DRAFT - DO NOT CITE OR QUOTE EPA/635/R-10/002 www.epa.gov/iris



# TOXICOLOGICAL REVIEW OF

FORMALDEHYDE

INHALATION TOXICITY

(CAS No. 50-00-0)



## In Support of Summary Information on the Integrated Risk Information System (IRIS)



# VOLUME IV of IV

Appendices

March 17, 2010

NOTICE

This document is an Inter-Agency Science Consultation draft . This information is distributed solely for the purpose of pre-dissemination peer re view under applicable information quality guidelines. It has not been formally disseminated by EPA. It does not represent and should not be construed to represent any Agency determination or policy. It is being circulated for review of its technical accuracy and science policy implicati ons.

U.S. Environmental Protection Agency Washington, DCThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-ii DISCLAIMER

This document is a preliminary draft for review pu rposes only. This information is distributed solely for the purpose of pre-dissemina tion peer review under applicable information quality guidelines. It has not been formally disse minated by EPA. It does not represent and should not be construed to represent any Agency det ermination or policy. Mention of trade names or commercial products does not constitute en dorsement or recommendation for use.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-iii CONTENTS—TOXICOLOGICAL REVIEW OF FORMALDEHYDE (CAS No. 50-00-0)

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

................................................... ................................. xi LIST OF FIGURES....................................

................................................... ................................ xx LIST OF ABBREVIATIONS AND ACRONYMS.................

................................................... xxv FOREWORD ...........................................

................................................... .............................. xxxii AUTHORS, CONTRIBUTORS, AND REVIEWERS............... ............................................. xxxiii

VOLUME I

1. INTRODUCTION...................................

................................................... ............................. 1-1

2. BACKGROUND .....................................

................................................... ............................ 2-1 2.1. PHYSICOCHEMICAL PROPERTIES OF FORMALDEHYDE... ............................. 2-1 2.2. PRODUCTION, USES, AND SOURCES OF FORMALDEHYDE ........................... 2-1 2.3. ENVIRONMENTAL LEVELS AND HUMAN EXPOSURE...... ............................... 2-4 2.3.1. Inhalation.................................

................................................... ...................... 2-5 2.3.2. Ingestion ..................................

................................................... .................... 2-10 2.3.3. Dermal Contact.............................

................................................... ............... 2-11

3. TOXICOKINETICS .................................

................................................... ........................... 3-1 3.1. CHEMICAL PROPERTIES AND REACTIVITY........... ............................................ 3-1 3.1.1. Binding of Formaldehyde to Proteins........

................................................... .... 3-1 3.1.2. Endogenous Sources of Formaldehyde .........

................................................... 3-3 3.1.2.1. Normal Cellular Metabolism (Enzymatic) ... ..................................... 3-3 3.1.2.2. Normal Metabolism (Non-Enzymatic)........ ...................................... 3-5 3.1.2.3. Exogenous Sources of Formaldehyde Product ion............................. 3-5 3.1.2.4. FA-GSH Conjugate as a Method of Systemic Distribution............... 3-6 3.1.2.5. Metabolic Products of FA Metabolism (e.g. , Formic Acid).............. 3-6 3.1.2.6. Levels of Endogenous Formaldehyde in Anim al and Human Tissues............................................ .................................................. 3 -6 3.2. ABSORPTION ...................................

................................................... ....................... 3-9 3.2.1. Oral.......................................

................................................... ......................... 3-9 3.2.2. Dermal .....................................

................................................... ...................... 3-9 3.2.3. Inhalation.................................

................................................... ...................... 3-9 3.2.3.1. Formaldehyde Uptake Can be Affected by Ef fects at the Portal of Entry .................................... ............................................ 3-10 3.2.3.2. Variability in the Nasal Dosimetry of For maldehyde in Adults and Children................................ ........................................ 3-12 3.3. DISTRIBUTION.................................

................................................... ..................... 3-13 3.3.1. Levels in Blood............................

................................................... ................ 3-13 3.3.2. Levels in Various Tissues..................

................................................... .......... 3-15 3.3.2.1. Disposition of Formaldehyde: Differentiat ing Covalent Binding and Metabolic Incorporation................ ............................. 3-16This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-iv CONTENTS (continued)

3.4. METABOLISM ...................................

................................................... .................... 3-20 3.4.1. In Vitro and In Vivo Characterization of Fo rmaldehyde Metabolism............ 3-20 3.4.2. Formaldehyde Exposure and Perturbation of M etabolic Pathways................ 3-23 3.4.3. Evidence for Susceptibility in Formaldehyde Metabolism ............................ 3-24 3.5. EXCRETION....................................

................................................... ....................... 3-25 3.5.1. Formaldehyde Excretion in Rodents ..........

................................................... . 3-26 3.5.2. Formaldehyde Excretion in Exhaled Human Bre ath...................................... 3-27 3.5.3. Formaldehyde Excretion in Human Urine...... ................................................ 3-3 1 3.6. MODELING THE TOXICOKINETICS OF FORMALDEHYDE A ND DPX.......... 3-32 3.6.1. Motivation .................................

................................................... .................. 3-32 3.6.2. Species Differences in Anatomy: Consequence s for Gas Transport and Risk...............................................

................................................... .............. 3-34 3.6.3. Modeling Formaldehyde Uptake in Nasal Passa ges....................................... 3-40 3.6.3.1. Flux Bins ................................

................................................... ...... 3-41 3.6.3.2. Flux Estimates ...........................

................................................... ... 3-41 3.6.3.3. Mass Balance Errors...................... .................................................. 3 -42 3.6.4. Modeling Formaldehyde Uptake in the Lower R espiratory Tract.................. 3-42 3.6.5. Uncertainties in Formaldehyde Dosimetry Mod eling .................................... 3-44 3.6.5.1. Verification of Predicted Flow Profiles.. ......................................... 3-44 3.6.5.2. Level of Confidence in Formaldehyde Uptak e Simulations............ 3-45 3.6.6. PBPK Modeling of DNA Protein Cross-Links (D PXs) Formed by Formaldehyde .......................................

................................................... ...... 3-48 3.6.6.1. PBPK Models for DPXs..................... ............................................. 3-48 3.6.6.2. A PBPK Model for DPXs in the F344 Rat and Rhesus Monkey that uses Local Tissue Dose of Formaldehyde . ................ 3-50 3.6.6.3. Uncertainties in Modeling the Rat and Rhe sus DPX Data.............. 3-51 3.6.7. Uncertainty in Prediction of Human DPX Conc entrations ............................ 3-53

VOLUME II

4. HAZARD CHARACTERIZATION........................

................................................... ............ 4-1 4.1. HUMAN STUDIES................................

................................................... ................... 4-1 4.1.1. Noncancer Health Effects...................

................................................... ........... 4-1 4.1.1.1. Sensory Irritation (Eye, Nose, Throat Irr itation)................................ 4-1 4.1.1.2. Pulmonary Function ....................... ................................................. 4- 11 4.1.1.3. Asthma...................................

................................................... ....... 4-19 4.1.1.4. Respiratory Tract Pathology.............. .............................................. 4-26 4.1.1.5. Immunologic Effects ...................... ................................................. 4- 30 4.1.1.6. Neurological/Behavioral.................. ................................................ 4-4 2 4.1.1.7. Developmental and Reproductive Toxicity.. ................................... 4-45 4.1.1.8. Oral Exposure Effects on the Gastrointest inal Tract ....................... 4-56 4.1.1.9. Summary: Noncarcinogenic Hazard in Humans ............................. 4-56This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-v CONTENTS (continued)

4.1.2. Cancer Health Effects......................

................................................... ............ 4-57 4.1.2.1. Respiratory Tract Cancer................. ................................................ 4-5 7 4.1.2.2. Non-Respiratory Tract Cancer............. ............................................ 4-84 4.1.2.3. Summary: Carcinogenic Hazard in Humans ... .............................. 4-107 4.2. ANIMAL STUDIES ...............................

................................................... ............... 4-109 4.2.1. Noncancer Health Effects...................

................................................... ....... 4-110 4.2.1.1. Reflex Bradypnea ......................... ................................................. 4- 110 4.2.1.2. Respiratory Tract Pathology.............. ............................................ 4-120 4.2.1.3. Gastrointestinal Tract and Systemic Toxic ity................................ 4-201 4.2.1.4. Immune Function.......................... ................................................. 4- 216 4.2.1.5. Hypersensitivity and Atopic Reactions.... ...................................... 4-225 4.2.1.6. Neurological and Neurobehavioral Function ................................. 4-250 4.2.1.7. Reproductive and Developmental Toxicity.. ................................. 4-285 4.2.2. Carcinogenic Potential: Animal Bioassays... ................................................ 4-3 24 4.2.2.1. Respiratory Tract ........................

................................................... 4-324 4.2.2.2. Gastrointestinal Tract ................... ................................................. 4- 326 4.2.2.3. Lymphohematopoietic Cancer............... ........................................ 4-328 4.2.2.4. Summary..................................

................................................... ... 4-335 4.3. GENOTOXICITY.................................

................................................... ................. 4-335 4.3.1. Formaldehyde-DNA Reactions .................

................................................... 4-335 4.3.1.1. DNA-Protein Cross-Links (DPXs)........... ..................................... 4-336 4.3.1.2. DNA Adducts .............................. .................................................. 4 -341 4.3.1.3. DNA-DNA Cross-Links (DDXs) ............... ................................... 4-343 4.3.1.4. Single Strand Breaks ..................... ................................................ 4-3 44 4.3.1.5. Other Genetic Effects of Formaldehyde in Mammalian Cells ...... 4-345 4.3.2. In Vitro Clastogenicity ....................

................................................... .......... 4-345 4.3.3. In Vitro Mutagenicity ......................

................................................... .......... 4-347 4.3.3.1. Mutagenicity in Bacterial Systems ........ ........................................ 4-347 4.3.3.2. Mutagenicity in Non-mammalian Cell System s............................ 4-353 4.3.3.3. Mutagenicity in Mammalian Cell Systems... ................................. 4-353 4.3.4. In Vivo Mammalian Genotoxicity.............

................................................... 4-360 4.3.4.1. Genotoxicity in Laboratory Animals ....... ...................................... 4-360 4.3.4.2. Genotoxicity in Humans................... ............................................. 4-362 4.3.5. Summary of Genotoxicity ....................

................................................... ..... 4-370 4.4. SYNTHESIS AND MAJOR EVALUATION OF NONCARCINOG ENIC EFFECTS ............................................

................................................... ................. 4-371 4.4.1. Sensory Irritation .........................

................................................... .............. 4-376 4.4.2. Pulmonary Function .........................

................................................... ......... 4-379 4.4.3. Hypersensitivity and Atopic Reactions ...... .................................................. 4 -382 4.4.4. Upper Respiratory Tract Histopathology..... ................................................. 4- 383 4.4.5. Toxicogenomic and Molecular Data that May I nform MOAs ..................... 4-385 4.4.6. Noncancer Modes of Actions .................

................................................... ... 4-387 4.4.7. Immunotoxicity .............................

................................................... ............ 4-389This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-vi CONTENTS (continued)

4.4.8. Effects on the Nervous System..............

................................................... .... 4-390 4.4.8.1. Irritant Threshold Detection ............. ............................................. 4-390 4.4.8.2. Behavioral Effects ....................... .................................................. 4 -391 4.4.8.3. Neurochemistry, Neuropathology, and Mecha nistic Studies......... 4-392 4.4.8.4. Summary..................................

................................................... ... 4-392 4.4.8.5. Data Gaps ................................

................................................... ... 4-393 4.4.9. Reproductive and Developmental Toxicity.... .............................................. 4-393 4.4.9.1. Spontaneous Abortion and Fetal Death..... .................................... 4-393 4.4.9.2. Congenital Malformations................. ............................................ 4-396 4.4.9.3. Low Birth Weight and Growth Retardation .. ................................ 4-396 4.4.9.4. Functional Development Outcomes (Developm ental Neurotoxicity)..................................... .......................................... 4-397 4.4.9.5. Male Reproductive Toxicity............... ........................................... 4-398 4.4.9.6. Female Reproductive Toxicity ............. ......................................... 4-399 4.4.9.7. Mode of Action...........................

................................................... 4-400 4.4.9.8. Data Gaps ................................

................................................... ... 4-402 4.5. SYNTHESIS AND EVALUATION OF CARCINOGENICITY.. ........................... 4-402 4.5.1. Cancers of the Respiratory Tract ...........

................................................... .... 4-402 4.5.2. Lymphohematopoietic Malignancies........... ................................................. 4- 408 4.5.2.1. Background...............................

................................................... .. 4-408 4.5.2.2. All LHP Malignancies ..................... .............................................. 4-410 4.5.2.3. All Leukemia .............................

................................................... . 4-414 4.5.2.4. Subtype Analysis ......................... .................................................. 4 -418 4.5.2.5. Myeloid Leukemia......................... ................................................ 4-4 19 4.5.2.6. Solid Tumors of Lymphoid Origin.......... ...................................... 4-421 4.5.2.7. Supporting Evidence from Animal Bio-Assay s for Formaldehyde-Induced Lymphohematopoietic Malignanci es...... 4-423 4.5.3. Carcinogenic Mode(s) of Action .............

................................................... .. 4-427 4.5.3.1. Mechanistic Data for Formaldehyde........ ...................................... 4-428 4.5.3.2. Mode of Action Evaluation for Upper Respi ratory Tract Cancer (Nasopharyngeal Cancer, Sino-Nasal) ......... .................... 4-439 4.5.3.3. Mode(s) of Action for Lymphohematpoietic Malignancies .......... 4-446 4.5.4. Hazard Characterization for Formaldlehyde C arcinogenicity...................... 4-453 4.6. SUSCEPTIBLE POPULATIONS ......................

................................................... ... 4-454 4.6.1. Life Stages ................................

................................................... ................. 4-454 4.6.1.1. Early Life Stages........................

................................................... . 4-455 4.6.1.2. Later Life Stages........................

................................................... . 4-459 4.6.1.3. Conclusions on Life-Stage Susceptibility. ..................................... 4-459 4.6.2. Health/Disease Status ......................

................................................... .......... 4-460 4.6.3. Nutritional Status.........................

................................................... .............. 4-461 4.6.4. Gender Differences.........................

................................................... ........... 4-462 4.6.5. Genetic Differences ........................

................................................... ........... 4-462This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-vii CONTENTS (continued)

4.6.6. Co-Exposures ...............................

................................................... ............. 4-464 4.6.6.1. Cumulative Risk .......................... .................................................. 4 -464 4.6.6.2. Aggregate Exposure....................... ................................................ 4-4 65 4.6.7. Uncertainties of Database..................

................................................... ........ 4-465 4.6.7.1. Uncertainties of Exposure ................ ............................................. 4-465 4.6.7.2. Uncertainties of Effect.................. ................................................. 4- 466 4.6.8. Summary of Potential Susceptibility........

................................................... . 4-467

VOLUME III

5. QUANTITATIVE ASSESSMENT: INHALATION EXPOSURE .. ..................................... 5-1 5.1. INHALATION REFERENCE CONCENTRATION (RfC)..... .................................... 5-2 5.1.1. Candidate Critical Effects by Health Effect Category...................................... 5-3 5.1.1.1. Sensory Irritation of the Eyes, Nose, and Throat............................... 5-3 5.1.1.2. Upper Respiratory Tract Pathology ........ ........................................... 5-5 5.1.1.3. Pulmonary Function Effects ............... ............................................... 5-6 5.1.1.4. Asthma and Allergic Sensitization (Atopy) ..................................... 5-10 5.1.1.5. Immune Function..........................

................................................... 5-16 5.1.1.6. Neurological and Behavioral Toxicity..... ........................................ 5-17 5.1.1.7. Developmental and Reproductive Toxicity.. ................................... 5-25 5.1.2. Summary of Critical Effects and Candidate R fCs.......................................... 5-33 5.1.2.1. Selection of Studies for Candidate RfC De rivation......................... 5-33 5.1.2.2. Derivation of Candidate RfCs from Key Stu dies ............................ 5-40 5.1.2.3. Evaluation of the Study-Specific Candidat e RfC ............................ 5-66 5.1.3. Database Uncertainties in the RfC Derivatio n ............................................... 5-6 9 5.1.4. Uncertainties in the RfC Derivation........

................................................... .... 5-72 5.1.5. Previous Inhalation Assessment.............

................................................... ..... 5-74 5.2. QUANTITATIVE CANCER ASSESSMENT BASED ON THE N ATIONAL CANCER INSTITUTE COHORT STUDY...................... ........................................ 5-74 5.2.1. Choice of Epidemiology Study...............

................................................... ..... 5-75 5.2.2. Nasopharyngeal Cancer......................

................................................... ......... 5-76 5.2.2.1. Exposure-Response Modeling of the Nationa l Cancer Institute Cohort................................... ............................................ 5-76 5.2.2.2. Prediction of Lifetime Extra Risk of Naso pharyngeal Cancer Mortality.......................................... ............................................... 5-79 5.2.2.3. Prediction of Lifetime Extra Risk of Naso pharyngeal Cancer Incidence.......................................... ............................................... 5-81 5.2.2.4. Sources of Uncertainty ................... ................................................. 5- 83 5.2.3. Lymphohematopoietic Cancer.................

................................................... .... 5-88 5.2.3.1. Exposure-Response Modeling of the Nationa l Cancer Institute Cohort................................... ............................................ 5-88This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-viii CONTENTS (continued)

5.2.3.2. Prediction of Lifetime Extra Risks for Ho dgkin Lymphoma and Leukemia Mortality ............................. .................................... 5-91 5.2.3.3. Prediction of Lifetime Extra Risks for Ho dgkin Lymphoma and Leukemia Incidence............................. .................................... 5-93 5.2.3.4. Sources of Uncertainty ................... ................................................. 5- 95 5.2.4. Conclusions on Cancer Unit Risk Estimates B ased on Human Data............. 5-99 5.3. DOSE-RESPONSE MODELING OF RISK OF SQUAMOUS CE LL CARCINOMA IN THE RESPIRATORY TRACT USING ANIMAL DAT A ...... 5-102 5.3.1. Long-Term Bioassays in Laboratory Animals.. ............................................ 5-104 5.3.1.1. Nasal Tumor Incidence Data ............... .......................................... 5-104 5.3.1.2. Mechanistic Data ......................... .................................................. 5 -105 5.3.2. The CIIT Biologically Based Dose-Response M odeling.............................. 5-106 5.3.2.1. Major Results of the CIIT Modeling Effort ................................... 5-111 5.3.3. This Assessment’s Conclusions from Evaluati on of Dose-Response Models of DPX Cell-Replication and Genomics Data, a nd of BBDR Models for Risk Estimation......................... ................................................ 5-1 11 5.3.4. Benchmark Dose Approaches to Rat Nasal Tumo r Data ............................. 5-118 5.3.4.1. Benchmark Dose Derived from BBDR Rat Mode l and Flux as Dosimeter ....................................... .......................................... 5-118 5.3.4.2. Comparison with Other Benchmark Dose Mode ling Efforts ........ 5-125 5.3.4.3. Kaplan-Meier Adjustment .................. ........................................... 5-128 5.3.4.4. EPA Time-to-Tumor Statistical Modeling ... ................................. 5-129 5.4. CONCLUSIONS FROM THE QUANTITATIVE ASSESSMENT OF CANCER RISK FROM FORMALDEHYDE EXPOSURE BY INHALATIO N... 5-133 5.4.1. Inhalation Unit Risk Estimates Based on Hum an Data................................ 5-133 5.4.2. Inhalation Unit Risk Estimates Based on Rod ent Data ................................ 5-133 5.4.3. Summary of Inhalation Unit Risk Estimates .. .............................................. 5-135 5.4.4. Application of Age-Dependent Adjustment Fac tors (ADAFs) .................... 5-136 5.4.5. Conclusions: Cancer Inhalation Unit Risk Es timates................................... 5-137

6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HA ZARD AND DOSE-RESPONSE......................................

................................................... ...................... 6-1 6.1. SUMMARY OF HUMAN HAZARD POTENTIAL............ ....................................... 6-1 6.1.1. Exposure...................................

................................................... ..................... 6-1 6.1.2. Absorption, Distribution, Metabolism, and E xcretion ..................................... 6-1 6.1.3. Noncancer Health Effects in Humans and Labo ratory Animals....................... 6-4 6.1.3.1. Sensory Irritation .......................

................................................... ..... 6-4 6.1.3.2. Respiratory Tract Pathology.............. ................................................ 6-5 6.1.3.3. Effects on Pulmonary Function ............ ............................................. 6-8 6.1.3.4. Asthmatic Responses and Increased Atopic Symptoms.................... 6-9 6.1.3.5. Effects on the Immune System ............. ........................................... 6-10 6.1.3.6. Neurological Effects .....................

................................................... 6-11 6.1.3.7. Reproductive and Developmental Effects ... .................................... 6-12This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-ix CONTENTS (continued)

6.1.3.8. Effects on General Systemic Toxicity ..... ........................................ 6-13 6.1.3.9. Summary..................................

................................................... ..... 6-14 6.1.4. Carcinogenicity in Human and Laboratory Ani mals...................................... 6-14 6.1.4.1. Carcinogenicity in Humans ................ ............................................. 6-14 6.1.4.2. Carcinogenicity in Laboratory Animals.... ....................................... 6-20 6.1.4.3. Carcinogenic Mode(s) of Action ........... .......................................... 6-21 6.1.5. Cancer Hazard Characterization.............

................................................... ..... 6-24 6.2. DOSE-RESPONSE CHARACTERIZATION ............... ............................................ 6-25 6.2.1. Noncancer Toxicity: Reference Concentration (RfC) .................................... 6-25 6.2.1.1. Assessment Approach Employed ............. ....................................... 6-25 6.2.1.2. Derivation of Candidate Reference Concent rations ........................ 6-25 6.2.1.3. Adequacy of Overall Data Base for RfC Der ivation ....................... 6-26 6.2.1.4. Uncertainties in the Reference Concentrat ion (RfC)....................... 6-29 6.2.1.5. Conclusions ..............................

................................................... .... 6-32 6.2.2. Cancer Risk Estimates......................

................................................... ........... 6-32 6.2.2.1. Choice of Data...........................

................................................... ... 6-32 6.2.2.2. Analysis of Epidemiologic Data........... ........................................... 6-33 6.2.2.3. Analysis of Laboratory Animal Data....... ........................................ 6-36 6.2.2.4. Extrapolation Aporoaches ................. .............................................. 6-37 6.2.2.5. Inhalation Unit Risk Estimates for Cancer ...................................... 6-41 6.2.2.6. Early-Life Susceptibility................ .................................................. 6 -41 6.2.2.7. Uncertainties in the Quantitative Risk Es timates ............................ 6-42 6.2.2.8. Conclusions ..............................

................................................... .... 6-45 6.3. SUMMARY AND CONCLUSIONS ......................

................................................... 6-45

REFERENCES .........................................

