways, which mediate anti-estr ogenic effects of dioxins and dioxin-like 1 polychlorinated biphenyls (PCBs; Bock, 1994), is probably causal for the well-documented 2 gender-specificity of the carcinoge nic effects of these agents (e .g., hepatocarcinogenicity of 3 TCCD in female as opposed to ma le rats) (Lucier et al., 1991). In addition, cro ss-talk between 4 AhR/Arnt and other nuclear recepto rs, their coactivators, and corepr essors, has been described. 5 In fact, cross-talk has been reported for AhR and numerous signaling pathways involved in a 6 broad range of physiological processes. The mo lecular mechanisms by which the AhR interferes 7 with these signaling networks are multifaceted and occur at multiple levels of regulation (many 8 beyond transcriptional c ontrol) (Haarmann-Stemmann et al., 2009). 9 Pertinent to human risk assessment, there are wide inter- and intraspecies differences in 10 the toxicological responses to TCDD (Ema et al., 1994; Poland and Glover, 1990; Poland et al., 11 1994) some of which can be explained by polym orphisms in AhR. For instance there is a 12 10-fold difference in susceptibility to TCDD- induced toxicity between the TCDD-sensitive 13
C57BL/6 and the resistant DBA/2 strains of mice (Poland and Glover, 1980) that can be 14 explained by polymorphic variations in the liga nd-binding domain and in the C-terminal region 15 of the AhR molecule of each strain (Dolwick et al., 1993). Depending on the system examined, 16 the estimated affinity of binding of TCDD (and related compounds) to the human AhR is about 17
10-fold lower than that observed to the AhR from “responsive” rode nt species and is comparable 18 to that observed to the AhR from “nonresponsiv e” mouse strains (Ramadoss and Perdew, 2004). 19 This reduced affinity is due, in part, to a single amino acid substitution within the ligand binding 20 domain of the human and “nonresponsive” mouse AhRs (Ramadoss and Perdew, 2004). 21 However, there is considerable tissue and species variability in response to TCDD that cannot be 22 ascribed solely to polymorphisms of the AhR gene (Pohjanvirta and Tuomisto, 1994; Geyer et 23 al., 1997). Although the affinity of binding of TCDD and related compounds to the human AhR 24 is reduced compared with rodent AhRs, the qua litative and quantitative rank-order potency of 25
these chemicals is similar. 26
27
5.1.2.3.1.1. Other AhR considerations. 28 In addition to the potent agonist TCDD, th ere are many other exogenous ligands for the 29 AhR, including certain polycyclic aromatic hydr ocarbons, polychlorinate d dibenzofurans, and 30 PCBs (Bock, 1994). Several natural and endoge nous compounds are al so regulators of AhR 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-13 (Chiaro et al., 2008). The classes of endogenous compounds that have been shown to induce 1 CYP1 and/or activate AhR include: (a) trypt ophan metabolites, othe r indole-containing 2 molecules, and phenylethylamines (Gielen and Nebe rt, 1971); (b) tetrapyrroles such as bilirubin 3 and biliverdin; (c) sterols such as 7-ketocholesterol and the horse st eroid equilenin; (d) fatty acid 4 metabolites, including at least six different pros taglandins (Seidel et al., 2001) and lipoxin A4; 5 and (e) the ubiquitous second messenger cAMP (re viewed in McMillan and Bradfield [2007] and 6 Barouki et al. [2007]). The existence of multiple ligands with varying affinity and responses 7 suggests that “selective receptor modulators” (or SR Ms) of the AhR exist. SRMs are ligands for 8 a receptor that, upon binding, elicit a conformati onal change in the receptor that results in 9 differential recruitment of coregulatory molecules to the target gene promoter region, thereby 10 imparting a different biological ac tivity relative to the prototypi cal ligand. This phenomenon has 11 been most studied for nuclear receptors such as the ER α with the classic example being 12 tamoxifen, which has estrogen-like activity in the uterus but anti-estrogen-like effects in the 13 breast. Thus, the relative abilit ies of compounds to stimulate ge ne expression or other effects 14 vary in promoter- and cell type-specific manners. It is now apparent th at SRMs exist for the 15 AhR as well (SAhRMs, Fretland et al. [2004]). For example, 16 6-methyl-1,3,8-trichlorodibenzofuran (6-MCDF), a SAhRM whose structure is similar to that of 17 the prototypical AhR agonist TC DD, can induce CYP1A1 gene expr ession in liver but does not 18 lead to the toxic responses associat ed with TCDD (Fritz et al., 2009). 19
It has been demonstrated that AhR-deficien t (AhR-/-) mice show no si gns of toxicity at 20 doses of TCDD approximating the lethal dose eliciting 50% response (LD 50 ) dose (200 μ g/kg) in 21 mice expressing the AhR (Fernandez-Salguero et al., 1996). However, a single high exposure of 22 2,000 μ g/kg to AhR-deficient mice produced several minor lesions including scattered necrosis 23
and vasculitis in the liver and l ungs. These data suggest that a pa thway leading to toxicity exists, 24 albeit at very high doses, that is independent of the AhR. Howeve r, these data also indicate that, 25 at least in mice, the major in vivo effects of TCDD are mediated through the AhR. The finding 26 of carcinogenicity in hamsters (Rao et al., 1988) is of special inte rest since hamsters have been 27 found to be relatively resistant to the lethal effects of TCDD (H enck et al., 1981; Olson et al., 28 1980). To date, there have been no chronic bioassay studies of TCDD carcinogenicity in 29 AhR-deficient transgenic animals. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-14 There are additional insights into the comp lexity of TCDD’s mechanism of action 1 involving AhR. Some biochemical responses to TCDD treatment in isolated cells have been 2 reported in cells lacking Arnt, in cells expressing a mutated Arnt protein and in cells with highly 3 reduced levels of AhR (Puga et al., 1992; Kolluri et al., 1999), im plying either a non nuclear role 4 of the AhR in mediating these events or an AhR-independent process. 5 Additionally, recent studies have linked Ah R activation in the absence of exogenous 6 ligand to a multitude of biological effects, rangi ng from control of mammary tumorigenesis to 7 regulation of autoimmunity (reviewed in Hahn et al., 2009). Finally, constitutively activated 8
AhR in rodents has been shown to induce st omach tumors (Andersson et al., 2002). This 9
indicates that AhR activation alone (i.e., in the absence of ligand) is sufficient to induce tumors. 10
11
5.1.2.3.2. TCDD as a tumor promoter. 12 The role of TCDD as a tumor promoter is di scussed in the 2003 Reassessment (Part II, 13 Chapter 6). The following is a brief summary of the information regarding TCDD as a tumor 14 promoter. 15 TCDD is typically designated as a nongenot oxic carcinogen because it does not damage 16 DNA directly through the formation of DNA adducts, is negative in most short-term assays for 17 genotoxicity, and is a potent tumor promoter and a weak initiator or noninitiator in multistage 18 models for chemical carcinogenesis (Pitot et al., 1980; Graham et al., 198 8; Lucier et al., 1991; 19 Clark et al., 1991; Flodstr om and Ahlborg, 1991; Poland et al ., 1982). However, mechanisms 20 have been proposed that support th e possibility that TCDD might be indirectly genotoxic, either 21 through the induction of oxidative stress or by altering the DNA-da maging potential of 22 exogenous and endogenous compounds, such as es trogens. In addition, there have been 23 numerous reports demonstrating TCDD-induced modifications of grow th factor signaling 24 pathways and cytokines in experimental animals and cell culture systems. Some of the altered 25 signaling pathways include those for epidermal gr owth factor, transformi ng growth factor alpha, 26 glucocorticoids, estrogen, tumor necrosis f actor-alpha, interleuki n 1-beta, plasminogen 27 inactivating factor-2, and gastri n. Many of these pathways are involved in cell homeostasis, 28 proliferation, and differentiation and provide plausible mechanisms responsible for the 29 carcinogenic actions of TCDD. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-15 5.1.2.3.3. Hypothesized modes of actio n of TCDD in rodents. 1 TCDD has been shown to consistently induce multiple tumors in both sexes in several 2 rodent species. These tumors are observed in various tissues, including (but not limited to): 3 liver, lung, thyroid, lymphatic syst em, soft tissue, nasal turbinates, hard palate, adrenal, pancreas, 4 and tongue. While the mode of action of TCDD in producing cancer has not been elucidated for 5 any tumor type, the best charac terized carcinogenic actions of TC DD are in rodent liver, lung, 6 and thyroid. The hypothesized mode of action fo r each of these three tumor types is briefly 7
discussed below and is describe d in Figure 5-2. The hypothesized sequence of events following 8 TCDD interaction with the AhR is markedly different for each of these three tumor types. No 9 detailed hypothesized mode of action information exists for a ny of the other reported tumor 10 types. Further, no single definitive mode of action of TCDD-mediated carcinogenicity has been 11
identified. 12
13
5.1.2.3.3.1. Liver tumors. 14 The mode of action of TCDD in producing liver cancer in rode nts has not been 15 elucidated. One hypothesized mode of carcinogenic action of TCDD in the liver is mediated 16 through hepatotoxicity. Generically speaking, TC DD activation of the AhR leads to a variety of 17 changes in gene expression, which then lead to hepatotoxicity, followed by compensatory 18 regenerative cellular proliferat ion and subsequent tumor development. The details of the 19 mechanism of TCDD-induced hepatotoxicity have not been fully determined but both CYP 20 induction and oxidative stress have been postula ted to be involved (Maronpot et al., 1993; 21 Viluksela et al., 2000). The enha nced cell proliferation arising from either altered gene 22 expression or hepatotoxicity, or both, could be the principal f actor leading to promotion of 23 hepatocellular tumors (Whysner and Williams, 1996). The sensitivity of female rat liver to 24 TCDD, which apparently does not extend to the mouse, depends on ovarian hormones (Lucier et 25 al., 1991; Wyde et al., 2001). This sensitivity has been ascribed to induction of estradiol 26 metabolizing enzymes (Graham et al., 1988) and is hypothesized to lead either to generation of 27 reactive metabolites of endogenous estrogen or to active oxygen species of estrogens. Oxidative 28 DNA damage has been implicated in liver tumor promotion (Umemura et al., 1999). 29 A dose-response relationship exists for TCDD-m ediated hepatotoxicity, and this parallels 30 the dose-response relationship for tumor formation (o r formation of foci of cellular alteration as a 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-16 surrogate of tumor formation). However, the dose-response re lationship for other 1 TCCD-induced responses such as enhanced gene e xpression is different from the dose-response 2 for tumor formation in terms of both efficacy and pot ency (see Popp et al. [ 2006] for review). It 3 is important to note that differe nces in potency between events (i.e., gene expression versus cell 4 proliferation) does not necessary imply alternative mechanisms of action. 5
6
5.1.2.3.3.2. Lung tumors. 7 The mode of action of TCDD in producing lung cancer in rodent s (predominantly 8 keratinizing squamous cell carcinoma, [Larse n, 2006]) has not been elucidated. One 9 hypothesized mechanism of the car cinogenic action of TCDD in th e lung involves disruption of 10
retinoid homeostasis in the liver. Retinoic ac ids and their correspondi ng nuclear receptors, the 11 retinoic acid receptors (RARs) and the retinoid X receptors (RXRs), work together to regulate 12 cell growth, differentiation, and apoptosis. It is hypothesized th at TCDD, through activation of 13 the AhR, can affect parts of the complex re tinoid system and/or ot her signaling systems 14 regulated by, and/or cross-talking with, the retinoid system (reviewed in Nilsson and Hakansson, 15 2002). These effects are then hypothesized to lead to lung tumor development; however, the 16 mechanisms underlying this hypothesis are not well- defined. Pulmonary squamous proliferative 17 lesions have been reported following oral exposur e to TCDD in rats (Trits cher et al., 2000). In 18 general, squamous metaplasia with some inflam mation is associated with significant forms of 19 injury via inhalation of toxic compounds but is also seen with vitamin A deficiency (Tritscher et 20 al., 2000) and gives some credence to this hypothesis. 21 Another hypothesized mechanism for the carc inogenic action of TC DD in the lung is 22 through induction of metabolic enzymes. Thro ugh activation of AhR an d subsequent induction 23 of metabolizing enzymes (such as CYP1A1), TCDD may enhance bioactivation of other 24 carcinogens in lung (Tritscher et al., 2000). There have been few studies to support this 25 hypothesis; however, in a long-term conti nuous-application study of carcinogenesis using 26 airborne particulate extract (APE), squamous cell carcinoma occurred in 8 of 17 AhR+/+ mice 27 (47%) while no tumors were found in AhR-/- mice (Matsumoto et al., 2007). In addition 28
CYP1A1 was induced in AhR+/+ mice but not in AhR-/- mice in this study. These results 29 suggest that AhR plays a significant role in APE-induced carcinogenesi s in AhR+/+ mice and 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-17 CYP1A1 activation of carcinogenic polycyclic ar omatic hydrocarbons (the primary carcinogenic 1 component of APE) is also of importance. 2
3
5.1.2.3.3.3. Thyroid tumors. 4 The mode of action of TCDD in producing thyroid cancer in rodents has not been 5 elucidated. It is hypothesized that TCDD increases the incidence of thyroid tumors through an 6 extrathyroidal mechanism. The prevailing hypot hesis for the induction of thyroid tumors by 7 TCDD involves the disruption of thyroid horm one homeostasis via induction of Phase II 8 enzymes UGTs in the liver (reviewed in Brouwer et al., 1998) by an AhR-dependent 9 transcriptional mechanism (Bock et al., 1998; Ne bert et al., 1990). This induction of hepatic 10 UGT results in increased conjugation and elimin ation of thyroxine (T4), leading to reduced 11 serum T4 concentrations. T4 synthesis is cont rolled by the thyroid st imulating hormone (TSH) 12 which is under negative and positive regulation fro m the hypothalamus, pituitary, and thyroid via 13 thyrotrophin-releasing hormone, TSH, T4, and tr iiodothyronine. Cons equently, the reduced 14
serum T4 concentrations lead to a decrease in the negative feedback inhibition on the pituitary 15 gland. This would then lead to a rise in s ecreted TSH and stimulati on of the thyroid. The 16 persistent induction of UGT by TCDD and the su bsequent prolonged stimulation of the thyroid 17 could result in thyroid follicu lar cell hyperplasia and hypertro phy of the thyroid, thereby 18 increasing the risk of progression to neoplasia. Increases in blood TSH levels are consistent with 19 prolonged stimulation of the thyroi d and may represent an early stag e in the induction of thyroid 20 tumors identified in animal bioassays. Statis tically significant increa ses in neonatal blood TSH 21 levels have been recently been reported in children born to TCDD-exposed mothers in the 22 Seveso cohort (Baccarelli et al., 2008; discussed in Section 2.4.1.1.1.4.4). Support for this 23 hypothesis comes from several st udies showing that TCDD decreas es serum total thyroxine and 24 free thyroxine concentrations in rats following both single dose and repeated dose exposures 25 (Bastomsky, 1977; Brouwer et al., 1998; Pohjanvirta et al., 1989; Potter et al., 1983, 1986; 26 Sewall et al., 1995; Van Birgelen et al., 1995). Further support come s from studies of transgenic 27 animals in which TCDD exposure resulted in a ma rked reduction of total thyroxin and free T4 28
levels in the serum of AhR+/- mice but not Ah R-/- mice (Nishimura et al., 2005). Additionally, 29 gene expression of UGT1A6, CY P1A1, and CYP1A2 in the liver was markedly induced by 30
TCDD in AhR+/- but not AhR-/- mi ce (Nishimura et al., 2005). 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-18 5.1.2.3.4. Summary of TCDD mode of action in rodents. 1 Overall, there are inadequate data to support the conclusion that any of the particular 2 mode of action hypotheses described above is operant in TCDD-induced carcinogenesis. 3 However, the wealth of scientific evidence ava ilable indicates that most, if not all, of the 4 biological and toxic effects of TCDD are medi ated by the AhR. Although the receptor may be 5 necessary for the occurrence of these events, it is not sufficien t because other proteins and 6 conditions are known to affect the activity of the receptor and its ability to alter gene expression 7
or to induce other effects. Ce rtain studies could be interpre ted to indicate AhR-independent 8 mechanisms, although these studies have not clear ly ruled out involvement of the AhR. The 9 only consistent, but limited, evidence for TCDD -induced effects that do not involve the AhR 10 comes from studies using AhR-deficient transgen ic animals. Here however, only minor effects 11 occurred following treatment with extremely high doses of TCDD. Thus, a toxic response to 12
TCDD has AhR interaction as a ke y event, but there are various species-, cell-, development-, 13 gender-, and disease-depende nt differences in the cellular milieu that can affect the nature and 14 extent of the response observed. 15 The findings that many AhR-modulated effects are regulated with distinct specificity 16 supports the understanding that the molecular and cellular pathways leading to any particular 17 toxic event are extremely complex. Precise dissec tion of these events represents a considerable 18 challenge, especially in that a toxic response may depend on timely modulation of several genes 19 rather than of just one particular gene, and po ssibly modulation of these genes in several rather 20 than just one cell type or tissue. 21
While a defined mechanism at the molecula r level or a defined mode of action for 22 TCDD-induced carcinogenicity is lack ing, EPA concludes the following 23
24

• interaction with the AhR is a necessary early event in TCDD carcinogenicity in 25 experimental animals. 26

• through interaction with the Ah R, TCDD modifies one or mo re of a number of cellular 27 processes, such as induction of enzymes, changes in growth f actor and/or hormone 28 regulation, and/or alterati ons in cellular proliferati on and differentiation. 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-19 • AhR activation is anticipated to occur in humans and to progress to tumors. AhR is 1 present in human cells and tissues, studies using human cells are consistent with the 2 hypothesis that the AhR mediates TCDD toxicity and no data exist to sugg est that the 3 biological effects of AhR activation by TCDD are precluded in humans. 4

• non-AhR mediated carcinogenic eff ects of TCDD are possible. 5
6
5.1.3. Summary of the Qualitative Weight of Evidence Classification for TCDD 7 Under the 2005 Cancer Guidelines (U.S. EPA, 2005), TCDD is characterized as 8 carcinogenic to humans, based on th e available data as of 2009. This conclusion is based on 9

• Multiple occupational epidemiologic studies showing strong evidence of an association 10 between TCDD exposure and increased mortality from all cancers. 11

• Epidemiological studies showing an associ ation between TCDD exposure and certain 12 cancers in individuals acci dentally exposed to TCDD in Seveso, Italy. 13

• Extensive evidence of carcinogenicity at multiple tumor sites in both sexes of multiple 14 species of experimental animals. 15

• General scientific consensus that the mode of TCDD’s carcinogenic action in animals 16 involves AhR-dependent key precursor events and proceeds through modification of one 17 or more of a number of cellular processes, such as induction of enzymes, changes in 18 growth factor and/or hormone regulation, and/or alterations in cellular proliferation and 19 differentiation. 20

• The human AhR and rodent AhR are similar in structure and f unction and human and 21 rodent tissue and organ cultures respond to TCDD in a similar manner and at similar 22 concentrations. 23

• General scientific consensus that AhR activation is anticipated to occur in humans and to 24 progress to tumors. 25

26
5.2. QUANTITATIVE CANCER ASSESSMENT 27
5.2.1. Summary of NAS Comments on Cancer Dose-Response Modeling 28
5.2.1.1. Choice of Response Level and Characterizati on of the Statistical Confidence Around 29 Low Dose Model Predictions 30 The NAS commented on the low dose model predictions in the 2003 Reassessment, 31 including EPA’s development of ED 01 (effective dose eliciting x percent response) estimates for 32 numerous study/endpoint combinations. The committee also suggested that EPA had not 33 appropriately characterized the statistical conf idence around such model pr edictions in the low34 response region of the model. 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-20 The committee concludes that EPA did not adequately justify the use of the 1% 1 response level (the ED 01 ) as the POD for analyzing epidemiological or animal 2 bioassay data for both cancer and noncan cer effects. The committee recommends 3 that EPA more explicitly address the im portance of the selection of the POD and 4 its impact on risk estimates by calcul ating risk estimates using alternative 5 assumptions (e.g., the ED 05 ) (NAS, 2006a, p. 18) 6 7 It is critical that the model used for determining a POD fits the data well, 8 especially at the lower end of the observed responses. Whenever feasible, 9 mechanistic and statistical information shoul d be used to estimate the shape of the 10 dose-response curve at lower doses. At a minimum, EPA should use rigorous 11 statistical methods to assess model fit, a nd to control and reduce the uncertainty of 12 the POD caused by a poorly fitted model. The overall quality of the study design 13 is also a critical element in deciding which data sets to use for quantitative 14 modeling (NAS, 2006a, p. 18). 15 16 EPA should ... assess goodness-of-fit of dos e-response models for data sets and 17 provide both upper and lower bounds on cen tral estimates fo r all statistical 18 estimates. When quantitation is not possi ble, EPA should clearly state it and 19 explain what would be required to achieve quantitation (NAS, 2006a, p. 10). 20 21 The NAS also suggested that EPA report in formation describing the adequacy of dose22 response model fits, particularly in the low-response region. For those cases where biostatistical 23 modeling was not possible, the NAS recomm ended that EPA identify the reasons. 24
25 The Reassessment should also explicitly address the importance of statistical 26 assessment of model fit at the lower end and the difficulties in such assessments, 27 particularly when using summary data from the literature instead of the raw data, 28 although estimates of the impacts of different choices of models would provide 29 valuable information about the role of this uncertainty in drivi ng the risk estimates 30 (NAS, 2006a, p. 73). 31 32
5.2.1.2. Model Forms for Predicting Cancer Risks Below the Point of Departure (POD) 33 The NAS focused much of its review on EP A’s derivation of a can cer slope factor. 34 Specifically, the NAS commented extensively on the selection of the appropriate point of 35 departure (POD) and the extrapolation of dose response modeling below the POD. 36 The NAS questioned EPA’s choice of a linear, no nthreshold model for extrapolating risk 37 associated with exposure levels below the POD, concluding that the current scientific evidence 38 was sufficient to justify the us e of nonlinear methods when extrapolating below the POD for 39This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-21 TCDD carcinogenicity. The committee further recommended that EPA include a nonlinear 1
model for low dose cancer risk estimates as a co mparison to the results from the linear model. 2
3
The committee concludes that EPA’s decisi on to rely solely on a default linear 4 model lacked adequate scientific sup port. The report recommends that EPA 5 provide risk estimates using both nonlin ear and linear methods to extrapolate 6 below PODs (NAS, 2006a, p. 5). 7 8 After reviewing EPA’s 2003 Reassessment and additional scientific data 9 published since completion of the Reassessment, the committee unanimously 10 agreed that the current weight of scie ntific evidence on the carcinogenicity of 11 dioxin is adequate to justify the use of nonlinear methods consistent with a 12 receptor-mediated response to extrapol ate below the POD. The committee points 13 out that data from NTP released af ter EPA generated the 2003 Reassessment 14 provide the most extensive informa tion collected to date about TCDD 15 carcinogenicity in test animals, and th e committee found the NTP results to be 16 compelling. The committee concludes that EPA should reevalua te how it models 17 the dose-response relationships for TCDD... (NAS, 2006a, p. 16). 18 19 Because EPA’s assumption of linearity at doses below the 1% excess risk level 20 for carcinogenic effects of TCDD, other dioxins, and DLCs is central to the 21 ultimate determination of regulatory values, it is important to critically address the 22 available scientific evidence on the most plausible shape of the dose-response 23 relationship at doses below the POD (LED 01 ). On the basis of a review of the 24 literature, including the detail ed review prepared by EPA and presented in Part II 25 of EPA’s Dioxin Risk Assessment and new literature available since the last EPA 26 review, the committee concludes that, although it is not possible to scientifically 27 prove the absence of linearity at low doses, the scientific evidence, based largely 28 on mode of action, is ade quate to favor the use of a nonlinear model that would 29 include a threshold response over the us e of the default linear assumption (NAS, 30 2006a, p. 122). 31 32 On the whole, the committee concluded th at the empirical evidence supports a 33 nonlinear dose-response below the ED 01 , while acknowledging that the possibility 34 of a linear response cannot be comple tely ruled out. The Reassessment 35 emphasizes the lack of such nonlinear mode ls, hence its adoption of the approach 36 of linear extrapolation below the POD level. Although this approach remains 37 consistent with the cancer guidelines (EPA 2005, see also Appendix B), EPA 38 should acknowledge the qualitative eviden ce of nonlinear dose response in a more 39 balanced way, continue to fill in th e quantitative data gaps, and look for 40 opportunities to incorporate m echanistic information as it becomes available. The 41 committee recommends adopting both lin ear and nonlinear methods of risk 42 characterization to account for the uncerta inty of dose-response relationship shape 43 below ED 01 (NAS, 2006a, p. 72). 44This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-22 5.2.2. Overview of EPA Response to NAS Comments on Cancer Dose-Response Modeling 1 EPA agrees with the NAS that the appro aches to cancer dose-response modeling for 2 TCDD should be clearly communicat ed and justified. Furthermor e, due to the abundance of new 3 information on TCDD carcinogenicity publishe d since the 2003 Rea ssessment, EPA has 4 reevaluated the cancer dose-response modeling fo r TCDD presented in the 2003 Reassessment. 5
As detailed below in Secti on 5.2.3, EPA has conducted an updated cancer dose-response 6 assessment for TCDD that incorporates key NAS recommendations discussed in this document, 7
reflects the current state-of-the science in can cer dose-response modeling and integrates new 8 TCDD carcinogenic information. Detailed respon ses to the NAS comments summarized above 9
are found in Section 5.2.3.3. 10 The 2003 Reassessment presents an extensiv e dose-response assessm ent of TCDD and 11 provides a comprehensive summary of dose-re sponse relationships. The analyses and 12 discussions synthesized a consid erable breadth of data and model types, highlighting the 13 strengths and weaknesses of the then-available scientific information. Modeling included both 14 administered dose and steady st ate body burden dose metrics, ta king into account variation in 15 half-lives of TCDD across species. These body burden calculations used a simple one16 compartment kinetic model based on the assumption of a first-order decrea se in the levels of 17
administered dose as a function of time. An ex cess risk of 1% was chosen to model the cancer 18 data, but comparative results were also shown for 5% and 10% ex cess risk (see Table 8-2 of the 19 2003 Reassessment). Dose response was also explor ed thoroughly for a numb er of in vitro and 20 biochemical endpoints in addition to the in vivo data analyses, and ranges of these values were 21 presented (see Figures 8-1, 8-2 and 8-3 of the 2003 Reassessment). Thus, the 2003 22 Reassessment provides an initial evaluation of the carcinogenic data base for TCDD and serves as 23 the foundation for the analyses presented below. 24
25
5.2.3. Updated Cancer Dose-Response Modeling fo r Derivation of Oral Slope Factor 26 The following sections describe the dose-res ponse analysis of the cancer data from 27 epidemiologic cohort studies (see Section 2.4.1) and rodent bioassays (see Section 2.4.2), 28 concluding with the derivation of oral slope factors for TCDD based on epidemiologic data (see 29 Section 5.2.3.1) and rodent bioassa y data (see Section 5.2.3.2). 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-23 5.2.3.1. Dose-Response Modeling Based on Epidemiologic Cohort Data 1 The 2003 Reassessment included dose-response an alyses and the development of oral 2 slope factors from the following three occupa tional cohorts: the NIOS H cohort, the Hamburg 3 cohort, and the BASF cohort. In this document, EPA determined that specific studies from each 4 of these cohorts (Steenland et al., 2001; Ott and Zober, 1996; Becher et al., 1998) met the 5 epidemiologic study inclusion criteri a (see Section 2.3.1 and Section 2.4.1). In Section 5.2.3.1.1, 6 the oral slope factors derived from these studies in the 2003 R eassessment are reviewed. Since 7 the publication of the 2003 Reassessment, additi onal cancer epidemiology studies based on these 8 cohorts have been published in the peer-reviewed literature. Of these, Cheng et al. (2006) met 9 the epidemiologic study inclusion criteria (s ee Section 2.3.1 and Section 2.4.1). In 10 Section 5.2.3.1.2, EPA evaluates the suitability of deriving an oral slope factor from this study 11 and derives oral slope factor estimates. 12 Another study that met the current epidemio logic study inclusion cr iteria (Warner et al., 13 2002) was also briefly discussed in the 2003 Reasse ssment, but an oral slope factor (OSF) was 14 not derived from that study. In Section 5.2.3.1. 2.2, EPA discusses its unsuccessful attempt to use 15 the categorical results published by Warner et al. (2002) to develop an oral cancer risk estimate. 16
17
5.2.3.1.1. Evaluation of Epidemiologic Studies in th e 2003 Reassessment for OSF Derivatio n. 18 In the 2003 Reassessment, EPA reported dos e-response modeling results for three 19 epidemiologic studies of human occupational coho rts: the NIOSH cohort with data published by 20 Steenland et al. (2001); the Hamburg cohort with da ta published by Becher et al. (1998); and the 21 BASF cohort with data published by Ott and Zober (1996). Each of these studies is summarized 22
in Section 2.4.1 of this document and in the 200 3 Reassessment (Part II, Chapter 8; Part III, 23 Chapter 5). Furthermore, EPA has evaluated th e suitability of these studies for use in TCDD 24 dose-response modeling and concluded that each of these studies meet the inclusion criteria for 25 epidemiology studies presente d in Section 2.3.1. 26 Each of these studies reports all cancer mort ality as an outcome. Steenland et al. (2001) 27 and Becher et al. (1998) anal yzed cohorts of primarily ma le workers who experienced 28 occupational exposures to TCDD ov er long periods of time, while Ott and Zober (1996) studied 29 a cohort of primarily male workers who were ex posed to high TCDD concentrations at a single 30 point in time due to an industrial accident. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-24 The authors of all three of these studies measured and then back-extrapolated TCDD 1 levels in a subset of workers to estimate expos ures during employment and then the authors used 2 this information to estimate exposures in the remainder of the cohort. These measured TCDD 3 samples generally were collected decades after the last known occupational exposure. In each 4 study, the authors relied on TCDD measures in the cohort to back -calculate serum lipid or body 5 fat levels of TCDD using a simple one-compartme nt kinetic model based on the assumption of a 6
first-order decrease in th e levels of exposure dose as a function of time. The assumed half-life of 7 TCDD used in the models varied from study to st udy. None of the studies sampled TCDD levels 8 from the entire cohort; for example, Ott a nd Zober collected samples from 138/243 workers 9 (57% of the cohort), which wa s the highest percentage of workers sampled among the three 10 studies. Steenland et al. ( 2001) and Becher et al. (1998) used the measured and back11 extrapolated TCDD concentrations to estimate the exposures that were associated with various 12 occupations within the cohort, and subsequently used this information to develop exposure 13 matrices (i.e., the TCDD load per unit time for an occupation) that then could be used to estimate 14
the cumulative dioxin dose for each cohort me mber. Ott and Zober (1996) used regression 15 procedures with data on time spent at various occupational tasks to estimate TCDD levels for all 16 members of the cohort. Following the estimation of worker exposures in each of these three 17 studies, the studies’ au thors divided these cohorts into exposure subgroups based on the 18
estimated TCDD levels. 19 In the 2003 Reassessment, EPA identified a POD based on a 1% response in cancer 20 mortality (ED 01 ) for the Steenland et al. (2001), and th e Ott and Zober (1996) studies. EPA 21 extrapolated from this POD to lower doses usin g a straight line drawn from the POD to the 22 origin—zero incremental dose, zero incremental re sponse—to give a probab ility of extra risk. 23 Because there was insufficient evidence to support an assumption of nonlinearity, EPA chose to 24 develop these models using a linear model. 25
26
5.2.3.1.1.1. Steenland et al. (2001). 27 Steenland et al. (2001) developed dose-res ponse models based on TCDD exposures and 28 all cancer mortalities from eight plants in the NIOSH cohort. Serum lipid levels of TCDD in 29
1988 were measured in 193 workers at one of thes e plants. Steenland and coauthors relied on a 30 first-order kinetic model (assuming a constant 8.7 year half-life) to back-extrapolate to serum 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-25 TCDD levels at the time of the last occupationa l exposure. The study authors assigned exposure 1 estimates to each of the 3,538 workers in the cohort based on a job-exposure matrix. This matrix 2 was based on (1) an estimated level of c ontact with TCDD, (2) the degree of TCDD 3 contamination of the products the workers produ ced, and (3) the fracti on of a workday during 4 which the worker likely contacted the TCDD-cont aminated products. They then estimated each 5
worker’s serum TCDD levels as an area under th e concentration curve (A UC) for lipid-adjusted 6 serum levels over time. The mortality an alysis was conducted on 256 cancer decedents. 7 Several different dose-response models were fit to these data to prov ide estimates of fatal 8 cancer risk. The best-fitting model was a C ox regression exposure-response model using the 9 log(AUC) of TCDD lipid concentration (ppt-year) lagged by 15 years as the exposure metric. 10 Steenland and colleagues also deve loped a piecewise linear regres sion model with no lag, in 11 which two separate linear slopes were estimate d. This analysis assumed a background exposure 12 of 0.5 pg/kg-day. The lipid concentrations we re converted to body burdens by dividing by 4. 13 The central tendency estimate and lower bound ED 01 s from the piecewise linear model and their 14 associated cancer slope factors for the most sensitive endpoint (male cancer mortality) are 15 presented in Table 5-1. 16
17
5.2.3.1.1.2. Becher et al. (1998). 18 Based on the Hamburg cohort, Becher et al. ( 1998) reported a dose-re sponse analysis for 19 all fatal cancers combined. The mortality analysis was conducted in 1992 on 124 cancer 20 decedents. The exposure variable in the st udy was the integrated blood levels for TCDD 21 concentration over time (AUC, ng/ kg-years), as estimated by Flesch-Janys et al. (1998); these 22 were converted to body burdens by dividing by 4. Estimates of th e half-life of TCDD, based on 23 the sample of 48 individuals with repeated measur es, were incorporated into a model that back24 extrapolated TCDD exposures to the end of th e employment after accoun ting for the workers’ 25 ages and body fat percentages. These estimate d exposure measures were then applied to the 26 entire cohort, which consisted of all 1,189 regul ar male employees who were employed for at 27 least 3 months between 1952 and 1984 at the Boehringe r chemical plant in Hamburg, Germany. 28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-26 Becher et al. (1998) used a Cox regression approach for the dose-respons e modeling and 1 developed three models: a multiplicative model, an additive model, and a power model. 25 The 2 response variable in each model was the SMR fo r total cancer mortality. The models were 3 calculated with lag times of up to 20 years. The multiplicative model provided the best fit; 4 however, the study authors judged the fits for al l three models to be acceptable. The model 5
results were used to calculate unit risk estimates derived as th e risk of cancer death through age 6 70 given a daily dose of 1 pg/kg body weight of TCDD minus the risk given no exposure to 7
TCDD. These calculations were based on bac kground German cancer mortality rates. The 8
model results were used to cal culate cancer risk estimates. The lower bound estimates on the 9 dose were not available for models published by Bech er et al. due to the absence of statistical 10 parameter measures. The central tendency estimate ED 01 s from the three statistical models and 11 their associated cancer slope factor s are presented in Table 5-1. 12 13
5.2.3.1.1.3. Ott and Zober (1996). 14 In the 2003 Reassessment, EPA also develope d a dose-response analysis based on a study 15 reported by Ott and Zober (1996) for cancer inci dence and mortality experienced by 243 men, 16 who were exposed to TCDD in 1953 during an accid ent at the BASF plant in Germany. The 17 cohort was followed through 1992. TCDD blood lipid levels were available for 138 of these 18 men 30 years after the accident. These levels we re back-extrapolated and used to estimate the 19 AUC for TCDD. Body burdens (ng/kg) were esti mated by dividing AUC by 4, and steady-state 20

25 The “multiplicative model” set relative risk (RR) equal to exp( β d ), where the dose d is the AUC. The “additive model” set RR = 1+ β d , and the “power model” set RR = exp( β

log ( kd +1)). The values β and k are estimated parameters.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-27 body burdens were estimated assuming a consta nt half-life of approximately 7.1 years. 26 Ott and 1 Zober (1996) used Cox proportional hazard approaches to estimate cancer mortality risk per unit 2
TCDD dose. 27 The central tendency estimate and lower bound ED 01 s from the modeling and 3 their associated cancer slope factor s are presented in Table 5-1. 4
5
5.2.3.1.2. Evaluation of Epidemiologic Studies Pub lished Since the 2003 Reassessment for 6 OSF Derivation. 7 Two additional epidemiological studies th at met the study inclusion criteria (see 8 Section 2.3) for use in dose res ponse modeling as set forth in this document are evaluated in this 9
section for the estimation of cancer risk estimate s. Each of these studies is summarized in 10
Section 2.4.1 of this document. 11
12
5.2.3.1.2.1. Cheng et al. (2006). 13 As discussed in Section 2.4.1.1.1, Cheng et al. (2006) analyzed th e relationship between 14 TCDD dose and all cancer mortality for the same subset of NIOSH workers as analyzed 15 previously by Steenland et al. (2001) . In contrast to Steenland et al., Cheng et al. (2006) used the 16 “concentration- and age-dependent eliminati on model” (concentration- and age-dependent 17 elimination [CADM], discussed in Section 3.3; s ee also Aylward et al. [2005a]), rather than a 18 constant 8.7-year half-life, and calculated serum-derived TCDD estimates for use in dose19 response analysis. An important feature of CADM is that it incorporates concentration- and age20 dependent elimination of TCDD from the body, m eaning that the effec tive half-life of TCDD 21

26 Based on the initial body burden (B 0 ) EPA estimated the body burden at time t using the following formula:

- 0 ()= k t e Bt Be , where k e is an elimination constant equal to ln(2)/(hal f-life in years). This implies that the AUC at

time T after initial exposure is 0 = (1- ) -k T

e

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.