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

VOLUME IV

APPENDIX A: SUMMARY OF EXTERNAL PEER REVIEW AND PU BLIC

COMMENTS AND DISPOSITIONS.......................... .................................... A-1 APPENDIX B: SIMULATIONS OF INTERINDIVIDUAL AND ADU LT-TO-CHILD

VARIABILITY IN REACTIVE GAS UPTAKE IN A SMALL SAMPL E OF PEOPLE (Garcia et al., 2009).................... .................................................B- 1 APPENDIX C: LIFETABLE ANALYSIS ....................

................................................... ..........C-1 APPENDIX D: MODEL STRUCTURE & CALIBRATION IN CONOL LY ET AL.

(2003, 2004).......................................

................................................... ........... D-1 APPENDIX E: EVALUATION OF BBDR MODELING OF NASAL C ANCER IN THE

F344 RAT: CONOLLY ET AL. (2003) AND ALTERNATIVE IMPLEMENTATIONS.................................... .................................................E- 1 APPENDIX F: SENSITIVITY ANALYSIS OF BBDR MODEL FOR FORMALDEHYDE

INDUCED RESPIRATORY CANCER IN HUMANS............... .....................F-1This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-x CONTENTS (continued)

APPENDIX G: EVALUATION OF THE CANCER DOSE-RESPONSE MODELING

OF GENOMIC DATA FOR FORMALDEHYDE RISK ASSESSMENT ... .. G-1 APPENDIX H: EXPERT PANEL CONSULTATION ON QUANTITAT IVE

EVALUATION OF ANIMAL TOXICOLOGY DATA FOR ANALYZING CANCER RISK DUE TO INHALED FORMALDEHYDE.. . H-1This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xi LIST OF TABLES

Table 2-1. Physicochemical properties of formaldeh yde................................................ ............ 2-2

Table 2-2. Ambient air levels by land use category

................................................... ................. 2-6

Table 2-3. Studies on residential indoor air level s of formaldehyde (non-occupational)........... 2-8

Table 3-1. Endogenous formaldehyde levels in anima l and human tissues and body fluids...... 3-8

Table 3-2. Formaldehyde kinetics in human and rat tissue samples..................................... .... 3-21

Table 3-3. Allelic frequencies of ADH3 in human po pulations .......................................... ..... 3-25

Table 3-4. Percent distribution of airborne [ 14 C]-formaldehyde in F344 rats....................... .... 3-26

Table 3-5. Apparent formaldehyde levels in exhaled breath of individuals attending a

health fair, adjusted for methanol and ethanol leve ls which contribute to the detection of the protonated species with a mass to charge ratio of 31 reported as formaldehyde ( m/

z = 31)............................................ ........................................3-29

Table 3-6. Measurements of exhaled formaldehyde co ncentrations in the mouth and nose,

and in the oral cavity after breath holding in thre e healthy male laboratory workers ............................................

................................................... ....................3-30

Table 3-7. Extrapolation of parameters for enzymat ic metabolism to the human.................... 3-53

Table 4-1. Cohort and case-control studies of form aldehyde cancer and NPC ........................4-59

Table 4-2. Case-control studies of formaldehyde an d nasal and paranasal cancer ...................4-71

Table 4-3. Epidemiologic studies of formaldehyde a nd pharyngeal cancer (includes

nasopharyngeal cancer) .............................

................................................... ..........4-78

Table 4-4. Epidemiologic studies of formaldehyde a nd lymphohematopoietic cancers...........4-98

Table 4-5. Respiratory effects of formaldehyde-ind uced reflex bradypnea in various

strains of mice ....................................

................................................... ...............4-112

Table 4-6. Respiratory effects of formaldehyde-ind uced reflex bradypnea in various

strains of rats ....................................

................................................... .................4-113

Table 4-7. Inhaled dose of formaldehyde to nasal m ucosa of F344 rats and B6C3F1

mice exposed to 15 ppm.............................

................................................... .......4-116This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xii LIST OF TABLES (continued)

Table 4-8. Exposure regimen for cross-tolerance st udy................................................ ..........4-117

Table 4-9. Summary of formaldehyde effects on muco ciliary function in the upper

respiratory tract..................................

................................................... ................4-127

Table 4-10. Concentration regimens for ultrastruct ural evaluation of male CDF rat

nasoturbinates.....................................

................................................... ...............4-129

Table 4-11. Enzymatic activities in nasal respirat ory epithelium of male Wistar rats

exposed to formaldehyde, ozone, or both............ .................................................4- 130

Table 4-12. Lipid analysis of lung tissue and lung gavage from male F344 rats exposed

to 0, 15, or 145.6 ppm formaldehyde for 6 hours.... .............................................4-138

Table 4-13. Formaldehyde effects on biochemical pa rameters in nasal mucosa and lung

tissue homogenates from male F344 rats exposed to 0 , 15, or 145.6 ppm formaldehyde for 6 hours ...........................

................................................... .......4-139

Table 4-14. Mast cell degranulation and neutrophil infiltration in the lung of rats

exposed to formaldehyde via inhalation............. ..................................................4 -140

Table 4-15. Summary of respiratory tract pathology from inhalation exposures to formaldehyde—short term studies....................

................................................... .4-143

Table 4-16. Location and incidence of respiratory tract lesions in B6C3F1 mice

exposed to formaldehyde............................

................................................... .......4-146

Table 4-17. Formaldehyde effects (incidence and se verity) on histopathologic changes in

the noses and larynxes of male and female albino SP F Wistar rats exposed to formaldehyde 6 hours/day for 13 weeks.............. .................................................4- 148

Table 4-18. Formaldehyde-induced nonneoplastic his topathologic changes in male

albino SPF Wistar rats exposed to 0, 10, or 20 ppm formaldehyde and examined at the end of 130 weeks inclusive of e xposure..............................4-149

Table 4-19. Formaldehyde-induced nasal tumors in m ale albino SPF Wistar rats

exposed to formaldehyde (6 hours/day, 5 days/week f or 13 weeks) and examined at the end of 130 weeks inclusive of expos ure ....................................4-150

Table 4-20. Formaldehyde effects on nasal epitheli um for various concentration-bytime products in male albino Wistar rats...........

................................................... 4-153This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xiii LIST OF TABLES (continued)

Table 4-21. Rhinitis observed in formaldehyde-trea ted animals; data pooled for male

and female animals.................................

................................................... ..........4-154

Table 4-22. Epithelial lesions found in the middle region of nasoturbinates of

formaldehyde-treated and control animals; data pool ed for males and females............................................

................................................... ..................4-155

Table 4-23. Cellular and molecular changes in nasa l tissues of F344 rats exposed to

formaldehyde.......................................

................................................... .............4-156

Table 4-24. Percent body weight gain and concentra tions of iron, zinc, and copper in

cerebral cortex of male Wistar rats exposed to form aldehyde via inhalation for 4 and 13 weeks.................................

................................................... ...........4-158

Table 4-25. Zinc, copper, and iron content of lung tissue from formaldehyde-treated

male Wistar rats...................................

................................................... .............4-158

Table 4-26. Total lung cytochrome P450 measurement s of control and formaldehydetreated male Sprague-Dawley rats...................

................................................... .4-159

Table 4-27. Cytochrome P450 levels in formaldehyde -treated rats...................................... .4-160

Table 4-28. Summary of respiratory tract pathology from inhalation exposures to formaldehyde, subchronic studies ...................

................................................... .4-162

Table 4-29. Histopathologic findings and severity scores in the naso- and

maxilloturbinates of female Sprague-Dawley rats exp osed to inhaled formaldehyde and wood dust for 104 weeks ........... ............................................4-166

Table 4-30. Histopathologic changes (including tum ors) in nasal cavities of male

Sprague-Dawley rats exposed to inhaled formaldehyde or HCl alone and in combination for a lifetime ......................

................................................... ......4-170

Table 4-31. Summary of neoplastic lesions in the n asal cavity of f344 rats exposed to

inhaled formaldehyde for 2 years ...................

................................................... ..4-173

Table 4-32. Apparent sites of origin for the SCCs in the nasal cavity of F344 rats

exposed to 14.3 ppm of formaldehyde gas in the Kern s et al. (1983) bioassay ...........................................

................................................... .................4-174

Table 4-33. Incidence and location of nasal squamo us cell carcinoma in male F344

rats exposed to inhaled formaldehyde for 2 years ... ............................................4-175

Table 4-34. Summary of respiratory tract pathology from chronic inhalation exposures to formaldehyde....................................

................................................... ............4-183This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xiv LIST OF TABLES (continued)

Table 4-35. Cell proliferation in nasal mucosa, tr achea, and free lung cells isolated

from male Wistar rats after inhalation exposures to formaldehyde.....................4-194

Table 4-36. The effect of repeated formaldehyde in halation exposures for 3 months on

cell count, basal membrane length, proliferation ce lls, and two measures of cell proliferation, LI and ULLI, in male F344 rats . .............................................4-196

Table 4-37. Formaldehyde-induced changes in cell p roliferation and (ULLI) in the nasal

passages of male F344 rats exposed 6 hours/day ..... ...........................................4-198

Table 4-38. Cell population and surface area estim ates in untreated male F344 rats and

regional site location of squamous cell carcinomas in formaldehyde exposed rats for correlation to cell proliferation rates.........................................4-199

Table 4-39. Summary of formaldehyde effects on cel l proliferation in the upper

respiratory tract..................................

................................................... ...............4-202

Table 4-40. Summary of lesions observed in the gas trointestinal tracts of Wistar rats

after drinking-water exposure to formaldehyde for 4 weeks...............................4-206

Table 4-41. Incidence of lesions observed in the g astrointestinal tracts of Wistar rats

after drinking-water exposure to formaldehyde for 2 years.................................4-209

Table 4-42. Effect of formaldehyde on gastroduoden al carcinogenesis initiated by

MNNG and NaCl in male Wistar rats exposed to formal dehyde (0.5% formalin) in drinking water for 8 weeks............ ..................................................4 -212

Table 4-43. Summary of benign and malignant gastro intestinal tract neoplasia

reported in male and female Sprague-Dawley rats exp osed to formaldehyde in drinking water at different ages... .............................................4-214

Table 4-44. Incidence of hemolymphoreticular neopl asia reported in male and

female Sprague-Dawley rats exposed to formaldehyde in drinking water from 7 weeks old for up to 2 years (experiment BT 7 001) .................................4-215

Table 4-45. Battery of immune parameters and funct ional tests assessed in female

B6C3F1 mice after a 3 week, 15-ppm formaldehyde exp osure ..........................4-218

Table 4-46. Summary of the effects of formaldehyde inhalation on the mononuclear

phagocyte system (MPS) in female B6C3F1 mice after a 3-week, 15 ppm formaldehyde exposure ( 6 hours/day, 5 days/week).. .........................................4-219

Table 4-47. Formaldehyde exposure regimens for det ermining the effects of

formaldehyde exposure on pulmonary S. aureus infection .................................4-221This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xv LIST OF TABLES (continued)

Table 4-48. Summary of immune function changes due to inhaled formaldehyde

exposure in experimental animals ...................

................................................... .4-226

Table 4-49. Study design for guinea pigs exposed t o formaldehyde through different

routes of exposure: inhalation, dermal, and injecti on..........................................4-232

Table 4-50. Sensitization response of guinea pigs exposed to formaldehyde through

inhalation, topical application, or footpad injecti on............................................4-233

Table 4-51. Cytokine and chemokine levels in lung tissue homogenate supernatants in

formaldehyde-exposed male ICR mice with and without Der f sensitization.....4-240

Table 4-52. Correlation coefficients among ear swe lling responses and skin mRNA

levels in contact hypersensitivity to formaldehyde in mice.................................4-249

Table 4-53. Summary of sensitization and atopy stu dies by inhalation or dermal

sensitization due to formaldehyde in experimental a nimals................................4-251

Table 4-54. Fluctuation of behavioral responses wh en male AB mice inhaled

formaldehyde in a single 2-hour exposure: effects a fter 2 hours.........................4-259

Table 4-55. Fluctuation of behavioral responses wh en male AB mice inhaled

formaldehyde in a single 2-hour exposure: effects a fter 24 hours.......................4-259

Table 4-56. Effects of formaldehyde exposure on co mpletion of the labyrinth test by

male and female LEW.1K rats ........................

................................................... .4-263

Table 4-57. Summary of neurological and neurobehav ioral studies in inhaled

formaldehyde in experimental animals............... .................................................4- 279

Table 4-58. Effects of formaldehyde on body and or gan weights in rat pups from

dams exposed via inhalation from mating through ges tation..............................4-289

Table 4-59. Formaldehyde effects on Leydig cell qu antity and nuclear damage in adult

male Wistar rats...................................

................................................... .............4-298

Table 4-60. Formaldehyde effects on adult male alb ino Wistar rats .................................... .4-299

Table 4-61. Formaldehyde effects on testosterone l evels and seminiferous tubule

diameters in Wistar rats following 91 days of expos ure .....................................4-300

Table 4-62. Effects of formaldehyde exposure on se miniferous tubule diameter and

epithelial height in Wistar rats following 18 weeks of exposure ........................4-302This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xvi LIST OF TABLES (continued)

Table 4-63. Incidence of sperm abnormalities and d ominant lethal effects in

formaldehyde-treated mice ..........................

................................................... .....4-302

Table 4-64. Body weights of pups born to beagles e xposed to formaldehyde during

gestation..........................................

................................................... ..................4-303

Table 4-65. Testicular weights, sperm head counts, and percentage incidence of

abnormal sperm after oral administration of formald ehyde to male Wistar rats........................................

................................................... .................4-305

Table 4-66. Effect of formaldehyde on spermatogeni c parameters in male Wistar rats

exposed intraperitoneally..........................

................................................... ........4-306

Table 4-67. Incidence of sperm head abnormalities in formaldehyde-treated rats ................4-307

Table 4-68. Dominant lethal mutations after exposu re of male rats to formaldehyde...........4-308

Table 4-69. Summary of reported developmental effe cts in formaldehyde inhalation

exposure studies ...................................

................................................... ............4-311

Table 4-70. Summary of reported developmental effe cts in formaldehyde oral exposure

studies............................................

................................................... ...................4-317

Table 4-71. Summary of reported developmental effe cts in formaldehyde dermal

exposure studies ...................................

................................................... ............4-318

Table 4-72. Summary of reported reproductive effec ts in formaldehyde inhalation

studies............................................

................................................... ...................4-319

Table 4-73. Summary of reported reproductive effec ts in formaldehyde oral studies...........4-322

Table 4-74. Summary of reported reproductive effec ts in formaldehyde intraperitoneal

studies............................................

................................................... ...................4-323

Table 4-75. Summary of chronic bioassays which add ress rodent leukemia and

lymphoma...........................................

................................................... ..............4-329

Table 4-76. Formaldehyde-DNA reactions (DPX format ion) ............................................... 4-340

Table 4-77. Formaldehyde-DNA reactions (DNA adduct formation) ...................................4-343

Table 4-78. Formaldehyde-DNA interactions (single stranded breaks).................................4- 344

Table 4-79. Other genetic effects of formaldehyde in mammalian cells ...............................4- 346
Table 4-80. In vitro clastogenicity of formaldehyd e.................................................. ............4-348This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xvii LIST OF TABLES (continued)

Table 4-81 Summary of mutagenicity of formaldehyde in bacterial systems........................4-350
Table 4-82. Mutagenicity in mammalian cell systems

................................................... ........4-355
Table 4-83. Genotoxicity in laboratory animals ....

................................................... .............4-361
Table 4-84. MN frequencies in buccal mucosa cells of volunteers exposed to

formaldehyde .......................................

................................................... .............4-364
Table 4-85. MN and SCE formation in mortuary scien ce students exposed to

formaldehyde for 85 days ...........................

................................................... ......4-364
Table 4-86. Incidence of MN formation in mortuary students exposed to formaldehyde

for 90 days ........................................

................................................... ................4-365
Table 4-87. Multivariate regression models linking genomic instability/occupational

exposures to selected endpoints from the MN assay.. .........................................4-369
Table 4-88. Genotoxicity measures in pathological anatomy laboratory workers and

controls ...........................................

................................................... ..................4-370
Table 4-89. Summary of human cytogenetic studies ..

................................................... ........4-372
Table 4-90. Summary of cohort and case-control stu dies which evaluated the

incidence of all LHP cancers in formaldehyde-expose d populations (ICD-8 Codes: 200-209) and all leukemias (ICD-8 Cod es: 204-207).................4-412
Table 4-91. Secondary analysis of published mortal ity statistics to explore alternative

disease groupings within the broad category of all lymphohematopoetic malignancies .......................................

................................................... ..............4-419
Table 4-92. Summary of studies which provide morta lity statistics for myeloid

leukemia sub-types .................................

................................................... ..........4-420
Table 4-93. Summary of mortality statistics for Ho dgkin’s lymphoma, lymphoma and

multiple myeloma from cohort analyses of formaldehy de exposed workers ......4-422
Table 4-94. Summary of chronic bioassays which add ress rodent leukemia and

lymphoma ...........................................

................................................... ..............4-424
Table 4-95. Incidence of lymphoblastic leukemia an d lymphosarcoma orally dosed in

Sprague-Dawley rats................................

................................................... .........4-425
Table 4-96. Available evidence for susceptibility factors of concern for formaldehyde

exposure...........................................

................................................... .................4-469This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xviii LIST OF TABLES (continued)

Table 5-1. Points of departure (POD) for nervous s ystem toxicity in key human and

animal studies ....................................

................................................... ................5-19

Table 5-2. Effects of formaldehyde exposure on com pletion of the labyrinth test by

male and female LEW.1K rats .......................

................................................... ...5-23

Table 5-3. Developmental and reproductive toxicity PODs including duration adjustments – key human study......................

................................................... ....5-31

Table 5-4. Summary of candidate studies for formal dehyde RfC development by

health endpoint category...........................

................................................... ..........5-36

Table 5-5. Adjustment for nonoccupational exposure s to formaldehyde ...............................5- 64

Table 5-6. Summary of reference concentration (RfC ) derivation from critical study and

supporting studies.................................

................................................... ..............5-68

Table 5-7. Relative risk estimates for mortality f rom nasopharyngeal malignancies (ICD-8 code 147) by level of formaldehyde exposure for different exposure metrics...................................

................................................... ..............5-78

Table 5-8. Regression coefficients from NCI log-li near trend test models for NPC

mortality from cumulative exposure to formaldehyde . .........................................5-79

Table 5-9. Extra risk estimates for NPC mortality from various levels of continuous

exposure to formaldehyde ..........................

................................................... .......5-80

Table 5-10. EC 0005 , LEC 0005 , and inhalation unit risk estimates for NPC mortal ity from

formaldehyde exposure based on the Hauptmann et al. (2004) log-linear trend analyses for cumulative exposure.............

................................................... .5-81

Table 5-11. EC 0005 , LEC 0005 , and inhalation unit risk estimates for NPC incide nce from

formaldehyde exposure based on the Hauptmann et al. (2004) trend analyses for cumulative exposure...................

................................................... ....5-82

Table 5-12. Relative risk estimates for mortality from Hodgkin lymphoma

(ICD-8 code 201) and leukemia (ICD-8 codes 204–207) by level of formaldehyde exposure for different exposure metric s.........................................5-90

Table 5-13. Regression coefficients for Hodgkin ly mphoma and leukemia mortality

from NCI trend test models .........................

................................................... .......5-90

Table 5-14. Extra risk estimates for Hodgkin lymph oma mortality from various levels

of continuous exposure to formaldehyde............. ..................................................5 -91This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xix LIST OF TABLES (continued)

Table 5-15. Extra risk estimates for leukemia mort ality from various levels of

continuous exposure to formaldehyde................

................................................... 5-91

Table 5-16. EC 0005 , LEC 0005 , and inhalation unit risk estimates for Hodgkin ly mphoma

mortality from formaldehyde exposure based on Beane Freeman et al. (2009) log-linear trend analyses for cumulative exp osure.....................................5-93

Table 5-17. EC 005 , LEC 005 , and inhalation unit risk estimates for leukemia m ortality

from formaldehyde exposure based on Beane Freeman e t al. (2009) log-linear trend analyses for cumulative exposure .. ..............................................5-93

Table 5-18. EC 0005 , LEC 0005 , and inhalation unit risk estimates for Hodgkin

lymphoma incidence from formaldehyde exposure, base d on Beane Freeman et al. (2009) log-linear trend analyses for cumulative exposure.............5-94

Table 5-19. EC 005 , LEC 005 , and inhalation unit risk estimates for leukemia i ncidence

from formaldehyde exposure based on Beane Freeman e t al. (2009) log-linear trend analyses for cumulative exposure .. ..............................................5-94

Table 5-20. Calculation of combined cancer mortali ty unit risk estimate at 0.1 ppm ...........5-100

Table 5-21. Calculation of combined cancer inciden ce unit risk estimate at 0.1 ppm ..........5-100
Table 5-22. Summary of tumor incidence in long-ter m bioassays on F344 rats....................5-105
Table 5-23. BMD modeling of unit risk of SCC in th e human respiratory tract ...................5-125
Table 5-24. Formaldehyde-induced rat tumor inciden ces................................................ ......5-128
Table 5-25. Human benchmark extrapolations of nasa l tumors in rats using

formaldehyde flux and DPX..........................

................................................... ...5-135
Table 5-26. Summary of inhalation unit risk estima tes ................................................ .........5-136
Table 5-27. Total cancer risk from exposure to a c onstant formaldehyde exposure

level of 1 μg/m 3 from ages 0–70 years .............................. ..................................5-137
Table 6-1. Summary of candidate Reference Concentr ations (RfC) for co-critical studies.....6-27
Table 6-2. Effective concentrations (lifetime cont inuous exposure levels) predicted

for specified extra cancer risk levels for selected formaldehyde-related

cancers ............................................

................................................... ....................6-36

Table 6-3. Inhalation unit risk estimates based on epidemiological and experimental

animal data........................................

................................................... ..................6-42This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xx LIST OF FIGURES

Figure 2-1. Chemical structure of formaldehyde ....

................................................... ................2-1
Figure 2-2. Locations of hazardous air pollutant m onitors............................................ ............2-5
Figure 2-3. Modeled ambient air concentrations bas ed on 1999 emissions ..............................2 -7
Figure 2-4. Range of formaldehyde air concentratio ns (ppb) in different environments...........2-9
Figure 3-1. Formaldehyde-mediated protein modifica tions.............................................. .........3-2
Figure 3-2. 3 H/ 14 C ratios in macromolecular extracts from rat tissue s following exposure

to 14 C and 3 H-labeled formaldehyde (0.3, 2, 6, 10, 15 ppm)..... .........................3-18
Figure 3-3. Formaldehyde clearance by ALDH2 (GSH-i ndependent) and ADH3

(GSH-dependent)....................................

................................................... .........3-20
Figure 3-4. Metabolism of formate.................

................................................... ......................3-22
Figure 3-5. Scatter plot of formaldehyde concentra tions measured in ppb in direct breath

exhalations (x axis) and exhaled breath condensate headspace (y axis) ............3-31
Figure 3-6. Reconstructed nasal passages of F344 r at, rhesus monkey, and human................3-36
Figure 3-7. Illustration of interspecies differenc es in airflow and verification of CFD

simulations with water-dye studies .................

................................................... 3-37
Figure 3-8. Lateral view of nasal wall mass flux o f inhaled formaldehyde simulated in

the F344 rat, rhesus monkey, and human ............. ..............................................3-38
Figure 3-9. CFD simulations of formaldehyde flux t o human nasal lining at different

inspiratory flow rates.............................

................................................... ..........3-39
Figure 3-10. Single-path model simulations of surf ace flux per ppm of formaldehyde exposure concentration in an adult male human ...... ..........................................3-43
Figure 3-11. Pressure drop vs. volumetric airflow rate predicted by the CIIT CFD

model compared with pressure drop measurements made in two hollow molds (C1 and C2) of the rat nasal passage (Cheng e t al., 1990) or in rats in vivo............................................

................................................... ..................3-45
Figure 3-12. Formaldehyde-DPX dosimetry in the F34 4 rat .............................................. .....3-47
Figure 4-1. Delayed asthmatic reaction following t he inhalation of formaldehyde after

“painting” 100% formalin for 20 minutes............ ..............................................4-20This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxi LIST OF FIGURES (continued)

Figure 4-2. Formaldehyde effects on minute volume in naïve and formaldehydepretreated male B6C3F1 mice and F344 rats .......... .........................................4-115

Figure 4-3. Sagittal view of the rat nose (nares o riented to the left)............................... .......4-121

Figure 4-4. Main components of the nasal respirato ry epithelium ...................................... ..4-122

Figure 4-5. Decreased mucus clearance and ciliary beat in isolated frog palates

exposed to formaldehyde after 3 days in culture .... ..........................................4-126

Figure 4-6. Diagram of nasal passages showing sect ion levels chosen for morphometry

and autoradiography in male rhesus monkeys exposed to formaldehyde.........4-135

Figure 4-7. Formaldehyde-induced cell proliferatio n in male rhesus monkeys exposed to

formaldehyde.......................................

................................................... ..........4-136

Figure 4-8. Formaldehyde-induced lesions in male r hesus monkeys exposed to formaldehyde

................................................... ................................................... ....................4-137

Figure 4-9. Frequency and location by cross-sectio n level of squamous metaplasia in

the nasal cavity of F344 rats exposed to formaldehy de via inhalation.............4-172

Figure 4-10. Effect of formaldehyde exposure on ce ll proliferation of the respiratory

mucosa of rats and mice ............................

................................................... ....4-190

Figure 4-11. Alveolar MP Fc-mediated phagocytosis from mice exposed to 5 ppm

formaldehyde, 10 mg/m 3 carbon black, or both ............................. ..................4-223

Figure 4-12. Compressed air in milliliters as para meter for airway obstruction

following formaldehyde exposure in guinea pigs afte r OVA sensitization and OVA challenge ......................................

................................................... ........4-235

Figure 4-13. OVA-specific IgG1 (1B) in formaldehyd e-treated sensitized guinea pigs

prior to OVA challenge .............................

................................................... ....4-235

Figure 4-14. Anti-OVA titers in female Balb/C mice exposed to 6.63 ppm

formaldehyde for 10 consecutive days, or once a wee k for 7 weeks................4-236

Figure 4-15. Vascular permeability in the tracheae and bronchi of male Wistar rats

after 10 minutes of formaldehyde inhalation........ ............................................4-238

Figure 4-16. Effect of select receptor antagonists on formaldehyde-induced vascular

permeability in the trachea and bronchi of male Wis tar rats............................4-239This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxii LIST OF FIGURES (continued)

Figure 4-17. The effects of formaldehyde inhalatio n exposures on eosinophil

infiltration (Panel A) and goblet cell proliferatio n (Panel B) after Der f challenge in the nasal mucosa of male ICR mice afte r sensitization and challenge..........................................

................................................... ..............4-241

Figure 4-18. NGF in BAL fluid from formaldehyde-ex posed female C3H/He mice

with and without OA sensitization .................. .................................................4- 243

Figure 4-19. Plasma Substance P levels in formalde hyde-exposed female C3H/He

mice with and without OVA sensitization ............ ...........................................4-244

Figure 4-20. Motor activity in male and female rat s 2 hours after exposure to

formaldehyde expressed as mean number of crossed qu adrants ± SEM..........4-256

Figure 4-21. Habituation of motor activity was obs erved in control rats during the

second observation period (day 2, 24 hours after fo rmaldehyde exposure) .....4-257

Figure 4-22. Motor activity was reduced in male an d female LEW.1K rats 2 hours

after termination of 10-minute formaldehyde exposur e ...................................4-258

Figure 4-23. The effects of the acute formaldehyde (FA) exposures on the

ambulatory and vertical components of SLMA......... .......................................4-260

Figure 4-24. Effects of formaldehyde exposure on t he error rate of female LEW.1K

rats performing the water labyrinth learning test.. ............................................4-264

Figure 4-25. Basal and stress-induced trunk blood corticosterone levels in male

LEW.1K rats after formaldehyde inhalation exposures ...................................4-269

Figure 4-26. NGF production in the brains of forma ldehyde-exposed mice .........................4-274

Figure 4-27. Mortality corrected cumulative incide nces of nasal carcinomas in the

indicated exposure groups ..........................

................................................... ...4-325

Figure 4-28. Leukemia incidence in Sprague-Dawley rats exposed to formaldehyde

in drinking water for 2 years......................

................................................... ....4-330

Figure 4-29. Unscheduled deaths in female F344 rat s exposed to formaldehyde for

24 months ..........................................

................................................... ............4-332

Figure 4-30. Cumulative leukemia incidence in fema le F344 rats exposed to

formaldehyde for 24 months.........................

................................................... .4-333

Figure 4-31. Cumulative incidence or tumor bearing animals for lymphoma in

female mice exposed to formaldehyde for 24 months.. ....................................4-334This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxiii LIST OF FIGURES (continued)

Figure 4-32. DNA-protein cross-links (DPX) and thy midine kinase ( tk ) mutants in

TK6 human lymphoblasts exposed to formaldehyde for 2 hours.....................4-357

Figure 4-33. Developmental origins for cancers of the lymphohematopoietic system..........4-409

Figure 4-34A. Association between peak formaldehyd e exposure and the risk of

lymphohematopoietic malignancy..................... ...............................................4-41 5

Figure 4-34B. Association between average intensit y of formaldehyde exposure and

the risk of lymphohematopoietic malignancy......... ..........................................4-416

Figure 4-35. Effect of various doses of formaldehy de on cell number in (A) HT-29

human colon carcinoma cells and in (B) human umbili cal vein epithelial cells (HUVEC)

................................................... ................................................... ...4-433

Figure 4-36. Integrated MOA scheme for respiratory tract tumors...................................... ..4-446

Figure 4-37. Location of intra-epithelial lymphocy tes along side epithelial cells in

the human adenoid..................................

................................................... .......4-450

Figure 5-1. Change in number of additions made in 10 minutes following formaldehyde

exposure at 32, 170, 390, or 890 ppb ............... ..................................................5 -21

Figure 5-2. Effects of formaldehyde exposure on the error rate of female LEW.1K rats

performing the water labyrinth learning test ....... ...............................................5-24

Figure 5-3. Fecundity density ratio among women ex posed to formaldehyde in the high

exposure index category with 8-hour time weighted a verage formaldehyde exposure concentration of 219 ppb..................

................................................... 5-27

Figure 5-4. Estimated reduction in peak expiratory flow rate (PEFR) in children in relation to indoor residential formaldehyde concent rations. ..............................5-41

Figure 5.5. Odds ratios for physician-diagnosed as thma in children associated with inhome formaldehyde levels in air ...................

................................................... .5-45

Figure 5-6. Prevalence of asthma and respiratory s ymptom scores in children

associated with in-home formaldehyde levels........ ............................................5-48

Figure 5-7. Prevalence and severity of allergic se nsitization in children associated

with in-home formaldehyde levels ................... ..................................................5 -49

Figure 5-8. Positive exposure-response relationshi ps reported for in-home

formaldehyde exposures and sensory irritation (eye irritation) ..........................5-53This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxiv LIST OF FIGURES (continued)

Figure 5-9. Positive exposure-response relationshi ps reported for in-home

formaldehyde exposures and sensory irritation (burn ing eyes) ..........................5-54

Figure 5-10. Age-specific mortality and incidence rates for myeloid, lymphoid, and

all leukemia .......................................

................................................... ..............5-98

Figure 5-11. Schematic of integration of pharmacok inetic and pharmacodynamic

components in the CIIT model ....................... ..................................................5 -110

Figure 5-12. Fit to the rat tumor incidence data u sing the model and assumptions in

Conolly et al. (2003)..............................

................................................... ........5-112

Figure 5-13. Spatial distribution of formaldehyde over the nasal lining, as

characterized by partitioning the nasal surface by formaldehyde flux to the tissue per ppm of exposure concentration, resul ting in 20 flux bins ..........5-120

Figure 5-14. Distribution of cells at risk across flux bins in the F344 rat nasal lining..........5-1 20

Figure 5-15. MLE and upper bound (UB) added risk o f SCC in the human nose for

two BBDR models....................................

................................................... .....5-124

Figure 5-16. Replot of log-probit fit of the combi ned Kerns et al. (1983) and

Monticello et al. (1996) data on tumor incidence sh owing BMC 10 and BMCL 10 ................................................... ................................................... ......5-127

Figure 5-17. EPA multistate Weibull modeling: nas al tumor dose response .......................5-131

Figure 5-18. Multistage Weibull model fit .........

................................................... ................5-132

Figure 5-19. Multistage Weibull model fit of tumor incidence data compared with KM estimates of spontaneous tumor incidence........ ........................................5-132This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxv LIST OF ABBREVIATIONS AND ACRONYMS

ACGIH American Conference of Governmental Industria l Hygienists ADAF age-dependent adjustment factors ADH alcohol dehydrogenase ADS anterior dorsal septum AIC Akaike Information Criterion AIE average intensity of exposure AIHA American Industrial Hygiene Association ALB albumin ALDH aldehyde dehydrogenase ALL acute lymphocytic leukemia ALM anterior lateral meatus ALP alkaline phosphatase ALS amyotrophic lateral sclerosis ALT alanine aminotransferase AML acute myelogenous leukemia AMM anterior medial maxilloturbinate AMPase adenosine monophosphatase AMS anterior medial septum ANAE alpha-naphthylacetate esterase ANOVA analysis of variance APA American Psychiatric Association ARB Air Resources Board AST aspartate aminotransferase ATCM airborne toxic control measure ATP adenosine triphosphate ATPase adenosine triphosphatase ATS American Thoracic Society ATSDR Agency for Toxic Substances and Disease Regis try AUC area under the curve BAL bronchoalveolar lavage BALT bronchus associated lymphoid tissue BBDR biologically based dose response BC bronchial construction BCME bis(chloromethyl)ether BDNF brain-derived neurotrophic factor BEIR biologic effects of ionizing radiation BfR German Federal Institute for Risk Assessment BHR bronchial hyperresponsiveness BMC benchmark concentration BMCL 95% lower bound on the benchmark concentratio n BMCR binuclated micronucleated cell ratefluoresce BMD benchmark dose BMDL 95% lower bound on the benchmark doseThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxvi LIST OF ABBREVIATIONS AND ACRONYMS (continued)

BMR benchmark response BN Brown-Norway BrdU bromodeoxyuridine BUN blood urea nitrogen BW body weight CA chromosomal aberrations CalEPA California Environmental Protection Agency CAP College of American Pathologists CASRN Chemical Abstracts Service Registry Number CAT catalase CBMA cytokinesis-blocked micronucleus assay CBMN cytokinesis-blocked micronucleus CDC U.S. Centers for Disease Control and Preventio n CDHS California Department of Health Services CFD computational fluid dynamics CGM clonal growth model CHO Chinese hamster ovary CI confidence interval CIIT Chemical Industry Institute of Toxicology CLL chronic lymphocytic leukemia CML chronic myelogenous leukemia CNS central nervous system CO 2 carbon dioxide COEHHA California Office of Environmental Health Ha zard Assessment CREB cyclic AMP responsive element binding protein s CS conditioned stimulus C × t concentration times time DA Daltons DAF dosimetric adjustment factor DDX DNA-DNA cross-links DEI daily exposure index DEN diethylnitrosamine Der f common dust mite allergen DMG dimethylglycine DMGDH dimethylglycine dehydrogenase DNA deoxyribonucleic acid DOPAC 3,4-dihydroxyphenylacetic acid DPC / DPX DNA-protein cross-links EBV Epstein-Barr virus EC effective concentration ED effective dose EHC Environmental Health Committee ELISA enzyme-linked immunosorbent assayThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxvii LIST OF ABBREVIATIONS AND ACRONYMS (continued)

EPA U.S. Environmental Protection Agency ERPG emergency response planning guideline ET ethmoid turbinates FALDH formaldehyde dehydrogenase FDA U.S. Food and Drug Administration FDR fecundability density ratio FEF forced expiratory flow FEMA Federal Emergency Management Agency FEV1 forced expiratory volume in 1 second FISH fluorescent in situ hybridization FSH follicle-stimulating hormone FVC forced vital capacity GALT gut-associated lymphoid tissue GC-MS gas chromatography-mass spectrometry GD gestation day GI gastrointestinal GO gene ontology G6PDH glucose-6-phosphate dehydrogenase GPX glutathione peroxidase GR glutathione reductase GM-CSF granulocyte macrophage-colony-stimulating fa ctor GSH reduced glutathione GSNO S-nitrosoglutathione GST glutathione S-transferase HAP hazardous air pollutant Hb hemoglobin HCl hydrochloric acid HCT hematocrit HEC human equivalent concentration 5-HIAA 5-hydroxyindoleacetic acid hm hydroxymethyl HMGSH S-hydroxymethylglutathione HPA hypothalamic-pituitary adrenal HPG hypothalamo-pituitary-gonadal HPLC high-performance liquid chromatography HPRT hypoxanthine-guanine phosphoribosyl transfera se HR high responders HSA human serum albumin HSDB Hazardous Substances Data Bank Hsp heat shock protein HWE healthy worker effect I cell initiated cell IARC International Agency for Research on CancerThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxviii LIST OF ABBREVIATIONS AND ACRONYMS (continued)

ICD International Classification of Diseases IF interfacial IFN interferon Ig immunoglobulin IL interleukin I.P. intraperitoneal IPCS International Programme on Chemical Safety IRIS Integrated Risk Information System K m Michaels-Menton constant KM Kaplan-Meier LD 50 median lethal dose LDH lactate dehydrogenase LEC 95% lower bound on the effective concentration LED 95% lower bound on the effective dose LHP lymphohematopoietic LI labeling index LM Listeria monocytogenes LMS linearized multistage LLNA local lymph node assay LOAEL lowest-observed-adverse-effect level LPS lipopolysaccharide LR low responders LRT lower respiratory tract MA methylamine MALT mucus-associated lymph tissues MCH mean corpuscular hemoglobin MCHC mean corpuscular hemoglobin concentration MCS multiple chemical sensitivity MCV mean corpuscular volume MDA malondialdehyde MEF maximal expiratory flow ML myeloid leukemia MLE maximum likelihood estimate MMS methyl methane sulfonate MMT medial maxilloturbinate MN micronucleus, micronuclei MNNG N-methyl-N-nitro-N-nitrosoguanidine MOA mode of action MoDC monocyte-derived dendritic cell MP macrophage MPD multistage polynomial degree MPS mononuclear phagocyte system MRL minimum risk levelThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxix LIST OF ABBREVIATIONS AND ACRONYMS (continued)

mRNA messenger ribonucleic acid MVE-2 Murray Valley encephalitis virus MVK Moolgavkar, Venzon, and Knudson N cell normal cell NaCl sodium choride NAD+ nicotinamide adenine dinucleotide NADH reduced nicotinamide adenine dinucleotide NALT nasally associated lymphoid tissue NATA National-Scale Air Toxics Assessment NCEA National Center for Environmental Assessment NCHS National Center for Health Statistics NCI National Cancer Institute NEG Nordic Expert Group NER nucleotide excision repair NGF nerve growth factor NHL non-Hodgkin’s lymphoma NHMRC/ARMCANZ National Health and Medical Researc h Council/Agriculture and Resource

Management Council of Australia and New Zealand NNK nitrosamine nitrosamine 4-(methylnitrosamino)- 1-(3-pyridyl)-butanone N 6 -hmdA N 6 -hydroxymethyldeoxyadenosine N 4 -hmdC N 4 -hydroxymethylcytidine N 2 -hmdG N 2 -hydroxymethyldeoxyguanosine NICNAS National Industrial Chemicals Notification a nd Assessment Scheme NIOSH National Institute for Occupational Safety an d Health NLM National Library of Medicine NMDA N-methyl-D-aspartate NO nitric oxide NOAEL no-observed-adverse-effect level NPC nasopharyngeal cancer NRBA neutrophil respiratory burst activity NRC National Research Council NTP National Toxicology Program OR odds ratio OSHA Occupational Safety and Health Administration OTS Office of Toxic Substances OVA ovalbumin PBPK physiologically based pharmacokinetic PC Philadelphia chromosome PCA passive cutaneous anaphylaxis PCMR proportionate cancer mortality ratio PCNA proliferating cell nuclear antigen PCR polymerase chain reaction PCV packed cell volumeThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxx LIST OF ABBREVIATIONS AND ACRONYMS (continued)

PECAM platelet endothelial cell adhesion molecule PEF peak expiratory flow PEFR peak expiratory flow rates PEL permissible exposure limit PFC plaque-forming cell PG periglomerular PHA phytohemagglutinin PLA2 phospholipase A2 PI phagocytic index PLM posterior lateral meatus PMA phorbol 12-myristate 13-acetate PMR proportionate mortality ratio PMS posterior medial septum PND postnatal day POD point of departure POE portal of entry PTZ pentilenetetrazole PUFA polyunsaturated fatty acids PWULLI population weighted unit length labeling ind ex RA reflex apnea RANTES regulated upon activation, normal T–cell exp ressed and secreted RB reflex bradypnea RBC red blood cells RD 50 exposure concentration that results in a 50% redu ction in respiratory rate REL recommended exposure limit RfC reference concentration RfD reference dose RGD regional gas dose RGDR regional gas dose ratio RR relative risk RT reverse transcriptase SAB Science Advisory Board SCC squamous cell carcinoma SCE sister chromatid exchange SCG sodium cromoglycate SD standard deviation SDH succinate dehydrogenase; sarcosine dehydrogena se SEER Surveillance, Epidemiology, and End Results SEM standard error of the mean SEN sensitizer SH sulfhydryl SHE Syrian hamster embryo SLMA spontaneous locomotor activityThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxxi LIST OF ABBREVIATIONS AND ACRONYMS (continued)

SMR standardized mortality ratio SNP single nucleotide polymorphism SOD superoxide dismutase SOMedA N 6 -sulfomethyldeoxyadenosine Sp1 specificity protein SPIR standardized proportionate incidence ratio SSAO semicarbozole-sensitive amine oxidase SSB single strand breaks STEL short-term exposure limit TBA tumor bearing animal TH T-lymphocyte helper THF tetrahydrofolate TK toxicokinetics TL tail length TLV threshold limit value TNF tumor necrosis factor TP total protein TRI Toxic Release Inventory TRPV transient receptor potential vanilloid TWA time-weighted average TZCA thiazolidine-4-carboxylate UCL upper confidence limit UDS unscheduled DNA synthesis UF uncertainty factor UFFI urea formaldehyde foam insulation ULLI unit length labeling index URT upper respiratory tract USDA U.S. Department of Agriculture VC vital capacity VOC volatile organic compound WBC white blood cell WDS wet dog shake WHO World Health Organization WHOROE World Health Organization Regional Office fo r EuropeThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE IV-xxxii 

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# Appendix AThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE A-2 APPENDIX A 1 SUMMARY OF EXTERNAL PEER REVIEW 2 AND PUBLIC COMMENTS AND DISPOSITIONS 3 4
5
6
7
8
9
10
11
12
13 

### [NOTE: This is a placeholder for Appendix A which will be drafted 14 

### following External Peer review and receipt of publi c comments.] 15
16

# Appendix BThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE B-2 APPENDIX B 1 2 SIMULATIONS OF INTERINDIVIDUAL AND ADULT-TO-CHILD V ARIABILITY IN 3 REACTIVE GAS UPTAKE IN A SMALL SAMPLE OF PEOPLE (Ga rcia et al., 2009) 4 5 Garcia et al. (2009) used computational fluid dynam ics to study human variability in the 6 nasal dosimetry of reactive, water-soluble gases in 5 adults and 2 children, aged 7 and 8 years 7 old. They considered two model categories of gases, corresponding to maximal and moderate 8 absorption at the nasal lining. The nasal airway (i ncluding the nasopharynx) geometries of these 9 individuals were mapped out using magnetic resonanc e imaging or computed tomography scans. 10 The scans chosen for the analysis were from individ uals who had normal nasal anatomies with no 11 pathology (as per a review carried out by a ear-nos e-throat surgeon). The minute volumes of 12 these individuals were estimated to range from 6.8 to 9.0 L/min (adults) and 5.5 to 5.8 L/min 13 (children). The sample size in this study is too sm all to consider the results representative of the 14 population as a whole (as also recognized by the au thors). Nonetheless, various comparisons with 15 the characteristics of other study populations add to the strength of this study. The range of adult 16 minute volumes in this study is reported by the aut hors to be in good agreement with that 17 obtained in many other studies in the literature; m inute volumes for the children in the study 18 were found to be similar to the average minute volu me of 6.1 ± 1.7 L/min obtained by Bennett 19 and Zeman (2004) in a study of 36 children aged 6 t o 13 years; the range of nasal surface area 20 values for the adults agreed well with that obtaine d by Guilmette et al. (1997) for 45 adults; and 21 the range of values for the surface area to volume ratio is in good agreement with that obtained 22 for 40 adult Caucasians studied by Yokley (2006). T he surface area to volume ratio is useful for 23 comparing the rate of diffusional transport of a ga s out of different cavities; however in the case 24 of the highly non-homogenously shaped nasal lumen, this might at best be considered a gross 25
indicator. 26 We focus here only on the “maximum uptake” simulati ons in Garcia et al. (2009). In this 27 case, the gas was considered so highly reactive and soluble that it was reasonable to assume an 28 infinitely fast reaction of the absorbed gas with c ompounds in the airway lining. Although such a 29 gas could be reasonably considered to represent for maldehyde, these results cannot be fully 30 utilized to inform quantitative estimates of formal dehyde dosimetry (and that does not appear to 31 have been the intent of the authors either). This i s because the same boundary condition 32 corresponding to maximal uptake was applied on the vestibular section as well as on the 33 transitional and transitional epithelial lining of the nasal cavity. This is not appropriate for 34 formaldehyde as the lining on the nasal vestibule i s made of keratinized epithelium which is 35 considerably less absorbing than the transitional o r respiratory epithelium (Kimbell et al. 2001). 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE B-3 Table B-1 provides results obtained by Garcia et al . (2009) for five adults and two 1 children for uptake for the maximal uptake scenario . Although the nasal cavities of the children 2 were smaller in surface area, volume and length, th e surface-area-to-volume ratios were similar 3 in the two age groups. Overall uptake efficiency, a verage flux (rate of gas absorbed per unit 4 surface area of the nasal lining) and maximum flux levels over the entire nasal lining did not 5 vary substantially between adults (1.6-fold differe nce in average flux and much less in maximum 6 flux), and the mean values of these quantities were comparable between adults and children. 7 These results are in agreement with conclusions rea ched by Ginsberg et al. (2005) that overall 8 extrathoracic absorption of highly and moderately r eactive and soluble gases (corresponding to 9 category 1 and 2 reactive gases as per the scheme i n USEPA [1994]) is similar in adults and 10 children. However, none of these three quantities s hould be considered as reasonable indicators 11 of variability in the interaction of the gas with t he nasal lining. For a very reactive and soluble 12 gas, regional absorption of the gas is highly nonho mogenously distributed; therefore 13 interindividual variability in this distribution wi ll be washed out when averaged over the whole 14 nose. Estimates of maximum gas flux, on the other h and, correspond to extremely small localized 15 regions of hot spots (see chapter 3), and thus may not be a proper measure of inter-individual 16 variability in flux distribution patterns over the whole nose. Furthermore, numerical error in the 17 calculation (such as mass balance and irregularly s haped elements of the finite-element mesh) is 18 likely to be most pronounced when estimates are con sidered over extremely small regions. 19
20 Table B-1: Variations in overall nasal uptake, whol e nose flux, and key parameters 21 22 % nasal uptake MV SA/V (L) (1/mm) left cavity right cavity left cavity right cavity

adult1 93.5 9 1.12 1.8 1.5 10.8 10.0
adult1 92.4 6.8 1.09 1.5 1.5 10.8 10.4
adult1 93.1 9 0.88 1.6 1.3 11 10.6
adult1 89.2 7.1 0.87 1.2 1.2 10.6 10.2
adult1 91.5 6.9 0.95 1.4 1.5 10.8 10.0
child1 92 5.5 1.13 1.9 1.5 11.8 11.0 child2 88.2 5.8 0.95 1.6 1.5 12.3 11.6 Avg flux Maximum flux
10 -8 kg/(s.m 2 ) 10 -8 kg/(s.m 2 )