27 The model from Ott and Zober has risk proportional to dose e β × 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).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-28 elimination varies based on expos ure history, body burden, and ag e of the exposed individuals. 1 As discussed in Section 3.3, the use of the CADM model to simulate TCDD kinetics in the 2 NIOSH cohort results in time-integrated body burden estimates four to five times greater than 3 those obtained with the simple first-order mode l, with smaller differences between the two 4 methods at lower exposures. 5 Following the estimation of dose using the CADM-derived AUC values, Cheng and 6 colleagues derived dose-response estimates for the NIOSH cohort using Cox regression and 7 piecewise linear modeling. Their results are summa rized in Table 5-2. For comparison, the Cox 8 regression coefficient from the analysis conducte d by Steenland et al. (2001), which relied on a 9 first-order elimination rate model assuming a cons tant 8.7-year half-life, is also shown on the 10
first line of the table. As in Steenland et al. (2001), Cheng et al. (2006) found a much stronger 11 relationship between cancer mortality and expos ure metrics lagged 15 years compared to the 12 relationships for unlagged exposures. Cheng et al. (2006) also noted that the dose-response 13 relationship plateaued above the 95 th percentile of exposure. For exposures lagged 15 years, the 14 regression coefficient of the linear slope derived by Cheng et al . (2006) was 3.3 × 10 − 6 per 15 ppt-year lipid-adjusted serum TCDD (in Table III in their analysis—the standard error of this 16 regression coefficient was 1.4 × 10 − 6 ). The upper 5% of the e xposure range (individuals 17
≥ 252,950 ppt-year lipid adjusted serum TCDD) was ex cluded in estimating this slope. Because 18
this exclusion reduces the upper portion of the response where the slope is sha llow, this likely 19 better represents the slope in the region of the curve where the fatal cancer risk is increasing with 20 dose, which is the equivalent of dropping the highest dose in an animal bioassay. 21 To develop an OSF for TCDD, EPA used data and equations from the Cheng et al. (2006) 22
in its calculations as follows 23
24

• Upper 95 th percentile slope ( β 95 ) . To estimate β 95 of this regression coefficient, EPA 25 summed the regression coefficient ( β ) and the product of 1.96 a nd the standard error of 26 the regression coefficient, yielding an estimate of 6.0 × 10 − 6 per ppt-year lipid adjusted 27 serum TCDD, as follows 28
SE *96.1 95 + = β β 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-29 • Background cancer mortality risk estimate ( R 0 ) . EPA used an R 0 of 0.112 as reported by 1 Cheng et al. (2006) 28 2

• Risk in the exposed group associated w ith a 1% extra risk of fatal cancer ( R exp ) . EPA 3 estimated R exp to be 0.12088, using the followi ng relationship for extra risk: 4

0 0exp 1 01.0 R RR

− −

= 5

• Incremental cancer mortality risk in the exposed population based on a 1% extra risk 6 ( R D )

. R D , was then calculated to be 8.9 × 10 − 3 using the following equation: 7
R D = R exp − R 0 8

• Cumulative TCDD concentration in the fat compartment for a 1% extra risk (AUC 01 )

. 9 EPA then estimated AUC 01 using Equation 3 from Cheng et al. (2006): 10
AUC 01 = ln( R D + R 0 / R 0 )/ β 95 11 Cheng et al. (2006) assume that the TCDD concen tration in fat is the same as ppt-yr lipid 12 adjusted serum concentration. The AUC 01 was calculated to be 1.26 × 10 4 ppt-yr. 13

• Oral slope factor associated with 1% extra risk [OSF(AUC 01 )] . AUC 01 of 14 1.26 × 10 4 ppt-yr is used by EPA as a POD for linear extrapolation to zero. OSF(AUC 01 ) 15 is calculated to be 7.92 × 10 − 7 by dividing 0.01 by the AUC 01 . OSF(AUC 01 ) is linear 16 with the TCDD concentration in fat. Ingest ed TCDD doses, however, are not linear with 17 the predicted TCDD fat concen trations in the Emond pharmacokinetic model. Thus, the 18 OSF(AUC 01 ) that is linear with TCDD in the fat is not linear with ingested TCDD dose. 19
20 Thus, to estimate the fatal cancer risk associat ed with an oral intake of TCDD, estimates 21 of both the average TCDD concentration in the fa t resulting from the oral intake and the risk 22 using the OSF(AUC 01 ) are needed. Next in this report, EPA presents estimates of OSFs for 23 specific TCDD intake rates based on ta rget risk levels (RLs) of 1 × 10 − 3 , 1 × 10 − 4 , 1 × 10 − 5

, 24
1 × 10 − 6 , and 1 × 10 − 7 , using the following calculations: 25
26

• Area under the TCDD fat concentration curve associated with a target risk level 27 (AUC RL ) (ppt-yr) . For each target risk le vel, EPA calculated an AUC RL by dividing the 28 target risk level by the OSF(AUC 01 ) [i.e., RL/7.92 × 10 -7 (ppt-yr) -1 ]. 29

• Lifetime averaged concentration of TCDD in the fat compartment associated with the 30 target risk level (FAT RL ) (ng/kg) . The AUC RL estimates were then further divided by 31

28 In Table IV, Cheng et al. (2006) report two estimates of background fatal cancer risk, R 0 , for males aged 75 years: 0.112 and 0.124. A R 0 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.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-30 70 years to identify (FAT RL ) (ng/kg). This step essentia lly reverses the integration 1 undertaken to calculate AUC 01 . 2

• Continuous daily TCDD intake D RL (ng/kg-day) associated with a target risk level over a 3 lifetime . Using the Emond human physiologi cally based pharmacokinetic (PBPK) 4 model, EPA estimated the D RL necessary to achieve the FAT RL . Table 5-3 shows the 5 ingested TCDD doses (D RL ) corresponding to these ta rget risk levels. 6

• Oral slope factor at th e target risk level (OSF RL ) (per mg/kg-day) . Table 5-3 also shows 7 that at the target risk levels, the associated OSF RL s range from 3.7 × 10 5 to 1.3 × 10 6 per 8 mg/kg-day. These ar e calculated as OSF RL = RL/D RL × 10 6

. 9
10 Table 5-4 shows analogous results based on th e best estimate of regression coefficient 11 (3.3 × 10 − 6 ) for all fatal cancers as re ported by Cheng et al. (2006) by comparing lipid-adjusted 12 serum concentrations, fat concentrations, risk sp ecific dose estimates and equivalent oral slope 13
factors for risk levels of 1 × 10 − 3 , 1 × 10 − 4 , 1 × 10 − 5 , 1 × 10 − 6 , and 1 × 10 − 7

. 14
15
5.2.3.1.2.2. Warner et al. (2002). 16 Warner et al. (2002) is a study of 981 females exposed to elevated TCDD levels 17 following the Seveso accident of 1976. The TCDD exposure pattern involving a single period of 18 elevated TCDD exposures followed by an exte nded period of lower ambient level TCDD 19 exposures and elimination is similar to that of the BASF cohort (Ott and Zober, 1996). TCDD 20 levels, measured or estimated in blood lipids shor tly after the time of the accident, were available 21
for all women. These women were divide d into four exposure groups of <20, 20

− 44, 44.1

− 100, 22 and >100 ppt. In this cohort, 21 total cancers have b een observed; 15 of these were breast cancer 23 cases and 3 were thyroid cancer cases. Cox proportional hazards modeling showed that the 24
hazard ratio for breast cancer a ssociated with a 10-fold increase in serum TCDD levels (log 10 25 (TCDD)) was significantly increa sed to 2.1 (95% CI = 1.0

− 4.6). Rate ratios (95% CI) for cancer 26 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 27 Cox proportional hazards model and assuming c ontinuous exposure, the rate ratio was 1.7 28 (0.9

− 3.4) for each 10-fold increase in serum TCDD; that is, a log 10 transformation of the 29 exposure estimates in their analysis was presen ted. They reported a test for trend of p = 0.09. 30 EPA attempted to estimate an ED 01 from the modeled results of Warner et al. (2002) from 31 the statistically significant hazard ratio for breast cancer. However, EPA had to estimate the 32 slope of the tangent to the log- linear relationship. Because the slope of a log-linear relationship 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-31 is not constant but varies w ith dose, and because the lowest exposure measure was well-above 1 the 1% response region of interest, EPA could not confidently estimate the tangent to the 2 log-linear relations hip. The transformation of the log 10 dose units to linear units of TCDD 3 yielded an implausibly low ED 01 and correspondingly high cancer risk that was inconsistent with 4 a visual inspection of the untransformed plot. EP A was not confident in these values for health 5
risk assessment because of uncerta inties in the transformation in the low response region of the 6 original model. Thus, EPA did not derive an ED 01 or oral slope factor for this study. 7
8
5.2.3.2. Dose-Response Modeling Based on Animal Bioassay Data 9 Figure 5-3 provides a summary of the process EPA has utilized to select and identify 10 candidate TCDD OSFs from key animal bioassays th at were identified in Section 2.4.3 of this 11
document. For each in vivo animal cancer study that qualified for TCDD dose-response 12 assessment using the study inclusion criteria specified in Section 2.3.2, EPA first selected the 13 species/sex/tumor data set combinations that had been characterized as having statistically 14 significant increases in tumor in cidence by either a pair-wise te st between the treated group and 15 the controls or by a trend test showing increases in tumors with increases in dose. Next, EPA 16
used the Emond animal kinetic model discussed in Section 3 to estimate blood concentrations 17 corresponding to each study’s average daily ad ministered doses for use in dose response 18 modeling. Benchmark dose lo wer confidence bounds (BMDL 01 s) were then estimated for the 19 blood concentrations by (1) using the multistage cancer model for each species/sex/tumor 20 combination within each study and (2) using a Bayesian Markov Chain Monte Carlo framework 21 that assumes independence of tumors, modeling all tumors together for each species/sex 22 combination within each study. The final selected models were subjected to goodness-of-fit tests 23 and visual inspections of fit to the raw data. Thus, for each sex/ species combination within each 24 study, this process generated a BMDL 01 for each single tumor type and another BMDL 01 for the 25 combined tumors. Finally, using the Emond huma n kinetic model discussed in Section 3, human 26 equivalent doses (BMDL HED s) were then estimated for each of the BMDL 01 s and, using a linear 27 extrapolation, OSFs were calculated by OSF = 0.01/BMDL HED . The lowest OSF for a 28 species/sex combination for either a single tumor t ype or all combined tumors was selected as a 29
candidate OSF for TCDD cancer assessment. Thes e steps in Figure 5-3 ar e further described in 30 detail in the following sections. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-32 5.2.3.2.1. Selection of key data sets. 1 Based on the study selection criteria outlined in Section 2.3.2 (see Figure 2-3), EPA 2 selected four animal bioassays for use in the cancer dose-response assessment for TCDD (see 3 Table 2-6 and Section 2.4.2 for detailed study desc riptions). Three of th ese studies (Kociba et 4 al., 1978; NTP, 1982; Toth et al., 1979) were evaluated in the 2003 Reassessment, while one 5 study (NTP, 2006) was published after the 2003 R eassessment was released. The NTP (2006) 6 study was specifically called out by the NAS (2006a ) report as cancer bioassay data that EPA 7 should evaluate prior to comple ting its TCDD dose-response assessment. As discussed below, 8 EPA has chosen to conduct dose-response modeli ng for a number of tumor types from each of 9 the sex/species combinations in these studies in order to maximize the amount of information 10 available to support OSF derivation. Because tumors occurred in multiple sites in the exposed 11 animals, each tumor type was considered separa tely (individual tumor models) and were also 12 combined into composite tumor incidence dose estimates (multiple tumor models). 13
The tumor incidence tables for these four bioassays are shown in Tables 5-5 through 5-8. 14
The data in these tables are summarized fr om each study’s reference publication and are the 15 species/sex/tumor incidence data used for TCDD dos e-response modeling in this report. In the 16 case of the Kociba et al. (1978) female rat co mbined hepatocellular ad enomas and carcinomas 17 only, EPA used data from a reanalysis of th e pathology slides that was published by Goodman 18 and Sauer (1992). The data sets in Tables 5-5 through 5-12 were selected because they had been 19 characterized by the study authors as having statistically significan t increases in tumor incidence 20 by either a pair-wise test between the treated group a nd the controls or by a trend test showing 21
increases in tumors with increases in dose. One exception is that Goodman and Sauer (1992) did 22 not statistically analyze the revised tumor inciden ce data from their reanalysis of the Kociba et 23 al. (1978) female rat combined hepatocellular adenomas and carcinomas, so a Fischer’s Exact 24 Test to evaluate the statistical significance of thos e data was used in this document. In the case 25 of the NTP (2006) study only, information was ava ilable regarding the length of time that the 26 animals stayed on test (105 weeks); animals who di ed within the first year were censored from 27 analysis in this document because animals who died within the first year were not considered to 28 have been alive long enough to develop tumors. Therefore, those animals were not included in 29 the denominators in Table 5-11. These adjusted incidence data were used in the analysis of 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-33 tumor dose-response for NTP (2006) in this docume nt. The tumor inciden ce data in Tables 5-5 1 through 5-12 include 2
3

• nasal, tongue and adrenal tumors in males, and liver, nasal and lung tumors in females 4 from the Kociba et al. (1978) 2- year study of Sprague-Dawley rats, 5

• liver, thyroid and adrenal tumors in males, and subcutaneous tissue, liver, adrenal and 6 thyroid tumors in females and from the NTP (1982) 2-year study of Osborne-Mendel rats, 7

• lung and liver tumors in males, and subcutan eous tissue, hematopoie tic system, liver and 8 thyroid tumors in females from th e NTP (1982) 2-year study of B6C3F 1 mice 9

• liver, oral mucosa, pancreas and lung tumo rs in females from the NTP (2006) 2-year 10 study of Sprague-Dawley rats, and 11

• liver tumors in males from the Toth et al . (1979) 1-year study of Swiss/H/Riop mice. 12
13 For each cancer endpoint, the reported (administered) doses from each study were converted, 14 where necessary, to average daily doses in ng/kg-day (e.g., doses administered 5 days/week were 15 adjusted by multiplying by 5 and dividing by 7 to get average daily doses). These doses were 16 then subjected to kinetic mode ling to generate blood concentr ations for use in TCDD dose17 response modeling. 18
19
5.2.3.2.2. Dose adjustment and extrapolation me thods for selected data sets. 20
5.2.3.2.2.1. Dose metric estimation fo r dose-response modeling. 21 Tables 5-5 through 5-12 show the blood con centrations that were used in TCDD dose22 response modeling of the animal bioassay data. Based on kinetic analys is (see Section 3), a 23
choice for whole blood concentration of TCDD wa s made for the purpose of dose extrapolation 24
between animals and humans. In order to estim ate blood concentrations for each study selected, 25
the Emond PBPK model was run us ing ACSLX® software, versi on 2.5.0.6 (see Section 3). 26 Depending on the selected study, either rat or mous e versions of the model were used. In each 27 case, the simulation was perf ormed using the exposure and observation durations, the body 28 weights, and the adjusted doses from the orig inal studies. Details of PBPK model input 29 parameters are given for each study’s m-file in A ppendix C.2. In the case of Toth et al. (1979) 30 study, which dosed the animals for a year and th en followed up for the lifetime of the animal, 31 only the one-year simulation was performed. The m- files were used to ru n the appropriate PBPK 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-34 model to estimate time-averaged, maximum, and te rminal (end of exposure) lipid adjusted blood 1 serum concentration (see Appendix C.3). Ot her model simulated dose metrics such as 2 concentrations for liver, fat, Ah-receptor bound in liver, body burden, and the time at which the 3
maximum concentration was reached for each dose metric are also reported for illustrative 4 purposes in Appendix C.3. The complete re sults for each study modeled are shown in 5 Appendix C.3. 6 Applying the Emond PBPK model to each study /endpoint combinations in Tables 5-5 7 through 5-12, whole blood concentrations were derived by converting the model estimated lipid8 adjusted to whole blood concentration using mode l constants for the lipi d fraction in blood serum 9 for rodents (0.0033) and the blood serum fraction (0.55). Once the Emond modeling runs were 10 performed, the model outputs of whole blood co ncentrations were used as dose metrics to 11 estimate BMDLs (in ng/kg). To obtain BMDLs, benchmark dose modeling was performed as 12 described below by substituting the model simu lated dose metrics (whole blood concentrations) 13 for the original study doses and calculating the corresponding BM DL (results appear below and 14 in Appendix F). 15
16
5.2.3.2.2.2. Calculation of human equi valent doses (HEDs). 17 Human equivalent doses (ng/kg-day), corre sponding to each BMDL (ng/kg) were 18 calculated using the Emond human PBPK model (see Section 3) and are denoted as BMDL HED s. 19 The Emond human PBPK model was run for 70 y ears assuming a constant daily dose starting 20
from birth. The model concentrations were averaged over both the entire 70 year lifetime 21 (lifetime average) and over the five years surroun ding the peak concentration (five-year average) 22 (see Section 3.3.1, describing first order body bur den estimation). The human equivalent doses 23 were estimated by adjusting the daily dose m odel input until the time-averaged whole blood 24
concentration matched the associated alternative dose BMDL (derived earlier from animal PBPK 25 model). For animal studies which lasted longer than 540 days, the lifetime average was used; for 26 studies lasting less than 540 days, the five year average was used. The process was iterative and 27 continued until the modeled human concentration was within 1% of the BMDL. In general, 28 however, the concentrations matched to within 0.1%. 29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-35 5.2.3.2.3. Dose-response modeling approache s for rodent bioassays. 1
5.2.3.2.3.1. Modeling of individual tumors. 2 EPA’s BMDS Software, version 2.1 was used to estimate the BMDL 01 s for each of the 3 species/sex/tumor combinations, using the blood concen trations and incidence data shown in 4 Tables 5-5 through 5-12. Each data set was mo deled using the multistage cancer model, and a 5
BMDL 01 in blood concentration was estimated. The multistage model has been used by EPA in 6 the majority of its quantitative cancer assessments because it is st atistically robust and able to 7 provide good fits to a wide range of dose-response patterns. It is also consistent with the 8 multistage nature of the carcinogenic process. The mathematical form of the multistage model is 9
10
P ( d ) = 1 – exp[–( q 0 + q 1 d + q 2 d 2 + ... + q k d

k )] (Eq. 5-3) 11 12
where 13 P ( d ) = lifetime excess risk (pr obability) of cancer at dose d 14 q i = parameters estimated in fitting the model, i = 1, ..., k

. 15
16
To estimate the BMD 01 s and BMDL 01 s, BMDS was run with all parameters set to their 17 defaults; up to three degrees of freedom were specified for the dichotomous, multistage cancer 18 model; and a 95% confidence level. A 1% benc hmark response (BMR) was used for each tumor 19 type to ensure the PODs were estimated in th e linear portion of the dose-response curve. The 20
BMDL 01 (ng/kg) was then converted to a BMDL HED (ng/kg-day) using the Emond human model, 21 and an OSF in units of (mg/kg-day) − 1 was calculated by, OSF = 0.01/BMDL HED ×10 6 . It may be 22
noted that the OSF is linear with the blood concentration, and is not linear with ingested TCDD 23 doses; thus, using the Emond et al. pharmacokinetic model, risk-specific doses of TCDD intake 24 (ng/kg-day) corresponding to the ta rget risk levels would need to be provided for use in human 25 health risk assessment. In the following secti ons, results are presented for the models that 26 provided the best overall fit to the data as j udged by comparison of likelihood ratios for models 27 that had an acceptable fit (chi -squared goodness of fit statistic p > 0.05). 28
29
5.2.3.2.3.2. Multiple tumor (Bayesian) models. 30 Statistically significant increased tumor incide nces were observed at multiple sites in 31 male and/or female rats (Kociba et al., 1978; NTP, 1982, 2006) and male and female mice (NTP, 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-36 1982) following oral exposures to TCDD. With this multiplicity of tumors, the concern is that a 1 potency or risk estimate based solely on one tumor site (e.g., the most sensitive site) may 2
underestimate the overall cancer ri sk associated with exposure to this chemical. Relevant 3 approaches in the 2005 Cancer Guidelines (U.S. EPA, 2005) for characteri zing total risk include 4 the following: (1) analyze the incidence of tumo r-bearing animals, or (2) combine the potencies 5 associated with significantly elevated tumors at each site. The NRC (1994) concluded that an 6 approach based on counts of animals with one or more tumors (tumor-bearing animals) would 7
tend to underestimate overall risk when tumor t ypes occur independently, and thus an approach 8 based on combining the risk estimates from each separate tumor type should be used. On 9 independence of tumors, NRC (1994) stated “...a general assumption of st atistical independence 10 of tumor-type occurrences within animals is not li kely to introduce substan tial error in assessing 11 carcinogenic potency.” 12 Because potencies are ty pically upper bound estimates, summing such upper bound 13 estimates across tumor sites is likely to oversta te the overall risk. Therefore, following the 14 recommendations of the NRC (1994) and the 2005 Cancer Guidelines (U.S. EPA, 2005), a 15 statistically valid upper bound on co mbined risk was derived, assuming independence, in order 16 to gain some understanding of the overall risk re sulting from tumors occurring at multiple sites. 17 In the case of TCDD, tumors are thought to be independent across the s ites found in these three 18 studies because: (1) they are in different organs and tissu es, specifically liver, lung, thyroid, 19 subcutaneous tissue, oral cavity, tongue, pancreas, adrenal cortex and the hematopoietic system; 20 (2) different kinds of tumors were f ound, even within the same organ (e.g., both 21 cholangiocarcinomas and hepatocellular adenomas were found in female rat livers in NTP, 22 2006); and (3) the tumors found in these studies were not progressive (i.e., they did not 23 metastasize to other sites in the body). It is important to note that th is estimate of overall 24 potency describes the risk of developing tumors at any combination of the sites and is not the 25 risk of developing tumors at all sites simultaneously. 26 For modeling individual tumor data, the multistage model is specified as shown in the 27 previous section (see Eq. 5-3). Under the as sumption of independence, the model for the 28 combined (or composite) tumor risk is still multista ge, with a functional form that has the sum of 29 stage-specific multistage coefficients as the corresponding multistage coefficient. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-37 P c ( d ) = 1

− exp [-( Σ q 0i + d Σ q 1i + d 2 Σ q 2i + ... + d m Σ q mi )], for i = 1,..., k, (Eq. 5-4) 1 2
where k = total number of sites. 3 The resulting equation for fixed extra risk (BMR) is polynomial in dose (when logarithms 4 of both sides are taken) and can be solved in a straightforward manner for the combined BMD. 5 However, the current version of BMDS cannot estimate confidence bounds for this combined 6
BMD. 7 Therefore, a Bayesian approach to find ing confidence bounds on the combined BMD was 8 implemented using WinBUGS (Spiegelhalter et al., 2003). WinBUGS software is freely 9 available and implements Markov Chain Mont e Carlo (MCMC) computations. Use of 10
WinBUGS has been demonstrated for derivati on of a distribution of BMDs for a single 11 multistage model (Kopylev et al., 2 007) and is easily generalized (Kopylev at al., 2009) to derive 12
the distribution of BMDs for the combined tumor load, following the NRC (1994) methodology 13 described above. The advantage of a Bayesian approach is that it pr oduces a distribution of 14
BMDs that allows better charac terization of statistical uncertainty. For the current analysis, a 15 diffuse (high variance or low tolerance) Gaussian prior restricted to be nonnegative was used. 16 The posterior distribution was ba sed on three simulation chains with 50,000 burn-in (i.e., the 17 initial 50,000 iterations were droppe d) and a thinning rate of 20, resulting in 150,000 interactions 18
total. The median and 5 th percentile of the posterior distribution provided the BMD 01 (central 19 estimate) and BMDL 01 (lower bound) for combined tumor load, respectively. 20 The methodology above was applied to the st atistically significan t dose-response data 21 from Kociba et al. (1978), NTP (1982), and NTP (2006) (see Section 2.3.2 for data set selection 22 criteria). As with the risk es timates generated for individual tumo r sites, the combined analysis 23 used the internal dose metric, whole blood con centration (see Section 3) . For the combined 24 tumors for each sex/species combination, a BMDL 01 in blood concentrations was estimated. The 25
BMDL 01 (ng/kg) was then converted to a BMDL HED (ng/kg-day) using the Emond human model, 26 and an OSF in units of (mg/kg-day) − 1 was calculated by, OSF = 0.01/BMDL HED × 10 6 . It may be 27
noted that the OSF factor is lin ear with the blood concentration, and is not linear with ingested 28 TCDD doses; thus, using the Emond et al. pharm acokinetic model, risk-s pecific doses of TCDD 29 intake (ng/kg-day) corresponding to the target risk levels would need to be provided for use in 30
human health risk assessment. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-38 5.2.3.2.4. Results of dose-response modeling for rodent bioassays. 1 Table 5-13 presents the benchmark dose mode ling results for both the individual tumors 2
and the combined tumors based on blood concentrations. The p -values in the table are for a 3 chi-square goodness of fit st atistic with significance of p > 0.05. Goodness of fit was acceptable 4

at p > 0.05 for all models. The difference in log likelihood (dLL) statistic documents the 5 difference in log likelihoods between stages of the models in cas es where the stage is above 1; it 6 shows the difference between the stage in the tabl e and the lower stage. For example, for the 7 NTP (2006) liver cholangiocarcinom as, twice the difference of 2. 92 would be >3.84, the test 8 statistic from the assumed chi-square distribution, 29 with p = 0.95, justifying the choice of 9 3 stages over 2 stages. The best fitting multistage models include a 1-stage (linear) model for all 10
of the individual tumor data sets from Kociba et al. (1978), NTP (1982), and Toth et al. (1979), 11 as well as for the pancreatic and oral mucosa tumors in NTP (2006), a 2-stage model for the lung 12 tumors in NTP (2006), and a 3-st age model for the liver cholangiocarcinoma and liver adenoma 13 data sets from NTP (2006). For th e Toth et al. (1979) liver tumors, the model fit to all of the data 14 was poor, and the highest dose group was dropped in order to achieve an acceptable fit 15 ( p = 0.29). The BMD 01 s and BMDL 01 s (ng/kg) were estimated from these multistage models for 16
the individual tumors. BMD 01 s and BMDL 01 s (ng/kg) were also provid ed in Table 5-13 for the 17 combined tumors for each sex/species combinati on within a study. Thes e were estimated from 18
the distributions of BMD 01 s produced by the Bayesian MCMC simulation (see 19 Section 5.2.3.1.2.3.2). The BMD 01 s and BMDL 01 s (ng/kg) for the combined tumors in 20 Table 5-13 are the mean and lower 95% percentile values from these dist ributions, respectively. 21
22
5.2.3.2.4.1. Individual tumor models. 23
Table 5-14 shows the BMDL HED s (ng/kg-day) that were estimated from the BMDL 01 s in 24 Table 5-13 using the Emond human model (see Sec tion 5.2.3.1.2.2.2) and the OSFs calculated 25 by, OSF = 0.01/BMDL HED × 10 6 to convert the OSF to (mg/kg-day) − 1 units. BMDS results, 26 details of the model fits and dose-response graphics for all endpoints are shown in Appendix F. 27

29 The 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.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-39 Although only the blood concentration results are presented in this section, for comparison 1 purposes, Appendix F also provides modeling results for the studies’ administered average daily 2 doses. Table 5-14 lists the OSFs in decreasing value. It can be seen that liver tumors in male 3 mice yield the highest slope fact ors; OSF values are 5.9 ×10 6 and 5.2 ×10 6 per mg/kg-day in 4 NTP (1982) and Toth et al. ( 1979), respectively. The OSFs for the new NTP (2006) study in 5
female rats are two orders of ma gnitude lower, ranging from 1.8 ×10 4 to 1.8 ×10 5 per mg/kg-day, 6 representing the lowest OSFs for TC DD from the individual tumor models. 7
8
5.2.3.2.4.2. Multiple tumor (Bayesian) models. 9
Table 5-15 shows the BMDL HED s (mg/kg-day) that were estimated from the BMDL 01 s in 10 Table 5-13 using the Emond human model (see Sec tion 5.2.3.1.2.2.2) and the OSFs calculated 11 by, OSF = 0.01/BMDL HED ×10 6 to convert the OSF to (mg/kg-day) − 1 units. Table 5-15 lists the 12 OSFs in decreasing value. It can be seen that the combined liver and lung tumors in male mice 13 yield the highest OSF value of 9.4 ×10 6 per mg/kg-day from NTP (1982), and the combined 14 adrenal, tongue and nasal tumors in male rats yield the lowest OSF value of 3.2 ×10 5 from 15 Kociba et al. (1978). The OSF for the combined liver, oral mucosa, lung, and pancreatic tumors 16 in female rats from the newer NTP (2006) study is 4.4 ×10 5