23 MV = minute volume, SA=nasal surface area, V=nasal volume 24 Source: Garcia et al. (2009). 25This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE B-4 On the other hand, Figure 6A of Garcia et al. (2009 ), reproduced here as Figure B-1, 1 shows significant interhuman variability in flux va lues at specific points on the nasal walls. The 2 local flux of formaldehyde varies among individuals by a factor of 3 to 5 at various distances 3 along the septal axis of the nose. However, interpr etation of the values in this plot is problematic 4 for reasons explained in the paper1: 5 The greater variability among individuals seen for wall fluxes at specific sites of the nasal 6 passages (Figure 6) in comparison to the minimal va riability in total uptake 7 (Table 2) and whole-nose dose (Tables 3 and Tables 4) indicates that fluxes of equal 8 magnitude do not exactly overlay the same anatomica l regions of the nasal cavity in each 9 individual. This implies that specific anatomical r egions subtended by maximum flux 10
could be offset from one individual to another. 11 Notwithstanding this difficulty in interpretion, we believe the quantities plotted in Figure B-1 12 provide a better perspective of the inter-individua l (adult) variability in local flux than the 13 variation in whole nose average or in maximum flux presented in Table B-1. 14
15

16 Figure B-1 : Gas flux across the nasal lining for the case of a “maximum 17 uptake” gas in Garcia et al. (2009) as a function of axial dist ance from the 18 nostril. The vertical bars show range of variation. See the paper for further 19 details. Figure is reproduced from Garcia et al. ( 2009). 20 21 22 Clearly, multiple measures of variability in dose c an be developed depending on the 23 adverse response. The advantage of models such as t hat developed by Garcia et al. is that they 24 make it possible to carry out these calculations. F or example, if deficit in pulmonary function is 25

1 The Figures and Tables in the cited text refer th ose in Garcia et al. (2009)This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE B-5 the adverse response, then variation in average who le nose flux or overall nasal uptake efficiency 1
would be most useful since one is then interested i n inter-individual variability in the overall 2 dose delivered to the lung. It is possible to conce ive of allergic or irritation responses being 3 triggered by some threshold value of local flux. In such a case it may be preferable to calculate 4 the variability associated with the net surface are a receiving flux values greater than that 5 threshold. On the other hand, the probability of de veloping a tumor at a nasal site may be non6 linearly related to the flux at that site and linea rly related to the number of cells at that site. In this 7 case, the appropriate metric may be the nasal surfa ce area associated with some intermediate 8 levels of local flux (see Appendix in Subramaniam e t al. 2008). 9 Various caveats presented by the authors as limitat ions of their study should be noted: 10 Possible nonuniform distribution of epithelial type s, enzymes, glands and other cellular 11 metabolic or clearance machinery were not considere d in the model; only effects pertaning to 12 resting breathing were considered; the study sample size was small; children younger than 7 13 years old were not studied; and, the model assumed a rigid nasal geometry. 14
15 Garcia et al. (2009) conclude their paper as follow s: 16 “..., our simulations predicted no differences in the nasal dosimetry of reactive, water17 soluble gases between children and adults, suggesti ng that the risk factor of 10 typically 18 used to accommodate inter-human variability is adeq uate.” 19
20 In addition to the obvious caveat already recognize d by the authors in regards extrapolating from 21 a study involving just two children, this conclusio n needs further qualification. Firstly, the safety 22 factor of 10 that is typically applied for interhum an variability does not specifically include 23 children. Instead, EPA practice is that unless ther e is reasonable evidence that childhood forms a 24 more susceptible lifestage, no additional factors a re applied for this population. Second, it is 25 important not to confuse dosimetric differences acr oss lifestages

− which actually contribute to 26
intra-individual differences

− with inter-individual differences that may contribu te towards the 10- 27 fold uncertainty factor. This interhuman variabilit y in susceptibility

− for a given life-stage

− may 28 be considered to arise from both pharmacokinetic an d pharmacodynamic factors, and the practice 29 in the US has been to split these into factors of 3 .3 each. Then, the roughly 3 to 5 -fold variation 30 estimated for adults (and also between the two chil dren) in Figure B-1 suggest that a factor of 10 31 may not be adequate to accommodate inter-human vari ability for those formaldehyde-induced 32 adverse responses for which the localized nature offormaldehyde flux, and therefore the inter33 individual differences in regional dosimetry, play a role. 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE B-6 

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# Appendix CThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE C-2 APPENDIX C 1 2 LIFETABLE ANALYSIS 3 4 A spreadsheet illustrating the extra-risk calculat ion for the derivation of the lower 95% 5
bound on the effective concentration associated wit h a 0.05% extra risk (LEC 0005 ) for 6 nasopharyngeal carcinoma (NPC) incidence is present ed in Table C-1. 7
8This document is a draft for review purposes only a nd does not constitute Agency policy C-3 DRAFT—DO NOT CITE OR QUOTETable C-1. Extra-risk calculationafor environmental exposure to 0.0461 ppm formaldehyde (the LEC0005forNPC incidence)busing a log-linear exposure-response model based on the cumulative exposure trend results ofHauptmann et al. (2004), as described in Section 5.2.2.A B C D E F G H I J K L M N O PIntervalnumber(i)AgeintervalAll causemortality(×105/yr)NPCincidence(×105/yr)Allcausehazardrate (h*)Prob ofsurvivinginterval(q)Prob ofsurvivingup tointerval(S)NPCcancerhazardrate (h)Condprob ofNPCincidenceininterval(Ro)Expdurationmidinterval(xtime)Cumexp midinterval(xdose)ExposedNPChazardrate (hx)Exposedall causehazardrate(h*x)Exposedprob ofsurvivinginterval(qx)Exposedprob ofsurvivingup tointerval(Sx)Exposedcondprob ofNPC ininterval(Rx)1 <1 728.70 0.0073 0.9927 1.0000 0.000000.0000000 0.0000 0.0000 0.0073 0.9927 1.0000 0.000002 1-4 32.90.05 0.0013 0.9987 0.9927 0.000000.0000020 0.0000 0.0000 0.0013 0.9987 0.9927 0.000003 5-9 16.40.03 0.0008 0.9992 0.9914 0.000000.0000010 0.0000 0.0000 0.0008 0.9992 0.9914 0.000004 10-14 20.90.09 0.0010 0.9990 0.9906 0.000000.0000040 0.0000 0.0000 0.0010 0.9990 0.9906 0.000005 15-19 68.20.12 0.0034 0.9966 0.9896 0.000010.0000062.5 0.3506 0.0000 0.0034 0.9966 0.9896 0.000016 20-24 960.16 0.0048 0.9952 0.9862 0.000010.0000087.5 1.0517 0.0000 0.0048 0.9952 0.9862 0.000017 25-29 990.23 0.0050 0.9951 0.9815 0.000010.00001112.5 1.7528 0.0000 0.0050 0.9951 0.9815 0.000018 30-34 116.30.48 0.0058 0.9942 0.9766 0.000020.00002317.5 2.4539 0.0000 0.0058 0.9942 0.9766 0.000039 35-39 162.20.55 0.0081 0.9919 0.9710 0.000030.00002722.5 3.1550 0.0000 0.0081 0.9919 0.9710 0.0000310 40-44 237.31.14 0.0119 0.9882 0.9631 0.000060.00005527.5 3.8561 0.0001 0.0119 0.9882 0.9631 0.0000811 45-49 3561.3 0.0178 0.9824 0.9518 0.000070.00006132.5 4.5572 0.0001 0.0178 0.9823 0.9517 0.0000912 50-54 518.61.72 0.0259 0.9744 0.9350 0.000090.00007937.5 5.2583 0.0001 0.0260 0.9744 0.9349 0.0001213 55-59 801.81.69 0.0401 0.9607 0.9111 0.000080.00007542.5 5.9594 0.0001 0.0401 0.9607 0.9110 0.0001214 60-64 1257.91.9 0.0629 0.9390 0.8753 0.000100.00008147.5 6.6605 0.0002 0.0630 0.9390 0.8751 0.0001415 65-69 1928.22.87 0.0964 0.9081 0.8219 0.000140.00011252.5 7.3616 0.0003 0.0965 0.9080 0.8217 0.0002116 70-74 2968.12.1 0.1484 0.8621 0.7464 0.000110.00007357.5 8.0627 0.0002 0.1485 0.8620 0.7461 0.0001417 75-59 4556.62.19 0.2278 0.7963 0.6434 0.000110.00006362.5 8.7638 0.0002 0.2279 0.7962 0.6431 0.0001318 80-84 7399.61.98 0.3700 0.6907 0.5123 0.000100.00004267.5 9.4649 0.0002 0.3701 0.6907 0.5120 0.00009Ro =0.000725Rx =0.001225Extra Risk = (Rx−Ro)/(1−Ro) = 0.0005This document is a draft for review purposes only a nd does not constitute Agency policy C-4 DRAFT—DO NOT CITE OR QUOTEColumn A: interval index number (i).Column B: 5-year age interval (except <1 and 1−4) up to age 85.Column C: all-cause mortality rate for interval i (× 105/year) (2000 data from NCHS).Column D: NPC incidence rate for interval i (× 105/year) (1996-2000 SEER data).Column E: all-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of years inage interval).cColumn F: probability of surviving interval i without being diagnosed with NPC (qi) (= exp(−h*i)).Column G: probability of surviving up to interval iwithout having been diagnosed with NPC (Si) (S1= 1; Si= Si−1× qi−1, for i>1).Column H: NPC incidence hazard rate for interval i(hi) (= NPC incidence rate × number of years in interval).Column I: conditional probability of being diagnosed with NPC in interval i (= (hi/h*i) × Si× (1−qi)), i.e., conditional upon surviving up to intervali withouthaving been diagnosed with NPC [Ro, the backgroundlifetime probability of being diagnosed with NPC =the sum of the conditional probabilitiesacross the intervals].Column J: exposure duration (in years) at mid-interval (xtime).Column K: cumulative exposure mid-interval (xdose)(= exposure level (i.e., 0.0461 ppm) × 365/240 × 20/10 × xtime) [365/240 × 20/10 converts continuousenvironmental exposures to corresponding occupational exposures].Column L: NPC incidence hazard rate in exposed people for interval i (hxi) (= hi× (1 +β× xdose), whereβ= 0.05183 + (1.645 × 0.01915) = 0.08333) [0.05183per ppm × year is the regression coefficient obtained, along with its SE of 0.01915, from Dr. Hauptmann (see Section 5.2.2.1). To estimate theLEC0005, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 0.05%, the 95% upper bound on the regression coefficient isused, i.e., MLE + 1.645 × SE].Column M: all-cause hazard rate in exposed people for interval i (h*xi) (= h*i+ (hxi− hi)).Column N: probability of surviving interval i without being diagnosed with NPC for exposed people (qxi) (= exp(−h*xi)).Column O: probability of surviving up to interval iwithout having been diagnosed with NPC for exposedpeople (Sxi) (Sx1= 1; Sxi= Sxi−1× qxi-1, for i>1).Column P: conditional probability of being diagnosed with NPC in interval i for exposed people (= (hxi/h*xi) × Sxi× (1−qxi)) [Rx, the lifetime probability ofbeing diagnosed with NPC for exposed people = the sum of the conditional probabilities across the intervals].aUsing the methodology of BEIR IV (1988).bThe estimated 95% lower bound on the continuous exposure level of TCE that gives a 0.05% extra lifetime risk of NPC.cFor the cancer incidence calculation, the all-cause hazard rate for interval i should technically bethe rate of either dying of any cause or being diagnosed withthe specific cancer during the interval, i.e., (theall-cause mortality rate for the interval + the cancer-specific incidence rate for the interval—the cancer-specificmortality rate for the interval [so that a cancer case isn’t counted twice, i.e., upon diagnosis and upon death]) × number of years in interval. This adjustmentwas ignored here because the NPC incidence rates are small compared with the all-cause mortality rates.MLE = maximum likelihood estimate, SE = standard error

# Appendix DThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-2 APPENDIX D 1 2 MODEL STRUCTURE & CALIBRATION IN CONOLLY ET AL. (20 03, 2004) 3 4 The various studies indicated in Section 5.4.1 were followed by the development of a 5 biologically motivated dose-response model for form aldehyde-induced cancer as represented in a 6 series of papers and in a health assessment report (CIIT model) (Conolly et al., 2004, 2003, 2000; 7 Conolly, 2002; Kimbell et al., 2001a, b; Overton et al., 2001; CIIT, 1999). EPA’s cancer 8 guidelines (U.S. EPA, 2005a) suggest using a BBDR m odel for extrapolation when data permits 9 since it facilitates the incorporation of MOA in ri sk assessment. The CIIT modeling and 10 available data were evaluated in a series of peer-r eviewed papers (Klein et al., 2009; Crump et 11 al., 2008; Subramaniam et al., 2008, 2007) and deba ted further in the literature (Conolly et al., 12 2009; Crump et al., 2009). In addition, alternativ es to the CIIT biological modeling (but based 13 on that original model) are further explored and ev aluated here. 14 In Conolly et al. (2003), tumor incidence data in t he above long-term bioassays were 15 modeled by using an approximation of the two-stage clonal growth model (Moolgavkar et al., 16 1988) and allowing formaldehyde to have directly mu tagenic action. Conolly et al. (2003) 17
combined these data with historical control data on 7,684 animals obtained from National 18 Toxicology Program (NTP) bioassays. These models a re based on the Moolgavkar, Venzon, and 19 Knudson (MVK) stochastic two-stage model of cancer (Moolgavkar et al., 1988; Moolgavkar 20 and Knudson, 1981; Moolgavkar and Venzon, 1979), wh ich accounts for growth of a pool of 21 normal cells, mutation of normal cells to initiated cells, clonal expansion and death of initiated 22 cells, and mutation of initiated cells to fully mal ignant cells. 23 The MVK model for formaldehyde accounted for two MO As that may be relevant to 24 formaldehyde carcinogenicity: 25 26

o An indirect MOA in which the regenerative cell prol iferation in response to formaldehyde 27 cytotoxicity increased the probability of errors in DNA replication. This MOA was modeled 28 by using labeling data on normal cells in nasal muc osa of rats exposed to formaldehyde. 29

o A possible direct mutagenic MOA, based on informati on indicating that formaldehyde is 30 mutagenic (Speit and Merk, 2002; Heck et al., 1990; Grafstrom et al., 1985), was modeled by 31 using rat data on formaldehyde production of DPXs ( Monticello et al., 1996, 1991). 32 33 The human model for formaldehyde carcinogenicity (C onolly et al., 2004) is conceptually 34 very similar to the rat model. The model uses, as input, results from a dosimetry model for an 35 anatomically realistic representation of the human upper airways and an idealized representation 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-3 of the lower airways. However, the model does not incorporate any data on human responses to 1 formaldehyde exposure. The rat and human formaldeh yde models are detailed further below. 2
3 The following notations are used in the rest of thi s chapter: 4 N cell, normal cell 5 I cell, initiated cell 6 LI, labeling index (number of labeled cells/(number labeled + unlabeled cells) 7 ULLI, unit length labeling index (number labeled ce lls/length of basement membrane) 8 N, number of normal cells that are eligible for pro gression to malignancy 9

α

N , division rate of normal cells (hours –1 ) 10

μ N , rate at which an initiated cell is formed by muta tion of a normal cell (per cell division 11 of normal cells) 12

α I, division rate of an initiated cell (hours –1 ) 13 β I, death rate of an initiated cell (hours –1 ) 14

μ I, rate at which a malignant cell is formed by mutati on of an initiated cell (per cell 15 division of initiated cells) 16
17
A novel contribution of the CIIT model is that cell replication rates and DPX 18 concentrations are driven by local dose, which is f ormaldehyde flux to each region of nasal tissue 19 expressed as pmol/mm 2 -hour. This dosimetry is predicted by computationa l fluid dynamics 20 (CFD) modeling using anatomically accurate represen tations of the nasal passages (see 21 Chapter 3). Such a feature is important to incorpo rating site-specific toxicity in the case of a 22 highly reactive gas like formaldehyde, for which up take patterns are spatially localized and 23 significantly different across species (see Chapter 3). In the CIIT model, each of these 24 parameters is characterized by local flux. The inp uts to the two-stage cancer modeling consisted 25 of results from other model predictions as well as empirical data as follows: 26
27

● Regional uptake of formaldehyde in the respiratory tract predicted by using CFD 28 modeling in the F344 rat and human (Kimbell et al., 2001a, b; Overton et al., 2001; 29 Subramaniam et al., 1998) 30

● Concentrations of DPXs predicted by a PBPK model (C onolly et al., 2000) calibrated to 31 fit the DPX data in F344 rat and rhesus monkey (Cas anova et al., 1994, 1991) and 32 subsequently scaled up to humans 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-4 ● α N inferred from LI data on rats exposed to formaldeh yde (Monticello et al., 1996, 1991, 1 1990) 2

D.1. DPX AND MUTATIONAL ACTION 3 Formaldehyde interacts with DNA to form DPXs. Thes e cross-links are considered to 4 induce mutagenic as well as clastogenic effects. C asanova et al. (1994, 1989) carried out two 5
studies of DPX measurements in F344 rats. In the f irst study, rats were exposed to 6 concentrations of 0.3, 0.7, 2, 6, and 10 ppm for 6 hours and DPX measurements were made over 7 the whole respiratory mucosa of the rat, while, in the second study, the exposure was to 0.7, 2, 6, 8 or 15 ppm formaldehyde for 3 hours and measurements were made at “high” and “low” tumor 9 sites. Overall, these studies showed statistically significantly elevated levels of DPXs at 10
concentrations ≥ 2 ppm, with the trend also indicating elevated DPXs at 0.7 ppm. In Conolly et 11 al. (2003), DPX formation is considered proportiona l to the intracellular dose that induces 12 mutations. Conolly et al. (2000) used data from th e second study to develop a PBPK model that 13 predicted the time course of DPX concentrations as a function of regional formaldehyde flux 14 (estimated in the CFD modeling and expressed as pmo l/mm 2 -hour). In Conolly et al. (2003), this 15 PBPK model was then used to predict regional DPX co ncentrations (that is, as a function of 16 regional formaldehyde flux) (Figure 5-11, Chapter 5 ). These data were incorporated into the 17 two-stage clonal expansion model by defining the mu tation rate of normal and initiated cells as 18
the same linear function of DPX concentration as fo llows: 19
20

μ N = μ I = μ Nbasal + KMU × DPX (1) 21
22
The unknown constants μ Nbasal and KMU were estimated by fitting model prediction s to the 23 tumor bioassay data. 24
25
D.2. CALIBRATION OF MODEL 26 The rat model in Conolly et al. (2003) involved six unknown statistical parameters that 27 were estimated by fitting the model to the rat form aldehyde bioassay data shown in Table 5-24 in 28 Chapter 5 (Monticello et al., 1996; Kerns et al., 1983) plus data from several thousand control 29 animals from all the rat bioassays conducted by the NTP. These NTP bioassays were conducted 30 from 1976 through 1999 and included 7,684 animals w ith an incidence of 13 SCCs (i.e., 0.17% 31 incidence). The resulting model predicts the proba bility of a nasal SCC in the F344 rat as a 32 function of age and exposure to formaldehyde. The fit to the tumor incidence data is shown in 33 Figure 5-12, Chapter 5. (Note: This figure also sh ows the fit to the data obtained by the 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-5 implementation of this model in Subramaniam et al. [2007], which is discussed later in this 1 chapter.) 2
3
D.3. FLUX BINS 4 The spatial distribution of formaldehyde over the n asal lining was characterized by 5 partitioning the nasal surface by formaldehyde flux to the tissue, resulting in 20 “flux bins” 6 (Figure 5-13, Chapter 5). Each bin is comprised of elements (not necessarily contiguous) of the 7 nasal surface that receive a particular interval of formaldehyde flux per ppm of exposure 8 concentration (Kimbell et al., 2001a). The spatial coordinates of elements comprising a 9 particular flux bin are fixed for all exposure conc entrations, with formaldehyde flux in a bin 10 scaling linearly with exposure concentration (ppm). The number of cells at risk varies across the 11 bins, as shown in Figure 5-14, Chapter 5. 12 13 D.4. USE OF LABELING DATA 14 Cell replication rates in Conolly et al. (2003) wer e obtained by pooling labeling data from 15 two phases of a labeling study in which male F344 r ats were exposed to formaldehyde gas at 16 similar concentrations (0, 0.7, 2.0, 6.0, 10.0, or 15.0 ppm). The first phase employed injection 17 labeling with a 2-hour pulse labeling time, and ani mals were exposed to formaldehyde for early 18 exposure periods of 1, 4, and 9 days and 6 weeks (M onticello et al., 1991). The second phase 19 used osmotic minipumps for labeling with a 120-hour labeling time to quantify labeling in 20 animals exposed for 13, 26, 52, and 78 weeks (Monti cello et al., 1996). The combined pulse and 21 continuous labeling data were expressed as one expo sure TWA over all sites for each exposure 22
concentration. α N was calculated from these labeling data by using a n approximation from 23 Moolgavkar and Luebeck (1992). A dose-response cur ve for normal cell replication rates (i.e., 24

α

N as a function of formaldehyde flux) was then calcu lated as shown in Figure D-1. These steps 25 are carefully detailed and evaluated in Subramaniam et al. (2008), and discussion of the data will 26
continue in the section on uncertainties in charact erizing cell replication rates. 27 28 D.5. UPWARD EXTRAPOLATION OF NORMAL CELL DIVISION RATE 29 The extensive labeling data collected by Monticello et al. (1996, 1991) present an 30 opportunity to use precursor data in assessing canc er risk. However, these empirical data could 31
be used to determine α Ν ( flux) only for the lower flux range, 0–9,340 pmol/m m 2 -hour (see 32 Subramaniam et al. [2008] for the reasons), as show n by the solid line in Figure D-1, whereas the 33 highest computed flux at 15.0 ppm exposure was 39,3 00 pmol/mm 2 -hour. Therefore Conolly et 34 al. (2003) introduced an adjustable parameter, α max , that represented the value of α Ν ( flux) at the 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-6 maximum flux of 39,300 pmol/mm 2 -hour. α max was estimated by maximizing the likelihood of 1 the two-stage model fit to the tumor incidence data . For 9,340 < flux ≤ 39,300 pmol/mm 2

- 2 hour, α Ν ( flux) was determined by linear interpolation from α Ν (9,340) to α max , as shown by the 3 dashed line in Figure D-1. 4 5

Flux (pmole/mm 2 /h) 0 10000 20000 30000 40000Cell Division Rate,α(1/h)

0.00 0.01 0.02 0.03 0.04 0.05 Empirical α N (from ULLI data)
Estimated α N Estimated α Ι

0.000 0.001 0.002

0 2000 4000 6000 0.000 0.001 0.002 J-shape

Hockey 

### α max

Flux (pmole/mm 2 /h) 0 10000 20000 30000 40000Cell Division Rate,α(1/h)

0.00 0.01 0.02 0.03 0.04 0.05 Empirical α N (from ULLI data)
Estimated α N Estimated α Ι

0.000 0.001 0.002

0 2000 4000 6000 0.000 0.001 0.002 J-shape

Hockey 

### α max

6 7 Figure D-1. Dose response of normal ( α N ) and initiated ( α I ) cell division rate 8 in Conolly et al. (2003). 9 10 Note: Empirically derived values of α N (TWA over six sites) from Table 1 in 11 Conolly et al. (2003) and optimized parameter value s from their Table 4 were 12 used. The main panel is for the J-shape dose respo nse. Insets show J-shape and 13 hockey-stick shape representations at the low end o f the flux range. The long 14 arrow denotes the upper end of the flux range for w hich the empirical unit-length 15 labeling data are available for use in the clonal g rowth model. α max is the value of 16

α

N at the maximum formaldehyde flux delivered at 15 p pm exposure and 17 estimated by optimizing against the tumor incidence data. α I < α N for flux greater 18 than the value indicated by the small vertical arro w. Conolly et al. (2004, 2003) 19 assumed β I

= α N at all flux values. 20 21 Source: Subramaniam et al. (2008). 22This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-7 D.6. INITIATED CELL DIVISION AND DEATH RATES 1 The pool of cells used for obtaining the LI data in Monticello et al. (1996, 1991) consists 2 of largely normal cells with perhaps increasing num bers of initiated cells at higher exposure 3
concentrations. Since the division rates of initia ted cells in the nasal epithelium, either 4 background or formaldehyde exposed, could not be in ferred from the available empirical data, 5 Conolly et al. (2003) made what they perceived to b e a biologically reasonable assumption for α I , 6 assuming α I

to be linked to α N via a two-parameter function: 7 8

α I

= α N ×{multb – multc × max[ α N – α N(basal) , 0]} (2) 9 10 where α

N ≡ α N (flux), α

N ( basal ) is the estimated average cell division rate in une xposed normal 11 cells, and multb and multc are unknown parameters e stimated by likelihood optimization against 12
the tumor data. 2 The value of α N(basal) was equal to 3.39 × 10 –4 hours –1 as determined by Conolly 13 et al. (2003) from the raw averaged LI data. The r atio α I / α N is plotted against flux in Figure D-2, 14
and α I (flux) is shown by the dotted line in Figure D-1. 15



### Flux (pmole/mm 2 /h) 0 10000 20000 30000 40000αI/αN

0.96 1.00 1.04 1.08

16 17 Figure D-2. Flux dependence of ratio of initiated and normal cell replication 18 rates ( α I / α N ) in CIIT model. 19 Note: Cell replication rate of initiated cells is l ess than normal cell replication rate 20 at flux exceeding the value denoted by the arrow. By assumption, the y-axis also 21 represents ( α I / β I ). 22 Source: Subramaniam et al. (2008). 23

2 multb and multc were equal to 1.072 and 2.583, re spectively (J-shaped α N )

, , and 1.070 and 2.515, respectively (hockey-stick shaped α N )

.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-8 Death rates of initiated cells ( β I ) are assumed to equal the division rates of normal cells 1 for all formaldehyde flux values, that is 2
3 β I (flux) = α N (flux) (3) 4
5 Conolly et al. (2003) stated that this formulation for α I and β I provided the best fit of the 6
model to the tumor data. 7
8
D.7. STRUCTURE OF THE CIIT HUMAN MODEL 9 Subsequent to the BBDR model for modeling rat cance r, Conolly et al. (2004) developed 10 a corresponding model for humans for the purpose of extrapolating the risk to humans estimated 11 by the rat model. Also, rather than considering on ly nasal tumors, it is used to predict the risk of 12 all human respiratory tumors. DPXs observed at pro ximal portions of the rhesus monkey LRT 13 (Casanova et al., 1991) suggested that the LRT may be at risk in addition to the URT. In 14 addition, some epidemiologic studies (Gardner et al ., 1993; Blair et al., 1990, 1986) reported an 15 increase in lung cancer associated with formaldehyd e exposure, while others reported no such 16 increases (Collins et al., 1997; Stayner et al., 19 88). The human model for formaldehyde 17 carcinogenicity (Conolly et al., 2004) is conceptua lly very similar to the rat model and follows 18 the schematic in Figure 5-11, Chapter 5. The follo wing points need to be noted: 19
20

• The model does not incorporate any data on human re sponses to formaldehyde exposure. 21

• The model is based on an anatomically realistic rep resentation of the human nasal 22 passages (in a single individual) and an idealized representation of the LRT. Local 23 formaldehyde flux to respiratory tissue is estimate d by a CFD model for humans 24 (Subramaniam et al., 1998; Kimbell et al., 2001a; O verton et al., 2001). 25

• Rates of cell division and cell death are, with a m inor modification, assumed to be the 26
same in humans as in rats. 27

• The concentration of formaldehyde-induced DPXs in h umans is estimated by scaling up 28 from values obtained from experiments in the F344 r at and rhesus monkey. This scaling 29 up was discussed in chapter 3. 30

• The statistical parameters for the human model are either estimated by fitting the model 31 to the human background data, assumed to have the s ame value as obtained in the rat 32 model, or, in one case, fixed at a value suggested by the epidemiologic literature. The 33 delay, D, is fixed at 3.5 years, based on a fit to the incidence of lung cancer in a cohort of 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-9 British doctors (Doll and Peto, 1978). The two oth er parameters in the rat model that 1 affect the background rate of cancer (multb and μ basal ) are estimated by fitting to U.S. 2 cancer incidence or mortality data. These paramete rs affect the baseline values for the 3
human α I, μ N , and μ I. Since α max , multfc, and KMU do not affect the background canc er 4 rate, they cannot be estimated from the (baseline) U.S. cancer incidence rates. Therefore, 5 in Conolly et al. (2004, 2003), α max and multfc are assumed to have the same values in 6 humans as in rats, and the human value for KMU is o btained by assuming that the ratio 7 KMU/μ basal is invariant across species. Thus, 8
9

( ) ( ) ( )
( ) rat Nbasal human Nbasal rat human KMU KMU μ

μ × = (4) 10This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE D-10 

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# Appendix EThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-2 APPENDIX E 1 EVALUATION OF BBDR MODELING OF NASAL CANCER IN THE F344 RAT: 2 CONOLLY ET AL. (2003) AND ALTERNATIVE IMPLEMENTATIO NS 3 4 A biologically based dose-response model for formal dehyde-induced cancer was in a 5 series of papers and in a health assessment report (CIIT model) (Conolly et al., 2004, 2003, 2000; 6 Conolly, 2002; Kimbell et al., 2001a, b; Overton et al., 2001; CIIT, 1999). The model structure, 7 notations, and calibration have been described in A ppendix D. In Chapter 5, an evaluation of the 8 uncertainties of this model and alternative approac hes based on its conceptual framework was 9 presented in a summary form. This Appendix provides the relevant details of that evaluation and 10 presents a range of dose-response curves for tumor risk in the rat. It is divided into the following 11 major sections. First, an overview of all the issue s evaluated is provided in tabular form. The rest 12 of the Appendix then presents only those issues whi ch have a significant impact on model 13 predictions. These issues pertain to the use of his tory controls, the uncertainty and variability in 14 the dose-response for normal cell-replication rates , and sensitivity of model results to uncertainty 15
in the kinetics of initiated cells. The issues have significant impact on mode of action inferences, 16
and this is discussed in some detail. 17
18
E.1. TABULATION OF ALL ISSUES EVALUATED IN THE RAT MODELS 19
Table E-1 summarizes model uncertainties and their impact as evaluated by EPA. The 20 key uncertainties are discussed in considerably mor e detail in additional sections in this 21 Appendix and in published manuscripts as denoted in the tables. 22
23
E.2. Statistical Methods Used in Evaluation 24
Parameters of the alternate models shown here were estimated by maximizing the 25 likelihood function defined by the data (Cox and Hi nkley, 1974). Such estimates are referred to 26 as maximum likelihood estimates (MLEs). Statistica l confidence bounds were computed by 27 using the profile likelihood method (Crump, 2002; C ox and Oakes, 1984; Cox and Hinkley, 28 1974). In this approach, an asymptotic 100(1 – α )% upper (lower) statistical confidence bound 29 for a parameter, β , in the animal cancer model is calculated as the l argest (smallest) value of β 30
that satisfies 31
32 2[L max – L*( β )] = x 1-2 α (5) 33
34This document is a draft for review purposes only a nd does not constitute Agency policy E-3 DRAFT—DO NOT CITE OR QUOTETable E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344ratAssumptions, approach,and characterization ofinput data Rationale EPA evaluationFurtherelaborationaHoogenveen et al. (1999)solution method, which isvalid only for timeindependent parameters, isaccurate enough.Errors due to thisassumption thought tobe significant only athigh concentration andnot at humanexposures.EPA implemented a solution method valid for time-dependentparameters. Results did not differ significantly from thoseobtained assuming Hoogenveen et al.(1999) solutions.Caveat: Impact not evaluated for the case where cellreplication rates vary in time.Crump et al.(2006);Subramaniamet al. (2007)All SCC tumors are rapidlyfatal; no incidental tumors.Death is expected tooccur typically within1–2 weeks of observedtumor (personalcommunication withR. Conolly).1) Overall, assumption does not impact model calibration orprediction.2) However, since 57 animals were observed to havetumorsat interim sacrifice times, EPA implementation distinguishedbetween incidental and fatal tumors. Time lag betweenobservable tumor and time of death was significantcomparedto time lag between first malignant cell and observable tumor.Subramaniamet al. (2007)Historical controls fromentire NTP database werelumped with concurrentcontrols in studies.Data is on controlanimals, and number islarge (7,684).Therefore, intercurrentmortality was notexpected to besubstantial.1) Tumor incidence in “all NTP” 10-fold higher thanin “allinhalation NTP” controls. Including all NTP controls isconsidered inappropriate.2) Low-dose response curve sensitive to use of historicalcontrols.3) Model inference on relevance of formaldehyde’s mutagenicpotential to its carcinogenicity varies from “insignificant” to“highly significant,” depending on controls used.(SeeAppendix F for impact on human risk.)Table E-2;Subramaniamet al. (2007);Sec E.3.1LI was derived fromexperimentally measuredULLI.Derived fromcorrelating ULLI to LImeasured in sameexperiment.Significant variation in number of cells per unit length ofbasement membrane. Spread in ULLI/LI ~25%. Impactonrisk not evaluated.Subramaniamet al. (2008);This document is a draft for review purposes only a nd does not constitute Agency policy E-4 DRAFT—DO NOT CITE OR QUOTETable E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344ratAssumptions, approach,and characterization ofinput data Rationale EPA evaluationFurtherelaborationaPulse and continuouslabeling data combined inderivingαNfrom LI.Continuous LInormalized by ratio ofpulse to continuousvalues for control data.Formula used for derivingαNfrom LI is not applicable forpulse labeling data. Pulse labeling is measure ofnumber ofcells in S-phase, not of their recruitment rate into S-phase; notenough information to deriveαNfrom pulse data. Impact onrisk predictions could not be evaluated.Subramaniamet al. (2008);Sec E.3.2.2To construct dose responseforαN, labeling data wereweighted by exposure time(t) and averaged over allnasal sites (TWA). Flux atan exposure concentrationwas averaged over all nasalsites.Site-to-site variation inLI large and did notvary consistently withflux. No reasonableapproach available forincorporating timevariation in labeling ininterspeciesextrapolation.1) TWA assigns low weight to early time LI, butαNfor early tis very important to the cancer process. Since pulse ULLI wasused for t < 13 weeks, impact of these ULLIs on risk could notbe evaluated.2) Time dependence inαNderived from continuous ULLIdoes not significantly impact model predictions.3) Site-to-site variation ofαNat least 10-fold and has majorimpact on model calibration. 10-fold variation intumorincidence data across sites.4) Large differences in number of cells across nasal sites(Table E-3), so averaging over sites is problematic.5) Histologic changes, thickening of epithelium andmetaplasia occur at later times for the higher doseand wouldaffect replication rate.Figures E-1, E2, E-3;Subramaniamet al. (2008);Sec E.3.2.3This document is a draft for review purposes only a nd does not constitute Agency policy E-5 DRAFT—DO NOT CITE OR QUOTETable E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344ratAssumptions, approach,and characterization ofinput data Rationale EPA evaluationFurtherelaborationaSteady-state flux estimatesnot affected by airway andtissue reconfiguration dueto long-term dosing.Histopathologicchanges not likely to berate-limiting factors indosimetry.1) Thickening of epithelium and squamous metaplasiaoccurring at later times for the higher dose will reduce tissueflux. Not incorporated in model.2) These effects will push regions of higher flux to moreposterior regions of respiratory tract. Likely toaffectcalibration of rat model. Uncertainty not evaluatedquantitatively.3) Calibration of PBPK model for DPXs seen to be highlysensitive to tissue thickness.Kimbell et al(1997);Subramaniamet al. (2008)TWAαN(flux) rises abovebaseline levels only atcytolethal dose. Abovesuch dose,αN(flux) risessharply due to regenerativeproliferation.Variability inαN(flux)rate is represented byalso consideringhockey-stick (thresholdin dose) when TWAindicates J-shape(inhibition of celldivision) description ofαN(flux).1) Uncertainty and variability inαNquantitatively evaluated tobe large. In addition, several qualitative uncertainties incharacterization ofαN(flux) from LI.2) Several dose-response shapes, including a monotonicincreasing curve without a threshold, were considered in orderto adequately describe highly dispersed cell replication data.Substantial impact on low dose risk, including negative addedrisk.FiguresE-1, E2, E-3, E-4, E5;Subramaniamet al. (2008);Sec E.3.2This document is a draft for review purposes only a nd does not constitute Agency policy E-6 DRAFT—DO NOT CITE OR QUOTETable E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344ratAssumptions, approach,and characterization ofinput data Rationale EPA evaluationFurtherelaborationaDose response forαIwasobtained fromαN,assuming ratio (αI/αN) tobe a two-parameterfunction of flux (seeFigures 5-7, 5-9).Parameters were estimatedby optimizing modelpredictions against tumorincidence data.(αI/αN) was >1.0 in linewith the notion of Icells possessing agrowth advantage overN cells.Satisfies Occam’s razorprinciple (Conolly etal., 2009).1) Estimated (αI/αN) in CIIT modeling is <1.0 (growthdisadvantage) for higher flux values and is >1.0 only at lowerend of flux range in model (see Figure 5-9).2) Since there are no data to informαI, sensitivity of riskestimates to various functional forms was evaluated. Riskestimates for the rat were extremely sensitive to alternatebiologically plausible assumptions forαI(flux) and varied bymany orders of magnitude at≤1 ppm, including negative risk.All these models described tumor incidence data and cellreplication and DPX data equally well.Figures D-2, E5, E-6;Subramaniamet al. (2008);Crump et al.(2009, 2008);Sec E.3.3Death rate of I cellsβI(flux) assumed = divisionrate of N cellsαN(flux).Based on homeostasis(αN=βN) andassumption thatformaldehyde is equallycytotoxic to N cells andI cells. SatisfiesOccam’s Razorprinciple (Conolly etal., 2009).1) In general, data indicate I cells are more resistant tocytolethality and that ADH3 clearance capacity is greater intransformed cells. Therefore, plausibility of assumption (βI=αN) is tenuous.2) Alternate assumption,βIproportional toαI, was examined.Risk estimates extremely sensitive to assumptions onβI(seeFigure 5-12).Subramaniamet al. (2008);Crump et al.(2009, 2008);Sec E.3.3DPX is dose surrogate forformaldehyde mutagenicpotential. DPX clearanceis rapid and complete in 18hours.Casanova et al. (1994). Half-life for DPX clearancein in vitroexperiments ontransformed cell lines was sevenfold longer than estimated byConolly et al. (2004, 2003) and perhaps 14-fold longer withnormal (non-transformed) human cells. Some DPXaccumulation therefore likely. However, model calibrationand dose response in rat is insensitive to this uncertainty.Quievryn andZhitkovich,(2000);Subramaniamet al. (2007);Chap 3This document is a draft for review purposes only a nd does not constitute Agency policy E-7 DRAFT—DO NOT CITE OR QUOTETable E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344ratAssumptions, approach,and characterization ofinput data Rationale EPA evaluationFurtherelaborationaFormaldehyde mutagenicaction takes place onlywhile DPXs are in place.DNA lesions remain after DPX removal. DPX inducesfurther DNA and protein damage. Potential forformaldehyde-induced mutation after DPX clearance.Thus,formaldehyde mutagenicity may be underrepresented.Couldnot quantitatively evaluate uncertainty (no data onclearanceof secondary lesions).Barker et al.(2005); Speitand Schmid(2006);Subramaniamet al. (2008);Chap 4aReferences stated here are in addition to Conolly et al. (2004, 2003).Note: Risk estimates discussed in this table are for the F344 rat.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-8 where L indicates the likelihood of the rat bioassay data, L max is its maximum value, L*( β ) is, for 1
a fixed value of β , the maximum value of the log-likelihood with resp ect to all of the remaining 2 parameters, and x 1-2 α is the 100(1– 2 α ) percentage point of the chi-square distribution w ith one 3 degree of freedom. The required bound for a parame ter, β , was determined via a numerical 4
search for a value of β that satisfies this equation. 5 The additional risk is defined as the probability o f an animal dying from an SCC by the 6 age of 790 days, in the absence of other competing risks of death, while exposed throughout life 7 to a prescribed constant air concentration of forma ldehyde, minus the corresponding probability 8 in an animal not exposed to formaldehyde. The MLE of additional risk is the additional risk 9 computed using MLEs of the model parameters. 10 The method described above for computing profile li kelihood confidence bounds cannot 11
be used with additional risk because additional ris k is not a parameter in the cancer model. 12 Instead, an asymptotic 100(1 – α )% upper (lower) statistical confidence bound for a dditional risk 13 was computed by finding the parameter values that p resented the largest (smallest) value of 14 additional risk, subject to the inequality 15
16 2[L max – L] ≤ x 1-2 α (6) 17
18 being satisfied, with the resulting value of additi onal risk being the required bound. This 19 procedure was implemented through use of penalty fu nctions (Smith and Coit, 1995). For 20 example, the profile upper bound on additional risk was computed by maximizing the “penalized 21 added risk,” defined as ( additional risk – penalty ), where 22
23 penalty = W × {[(L max – L) - x 1-2 α