. 17
18
5.2.3.2.5. Summary evaluation of slope factor estimates from rodent bioassays. 19 To estimate a range of candidate TCDD OSFs from the animal data, dose-response 20 modeling of the four chronic rode nt bioassays identified in Sec tion 2.4.3 was conducted. Dose21 response modeling was performed using whole blood concentrations, and BMDL HED values 22 (ng/kg-day) were derived for th e 25 species/sex/endpoint data sets individually (see Table 5-14) 23 and for seven species/sex combined tu mor data sets (see Table 5-15). 24 For each sex/species combination within a st udy, the combined tumor OSFs presented in 25 Table 5-15 represent the highest OSFs that have been derived from the animal cancer bioassay 26 multistage models. The most sensitive species and sex is male mice, for which the estimated 27
BMDL HED for combined tumors is 1.1 × 10 -3 ng/kg-day. This result, which is derived under the 28 assumption that multiple tumor types occur inde pendently in the exposed animals, is, as 29 expected, lower than the BMDL HED for the most sensitive individual tumor. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-40 Based on these results, EPA believes that a credible value for the BMDL HED derived from 1 the animal studies lies in the rang e shown in Table 5-15 between 3.1 × 10 -2 and 2
1.1 × 10 − 3 ng/kg-day. These values, which correspond to oral slope factor values of 3.2 × 10 5 3
and 9.4 × 10 6 per mg/kg-day, respectively, encompass the range at which elevated cancer risks 4 can be detected for the most sensitive species, se x, and endpoints in the animal bioassay data. 5
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 6
linear with blood concentration. The OSFs shown in Tables 5-14 and 5-15 correctly depict the 7 cancer risks generated from the multistage m odels for their specific associated BMDL HED s. 8 However, ingested TCDD doses are not linear with the predic ted TCDD blood concentrations 9 generated by the Emond pharmacokinetic model. Thus, the OSFs associat ed with the ingested 10 doses are not linear with ingested dose. If the OS Fs derived in this sectio n were to be used in 11 human health risk assessment, ta rget risk levels (e.g., 1 × 10 − 3 , 1 × 10 − 4 , 1 × 10 − 5 , 1 × 10 − 6 , etc.) 12 would need to be identified. Then, the Emond et al. pharmacokinetic model could be used to 13 generate risk-specific doses of TCDD intake (n g/kg-day) corresponding to th e target risk levels. 14
15
5.2.3.2.6. Qualitative uncertainties in slope fact or estimates from rode nt bioassays. 16 This section presents a qualitative discu ssion of the uncertaintie s associated with 17 calculating the OSF for TCDD from chronic animal bioassay data. Discussions on the feasibility 18 of conducting a quantitative uncertainty analys is for TCDD using dose-response methods are 19 provided in Section 6.4. 2 of this document. 20
21
5.2.3.2.6.1. Quality of studies relied upon for determining POD. 22 EPA considers the overall quality and breadth of the studies used for the cancer dose23 response analysis to be excelle nt. All of the studies were published in the peer-reviewed 24 literature, and two of them were conducted by NT P (1982, 2006). Kociba et al. (1978) and Toth 25 et al. (1979) are older studies, but appear to have been c onducted according to good laboratory 26 practice standards. The control and dose group sample sizes were relatively large, ~50 animals 27 or more per group for all of the studies except for Toth et al. (1979), where the dose group sizes 28 were ~40 animals per group. All four studies ex posed the test animals via the oral route to 29 TCDD alone, as was stipulated in EPA’s study inclusion criteria. Collectively, these four studies 30 reported development of numerous cancer endpoints (tumors) in bot h sexes in two strains of rats 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-41 (Sprague-Dawley and Osborne-Mendel) and two strains of mice (i.e., B6C3F 1 , Swiss/H/Riop). 1 The overall high quality of these studies and the strong, positive association between TCDD 2 exposure and cancer suggests that study quality is not a major contributing factor to uncertainty 3
in the risk estimates. 4
5
5.2.3.2.6.2. Interpretation of results from studies relied upon for determining POD. 6 As discussed in Section 3.4.3.2.1, questions arose about the interpretation of liver tumor 7 responses in female rats in the Ko ciba et al. (1978) study. Three re-evaluations of the slides have 8 been reported (Kociba et al., 1978; Squire, 1980 ; Goodman and Sauer, 1992). The decision to 9 use the Goodman and Sauer (1992) evaluation was based on the fact that the authors used the 10 most current tumor classification procedures. The incidence of hepatocellular adenomas and 11 carcinomas (individually and combined), however , did vary (sometimes widely) for each dose 12 group across the three evaluations. Although the state-of-the-science is reflected in the incidence 13 data used for analysis, a small de gree of additional uncer tainty (i.e., above th at associated with 14 data collection and reporting erro rs) could be associated with the female liver tumor risk 15 estimates from the Kociba et al. (1978) study due to this variability. No controversy has arisen 16 with regard to the interpretation of the NTP (1982, 2006), or Toth et al. (1979) tumor 17
identification and classification. 18
19
5.2.3.2.6.3. Consistency of results across chronic rodent bioassays. 20 The existence of four high-quality chronic bioassays for TCDD increases confidence and 21 reduces uncertainty in the cancer OSFs. Consider ed together, these studies tested two species 22 and both sexes of mice and rats, and a wide range of well-characterized tu mor types. All four 23
studies were consistent in obser ving increases (at some dose level) in rates of liver tumors (in 24 both species and sexes). While tumors at other sites were observed (and those sites varied across 25 study, species, and sex), the liver tumors were consistently the most sensitive indicators of 26 carcinogenic response (with respect to BMDL HED estimates). Lung tumors were also 27 consistently observed across three of the studies, in male mice in the NTP (1982) study and in 28
female rats in Kociba et al. (1978) and NTP (2006). As discusse d above, the two most sensitive 29 single-tumor endpoint s as judged by BMDL 01 values were associated with elevated liver tumor 30 risks, followed by lung, lymphoma or leukemia, t hyroid and adrenal cancers. The consistency of 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-42 tumor types and sensitivities acr oss endpoints and studies lends c onfidence to the multistage 1 modeling results. 2
3
5.2.3.2.6.4. Human relevance of rodent tumor data. 4 There is some concordance in the tumor respon ses observed in the rode nt test species and 5 humans, however, the most sensitive tumor site in the animals, the liver, has not been associated 6 with cancer from TCDD exposures in humans. On the other hand, lung cancer and leukemia 7
have been are found both in the an imal studies and in epidemiologi c studies of exposed workers. 8 The consistency across sex, species and strains in the animal studies suggests that the occurrence 9 of several of these tumors, in particular, liver a nd lung tumors is not an idiosyncratic response of 10 a particular combination of species, strain, or sex. As discussed in Section 5.2.1, the likely AhR 11 related carcinogenic mechanism is credible for humans as well as for rodent species. 12
13
5.2.3.2.6.5. Relevance of rodent exposure scenario. 14
The four chronic rodent bioassa ys exposed the test animals for ~2 years, the majority of 15 their lifespans. The exception is the Toth et al. (1979) study, where the animals were exposed 16 only for one year, but were kept on the study for a second year before they were evaluated for 17 cancer. These lifetime bioassays in animals ha ve long been used by EPA to assess potential 18 lifetime exposures and effects in humans. However, in the case of TCDD, th e half life of TCDD 19 in the body for rats, mice, and humans is very different (see Section 3) . Thus, there is a 20 significant amount of uncertainty in the use of rat and mouse data to develop OSFs for human 21
cancer risk assessment of TCDD. 22
23
5.2.3.2.6.6. Impact of background TCDD exposures. 24
It is known that TCDD has been found in the feed used in animal bioassays, and that this 25 is a confounding factor, particularly in older studies. The effect of TCDD in the diets of test 26 species has the potential to be quite significant given the low levels of TCDD at which adverse 27
effects have been observed. Insofar as that is an issue, the risks associated with TCDD 28 exposures in the animal bioassays , and therefore the OSFs, would be biased high, which could be 29 the case for the NTP (1982), Kociba et al. (1978) and Toth et al. (1979) studies . The impact of 30 this issue is that the newer study, NTP (2006) , accounted for TCDD exposures in the animal 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-43 feed. Thus, there is likely to be less uncer tainty in the TCDD dose-response information 1 presented in NTP (2006) than in the othe r three studies conducted before 1990. 2
3
5.2.3.2.6.7. Choice of endpoint for POD derivation. 4 As noted above, the liver tumor PODs represent the most sensitive single-tumor endpoint 5 across the four cancer bioassays. Thus, the liver cancer endpoints must be seriously considered 6
for derivation of a TCDD OSF. As discussed in the previous s ection, EPA has also developed 7 Bayesian dose-response estimates for combined tumors, which yield BMDL 01 values slightly 8 lower than those for any individu al tumor type. Although it is th e most conservative choice to 9
select the lowest combined tumor POD for OSF derivation, there are unc ertainties associated 10 with the multiple tumor analysis. The assumption of independence of tumors across sites is 11 reasonable, particularly since the tumors fr om TCDD do not metastasize. However, the 12 independence assumption lacks hard evidence an d needs further laboratory confirmation. 13
14
5.2.3.2.6.8. Choice of animal-to-human extrapolation method. 15 The analyses presented here have used the Emond human kinetic model for extrapolating 16 dose from animals to humans (as discussed in Secti on 3.4.2). The rationale for this choice is that 17
the blood concentration metric most accurately reflects the concentr ation of TCDD in the various 18 tissues. As discussed in Section 3.4.3.2.4, use of the blood concentration dose metric results in 19 critical dose estimates (HEDs) that are considerab ly lower (10- to more than 100-fold) than those 20
derived based on administered dose. This does not reflect bias in the blood-based measure; 21
rather it is a reflection of th e highly nonlinear biokinetics of TCDD in the body. EPA has also 22 explored the impacts of usi ng other dose metrics, includi ng liver bound TCDD concentration 23
calculated based on the Emond model. As discussed in Section 3.4.3.2.6.2, this also results in 24
HED estimates much lower than those obt ained based on administered dose. 25
26
5.2.3.2.6.9. Choice of model for POD and model uncertainty for POD derivation. 27 The bioassay-based cancer dose-response a ssessment in this section has used the 28 multistage model which is the standard model choice for such assessments and has been the basis 29
for most of EPA’s cancer risk asse ssments. In that sense, there is no associated uncertainty for 30
model choice. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-44 There is some model choice uncertainty associat ed with instances of lack of fit. When 1 the multistage model does not adequately descri be the observed pattern of responses (typically 2 determined by examining the p -value for lack of fit), a deci sion must be made about possible 3 adjustments, including the dropping of higher dos e groups thought to be less relevant to the 4 estimation of low-dose slopes. In this analysis, poorer fits ( p -values less than 0.10) were 5 observed in four cases, all from NTP ( 1982) (see Table 5-13). The lowest BMDL 01 associated a 6
low p -value ( p = 0.09) was for the lung tumors in the NTP (1982) male mouse, the third lowest 7
POD behind the liver PODs in the individual tumo r data sets. The other instances were for 8 adrenal cortex and thyroid follicular cell adenomas in male rats and for subcutaneous tissue in 9 female mice in the NTP (1982) st udy. In those instances, the p -values were 0.06, 0.06, and 0.09, 10 respectively; the fits were considered adequate for describing the low-dose response patterns and 11 estimating slope factors. These poorly fit data sets contribute to uncertainty in the combined 12
tumor PODs. The lowest BMDL 01 in the combined tumors is fo r the male mice combined liver 13 and lung tumors, thus estimates from this sex/ species combination from NTP (1982) is highly 14 uncertain and impacts its choice as a POD. 15
16
5.2.3.2.6.10. Statistical uncertainty in model fits. 17 Every model fit to a data set is associated w ith some inherent statistical uncertainty. For 18 this reason, bounds were calcu lated and used for OSF de rivation (e.g., lower bounds on 19 benchmark doses, in this case the BMDL 01 s). Those bounds account for uncertainties associated 20 with finite samples of test animals, both in terms of the number of dose groups and of the 21 number of animals per dose group. Valid and accep ted statistical procedures have been applied 22 to ascertain the impact of those limitations on the estimates of interest. That being the case, the 23
statistical uncertainties associated with finite samples have been adequately addressed. 24
25
5.2.3.2.6.11. Choice of risk level for POD derivation. 26
The BMR level that has been used for the P OD in deriving the cancer OSF is one percent 27 extra risk. This choice is supported by the fact th at risks need to be lo w enough so that the linear 28 extrapolation from them reflects low-dose beha vior, reducing the impact of any higher-dose 29 changes (inflexions) in the dose-response. The mu ltistage model has the characteristic that it can 30 be approximated by a linear model at low enough doses . Thus, a risk level in the range of one 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-45 percent is generally suitable for characterizing that approximate linear function, while not falling 1 too far below the observed range of the data. 2
3
5.2.3.3. EPA ’ s Response to the NAS Comments on Choice of Response Level and 4 Characterization of the Statistical Confidence Around Low Dose Model Predictions 5 The NAS was concerned with the statistical power to determine the shape of the dose 6 response curve at low doses, well below observed dose-response information. EPA shares this 7 concern in that the shape of th e dose-response curve in the low-dose region cannot be determined 8 with confidence when based on higher dose information. 9 When tumor data are used for dose-response modeling, a POD is obtained from the 10
modeled tumor incidences. Wh en assessing carcinogenicity us ing a linear extrapolation 11 approach from a POD, a balance must be stru ck between staying within the range of the 12 observations and obtaining a representative estima te of the low-dose slope. Traditional cancer 13 bioassays, with approximately 50 animals per gr oup, can typically support modeling down to an 14 increased incidence of 1–10%; epid emiologic studies, with larger sample sizes, below 1%. For 15 the TCDD animal cancer bioassay data, most of the low-dose tumor incidence responses are 16 under 10% (relative to controls), with some as low as 2%. After evaluating the magnitude of the 17 uncertainty in BMDL 01 s against the impact of using BMDL 10 s, EPA has chosen to use a 1% 18 BMR in all cases, determining that the uncertainty bounds on the BMDL 01 values are reasonable 19 (see Appendix F for details). For comparison purposes, BMDL 01 s, BMDL 05 s, and BMDL 10 s are 20 presented for all modeled tumor incidences in Appendix F. 21 In the analysis of the animal cancer bioassays presented in this document, the multistage 22 cancer model was applied with a linear dose extra polation to zero. EPA used a 1% excess risk 23 estimate, i.e., a BMDL 01 , as the POD for development of candidate TCDD cancer oral slope 24 factors using a Bayesian multitumor approach (see Section 5.2.3.2. The advantage of a Bayesian 25 approach is that it produces a distribution of BMDs that a llows better characterization of 26 statistical uncertainty. 27 Central tendency and lower bound slope factor estimates are part of the standard BMDS 28 multistage cancer model and are included in each out put file for the animal bioassay single tumor 29 analyses in Appendix F. Centra l tendency BMDs are also reported for the results of the animal 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-46 bioassay multitumor analysis (see Table 5-13). Cent ral tendency slope factor estimates are given 1 for all the qualifying epidemiological studies as well (see Tables 5-1 and 5-4). 2
3
5.2.3.4. EPA ’ s Response to the NAS Comments on Mo del Forms for Predicting Cancer Risks 4 Below the POD 5 The NAS offered extensive comments on th e cancer dose-response modeling in the 2003 6 Reassessment. Although epidemiologic and rodent bioassay data are useful for the evaluation of 7 the dose-response curve within th e range of the observed response data, they have traditionally 8
not been useful sources of information for iden tifying a threshold or for estimating the shape of 9 the dose-response curve below the POD. Rather, mechanistic toxicologica l data have been the 10 evidentiary sources of choice for those types of analyses. As noted above, any quantitative 11 estimation of carcinogenic risk associated with TCDD exposure requires low-dose extrapolation 12 of experimental data. Unfortunately, the shape of the dose-response curve in the low dose region 13
is unknown. 14 Several of the analyses of ep idemiological cohort data evalua ted the fit of different dose15 response models to the data. Log-dose models accentuate the importance of low-dose low16 magnitude responses and can yield implausible result s . The most re levant m odels used in these

17
studies are the untransformed-dos e Cox regression m odels, which are the most similar to the 18 multistage (1-degree) model used for animal bioassay analysis. Better results have been obtained 19 in the cohort analyses when the flattening of th e hazard-ratio curve is taken into account. The 20 latter has been modeled explicitly by Steenla nd et al. (2001), who use a piecewise linear model 21 and implicitly by Cheng et al. ( 2006), who drop out a percentage of the high-dose response data 22 and fit a linear model to the remainder. Importa ntly, the analyses of th e epidemiologic cohorts 23 presented in Section 5.2.3.1 are limited to ev aluation and reanalyses of published data as 24 reported by the study authors. EPA does not have access to the raw data from these 25 epidemiologic studies and, therefore, could not conduct de novo analyses. 26
27
5.2.3.4.1. Choice of extrapolation approach 28
5.2.3.4.1.1. TCDD and receptor theory. 29 TCDD is considered to be a receptor-mediated carcinogen in animals. Nearly all TCDD 30 experimental data are consistent with the hypothesis that the bindi ng of TCDD to the AhR is the 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-47 first step in a series of biochemical, cellular, and tissue changes that ultimately lead to toxic 1 responses observed in both experimental an imals and humans (Part II, Chapter 2 of the 2003 2 Reassessment). Ligand-receptor binding, like any bimolecular interaction, obeys the law of mass 3 action as originally formulated by A.J. Clark (Limbird, 1996). The law of mass action predicts 4 the fractional receptor occupancy at equilibrium as a function of ligand concentration. Fractional 5 occupancy (Y) is defined as the fraction of all receptors that are bound to ligand: 6
7

Y = [ TCDD

− A hR ] [ AhR ] TOT = [ TCDD

− A hR ] [ AhR ] + [ TCDD

− AhR ] = [ TCDD ] [ TCDD ] + K d (Eq. 5-5) 8
9 where [TCDD] is the concentration of the ligand, [AhR] is the concentration of the receptor and 10 [TCDD-AhR] is the amount of liganded recepto r. The equilibrium dissociation constant K d 11 describes the affinity of the interaction and is the concentration of TCDD that results in 50% 12 receptor occupancy. This simple equation defines a rectangular hyperbola, which is the 13 characteristic shape of the vast majority of biological dose -response relationships. 14 In certain cases, no response occurs even when there is some receptor occupancy. This 15 suggests that there may be a threshold phenomenon th at reflects the biolog ical “inertia” of the 16 response (Ariens et al., 1960). In other cases , a maximal response occurs well before all 17 receptors are occupied, a phenomenon that refl ects receptor “reserve” (Stephenson, 1956). 18 Therefore, the law of mass action cannot by itself fully explain the eff ect or response observed 19 after TCDD interacts with AhR. The ligand-re ceptor complex is associated with a signal 20 transduction or effector system. In the case of the AhR, this effector system can be considered to 21 be the transcriptional machinery its elf. The key feature of this formulation is that a response is 22 proportional, or a function of, th e number of receptors occupied. 23 Furthermore, for a ligand such as TCDD that elicits multiple receptor-mediated effects, 24
one cannot assume that the bindi ng-response relationship for a simple effect (such as enzyme 25 induction) will necessarily be identical to that fo r a different and more complex effect (such as 26 cancer). The cellular cas cades of events leading to different complex responses (e.g., altered 27 immune function, developmental effects, or can cer) are different, and other rate-limiting events 28 likely influence the final biological outcome resu lting in different dose-response curves. Thus, 29 even though TCDD binding to AhR is assumed to be the initial ev ent leading to a spectrum of 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-48 biological responses, TCDD-AhR bi nding data may not always corr elate with the dose-response 1 relationship observed for particular effects. 2 A receptor-based mechanism would predict that , except in cases where the concentration 3 of TCDD is already high (i.e., [TCDD]~K d ), incremental exposure to TCDD will lead to some 4 increase in the fractional occupa ncy of AhR. However, as discussed above, it cannot be assumed 5 that an increase in receptor occupancy will ne cessarily elicit a proportional increase in all 6 biological response(s), because numerous molecu lar events contributing to the biological 7 endpoint are integrated into the overall response. That is, the fi nal biological response could be 8 considered as an integration of a series of inte rdependent dose-response curves with each curve 9 dependent on the molecular dosimetry for each part icular step. Dose-response relationships that 10 will be specific for each endpoint must be considered when using mathematical models to 11 estimate the risk associated with exposure to TC DD. It remains a challenge to develop models 12 that incorporate all the complexities associated with each biological response as the modes of 13 action for various toxicological endpoints appear to vary greatly. For TCDD, extensive 14 experimental data from studies using animal and human tissues indicate that cell- or tissue15 specific factors determine the quantitative relationship betw een receptor occupancy and the 16 ultimate biological response. This would suggest that the parameters for each mathematical 17 model might only apply to a singl e biological response within a given tissue and species, making 18 extrapolation to othe r systems challenging. 19
20
5.2.3.4.1.2. Low-dose extrapolation: threshold or no threshold? 21 As indicated in the 2005 Cancer Guidelines, 30 toxicity reference values for human 22 noncancer endpoints have historic ally been estimated based on a no-observed-adverse-effect 23 level (NOAEL) or lowest-observed- adverse-effect level (LOAEL) from animal bioassay studies. 24 This terminology suggests a biological populat ion threshold beneath which no harm is 25 anticipated. Reference values such as the oral reference dose (RfD) or inhalation reference 26 concentration (RfC) are derived by applying uncertainty factors (UFs) to a POD. Depending on 27 the nature of available data and modeling choice, a POD can be selected from values other than 28

30 As stated in the 2005 Cancer Guidelin es (U.S. EPA, 2005): “For effects othe r 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, 199 8b), 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.”This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-49 an NOAEL or LOAEL, such as an ED x , or a benchmark dose (BMD) or its BMDL. An RfD is 1 described as “likely to be wit hout appreciable risk” but the proba bilistic language has not as yet 2 been operationalized. There is no quantitative definition of “appreciable” and no mechanism to 3 compute risk as a function of dose, so as to ascert ain that the risk is ind eed not appreciable. The 4 risk at the RfD is not calculated, and it cannot be calculated within the current UF framework. 5 Instead, a hazard quotient is computed as the ratio of a given exposure to the RfD, or a margin of 6 exposure is estimated as the ratio of the POD to the human exposure level. 7 Cancer endpoints are predominantly thought to have no population biological threshold. 8 Although the terminology “thres hold/nonthreshold” is still common in cancer dose-response 9 discussions, the 2005 Cancer Guidelines propose a different terminology, whereby “nonlinear 10 models” are those whose dose-response slope is zero at or above zero. In the natural language, 11 and indeed in data analysis, it is difficu lt to distinguish the following situations: 12
13

• The response approaches zero as dose goes to zero, versus 14

• The response slope goes to zero as dose goes to zero (nonlinear model). 15
16
This use of “nonlinear” is ac knowledged to be idiosyncratic. 31 The NAS review (NAS, 17 2006a) does not consistently apply the terminology from the 2005 Ca ncer Guidelines, nor does it 18 consistently distinguish the above two circumst ances: “...the observed data are more consistent 19 with a sublinear response that a pproaches zero at low doses ra ther than a linear dose response” 20 (NAS, 2006a). The point of a nonlinear model in th e sense of the 2005 Cancer Guidelines is that 21 the response slope approaches zero. Bo th linear and nonlinear responses approach zero at low 22 dose (in the absence of backgr ound). Since the terms “linear,” “sublinear,” and “nonlinear” 23 invite confusion in this context, the fo llowing terminology is used in this document: 24
25

31 From 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 perhap s above) a dose of zero. .... Use of nonlinear approaches does not imply a biological threshold dose below which the response is zero.”This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-50 Threshold Model : There is some threshold T > 0 such that the probability of response for 1 any dose less than or equal to T is zero, and the probability is nonzero for any dose 2 greater than T. 3

Linear/ Linear above Threshold Model : For the linear model, the probability of response 4 is proportional to the dose. For the linear over threshol d model, the probability of 5 response is zero for a dose below the threshol d, and it is proportiona l to the excess dose 6 over the threshold otherwise. 7

Nonlinear Model : Any model that is not linear. 8
Supralinear/ Supralinear above Threshold Model : For the supralinear model, the slope of 9 the probability of response decreases as dose increases; in other words, the second 10 derivative of the response curv e is negative. For the supral inear above threshold model, 11 the second derivative is negative above the threshold, and the response probability is zero 12 below the threshold. 13

Sublinear/Sublinear above Threshold Model : For the sublinear model, the slope of the 14 probability of response increases as dose incr eases; in other words, the second derivative 15 of the response curve is positive. For the sublinear above threshold model, the second 16 derivative is positive above the threshold, and the response probability is zero below the 17 threshold. 18
Zero Slope at Zero Model : The slope of the response curv e is zero at or above dose zero. 19 20 All of these models may be understood in an individual or population sense. According 21 to the 2005 Cancer Guidelines, the trigger fo r applying the basic Rf D methodology for cancer 22 endpoints is sufficient evidence for the “zero sl ope at zero” model fo r the population. By 23 definition, any sublinear, s upralinear, or linear model above the threshold is a zero slope at zero 24 (“ZS@Z”) model. 25
The relation between indivi dual and population models is not simple. Suppose for 26 purposes of illustration that each individual has a threshold, and that the threshold values are 27 uniformly distributed in the popul ation, for some neighborhood of zero. (Note that “in the 28 neighborhood of zero” has the mathematical meani ng that all values are sufficiently close to 29 zero; i.e., for some δ >0, the thresholds are uniformly distributed over the interval [0, δ ].) 30 Then (see T e xt Box 5-1) 31
32
A. If each person’s response func tion is linear for a dose above the threshold, then the 33 population dose-response curve is quadratic, and the slope of the population curve is zero 34 for dose zero (ZS@Z). 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-51 Text Box 5-1. Individual vs. Population Thresholds
Suppose each individu al 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 = δ 2

− δ 2 /2 = δ 2 /2.
The slope of the response function is found by taking the derivative with respect to δ : ( d

/ d

δ ) δ 2 /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. B. If each person’s response function is log 2 linear, for a dose above the threshold, 4 then the population dose-response curve 6 is linear without a threshold, and the 8 slope of the population curve is positive 10 for dose zero. 12 14 Of course these are not the only 16 possibilities; in general, the population dose18 response curve depends on (1) the distribution of 20
individual thresholds in the neighborhood of 22 zero, (2) the dose-response curve for each 24 individual, and (3) the dose metric. 26
On the nature or the distribution of 28 individual thresholds, often referred to as the 30 population tolerance distribution, there is 32 ongoing debate as to how receptor kinetics 34 influence the shape of that distribution. Even 36 within an individual, there is a lack of consensus 38 as to whether receptor kinetics confer linear or 40 sublinear attributes to downstream events, or 42 whether receptor kinetics, themselves, are linear, 44 sublinear, or supralinear. Whatever the nature of 46 the form of receptor kinetic s, it may have little or 48 no influence on the ultimate population response. The kinetics of receptors is in the domain of 49 the individual, rather than the population. As described previously, receptor kinetics are 50 governed by the law of mass action, which lead s to a low-dose propor tional (linear) response 51 model, generally modeled by some form of H ill function, the linear form being Michaelis52 Menten kinetics. Linearity can be violated by a number of inhibitory or stimulatory processes, 53
but is a fundamental conserved characteri stic in living systems. There is no a priori reason to 54 believe that the shape of the dos e-response curve in an individu al has any relationship to the 55 shape of the population response, particularly fo r quantal endpoints. Lutz and Gaylor (2008) 56 present a strong argument for c onsidering the population res ponse in terms of the more 57This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-52 traditional tolerance distribution, which is likely the result of more variable factors than the 1 shape of receptor kinetics. Perhaps more to the point, receptor activatio n is only the first of 2 many events in the path to the apical event (a tumor in this example). Because there are 3 undoubtedly numerous additional downstream events th at must occur before the apical effect is 4 observed, there are many opportunities for interindi vidual variability to b ecome manifest in the 5 tolerance distribution. Even at the first step , a more likely contribu tor to interindividual 6 variability than the shape of the response is the do se resulting in the response, as measured by the 7
ED 50 (the aforementioned equili brium dissociation constant K d , K m in the Michaelis-Menten 8 formulation), which shifts the response curve. Factors that influence sh ifts in response curves 9 are generally modeled as normal or log-normal di stributions and will most likely confer a log10 normal shape on the population tolerance distribution, particularly if there are a number of 11 dependent sequential steps or distinct subpopulati ons (Lutz, 1999; Hattis et al., 1999; Hattis and 12 Burmaster, 1994). 13 Pertaining to the shape of the dose-response cu rve, because the criterion for applying the 14 RfD model is a zero slope at dose zero, the role of dose metric must be fully appreciated. A 15 slope is simply the ratio of a ch ange on the vertical axis (the pr obability of response) relative to a 16 change on the horizontal axis (t he dose). Changing the dose metric from the dose to the 17 logarithm of the dose dramatically changes this ratio. As dose goes to zero, the rate of change of 18 log(dose) becomes infinite. Therefore, ANY dose-re sponse relation with a finite slope at zero 19
will appear to have a zero slope when graphed agains t log(dose). Text Box 5-2 illustrates this 20 point for the mass action dose response, which ha s been proposed for r eceptor-mediated modes 21
of action. 22
To see how the discussion over threshold/ nonthreshold might play out for TCDD, 23 consider the equilibriu m dissociation constant K d for TCDD, which measures the binding affinity 24
of TCDD to the AhR. Lower values indicate hi gher binding affinity a nd (other things being 25 equal) greater risk. For Han/Wistar rats, the value K d = 3.9 is reported; human values are 26 reported as K d =9.6 ± 7.8(0.3 – 38.8 with 15 of 67 dono rs without detectable binding) (Connor 27 and Aylward, 2006). 28 If AhR binding is necessary for carcinogenesis, then the majority of a human population 29 may be much less susceptible than Han/Wistar ra ts, whereas a population th reshold, if it exists, 30 might be well below the Han/Wistar rat threshold (see Section 4.4.2.9). The NAS contends that 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-53 1 2 3 an AhR-mediated mode of action indicates a threshold dose-response relation (NAS, 2006a). 4 Presumably, the value of the threshold, if it exists, depends on the AhR binding affinity. 5 Text Box 5-2. Logarithmic Transform of Mass Action Hyperbolic Mode l
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. Th is is a hyperbolic function written as [LR] = [R][L]/([L] + K d ). 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] + K E ) where K E 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] + K d /( τ +1)

where τ is the tissue-/endpoint-specific ‘t ransducer constant’ [R]/KE. Writing δ for dose [L], this is proportional to the hyperbolic equation f( δ ) = δ /( δ + K ); K = K d /( 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/d δ * = (df/d δ )(d δ /d δ *), and d δ /d δ * = e δ * → 0 as δ * → −∞ . The change in response relative to the change in log dose becomes infinitesimal, since log dose changes infinitely fast as dose g oes to zero. f( δ *) is not a zero slope at zero model.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-54 Arguing for a population threshold in this ca se requires two types of information: 1
2
1. The distribution of the indivi dual thresholds induced by, inter alia (among other things), 3 the individual K d values; and 4
2. The dose-response function for values above the threshold induced by K d . 5
6 Without this information, the two possibili ties {A,B} enumerated on Pages 5-50 through 7 5-51 cannot be distinguished, and the default linear relationship applies response probability is 8
modeled as a linear function of dose, for dose near zero. However, from the 2005 Cancer 9 Guidelines: “When adequate data on mode of action provide sufficient evidence to support a 10
nonlinear mode of action for the general population (emphasis added) and/ or any subpopulations 11 of concern, a different appro ach—a reference dose/reference c oncentration that assumes that 12 nonlinearity—is used.” In curr ent terminology, the reference dose methodology applies if there 13 is sufficient evidence supporting a “ zero slope at zero” model; otherw ise, the linear nonthreshold 14 model applies by default. 15 In principle, the choice betw een the above models could fa ll within the purview of dose16 response modeling. However, standard statistical methods encounter well-known difficulties in 17 detecting thresholds. Without going into deta il, suffice to say that the maximum likelihood 18 estimate of response probability when no responses are observed in a finite sample is always 19 zero. That said, some researchers have attemp ted to identify thresholds (Mackie et al., 2002; 20 Alyward et al., 2003) or nonlineari ty (Hoel and Portier, 1994) by means of parameter estimation 21 of appropriate models. A review of 344 rodent bioassays on 315 chemicals led to the following 22 conclusion by Hoel and Portier (1994): 23
24
We have also found that the oft-held belief that genotoxic compounds typically 25 follow a linear dose-response pattern and that nongenotoxic compounds follow a 26 nonlinear or threshold dose response pattern is not suppor ted by the data. In fact 27 we find the opposite with genotoxic co mpounds differing from linearity more 28 often than nongenotoxic compounds. 29 30 The choice between a linear and “zero slope at zero” model in current practice does not 31 fall under dose-response model fitting, it is made on the basis of a structured narrative as set 32 forth in the 2005 Cancer Guidelines (U.S. EPA, 2005): 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-55 In the absence of sufficiently, scientifically justifiable mode of action information, 1 EPA generally takes public health-protective, defau lt positions regarding the 2 interpretation of toxicologic and epidem iologic data: animal tumor findings are 3 judged to be relevant to humans, and can cer risks are assumed to conform with 4 low dose linearity. ... The linear approach is used when: (1) there is an absence of 5 sufficient information on modes of action or (2) the mode of action information 6 indicates that the dose-respons e curve at low dose is or is expected to be linear. 7 Where alternative approaches have significant biological support, and no 8 scientific consensus favors a single approach, an assessment may present results 9 using alternative approaches. A nonlinear approach can be used to develop a 10 reference dose or a reference concentration. 11 12
5.2.3.4.1.3. Extrapolation method. 13 The 2005 Cancer Guidelines (U.S. EPA, 2005) emphasize that the method used to 14 characterize and quantify cancer ri sk from a chemical is determ ined by what is known about the 15 MOA of the carcinogen and the shape of the cancer dose-response curve. 16 The NAS was critical of EPA’s decision to apply linear low-dose extrapolation for 17 TCDD cancer assessment in the 2003 Reassessment and encouraged the use of a nonlinear 18 approach. The 2005 Cancer Guidelines state th at a nonlinear approach should be used when 19
“there are sufficient data to ascertain the mode of action and conclude that it is not linear at low 20 doses and the agent does not demonstrate mutagenic or other activity consistent with linearity at 21
low doses.” 22 Receptor modeling theory (as outlined in th e 2003 Reassessment, Part II, Chapter 8) 23 indicates that exogenous compounds which opera te through receptor bindi ng mechanisms, such 24 as TCDD, will follow a linear dose-response binding in the 1