/2] + } 2 (7) 24
25
and [] + equals the quantity in the brackets whenever it is positive and zero otherwise. The 26 multiplicative weight, W , was selected by trial and error so that the final solution satisfied the 27 following equation sufficiently well. 28
29 2(L max – L) = x 1-2 α (8) 30
31 The computer code was written in Microsoft Excel® 2 002 SP3 Visual Basic. Either the 32 regular Excel Solver or the Frontline Systems Premi um Solver® was used to make the required 33 function optimizations. Computation of confidence bounds was highly computationally 34 intensive, and, consequently, confidence bounds wer e computed only for selected parameters in 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-9 selected runs. For select cases, the bootstrap met hod was also used to calculate confidence 1 bounds in order to confirm their accuracy. Values so calculated were found to be in agreement 2 with those calculated by using the likelihood metho d. 3
4
E.3. PRIMARY UNCERTAINTIES IN BBDR MODELING OF THE F344 RAT DATA 5
The evaluation of the CIIT model and alternatives t o this model that were implemented 6
for the F344 rat will be discussed first. Of the i ssues tabulated above, the following uncertainties 7 in the modeling of the F344 rat data will be discus sed in considerably more detail: use of 8 historical controls, uncertainty and variability in characterizing cell replication rates from the 9 labeling data, and uncertainty in model specificati on of initiated cell kinetics. 10 In their evaluation, Subramaniam et al. (2007) firs t attempted to reproduce the Conolly et 11 al. (2003) results under similar conditions and ass umptions as employed in their paper, which 12 included the assumption that tumors were rapidly fa tal. Figure 5-12 in Chapter 5 shows the 13 results for this case. The predicted probabilities shown in this figure were obtained by 14 Subramaniam et al. (2007) by using the source code made available by Dr. Conolly. These are 15 compared with the best-fitting model and plotted ag ainst the Kaplan-Meier (KM) probabilities. 16 Although the results are largely similar, there are some differences (Subramaniam et al., 2007). 17 Given the scope of issues to examine for the uncert ainty analyses, the evaluation 18 proceeded in stages. First, the Hoogenveen et al. (1999) solution was replaced by one that is 19 valid for a model with time varying parameters (fir st entry in Table E-1), and tumors found at 20
scheduled sacrifices were assumed to be incidental rather than fatal (second entry in Table E-1 ). 21 Second, weekly averaged solutions for DPX concentra tion levels were used instead of hourly 22 varying solutions (predicted by a PBPK model). The log-likelihood values and tumor 23 probabilities remained essentially unchanged. Upon quantitative evaluation, these factors, 24 although important from a methodological point of v iew, were not found to be major 25 determinants of either calibration or prediction of the model for the F344 rat data (Subramaniam 26 et al., 2007). 27 Subramaniam et al. (2007), as in Georgieva et al. ( 2003), used the DPX clearance rate 28
constant obtained from in vitro data instead of the assumption in Conolly et al. (2003) that all 29 DPXs cleared within 18 hours (Subramaniam et al., 2 007). With this revision, weekly average 30 DPX concentrations were larger than those in Conoll y et al. (2003) by essentially a constant ratio 31 equal to 4.21 (range of 4.12–4.36) when averaged ov er flux bin and exposure concentrations. 32 Accordingly, cancer model fits to the rat tumor inc idence data using the two sets of DPX 33 concentrations (everything else remaining the same) provided very similar parameter estimates, 34 except that the parameter KMU rat in equation 1 (and equation 4) was 4.23 times larg er with the 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-10 Conolly et al. (2003) DPX concentrations. In other words, the product KMU × DPX remained 1 substantially unchanged. However, the different cle arance rate does significantly impact the 2 scale-up of the two-stage clonal growth model to th e human. 3 After making the above modifications, the impact of the following uncertainties, which 4 had a large impact on modeling, was examined sequen tially. 5
6 E.3.1. Sensitivity to Use of Historical Controls 7
E.3.1.1. Use of Historical Controls 8 Conolly et al. (2003) combined the historical contr ols arising from the entire NTP 9 database of bioassays. Tumor and survival rates in control groups from different NTP studies are 10 known to vary due to genetic drift in animals over time and differences in laboratory procedures, 11 such as diet, housing, and pathological procedures (Haseman, 1995; Rao et al., 1987). In order to 12 minimize extra variability when historical control data are used, the current NTP practice is to 13 limit the historical control data, as far as possib le, to studies involving the same route of 14 exposure and to use historical control data from th e most recent studies (Peddada et al., 2007). 15 Bickis and Krewski (1989) analyzed 49 NTP long-term rodent cancer bioassays and 16 found a large difference in determinations of carci nogenicity, depending on the use of historical 17
controls with concurrent control animals. The hist orical controls used in the CIIT modeling 18
controls came from different rat colonies and from experiments conducted in different 19 laboratories over a wide span of years, so it is cl early problematic to assume that background 20
rates in these historical control animals are the s ame as those in the concurrent control group. 21 There are considerable differences among the backgr ound tumor rates of SCCs in all NTP 22 controls (13/7,684 = 0.0017), NTP inhalation contro ls (1/4,551 = 0.0002), and concurrent 23 controls (0/341 = 0.0). The rate in all NTP contro ls is significantly higher than that in NTP 24 inhalation controls ( p = 0.01, Fisher’s exact test). Given these differe nces, the inclusion of any 25 type of historical controls is problematic and is t hought to have limited value if these factors are 26 not controlled for (Haseman, 1995). 27
28
E.3.1.2. Influence on Model Calibration and on Human Model 29 To investigate the effect of including historical c ontrols in the CIIT model, the analyses in 30 Subramaniam et al. (2007) were conducted by using t he following sets of data for controls (the 31 fraction of animals with SCCs is denoted in parenth eses): only concurrent controls (0/341), 32 concurrent controls plus all the NTP historical con trol data used by Conolly et al.(2003) 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-11 (13/8,031), and concurrent controls plus data from historical controls obtained from NTP 1 inhalation studies (1/4,949) (NTP, 2005). 3 2
The results of the evaluation are shown in Table E- 2. For these analyses, the same 3 normal cell replication rates and the same relation ship (see eq 2) between initiated cell and 4 normal cell replication rates as used in Conolly et al. (2003) were used. In all cases, weekly 5 averaged values of DPX concentrations were used. M odel fits to the tumor incidence data were 6 similar in all cases (see Figure 5-12 in Chapter 5 and Subramaniam et al. [2007] for a more 7 complete discussion). The biggest influence of the control data was seen to be on the estimated 8 basal mutation rate in rats, μ Nbasal(rat) , which, in turn, influences the estimated mutation effect in 9 humans through eq 4 (Appendix D). α max was also seen to be a sensitive parameter and is 10 discussed later. See Subramaniam et al. (2007) for other parameters in the calibration. 11 The ratio KMU/μ Nbasal is of particular interest because extrapolation to human in Conolly 12 et al. (2004) assumed its invariance as given by eq 4 (Appendix D). Now, μ Nbasal in the human is 13 estimated independently by fitting a scaled-up vers ion of the two-stage model to human baseline 14 rates of tumor incidence. Thus, a decrease in the value of μ Nbasal estimated in the rat modeling 15 increases the formaldehyde-induced mutational effec t in the human. 16 While the MLE of KMU/μ Nbasal is zero in the CIIT animal model (Conolly et al., 2003), it 17 takes a range of values from 0 to 0.9 mm 3 /pmol and undefined (or infinite, when μ Nbasal = 0) in 18 the various cases examined in this paper. The 95% upper confidence bound on this ratio ranges 19 from 0.25–6.2 (these values would be four times lar ger had the Conolly et al. [2003] DPX 20 concentrations been used) to infinite. Thus, the e xtrapolation to human risk by using the 21 approach in Conolly et al. (2004) becomes particula rly problematic when only concurrent 22 controls are used, because then the mutational cont ribution to formaldehyde-induced risk in 23
humans becomes unbounded. This issue will be discu ssed again toward the end of the discussion 24
on historical controls. 25
26

3 Three animals in the inhalation historical control s were diagnosed with nasal SCC. Of these, two of the tumors were determined to have originated in tissues other than the nasal cavity upon further review (Dr. Kev in Morgan and Ms. Betsy Gross Bermudez, personal communication). These two tumors were therefore not included on th e advice of Dr. Morgan. See Subramaniam et al. (2007) for mo re details.This document is a draft for review purposes only a nd does not constitute Agency policy E-12 DRAFT—DO NOT CITE OR QUOTETable E-2. Influence of control data in modeling formaldehyde-induced cancer in the F344 ratCase A D B E C FControl animals (combinedwith concurrent controls)All NTPhistoricalaAll NTPhistoricalaNTPinhalationhistoricalaNTPinhalationhistoricalaConcurrentonlyaConcurrentonlyaCell replication doseresponseJ-shaped Hockey stick J-shaped Hockey stick J-shaped Hockey stickLog-likelihood −1692.65 −1693.68 −1,493.21 −1,493.35 −1,474.29 −1,474.29μNbasal1.87 × 10–62.12 × 10–67.32 × 10–79.32 × 10–70.0 0.0KMU 1.12 × 10–70.0 6.84 × 10–76.18 × 10–71.20 × 10–61.20 × 10–6KMU/μNbasal0.06(0.0, 0.40)0.0(0.0, 0.25)0.94(0.26, 6.20)0.66(0.2, 5.20)∞(0.42,∞)∞(0.41,∞)αmax0.045(0.029, 0.045)0.045(0.029, 0.045)0.045(0.026, 0.045)0.045(0.027, 0.045)0.045(0.027, 0.045)0.045(0.027, 0.045)aValues in parentheses denote lower and upper 90% confidence bounds.Source: Adapted from Subramaniam et al. (2007).This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-13 It may be noted, however, that absence of tumors in the limited number of concurrent 1 animals does not imply that the calculation will ne cessarily predict a zero background probability 2 of tumor (i.e., a parameter estimate of μ Nbasal = 0). We observed such a counterexample estimate 3
for μ Nbasal in simulations involving the alternate dose-respons e curves for α N and α I that are 4 discussed later. Nonetheless, when μ Nbasal = 0, an upper bound for μ Nbasal using the concurrent 5 controls can be inferred. Accordingly, the 90% sta tistical lower confidence bound on the ratio 6 KMU/μ Nbasal is also reported in Table E-2. Such a value would of course provide a lower bound 7 on risk by using this model and would therefore not be conservative. 8 Conolly et al. (2003) estimated KMU to be zero for both the hockey-stick and J-shape 9 cell replication models. However, the estimate for the coefficient KMU (obtained using the 10 solution of Crump et al. [2005]) is zero only for the case of the model with the hockey -stick 11 curve for cell replication and with control data as used by Conolly et al. (2003). It is positive in 12 all other cases and statistically significantly so in all cases in which either inhalation control dat a 13
or concurrent controls were used. With concurrent controls only and the J-shape cell replication 14 model, the MLE estimate for KMU (1.2 × 10 –6 ) is larger than the statistical upper bound obtain ed 15 by Conolly et al. (2003) (8.2 × 10 –7 ). It should also be kept in mind that the estimat e would be 16 about 4.2 times larger still had the Conolly et al. (2003) DPX model been used. 17 18
E.3.1.3. Influence on Dose-Response Curve 19 Subramaniam et al. (2007) showed that inclusion of historical controls had a strong 20 impact on the tumor probability curve below the ran ge of exposures over which tumors were 21 observed in the formaldehyde bioassays. As shown t here, the MLE probabilities for occurrence 22 of a fatal tumor at exposure concentrations below 6 ppm were roughly an order of magnitude 23 higher when all the NTP historical controls were us ed, compared with MLE probabilities 24 predicted when historical controls were drawn only from inhalation bioassays, and many orders 25 of magnitude higher than MLE probabilities predicte d when only concurrent controls were used 26 in the analysis. (Note that this comparison should not be inferred to apply to upper bound risk 27 estimates since there were many fewer concurrent th an historical controls, so error bounds could 28 be much larger in the case where concurrent control s were used.) 29 However, as shown by these authors, model fits to t he tumor data in the 6–15 ppm 30 exposure concentration range were qualitatively ind ifferent to which of these control data sets 31 was used. This observation emphasizes the statisti cal aspect of the CIIT modeling—that 32 significant interplay among the various adjustable parameters allows the model to achieve a good 33 fit to the tumor incidence data independent of the control data used. On the other hand, the 34 results in Subramaniam et al. (2007) show that chan ges in the control data affect parameter 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-14 KMU, resulting in significantly different tumor pre dictions at lower exposure concentrations. 1 Therefore, the strong influence of using all the NT P historical controls on the low-dose region of 2 the time-to-tumor curves presented in Subramaniam e t al. (2007) suggests that large uncertainties 3 may arise in extrapolating to both human and rat (i n the low-dose region) from such 4
considerations alone. 5
6 E.3.1.4. Problem Including 1976 Study for Inhalation Histori cal Control 7 A crucial point needs to be noted with regard to th e use of inhalation NTP historical 8 controls (i.e., cases B and E) in the two-stage clo nal growth modeling. The single relevant tumor 9 in the NTP inhalation studies came from the very fi rst NTP inhalation study, dated 1976, and the 10 animals in this study were from Hazelton Laboratori es, whereas the concurrent animals were all 11 from Charles River Laboratories. Similar problems arise with inclusion of several other NTP 12 inhalation studies. As mentioned before, genetic a nd other time-related variation can lead to 13 different tumor and survival rates, and in general it is recommended that use of historical controls 14 be restricted to the same kind of bioassays and to studies within a 5–7 year span of the concurrent 15 animals (Peddada et al., 2007). Thus, it is proble matic to assume that the tumor in the 1976 NTP 16 study is representative of the risk of SCCs in the formaldehyde bioassays. Even if it were 17 appropriate to consider the 1976 study, this leads to the unstable situation in which, despite all of 18 the “upstream” mechanistic information used to cons truct the BBDR model, the only piece of 19 data that might keep the model predictions of human risk bounded is a single tumor found among 20 several thousand rats from NTP bioassays (Crump et al., 2008). In summary, although it can be 21 argued that the rate of SCCs among the controls in the rat bioassay is probably not zero, it is also 22 problematic to assume that this rate can be adequat ely represented by the background rate in NTP 23
historical controls or even in NTP inhalation histo rical controls. 24 25
E.3.1.5. Inference on MOA 26 Subramaniam et al. (2007) also examined the contrib ution of the DPX component (which 27 represents the directly mutagenic potential of form aldehyde in the model) to the calculated tumor 28 probability, choosing for their case study the opti mized models that use the NTP inhalation 29 control data. In the range of exposures where tumo rs were observed (6.0–15.0 ppm), the DPX 30 term was found to be responsible for 58–74% of the added tumor probability. Below 6.0 ppm the 31 estimated DPX contribution was extremely sensitive to the shape of apha_n and varied between 32
2 and 80%. 33 The CIIT BBDR cancer modeling has contributed to th e weight-of-evidence process in 34 various formaldehyde risk assessment efforts and pa pers by lending weight to the argument that 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-15 the direct mutations induced by formaldehyde are re latively irrelevant compared to the 1 importance of cytotoxicity-induced cell proliferati on in explaining the observed tumorigenicity in 2 rodent bioassays and in projecting those observatio ns to human exposures (Conolly et al., 2004, 3 2003; Slikker et al., 2004; Bogdanffy et al., 2001, 1999; Conolly, 1995). The reanalyses in 4 Subramaniam et al. (2007) (in particular, the resul ts in the above paragraph) indicate that, if the 5 CIIT mathematical modeling were utilized to inform this debate, it would in fact indicate the 6 contrary—that a large contribution from formaldehyd e’s mutagenic potential may be needed to 7 explain formaldehyde carcinogenicity. This discuss ion is resumed in the context of uncertainties 8 in model specification for initiated cells. 9 10 E.3.2. Characterization of Uncertainty-Variability in cell Replication Rates 11
E.3.2.1. Dose-Response for α N as Used in the CIIT Clonal Growth Modeling 12 Monticello et al. (1996, 1991) used ULLI to quantif y cell replication within the respiratory 13 epithelium. ULLI is a ratio between a count of lab eled cells and the corresponding length (in 14 millimeters) of basal membrane examined, whereas th e per-cell LI is the ratio of labeled cells to 15 all epithelial cells, in this case, along some leng th of basal membrane and its associated layer of 16 epithelial cells. Monticello et al. (1996, 1991) p ublished ULLI values averaged over replicate 17 animals for each combination of exposure concentrat ion, exposure time, and nasal site. These 18 values are plotted in Figure E-1. Conolly et al. (2003) adopted the following procedu re in using 19 these values (Subramaniam et al., 2008): 20
1. The injection labeled ULLI data were first normaliz ed by the ratio of the average 21 minipump ULLI for controls to the average injection labeled ULLI for controls. 22

2. Next, these ULLI average values were weighted by th e exposure times in Monticello et al. 23 (1996, 1991) and averaged over the nasal sites. Th us, the data were combined into one 24 TWA for each exposure concentration. 25

3. LI was linearly related to the measured ULLI by usi ng data from a different experiment 26 (Monticello et al., 1990) where both quantities had been measured for two sites in the 27

nose. 28

4. Cell replication rates of normal cells ( α N ) were then calculated as α N = (−0.5/t)log(1 − LI) 29 (Moolgavkar and Luebeck, 1992), where LI is the lab eling index and t is the period of 30 labeling. 31

5. This was repeated for each exposure concentration o f formaldehyde, resulting in one 32 value of α N for each exposure concentration. 33

6. Correspondingly, for a given exposure concentration , the steady-state formaldehyde flux 34 into tissue, computed by CFD modeling, was averaged over all nasal sites. Thus, the 35

α N (flux) constructed by Conolly et al. (2003) consist ed of a single α N and a single average 36 flux for each of six exposures. 37This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-16 1 Figure E-1. ULLI data for pulse and continuous lab eling studies. 2 Note: Data are from pulse labeling study, left-hand side, at 1–42 days of exposure 3 and from the continuous-labeling study, right-hand side, at 13–78 weeks of 4 exposure for five nasal sites ALM, AMS, MMT, PLM, a nd posterior mid septum 5 [PMS]). Within each graph, lines with more breaks correspond to shorter 6 exposure times. Data source: Monticello et al. (1 996, 1991). 7 0 5 10 150.5 5.0 50.0 Exposure < 12 wk (pulse labeling)

ALM

0 5 10 150.5 5.0 50.0

Exposure > 12 wk (continuous labeling)

ALM

0 5 10 150.5 5.0 50.0

AMS

0 5 10 150.5 5.0 50.0

AMS

0 5 10 150.5 5.0 50.0

MMT

0 5 10 150.5 5.0 50.0

MMT

0 5 10 150.5 5.0 50.0

PLM

0 5 10 150.5 5.0 50.0

PLM

0 5 10 150.5 5.0 50.0

PMS

0 5 10 150.5 5.0 50.0

PMS

Formaldehyde exposure concentration, ppmMean values of ULLI (log scale)This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-17 This yielded a J-shaped dose-response curve for ce ll replication (when viewed on a non1
transformed scale for α N ), as shown in Figure D-1 (Appendix D) for the full range of flux values 2 used in their modeling. The authors also considere d a hockey-stick threshold representation of 3 their J-shaped curve for α N in order to make a health-protective choice, and t he differences 4 between the two can be seen from the insets in Figu re D-1. In these curves, the cell replication 5
rate is less than or the same as the baseline cell replication rate at low formaldehyde flux values. 6 The shape of the dose-response curve for cell repli cation as characterized in Conolly et al. (2003) 7 is seen as representing regenerative cell prolifera tion secondary to the cytotoxicity of 8 formaldehyde (Conolly

, 2002). Considerable uncertainty and variability, b oth quantitative and 9 qualitative, exist in the use and interpretation of these labeling data for characterizing a dose 10 response for cell replication rates. The primary i ssues are discussed here. Unlike the preceding 11 sections, these have largely not been published els ewhere, so more details are provided. 12
13
E.3.2.2. Uncertainty in the Use of Pulse Labeling Data, and Short-Time Exposure Effects on 14 Cell Replication 15 The formula used for obtaining α N from LI in Conolly et al. (2003) was due to 16 Moolgavkar and Luebeck (1992) who derived this form ula for continuous LI, cautioning that it is 17 not applicable for pulse labeled data. However, Co nolly et al. (2003) applied this formula to the 18 injection (pulse) labeled data also. Such an appli cation is problematic because 2-hour pulse 19 labeled data represent the pool of cells in S-phase rather than the rate at which cells are recruited 20 to the pool and because the baseline values of α N obtained in this manner from both data sets 21 differ considerably. As such, we are not aware of any reasonable manner to derive cell 22 replication rates from these pulse data without acq uisition of data at additional time points. 23 Therefore, the quantitative analysis of cell replic ation rates is restricted in this document to the 24 continuous labeled data (Monticello et al., 1996), which does not include measurements made 25 before 13 weeks of exposure. 26
It is unfortunate that the continuous labeled data do not include any early measurements 27 because, as indicated by Figure E-1, the temporal v ariation in the unit-length LI (ULLI, the raw 28 data) is quite different between the “early time” ( left panel) and “later time” (right panel) and 29 these early-time effects may be quite important to the cancer modeling. At the earliest times in 30 the left panel, the data show an increased trend in labeling at 2 ppm for the sites anterior lateral 31 meatus (ALM), anterior medial septum (AMS), posteri or lateral meatus (PLM), and medial 32 maxilloturbinate (MMT) relative to control. (Also see the dose-response plotted as a function of 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-18 flux in Figure E-4 for the 13-week exposure time, w here such an increase is generally indicated 1 for low flux values.) 2 The early times would be important if, say, repeate d episodic exposures were considered, 3 where adequate time has not elapsed for adaptive ef fects to take place. Such an exposure 4 scenario may be the norm in the human context. How ever, the contribution of the early-time 5 labeling data is minimized in the CIIT cancer model ing since the LI was weighted by exposure 6 time. Because of the problems described above in i ncorporating the pulse-labeled data, the 7 sensitivity analysis will be restricted to only the continuous labeling data. 8 9 E.3.2.3. Site and Time Variability 10 In the remainder of this section, the factors that are considered in order to represent the 11 uncertainty and variability in the cell replication data when developing alternate dose-response 12
curves for α N (flux) will be elaborated. Figure E-2 (from Subram aniam et al., 2008) shows the 13 variability due to replicated animals, exposure tim es, and nasal sites in the continuous labeled 14 data obtained by Monticello et al. (1996). The ULL I data for individual animals were provided 15 by CIIT. In this figure, log α N versus site-specific flux are plotted for six site s and four exposure 16 times for four to six replicate animals in each cas e. (The mean ULLI over these replicates were 17 shown in Figure E-1 for each site and time as a fun ction of exposure concentration.) It needs to 18 be noted that these nasal sites differ considerably in the number of cells estimated at these 19 locations as shown in Table E-3. Each point in Fig ure E-2 represents data from a single site for a 20 single animal at a given time. For comparison, the α N (flux) in Conolly et al. (2003) is also 21 plotted in this figure at their averaged flux value s (filled circles). For flux >9,340 pmol/mm 2

- 22 hour, Conolly et al. (2003) extrapolated this empir ically derived α N (flux) by using a scheme 23 discussed in Appendix D (section D.5) on the upward extrapolation of cell replication rate. The 24 curves shown connecting the filled circles in the f igure represent their linear interpolation (long 25 dashes) between the six points. Their linear extrap olation for flux value >9,340 pmol/mm 2 -hour 26 is also shown (short dashes). Note that the linear interpolation/extrapolation is shown 27 transformed to a logarithmic scale. 28 In Figures E-3, fitted dose-response curves are plo tted for log 10 ( α N ) versus flux with 29 simultaneous confidence limits separately for each time point for two of the largest sites in Table 30 E-3 (ALM and PLM). Note that flux levels are diffe rent at each site. Simple polynomial models 31 in flux (as a continuous predictor), with time incl uded as a factor (i.e., a class or indicator 32 variable, τ i representing the effect of the i th time) were used as follows: 33 34 log( α N ) = a + b×flux + c×flux 2 + d×flux 3 + τ i (9) 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-19 0 2000 4000 6000 8000 10000 12000-5 -4 -3 -2 ADS ALM AMS MMT PLM PMS

1 2 Figure E-2. Logarithm of normal cell replication r ate α N versus 3 formaldehyde flux (in units of pmol/mm 2 -hour) for the F344 rat nasal 4 epithelium. 5 6 Note: Values were derived from continuous unit leng th labeled data obtained by 7 Monticello et al. (1996) for four to six individual animals at all six nasal sites 8 (legend, sites as denoted in original paper) and fo ur exposure durations (13, 26, 9 52, 78 weeks). Each point represents a measurement for one rat, at one nasal site, 10 and at a given exposure time. Filled red circles: α N (flux) used in Conolly et al. 11 (2003) plotted at their averaged flux values (see t ext for details). Long dashed 12 lines: their linear interpolation between points. Short dashed line: their linear 13 extrapolation for flux value >9,340 pmol/mm 2 -hour (see Figure D-1 for full range 14 of extrapolation). Linear interpolation/extrapolat ion is shown with y-axis 15 transformed to logarithmic scale. 16 17 Source: Subramaniam et al. (2008). 18This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-20 Table E-3. Variation in number of cells across nas al sites in the F344 rat 1 2 Nasal site No. of cells

Anterior lateral meatus 976,000 Posterior lateral meatus 508,000 Anterior mid septum 184,000 Posterior mid septum 190,000 Anterior dorsal septum 128,000 Anterior medial maxilloturbinate 104,000 3 Note: Mean number of cells in each side of the nose of control animals. 4 5 Source: Monticello et al. (1996). 6This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-21 1