− 10% receptor occupancy region. 25 This theory has been supporte d by empirical findings and suggest s that the proximal biochemical 26 effects (such as enzyme induction) and transc riptional reactions for TCDD may also follow 27 linear dose-response kinetics. More distal toxic effects could take any one of multiple forms 28 (i.e., linear, sublinear, supralinear or thre shold) depending on (1) the toxic mechanism; 29 (2) location on the dose-response cu rve; and (3) interactions w ith other processes such as 30 intracellular protein binding and cofactor induction/repression. 31 In the case of TCDD, many adverse effects e xperienced at low exposure levels have too 32 much data variability to disti nguish on a statis tical basis (goodness-of-f it) between dose-response 33 curve options, and whether the dose-response is linear, sublinear or supralinear. For tumor 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-56 responses, with the exception of squamous cell carcinoma of the oral mucosa and adenomas or 1 carcinomas of the pancreas, whic h were fit with a linear multista ge model, the tumor endpoints 2 in the NTP (2006) study using female Sprague-Dawley (S-D) rats are all best fit with a sublinear 3 model (i.e., the multistage model fits to tumor incidence data were second or third degree; see 4 Table 5-13 and Appendix F). For all other tumor in cidence data from all other cancer bioassays 5 that met the study inclusion criteria (NTP, 1982, Toth et al., 1979, and Kociba et al., 1978), the 6 multistage model fit was linear (first degree), when based on either administered dose or 7
modeled blood concentrati ons (see Appendix F). 8 Another issue of potential importance when evaluating the shape of the dose-response 9 curve for low dose effects is the concept of “int eracting background.” The concept of interacting 10 background refers to a pathologi cal process in the exposed popul ation that shares a causal 11 intermediate with the toxicant being evaluated. On this issue, a recent NAS committee (NAS, 12 2009) contended that 13
14 ...the current EPA practice of determin ing “nonlinear” MOAs does not account 15 for mechanistic factors that can create linearity at low dose. The dose-response 16 relationship can be linear at a low dos e when an exposure contributes to an 17 existing disease process (Crump et al.1976, Lutz 1990). Effects of exposures that 18 add to background processes and background endogenous and exogenous 19 exposures can lack a threshold if a base line level of dysfunction occurs without 20 the toxicant and the toxicant adds to or augments the background process. Thus, 21 even small doses may have a relevant biologic effect. That may be difficult to 22 measure because of background noise in th e system but may be addressed through 23 dose-response modeling procedures. Huma n variability with respect to the 24 individual thresholds for a nongenotoxic cancer mechanism can result in linear 25 dose-response relationships in the popul ation (Lutz 2001) (NAS, 2009, p. 130). 26 27
AhR activation could be considered a causal in termediate in several disease processes. 28
Recent studies have linked AhR activation in th e absence of exogenous ligand to a multitude of 29 biological effects, ranging from control of mammary tumorigenesis to regulation of 30 autoimmunity (reviewed in Hahn et al., 2009). Wh ile the level of backgr ound activation of AhR 31 by endogenous compounds (and/or exogenous compounds other th an TCDD) in the human 32 population is unknown, given the ubiquitous natu re of several of the known endogenous and 33 exogenous AhR ligands, it is reasonable to assume that a certain baseline level of AhR activation 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-57 exists in the population. The degree to which TCDD exposure augm ents this baseline level of 1
AhR activation is unknown. 2 The 2005 Cancer Guidelines (U.S. EPA, 2005) recommend that the method used to 3 characterize and quantify cancer ri sk from a chemical be determined by what is known about the 4 mode of action of the compound and the shape of the cancer dose-response curve. The linear 5 approach is used if the mode of action is not understood (U.S. EPA, 2005). In the case of 6 TCDD, (1) the mode of action of TCDD- induced carcinogenesis beyond potential AhR 7 activation is unknown; (2) informa tion is lacking to determine c onfidently the shape of the dose8 response curves for various adverse endpoints (i ncluding cancer) in humans or experimental 9 animals; (3) there is undoubtedly a certain level of interactin g background (i.e., AhR activation 10 by endogenous ligands) in the human population; (4) many of the rodent cancer dose-response 11 relationships (NTP, 1982; Toth et al., 1979; Kociba et al., 1978) are linear (first degree 12 multistage model fit) when based on either admi nistered dose or modeled blood concentrations; 13 and (5) higher human interindivi dual variability compared to expe rimental rodents will tend to 14 shift the shape of the dose-respons e towards linear (rela tive to rodents). Therefore, a linear low15 dose extrapolation approach was used to esti mate human carcinogenic ri sk associated with 16 TCDD exposure. 17
18
5.2.3.4.1.4. Consideration of nonlinear methods. 19 While the 2005 Cancer Guidelines deem linear extrapolation to be most appropriate for 20 TCDD, EPA has carefully considered the NAS r ecommendation to provide risk estimates using 21
both linear and nonlinear methods. 22
The 2005 Cancer Guidelines state 23
24 For cases where the tumors arise through a nonlinear mode of action, an oral 25 reference dose or an inhalation refere nce concentration, or both, should be 26 developed in accordance with EPA’s es tablished practice for developing such 27 values ... This approach expands the pa st focus of such reference values 28 (previously reserved for e ffects other than cancer) to include carcinogenic effects 29 determined to have a non linear mode of action. 30 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-58 In this section, EPA presents two illu strative examples of RfD development for 1 carcinogenic effects of TCDD. Each of these ex amples focuses on data derived from animal 2 bioassays as described in Section 2.4.2. 3
4
5.2.3.4.1.4.1. Illustrative RfDs based on tumorige nesis in experimental animals. 5 TCDD has been shown to be a multisite carcinogen in both sexes of several species of 6 experimental animals. It also has been s hown to be carcinogenic to humans. Most of the 7 available quantitative human epidemiologic da ta related to TCDD carcinogenesis are for all 8 cancer mortality. Mortality is a frank effect and is generally considered to be inappropriate for 9 RfD development, therefore, the illustrative ex ample below utilizes available evidence from 10 experimental animals. Table 5-16 presents ca ndidate PODs and RfDs for TCDD carcinogenicity 11 based on combined tumor responses from the animal bioassays described in Section 2.4.2. The 12 PODs from the NTP (1982, 2006) and Kociba et al. (1978) animal studies were derived from 13 Bayesian multitumor dose-response modeling (as described in Secti on 5.2.3.2, Table 5-15) using 14 a BMR of 1%. Because only TCDD-induced liver tumors were reported by Toth et al. (1979), 15 the BMR of 1% (POD) from that study was generated using a first degree linear multistage 16 model (see Table 5-14). Following BMD modeling, BMDL HED s were then estimated using 17
alternative dose metrics from the Emond model as described in Section 3. The illustrative RfDs 18 were derived by dividing the BMDL HED s by appropriate uncertainty fact ors. In each instance, a 19 total UF of 30 was applied, comprising factor s of 3 for the toxicodynamic component of the 20 interspecies extrap olation factor (UF A ) and a factor of 10 for huma n interindividual variability 21 (UF H ). 22 As shown in Table 5-16, the illustrative RfDs for TCDD-induced tumors range from 23 3.6E-11 for liver and lung tumors in male mice (NTP, 1982) to 1.0E-9 for adrenal cortex, tongue 24 and nasal/palate tumors in male rats (Kociba et al., 1978). This illustrative RfD range for TCDD 25 tumorigenesis falls within the range of candida te RfDs for noncancer TCDD effects presented in 26
Table 4-5. 27
28
29
30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-59 5.2.3.4.1.4.2. Illustrative RfDs based on hypothesized key events in TCDD’s MOAs for liver 1 and lung tumors. 2 As described in Section 5.1, most evidence sugg ests that the majority of toxic effects of 3 TCDD are mediated by interaction with the AhR. EPA considers interaction with the AhR to be 4 a necessary, but not sufficient, event in TCDD carcinogenesis. The sequence of key events 5 following binding of TCDD to the AhR and that ultim ately leads to the development of cancer is 6
unknown. While the mode of action of TCDD in producing cancer has not been elucidated for 7 any tumor type, the best charac terized carcinogenic actions of TC DD are in rodent liver, lung, 8 and thyroid. The hypothe sized sequence of events following TCDD interaction with the AhR is 9 markedly different for each of these three tumo r types. Additionally, no detailed hypothesized 10
mode of action information exists for a ny of the other reported tumor types. 11 The endpoints selected for this illustration we re evaluated to provi de insight into the 12 quantitative relationships betw een tumor development and precursor events in TCDD-induced 13 carcinogenesis. The endpoints described below may or may not be biol ogically adverse in 14 themselves; the intent herein was to consider TCDD-induced biochemical and cellular changes 15 that could lead to subsequent tumor development. 16 In the following exercise, illustrative RfDs were derived for key events in TCDD’s 17 hypothesized modes of action in the liver and lu ng. No appropriate dose-response data were 18 identified for key events in TCDD’s hypot hesized MOA for thyroid tumors in a 19 sex/species/strain that has been shown to deve lop thyroid tumors (i.e., female B6C3F1 mice and 20 male and female Osborne-Mendel rats [NTP, 1982]). 21 As this is an illustrative exercise only, only studies that were originally identified in 22 Section 2 for potential n oncancer dose-response modeling were evaluated here (see Section 2.4.2 23 for study details). There may be additional studies available in the literatu re that would further 24 inform the dose-response asse ssment of these endpoints. 25 Additionally, for animal model consistency, only results from studies conducted in 26 female S-D rats are presented here. The majority of the available information on TCDD 27 carcinogenicity (and TCDD carcinoge nic precursor events) comes from studies conducted in 28
female S-D rats and the most recent TCDD car cinogenicity study was conducted in female S-D 29 rats (NTP, 2006). While both Kociba et al . (1978) and NTP (2006) have conducted TCDD 30 carcinogenicity studies in female S-D rats, different substrains were used; this difference in 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-60 substrain may have resulted in the different carcinogenic responses re ported from the two 1 studies. While the carcinogenicity of TCDD in fe male S-D rats has been well characterized, this 2
animal model does not exhibit th e full suite of tumor responses reported for TCDD (for instance, 3
female S-D rats have not been shown to deve lop thyroid tumors). Additionally, the most 4 sensitive single tumor response in female S-D rats from NTP (2006) is squamous cell carcinoma 5 of the oral mucosa (see Section 5.2.3.2), a tu mor type for which no mode of action information 6 exists. Therefore, the illustrative RfDs descri bed below may not be protective against all tumor 7 types. 8 For each endpoint, PODs for illustrative cancer RfD development were identified as 9
described for the noncancer RfD de rivation in Section 4. Briefl y, for the endpoints identified 10 below, the NOAEL HED s and/or LOAEL HED s were determined based on EPA analysis of the 11 original data presented by the study author (see Section 2.4.2 for details) and by application of 12
the Emond PBPK models as descri bed in Section 3.3.4. BMDL HED s were determined as 13
described in Section 4.2 for all data sets amen able to BMD modeling. Modeling outputs for the 14 endpoints are presented in Appendi ces E and G as noted in Table 5-17. The illustrative RfDs 15 were derived by dividing the POD by appropriate uncertainty factor s as indicated in Table 5-17. 16
17
5.2.3.4.1.4.2.1. Liver tumors. 18 Figure 5-4 presents one hypothe sized mode of action for TC DD-induced liver tumors in 19
rats. TCDD activation of the AhR leads to a va riety of changes in ge ne expression, including 20
increased CYP1A1 mRNA and subse quent increases in CYP1A1 activity. These alterations in 21 gene expression are hypothesized to lead to hepatotoxicity, followed by compensatory 22 regenerative cellular proliferat ion and subsequent tumor development. The details of the 23 mechanism of TCDD-induced hepatotoxicity have not been fully determined but both CYP 24 induction and oxidative stress have been postula ted to be involved (Maronpot et al., 1993; 25 Viluksela et al., 2000). Additiona lly, oxidative DNA damage has been implicated in liver tumor 26 promotion (Umemura et al., 1999). The enhanced cell proliferation arisin g from either altered 27 gene expression or hepatotoxicity, or both, could be the principal factor leading to promotion of 28 hepatocellular tumors (Whysner and Williams, 1996). 29 A dose-response relationship exists for TCDD-m ediated hepatotoxicity, and this parallels 30 the dose-response relationship for tumor formation (o r formation of foci of cellular alteration as a 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-61 surrogate of tumor formation). However, the dose-response re lationship for other 1 TCCD-induced responses such as enhanced gene e xpression is different from the dose-response 2 for tumor formation in terms of both efficacy a nd potency (see Popp et al. [2006] for review). 3 A representative endpoint for each of the hypothesized key events following AhR 4
activation for TCDD-induced liver tumors was identif ied and is shown in Fi gure 5-4. Illustrative 5 RfDs based on each representative e ndpoint are shown in Table 5-17. 6
7
5.2.3.4.1.4.2.2. Lung tumors. 8
Far less is known about TCDD’s mode of acti on in the lung. Figur e 5-5 presents two 9 hypothesized modes of action for TCDD-induced l ung tumors in rats. The first hypothesized 10 mode of action of TCDD in the lung involves di sruption of retinoid homeostasis in the liver. 11 Retinoic acids and their corresponding nuclear recep tors, the RARs and the RXRs, work together 12 to regulate cell growth, differe ntiation, and apoptosis. It is hypothesized that TCDD, through 13 activation of the AhR, can affect parts of the complex retinoid system and/or other signaling 14 systems regulated by, and/or cr oss-talking with, the retinoid system (reviewed in Nilsson and 15 Hakansson, 2002). These effects are then hypothe sized to lead to lung tumor development, 16 however the mechanisms underlying this hypoth esis are not well-defined. The second 17 hypothesized mechanism for the carcinogenic action of TCDD in the lung is through induction of 18 metabolic enzymes. Through activation of Ah R and subsequent induction of metabolizing 19 enzymes (such as CYP1A1), TCDD may enhance bioactivation of other carcinogens in lung 20 (Tritscher et al., 2000). However, there are few studies to suppor t this hypothesis. 21 Representative endpoints could only be identified for two of the hypothesized key events 22 following AhR activation for TCDD-induced lung tumors. These endpoints are presented in 23 Figure 5-5. Illustrative RfDs based on each of these two repres entative endpoints are shown in 24
Table 5-17. There is insufficient informati on to form any conclusions on the quantitative 25 progression to tumorigenicity or on the relative protection afford ed by preventing the key events 26
shown. 27
28This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-62 5.2.3.4.1.4.2.3. Limitations of illustrative RfDs based on hypothesized key events in TCDD’s 1 MOAs for liver and lung tumors. 2 A trend for increasing RfD values that follo ws the progression of endpoints towards the 3 production of tumors is evident. However, th ere are a number of fact ors that prevent making 4 strong conclusions based on this exercise. These limitations include the following 5
6

• This example addresses only two tumor types in one species, strain and sex (female S-D 7 rats), with little informa tion available on the hypothesi zed mode of action for lung 8 tumors. No mode of action information is available for the most sensitive tumor type in 9 this animal model (squamous cell carcinoma of the oral mucosa). Therefore, it is 10 possible that the illustrative RfDs presente d in this example would not be protective 11 against all tumor types in female S-D rats. Importantly, other animal models have been 12 shown to be more sensitive to TCDD-indu ced carcinogenesis based on combined tumor 13 analysis (see Section 5.2.3.2); an RfD based on tumorigenesis in this animal model may 14 not be protective against tumori genesis in other, more sensit ive, animal models (or, by 15 extension, in humans). 16

• Several of the BMDLs are based on poorly-fitting models, such that the RfD is based on 17 a LOAEL, which is not a particularly good measure for comparison across endpoints 18 (e.g., LOAELs are subject to influences of dose spacing in bioassays). Furthermore, the 19 hepatotoxicity BMDL based on a dichotomous 10% BMR, is not directly comparable to 20 all the other BMDLs based on a continuous 1 standard-deviation BMR (Crump, 2002). 21

• The endpoints selected as representative of each hypothesized key event may not be the 22 most appropriate choices. These particular e ndpoints were chosen because they were the 23 most sensitive indicator (i.e ., lowest POD) from the avai lable data or were the only 24 available choice based on a lack of data for other effects related to the hypothesized key 25 event. 26

• The optimum timing of these ev ents may not be reflected in the endpoints selected. 27 Almost certainly, changes in gene expre ssion are early events, such that a single 28 exposure should be relevant, as in the mRNA changes re ported after a single TCDD 29 exposure (Vanden Heuvel et al., 1994), alt hough it is not known whether the magnitude 30 of these changes would be altered after l onger-term exposure, or whether longer-term 31 exposure would be more relevant to downstr eam events. Similarly, single exposures for 32 induction of CYP enzymes would seem to releva nt as a measure of the immediate effect, 33 but it may be longer-term repeated CYP ac tivity that is important for longer-term 34 downstream events; Table 5-17 shows a nominal order-of-magnitude difference in effect 35 levels for similar effect magnitudes (ca. 20-fold) from single e xposures (Kitchin and 36 Woods, 1979) and long-term exposures (53-we eks; NTP, 2006). The relevant exposure 37 durations for oxidative stress and later eff ects are longer term, so a measurement of 38 oxidative stress at 90-days in a rodent may be appropriate; Wyde et al. (2001) suggest 39 that induction of 8-oxo-dG DNA adducts are a result of longer-term oxidative stress 40 because of the lack of effect of single e xposures. Hepatotoxicity and hepatocellular 41This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-63 proliferation events would appear at successive ly later times, but the effective exposure 1 levels would depend heavily on the endpoints ch osen to represent those events and the 2 time at which they were measured. The t oxic hepatopathy endpointreported in NTP 3 (2006), is a general measure of mild to modera te liver toxicity, but is measured only at 4 the end of the study when tumors have al ready appeared. Hepatocyte hypertrophy, 5 measured at 31weeks may be more durati on-relevant, but may not indicate actual 6 hepatocellular toxicity. 7

• The lowest of the tested doses may well be mu ch higher, given that all animal diets are 8 contaminated to a certain extent by TCDD, resulting in initial TC DD body burdens in all 9 animals. Vanden Heuvel et al. (1994) repor ted TCDD liver concentrations in control 10 animals almost as high as for the low-dose group, which could equate to a significant 11 increase in the actual exposure experienced by the low-dose group. A similar effect on 12 the low-dose group (0.45 ng/kg) in Kitchin a nd Woods (1979) is possible, although they 13 did not report control animal tissue concentrations. Higher exposure levels or longer14 term exposures would not be affected to th e same degree, as admi nistered TCDD levels 15 would likely be large compared to initial body burden or low-level feed stock exposure. 16
17 Given the limitations described above, es tablishing an unambiguous progression of 18 effects is extremely problematic given the lack of sufficient data. Identifying a RfD that could 19 be considered to be protective against tumorige nesis in humans based on these data and models 20 is subject not only to the determination of effec tive low doses for the RfDs in Table 5-17 but also 21 to the determination of effective exposures that c ould be considered to be protective of all other 22 tumor types in female S-D rats as well as all other animal models. The latter would entail 23 identifying precursors that are sufficient in them selves for progression to tumorigenesis for all 24 tumor types. Given the disparate sequence of hypothesized key events following TCDD-induced 25 AhR activation for the tumor types for which some information is available, AhR 26 binding/activation is the only key ev ent that is likely to be sh ared across tumor types. No 27 appropriate quantitative data on AhR binding/activation by TCDD in relevant animal models 28 were located; therefore, an illustrative RfD based on TCDD AhR activation could not be 29 developed. 30 Simon et al. (2009) present a similar analysis for the liver tumors observed in the NTP 31 (2006) study, showing a progr ession of effects from early biochemi cal events to irreversible liver 32 toxicity, culminating in tumorigenesis. While illustrative of the putative tumor-promoting MOA 33 for TCDD, the limitations of using such an approach within the context of an assessment of the 34 overall carcinogenic risk of TCDD as detailed ab ove still apply. Simon and colleagues also 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-64 present RfDs for liver tumors a nd several precursor endpoints. A ll the RfDs presented in Simon 1 et al. (2009) are essentia lly equivalent and are 1 to 3 orders of magnitude higher than the RfDs 2 for equivalent endpoints presented in Table 5-17. These discrepanc ies are partly due to the fact 3 that the Emond PBPK models (Emond et al., 2004, 2005, 2006; see also Section 3.3.4) used in 4 this document predicts lower TCDD intakes for similar tissue concentrations than the CADM 5 kinetic model (Carrier et al., 1995a, b; Aylwar d et al., 2005b) used by Simon and colleagues. 6 However, a larger contributor to these discrepanc ies is the use of a chem ical-specific adjustment 7 factor (CSAF) of 0.1 for the toxicodynamic com ponent of the interspecies uncertainty factor by 8 Simon et al. (2009), while EPA used an uncertain ty factor of 3. EPA does not find that the in 9
vitro evidence presented by Simon et al. in suppo rt of a CSAF of 0.1 for interspecies 10 toxicodynamics meets the burden of proof necessary for a reduction in this uncertainty factor. 11
12 5.2.3.4.1.4.3. Effect of dose metric on linearity of response. 13 EPA agrees that there is evidence for sublin earity in the TCDD car cinogenic response in 14 rats. The lung and liver tumor endpoints in the NTP (2006) study using female S-D rats are best 15 fit with a sublinear model. Except for squa mous cell carcinoma of the oral mucosa and 16 combined carcinomas and adenomas of the panc reas, the multistage model fits to tumor 17
incidence data were second or third degree (see Table 5-13 and Appendix F). Figure 5-6 shows 18 the multistage model fit for cholangiocarcinomas as a representative example. For all other 19 tumor incidence data in all other cancer bioassays, the multistage model fit was linear (first 20 degree), when based on either administered dose or modeled blood concentrations (see 21 Appendix F). In further evaluating the combined liver tumor incidence for female S-D rats in 22 Kociba et al. (1978) (as re-eva luated by Goodman and Sauer, 1992; “Kociba/G&S data”), using 23 an unconstrained dichotomous Hill model, a progression from supralinearity to linearity to 24 sublinearity is obtained when fitting the model to the data based on progressively more relevant 25 dose metrics. Figure 5-7 shows the Hill model fits to the Kociba/G&S data based on 26 administered dose, modeled blood con centrations and modeled liver AhR-bound 27
concentrations. 32 Details of the model fits are presented in Table 5-18. The Hill coefficient 28 parameter values are also given in the caption to Figure 5-7. A Hill coefficient of 1 indicates 29

32 The modeled TCDD compartmental concentrations were obtained using the Emond rodent PBPK model described in Section 3.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-65 linearity, while values less than 1 indicate supr alinearity and values greater than 1 indicate 1 sublinearity. The TCDD blood concentration me tric is used in this document as it was 2
determined to be the most rele vant toxicokinetically-e quivalent metric across species. However, 3 as discussed in Section 3, the AhR-bound TCDD c oncentration may be the most relevant metric 4 for liver effects, but this metric is also the mo st uncertain. Uncertainty aside, the tendency for 5 the model fits to move towards sublinearity with increasing relevance of dose metric suggests 6 that the “true” tumorigenic respon se may be sublinear in female S-D rat liver. The progression 7 towards sublinearity is likely to be due to the fa ilure of the less-relevant metrics to reflect the 8 dose-dependent elimination of TC DD in the liver. The range of the administered doses in 9 Kociba et al. (1978) is much larger than that of the effective internal TCDD concentrations. This 10 “stretches out” the response relative to dose, resulting in a supralinear model fit. The blood11 concentration metric (as determined using th e Emond PBPK model) partially accounts for dose12 dependent elimination, but doe s not account for the partiti on of effective (AhR-bound) and 13 ineffective (CYP1A2-bound) TCDD in the liver 33 , thus yielding only a partial correction to the 14 shape of the dose-response curv e (see Section 3). Thus, hypothe sizing that the “true” response 15
for liver tumors is sublinear in female S-D ra ts and assuming that the same mechanisms are 16 operant in humans, a zero-slope at zero dose re lationship could be proposed for liver tumors, 17 allowing for an RfD approach for liver tumorige nsis (as described above). This approach, 18 however, would not be considered to be protective against all tumors given the lack of 19 appropriate mode of action information for ma ny of the known tumor types, particularly 20 squamous cell carcinoma of the oral mucosa in this animal model. 21 On balance, however, the evidence is only s uggestive and the arguments are speculative. 22 Even if a sublinear response could be shown for rodent tumors, the greater interindividual 23 variability in the human populati on would tend to shift the res ponse towards linear (Lutz, 1999; 24 Hattis, 1996). Hattis (1996), however, points out that, assuming a lognormal distribution of 25 susceptibilities, increasing the variance may resu lt in a linear or supralinear shape for a large 26 portion of the response curve but never brings th e relationship to a true low-dose linear form. 27 How close to zero the linearity extends and how much it encompasses the risks of concern 28 depends on the specifics of the part icular human variability scenario. 29

33 Modeling the response against the whole liver concentratio ns results in a supralinear fit similar to administered dose (Hill coefficient = 0.71).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-66 5.3. DERIVATION OF THE TCDD ORAL SLOPE FACTOR AND CANCER RISK 1 ESTIMATES 2 EPA was able to derive ca ndidate OSFs for all cancer mortality from human 3 epidemiologic studies as well as for individua l and combined tumor incidence from rodent 4 cancer bioassays. Each of these studies were selected for TCDD dose-response modeling using 5 the study inclusion criteria outlined in Section 2. The deriva tion of these OSFs can be found for 6 the epidemiologic data in Section 5.2.3.1 and for the rodent bioassay data in Section 5.2.3.2. 7 The OSFs based on epidemiologic studies from three cohorts ranged from 3.75 × 10 5 to 8
2.5 × 10 6 per mg/kg-day (see Tables 5-1 and 5-3) . For the animal data, OSFs based on 9
individual tumors were devel oped for 25 study/sex/endpoint combin ations, and the results ranged 10
from 1.8 × 10 4 to 5.9 × 10 6 per mg/kg-day (see Table 5-14). The OSFs based on combined 11 tumors were developed for 7 study/sex combina tions, and the results ranged from 3.2 × 10 5 to 12
9.4 × 10 6 per mg/kg-day (see Table 5-15). 13 As recommended by expert panelists at EPA’s 2009 Dioxin Workshop (U.S. EPA, 14 2009c) and in the 2005 Cancer Guidelines (U.S. EPA, 2005), EPA has chosen to give higher 15 consideration to the human epidemiologic data rather than the animal bioassay data in 16 developing an OSF for TCDD. Cand idate OSFs derived from the hum an data are consistent with 17 the animal bioassay OSFs; specifically, the human OSFs fall within the same range as the animal 18 bioassay OSFs. Because all the human and animal studies were considered to be of high quality 19 and yielded similar ranges of OSFs, EPA has chos en to rely on the epidemiologic data for OSF 20
derivation. 21 The strengths and limitations of the five ep idemiological studies meeting the inclusion 22 criteria for cancer dose-response modeling ar e summarized in Table 5-19. Among the human 23 studies, the occupational TCDD exposures in th e NIOSH and Hamburg cohorts are assumed to 24 be reasonably constant over the duration of occupational exposure. In contrast, the TCDD 25 exposure patterns in the Seveso and BASF cohorts are associated w ith industrial accidents; as a 26 consequence, the exposure patterns are acu te, high dose followed by low-level background 27 exposure. Such exposure patterns similar to those experienced by the BASF and Seveso cohorts 28 have been shown to yield higher estimates of risk when compared to constant exposure scenarios 29 with similar total exposure magnitudes (Kim et al., 2003; Murdoch and Krewski, 1988; Murdoch 30 et al., 1992). Thus, EPA has judged that the NI OSH and Hamburg cohort response data are more 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-67 relevant than the BASF and Seveso data fo r assessing cancer risks from continuous ambient 1 TCDD exposure in the general population. 2 The NIOSH (Steenland et al., 2001; Cheng et al., 2006) and Hamburg (Becher et al., 3 1998) cohort studies report cumulative TCDD levels in the serum for cohort members. The most 4 significant difference among the Che ng et al. (2006) analysis and those of Steenland et al. (2001) 5 and Becher et al. (1998) is th e method used to back-extrapolat e exposure concentrations based 6
on serum TCDD measurements. Steenland et al . (2001) and Becher et al. (1998) back7 extrapolated exposures and body burdens using a firs t-order model with a cons tant half-life. In 8 contrast, Cheng et al. (2006) b ack-extrapolated body burdens usi ng a kinetic modeling approach 9 that incorporated concentration- and age-dependent elimination kinetics. 10 Although all three of these are high-quality st udies, the kinetic modeling used by Cheng 11 et al. (2006) is judged to bette r reflect TCDD pharmacokinetics, as currently understood, than the 12 first-order models used by Steen land et al. (2001) and Becher et al. (1998). EPA believes that 13 the representation of physiological processes provid ed by Cheng et al. (2006) is more realistic 14 than the assumption of simple first-order kine tics and this outweighs the attendant modeling 15 uncertainties. Furthermore, the use of kinetic mo deling is consistent with recommendations both 16 by the NAS and the Dioxin Workshop panel. 17 However, as discussed in Section 3.3.2, th e kinetic model that they employed does have 18 certain limitations, including the fact that it has been calibrated based on a relatively small 19 number of human subjects. In addition, th eir kinetic model does not allow body mass index 20 (BMI; and hence fat content) to vary with age, which may bias the model results. Nonetheless, 21 EPA prefers the increased techni cal sophistication of the dose es timates used in the cancer 22 mortality risk estimates derived from Cheng et al. (2006) to thos e derived from Steenland et al. 23 (2001). 24 EPA, therefore, has decided to use the resul ts of the Cheng et al. (2006) study for 25
derivation of the TCDD OSF based on total ca ncer mortality as calculated by EPA using 26 data and models from the Cheng et al. ( 2006) study as described in Section 5.2.3.1.2. 27 Table 5-3 shows the oral slope factors at specific target risk levels (OSF RL s) which range 28
from 3.7 × 10 5 to 1.3 × 10 6 per (mg/kg-day). EPA recommends the use of an OSF of 29
1.3 × 10 6 per (mg/kg-day) when the target risk range is 10 − 5 to 10 − 7

. 30
31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-68 5.3.1. Uncertainty in Estimation of Oral Slope Factors From Hu man Studies 1 A substantial degree of uncertainty is asso ciated with the estimation of slope factor 2 values and cancer risk specific doses for TCDD based on the epid emiological studies. In some 3 instances, the influence of a given factor is theoretically amenable to analysis, but such 4 investigation is limited by the av ailability of sufficiently detailed data to support such an 5 analysis. In other cases, only ve ry broad ranges can be placed on the uncertainty associated with 6 a given feature of the analysis, or uncerta inties must be disc ussed qualitatively. 7
8
5.3.1.1. Uncertainty in Exposure Estimation 9 The major technical challenge within each of the epidemiological studies was developing 10 relevant and precise estimates of exposure. Wh ile Warner et al. (2002) collected blood samples 11 relatively close to the time of the Sevesso accident and could r easonably estimate peak exposures 12 based on these collected samples, in the case of the Becher et al. ( 1998), Ott and Zober (1996), 13 Steenland et al. (2001), and Cheng et al. (2006) studies, the majo r exposure issues included the 14 following 15
16

• Selecting (an) appropriate dose metr ic(s) for dose-res ponse modeling, 17

• Estimating serum TCDD levels for the entire cohort based on measurements from a 18 smaller number of the subject s in the cohort co llected long after the occupational 19 exposures had occurred, and then assigning exposures to the remaining members of the 20 cohort based on qualitative job classifications. 21

• Estimating time-weighted average tissue doses (e.g., lipid-average serum concentration 22 over time) based on single samples taken at one point in time. (Except for the Becher et 23 al. [1998] analysis where one of the study stre ngths was their estimate of TCDD half life, 24 which utilized repeated measurements from a subset of their cohort). 25
26 In the Becher et al. (1998), Steenland et al . (2001), and Cheng et al . (2006) studies, dose27 response modeling was performed using ppt-years lipid-adjusted serum concentration as the 28 primary dose metric for TCDD. The choice of seru m concentration was natural in the sense that 29 the serum TCDD was the only direct measurement of exposure or dose that was available. In 30 addition, as discussed in Section 3.3.4, serum concentration is a reasonable index of total tissue 31 concentration (target organ dose), and lipid-adj usted serum concentrati on provides a reasonable 32 index of TCDD in the fatty components of ti ssues. Ott and Zober (1996) used ng/kg body 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-69 weight at the time of the accident as the primar y dose metric, and EPA later converted these to 1 units of ppt-years lipid-adju sted serum concentration. 2 The decision to use cumulative serum concentrations (ppt-years) as the primary dose 3 metric for carcinogenicity is based on the unders tanding that time weighted concentrations (over 4 a chronic exposure period) are the most appropriat e dose measures for cancer risk assessment. 5 This may not be strictly true if cancer inducti on by TCDD is considered to be a “threshold 6 process.” However, as discussed in Section 5.2, there are reasonable gro unds to believe that the 7 assumption of low-dose linearity is reasonabl e for TCDD, especially when calculating 8 population risks where the effects of interindividual variability must be taken into account. 9
In addition to the issue of low-dose threshol ds, the rationale for using cumulative dose 10 metrics also can fail at high doses if the advers e response in question involves a step that is 11 saturable (e.g., where there is a maximum level of response that cannot be exceeded owing to a 12 rate-limited process). There is some evid ence for such a phenomenon in the NIOSH cohort 13 where cancer risks in the highest exposure group (>50,000 ppt-years) appear to sa turate, and the 14 response decreases at this level. Steenland et al. (2001) suggest that the apparent saturation of 15 dose-response in this co hort may be due, at least partially, to exposure misclassification, rather 16
than to an actual reduction in response per unit exposure. 17 The uncertainty associated with differences in the exposure patte rns is important to 18 consider across the five epidemiologic studies. Steenland et al. (2001), Cheng et al. (2006), and 19 Becher et al. (1998) stud ied cohorts exposed to elevated TC DD levels over a long period of time, 20 while Ott and Zober (1996) and Wa rner et al. (2002) studied c ohorts exposed to TCDD levels 21 significantly above background at one point in time but the e xposures and likely the TCDD body 22 burdens declined significantly following these periods of elevated exposure. Both these chronic 23 and acute exposures can be analyz ed in terms of cumulative expos ure to TCDD. Use of such a 24 metric requires an assumption that the “actual” ca ncer potency associated with a cumulative dose 25 where much of the dose is received at a single point in time and then gradually eliminated would 26 be similar to the cancer potency of the same cumulative dose received over a longer period of 27 time and also gradually eliminated. While EPA believes that such an assumption is not 28 unreasonable, the experiment of Kim et al. ( 2003), which showed statistically significant 29 increase in liver effects due to a peak TCDD dos e when comp ared to ch ronically-dosed Sprague30 Dawley rats administered the same levels of TCDD when measured as a cumulative dose, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-70 suggests that additional analyses of cumulative and peak TCDD dose measures may need to be 1
conducted. 2
There are uncertainties associated with the approaches used to estimate TCDD exposures 3 in the members of the occupational epidemiologi c studies for which no measurement data were 4 available. To impute TCDD levels for workers wi thout measured samples, all four occupational 5 epidemiologic studies matched workers for w hom measured TCDD samples had never been 6 reported to workers with measur ed TCDD levels based on job hist ories. The NIOSH cohort is 7
used to illustrate some of the uncertainties. In the NIOSH cohort, the subset of workers (roughly 8 five percent of the to tal cohort) with blood serum data co mprised surviving members of the 9 cohort (in 1988), and therefore, th eir age distribution would have differed from the rest of the 10 cohort. For each worker in this subset, the foll owing data were availabl e: (1) job classification 11 information, (2) employment history, and (3) seru m TCDD measures. All of the workers in this 12 subset were employed at a single plant where the work histories we re less detailed than at other 13 plants, and many of the workers at this plant had the same job tit le and were employed during the 14 same calendar period. There is an assumption that workers with same job title and work history 15 were exposed to the same TCDD levels within a plant and across plants ; this obviously does not 16 account for exposure heterogeneity. 17 Both Steenland et al. (2001) and Cheng et al. (2006) addressed the potential for exposure 18 measurement error in TCDD estimates and possibl e exposure misclassificati on. For the highest 19 exposure workers, Steenland et al. (2001) and Ch eng et al. (2006) found weak, “noisy,” and/or 20 negative exposure-response relationships. St eenland et al. (2001) suggests that possible 21 explanations for this observation include the saturation of effect s at the upper end of the dose22 response curve, instability of the TCDD expos ure estimates based on the limited number of 23 highly exposed individuals, and the increased probability of exposure misclassification for 24 workers whose job historie s indicate the highest exposures. As Steenland et al. (2001) reported, 25 some of the highest exposures might have been inaccurately estimated because they occurred in 26 workers exposed to short-term, high-dose exposur es during spill clean-up. Cheng et al. (2006) 27 used sensitivity analyses to examine this measur ement error issue and evaluated the potential for 28 exposure misclassification by usi ng ln-transformed TCDD ppt-years. The authors also removed 29 all observations with exposures within the lower and upper 1, 2. 5, or 5th percentiles of the 30 TCDD ppt-year distribution and also removed observations w ith in just the upper 1, 2.5, or 5th 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-71 percentile of TCDD ppt-years. These sensitivity analyses provided similar results. An 1 additional concern is that exposure errors mi ght distort the exposur e distribution in the 2 population, which generally spread s the response out over a wider dose range. This serves to 3 increase the variance of the regression model, al tering both the POD and the corresponding OSF. 4 Becher et al. (1998) only considered workers from a single plant but their analysis 5 included workers employed in five different job locations within the plant. The influence of 6 worker location on slope factor estimates does not appear to be further explored and may 7 represent a source of uncertainty. 8 To estimate long-term body burden metric s from the serum TCDD measurements, 9 Steenland et al. (2001) empl oyed simple first order kinetic elimin ation rate model with a half-life 10 of 8.7 years. Limitations of this approach in clude (1) the average elim ination half-life among the 11 study subjects may not be 8.7 years given di fferences between the study population and the 12 Ranch Hand population from which the value was estimated, (2) use of a single-value estimate 13
fails to take into account the inherent variability in elimina tion half life am ong the individual 14 workers, and (3) it fails to take into account variations in eliminati on kinetics throughout the 15 lifetime of the exposed worker due to change in body fat, age, etc. The impact of these potential 16
sources of bias on the estimates of time-in tegrated body burden cannot be quantitatively 17 assessed. However, Steenland et al. (2001) noted that modest cha nges in elimination half-life (to 18 7.1 years) had only a very small impact on risk estimates. 19 Cheng et al. (2006) estimated past body burdens using the C ADM approach (described in 20 Section 3) (Aylward et al., 2005a , b) rather than a half-life estimate. As noted above, the 21 incorporation of concentration- and age-dependent elimination into this approach has significant 22 advantages over the use of a constant elimina tion half-life. However, as discussed in 23 Section 3.3, the CADM has only been subject to limited testing against human validation data 24 sets, so the degree to which its advantages are realized in practice cannot be easily assessed. 25
There are no available human data in the low dose region, the region of interest to this 26 assessment, to compare with the C ADM (or Emond) model predictions. 27 Becher et al. (1998) developed half lif e estimates based on multiple TCDD blood 28
measures in 48 individuals from this cohort. Th ese half life estimates were then used to back 29
calculate TCDD concentrations at the end of each worker’s em ployment, accounting for age and 30 percentage of body fat. This cohort-specific information m ay pr ovide a better exposure estimate 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-72 than Steenland et al. (2 001) or Ott and Zober (1996) who used similar kinetic approaches. 1 However, the comparison of the accuracy of th e exposure estimates acros s the cohorts is not 2 easily assessed. There are several assumptions and important uncertainti es involved in modeling 3 TCDD exposures in these cohorts. The study aut hors have invoked different kinetic assumptions 4 when extrapolating measured levels of TCDD in sera backward in time to estimate higher 5 chronic or peak dosage (i.e., there is uncertain ty in these back-calcu lations that includes 6 assumptions regarding elimination kinetics). There is also uncertainty in applying such estimates 7
to other members of the cohort based on sim ilar characteristics (e.g., job category). 8
9
5.3.1.2. Uncertainty in Shape of the Dose-Response Curve 10 Another source of uncertainty is the nature of the dose-re sponse curve in the low dose 11 region of interest for risk assessmen t for environmental exposures (e.g., 5

− 10 ng/kg-day). Most 12 of the dose-response curves appear reasonably lin ear in this region. These epidemiologic data, 13 however, are based on occupational studies in which exposures we re often several orders of 14 magnitude higher than environmental exposures. Da ta from these studies are quite sparse in the 15 low dose region, and only one study examined uncertainty due to the low dose region. Steenland 16 and Deddens (2003) attempted to analyze this region sp ecifically by fitting threshold curves to 17
the NIOSH data in which there was no extra ri sk from exposure until some specific level. 18 However, this model did not fit as well as models without a threshold. In general, the usual 19 assumption of linearity in the low dose region se ems reasonable when using epidemiologic data 20 given the lack of data in this region that precludes the rejection of linearity. 21 There is uncertainty in the extrapolati on of the OSF to the low dose region (e.g., 22 <1 ng/kg-day). EPA developed th e cancer assessment in this document assuming the slope in the 23 low-dose region of the dose-respons e curve is linear; the decision wa s made due to the lack of 24 sufficient evidence to support an assumption of nonlinearity as outlined in the 2005 Cancer 25 Guidelines (U.S. EPA, 2005). Sim ilarly, there is uncertainty as to whether a threshold exists for 26 TCDD-induced toxicity leading to tumorigenesis and the dose associ ated with such a threshold, 27 if it exists, is unknown. EPA chose to model th is dose-response without a threshold because 28 there is insufficient evidence to support an assumption of a threshold. 29 It also is noteworthy that the shapes of the exposure-response in several of these studies, 30 based on the published statistical models, is indi cative of a response that tends to tail off or 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-73 “plateau” at high cumulative exposures to TC DD. This phenomenon has been seen in many 1 studies of occupational carcinogens, and may re flect a number of things including exhaustion of 2 people susceptible to cancer, satura tion of biological pathways whic h are part of the pathway to 3 cancer, and increased error measurement of dose at high levels biasing dose-response towards 4 the null (Stayner et al., 2003). 5
6
5.3.1.3. Uncertainty in Defining the Reference Population 7 Another source of uncertainty using human ep idemiologic data is due to the lack of 8 completely unexposed populations. Epidemiologic data in TCDD studies is based on comparing 9 health outcomes from populations which expe rienced elevated TCDD exposures to outcomes 10 from populations which experienced lower exposur es, rather than to a population with zero 11 TCDD exposure (They also compared outcomes in members of the cohort that were more highly 12 exposed to TCDD to outcomes in members that were less exposed to TCDD). This lack of a 13 completely unexposed population in epidemiol ogy studies is inevitabl e given that truly 14 unexposed human populations do not exist. Therefore, the extra risk calculated in the form of 15
ED 01 s, is inevitably the extra risk above a low background exposure due to dioxins in the general 16 environment. Typically, the ge neral population in western count ries where the epidemiologic 17
studies have been done have had serum levels on the order of 5 ppt. Hence, the extra risks may 18 be considered as those incurred by added exposur e above these background doses. For example, 19
an ED 01 of 18 ng/kg, which yields an excess risk of cancer mortality of 1%, would mean 20 18 ng/kg above a typical background level of 5 ng/kg (5 ppt). 21
22
5.3.1.4. Uncertainty in Cancer Risk Estimates 23 It is important to remember the overall uncer tainty associated with cancer risk estimates 24 for TCDD can be approximately bounded by evaluati ng the spread of estimates from the several 25 epidemiological studies that have been conducte d. In the 2003 Reassessment, EPA noted that 26 the range of ED 01 /lower bound of the 95% confidence interval on the dose that yields a 1% effect 27 (LED 01 ) values identified in the then-available dose-respons e studies (Steenland et al., 2001; 28 Becher et al., 1998; Ott and Zober, 1996) implie d an approximate range of all-cancer mortality 29 slope factors between 1.2 to 2.5 per ng/kg-day. The equivalent oral cance r slope factors resulting 30 from the analysis of the newer Cheng et al. ( 2006) study are below and at the low end of this 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-74 range (0.37

− 1.2 per ng/kg-day for fatal cancer risks between 1 × 10 − 3 and 1 × 10 − 7 ). This 1 suggests that the highest quality epidemiological studies all yield risk es timates that agree to 2 within about a factor of 7. This relatively cl ose agreement between the studies of the different 3
cohorts lends additional confidence to the slope factor derivation. EPA notes that each of these 4 estimates is derived from an occupational cohort, and significant differences likely exist (e.g., 5 exposure levels and routes) between these cohorts and those exposed to TCDD environmentally 6 (i.e., primarily through the food chain). 7
8
5.3.2. Other Sources of Uncertainty in Risk Es timates From the Epidemiological Studies 9 There are a number of other aspects of the Steenland et al . (2001), Cheng et al. (2006), 10 Becher et al. (1998), and Ott a nd Zober (1996) studies that may contribute uncertainty to the 11 cancer risk estimates. First, all studies that meet the criteria (with the excep tion of Warner et al., 12 2002) measure cancer mortality rather than cancer incidence. This presumably biases the slope 13
factor downward relative to that which would be calculated for cancer incidence, which would 14 give a truer picture of the tota l health impacts of TCDD exposure s on the general population. In 15 the NIOSH cohort, roughly one-third of the fata l cancers were from lung cancer. Because of the 16 high case mortality rate associated with lung can cer during the period of cohort evaluation, the 17 slope factor estimated for cancer mortality might not be much lo wer than that calculated for 18
cancer incidence. Estimation of cancer incidence in the genera l population associated with 19 TCDD exposure would require a ssumptions related to the relative survival and age-specific 20 cancer risks in the exposed popul ation compared to the NIOSH cohort or the Hamburg cohort; 21
insufficient data are available to support such an analysis. 22 The general issues associat ed with potential confoundi ng effects were noted and 23
discussed in the 2003 Reassessment. In additi on to smoking and lifestyle factors that might 24 affect cancer risks, intra-indi vidual variation in TCDD kinetics and susceptibility could also 25 affect the relationship between exposure and cancer risk. One specific example of such a 26 confounding factor would be varia tion in TCDD elimination half-liv es. For example, if a large 27 proportion of the 256 observed cancer deaths occu rred in NIOSH workers w ith longer half lives, 28 this could bias the slope factor downward because higher doses would be associated with cancer 29
cases in the deceased workers. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-75 Another source of uncertainty in the exposure/ dose assessment in the Steenland et al. 1 (2001), Cheng et al. (2006), Becher et al. (1998), and Ott and Zober (1996) studies is the limited 2 of information related to expos ure to dioxin-like com pounds (DLCs). In addition, the Hamburg 3 cohort was also exposed to hexachlorocyclohexane (HCH) and lindane. The oral slope estimates 4 derived by these studies attribute all the obser ved excess cancer mortality solely to TCDD 5 exposures. This assumes that occupational e xposure was entirely to TCDD, with no other 6 occupational exposure to dioxins and furans. However, because TCDD typically occurs as a 7 component of a mixture with othe r DLCs, this assumption could l eads to a positive bias in the 8 slope factors estimates derived from these epidem iologic studies if confounding is present. The 9 magnitude of the potential bias can be estimated in a general way through the estimation of risks 10 for plausible mixtures of DLCs and TCDD exposures in the cohor t with the same composition as 11 the Steenland et al. (2001) and Cheng et al. ( 2006) studies, but the deta iled data required to 12 perform such an analysis on the NI OSH cohort are not available. 13 Cheng et al. (2006) assessed the impact of possible confounding by conducting excluding 14 individual plants in the modeli ng. If the estimated cancer risks as a function of exposure did not 15 change too much when specific facilities were left out, then confounding was deemed unlikely. 16 Cheng et al. (2006) likewise found little variation in risks based on these analyses. In addition to 17 the slope factor estimated for TCDD, Becher et al. (1998) also evaluated the slope based on 18 TEQs. They found a dose-response effect for TCDD but not for TEQ (excluding TCDD) which 19 suggests that confounding by DLCs did not occur. 20 There is adequate evidence to believe age, gender, and body fat content all can have a 21 significant impact on elimination kinetics and co nsequent cancer risks associated with TCDD 22 exposure (U.S. EPA, 2003). The Hamburg cohort acc ounted for impacts in their kinetic analysis. 23 There may be gender differences that affect su sceptibility to TCDD exposure, and the NIOSH 24 cohort and the Becher et al. (1998) analysis were comprised almost exclusively of men, so these 25 differences were not systematically addressed. There are few quantitative data on which to 26 examine the potential variation of these modi fying factors on exposur e estimates; however 27
several of the studies have re ported similar results based on va riable half-life estimates. 28 Finally, none of these cancer cohorts containe d any children, and th e unique sensitivities 29 of infants, toddlers, and children were not a ddressed. Aside from differences in exposure 30 patterns and body fat content, the unique develo pm ental status of children m ay result in a 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-76 substantially different profile of health risks (and magnitudes of those risks) than can be 1 addressed by simply compensating on the basis of differences in body weight, food intake, etc. 2 Further, because EPA could not develop an estim ate from the Warner et al. (2002) cohort, none 3 of the studies for cancer dose-response analyzed contained a significant pe rcentage of women. 4 Thus, the generalizability of th e slope factor estimates to women and children is uncertain. 5
6
5.3.3. Approaches to Combining Estimates Fr om Different Epidemiologic Studies 7 Meta-analyses and pooled analyses are two common approaches for combining 8 epidemiologic study data. Meta-analyses are a us eful way to combine epidemiologic data from 9
different studies and derive a common estimate of effect, particularly when there are a large 10 number of comparable studies that are fairly homogenous as to make them impossible to 11 combine. A meta-analysis often involves a weighted average of effect measures, dose-response 12 coefficients, or ED 01 s. 13 Unlike a meta-analysis, a pooled analysis co mbines the original exposure and health 14 outcome data across multiple studies, enabling a fit of new models to the data which were not 15 used in the original publications. Whereas a pooled analysis of the four different cohorts 16 considered here would be useful to explore the functional form and fit of models (either 17 statistical or multistage) across all four cohorts, th is would entail a lengthy undertaking and is not 18 being contemplated here, due in part to concerns about the confidence in the results of such an 19 undertaking. 20
21
5.3.3.1. The Crump et al. (2003) Meta-analysis 22 Crump et al. (2003) published a meta-analysis that incorporated data from the three 23 studies EPA used in the quantitative dos e-response modeling presented in the 2003 24 Reassessment (U.S. EPA, 2003). These three st udy populations were the NIOSH (Steenland et 25 al., 2001), the Hamburg (Becher et al., 1998), and the BASF (Ott and Zober, 1996) cohorts. The 26 data for the NIOSH study incl uded six additional years of follow-up and improved TCDD 27 exposure estimates that had not been applie d to EPA’s dose-response modeling in the 2003 28 Reassessment. This study examined the rela tionship between TCDD exposure and all-cancer 29 mortality. SMR statistics that had been used in all three studies were applied. 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-77 The Crump et al. (2003) analysis was based on published data, and therefore, selection of 1 the dose metric was limited to how aggregated da ta had been presented in the publications. For 2 the NIOSH component of the analysis, the exposur e data were based on wo rker-specific data and 3 specific processes performed at each plant (Steenland et al., 2001). The previous approach 4 assigned workers that had broad categories of exposure duration w ith the same cumulative serum 5 level, and did not take into accoun t the particular plant or the job assignment within the plant. 6 The Crump et al. (2003) approach did take into account wh en exposure occurre d in relation to 7 the follow-up interval. The TCDD exposure me tric used was a cumulative serum lipid 8 concentration (CSLC). For the Hamburg cohort, Cr ump et al. (2003) used an average value from 9 the exposure ranges provided in Flesch-Janys et al. (1998). For the BASF cohort, arithmetic 10 averages for the dose categories were converted to TCDD CSLC intakes by dividing them by 11 0.25 (average body fat of 25%) and a decay rate that corresponded to a half-life of 7 years. 12 The outcome variable for the dose-respons e modeling was all cancer mortality, and 13 CSLC was the independent variable. Crump et al . (2003) performed a series of trend tests to 14
determine the lowest dose for which a statistical ly significant trend in SMR could be shown and 15 all other lower doses. These tests also examined the highest dose in which there was no 16 statistically significant trend usi ng data from this dose and all ot her lower doses. Estimates of 17
ED 10 , ED 05 , and ED 01 for TEQ with respect to the lifetime probability of dying from cancer were 18
calculated. This calculation assumed a first-or der elimination process with a half-life of 19 7.6 years, a 50% systemic uptake of ingested dioxin , that dioxin concentration in serum lipid is a 20
suitable measure for dioxin concentration in all li pid, and that all dioxin is sequestered in lipid 21 (which comprises 25% of body weight). Age-speci fic mortality rates in the presence of dioxin 22 exposure were then generated. Life-table me thodology was used to calculate lifetime risks of 23 cancer mortality. 24 Based on the modeling results, the hypothesis of a baseline SMR of 1.0 was rejected, and 25 the linear model produced an SMR estimate of 1.17 (95% CI = 1.04

− 1.30) from these studies. 26 The dose-response curves for the three studies were not homogene ous. Namely, the points from 27 the BASF cohort fell below the predicted curve. Because the heterogeneity was not judged to be 28 extreme by different statistical tests, however , the investigators used a common model in a 29 combined analysis of the data from the three stud ies. The linear model provided an adequate fit 30 of the data, and the slope associ ated with CSLC-ppt was 6.3 × 10 − 6 (95% CI = 8.8 × 10 − 7 to 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-78 1.3 × 10 − 5 ). Based on goodness of fit analys is, the preferred estimate of ED 01 was 7.7 pg/kg/day, 1 which was six times higher than the estim ate derived Steenland et al. (2001). 2
3
5.3.3.2. EPA ’ s Decision Not to Conduct a Meta-analysis 4 From a statistical perspective, meta-analyses may not be very reliable when applied to a 5 small number of studies. Crump et al. (2003) used only three studies. Had EPA undertaken a 6 meta-analysis for the studies that met its criteria , most of the weight w ould come from the two 7 large studies on the NIOSH and Hamburg cohorts. However, such an analysis relies on an 8 assumption of a normally distribut ed between-study effect. This normality assumption cannot be 9 assessed with only three observations, yet the meta-analysis estimate is highly sensitive to this 10 distributional assumption (Higgens et al., 2009). Because of this limitation and the imprecision 11 of the between-study variance estimate, statistici ans often recommend forgoing meta-analysis in 12 favor of discussing the individual studies when few studies are availabl e (Cox, 2006; Higgens et 13 al., 2009). Based on these considerations, EPA deci ded not to undertake a meta-analysis in this 14
document. 15 As noted previously, Crump et al. (2003) has conducted a meta-analysis of the three 16 cohorts considered here, i.e., the NIOSH, Hambur g, and BASF cohorts. However, Crump et al. 17
modeled SMR data in which the co horts were compared to the gene ral population, rather than on 18 internal exposure-response analyses as relied up on in this document. Th eir analysis included a 19
total of 15 different SMRs from the three studi es. A prior analysis of the dose -responses by 20 Becher et al. (1998) was used (i .e., the categorical SMR analysis by Flesch-Janys et al. [1998]). 21 Additionally, a prior anal ysis of the NIOSH cohort (Steenland et al., 1999) in which SMRs were 22 calculated was used. Crump et al. (2003) found that a linear dose-response gave a good fit to the 23 data, and used that for deriving an ED 01 . However, they found that a supra-linear dose-response 24 provided a better fit to the data, but rejected the supra-linear model (a power model) because of 25 an infinite slope at zero dose. In the origin al publications by Becher et al. (1998) and Steenland 26 et al. (2001), both observed a supr a-linear dose-response trend. Crump et al. (2003) concluded 27
that the ED 01 was 45 pg/kg-day, similar to the ED 01 of 8 pg/kg-day calculated by Steenland et al. 28 (2001) using the same diet ary units (pg/kg-day). 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-79 Table 5-1. Cancer slope factors calculated from Becher et al. (1998), 1 Steenland et al. (2001) and Ott and Zo ber (1996) from 2003 Reassessment 2 Table 5-4 3 4

Study ED 01 (LED 01 ) (ng/kg) Cancer slope factor per ng/kg-day above background a (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 Assumes 25% of body weight is lipid; in humans 80% of dioxin dose is absorbed from the normal 6 diet; the TCDD half-life is 7.1 years in humans. Background all cancer mortality rate calculated 7 through lifetable analysis to 75 years. Summary results are for male all cancer risk, because the 8 male lifetime (to 75 years) all cancer risk is gr eater than for females, leading to correspondingly 9 higher cancer slope factors. As detaile d in Part III, Chapter 8, RelRisk(ED 01 ) = 0.99 + 10 0.01/Risk (0 dose) . Based on the manner in which the dose-response data were calculated using Cox 11 regression rate ratio analyses, risks are give n as cancer slope factors for 1 pg/kg-day above 12 background, assumed 5 ppt TCDD in lipid. 13 UCL = upper confidence limit. 14 15 Source: U.S. EPA (2003; Part III, Chapter 5, Table 5-4). 16This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-80 Table 5-2. Cox regression coefficien t estimate and incremental risk for 1 NIOSH cohort data as presented in Cheng et al. (2006) compared with 2 Steenland et al. (2001) 3 4 Incremental risk a

Model Cox regression coefficient estimate (ppt-year) − 1 R 0 = 0.124 R 0 = 0.112 Steenland et al. (2001) result
Piecewise linear b 1.5 × 10 − 5 7.0 × 10 − 4 6.3 × 10 − 4 Unlagged exposure, Cheng et al. (2006)
Piecewise linear c 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 year s), 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 Assumes 5 ppt serum lipid TCDD concentration for 75 years (unlagged) or 60 years (taking into account a 15-year 6 lag). 7 b Cox regression coefficient from the Steenla nd et al. (2001) analysis as reported in the 2003 Reassessment, Part III, 8 pp. 5–34, males, no lag. The value of this coefficient for the piecewise lin ear modeling was not explicitly reported 9 by Steenland et al. (2001). 10

c Piecewise linear with cutpoint set at maximum likelihood for model fit, 452,000 ppt-years. 11

d Not statistically significantly different from 0. 12 13 Source: Cheng et al. (2006; Table IV). 14This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-81 Table 5-3. Comparison of lipid-adjusted serum concentrations, fat 1 concentrations, risk specific dose esti mates and equivalent oral slope factors 2 based on upper 95 th percentile estimate of regression coefficient a of all fatal 3 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) AUC RL , (ppt-yr) FAT RL (ng/kg) Risk specific dose (D RL ) (ng/kg-day) Equivalent oral slope factors (OSF RL ) per (mg/kg-day)
1 × 10 − 3 1.26 × 10 − 3 1.803 × 10 1 2.73 × 10 − 3 3.7 × 10 5

1 × 10 − 4 1.26 × 10 2 1.803 × 10 1.23 × 10 − 4 8.1 × 10 5

1 × 10 − 5 1.26 × 10 1 1.803 × 10 − 1 8.57 × 10 − 6 1.2 × 10 6

1 × 10 − 6 1.26 × 10 1.803 × 10 − 2 7.77 × 10 − 7 1.3 × 10 6

1 × 10 − 7 1.26 × 10 − 1 1.803 × 10 − 3 7.62 × 10 − 8 1.3 × 10 6
7

a Based on regression coefficient of Ch eng et al. (2006; Table III), excluding observations in the upper 5% range 8 ( ≥ 252,950 ppt-year lipid adjusted serum TC DD) of the exposure s; where reported β = 3.3 × 10 − 6 ppt-years and 9 standard error = 1.4 × 10 − 6 . Upper 95%tile estimate of regression coefficient ( β 95 ) calculated to be 10 6.04 × 10 − 6 = (3.3 × 10 − 6 ) + 1.96 × (1.4 × 10 − 6 ). 11 12 13 Table 5-4. Comparison of lipid-adjusted serum concentrations, fat 14 concentrations, risk specific dose esti mates and equivalent oral slope factors 15 based on best estimate of regression coefficient a of all fatal cancers reported 16 by Cheng et al. (2006) fo r 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) AUC RL , (ppt-yr) FAT RL (ng/kg) Risk specific dose (D RL ) (ng/kg-day) Equivalent oral slope factors (OSF RL ) per (mg/kg-day)
1 × 10 − 3 2.31 × 10 3 3.303 × 10 1 6.63 × 10 − 3 1.5 × 10 5

1 × 10 − 4 2.31 × 10 2 3.303 × 10 2.63 × 10 − 4 3.8 × 10 5
1 × 10 − 5 2.31 × 10 1 3.303 × 10 − 1 1.67 × 10 − 5 6.0 × 10 5
1 × 10 − 6 2.31 × 10 3.303 × 10 − 2 1.44 × 10 − 6 6.9 × 10 5

1 × 10 − 7 2.31 × 10 − 1 3.303 × 10 − 3 1.40 × 10 − 7 7.1 × 10 5 20

a Based on regression coefficient of Cheng et al. (2006; Table III), excluding observations in the upper 5% range 21 ( ≥ 252,950 ppt-year lipid adjusted serum TC DD) of the exposure s; where reported β = 3.3 × 10 − 6 ppt-years. 22This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-82 Table 5-5. Kociba et al. (1978) male rat tumor incidence data a and blood 1 concentrations for dose-response modeling 2 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 4/50 b

Stratified squamous cell carcinoma of tongue 0/85 1/50 1/50 3/50 b

Adenoma of adrenal cortex 0/85 0/50 2/50 5/50 b
4

a Source: Kociba et al . (1978; Table 4). 5 b Statistically significant by Fischer Exact Test ( p < 0.05). 6 7 8 Table 5-6. Kociba et al. (1978) female rat tumor incidence data a and blood 9 concentrations for dose-response modeling 10 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 9/50 a 18/45 b

Stratified squamous cell carcinoma of hard palate or nasal turbinates 0/86 0/50 1/50 4/49 b

Keratinizing squamous cell carcinoma of lung 0/86 0/50 0/50 7/49 b

12

a Source: Kociba et al. (1978; Table 5). Incidence for He patocellular adenomas or carcinomas is from Goodman and 13 Sauer (1992; Table 1); EPA calculated statistical significance as the study authors did not provide this. 14 b Statistically significant by Fischer Exact Test ( p < 0.05). 15This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-83 Table 5-7. NTP (1982) female rat tumor incidence data a and blood 1 concentrations for dose-response modeling 2 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: fibr osarcoma 0/75 2/50 3/50 4/49 b Liver: neoplastic nodule or hepatocellular carcinoma 5/75 c 1/49 3/50 14/49 b

Adrenal: cortical adenoma, or carcinoma or adenoma, NOS 11/73 c 9/49 5/49 14/46 b

Thyroid: follicular-cell adenoma 3/73 c 2/45 1/49 6/47 4

a Source: NTP (1982; Table 10). 5 b Statistically significant by Fischer Exact Test ( p < 0.05). 6

c Statistically significant trend by Chochran-Armitage test ( p < 0.05). 7 8 9 Table 5-8. NTP (1982) male rat tumor incidence data a and blood 10 concentrations for dose-response modeling 11 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 0/74 b 0/50 0/50 3/50

Thyroid: follicular-cell adenoma or carcinoma 1/69 b 5/48 c 8/50 c 11/50 c

Adrenal cortex: adenoma 6/72 9/50 12/49 b 9/49 13

a Source: NTP (1982; Table 9). 14 b Statistically significant trend by Chochran-Armitage test ( p < 0.05). 15

c Statistically significant by Fischer Exact Test ( p < 0.05). 16This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-84 Table 5-9. NTP (1982) female mouse tumor incidence data a and blood 1 concentrations for dose-response modeling 2 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 1/74 b 1/50 1/48 5/47 c Hematopoietic system: lymphoma or leukemia 18/74 b 12/50 13/48 20/47 c

Liver: hepatooellular adenoma or carcinoma 3/73 b 6/50 6/48 11/47 c

Thyroid: follicular-cell adenoma 0/69 b 3/50 1/47 5/46 c 4

a Source: NTP (1982; Table 15). 5 b Statistically significant trend by Chochran-Armitage test ( p < 0.05). 6

c Statistically significant by Fischer Exact Test ( p < 0.05). 7 8 9 Table 5-10. NTP (1982) male mouse tumor incidence data a and blood 10 concentrations for dose-response modeling 11 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 10/71 b 2/48 4/48 13/50

Liver: hepatocellular adenoma or carcinoma 15/73 b 12/49 13/49 27/50 c

13

a Source: NTP (1982; Table 14). 14 b Statistically significant trend by Chochran-Armitage test ( p < 0.05). 15

c Statistically significant by Fischer Exact Test ( p < 0.05). 16This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-85 Table 5-11. NTP (2006) female rat tumor incidence data a and blood 1 concentrations for dose-response modeling b 2 3 Vehicle control (ng/kg) Low dose (ng/kg) Low-med dose (ng/kg) Median dose (ng/kg) Med-high dose (ng/kg) Hi g h dose (ng/kg) System: morphology: topography 0 1,408 3,137 5,393 9,129 16,361
Liver: cholangiocarcinoma 0/49 c 0/48 0/46 1/50 4/49 25/53 c