### Fluxlog10(alpha)



### 0 4000 8000 12000-5 -4 -3 -2 

### 13 weeks

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### 0 4000 8000 12000-5 -4 -3 -2 

### 26 weeks

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### 0 4000 8000 12000-5 -4 -3 -2 

### 52 weeks

Fluxlog10(alpha)



### 0 4000 8000 12000-5 -4 -3 -2 

### 78 weekslog10αlog10αlog10αlog10α 

### Fluxlog10(alpha)



### 0 4000 8000 12000-5 -4 -3 -2 

### 13 weeks

Fluxlog10(alpha)



### 0 4000 8000 12000-5 -4 -3 -2 

### 26 weeks

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### 0 4000 8000 12000-5 -4 -3 -2 

### 52 weeks

Fluxlog10(alpha)



### 0 4000 8000 12000-5 -4 -3 -2 

### 78 weekslog10αlog10αlog10αlog10α

2 3 Figure E-3A. Logarithm of normal cell replication rate versus formaldehyde 4 flux with simultaneous confidence limits for the AL M. 5 6 Source: Subramaniam et al. (2008). 7This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-22 

### Fluxlog10(alpha)



### 0 2000 6000-5 -4 -3 -2 

### 13 weeks

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### 0 2000 6000-5 -4 -3 -2 

### 26 weeks

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### 0 2000 6000-5 -4 -3 -2 

### 52 weeks

Fluxlog10(alpha)



### 0 2000 6000-5 -4 -3 -2 

### 78 weekslog10αlog10αlog10αlog10αlog10α 

### Fluxlog10(alpha)



### 0 2000 6000-5 -4 -3 -2 

### 13 weeks

Fluxlog10(alpha)



### 0 2000 6000-5 -4 -3 -2 

### 26 weeks

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### 0 2000 6000-5 -4 -3 -2 

### 52 weeks

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### 0 2000 6000-5 -4 -3 -2 

### 78 weekslog10αlog10αlog10αlog10αlog10α

1 Figure E-3B. Logarithm of normal cell replication rate versus formaldehyde 2 flux with simultaneous confidence limits for the PL M. 3 4 Source: Subramaniam et al. (2008). 5 6 7 The variability considered is that among animals an d any measurement error as well as 8 any other design-related components of error. Simu ltaneous 95% confidence limits for log( α N ) 9 were produced using Scheffe’s method (Snedecor and Cochran, 1980). These 95% confidence 10 limits span a range of 0.96 in log10( α N ), or nearly a 10-fold range in median α N . There is 11 additional dispersion in these data that does not a ppear in Figures E-2 and E-3; due to variation 12 in the number of cells per mm basement membrane, th e ratio of ULLI/LI had a spread of 13 approximately ±25% (0.45 to 0.71, mean 0.60) among the eight observations considered in 14 Monticello et al. (1990). Thus: 15This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-23 1. As suggested by Table E-3, and Figures E-2 and E-3, the shape of α N (flux) in Conolly et 1 al. (2003) is therefore likely to be very sensitive to how α N is weighted and averaged over 2
site and time. 3
2. Averaging of sites could significantly affect model calibration because of substantial 4 nonlinearity in model dependence on α N at the 10 and 15 ppm doses associated with high 5
cancer incidence. 6
3. Monticello et al. (1996) found a high correlation b etween tumor rate and the ULLI 7 weighted by the number of cells at a site. Therefo re, considering these factors while 8 regressing α N against tissue dose would be important in the conte xt of site differences in 9
tumor response. 10
4. A further complexity arises because of histologic c hanges and thickening that occurs in 11 the nasal epithelium over time in the higher dose g roups (Morgan, 1997), factors that are 12 likely to affect estimates of local formaldehyde fl ux, uptake, and replication rates 13 (Subramaniam et al., 2008). 14
15 Figure E-1 indicates that the time dependence in UL LI is significant. It would also be 16 useful to examine if the time dependence affects th e results of the time-to-tumor modeling and if 17 early temporal changes in replication rate are impo rtant to consider because of the generally 18
cumulative nature of cancer risk. The time window over which formaldehyde-induced cancer 19 risk is most influenced is not known, but the time weighting used by Conolly et al. (2003) assigns 20 a relatively low weight to labeling observed at ear ly times compared with those observed at later 21 time points. Finally, initiated cells are likely t o be replicating at higher rates than normal cells as 22 evidenced in several studies on premalignant lesion s (Coste et al., 1996; Dragan et al., 1995; 23
Rotstein et al.

, 1986). Therefore, LI data as an estimator of norm al cell replication rate would be 24 most reliable at early times when the mix of cells sampled include fewer preneoplastic or 25 neoplastic cells. 26 The more relevant question, therefore, is whether α N (flux) derived by a TWA over all 27 sites (as carried out by Conolly et al. [2003]) has an effect on low-dose risk estimates. Given the 28 above uncertainties and variability not characteriz ed in CIIT (1999) or in Conolly et al. (2003), it 29 is important to examine whether additional dose-res ponse curves that fit the cell replication data 30 reasonably well have an impact on estimated risk. Such sensitivity analyses are carried out in the 31 sections that follow. Clearly, a large number of a lternative α N (flux) can be developed. In 32 conjunction with the other uncertainties, mainly th e use of control data and alternative model 33 structures for initiated cell kinetics, the number of plausible clonal growth models to be exercised 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-24 soon require a prohibitively large investment of ti me. Therefore, detailed analyses were 1 restricted to a select set of biologically plausibl e choices of curves for α N (flux), which would 2 allow the identification of a range of plausible ri sk estimates (MLEs and statistical bounds). 3
4
E.3.2.4. Alternate dose-response curves for cell replicatio n. 5 Six alternative equations for α N were developed by regression analysis of the Monti cello 6 et al. (1996) ULLI data. The replicate data corres ponding to the summary data presented in this 7 paper were kindly provided to EPA by CIIT for furth er analyses. In each of these equations, α N is 8 expressed as a function of formaldehyde flux to nas al tissue (pmol/mm 2 -hour) and, in one 9 equation (eq 15) that explored time-dependence, the duration of exposure to formaldehyde in 10 weeks. All the graphs use flux/10,000 for the x-ax is, and the y-axis expresses log 10 α N . 11 One source of uncertainty in the cell proliferation dose response in Conolly et al. (2003) 12 is the large value of α max in the upward extrapolation (the cell replication r ate corresponding to 13 the upper end of the flux range at 15 ppm exposure) . The optimal value of α max was found by 14 Conolly et al. (2003) to be 0.0435 hour –1 . As noted by the authors, an argument in support of this 15 value is that it corresponds to the inverse of the fastest cell cycle times found in the literature. 16 Since the model treats the induced replication rate s as being time invariant, this means that cells 17 in the high-flux region(s) divide at the highest ce ll turnover rate ever observed throughout most 18
of an animal’s life. This does not seem to be biol ogically plausible (Subramaniam et al., 2008). 19 In the analysis, it was found that a 20% increase o r decrease in the estimated value for 20

α max degraded the fit to the tumor incidence data consi derably. Because of the interplay between 21 the parameters estimated by optimization, this sens itivity of the model to α max indicates that it is 22 necessary to examine if other plausible values of α max are also indicated by the data and to what 23 extent low dose estimates of risk are influenced by the uncertainty in its value. The need for such 24 an analysis is also indicated by Figure E-2. The v alue of α max (log 10 α max = −1.37) in the 25 modeling of Conolly et al. (2003) is roughly an ord er of magnitude greater than the values of 26

α

N (flux) at the highest flux levels in this figure. If the data pooled over all sites and times are to 27
be used for α N (flux), then, based solely on the trend in α N (flux) in Figure E-2, it appears unlikely 28
that α

N (flux) could increase up to this value of α max . Visually, these empirically derived data 29 collectively suggest that α N versus flux could be leveling off rather than incr easing 10-fold. 30 Therefore, as an alternative to the approach taken in Conolly et al. (2003) of estimating α max via 31 likelihood optimization against the tumor data, reg ressions of the empirical cell replication data 32 were used to extrapolate α N (flux) outside the range of observation (recognizin g the uncertainty 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-25 and model dependence that still results from extrap olating well outside the range of observed 1 data). 2 In fitting dose-response curves to the cell replica tion data, a functional form was used that 3 was flexible to allow a variety of monotonic and no n-monotonic shapes, with a parameter that 4 determined the asymptotic behavior of the dose-resp onse function. This allowed the 5 extrapolation by only relying on the empirical cell replication data without using an adjustable 6 parameter estimated by fitting to the tumor data. However, the plausible asymptotes obtained 7 spanned a large range. In one case below, the asym ptote suggested by the fit was judged to be 8 abnormally high. In this case, the α N versus flux curve was followed until the biologica l 9
maximum of α max (as given in Conolly et al. [2003]) was reached. 10 In three of the regression models below, the data w ere restricted to the earliest exposure 11 time (13 weeks) in Monticello et al. (1996) for whi ch the cell proliferation rate ( α N ) could be 12 calculated. The interest in using only the 13-week exposure time arises from observations 13 (Monticello et al., 1996, 1991) that at later times there were more frequent and severe histologic 14 changes, which may have altered formaldehyde uptake and cell proliferation response. 15 Consequently, given that the data in Monticello et al. (1991) for times earlier than 13 weeks 16 could not be utilized as explained earlier, the 13- week responses might better represent 17 proliferation rates for use in a two-stage model of the cancer process than the rest of the 18 Monticello et al.(1996) data. 19 Second, the LI data showed considerable variation a mong nasal sites, which may be 20 related to the variation in tumor response among si tes. Since the cell replication dose-response 21 curves used in the cancer model represent all of th e sites, it was attempted to include this 22 variation by weighting the regression by the relati ve cell populations at risk at each of the sites. 23
This was carried out for some of the models as stat ed below. The following models (denoted 24 N1–N6), shown in Figure E-4, have been included in addition to using the hockey stick- and J25 shaped curves in Conolly et al. (2003). Applicable equations are as follows: 26
27
N1 : Quadratic; monotone increasing in flux, derived f rom fit to all of the Monticello et al. (1996) 28
ULLI data. 29
30

α N

= Exp{–2.015 – 6.513 × Exp[– (6.735×10 –4 × flux) 2 ]} (10) 31
32
N2 : Linear-quadratic; decreasing in flux for small va lues of flux, derived from fit to all of the 33 Monticello et al. (1996) ULLI data. 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-26 xy

0.0 0.2 0.4 0.6 0.8 1.0-9 -8 -7 -6 Y = a + c * exp(- b1*X - sgn(b2)*[b2*X]^2 - ...), param = -2.01; -6.51; 0; 0.673

1 Figure E-4, N1. Various dose-response modeling of normal cell replication 2 rate. 3 Note: See text for definitions of N1–N6. N1: Quadr atic; monotone increasing in 4 flux, derived from fit to all of the Monticello et al. (1996) ULLI data. 5

weighted mean flux/10,000weighted mean log10(alpha)

0.0 0.2 0.4 0.6 0.8 1.0-4.0 -3.5 -3.0 -2.5 log10( alpha) = -2.565 -0.987 * exp{+2.188*X - (2.162 *X)^2 }

6 Figure E-4, N2: Various dose-response modeling of normal cell replication 7 rate. 8 Note: See text for definitions of N1–N6. N2: Linea r-quadratic; decreasing in flux 9 for small values of flux, derived from fit to all o f the Monticello et al. (1996) 10 ULLI data. 11This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-27 weighted mean flu x/10 ,000weig hted m ean alpha

0.0 0.2 0 .4 0. 6 0.8 1.0-8 -7 -6 Time = 13 weeks

1 Figure E-4, N3. Various dose-response modeling of normal cell replication 2 rate. 3 4 Note: See text for definitions of N1–N6. N3: Linea r-quadratic; decreasing in flux 5 for small values of flux, derived from fit to the 1 3-week Monticello et al. (1996) 6 ULLI data, using average flux over all sites for a given ppm exposure and 7 weighting regression by estimates of the numbers of cells at each of five sites. 8

flux/10,000log(alpha)

0.0 0.2 0.4 0.6 0.8 1.0 1.2-10 -8 -6 -4 Time = 13 weeks

9 Figure E-4, N4. Various dose-response modeling of normal cell replication 10 rate. 11 12 Note: See text for definitions of N1–N6. N4: Quadr atic; monotone increasing in 13 flux, derived from unweighted fit to 13-week Montic ello et al. (1996) ULLI data. 14This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-28 weighted mean flux/10,000weighted mean alpha

0.0 0.2 0.4 0.6 0.8 1.0-8 -7 -6 Time = 13 weeks

1 Figure E-4, N5. Various dose-response modeling of normal cell replication 2 rate. 3 4 Note: See text for definitions of N1–N6. N5: Linea r-quadratic-cubic; initially 5 increasing slightly with increasing flux, then decr easing slightly, and finally 6 increasing, derived from fit to 13-week Monticello et al. (1996) ULLI data, using 7 average flux over all sites for a given ppm exposur e and weighting regression by 8 estimates of the numbers of cells at each of five s ites. 9 10This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-29 1 Figure E-4, N6. Various dose-response modeling of normal cell replication 2 rate. 3 4 Note: See text for definitions of N1–N6. N6: Linea r-quadratic-cubic; initially 5 increasing slightly with increasing flux, then decr easing slightly, and finally 6 increasing, derived from fit to all Monticello et a l. (1996) ULLI data, using weeks 7 of exposure as a covariate. In this model, time wa s a regression (continuous) 8 predictor, not a class variable, and its coefficien t represents the decrease in log 10 9

α

N per week of exposure time. 10 11 12

α N

= Exp{–5.906 – 2.272 × Exp[2.188×10 –4 × flux – (2.162×10 –4 × flux ) 2 ]} (11) 13
14
N3 : Linear-quadratic; decreasing in flux for small va lues of flux, derived from fit to the 13-week 15 Monticello et al. (1996) ULLI data, using average f lux over all sites for a given ppm exposure 16 and weighting regression by estimates of the number s of cells at each of five sites. 17 xy

0.0 0.2 0.4 0.6 0.8 1.0 1.2-12 -8 -6 -4 Time = 13

xy

0.0 0.2 0.4 0.6 0.8 1.0 1.2-12 -8 -6 -4 Time = 26

xy

0.0 0.2 0.4 0.6 0.8 1.0 1.2-12 -8 -6 -4 Time = 52

xy

0.0 0.2 0.4 0.6 0.8 1.0 1.2-12 -8 -6 -4 Time = 78 All Sites, ~ Time + 2nd order in FluxThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-30 . α N = Exp{–5.274 – 2.792 × Exp[1.407×10 –4 × flux – (1.986×10 –4 × flux) 2 ]} (12) 1
2
N4 : Quadratic; monotone increasing in flux, derived f rom unweighted fit to 13-week Monticello 3 et al. (1996) ULLI data. 4
5

α N

= Exp{–3.858 – 4.809 × Exp[– (9.293×10 –5 × flux) 2 ]} (13) 6
7
N5 : Linear-quadratic-cubic; initially increasing slig htly with increasing flux, then decreasing 8 slightly, and finally increasing, derived from fit to 13-week Monticello et al. (1996) ULLI data, 9 using average flux over all sites for a given ppm e xposure and weighting regression by estimates 10
of the numbers of cells at each of five sites. 11
12

α N

= Exp{–5.488 – 2.755 × Exp[–7.808×10 –5 × flux + (2.349×10 –4 × flux) 2 13 – (2.166×10 –4 × flux) 3 ]} (14) 14
15
N6 : Linear-quadratic-cubic; initially increasing slig htly with increasing flux, then decreasing 16 slightly, and finally increasing, derived from fit to all Monticello et al. (1996) ULLI data, using 17 weeks of exposure as a covariate. In this model, t ime was a regression (continuous) predictor, 18 not a class variable, and its coefficient represent s the decrease in log 10 α N per week of exposure 19
time. 20
21

α N

= Exp{7.785×10 –3 × (weeks) – 5.722 – 2.501 × Exp[1.103×10 –4 × flux 22 – (7.223×10 –5 × flux) 2 – (1.575×10 –4 × flux) 3 ]} (15) 23
24 Further details on the above regressions are provi ded in the appendix. These regressions 25 of the cell replication data as well as the hockey- stick and J-shaped curves used by Conolly et al. 26 (2003) (shown in Figure D-1, Appendix D) are used n ext as inputs to the clonal growth model for 27

cancer. 28
29 E.3.3. Uncertainty in Model Specification of Initi ated Cell Replication and Death 30 E.3.3.1. Biological Inferences of Assumptions in C onolly et al. (2003) 31 The results of a two-stage MVK model are extremely sensitive to the values for initiated 32 cell division ( α I ) and death ( β I ) rates, particularly in the case of a sharply risi ng dose-response 33 curve as in the case of formaldehyde. The pool of cells used for obtaining the available LI data 34 (Monticello et al., 1996, 1991) consists of largely normal cells with perhaps increasing numbers 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-31 of initiated cells at higher exposure concentration s. As such there is no way of inferring the 1 division rates of initiated cells in the nasal epit helium, either spontaneous (baseline) or induced 2 by exposure to formaldehyde, from the available emp irical data. Conolly et al. (2003) considered 3

α I (flux) as a function of α N (flux) as given by eq 2 in Appendix D. As shown in Figure D-1 4 (Appendix D), α I is estimated in Conolly et al. (2003) to be very si milar to α N . That is, with eq 2 5
assumed to relate α I (flux) to α N (flux), a J- or hockey-shaped dose-response curve f or α N (flux) 6 results in a J or hockey shape for α I (flux). 7 The J shape for the TWA α N (flux) in Conolly et al. (2003) could plausibly be explained, 8 as suggested by the examples in Conolly and Lutz (2 004), by a mathematical superposition of 9 dose-response curves describing the effects of the inhibition of cell replication by the formation 10 of DPXs (Heck and Casanova, 1999) and cytotoxicity- induced regenerative replication (Conolly

, 11 2002). However, as explained earlier, there is con siderable uncertainty and variability, both 12 qualitative and quantitative, in the interpretation of the LI data and in the derivation of normal 13 cell replication rates from the ULLI data. While t he TWA values of ULLI indicate a J-shaped 14 dose response for some sites, as also concluded by Gaylor et al. (2004), this is not consistently 15 the case for all exposure times and sites as discus sed earlier. Notwithstanding this uncertainty 16 variability, and in the absence of data, the follow ing essential questions have a significant impact 17 on risk predictions and need resolution if the mode l structure in eq 2 is to be used in a 18 biologically based (or motivated) sense to predict risk outside observable data: 19
20

● Should mechanisms that might explain a J-shaped dos e response for normal cell 21 replication or a cytotoxicity-driven threshold in d ose response (as indicated by a hockey22 stick-shaped curve) be expected to prevail also for initiated cells? 23

● Would the formaldehyde flux at which the cell repli cation dose-response curve rises 24
above its baseline be similar in value for both nor mal and initiated cells as inferred by the 25 CIIT model in Figure D-1? 26
27 The next critical assumption was that made for β I (the death rate of initiated cells), 28 namely, β I (flux) = α N (flux) (eq 3). In Subramaniam et al. (2008), the r ationale for this 29 assumption in Conolly et al. (2003) is explained by assuming formaldehyde to be equally 30 cytotoxic to initiated and normal cells (since the mechanism is presumed to be via its general 31 chemical reactivity). In essence, this assumption brings the cytotoxic action of formaldehyde to 32 bear strongly on the parameterization of the CIIT m odel. 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-32 There are no data to evaluate the strength of thes e assumptions, so Subramaniam et al. 1 (2008) studied the plausibility of various inferenc es that arise as a result of these assumptions. 2 These inferences are only briefly listed here (see the paper for further discussion). 3
4

● For flux <27,975 pmol/mm 2 -hour, α I > α N (Figures D-1 & D-2 of Appendix D). 5 Qualitatively, this is in line with data on epithel ial and other tissue types with or without 6 exposure to specific chemicals. 7

● For higher flux levels, however, the model indicate s α I < α N (Figure D-2). There are no 8 data to shed further light on this inference. 9

● At these higher flux levels, initiated cells in the model die at a faster rate than they divide, 10 indicating the extinction of initiated cell clones in regions subject to these flux levels. 11 There are no data indicating formaldehyde to have t his effect. 12
13 In evaluating these inferences, Subramaniam et al. (2008) point to various data that 14 indicate that initiated cells represent distinctly different cell populations (from that of normal 15 cells) with regard to proliferation response (Ceder et al.