Liver: hepatocellular adenoma 0/49 c 0/48 0/46 0/50 1/49 13/53 c

Oral mucosa: squamous cell carcinoma 1/49 c 2/48 1/46 0/50 4/49 10/53 c

Pancreas: adenoma or carcinoma 0/48 c 0/48 0/46 0/50 0/48 3/51

Lung: cystic keratinizing epithelioma 0/49 c 0/48 0/46 0/49 0/49 9/52 c

4

a Source: NTP (2006; Table A3a). 5 b Incidence adjusted for anim als <365 days on study. 6

c Statistically significant by Poly-3 Test ( p < 0.05). 7 8 9 Table 5-12. Toth et al. (1979) male mouse tumor incidence data a and blood 10 concentrations for dose-response modeling 11 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 21/44 b 13/43 13

a Source: Toth et al. (1979; Table 1). 14 b Statistically significant by Chi 2 Test ( p < 0.01). 15This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 5-86 DRAFT: DO NOT CITE OR QUOTETable 5-13. Comparison of multi-stage modeling resultsacross cancer bioassays using blood concentrationsStudy Species SexMorphology: topographyMulti-stage modeling*:stage, GoFp-value, LL differenceBMD01(ng/kg)BMDL01(ng/kg)Stratified squamous cell carcinoma of hard palate or nasal turbinates1,p= 0.813,175 1,540Stratified squamous cell carcinoma of tongue1,p= 0.4733561433Adenoma of adrenal cortex1,p= 0.781,793 1,020MaleCombined tumors Bayesian analysis849 528Hepatocellular adenoma(s) or carcinoma(s)1,p= 0.24,387 277Stratified squamous cell carcinoma of hard palate or nasal turbinates1,p= 0.972,484 1,289Keratinizing squamous cell carcinoma of lung1,p= 0.631,730 984Kocibaet al.,1978RatFemaleCombined tumors Bayesian analysis280 206Subcutaneous tissue: fibrosarcoma1,p= 0.181,700 751Liver: neoplastic nodule or hepatocellular carcinoma1,p= 0.22638 402Adrenal: cortical adenoma, orcarcinoma or adenoma, NOS 1,p= 0.34 878 444Thyroid: follicular-cell adenoma1,p= 0.571,840 846FemaleCombined tumors Bayesian analysis251 172Liver: neoplastic nodule or hepatocellular carcinoma1,p= 0.853,345 1,472Thyroid: follicular-cell adenoma or carcinoma1,p= 0.06657 380Adrenal cortex: adenoma1,p= 0.062,161 665RatMaleCombined tumors Bayesian analysis410 243Subcutaneous tissue: fibrosarcoma1,p= 0.931,849 916Hematopoietic system: lymphoma or leukemia1,p= 0.98622 331Liver: hepatocellular adenoma or carcinoma1,p= 0.34807 449Thyroid: follicular-cell adenoma1,p= 0.09, no improvement withhigher orders1,653 779NTP,1982Mouse FemaleCombined tumors Bayesian analysis244 162This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 5-87 DRAFT: DO NOT CITE OR QUOTETable 5-13. Comparison of multi-stage modeling resultsacross cancer bioassays using blood concentrations(continued)Study Species Sex Morphology: topographyMulti-stage modeling*:stage, GoFp-value, LL differenceBMD01(ng/kg)BMDL01(ng/kg)Lung: alveolar/bronchiolar adenoma or carcinoma1,p= 0.09354 191Liver: hepatocellular adenoma or carcinoma1,p= 0.93115 75NTP,1982cont.Mousecont.MaleCombined tumors Bayesian analysis88 58Liver: cholangiocarcinoma3,p= 0.99, dLL = 2.93 4,173 2,277Liver: hepatocellular adenoma3,p= 0.93, dLL = 2.10 5,631 3,596Oral mucosa: squamous cell carcinoma1,p= 0.271,214 765Pancreas: adenoma or carcinoma1,p= 0.645,794 2,551Lung: cystic keratinizing epithelioma2,p= 0.51, dLL = 3.55 4,575 2,890NTP,2006Rat FemaleCombined tumors Bayesian analysis651 431Toth etal.,1979Mouse Male Liver: tumors1,p= 0.29203 115*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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-88 Table 5-14. Individual tumor points of departure and slope factors using 1 blood concentrations 2 3

Study Tumor Site (Sex/Species) BMDL HED (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 fibrosar coma (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 4This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-89 Table 5-15

. Multiple tumor points of depart ure and slope factors using blood 1 concentrations 2 3

Study Sex/species: tumor sites BMDL HED (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 squa mous 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

4This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 5-90 DRAFT: DO NOT CITE OR QUOTETable 5-16. Illustrative RfDs based ontumorigenesis in experimental animalsStudySpecies, strain(sex) ProtocolEndpointBMDLHEDa(ng/kg-day)RfDb(mg/kg-day)NTP, 1982 Mouse, B6C3F1,male2-year gavage;n= 50Liver adenoma and carcinoma, lung1.1E-3 3.6E-11NTP, 1982 Mouse, B6C3F1,female2-year gavage;n= 50Liver adenoma and carcinoma, thyroid adenoma,subcutaneous fibrosarcoma, all lymphomas5.3E-3 1.7E-10NTP, 1982 Rat, OsborneMendel, female2-year gavage;n= 50Liver neoplasitc nodules, thyroid follicular cell adenoma,liver adenoma and carcinoma, adrenal cortex adenoma orcarcinoma5.7E-3 1.9E-10Toth et al., 1979 Mouse, Swiss/H/Riop, male1-year gavage(1-year average);n= 38−44Liver tumors6.1E-3 2.0E-10Kociba et al.,1978Rat, S-D, female 2-year dietary;n= 50Liver adenoma carcinoma, oral cavity, lung7.3E-3 2.4E-10NTP, 1982 Rat, OsborneMendel, male2-year gavage;n= 50Thyroid follicular cell adenoma, adrenal cortex adenoma 9.6E-3 3.2E-10NTP, 2006 Rat, S-D, female 2-year gavage;n= 53Liver cholangiocarcinoma, hepatocellular adenoma, oralmucosa squamous cell carcinoma, lung cystic keratinizingepithelioma, pancreasadenoma, carcinoma3.1E-21.0E-9Kociba et al.,1978Rat, S-D, male 2-year dietary;n = 50Adrenal cortex adenoma, tongue carcinoma, nasal/palatecarcinoma3.1E-2 1.0E-9aBMR = 0.01.bUF = 30; UFA= 3, UFH= 10.This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 5-91 DRAFT: DO NOT CITE OR QUOTETable 5-17. Illustrative RfDs based on hypothesizedkey events in TCDD’s MOAs for liver and lung tumorsKey eventEndpoint and exposure durationNO(A)ELHED(ng/kg-day)LO(A)ELHED(ng/kg-day)BMDLHEDa(ng/kg-day)RfDb(mg/kg-day)StudyLiver tumorsChanges in gene expression CYP1A1 mRNA,1 day1.8E-053.4E-04 1.1E-01c(Appendix G)6E-13dVanden Heuvel et al.,1994Benzo(a)pyrene hydroxylase (BPH)activity (CYP1A1), 1 day9.2E-04 6.0E-034.6E-04c(Appendix G)2E-11dKitchin and Woods,1979Changes in gene expressionEROD (CYP1A1), 53 weeks none 1.4E-019.5E-03c(Appendix G)3E-10dNTP, 2006DNA single-strand breaks,90 daysnone 3.3E-022.2E-02c(Appendix G)7E-10dHassoun et al., 2000TBARS, 90 days--4.4E-02(Appendix G)2E-09dHassoun et al., 2000Oxidative stressCytochrome C reductase, 90 days --8.8E-02(Appendix G)3E-09dHassoun et al., 2000Toxic hepatopathy,2 yearsnone1.4E-011.8E-01c(Appendix E)5E-09eNTP, 2006HepatotoxicityHepatocyte hypertrophy, 31 weeks 9.3E-02 3.3E-018.8E-03(Appendix E)3E-10dNTP, 2006Hepatocellular proliferation Labeling index,31 weeksnone 1.4E-016.6E-02c(Appendix G)2E-09dNTP, 2006Lung tumorsMetabolic enzyme induction EROD (CYP1A1), 53 weeks none 1.4E-012.9E-04(Appendix G)1E-11dNTP, 2006Retinoid homeostatsis Hepatic retinol and retinylpalmitate, 90 daysnone 1.1E+001.7E-01c(Appendix E)6E-09dVan Birgelen et al.,1995aBMR 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; UFA– 3, UFH– 10.eUF = 300; UFA – 3, UFH – 10; UFL – 10.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-92 Table 5-18. Dichotomous Hill model fits to combined adenoma and 1 carcinoma data from Kociba et al. (1987) as re-evaluated by Goodman and 2 Sauer (1992) a 3 4

TCDD dose metric Background response ED 50 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 × 10 4 0.714 0.172 5

a Calculations performed in S-PLUS ® 6.2 for Windows ®

. 6 b ng/kg tissue concentration except for administered dose, which is ng/kg BW per day (ng/kg-day). 7This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-93 Table 5-19. Comparison of prin cipal epidemiological studies 1 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 compar isons (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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-94 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-95 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 confounder s (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 adjust ment 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 log 10 transformation of the exposure

estimates in their regression analysis. Warner et al. (2002)This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-96 1 Figure 5-1. Mechanism of altered gene expression by AhR. The regulation of 2 gene expression by TCDD in mammalian ce lls requires binding of the xenobiotic 3 to the aryl hydrocarbon receptor (AhR). The AhR is part of a multi-protein 4 complex that includes heat shock protei ns and various kina ses and other post5 translational modifying fact ors. Upon ligand binding, the AhR heterodimerizes 6 with the aryl hydrocarbon receptor nuclear translocator (Arnt) and binds to dioxin 7 response elements (DREs) found in target genes. Alternatives to DRE-dependent 8 gene expression exist whereby the Ah R complex associates with other 9 transcription factors and results in a cr oss-talk between these systems. The 10 culmination of regulation of AhR targets genes (both increases ad decreases in 11 transcription) results in an alteration in cellular phenotypes, including changes in 12 intracellular metabolism and cha nges in cell cycle regulation. 13This document is a draft for review purpos es only and does not constitute Agency policy 1/15/10 5-97 DRAFT: DO NOT CITE OR QUOTEFigure 5-2. TCDD’s hypothesized modes ofaction in site-specific carcinogenesis.See text for details. In eachinstance, the solid arrows depict pathways that are well-established and are associated with low uncertainty. Thedashed arrows represent connections that are less established and are associatedwith higher uncertainty.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-98 1 Figure 5-3. EPA’s process to select a nd identify candidate OSFs from key 2 animal bioassays for use in the ca ncer risk assessment of TCDD. 3 For each cancer study that qualified for TCDD dose-res ponse assessment using the study inclusion criteria, 4 EPA first selected the species/sex/tu mor combinations with statistically significant increases in tumor 5 incidence by either a pair-wise test between the treated group and the controls or by a trend test showing 6 increases in tumors with increases in dose. Next, EPA used an animal kinetic model to estimate blood 7 concentrations corresponding to the study average daily administer ed doses for use in dose response 8 modeling. BMDL 01 ’s were then estimated for the blood c oncentrations by, (1) using the linearized 9 multistage model for each species/sex/tumor combinati on within each study, and (2) using the linearized 10 multistage model within a Bayesian Markov Chain Monte Carlo framework that assumes independence of 11 tumors and modeling all tumors together for each speci es/sex combination within each study. Using the 12 human kinetic model, human equivalent doses (BMDL HED s) were then estimated for each of the BMDL 01 s 13 and oral slope factors were calculated by OSF = 0.01/BMDL HED . The lowest OSF for a species/sex 14 combination for either a single tumor type or all tu mors combined was selected as a candidate OSF for 15 TCDD risk assessment. 16 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 BMDL 01 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 BMDL 01 for each species/sex combination within a study

Using the human kinetic model, estimate a Human Equivalent Dose (BMDL HED s) for each BMDL 01 and derive a candidate OSF by dividing 0.01 by the BMDL HED s 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 BMDL 01 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 BMDL 01 for each species/sex combination within a study

Using the human kinetic model, estimate a Human Equivalent Dose (BMDL HED s) for each BMDL 01 and derive a candidate OSF by dividing 0.01 by the BMDL HED sThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-99 1 Figure 5-4. Representative endpoints for each of the hypothesized key events 2 following AhR activation for TCDD-induced liver tumors. 3 

## 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, 2006This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-100 1 2 Figure 5-5. Representative endpoints for two hypothesized key events 3 following AhR activation for TCDD-induced lung tumors. 4 

### 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, 2006This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 5-101 dose (ng/kg-day)cumulative response

1 5 10 50 1000.0 0.2 0.4 0.6 0.8 1.0 Multistage model fit Linear fit

1 Figure 5-6. Multistage mode l fit to cholangiocarcinoma response data (NTP, 2 2006) with comparison to linear model fit. 3

Exposure measureTumor incidence

0 20 40 60 80 1000.0 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 Figure 5-7. Weibull model fits to Kociba/G&S liver tumor response data 5 with alternative dose metrics. Weibull powers are 0.68, 0.95, and 1.4 for the 6 administered dose, blood concentrat ion and AhR-bound concentration fits, 7 respectively. 8This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-1 6. FEASIBILITY OF QUANTITATIVE UNCERTAINTY ANALYSIS 1 FROM NAS EVALUATION OF THE 2003 REASSESSMENT 2
3 4 6.1. INTRODUCTION 5 This section focuses on the third area for im provement in the 2003 Reassessment that was 6 identified by the National Academy of Sciences (NAS) review committee (NAS, 2006a), i.e., 7 improving transparency, t horoughness, and clarity in quantitative uncertainty analysis

. 8 Although the NAS committee summarized the shortf alls in the 2003 Reassessment categorically, 9
the elaborations within their re port often contain the qualification “if possible” and do not take a 10 position with regard to the feasibility of many of its suggestions. With appreciation for the 11 extent of information available for dioxin, the goal of this section is to circumscribe the 12 feasibility of a data-driven quantitative uncertainty analysis for 13 2,3,7,8-tetrachlorodibenzo- p -dioxin (TCDD) dose-response assessment. Following brief 14 highlights of the evolution of quantitative uncertainty analysis for such applications, this section 15 lays out definitions of key terms, reviews U.S. Environmental Protection Agency (EPA)’s 16 position regarding cancer and nonc ancer endpoints, summarizes th e NAS critique, and evaluates 17 the feasibility of quantitative uncertainty analys is for TCDD within the framework of EPA’s 18 reference dose (RfD) and cancer dose-response methodologies. 19
20
6.1.1. Historical Context for Quantitative Uncertainty Analysis 21 The basic methods of probabilistic risk assessment were developed in the aerospace 22 program in the 1960s, and they found their first fu ll-scale application in the Nuclear Regulatory 23
Commission’s Reactor Safety Study of 1975 —including accident cons equence analysis and 24 uncertainty analysis (U.S. NRC, 1975). This study, commonly referred to as the Rasmussen 25 Report after its lead author, is considered to be the first modern probabilistic risk assessment. In 26
the aftermath of the 1979 Three Mile Island accid ent, a new generation of probabilistic risk 27 assessments (PRAs) appeared in which some of the methodological problems of the 1975 study 28
were avoided. These advances we re reflected in the Commission’s Fault Tree Handbook 29 (U.S. NRC, 1981) and PRA guide (U .S. NRC, 1983), which shored up and standardized much of 30 the risk assessment methodology. An extensive chap ter of the latter was devoted to uncertainty 31 and sensitivity analysis. These documents fo rmed the basis for standards and guidelines 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-2 established by other agencies, in cluding the U.S. Department of Energy (U.S. DOE, 1992) and 1 National Aeronautics and Space Ad ministration (NASA, 2002). 2 In 1991, a set of U.S. Nuclear Regulator y Commission (U.S. NRC) studies known as 3 NUREG 1150 used structured expe rt judgment to quantify uncertainty and set new standards for 4 uncertainty analysis, in particul ar with regard to expert elic itation (U.S. NRC, 1991). This was 5 followed by a joint U.S.-European Union (EU) pr ogram for quantifying uncertainty in accident 6 consequence models. Expert judgment methods were further elaborated in those evaluations, as 7 well as screening, dependence modeling and sensitivity analysis (EC, 2009). Studies building 8 off of this work have performed a large-scale uncertainty analysis of European consequence 9 models and provided extensive guidance on iden tifying important variables; selecting, 10 interviewing and combining expe rts; propagating uncertainty; in ferring distributions on model 11 parameters; and communicating results, as docu mented by Goossens et al. (1996, 1997, 1998, 12 2001a-g) and others (Brown et al ., 1997; Harper et al., 1995, 2002). 13 The National Research Council (NRC) has been a persistent voice in urging the 14 government to enhance its risk a ssessment methodology since its repor t on risk assessment in the 15 federal government (NRC, 1983). The Council’s 1989 report, Improving Risk Communication , 16 inveighed against minimizing the existence of uncertainty and noted the importance of 17 considering the distribution of exposure and se nsitivities in a population (NRC, 1989). The issue 18 of uncertainty was a clear concern in subse quent reports, includi ng those assessing human 19 exposure to airborne pollutants (NRC, 1991). Building on its evaluation of Issues in Risk 20
Assessment (NRC, 1993), the landmark study Science and Judgment in Risk Assessment (NRC, 21 1994) gathered many of these themes in a plea fo r quantitative uncertainty analysis as “the only 22 way to combat the ‘false sense of certainty,’ which is caused by a refusal to acknowledge and 23 (attempt to) quantify the uncertainty in ri sk predictions.” A subsequent report Estimating The 24 Public Health Benefits of Proposed Air Pollution Regulations (NRC, 2002) identified three 25
barriers to the broad ac ceptance of recent EPA health benef it analyses: (1) th e large amount of 26 uncertainty inherent in these analyses, (2) the manner in which EPA deals with this uncertainty, 27 and (3) “... projected health bene fits are often reported as absolute numbers of avoided death or 28
adverse health outcome s without a context of population size or total numbers of outcom es.” 29 The Council encouraged EPA to “explore alternative options for incorporating expert judgment 30 into its probabilistic uncertainty analyses.” 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-3 In 2006, the Office of Management and B udget released a draft bulletin proposing 1 technical guidance for risk assessments produc ed by the federal government (OMB, 2006). An 2 NRC (2007) review found many shortfalls in this proposal and recommended its retraction, and a 3
revision was undertaken. In an early 2009 report, Science and Decisions: Advancing Risk 4
Assessment , the NRC committee on improving risk anal ysis encouraged EPA to harmonize 5 approaches for cancer and noncancer dose-res ponse assessment (NRC, 2009), which involves 6 uncertainty issues discussed in this section. Even more recently, EPA released a draft white 7

paper, Using Probabilistic Methods to Enhance the Ro le of Risk Analysis in Decision Making 8 (U.S. EPA, 2009d). Although not focu sed specifically on quantitative uncertainty analysis, there 9 is overlap with the issues treate d here, and relevant insights ar e anticipated from ongoing efforts 10
in this area. 11
12
6.1.2. Definition of Terms 13 For purposes of this study, the following definitions are adopted: 14
15 Uncertainty Characterization : This consists of a Structured Uncertainty Narrative and, if 16 the uncertainty is support ed by quantitative models, Quantitative Uncertainty Analysis

. 17 Structured Uncertainty Narrative : This identifies assumptions conditional on which 18 uncertainty is to be characterized and delin eates the type of ar guments with supporting 19 evidence that buttress these assumptions. 20 Joint Distribution/Marginal Distribution : For a set of uncertain quantities, a joint 21 distribution is an assignment of probabiliti es (or probability densities) for each possible 22 combination of values of these quantities. Each uncertain quantity has a marginal 23 distribution, that is, an assi gnment of probabilities (or proba bility densities) to each 24 possible value of that quantity. Assigning a marginal distribution to each quantity is not 25 equivalent to assigning a joint distribution to the set of quantities , unless the quantities 26 are independent; in this case th e joint distribution is just the product of the margins. 27 Quantitative Uncertainty Analysis : This is a quantification of the uncertainty attending 28 the use of quantitative models . It applies to a mathematical model of physical 29 phenomena, some of whose parameter values are not known with certainty. A joint 30 distribution is assigned to un certain model parameters and propagated through the model 31 to yield a joint distribution over the model output. Thus, a quantitative uncertainty 32 analysis always has a joint distribution over model outputs as its result. 33 Qualitative/Informal Uncertainty Analysis : This assembles the arguments and evidence 34 and provides an assessment of their plausibility in terms of verbal modifiers. The 35 meaning of verbal modifiers such as “likel y/unlikely” or “plausible/implausible” in the 36 natural language is indete rminate and context dependent. The way in which these 37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-4 qualifiers combine in the natural language requires critical attention from a quantitative 1 viewpoint (if A is likely and B is likely and C is likely, is A and B and C likely?). Before 2 the advent of personal computers, various shorthand techniques were developed for 3 computing system risk. In control theory schemes of ‘interval probabilities’ were 4 proposed which could be propagated thr ough a system to yield bounds on system 5 reliability. Whereas these bounds origina lly reflected accuracy of shorthand 6 approximations of complex formulae, th eir offspring have been proposed as 7 quantifications of uncertainty. Alternative notions of uncerta inty are also proposed with 8 the goal of simplifying the assessment and co mputational burden or capturing putative 9 features of uncertainty which are overlooke d in probability theory. These include 10 possibility theory, fuzzy numbers, qualitative algebra, imprecise probabilities, belief 11 functions, certainty factors, and the like. Nonmonotonic reasoning systems attempt to 12 capture reasoning about knowledge, or reason ing from partial knowledge; they include 13 default logic, defeasible logic, abductive logic, autoepistemic logic, to name a few. This 14 is not the place to discuss foundational issues , except to remark that the practitioner 15 should carefully explore the whole range of alternatives and critically examine the 16 operational meaning of the primitive notions in each alternative. 17 Sensitivity Analysis : If a quantitative model uses “ nominal values” for various input 18 parameters, a sensitivity anal ysis is performed by choosing different values for these 19 parameters and re-running the model to assess the impact of changes in these parameters 20 on model output. Applicable methods include one- and two-at-a-tim e methods, design of 21 experiments and Morris’s method. They aim at estimating first-, and perhaps 22 higher-order effects with a mini mal number of model runs, by systematically varying the 23 nominal values. In large uncertainty analyses , sensitivity analysis is used to screen 24 variables for in-depth uncertainty quantification, and thus is part of a quantitative 25 uncertainty analysis. As a note, the NAS committee report does not distinguish between 26 uncertainty and sensitivity analysis. In fi elds which have not de veloped a tradition in 27 uncertainty quantification, the spread of va lues generated by a sensitivity analysis is 28 sometimes presented as a representation of un certainty (Murphy et al., 2004). That may 29 or may not be the case; the question is moot so long as the uncertainty on model input 30 parameters is not quantified. Systematically varying input values is not the same as 31

sam p ling input param eter values from their unc ertainty distributions. In any event, a 32 systematic approach to parameter variation is essential; simply changing a few values 33 happenstance and generating diffe rent output does not serve a scientific purpose, and 34 inevitably raises questions of selection bias. That said, if alternative values recommend 35 themselves, then running these through the models can help sensitize users to parameter 36 variations. 37 Cognitive Uncertainty : This concerns uncertainty regarding what is the case. This may 38 be conceived as uncertainty over the set of possible worlds. Uncertainty over possible 39 worlds may be represented as probability. Two interpretations or operationalizations of 40 the probability formalism are current, the obj ective or frequentist interpretation and the 41 subjective or Bayesian interpretation. These interpretations are not mutually exclusive, 42 as subjective probabilities can and often do track relative frequencies. 43This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-5 Volitional Uncertainty : This concerns uncertainty regard ing what to do. In the natural 1 language, being unsure which course of action to choose is also called “uncertainty.” 2 Insofar as uncertainty on the best course of action can be translated into a claim about the 3 state of the world, volitional uncertainty can be translated into cognitive uncertainty. A 4 regulatory body charged with setting a speed li mit is obliged to make a decision. The 5 decision may be cautious or reckless, well or poorly motivated, wise or foolish; but it 6 cannot be true or false. Si nce the decision makes no claim about the state of the world, it 7 cannot be uncertain in the cognitive sense. The uncertainty cannot be analyzed by 8 sampling from some distribution. However, if the decision is based on the claim that the 9 chosen speed limit minimizes accidents while maintaining a prescribed traffic volume, 10 that claim may be uncertain and may be subj ected to quantitative uncertainty analysis. A 11 discretionary decision of a regulatory body may entrain cognitive uncertainty, but it 12 becomes amenable for quantitative uncertainty analysis only when it is linked to a claim 13 about the state of the world. 14 Aleatoric/Epistemic Uncertainty : This distinction is also termed Variability/Uncertainty

. 15 A variable whose uncertainty is aleatoric for a given populati on takes different, uncertain, 16 values for each member of the population. If its uncertainty is epistemic, it takes the 17 same uncertain value for all members of the population. Issues involving uncertainty and 18 variability or epistemic and aleatory uncertainty translate into issues of dependence, when 19 conducting a quantitative uncer tainty analysis (see Section 6.1.3.3). In its Science and 20 Judgment report, NRC (1994) correctly remarks th at “the amount of variability is 21 generally itself an uncertain parameter.” It is natural to ask whether a given uncertainty 22 is aleatoric or epistemic, whereas it is awkward to ask whether this uncertainty is 23 uncertain or variable—which explains th e preference for the epistemic/aleatoric 24 terminology. 25 26
6.1.3. Basic Requirements of a Quantitative Uncertainty Analysis 27 The uncertainty propagation can be performed by some rough estimation, as for example 28 the delta method (Oehlert, 1992) or in rare cases it can be performed analytically, as in simple 29 error propagation. Most often, however, it will be performed using Monte Carlo simulation. A 30 joint distribution is assigned to the parameters of a quantitative model and then propagated 31 through the model by sampli ng repeatedly from this joint di stribution, compu ting model output 32 and generating a distribution of m odel output. Every uncertainty an alysis is conditional on initial 33 assumptions. A “complete” uncertainty analysis is an unattainable goal; the best that can be 34 done in practice is to identify a nd motivate the assumptions that ar e used. This section is not a 35 how-to guide, but a to-do list of ke y tasks involved in any quantitative uncertainty analysis. 36
37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-6 6.1.3.1. Quantitative Model 1 The starting point of any quant itative uncertainty analysis is a mathematical model or 2 prescription for calculating quantitie s of interest. A structured narrative explains the choice of 3 quantitative models. If some valu es of input parameters for this calculation are not known with 4 certainty, then the question arises: “What is the uncertainty attending the use of this model?” 5 This is the question a quantitative un certainty analysis seeks to answer. 6
7
6.1.3.2. Marginal Distributions over Model Parameter 8 If the model parameters are directly measur able with sampling error, then the sampling 9 distribution may itself be used in the quantitative uncertainty analysis. If the model parameters 10 are fit to data that are samp led from a known or hypothesized di stribution, then by resampling 11 this distribution and refitting the model, di stributions over the model parameters may be 12 constructed. Physically-based simulation m odels, such as pharmacokinetic models or 13 environmental transport models, may be solved an alytically if equilibrium reaction rates (the 14 transfer coefficients) are constant . If these rates are not consta nt, as when concentrations are 15 near saturation levels, then simulation is indicated . Structured expert ju dgment has been applied 16 for uncertainty quantification within the engin eering community since the time of the Rasmussen 17 Report. More recently, this approach has been “test-driven” by EPA in assessing health effects 18 of fine particulates (Walker et al., 2009), and its potential applic ation has been identified in the 19 Agency’s Guidelines for Carcinogen Risk Assessment , commonly referred to as the cancer 20 guidelines (U.S. EPA, 2005). 34 21
22
6.1.3.3. Dependence Between Parameter Uncertainties: Aleatoric and Epistemic (Uncertainty 23 and Variability) 24 Two uncertain quantities are in dependent if knowledge about one of them does not alter 25 our uncertainty regarding the othe r. The quantities are dependent if they are not independent. 26 The role of dependence modeling in quantitative uncertainty analys is must be addressed. To 27 illustrate, cigarette smoking and body fat are both thought to influence biomarkers for toxic 28

34 The U.S. EPA (2005) cancer guidelines st ate: “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 e licitation 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 risk s is considered to be quality science.”This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-7 response to dioxin exposure , such as ethoxyresorufin- O -deethylase (EROD) activity (Pereg et al., 1 2002). Both cigarette smoking and percent body fa t in a random individual sampled from a 2 target population are uncertain. 35 However, these uncertainties are not independent, inasmuch as 3 smokers tend to have less body fat (Vanni et al., 2009). 4 Issues involving uncertainty and variability or epistemic and aleatory uncertainty 5 translate into issues of dependence, when c onducting a quantitative uncer tainty analysis. For 6 example, a constant used to estimate the biokine tic behavior of dioxin may be uncertain. If it is 7 believed to be the same for every member of the population, the uncertainty is termed 8 “epistemic.” In a quantitative uncertainty analysis, this factor would be sampled from its 9 uncertainty distribution on each Monte Carlo run and used for all members of the population. 10 Body fat, in contrast, is aleatoric. We do not sample one value from the body fat distribution and 11
use this value for all members of the population on each Monte Carlo run. Instead we sample a 12 body fat value for each individual on each run. Because body fat is correlated with other 13 relevant variables (e.g., smoking, sex, age, and socioeconomic status), all of these variables 14 should be sampled in a manner that reflects their dependences. Kinetic constants whose 15 uncertainty is epistemic are completely correlated across individuals: if th e value is 0.5 for one 16 individual, it is 0.5 for everyone. Body fat values vary from individual to individual, and they 17 are correlated through a hos t of other variables. 18
19
6.1.3.4. Model Uncertainty 20 All models, being idealizations, are false; on this there is no uncertainty to quantify. 21 However, the choice of model may constrain the ab ility to represent uncertainty in observable 22 phenomena. Thus, in a linear low-dose model, uncertainty over a cancer slope factor may be 23 quantified, but uncertainty rega rding changes in slope at dis tinct low-dose regimes cannot be 24 captured. When the model choice imposes se vere and potentially unw elcome constraints on 25 uncertainty quantification, this must be addresse d. Distributions over model parameters may be 26

35 Because 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 popula tion 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 purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-8 selected and evaluated based on their ability to reflect uncertainty distri butions over observable 1 phenomena predicted by the models. In such cases, the uncertainty propagated through the 2 quantitative model is not strongl y model-dependent. In other cases, multiple model alternatives 3 may be applied, whose “probability of being th e true model” is known or assumed. Since 4 different models can always be regarded as specializations of more general models, the 5 distinction between parameter and model uncertainty is sometimes more apparent than real. The 6 multistage and Weibull dose-response models both contain the model Pr(x) = γ + (1

− γ ) 7 (1

− e −β 1x ) as a sub model, to which they collapse if other parameters are zero (multistage) or one 8 (Weibull). Recalling that the function 1/(1 + x) is first order equivalent to (1

− x) for small x, the 9 same may be said for logistic models as well. In this case, these models could easily be 10 parameterized within one family, rendering th e choice between them a choice of parameter 11 values. Similarly, the choice be tween sub-, supra-, and linear models is sometimes reduced to 12 parameter estimation within a more genera l class of model (Hoel and Portier, 1994). 13 In other cases, the reduction of model uncertainty to parameter uncertainty is less natural. 14 For example, according to the “chemoprotection model” of Greenlee et al. (2001), dioxin’s 15 binding to the aryl hydro carbon receptor (AhR) inhibits prolif eration in tumor cells and thus 16 suppresses mammary tumors. Dose-dependent pr otection and cancer induc tion can both be true, 17 each applying to different tissues. Although mathem atical models exhibiting these twin features 18 are under development (Kohn and Melnick, 2002), these models are not yet readily estimable 19 from data, and the choice between them is referred to the structured narrative. 20
21
6.1.3.5. Sampling Method 22 All sampling on a computer is “pseudo random.” Significant issues arise in choosing a 23 method for sampling high-dimensional distributio ns with dependence. If evaluating the 24 quantitative model is very time consuming, vari ous ‘quasi random’ schemes may be applied, 25 including Latin hypercube sampli ng, importance sampling, and Hammersley sampling. These 26 methods involve trade-offs between economy and accuracy of the dependence modeling. 27
28
6.1.3.6. Method for Extracting and Communicating Results 29 When a large quantitative uncertainty analysis has been performed, the method for 30 identifying important contribu tors and communicating this in formation to users is not 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-9 straightforward. Analysts have proposed many ways to quantify the uncer tainty contribution of 1 one variable, or set of variables, on others, 36 and his/her decision at this juncture may strongly 2 impact the “take-home” message from the study. An importance measure that averages over an 3 entire sample space may obscure the features of r eal interest. For exampl e, the drivers of cancer 4 induction at low doses might be different from the drivers at high doses. If the drivers of 5 low-dose cancer induction are of interest, then importance measures that average over all doses 6
should not be considered. 7
8
6.2. EPA APPROACHES FOR ORAL CANCER AND NONCANCER ASSESSMENT 9 EPA typically develops differe nt types of toxicity inform ation in its oral cancer and 10 noncancer dose-response assessments, although effort s to harmonize these approaches continue 11 to be made. Noncancer endpoints are usually assessed using the RfD methodology to derive a 12
threshold below which there is likel y no appreciable risk. (Note “ris k” is used here simply as a 13 general term indicating the potential for adverse effects.) In contrast, cancer endpoints are 14 commonly assessed using a dose-response function with the probability of excess risk above 15 background modeled as a linear f unction of dose, for doses dow n to zero. The RfD method 16 relies on a point of departure (POD), and the dose-response method uses a POD if the linear 17 model is chosen by default. From the U.S. EP A (2005) guidelines, cancer endpoints can also be 18 assessed using the RfD methodology if the proo f burden is satisfactor ily met (previously 19 described in Section 5.2.3.3). 20 Toxicity reference values have generally been derived for human noncancer endpoints 21 based on a no-observed-adverse-effect level (NOA EL) or lowest-observed- adverse-effect level 22 (LOAEL) from animal bioassay studies. Th is terminology suggests a biological population 23 threshold beneath which no harm is anticipated. Reference values such as the RfD or reference 24

36 A few examples may suffice. The standard Pearson correlation coefficient meas ures the linear dependence between two variables, positive or negative. The rank or Spearman correlation coeffi cient measures the monotone dependence. The correlation ratio measures the (unsigned) variance contribution of an explanatory variable on a target variable. The regression coeffici ent measures the expected change in sta ndard (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 regressi on coefficient are not symme tric, the correlation multip le regression are always non-negative (Saltelli et al., 2000; Kurowicka and Cooke, 2006).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-10 concentration are derived by applying uncertain ty factors (UFs) to a POD. Depending on the 1 nature of available data and modeling choice, a POD can be selected from values other than a 2 NOAEL or LOAEL, such as an ED x (effective dose eliciting x pe rcent response), or a benchmark 3 dose (BMD) or its lower conf idence bound (BMDL). The BMD is the dose that induces a 4 benchmark response (BMR), which is often chosen to represent a 5 or 10% increase in excess 5 risk above background. The POD is divided by one or more uncer tainty factors that represent 6 knowledge gaps (see Section 6.4.1.2 for de tails on specific types of UFs). 7 An RfD is described as “likely to be wit hout appreciable risk” but the probabilistic 8 language has not as yet been operationalized. There is no quantitative definition of 9 “appreciable” and no mechanism to compute risk as a function of dose, so as to ascertain that the 10 risk is indeed not appreciable . In addition, different par ticipants in discussions over 11
threshold/nonthreshold models for dioxin may have different pe rspectives regarding how to 12 define “appreciable risk.” Under the current POD/UF framework, dose- response functions are 13 not provided for calculating the actual risk at or above the RfD. Inst ead, to provide a “risk 14 indicator” for use in screening for health hazar ds, a hazard quotient (HQ) is computed as the 15 ratio of a given exposure to the RfD, or a margin of exposure (MOE) is estimated as the ratio of 16 the POD to the human exposure level. 17 For the cancer endpoint, an oral cancer slope factor may be derived for human health risk 18 assessment, typically based on tumor incidence data from an animal bioassay or on cancer 19 incidence or deaths from an epidemiologic study . In U.S. EPA’s Cancer Guidelines, cancer is 20 predominantly thought to have no population biological threshold and a linear extrapolation to 21 zero is applied from the POD based on extra risk above background, i.e., the probability of the 22 endpoint decreases linearly in dose from the POD to zero or to a population background level. In 23 the absence of sufficient evidence supporting low-dose cancer i nduction, the linear model is 24 applied as a default. Cancer endpoints can also be evaluated using a “nonlinear” model. In this 25 case, the proof burden clearly rests on the nonl inear model; it must have a preponderance of 26 evidence to override the health-protective defaul t choice, as described in U.S. EPA Cancer 27 Guidelines. U.S. EPA (2005) can cer guidelines state, “When ade quate data on mode of action 28 provide sufficient evidence to s upport a nonlinear mode of action for the general population 29 (emphasis added) and/or any subpopulations of concern, a different approach—a reference 30
dose/reference concentrati on that assumes that nonlinearity—i s used.” In current terminology, 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-11 the RfD methodology applies to the cancer endpoint if there is sufficient evidence supporting a 1 “zero slope at zero” model; otherw ise, the linear nonthreshold model applies by default. (See 2
Section 5.2.3.3 for a detailed discussion of linea r vs. nonlinear extrapolations below the observed 3 data, population vs. indivi dual thresholds, and for the how U. S. EPA (2005) Cancer Guidelines 4 are applied in choosing dose-response m odel forms for risk assessment.) 5
6
6.3. HIGHLIGHTS OF NAS REVIEW COMMENTS ON UNCERTAINTY 7 QUANTIFICATION FOR THE 2003 REASSESSMENT 8 The NAS (2006a, b) identified a number of un certainty characteriza tion issues for the 9 2003 Reassessment; key sources of uncertainty for which quantificati on is suggested are 10 highlighted in Table 6-1. The discussion in th is section focuses on comments related to dose 11

response. 12 There are several nuances in the NAS positi on relative to the need for substantial 13 improvement in transparency, thoroughness, and clar ity in quantitative un certainty analysis for 14
the 2003 Reassessment. These nuances concern wh ether the nonlinear mode l (note that the NAS 15
committee uses “sublinear” and “nonlinear” inte rchangeably) is scientif ically better supported 16 than the linear model, and if the sublinear model is better supported, whether this is based on 17 data or on apodictic knowledge (knowledge without uncertainty) of the mode of action (MOA). 18 The NAS committee does not distinguish betw een individual and population dose-response 19 models; however the criteria from U.S. EPA Ca ncer Guidelines clearl y apply to population 20 models. Assuming that the AhR-mediated MOA imp lies a threshold for each individual, the step 21 to a population “zero slope at zero” model requires the following, as identified and discussed in 22
detail in Section 5.2.3.3: 23
24
1. The distribution the indi vidual thresholds induced by the MOA, and 25
2. The dose-response function for values above the thresholds. 26 27 This information can either come from data, or from apodictic knowledge of the MOA, 28 but the burden of proof clearly rests on the nonlinear model. This section summarizes the NAS 29 committee’s overall positions. Responses to spec ific suggestions are given in Section 6.4 and 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-12 summarized in Section 6.5. Several excerpts of specific comments from NAS (2006a) illustrate 1 key issues. 2
The NAS committee favors the nonlin ear model with a threshold: 3
4 ...the committee concludes that , although it is not possible to scientifically prove 5 the absence of linearity at low doses, th e scientific eviden ce, based largely on 6 mode of action, is adequate to favor the use of a nonlinear model that would 7 include a threshold response over the us e of the default linear assumption. 8 (p. 126) 9 10 The committee does not state whether the thresh old applies to the population, or whether each 11
individual has his/her own threshold. 12 The NAS also comments on whether the nonlinear model should be used to compare with 13 the health-protective linear default: 14 15 Because the committee concludes that th e data support the hypothesis that the 16 dose-response relationship for dioxin and can cer is sublinear, it recommends that 17 EPA include a nonlinear model for cancer ri sk estimates, but that EPA also use 18 the current linear models for comparative purposes. (p. 12) 19 20 The committee does not suggest what the (popula tion/individual) threshol d might be, nor how it 21 might be supported on the basis of data. Rather, the apodictic knowledge that there is a 22 (population/individual) threshold places the dioxin risk assessme nt within the RfD framework, 23 using a HQ or MOE as the basis for indicating the potential risk s from exposure. The committee 24 further asks for a quantitative characte rization of the range of uncertainty: 25 26 The committee determined that the ava ilable data support th e use of a nonlinear 27 model, which is consistent with rece ptor-mediated responses and a potential 28 threshold, with subsequent calculations and interpretation of MOEs. EPA’s sole 29 use of the default assumption of linearity and selection of ED 01 as the only POD 30 to quantify cancer risk does not provide an adequate quantitative characterization 31 of the overall range of uncertainties asso ciated with the final estimates of cancer 32 risk. (p. 17) 33 34
Regarding the preponderance of evidence, the committee indicates that quantitative 35
evidence will not decide the lin earity/nonlinearity (nonthreshold/threshold) i ssue, but knowledge 36 (without uncertainty) of the MOA should be used: 37This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-13 Quantitative evidence of nonlinearity below the POD, the ED 01 , will never be 1 available because the POD is chosen to be at the bottom end of the available 2 dose-response data. ... EPA should give gr eater weight to knowledge about the 3 mode of action and its imp act on the shape of the dose-response relationship. 4 (p. 126) 5 6 The comment continues, with the committee implicitly acknowledging that there is no 7 evidence arguing against linearity, but that the lack of evidence s hould not justify using the linear 8
model: 9
10
The committee considers that the absence of evidence that argues against linearity 11 is not sufficient justification for adopti ng linear extrapolation, even over a dose 12 range of one or two orders of magnitude or to the assumption of linearity through 13 zero, which would not normally be app lied to receptor-mediated effects. (p. 126) 14 15 Whereas EPA has applied its own guidance on cancer risk assessment and adopted 16 linearity as a health-protective default in th e absence of sufficient evidence (volitional 17 uncertainty), the NAS committee views the absen ce of evidence as impos ing a burden of proof 18 on the linear model (cognitive uncertainty). (As a note, terminology issues and conceptual 19 inconsistencies within the NAS report illustrate the importance of clarity and are not unexpected, 20 given the complexity of these issues a nd the nature of a committee process.) 21 The thrust of the NAS remarks regarding transparency, thoroughness and clarity in 22 quantitative uncertainty analysis relevant to dose-re sponse can be summarized as follows: 23
24
1. The uncertainty of cancer risks due to dioxin exposure should be quantified. 25
2. Dioxin cancer risk should be treated either as a threshold phenomenon, thus following the 26 basic RfD methodology, or shoul d be modeled using a sub linear dose-resp onse function 27 below the observed data, with the linear model used for comparison. 28
3. The POD should be subjected to qua ntitative uncertainty analysis. 29 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-14 A similar point of view has been indicated by others. 37 Detailed suggestions regarding specific 1 improvements for quantitative uncer tainty analysis in the 2003 Reassessment are outlined in the 2
next section and summarized in Section 6.5. 3
4
6.4. FEASIBILITY OF CONDUCTING A QUANTITATIVE UNCERTAINTY 5 ANALYSIS FOR TCDD 6 This section focuses on uncertainty analysis for TCDD dose response, which involves a 7 range of issues as highlig hted in Table 6-1. 8 9
6.4.1. Feasibility of Conducting a Quantitative Uncertainty Analysis under the RfD 10 Methodology 11 This discussion applies to all noncancer e ndpoints of TCDD, and to cancer endpoints 12 insofar as they fall under the RfD methodology. An RfD is obtained through the following steps 13
14 1. Choose a POD, then 15
2. Apply uncertainty factor s (UFs) to account for knowledge shortfalls. 16 17 The method of uncertainty fact ors harkens back to the engineering practice of safety 18 factors (Lehman and Fitzhugh, 1954). To illustrate , if the reference load for an engineered 19 structure is X, then engineers might design the structure to with stand load 3X, using a safety 20 factor of 3 to create a margin of safety. If the structure func tions in a corrosive environment, 21 another factor could be multiplied to guarantee safety for that c ondition, and another factor could 22 be applied for heat, another for vibrations, and so on. The choice of values is simply based on 23 good engineering practice, i.e., reflecting what ha s worked in the past. Although safety factors 24 are still common in engineering, they are giving way to probabilistic design in many 25 applications. The reason is that compounding sa fety factors quickly l eads to overdesigning. 26 Compounding safety margins for spaceflight systems may render them too heavy to fly. As our 27 understanding of a system increases, it becomes possible to guarantee the requisite safety by 28 leveraging our scientific understand ing of the materials and processe s. That of course requires 29

37 For example, from Popp et al. (2006): “Overall, th e evidence indicates that (1) TCDD causes cancer via a receptor-mediated process; (2) this do se-response is non-linear; and (3) a threshold region exists for TCDD-induced cancer below which adverse effects are unlikely to occur.”This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-15 formulating clear probabilistic safety goals a nd developing the techniques to demonstrate 1 compliance. 2 The engineering community has never sought to account for uncertainty by treating 3 safety factors as random variable s and assigning them (marginal) di stributions; such an approach 4 would not counteract the overdesig ning inherent in safety factor s. Many authors, including the 5
recent national committee for Science and Decisions (NRC, 2009), have advocated just such a 6 probabilistic approach to the apparent “overdesig ning” of the RfD when multiple UFs are used in 7
its derivation. 8
The NAS committee that evaluated the 2003 Reassessment does not discuss how to 9 perform uncertainty analysis. But their call for substantial improve ment in quantitative 10 uncertainty analysis with TCDD falling under the RfD framework entails examining the 11 feasibility of quantitative uncertainty analysis within this framework. (Note that the EPA 12 Integrated Risk Information System database uses uncertainty factor s without probabilistic 13 interpretations; some context for this is offered in Section 6.4.1.2.) 14
15
6.4.1.1. Feasibility of Conducting a Quantitative Un certainty Analysis for the P oint of 16 Departure 17 The POD plays a role in both the noncancer RfD methodology and the cancer 18 dose-response methodology. The POD can be sele cted from various options, such as a NOAEL 19 or LOAEL, a BMDL, or an ED x . The feasibility of quantitative uncertainty analysis for each of 20 these three options is considered below. 21 As described by Swartout et al . (1998), “The NOAEL is the highest of the tested doses in 22 a toxicological experiment that is judged not to have caused an adverse effect” (with dose 23 expressed as a dose rate, in mg/kg-day). A qua ntitative uncertainty analysis for a NOAEL or 24 LOAEL encounters the following problem. In an experiment involving a small, positive 25 response, the probability of seeing no response can be calcu lated using a binomial model with 26 the number of exposed animals and the observed number of responses. However, in an 27 experiment with no response, the probability of having observed a response cannot be calculated 28 without assuming a response probability. Such an assumption could not be based on observed 29 data. If the analysis is restricted to experiments showing a positive response, the results will be 30 biased. The probability of a hi gher NOAEL or higher LOAEL can be computed, but not that of a 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-16 lower NOAEL or LOAEL. In other words, the probability that an experiment with a positive 1 result may have yielded a null response can be estimated, but not the probability that an 2 experiment with a null response might have yielded a positive response. 3 In addressing uncertainty quantification for a BMDL or ED x , two questions must be 4 distinguished regarding the response: 5
6
1. What is the distribution of possible doses that causes an x% increase over background? 7
2. What is the distribution fo r possible values of increas e over background caused by a 8 given dose? 9 10
The BMD is defined as the dose th at realizes a BMR. It is an estimate from bioassay data 11 that requires choosing a BMR and fitting a dose-re sponse curve. The BMR, being a choice, is 12 not amenable to quantitative uncertainty analysis, but the choice can be motivated in a structured 13
narrative. The BMDL is the lo wer confidence limit (e.g., 5%) that can be found based on the 14 uncertainty in the parameters of the dose-response relationship. Thus, the BMDL is addressed to 15 the first question above, and re presents in this case the 5% lower confidence band of the 16 distribution of possible doses causing an x% increase over background. In the standard 17 approach, the uncertainty captured by the BMDL is sampling uncertainty conditional on the truth 18 of the dose-response model. Different models might fit the data equally we ll yet lead to different 19
BMDLs. 20 The BMDL is also influenced by the constr aints imposed on the parameter fitting. 21 Suppose that the slope is expected to be gr eater than one, and that the maximum likelihood 22 estimate of the slope is slightly greater than one. Since the constrai nt is not binding, the 23
constrained and unconstrained m odel would have the same Akaike Information Criterion and 24 would be equivalent in this sense. However, computing the BMDL with the slope constraint can 25 lead to very different values than without this constraint. In the latter case, slope values less than 26
one contribute to the uncer tainty in the dose causing the BMR (see Cooke, 2009). 27
The ED x can also be taken as a POD. It is sim ilar in spirit to the BMD; however, as used 28 here, the term ED x applies when the dose causing an x% extra risk over background has actually 29This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-17 been observed, not estimated from a fitted dose-response model. 38 The observations are subject 1 to sample fluctuations, and if the experiment on which the ED x is based were repeated, different 2 values might be found. It is helpful to c onsider a numerical exam ple. Suppose a background 3 response rate of 10% is established based on ma ny observations of nonexposed individuals. In a 4 given experiment, involving sa y 100 individuals given dose d , 14 individuals responded. The 5 percent increase x over background (extra ri sk) is found by solving 14/100 = 10/100 + x 6
× 90/100 , or x = 4.4%. We conclude that d = ED 4.4 . We may assume that if the experiment 7 were repeated with 100 new individuals sample d independently from the whole population, the 8 response would be given by a binomial distribution with parameters (14, 100). The number of 9 responses might be greater or smaller than four , there is a 16% chance of observing 10 or fewer 10 responses. The response to dose d would not be distinguished fr om the background in that case, 11 and a higher dose would be used for the POD. 12 The uncertainty analysis of ED x as the POD involves addressing the second question 13 above, without a quantitative dose-response model. A quantitative uncertainty analysis is 14 hampered, however, by the possibility that dose d would produce a response less than or equal to 15 the background, in which case the POD is indete rminate—another experiment with a different 16
dose would be chosen as the POD. A tr ue quantitative uncertainty analysis of ED x as the POD 17 would thus require a full bioassa y experimental design, with bi nomial sampling of response rates 18 at each dose level in the assay. Absent that, quantitative uncertainty analysis is not possible. 19 The interplay of choice and estimation ingred ients in the POD depends on the type of 20
POD. The main features of the above discussion are captured in Table 4-2. This table notes that 21 the BMDL captures the uncertainty caused by sampling fluctuations given that the dose-response 22
model is true. Other methods are available to compute the BMDL usi ng (1) model-independent, 23 observable uncertainty; (2) nonparametric Bayesi an dose-response models; or (3) Bayesian 24 model averaging (Cooke, 2009). Only the ED x can be subject to a quantitative uncertainty 25 analysis, and then only if a full bioassay data set is available. 26
27

38 This definition of ED x is adopted to distinguish the modeled response (BMD) and the observed response (ED x ), and it is more restrictive than usages common in the literature.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-18 6.4.1.2. Feasibility of Conducting a Quantitative Uncertainty Analysis with Unc ertainty 1 Factors 2 Uncertainty factors are chosen based on a st ructured narrative ch aracterizing knowledge 3 shortfalls involving 4
5 1. Interspecies extrapolation, (UF A : from animal data to humans). 6
2. Intraspecies extrapolation (UF H : to account for human interindividual variability, 7 considering sensitive subgroups). 8
3. LOAEL to NOAEL extrapolation (UF L : to estimate the dose corresponding to no adverse 9 effect from a reported LOAEL). 10
4. Subchronic to chronic extrapolation (UF S : to estimate effects of chronic exposures from a 11 subchronic study). 12
5. Database deficiency (UF D : to extrapolate from an incomplete data set, e.g., in terms of 13 endpoints assessed or study design, i.e., from a poor to a suffici ent or rich data context). 14 15 The standard chronic RfD can represent a sensitive human (H) response to a toxic 16 substance under chronic (C) exposure conditions . Suppose a BMDL POD were based on animal 17 (A) data from a subchronic (S) study. The database for that chemical could be rich (R), e.g., 18 involving multiple (and at least sensitive) species/ strains, both genders, multiple life stages, with 19 multiple endpoints observed under sound study designs . Or the data could be poor (P), with 20 limited measurements from only a subchronic animal study (ASP) forming the basis for 21 estimating a general reference value for humans (including sensitive subgroups) under chronic 22 exposure conditions. For that case, the UF method would be applied as follows 23
24

H DSA UFUFUFUF ASP RfD ××× = (Eq. 6-1) 25

26
where UF A , UF S , UF D , and UF H are the uncertainty factors for extrapolating from animals to 27 humans (UF A ), subchronic to chronic exposure conditions (UF S ), without adequate endpoint 28 coverage (UF D ), and considering sensitive human subpopulations (UF H ). It is possible to assign 29 distributions to the UFs in Eq. 6-1, and to perform a Monte Carlo analysis to produce a 30 quantitative uncertainty distributi on over the dose or value likely to be without appreciable risk 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-19 to humans for chronic exposures. Many auth ors have proposed such an approach, 39 and the 1 recent NRC (2009) report on scien ce and decisions emphatically counsels EPA to move in this 2
direction. 3 The idea of using a Monte Carlo analysis to develop quantitative uncer tainty distributions 4 for the RfD is simple, but the data on which th e UFs are based and the assumptions that would 5 need to be made should be further explored. Fo r example, it is assumed that the extrapolation 6 from subchronic to chronic exposure (UF S ) is the same whether applied to animals or humans, 7 and whether applied to sufficient (r ich) or deficient (poor) data c ontexts. Swartout et al. (1998) 8 write “Within the current RfD methodology, UF S does not consider differences among species, 9 endpoints, or severity of effects; the same factor is applied in all cases.” In addition, due to the 10 paucity of relevant human data, the same author s suggest the use of othe r endpoints as surrogates 11 in estimating the extrapolation from animals to humans, UF A . Further, few data exist in humans 12 to accurately portray the interindividual variability in humans represented by UF H . Much of the 13 data gathered to date on distribut ions of UFs have aggregated acro ss other extrapolations; that is, 14
data from subchronic to chronic ratios are aggregated over differe nt species and different data 15 contexts. Finally, it may be noted that an im portant issue is the data on which empirical 16 distributions of response ratios are based. Brand et al. (1999, 2001) studied the sampling 17 behavior of response ratios and raise concerns with regard to their informativeness. 18 Detailed analyses of the data underlying a Monte Carlo uncertainty analysis of Eq. 6-1 19 would afford the possibility of verifying at least some of the assumptions and numerical 20 estimations such an analysis must make. Even if the assumption that the same UF S is applicable 21 for all species, endpoints, and effect severities is thought to be biological plausible, the question 22 of whether a given set of chemicals reflect th is assumption, and hence are suitable for Monte 23

39 There 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. (199 6), Swartout et al. (1998), Sl ob and Pieters (1998), Evans and Baird (1998), Calabrese and Gilber t (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 multiplyi ng the median values, and combining the “error factors” according to the formula K S × H = exp[1.6449 × √ ( σ

S 2 + σ H

2 )], where σ S 2

, σ H

2 are the variances of ln(UF S ) and ln(UF H ). Thus UF S × UF H is a lognormal variable with Median(UF S × UF H ) = Median(UF S ) × Median(UF H ), and 95 th percentile given by Median(UF S × UF H ) × K S × H . If U S and U H each have an error factor or 10, then the error factor of UF S × UF H is not 100 but 25.95. Several authors sugges t that multiplying uncertainty factors might overprotect. Recent proposals from the Na tional Research Council reflect the rando m chemical concept, and they inherit its problems (NRC, 2009).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-20 Carlo analysis of Eq. 6-1, can only be decided by data evaluation. Data are the ultimate arbiter 1 of whether quantitative uncertainty analysis with uncertainty factors, as currently envisioned, has 2 sufficient evidentiary support. 3
4
6.4.1.3. Conclusion on Feasibility of Quantita tive Uncertainty Analysis with the RfD 5 Approach 6 A quantitative uncertainty analysis of the POD is not feasible for PODs based on 7 NOAELs or LOAELs. For the BMDL, such an anal ysis is not appropriate because the BMDL is 8 already a quantile from an uncertainty distribut ion of the BMD. However, this uncertainty 9
distribution can be obtained in di fferent ways that capture different aspects of uncertainty. 10 Quantitative uncertainty analysis is feasible if the POD is based on the ED x (as defined above) 11 and is supported by a full set of bioassay data . A quantitative uncertain ty analysis based on a 12 probabilistic interp retation of uncertainty factors in their present form invokes strong 13 assumptions. The data on which the distributions of uncertainty factors ar e based could be used 14 to check at least some of these assumptions. 15
16
6.4.2. Feasibility of Conducting a Quantitative Uncertainty Analysis for TCDD Under the 17 Dose-Response Methodology 18 Quantitative uncertainty analysis starts with a mathematical model and seeks to quantify 19 the uncertainty attending the use of this model. Dose-response relations are mathematical 20 models expressing the probability of response as a mathematical function of dose. For several 21 decades, the uncertainty attending the use of dose -response models has been an abiding concern 22 in many sectors, including the chemical and nuclear industries as well as the public health sector. 23 Given a set of animal bioassay da ta, quantifying dose-response uncer tainty may be approached in 24 different ways. The differences re flect different types of uncertain ty that are captured. A recent 25 evaluation enumerates the following possi ble methodologies (Bussard et al., 2009): 26
27 Benchmark Dose Modeling (BMD) : Choose the ‘best’ model, and 28 assess uncertainty assuming this model is true. Supplemental results can compare 29 estimates obtained with different models , and sensitivity analyses can investigate 30 other modeling issues. 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-21 Probabilistic Inversion with Iso tonic Regression (PI-IR) : Define 1 model-independent ‘observati onal’ uncertainty, and look for a model that captures 2 this uncertainty by assuming the select ed model is true and providing for a 3 distribution over its parameters. 4 Non-Parametric Bayes (NPB) : Choose a prior mean response (potency) 5 curve (potentially a “non-informative prior” ) and a precision parameter to express 6 prior uncertainty over all in creasing dose-response relati ons, and update this prior 7 distribution with the bioassay data. 8 Bayesian Model Averaging (BMA) (as considered here): Choose an 9 initial set of models, a nd then estimate the parame ters of each model with 10 maximum likelihood. Use classical methods to estimate parameter uncertainty, 11 given the truth of the model. Determ ine a probability weight for each model 12 using the Bayes Information Criterion, a nd use these weights to average the model 13 results. 14 15 The first of the above methods involves st andard classical statistical methods and 16 captures sampling uncertainty cond itional on the truth of the mode l used. The other methods are 17 “exotic” in the sense that they attempt to capture uncertainty th at is not conditional on the truth 18 of a given model. All have b een subjected to peer review a nd published, but they do not enjoy 19 the market penetration of the standard clas sical methods. The Baye sian models involve 20 subjective choices of prior distribu tions. Insofar as the final result is largely independent of the 21 choice of prior, these methods conform to the current starting point of focusing on data-driven 22 methods and not appealing to stru ctured expert judgment. (Struc tured expert judgment can also 23 be considered an exotic method; an explanation of this approach falls outside the scope of this 24 report.) 25 It should be emphasized that th e four approaches above were illustrated as part of an 26 integrated bench-testing exercise using simplifie d data for demonstrati on purposes. Application 27 to actual, complicated data sets would provide a dditional insights into the feasibility of each per 28
the nature of those data. 29 Many steps are involved in arriving at such a starting point, to frame the extrapolation of 30 data from animal bioassays to human refere nce values together with consideration of 31 epidemiological data from studies of workers (r outine exposures) or the general public (including 32 dietary exposures and those via di screte poisonings or accidental releases). Major issues are 33
summarized below. 34
35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-22 6.4.2.1. Feasibility of Quantitatively Characterizi ng the Uncertainties Encountered when 1 Determining Appropriate Types of Studie s (Epidemiological, Animal, Both, and 2 Other) 3 The risk assessor must choose the data set(s) that will serve as a starting point for 4 dose-response modeling. With respect to TCDD, a wealth of animal bioassay data exist in the 5 scientific literature, across species ranging from rats, mice, guinea pigs, and hamsters to mink, 6 dogs and monkeys, and a variety of tissues, organs, and systems. In addition, there are historical 7
human data such as from clini cal/case reports, accidental rele ases, and occupational exposures, 8 including epidemiological data for several cohorts. Historical da ta are usually attended with 9 large uncertainties regarding the doses actually received by individuals. Both clinical/case 10 reports and epidemiological data pose issues wi th regard to possible confounders. Different 11
TCDD studies have assessed vari ous endpoints, ranging from ch loracne to hepatic enzyme 12 induction, abnormal fasting glucos e, diabetes, lung and cardiovas cular effects, neurological 13 effects, developmental delay, dental defects, se x ratio, epididymal sper m count, endometriosis, 14 non-Hodgkin’s lymphoma, susceptibility to infec tion, depression, hostility, and anger, among 15
others. Confounders that have been evaluated in the epidemiological studies include gender, 16 body mass index, age, cigarette and alcohol consumption, and hair and eye color (Eskenazi et al., 17 2002a, b; Pereg et al., 2002; Baccarelli et al., 2005, 2006). 18 There is disagreement in the literature over th e nature, scope, and quali ty of the historical 19 and evolving epidemiological data for TCDD. P opp et al. (2006) put fort h a position similar to 20 that taken by the NAS: 21
22 According to IARC [1997], the strongest overall evidence for the carcinogenicity 23 of TCDD is for all cancers combined, rather than for any specific site. The 24 relative risk for all can cers combined in the most highly exposed and 25 longer-latency subcohorts is 1.4. Although IA RC indicated that this relative risk 26 does not appear likely to be explained by confounding, relative risks of this 27 magnitude for studies of other substances , particularly with a lower confidence 28 interval at or near one, are generally found to be the result of confounders. Few 29 examples (perhaps only smoking and ionizing radiation in atomic bomb 30 survivors) exist of agents known to cause an increase in cancers at many sites. 31 This lack of precedent for a multi-site carcinogen without particular sites 32 predominating, combined with the very sm all excess relative risks, means that the 33 epidemiological findings must be treated with caution. 34 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-23 On the other hand, U.S. EPA Cancer Guid elines express a clear preference for 1 epidemiological studies over animal data. The qu estion here is whether quantitative uncertainty 2 analyses based on bioassay data and different epidemiological da ta can be combined in some 3 overall uncertainty assessment. Diverse studies are sometimes combined in meta-analyses. 4
Different studies have different protocols, and ignoring these differences can distort results. To 5 illustrate, placental EROD activity was 4-fold higher in a group of highly polychlorinated 6 biphenyl (PCB)-exposed Inuit women compared to a group of women from southern Quebec 7 with lower exposure. However the differences in EROD activity disappeared when smoking was 8 taken into account. Had tobacco consumption been unknown in one of the groups, spurious 9
conclusions would have been drawn (Pereg et al., 2002). Can uncertainty arising from 10 combining studies with different protocols be taken into acco unt in quantitative uncertainty 11 analysis? The question is similar to that of accounting for uncertainty due to missing covariates 12 in Cox regression (see Section 6.4.2.2). 13
Standard statistical tools will be of no av ail, as they quantify uncertainty in model 14 parameters estimated from data. However, exotic methods, such as Bayesian methods, 15 probabilistic inversion, or structured expert judgment may be applicable. These methods can be 16 applied when a quantitative model predicts other phenomena, even though these phenomena 17
could not be used to estimate the model. Th e question of whether such methods could remain 18 sufficiently tethered to data, or whether structur ed expert judgment is unavoidable, is a subject 19
for future research. 20
21
6.4.2.2. Uncertainty in TCDD Exposure/Dose in Epidemiological Studies 22 There are many types of epidemiological studies including case controls, longitudinal and 23 cross-sectional cohorts, and simple exposed/unex posed studies. The variety of mathematical 24 models includes Cox proportional hazard, Poisson regression, and logistic regression. The 25 outputs vary accordingly: relative risk ratios, odds ratios, and standardized mortality ratios 26 (SMR, ratio of observed to expected deaths). Exposure uncertainties of ten concern back-casted 27 exposures based on current serum lipid concentra tions, estimated/self re ported dietary habits, 28 fish consumption, placenta lipid co ncentrations, and other measures. 29 Uncertainty in exposure is often compen sated by coarsening the output, e.g., SMRs 30 instead of dose-dependent risk factors. P ackages computing the outputs routinely produce 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-24 confidence intervals that reflect sampling fluctua tions, assuming truth of the model. Additional 1 uncertainty could be factored in with exotic methods. A signi ficant issue in epidemiological 2
studies is the effect of omitted covariates. Om itted covariates in Cox regression will bias the 3 estimates of effects of included covariates. If the omitted covariates are independent of the 4 included covariates, the bias is toward zero in absolute valu e (Bretagnolle and Huber-Carol, 5 1988); if the omitted covariates are not independent, little can be said. 6 With regard to individual studies, it might be possible to identify specific opportunities 7 for uncertainty quantification. This is illustra ted with the study of Steenland et al. (2001) of 8 more than 3,500 male workers exposed to TCDD-c ontaminated products at eight U.S. chemical 9 plants. Each worker was assigned an exposure sc ore based on estimated level of contact with 10 TCDD, the degree of TCDD contamin ation of product at each plant over time, and the fraction of 11 a workday in contact with the product. For 170 workers, the serum TCDD levels were also 12 measured. The serum levels were back-extrapolate d to the last time of exposure using a constant 13 biological half life, and regresse d on the exposure scores. This regression model was used to 14 predict the dose in all workers, and predicted dose was correlated with cancer mortality. 15 Figure 6-1 shows a scatter plot of back-caste d versus predicted TCDD serum levels for the 16 170 workers on which the regression was based. 17 Given a predicted TCDD level, the uncertainty on the back-casted TCDD value could be 18 inferred from such data by vari ous techniques. A key question is whether the actual cancer 19 mortalities among 170 back-casted workers are ra ndomly placed in the conditional distribution 20 given predicted TCDD. Imagine, in other words, that the mortalities among the 170 back-casts 21 are colored red in the above picture. At any given level of TCDD predic tion, are the red points 22 evenly distributed, or are they shifted to the right ? In principle, the co rrelation between mortality 23 and back-casted TCDD level, given the predicted le vel, could be estimated. This amounts to 24 estimating heteroscedasticity in the regression m odel. Then, for each of the 3,538 workers, given 25 his predicted TCDD level, we could sample a b ack-casted TCDD level, appropriately correlating 26 with mortality, and recompute th e dose response analysis. Rep eating this many times we could 27 build up a distribution for excess lifetime cancer mortality risk. 28 It is instructive to step th rough similar issues with rega rd to biological half life, 29 background and body fat. The Steenland et al. (2001) analysis assumed a constant TCDD 30 biological half life (8.7 years). A distribution over th is half life could plausibly be developed 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-25 from published sources. Assuming this half life is constant for all workers, but uncertain 1 (epistemic uncertainty), this distribution could easily supplement the previ ous distribution: first 2 sample a half life (to be applied to all workers), then estimate the regression model for this half 3 life, and sample back-casted TCDD levels given each worker’s exposure score, taking account of 4 correlation with mortality. This works if the half life uncertainty is epistemic. However, since 5 the half life is estimated from data, it is more reasonable to suppose that th e half life varies from 6 worker to worker (aleatoric uncertainty). Here again the correlation with mortality must be taken 7 into account, indeed it seems reasonable to suppo se that the 256 cancer deaths involved workers 8 with longer half lives. However, there is no way ex post of determin ing the biological half life in 9
the deceased workers. 10 Background exposure and body fat are similar to half life. The study authors held 11 background level constant at the me dian level (6.1 ppt, range 2.0

− 19.7) for 79 nonexposed 12
workers from whom blood was also drawn (see also Section 6.4.2. 4). The full distribution of 13 TCDD levels for these nonexposed workers could be used as well. Is it reasonable to suppose 14 that responsive workers have background leve ls that are sampled randomly from this 15 distribution, or might they not plausibly come from the high end of the distribution? The 16 analysis also assumed a constant percentage of body fat (30%), whereas body fat percentage 17 varies in the general pop ulation, e.g., for men this has been reported to range from 2 to 38% or 18 more (see Footnote 32). The body fat distributio n in the worker population could have been 19 ascertained, but again th e question arises, are the responsiv e workers sampled randomly from 20
this distribution? 21 These three factors, variable half life, variable background, variable body fat percentage, 22 might conspire to make the effective dose level among the responsiv e workers significantly 23 higher than would appear in a st udy that assumes these factors to be constant. However, such 24 concerns cannot be addressed in a quantitative uncertainty analysis, unless cancer mortality can 25
be correlated with these variab les. In an optimal study design, this information could be 26 retrieved from the data, but optimality is a luxury that is seldom afford ed. The optimal should 27 not be the enemy of the good; it might be possibl e to estimate these corre lations in some other 28 defensible manner, in which case the effect of exposure uncertainty co uld be quantified and 29 propagated. Such an analysis would involve substantial effo rt and should not be undertaken 30 under assump tions that are them selves implausibl e. Experimental protocols do not currently 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-26 require such uncertainty quantification; perhaps they should and perhaps someday they will. In 1 any event Steenland et al. (2001) must be appl auded for conscientiously identifying these key 2
issues. 3
4
6.4.2.3. Uncertainty in Toxicity Equivalence (TEQ ) Exposures in Epidemiological Studies 5 Toxicity equivalence factors (TEFs) are used to infer the health effects of dioxin-like 6 compounds based on their relative potencies comp ared to TCDD. These factors are not known 7 with certainty, and the question arises whether un certainty in TEFs can be incorporated into a 8 quantitative uncertainty analysis. The process of deriving TEFs applied by the World Health 9 Organization (WHO, 2005) is described in Van den Berg et al. (2006). Dist ributions of relative 10 potencies (REPs) were developed from the scie ntific literature, with preference for in vivo 11 studies, as supplemented by in vitr o studies. An expert panel used a consensus process to select 12 a TEF value for each congener, in half log steps “Thus, the TEF is a central value with a degree 13 of uncertainty assumed to be at least ± half a log, which is one order of magnitude. However, it 14 should be realized that TEF assignments are usually within the 50 th to 75 th percentile of the REP 15 distribution, with a general inclination toward the 75 th percentile in order to be health protective” 16 (Van den Berg et al., 2006) (see Figure 6-2). 17 The WHO considers the uncertainty in TEFs to span one order of magnitude (presumably 18 log uniformly distributed). It w ould be tempting to use the distri butions in Figure 6-2 to quantify 19 uncertainty in the TEFs in a quantitative uncertain ty analysis. However, the issue of dependence 20 in this case is daunting. For example, should va lues of 1,2,3,7,8,-pentachlorodibenzofuran and 21 2,3,4,7,8-pentachlorodibenzofuran be sample d independently? The choice of dependence 22 structure will have a large effect. As described by Van den Berg et al. (2006), the differences in 23 REPs reflects differences in dosing regimens, species, endpoints, mechanisms, and calculation 24 methods. In a quantitative uncertainty analysis one must insure that these are not double 25
counted. 26 27 Reasons for significant differences in REPs for the same congener can be caused 28 by the use of different dosing regimens (acute vs. subchronic) , different endpoints, 29 species, and mechanisms (e.g., tumor promotion caused by at least two different 30 mechanisms as for mono- ortho -substituted PCBs), as well as different methods 31 used for calculating REPs. Thus, different methodological approaches used in 32 different studies clearly provide uncertainties when deriving and comparing REPs. 33This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-27 If future study designs to derive REPs were more standardized and similar, the 1 variation in REPs when using the same congener, endpoint, and species might be 2 expected to be smaller (Van den Berg et al., 2006). 3 4 Although the TEFs themselves a nd the distributions underlying them are based on expert 5 judgment, it is possible to incor porate these into a qua ntitative uncertainty analysis; however, it 6 is not simply a matter of taking the distributions in Figure 6-2 to predict the results, with 7 uncertainty, of exposure to dioxin-like com pounds. The issues of dependence and double 8 counting must first be addressed. Inasmuch as th e distributions ar e the result of expert judgment, 9 this would reasonably involve struct ured expert judgment as well. (Procedures for this type of 10 assessment have been developed and applied, and it would entail a significant level of effort.) 11
12
6.4.2.4. Uncertainty in Background Feed Exposures in Bioassays 13 TCDD is not produced intenti onally but rather is form ed as a byproduct of volcano 14 eruptions, forest fires, manufactur ing of steel and certain chemi cals (including some pesticides 15 and paints), pulp and paper bleaching, exhaust em issions, and incineration. It enters the food 16 supply primarily via aerial trans port and deposition of emissions, and it bioaccumulates in animal 17 fat. In general, food of animal origin contribut es to about 80% of the overall human exposure. 18 For example, Schecter et al. (1997) measured dioxins in pooled food samples collected in 1995 19 from supermarkets across the United States. Reported as parts per tr illion (ppt) toxicity 20 equivalences (TEQs), fresh water fish had the highest level (1.43); followed by butter (1.07); 21 hotdog/bologna (0.54); ocean fish (0.47); cheese (0.40); beef (0.38); eggs (0.34); ice cream 22 (0.33); chicken (0.32); pork (0.32); milk (0. 12); and vegetables, fruits, grains, and legumes 23 (0.07). (More recent exposure studies by Lorber et al. [2009] and others have updated these 24 older estimates of dietary levels.) 25 These results illustrate that a person’s diet ary intake of dioxins depends on the relative 26 intake of foods with high or low levels of c ontamination, and human background levels will vary 27 accordingly. The same applies to experimental animals in bioassays, although in those cases the 28 background intake can in principl e be controlled. Some of th e effects of TCDD and other AhR 29 agonists in enhancing the early init iation stages of cancers are consid ered to occur as a result of 30 prenatal exposures that are not included in the standard National T oxicology Program (NTP) 31 bioassay protocol (Brown et al., 1998; Muto et al., 2001). Further, to enhance reproducibility 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-28 and keep statistical fluctuations to a minimum, the standard NTP assays are deliberately run on 1 groups of animals that are relatively uniform genetically, fed uniform diets, and have the 2 minimum possible exposures to toxicants other than the agent(s) being tested. This tends to 3 reduce the potential for observing the consequences of potential interactive effects that might 4 occur in the diverse human populatio n with its variety of dietary and other exposures to a wide 5 range of potentially interacti ng substances and conditions. 6 A critical question is the extent to which the backgr ound exposure influences the 7 dose-response curve, and how this background should be taken into account. 40 One idea, 8 articulated in the recent NRC ( 2009) report on science and decisi ons, involves an “interacting 9 background.” This can be implemented by computing a virtual dose B which, according to the 10 selected dose-response model, would explain a chosen fraction of the background response. If 11
the chosen model for dose δ is f( δ ), the model can be adapted to account for an interacting 12 background by writing f*( δ ) = f( δ + B)

− f(B). This can alter the mode l’s behavior at zero dose. 13 For example, if f( δ ) = δ n /( δ n + EC 50 n ), the derivative d(f)/d( δ ) is n δ n-1 EC 50 n /( δ n + EC 50 n ) 2

, 14 which goes to zero as δ→ 0, if n > 1. However, replacing δ with ( δ + B) evidently changes the 15
derivative at zero to nB n − 1 EC 50 n /(B n + EC 50 n ). This model is not yet estimable from data, as we 16 have no way of choosing from the available anim al data the fraction of background response to 17 be explained by the model when applied to hum ans (although judgments could be made if we 18
had better information about the details of the processes that are involved in causing various 19 human health effects). However, as a conceptual model, it serves to remind us that the manner 20 of accounting for background exposures can influe nce a model’s behavior in the low-dose 21 region. 22
23
6.4.2.5. Feasibility of Quantifying the Uncertainties Encountered When Choosing Specific 24 Studies and Subsets of Data (e.g., Species and Gender) 25 Species, strain, gender, life stage, and other characteristics of experimental animals are 26 selected for a given study base d on previous knowledge (e.g., of the species sensitivity, 27

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).This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-29 availability of strains having li ttle genetic variation for the en dpoints in question, relevance of 1 the MOA, and degree to which the endpoints are si milar for humans). Many other decisions are 2 made in designing a bioassay study; will the animals be sacrificed at the termination of the study 3 (if not a lifetime study), or will they be allowed to live out their natural lives? What dosing 4 regimen should be applied? How will the animal s be fed and handled? Although such questions 5 may engender uncertainty in the minds of the expe rimenters, and reviewers; such uncertainty is 6 not amenable for quantitative uncertainty analysis unless and until there are quantitative models, 7 with parameters estimable from data, that can pr edict the effect of thes e choices on the response 8
function. 9
10
6.4.2.6. Feasibility of Quantifying the Uncertain ties Encountered when Choosing Specific 11 Endpoints for Dose-Response Modeling 12 Standard experimental protocols guide the selection of exposure/dosing conditions for a 13 given bioassay, including the amount, delivery vehicle, route, timing, dosing frequency and 14 duration, and dose spacing. The goal is to find the dose range where the experimental animals 15 begin to respond adversely, to he lp anchor the lower end of th e dose-response relationship, and 16 to avoid multiple experiments in which all or none of the animals respond. A common 17
recommendation is that the dose levels be chosen such that the increments in probability of 18 response are roughly equal. Hence, the choice of endpoint, spacing, and number of animals 19
should be made with these factors in mind. Of particular importan ce is the number of animals at 20
each dose level in relation to th e choice of endpoint and probabili ty of response. Using more 21 animals at the lower dose levels increases the pr obability of seeing some animals respond; on the 22 other hand, this approach will give higher weight to the low-dose responses in model fitting and 23 uncertainty quantification. In cluding many experiments at low dose with no expected response 24 could provide deceptive graphs. 25 One of the most clearly nonlinear responses to high-dose dioxin exposure cited by the 26 committee is cholangiocarcinoma (bile duct can cer) in rats (see Figure 6-3). The model 27 Prob(dose) = dose n /(dose n + EC 50 n ) with Hill coefficient n was fit to these data, yielding a 28 maximum likelihood estimate of n = 2.79 (Hattis, 2009) . For n > 1, this is a zero slope at zero 29 dose model (see Section 5.2.3.3). If we choos e a linear extrapolation below the POD at dose 30 3.82 ng/kg-day with probability of response = 0.02 , then at dose 1.7 ng/kg-day, the predicted 31This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-30 probability of response is 0.009; with 46 rats exposed to this dose we expect to see no responses, 1 which is what we see. However, sublinear mo dels will yield a higher probability of seeing 2 nothing. A graph like Figure 6- 3 would suggest that the linear extrapolation was missing the 3 experiments at doses 1.7 and 0.52 ng/kg-day, indicating lack of f it, whereas the null response is 4 what we should expect on the linear model. 5 The conclusion with regard to the feasibility of this quantitative uncertainty analysis 6 echoes that of the previous paragr aph: such uncertainty is not am enable for quantitative analysis 7 unless and until there are quantitative models, with parameters estimable from data, that predict 8
the effect of these choices on the response function, although sensitivity analyses can be done 9 showing the consequences of assuming differe nt amounts of interacti ng background within the 10 context of a specific nonlinear model. 11
12
6.4.2.7. Feasibility of Quantifying the Uncertainties Encountered when Choosing a Specific 13 Dose Metric (Trade-Off Between Confiden ce in Estimated Dose and Relevance of 14 MOA) 15 The concept of dose is not straightforwar d. To review, U.S. EPA (2005) Cancer 16 Guidelines provide the following taxonomy: 17
18

• Exposure is contact of an agen t with the outer boundary of an organism. 19

• Exposure concentration is the concentration of a chemical in its transport or 20 carrier medium at the point of contact. 21

• Dose is the amount of a substance availa ble for interaction with metabolic 22 processes or biologically significant recepto rs after crossing the outer boundary of 23 an organism. 24

• Potential dose is the amount ingested, inhaled, or applied to the skin. 25

• Applied dose is the amount of a substance presented to an absorption barrier and 26 available for absorption (although not nece ssarily having yet crossed the outer 27 boundary of the organism). 28

• Absorbed dose is the amount crossing a specifi c absorption barrier (e.g., the 29 exchange boundaries of skin, lung, and digestive tract) through uptake processes. 30

• Internal dose is a more general term, used without respect to specific absorption 31 barriers or exchange boundaries. Delivered dose is the amount of the chemical 32 available for interaction by a ny particular organ or cell. 33 34This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-31 Using the applied dose or absorbed dose woul d yield statistically more powerful results 1 and enable more precise predictions. If it is no t possible to measure thes e, then other available 2 dose metrics such as potential dose or exposure are used. Due to toxicokinetic variability, 3 different individuals receiving the same exposure may not have the same applied or absorbed 4 dose. Hence, use of an exposure metric would a dd variability to the pred icted results. The dose 5 metric should be selected that (1) has the most proximate possible causal relation to the 6 production of an adverse health endp oint, and (2) is most easily rela ted to the units of (external) 7 exposure that will be the basis for assessing and controlling human exposures. 8
9
6.4.2.8. Feasibility of Quantifying the Uncertainties Encountered When Choosing Model 10 Type and Form 11 U.S. EPA (2009d) draft white paper on proba bilistic methods not es: “There is no 12 consensus on any one well-accepted general me thodology for dealing with model uncertainty, 13 although there are various examples of efforts to do so.” Model uncertainty was introduced in 14 Section 6.1.3.4. Many statistical techniques are available to ev aluate model adequacy or to 15 choose a “best” model. Although it is tempting to qualify such deliberations as “uncertainty that 16 a model is true,” one must remember that all models, being idealizations, are false. Ultimately, 17 one is interested in uncertainty with regard to observable phenomena, not w ith regard to models. 18 Models are merely tools for describing the pheno mena. Nonetheless, the choice of a model 19 constrains the ways in which uncertainty can be represented, and the question addressed in this 20 paragraph is: How should one deal with these constraints? A recent study of uncertainty 21 modeling in dose response (Cooke, 2009) addresses precisely th is issue and provides ample 22
technical detail to fr ame possible options. 23 Before elaborating on exotic approaches to model uncertainty, it is well advised to 24 appreciate a feature in the standa rd statistical treatment of uncertainty. Consider a model based 25 on experimental data, typically bioassay data, in which a certain number of study subjects are 26 exposed to varying doses of a test substance, a nd in which the numbers of subjects exhibiting a 27 response are tallied. Values for the parameters in the model are chosen by the principle of 28
maximal likelihood: those values are chosen wh ich render the data as likely as possible. 29 According to standard practice, a model is chosen that best fits the data according to one of the 30This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-32 accepted criteria, such as reduced R 2 , or the Akaike Information Criterion. There might be many 1 incompatible models that are nearly as good. 2 One can ask the following: If the experiments on which the model is based were repeated, 3 sampling the same number of experimental subj ects from the distribut ion posited by the model, 4 how much could our parameter estimates change? This is described by a joint distribution over 5 the model’s parameters, which captures samplin g uncertainty under the assumption that the 6 model is true. Now, all models are false, and as our sample sizes grow the lack of fit in the 7 model becomes increasingly apparent . At the same time, the sample fluctuations in parameter 8
estimates— assuming the model is true —become ever smaller. In very large epidemiological 9 studies, standard statistical methods can produce razor-th in confidence bands in this way, which 10 fail to capture experts’ uncertain ty regarding observable phenomena. 41 11 The exotic methods sketched in the beginning of Section 6.4.2 may be viewed as attempts 12
to deal with this feature. Pr obabilistic inversion me thods were deployed on a large scale in the 13 joint U.S. NRC-EU uncertainty analyses noted in Section 6.1. Distributions over model 14 parameters are intended to capture an antecedently defined uncertainty over observable 15 phenomena predicted by the model. This met hod was applied in dispersion and deposition 16 modeling and further environmenta l transport models (including upt ake) for radionuclides. In 17 most cases, the observable uncertainty was based on structured expert judg ment, but it has also 18 been based on binomial uncertainty in bioassay st udies. A potential drawb ack is that it may not 19 prove possible to capture the obser vable uncertainty in this way with a classically best-fitting 20 model, and new models may be required. 21 Nonparametric Bayesian methods arose in the biomedical and reliability fields. They 22 start with a prior distributi on over all nondecreasing dose-response functions, and update these 23 with observations from a bioassay study. No furt her assumptions regarding parametric form are 24 introduced, but the prior distri bution remains important for doses outside the range of 25 observations. Bayesian model aver aging starts with a prior distri bution over a se t of candidate 26 models, and updates this distributi on with bioassay data. The met hod is flexible and intuitive, 27 though attenuation of the effect of the prio r on the posterior must be verified. 28

41 See for example Tuomisto et al. (2008, Table 6) for a compar ison of experts’ uncertainty in health effects of fine particulates with uncertainties derived from sampli ng uncertainty from large epidemiological studies.This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-33 All these approaches represent attempts to capture “extramodel uncertainty,” that is, 1 uncertainty that is not conditional on the truth of th e model. This is an active research area, and 2 the last word on capturing extramodel uncertainty in quantitative uncertainty analysis has not 3 been spoken. A major effort with regard to dioxin would be indicated when the strengths and 4
weakness of the exotic me thods are well understood. 5
6
6.4.2.9. Threshold MOA for Cancer 7 The NAS committee avers that knowledge of th e AhR binding MOA enta ils that there is 8 a response threshold for TCDD cancer induction. The differences betw een individual and 9 population thresholds are not disc ussed, but the following two possi bilities are distinguishable: 10
11
1. The threshold is the same for each indivi dual; since human variability in AhR binding 12 affinity is rather large (see Section 5.2.3.3), th is entails that the threshold is not affected 13 by the binding affinity. 14
2. The threshold varies across individuals a nd is related to the individual AhR binding 15 affinity. 16 17 These two positions are different. As shown in Section 5.2.3.3, it is quite possible that 18 each individual in a population has a threshold whereas the population dose-response relation is 19 linear. Because the NAS committee does not distinguish which of these positions it holds, the 20 feasibility of quantitative uncertainty an alysis is examined here for both. 21
22
i. Quantitative uncertainty analysis concerns a math ematical model. In case (i), this model 23 would show how the existence of the AhR binding would induce a threshold, 24 independently of the strength of the binding. Assessing the feasibility of quantitative 25 uncertainty analysis must await th e elaboration of such a model. 26 ii. In this case, it must be shown that the di stribution of thresholds, and the dose-response 27 function above the threshold, is such as to induce a population “zero slope at zero dose” 28 (ZS@Z) model. Recall, the burden of proof is on this (ZS@Z) model. Scoping the 29 population variability with regard to AhR-medi ated mechanisms in general, and dioxin 30 sensitivity in particular, is an active area of research. It i nvolves phenotyping human 31 AhR-mediated responsiveness and relati ng this to polymorphisms in the human 32 population. Harper et al. (2002) report that a 10-fold va riation in binding affinity of AhR 33 for TCDD in human placental tissue did not reveal any polymorphisms, suggesting that 34 the relation between phe notypical and genotypical variation is tenuous at best. Tuomisto 35 et al. (1999) demonstrate larg e variations in efficacy in two rat strains whose binding 36This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-34 affinity is similar (Long-Evans, K d = 3.4, Han/Wistar, K d = 3.9 [Connor and Alyward, 1 2006]), and they also show that this variation is endpoint-sp ecific. The responses in both 2 strains are similar for cytochrome P450 (C YP)1A1 induction, but very dissimilar for 3 thymus atrophy, serum bilirubin, and mortality. Toide et al. (2003) suggest that common 4 biochemical measures of EROD activity might be mediated by CYP1B1 and CYP1A2. 5 The differences in serum bilirubin at doses around 10 μg/kg are about a factor of 30. 6 Han/Wistar rats seldom die at this dose, while mortality of Long Evans rats is about 50%. 7 The mechanisms are not understood. 8 9 Although the mass action dose-response model does not have a threshold, it is possible 10 that certain enzymes block the receptor bindi ng, and until these are overwhelmed, no response 11 occurs. The availability of such enzymes may vary from individual to individual, and may or 12 may not covary with the dissociation constant K d . Pursuing these lines of research may result in 13 a convincing demonstration of a population (ZS@Z) model. Su ch a model would express the 14
individual threshold in terms of parameters that could be estimated with uncertainty from the 15
data. 16
17
6.4.2.10. Feasibility of Quantifying the Uncertain ties Encountered when Selecting the BMR 18 The NAS committee explicitly requested that the uncertainty attending the choice of a 19 BMR be quantified. First of all, simply plugging other values in for the BMR does not constitute 20 a quantitative analysis of uncertainty. The pl ugged-in values must be sampled from some 21 uncertainty distribution. Sin ce this concerns volitional un certainty, there is no underlying 22 distribution from which to sample , unless the choice of BMR is re lated to some claim about the 23
state of the world. 24
25
6.5. CONCLUSIONS 26 The main conclusions regarding the feasibilityof quantitative uncertainty analysis are 27 first summarized in relation to specific sugge stions made by the NAS committee. A suggested 28 research agenda follows. 29
6.5.1. Summary of NAS Suggestions and Responses 30 On page 130 of their report (NAS, 2006a), NAS makes specific suggestions regarding 31 uncertainty quantification. These are reformatted and presented in italics below. Following each 32This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-35 suggestion, a summary of the discus sion in this section is given, w ith reference to the section in 1
which it is addressed. 2
3 EPA should have addressed quantitatively the following sources of uncertainty: 4
5

• Basis for risk quantification: 6
1. bioassay data, 7
2. occupational cohort data. 8 9 Response : (a) Classical statistical methods yi eld distributions on model parameters 10 which reflect sample fluctuations, assuming that the model is true. This type of 11 uncertainty is taken into account in th e BMDL. Exotic methods can account for 12 uncertainty which is not conditional on the trut h of a model, at least for bioassay data 13 (see Section 6.4.2). (b) For epidemiological data, the dose recons truction often involves 14 assumptions which may support data driven un certainty analysis, if sufficient data can 15 be retrieved. Examples discussed above include back-casted TCDD level, biological 16 half life, body fat and background (see Section 6.4.2.2). Uncertainty in the choice of 17 bioassay data sets or choice of occupational c ohort data sets is vo litional, and is not 18 quantified by sampling an input distributi on. To be amenable for quantitative 19 uncertainty analysis, the choice must be li nked to a statement about the state of the 20 world (see Section 6.1.1). 21

• Epidemiology data to use: 22
1. risk estimate developed with data aggre gated from all suitable studies, 23
2. risk estimate or estimates developed using each study individually. 24

• Factors affecting extrapolation from occupational to general population cohorts, 25 including differences in baseline health st atus, age distribution, the healthy worker 26 survivor effect, and background exposures. 27 28 Response: (a) Quantitative uncertainty analysis based on meta-analysis data poses 29 challenges owing to differences in study protocols. Exotic methods might take us further, 30 the question is whether the re striction to data driven methods (as opposed to expert 31 judgment or Bayesian methods) could be maintained (see Sections 6.4.2.2 and 6.4.2.3). 32 (b) If the general population is characterized by distri butions over known confounders 33 whose coefficients are estimated from the ep idemiological studies, then uncertainty over 34 these coefficients can be extracted with th e methods mentioned in Section 6.4.2.1. 35 Uncertainty due to missing covariates is intractable for data driven uncertainty analysis 36 (see Section 6.4.2.2). 37

• Bioassay data to use: 38
1. risk estimate developed with the single data set implying the greatest risk (that is, 39 single study, tumor site, gender), 40This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-36 2. risk estimate developed with multiple dat a sets satisfying an a priori set of 1 selection criteria. 2 3 Response: (a) Echoing response 1.b, uncertainty in choi ce of data sets is volitional and is 4 not quantified by sampling an input distribution. To be amenable for quantitative 5 uncertainty analysis the choice must be linked to a statement about the state of the world 6 (see Section 6.1.1). (b) The issue here is sim ilar to the meta-analysis addressed in (2.a). 7

• Dose-response model: 8
1. linear dose response, 9
2. nonlinear dose. 10 11 Response : (a) Presumably the low dose extrapolati on is meant. When the linear model is 12 used by default, the uncertainty is volitional. To be amenable for quantitative uncertainty 13 analysis the choice must be linked to a st atement about the state of the world (see 14 Section 6.1.1). The ED x as POD for the linear extrap olation can be subjected to 15 quantitative uncertainty analysis, if based on sufficient bioassay data. (b) presumably 16 ‘nonlinear dose response’ is meant. It is pos sible that human thresholds exist, and that 17 the distribution of thresholds can be characterized in the hum an population. In as much 18 as the mechanisms for this are not yet understood, there is no quantitative model 19 expressing threshold as a function of para meters which could be estimated, with 20 uncertainty, from data. This currently limits the application of uncertainty quantification 21 (see Section 6.4.2.9). 22

• Dose metric: 23
1. average daily intake, 24
2. area under the blood concen tration-time curve, 25
3. lifetime average body burden, 26
4. peak body burden, 27
5. other. 28 29 Response: The dose metric is chosen to maximize causal proximity to the endpoint, 30 while maintaining the link to measured expos ure (see Section 6.4.2.7). There may be 31 uncertainty with regard to which metric is opt imal. If an inappropriate metric is chosen 32 in a bioassay study, this woul d be expressed in noisier responses which would tend to 33 suppress the dependence of e ndpoint on dose. A data driv en quantitative uncertainty 34 analysis of dose metric woul d require a mathematical mode l expressing endpoints as a 35 function, inter alia, of dose metric, with parameters estimated from data. 36

• Dose metric—biological measure: 37
1. free dioxin, 38
2. bound dioxin. 39 40This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-37 Response: The issue is whether all TCDD availa ble for AhR binding, or only the bound 1 TCDD, should be used as a dose metric. Bind ing affinity is determined by more factors 2 than genetic polymorphisms and these other factors are poorly understood (see 3 Section 6.4.2.9). A quantita tive uncertainty analysis must await the formulation of a 4 quantitative model expressing bi nding affinity in terms of parameters which can be 5 estimated from data. 6

• POD: 7

a. ED 10 , 8 b. ED 05 , 9

c. ED 01 10 11 Response : Uncertainty in choosing a POD is voliti onal. Uncertainty in the value of an 12 ED x can be quantified in a data driven manner if sufficient bioassay data is at hand (see 13 Section 6.4.1.1). 14

• Value from ED distribution to use: 15
1. ED, 16
2. lower confidence bound value for the ED (LED), 17
3. upper confidence bound for the ED (UED). 18 19 Response : Given that uncertainty on the POD is quantified, it is a trivial exercise to 20 derive a distribution of th e slopes of a linear low dose extrapolation, and hence a 21 distribution of a risk specific dose. This would seem pref erable to choosing between a 22 lower, upper, or nominal value. 23

• Where alternative assumptions or methodologies could not be ruled out as implausible or 24 unreasonable, EPA could have estimated the corresponding risks and reported the 25 impact of these alternatives on the risk assessment results. The potential impacts of four 26 sources of uncertainty are discussed below. 27

• The full range of plausible parameter valu es for the dose-response functions used 28 to characterize the dose-response relati onship for the three occupational cohort 29 studies selected by EPA (Ott and Zober, 1996; Becher et al., 1998; Steenland et 30 al., 2001). 31

• Use of other points of de parture, not just the ED 01 (or LED 01 ), to develop a CSF. 32

• Alternative dose-response functional forms as well as goodness of fit of all 33 models, especially at low doses. 34

• Uncertainty introduced by estimation of historical occupa tional exposures. 35 36 Response: (a) The study of Steenland et al. (2001) was selected to illustrate the 37 possibilities and limitations of qua ntitative uncertainty analysis for this type of study (see 38 Section 6.4.2.2). (b) The possi bilities for uncertainty quantif ication with regard to the 39 POD are discussed in Section 6.4.1.1 and in the POD bullet above. (c) Goodness of fit at 40This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-38 any measured dose is evaluated in standard packages. There may be different models 1 with comparable goodness of fit at observed doses which differ strongly at doses outside 2 the measured range. Extra model uncertainty, that is, uncertainty wh ich is not conditional 3 on the truth of any given model, is addressed by the exotic methods (see Section 6.4.2). 4 (d) The feasibility of quantif ying uncertainty in occupationa l exposure is study specific. 5 The example of Steenland et al. (2001) was discussed in some detail (see 6 Section 6.4.2.2). In general, the proble m is not so much quantifying the exposure 7 uncertainty, but in quantifying the depende nce between the endpoi nts and the exposure 8 uncertainty. 9 10
6.5.2. How Forward? Beyond RfDs and Cancer Slope Factors to Development of 11 Predictive Human Dose-Response Functions 12 Uncertainty quantification is an emerging area in science. There are many examples of 13 highly vetted and peer-reviewed unc ertainty analyses based on st ructured expert judgment. 14 Under this process, experts in effect synthesi ze a wide diversity of in formation in generating 15 their subjective probability distri butions. Where considerable da ta exist for an environmental 16 pollutant, such as for the well-studied TCDD, it is natural to ask whether these extensive data can 17 be leveraged more directly in uncer tainty quantification. This is an area where research could be 18 focused. The requisite knowledge does not yet exist, but there are promising lines of attack. It is 19 therefore not a question of convening blue-ribbon panels to reveal the proper approach; instead 20 multiple approaches should be encouraged to try out new ideas and share experiences. 21 An important idea that has been pioneered in Europe is to organize bench-test exercises 22 where different approaches are applied to a common problem. This focuses the discussion on 23 real issues and builds a community of capable practitioners. Such initiatives have proven much 24 more productive than simply supporting individu al researchers to ex plore their ideas. 25
Areas for which bench-test exerci ses might be appropriate include: 26
27

• Testing “exotic methods” for capturing model uncertainty 28

• Combining bioassay and epidemiological data for uncertainty quantification 29

• Applicability of structured expert ju dgment, e.g., for low-dose extrapolation 30

• Conducting dependence modeling, dependence inference, and dependence elicitation 31 (such as with regard to TEFs). 32 33 Looking beyond compounds for which considerable data exist, there will always be a 34
need to evaluate new substances. The target will be a simple method that: 35This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-39 1. Can yield predictions of toxico logical indicators with uncertainty via a valid probabilistic 1 mechanism. 2
2. Could evolve from approaches based on sim ilarities (such as a random chemical model) 3 under which the new substance could be seen as a random sample from a reference 4 distribution of chemicals considered sufficien tly similar, e.g., in terms of structure, 5 physicochemical properties, and biological activity (potency). 6
3. Is nondisruptive to the traditional process. 7 8 This last feature is very important because as a practi cal matter, any collective push 9 forward in this area must extend from (not ab ruptly depart from) cu rrent methodology. For 10 example, the discussion surrounding uncertainty f actors suggests that a probabilistically valid 11 inference system could substantially differ from th e current system. Noneth eless, to meld with 12 current practice, it must initialize on the current system and allow this system to evolve in a 13
measured fashion. 14 Additional research topics rele vant to dioxin that could furt her inform health assessments 15 include population variability of biokinetic cons tants, threshold mechanisms for the mass action 16 model, and polymorphism at frequencies lo wer than 1%. Further data and improved 17 methodologies in these areas, combined with devel opments illustrated elsewhere in this report, 18 will help reduce uncertainties and strengthen our unde rstanding of potential health implications 19
of environmental contaminants. 20This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-40 Table 6-1. Key sources of uncertainty 1 2 Selection of endpoint and of species/strain, ge nder, life stage, other subject characteristics - critical effect - sensitivity (e.g., species, life stage) - human relevance Selection of key study(ies): human data a nd bioassays (strength, inclusion criteria) - epidemiological studies, clini cal/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 phar macokinetic (PBPK) modeling - scaling (human equivalents), adjust ments (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, ED 01 , 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., occupationa l, general public, sensitive groups) - polymorphisms - life stage, other features - individual vs. population threshold Characterization of risk/effect - adversity of effect (vs. in norma l 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 PBPK = physiologically-based pharmacokinetic; SF = slope factor; TD = toxicodynamic; TK = 3 toxicokinetic

. 4This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-41 Table 6-2. PODs and amenability for uncertainty quantification 1 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
ED x Estimate from set of exposure-response data Choose bioassay experiments to estimate ED x Yes, if full bioassay data are available 3This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-42 1 2

3 4 5 Figure 6-1. Back-casted vs. predic ted TCDD serum levels for a worker 6 subset. 7 8 Source: Steenland et al. (2001). 9Predicted TCDD

Back-Extrapolated TCDDThis document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-43 1 Figure 6-2. Distribution of in vivo unweighted REP values in the 2004 database. 2 3 Source: Van den Berg et al. (2006), reprinted with permissi on from Haws et al. (2006). 4This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/10 DRAFT

— DO NOT CITE OR QUOTE 6-44 1

Human equivalent dose (ng/kg-day ), from simple body weight

− 1/4 projection 2 3 Figure 6-3. Plot of observed rat ch olagiocarcinoma incidence and central 4 estimate of Hill Model fit to the data vs. human equivalent dose. 5 6 Source: Hattis (2009). 7 8 20 18 16 14 12 10 8 6 4 2 0 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)This document is a draft for review purpos es only and does not constitute Agency policy. 1/15/2010 DRAFT

— DO NOT CITE OR QUOTE R-1 REFERENCES 1

2

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