, 2007; Bull

, 2000; Schulte-Hermann et 16 al., 1997; Coste et al., 1996; Dragan et al., 1995) , have excess capacity to clear formaldehyde 17 and, in general, are considerably more resistant to cytotoxicity (such a resistance is manifested 18 variably as decreased ability of the toxicant to in duce cell death or to inhibit cell proliferation 19 compared to corresponding effects in normal cells), and may already have altered cell cycle 20 control; thus, the influence of formaldehyde on apo ptosis likely differs between normal and 21
initiated cells. 22 As concluded in Subramaniam et al. (2008), taken to gether, there is much data to suggest 23 that inferring α I

< α N at cytotoxic formaldehyde flux levels is problemat ic and that death rates of 24 initiated cells are likely to be very different fro m those of normal cells. In the absence of data to 25 indicate that eq 2 and eq 3 (in Appendix D) are bio logically reasonable approaches to link the 26
kinetics of initiated cells with those of normal ce lls, alternate model structures other than those 27 represented by these relationships considered by Co nolly et al. (2003) need to be explored, given 28 that the two-stage model is extremely sensitive to α I and β I . Such an evaluation needs to 29 primarily explore if the assumptions in eq 2 and eq 3 significantly impact the intended use of the 30 model, namely extrapolation to low-dose human cance r risk and the calculation of an upper 31 bound on human risk. Any such alternate model stru cture needs to provide a good fit to the time32
to-tumor data. 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-33 E.3.3.2. Plausible Alternative Assumptions for α I and β I 1 Therefore, in the additional sensitivity analysis p resented here, initiated cell kinetics are 2 considered to be independent of normal cells, and i nitiated cell proliferation cannot take a J shape 3 (motivated by the consideration that lower-than-bas eline turnover rate represents an increased 4 amount of DNA repair taking place, which may not be consistent with impaired DNA repair in 5 initiated cells). 6 Thus, two alternatives were considered to eq 2 for α I (flux): 7
8
I1: α I

= γ 1 × [1 + exp( γ 2 / γ 3 )] / {1 + exp[–(flux – γ 2 ) / γ 3 ]} (16) 9
10
I2: α I

= max[ α I (eq. I1), α NBasal ] (17) 11
12
Here γ 1 , γ 2 , and γ 3 are parameters estimated by fitting the cancer mode l to the rat bioassay 13 data. In eq 16, α I

increases monotonically with flux from a backgroun d level of γ 1 asymptotically 14 up to a maximum value of γ 1 × [1 + Exp( γ 2 / γ 3 )]. The choice of this functional form in eq 16 15 and eq 17 was considered in order to be parsimoniou s while at the same time allowing for a 16 flexible shape to the dose-response curve. The sig moidal curve allows for the possibility of a 17 slow rise in the curve at low dose and an asymptote

. 18 Equation 17 is a modification of equation 16 that r estricts the rate of division of initiated 19 cells to be at least as large as the spontaneous di vision rate of unexposed normal cells. There is 20 evidence to suggest (e.g., in the case of liver foc i) that initiated cells have a growth advantage 21 over normal cells, with or without exposure to spec ific chemicals (Ceder et al., 2007; Grasl22 Kraupp et al., 2000; Schulte-Hermann et al., 1999; Coste et al., 1996; Dragan et al., 1995). 23 In addition, in most runs, an upper bound ( α high ) is selected for both α N and α I . This value 24 is assumed to represent the largest biologically pl ausible rate of cell division. Following Conolly 25 et al. (2003), in most cases α high is set equal to 0.045 hours –1 . If a value of α I or α N computed 26 using one of the above formulas exceeded α high , the value of α high was used in the computation 27 rather than the value obtained by using the formula

. 28 As noted above, Conolly et al. (2003) set the rate of death for intermediate cells, β I , equal 29 to the division rate of normal cells, β I = α N . On the other hand, apoptotic rates and cell 30 proliferation rates are thought to be coupled (Schu lte-Hermann, 1999; Moolgavkar, 1994), so that 31
death rates of initiated cells would rise concomita ntly with an increase in their division rates 32 (Grasl-Kraupp et al., 2000; Schulte-Hermann et al., 1999). Therefore, as an alternative to the 33 Conolly et al. (2003) formulation, it is assumed th at the death rate of intermediate cells is 34 proportional to the division rate of intermediate c ells. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-34 β I = Κ β × α I (18) 1
2 where the constant of proportionality, Κ β , is an additional parameter to be estimated by 3 optimization against the tumor incidence data. Suc h an assumption has also been made by other 4 authors (Luebeck et al., 2000, 1995; Moolgavkar et al., 1993). 5 Since most of the SCCs in the rat bioassays occurre d in rats exposed to the highest 6 formaldehyde concentration (15 ppm), the data from this exposure level have a big impact on the 7 estimated model parameters. In most runs that inco rporated the 15 ppm data, the model 8 appeared, based on inspection of the KM plots, to f it the 15 ppm data quite well but to fit the 9 lower exposure data less well. Because of the high level of necrosis occurring at 15 ppm, it is 10 possible that the data at this exposure may not be particularly relevant to modeling the sharp 11 upward rise in the dose response at 6 ppm. Further more, the principal interest is in the 12 predictions of the model at lower levels to which h uman populations may be exposed. 13 Consequently, in order to improve the fit of the mo del at lower exposures, some of the alternative 14 models were constructed with the 15 ppm data omitte d. 15
16 E.3.4. Results of Sensitivity Analyses on α N , α I , and β I 17
E.3.4.1. Further Constraints 18 The number of models that might be constructed if a ll the possibilities listed above for 19

α

N , α I , and β

I are to be tried in a systematic manner clearly bec ome exponential and daunting. 20 (Optimally, it would have been desirable to elucida te the role of a specific modification while 21 keeping others unchanged to determine risk.) There fore, in order to carry out a viable sensitivity 22 analysis while at the same time examining the plaus ible range of risks resulting from variations 23 in parameters and model structures, various uncerta inties were combined in any given 24 simulation. By using the constraints described abo ve (eq 10–17 and associated text) for α I , β I , 25
and α

N , 19 models were obtained that provided similarly g ood fits to the time-to-tumor data 26 (which in some cases contained only five dose group s). 27 However, for many of these models, the optimal α I (flux) displayed a threshold in flux 28
even when the model utilized for α N (flux) was a monotonic increasing curve without a t hreshold 29 (i.e., model N4 for α N in Figure E-4). Indeed, if a thresholded dose-res ponse curve was plausible 30
for α I

based on arguments of cytotoxicity, then a thresho ld is all the more plausible for α N , and 31
such models are removed from consideration. 32 Secondly, the basal value of α I was required to be at least as large as the basal value of α N . 33 Another constraint was placed on the baseline initi ated cell replication rate. In the absence of 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-35 formaldehyde exposure, α I was not allowed to be greater than two or four tim es α N , even if such 1 models described the tumor data, including the cont rol data, very well. There are some data that 2 suggest that baseline initiated cells have a small growth advantage over normal cells, so a huge 3 advantage was thought to be biologically less plaus ible. 4
5
E.3.4.2. Sensitivity of Risk Estimates for the F344 Rat 6 Figure E-5 contains plots of the MLE of additional risk computed for the F344 rat at 7 formaldehyde exposures of 0.001, 0.01, 0.1, and 1 p pm for eight models. Two log-log plots are 8 provided. For those models for which the estimates of additional risk are all positive, the 9 additional risks are plotted (panel A), and, for th ose for which estimates of additional risk are 10 negative, the negatives of additional risks are plo tted (panel B). Only five dose groups were 11 considered (i.e., 15 ppm data omitted) for models 8 , 5, 15, and 16. Figure E-6 shows the dose12 response curves for α N and α I for these eight cases (panels A and B correspondin g to those in 13 Figure E-5). The primary results are as follows: 14
15
1. Among the models considered, negative values for ad ditional risk can arise only in 16 models in which the dose response for normal cells is J shaped. Thus, all of the models 17 with negative dose responses for risk have J-shaped dose responses for normal cells. 18 However, the converse is not necessarily true as ma y be noted from model 8. This model 19 has both a positive dose response for risk and a J- shaped dose response for normal cells. 20 In this case, the strong positive increase in respo nse of initiated cells at low dose was 21 sufficient to counteract the negative response of n ormal cells. 22
2. The risk estimates predicted by the different model s span a very large range for doses 23 below which no tumors were observed. This result p oints to large uncertainties in model 24 specification (how to relate the kinetics of normal and initiated cells) as well as in 25 parameter values. As mentioned above, the analysis does not attempt to separate the 26 influence of the different sources of uncertainty, so this range also incorporates the 27 uncertainty arising from the use of different contr ol data and that due to α max . 28
3. At the 10 ppb (0.01 ppm) concentration, MLE risks r ange from −4.0 ×10 –6 to +1.3 ×10 –7

. 29 At this dose, models that gave only positive risks resulted in a five orders of magnitude 30 risk range from 1.2 ×10 –12 to 1.3 ×10 –7 , while narrowing to a four orders of magnitude 31 risk range from 1.2 ×10 –10 to 1.3 ×10 –6 at the 0.1 ppm level. This narrowing continues as 32 exposure concentration increases, and the curves co alesce to substantially similar values 33 at 6 ppm and above (not shown). 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-36 1 Figure E-5A. BBDR models for the rat—models with p ositive added risk. 2 3 Note: All four models provide “similar” fits to tumor dat a (see text). 4 As in Conolly et al. (2003), Hockey stick α N and α I but using concurrent controls A l l 8 m o d e l s i n A & B p

r o v i d e s i m i l a r f i t s t o t u m o r d a t a

1.E-15 1.E-14 1.E-13 1.E-12 1.E-11 1.E-10 1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04

0.001 0.01 0.1 1 formaldehyde exposure conc (ppm)Added risk model 8 model 15 model 16
model 17

1.E-15 1.E-14 1.E-13 1.E-12 1.E-11 1.E-10 1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04

0.001 0.01 0.1 1 formaldehyde exposure conc (ppm)Added risk model 8 model 15 model 16
model 17This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-37 1 Figure E-5B. BBDR rat models resulting in negative added risk. 2 3 Note: All four models provide “similar” fits to tumor dat a (see text). 4 5 1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03 0.001 0.01 0.1 1NEGATIVE Added risk

model 3
model 4
model 5
model 13 Exposure conc (ppm)

As in Conolly et al. (2003), J-shape α N and α I but using Inhalation NTP controls added to concurrent Al l 8 m o d e l s i n A & B p r o v i d e s i m i l a r f i t s t o t u m o r d a t aThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-38 1

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/hr)

Flux (pmole/m m 2 /hr)

α N

α I

Model 8

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/hr)

Flux (p mole/m m 2 / hr) α N

α I

Model 15

0 10000 20000 30000 40000 1E- 4 1E- 3 0.01 0.1 1α(divisions/hr)

Flux (pm ole/mm 2 /hr) α N

α I

Model 16

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisio ns/hr)

Flux (pmole/m m 2 /hr) α N

α I

Model 17 Hockey stick α N and α I with concurrent controls All 8 models in A & B provide similar fits to tumor data

2 Figure E-6A. Models resulting in positive added ra t risk: Dose-response for 3 normal and initiated cell replication 4 5This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-39 1

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/h r)

Flux (pmole/m m 2 / hr) α N

α I

Model 3

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/hr)

Flux (pm ole /mm 2 /hr)

α N

α I

Model 4

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/hr)

Flux (pm ole/mm 2 /hr) α N

α I

Model 5

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1 1α(divisions/hr)

Flu x (pm ole/mm 2 /hr) α N

α I

Model 13 J-shape α N , α I in Conolly (2003) with Inhalation NTP + concurrent controls All 8 models in A & B provide similar fits to tumor data

2 Figure E-6B. Models resulting in negative added ra t risk: Dose-response for 3 normal and initiated cell replication 4 5 6 4. There does not seem to be any systematic effect on additional risk that depends on 7 whether the 15 ppm data are included in the analysi s. 8
5. For all of the models except models 13 and 17 in Fi gures E-5 & E-6, the additional risk 9 varies substantially linearly with exposure at low exposures between 0.001 and 1.0 ppm 10 (departing only to a small extent from linearity be tween 0.1 and 1.0 ppm). Models 13 and 11 17 (the models in Conolly et al. [2003] except for different control data being used) show 12 a quadratic dependence. 13
14 The various model choices presented in Figure E-5 all provided equally good fits to the 15 time-to-tumor data although within the context of a significant qualification. It was not possible 16This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-40 to simply use the log-likelihood values as a means of comparing the goodness-of-fit to the tumor 1
incidence data across these model choices. This is because many of the model choices differed 2
in the number of doses or in the number of control animals that were used, so the fits were 3 compared across such models only visually. Within model choices where such a comparison did 4 not pose a problem, the log-likelihood values did n ot differ statistically significantly. 5 Wherever results from the BBDR modeling are discus sed, values of added risk, as 6 opposed to extra risk, are reported. This is purel y for convenience in interpretation. Because of 7 the low background incidence, these values are only negligibly different from the corresponding 8 extra risk estimate. The final risk (or unit risk) estimates provided in this document are based on 9
extra risk estimates. 10
11
E.3.4.3. MOA Inferences Revisited 12
The ratio KMU/ μ Nbasal represents the added fractional probability of mut ation per cell 13 generation (

μ N – μ Nbasal )/

μ Nbasal due to unit concentration of DPXs. As discussed e arlier, this 14 parameter has a critical impact on the extrapolatio n as well as on inferring whether the mutagenic 15 action of formaldehyde is relevant to the quantitat ive risk characterization. In that prior 16 discussion, this ratio was found to be extremely se nsitive to the choice of historical control data. 17 The analysis indicates that, for a given set of con trol data that is used, uncertainties associated 18
with α

N and α I also have a large impact on this ratio. In that d iscussion, this ratio was infinite 19
when concurrent controls were used because the MLE value for μ Nbasal was found to be zero. The 20 use of these concurrent controls, however, does not necessarily imply that μ Nbasal will be 21
determined to be zero. In one of the scenarios exa mined in the sensitivity analysis, where 22 concurrent controls were used along with the combin ation of dose-response curves eq 13 for α N 23 (Figure E-4) and eq 17 for α I , the optimal value of the ratio KMU/ μ Nbasal was equal to 0.25. For 24 the models in Figure 5-13A, this ratio was 0 for al l except model 17 for which it was infinite. 25 For the models in Figure 5-13B with negative added risk, the ratio ranged from 0–4.5. For some 26
of those models where KMU/ μ Nbasal was finite, the upper confidence bound on this rati o was 27 found to increase by an order of magnitude from the MLE value. Thus, we conclude that the 28 modeling does not help resolve the debate as to the relevance of formaldehyde’s mutagenic 29 potential to its carcinogenicity. 30
31
E.3.4.4. Confidence Bounds: Model Uncertainty Versus Statist ical Uncertainty 32 For models 15 and 17 in Figures E-5A and E-6A, 90% CIs for additional risk were 33 calculated by using the profile likelihood method. Table E-4 compares the lower and upper 34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-41 confidence bounds for these models for 0.001 ppm, 0 .1 ppm (doses well below the range where 1 tumors were observed), and 6 ppm (the lowest dose w here tumors were observed) with the MLE 2 risk estimates at these doses. In both cases, thes e intervals were quite narrow compared with the 3 differences in risk predicted by different models i n Figure E-5. This suggests that model 4 uncertainty is of more consequence in the formaldeh yde animal model than is statistical 5 uncertainty. We also estimated confidence bounds u sing the bootstrap method for select models, 6 and determined that these estimates were in agreeme nt with the bounds calculated using the 7 profile likelihood method. These results are not p resented here. We return to the calculation of 8 confidence limits when determining points of depart ure (PODs). 9
10 Table E-4. Comparison of statistical confidence bo unds on added risk for 11 two models 12 13

Dose (ppm) Model Lower bound MLE Upper bound 0.001 Model 15 4.4 × 10 –9 1.3 × 10 –8 1.6 × 10 –8

Model 17 1.2 × 10 –14 1.2 × 10
– 14 1.3 × 10 –14

0.1 Model 15 4.5 × 10 –7 1.3 × 10 –6 1.7 × 10 –6

Model 17 1.2 × 10 –10 1.2 × 10
– 10 1.3 × 10 –10

6 Model 15 1.8 × 10 –2 2.1 × 10 –2 2.3 × 10 –2
Model 17 1.3 × 10 –2 1.8 × 10 –2 3.0 × 10 –2
14 15 In conclusion, it is demonstrated that the differen t formaldehyde clonal growth models 16 can fit the data about equally well and still produ ce considerable variation in additional risk and 17 biological inferences at low exposures. However, e ven with these large variations, the highest 18 MLE added risk for the F344 rat is only of the orde r of 10 –6 at 0.1 ppm. Thus, with regard to 19 calculating a reasonable upper bound that includes model and statistical uncertainty, the relevant 20 question is whether the range arising out of uncert ainties in the rat model amplifies when 21 extrapolated to the human. Thus, in Appendix F, th e human model in Conolly et al. (2004) will 22
be examined. 23This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE E-42 

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# Appendix FThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-2 APPENDIX F 1

2 SENSITIVITY ANALYSIS OF BBDR MODEL FOR FORMALDEHYDE INDUCED 3 RESPIRATORY CANCER IN HUMANS 4 5

F.1. MAJOR UNCERTAINTIES IN THE FORMALDEHYDE HUMAN BBDR MODEL 6 Subsequent to the BBDR model for modeling rat cance r, Conolly et al. (2004) developed 7 a corresponding model for humans for the purpose of extrapolating the risk to humans estimated 8 by the rat model. Also, rather than considering onl y nasal tumors, it is used to predict the risk of 9 all human respiratory tumors. The human model for formaldehyde carcinogenicity (Conolly et 10 al., 2004) is conceptually very similar to the rat model and follows the schematic in Figure 5-11 11 in Chapter 5. The model structure, notations, and c alibration are described in Appendix D. 12 Unlike the sensitivity analysis of the rat modeling where a number of issues were examined, a 13 much more restricted analysis will be presented her e for the sake of brevity. A more extensive 14 analysis was carried out initially that carried for ward several of the rat models to the human, and 15 the lessons learned from those exercises are in agr eement with the more restricted presentation 16 that follows. Table F-1 lists the major uncertainti es and assumptions in the human extrapolation 17 model in Conolly et al. (2004). 18This document is a draft for review purposes only a nd does not constitute Agency policy F-3 DRAFT—DO NOT CITE OR QUOTETable F-1. Summary of evaluation of major uncertainties in CIIT human BBDR modelAssumptions, approach, andcharacterization of input dataaRationale in Conolly et al. (2003) orCIIT (1999) EPA uncertainty evaluation Further elaborationCell division rates derived from ratlabeling data assumed applicable forhuman (except for assumingdifferent fraction of cells withreplicative potential).No equivalent LI data for human orguidance in extrapolating cell divisionrate across species.Enzymatic metabolism plays a role in mitosis.Therefore, we expect interspecies difference in celldivision rate. Basal cell division rates in humansexpected to be much more variable than in laboratoryanimals.Subramaniam et al.(2008)Development of PBPK model forDPX concentration in humanrespiratory lining.See text (Chap 3) See text (Chap 3) Chap 3; Conollyet al.(2000); Subramaniamet al. (2008); Klein etal. (2009)Anatomically realistic representationof nasal passages.Reduces uncertainty (over defaultcalculation carried out by averaging doseover entire nasal surface).Computer representation pertains to that of oneindividual (Caucasian male adult). Considerableinterindividual variability in nasal anatomy.Susceptible individuals even more variable.Kimbell et al. (2001a,b); Subramaniam etal. (2008, 1998)KMU/μNbasalis species invariant(used to estimate human).Human cells are more difficult totransform than rodent, both spontaneouslyand by exposure to formaldehyde.μNbasalis 0 when concurrent controls or inhalation NTPcontrols in time frame of concurrent bioassays areused.Leads to infinitely large KMU for human.Subramaniam et al.(2007); Crump et al.(2009, 2008).Conservative assumptions weremade. Results are conservative inthe face of model uncertainties.1) Hockey-stick dose-response forαNwasincluded even though TWA indicatedJ-shape.2) Overall respiratory tract cancerincidence data for human baseline rateswere used.3) Risk was evaluated at statistical upperbound of the proportionality parameterrelating DPXs to the probability ofmutation.CIIT result cannot be characterized as conservativeinthe face of model uncertainties and as a plausibleupperbound on human risk. Human model is unstable.Conolly et al. (2004);Subramaniam et al.(2007); Crump et al.(2009, 2008).aAssumptions in this table are in addition to thoselisted for the BBDR model for the F344 rat.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-4 F.2. SENSITIVITY ANALYSIS OF HUMAN BBDR MODELING 1 Crump et al. (2008) carried out a limited sensitivi ty analysis of the Conolly et al. (2004) 2 human model. This analysis was limited to evaluati ng the effect on the human model of the 3 following. These evaluations have been the subject of some debate in the literature and in 4 various conferences (Conolly, 2009; Conolly et al., 2009, 2008; Crump et al. 2009). 5
6
1. The use of the alternative sets of control data for the rat bioassay data that were 7 considered in the sensitivity analysis of the rat m odel (Subramaniam et al., 2007). 8
2. Minor perturbations in model assumptions regarding the effect of formaldehyde on the 9 division and death rates of initiated cells ( α I , β I ). Now, recall from the description of the 10 structure of the human model that one (of the two) adjustable parameter in the expression 11
for the human α I was determined from the model fit to the rat tumor incidence data while 12 the second parameter was determined from background rates of cancer incidence in the 13 human. Therefore, variations considered in α I were constrained to only those that (a) did 14 not meaningfully degrade the fit of the model to th e rat tumor incidence data and (b) were 15 in concordance with background rates in the human. Crump et al. (2008) also evaluated 16 these variations with respect to their biological p lausibility. The sensitivity analysis on 17 assumed initiated cell kinetics was thought to be p articularly important since there were 18 no data to even crudely inform the kinetics of init iated cells for use in the models, even in 19 rats, and the two-stage clonal expansion model is v ery sensitive to initiated cell kinetics 20 (Gaylor and Zheng, 1996; Crump, 1994a, b). 21
22 Crump et al. (2008) note that, since the purpose o f their analysis was to carry out a 23 sensitivity analysis, in order to illustrate certai n points, only risks to the general U.S. population 24 from constant lifetime exposure to various levels o f formaldehyde under the Conolly et al. (2004) 25 environmental scenario (8 hours/day sleeping, 8 hou rs/day sitting, and 8 hours/day engaged in 26 light activity) are considered. Fits based on the hockey-stick and J-shape models were identical, 27 and, of the three estimated parameters (μbasal, mul tb, and D), only the estimate of μbasal 28
differed between the two models. 29
30
F.2.1. Effect of background Rates of Nasal Tumors in Rats on Human Risk Estimates 31 Crump et al. (2008) quantitatively evaluated the im pact of different control groups on 32
estimates of additional human risk as follows: 33
34This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-5 1. Concurrent controls plus all NTP controls:, the sam e as used by Conolly et al. (2004); 1
2. Concurrent controls plus controls from NTP inhalati on studies; 2
3. Only concurrent controls; 3
4. Each set of control data was applied with both the J shape and hockey-stick models in 4 Conolly et al. (2004) for α N (flux) and α I (flux) for a total of six analyses;. 5
5. Uncertainties associated with α N or α I are not addressed. Parameters α max , multfc, and 6 KMU were estimated in exactly the same manner as in Conolly et al. (2004). 7
8 Crump et al. (2008) present the following dose-res ponse predictions of additional risk in 9 humans from constant lifetime exposure to various l evels of formaldehyde arising from 10 exercising the above six cases. Their plots are re produced in Figure F-1 , where the 11 corresponding curves based on Conolly et al. (2004) are also shown for comparison. 12 The lowest dotted curve in Figure F-1 represents th e highest estimates of human risk 13 developed by Conolly et al. (2004). This resulted from use of the hockey-stick model for cell 14 division rates in conjunction with the statistical upper bound for the parameter KMU. As 15 indicated by the downward block arrows in the figur e, their corresponding estimates based on the 16 J-shape model were all negative for exposures below 1 ppm. 17 Consider next the solid curves in the figure, which show predicted MLE added risks that 18 were positive and less than 0.5. Crump et al. (200 8) next examined the added risk obtained when 19 the MLE estimate of ( KMU / μ basal ) in these cases is replaced by the 95% upper bound of this 20 parameter ratio. The upper bound risk estimates in Conolly et al. (2004) were calculated in a 21 similar manner (but using all NTP historical contro ls). Except for minor differences, risk 22 estimates corresponding to such an upper bound and using all NTP controls were very similar in 23 the two efforts (Crump et al., 2008; Conolly et al. , 2004). 24 Figure F-1 shows that the choice of controls to inc lude in the rat model can make an 25
enormous difference in estimates of additional huma n risk. For the J-shaped model for cell 26 replication rate both estimates based on the MLE an d those based on the 95% upper bound on 27
KMU

/ μ basal are negative for formaldehyde exposures below 1 pp m. However, when only 28 concurrent controls are used in the model in Crump et al. (2008), the MLE from the J-shape 29 model is positive and is more than three orders of magnitude higher than the highest estimates 30 obtained by Conolly et al. (2004). Using only conc urrent controls, estimates based on the 95% 31 upper bound on KMU / μ basal are unboundedly large (block arrows at the top of the figure). For the 32 hockey-stick shaped model for cell replication rate , when all NTP controls are used, the estimates 33 based on the MLEs are zero for exposures less than about 0.5 ppm. If only inhalation controls 34 are added, the MLEs are about seven times larger th an the Conolly et al. (2004) upper bound 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-6 estimates, and the estimates based on the 95% upper bound on KMU

/ μ basal are about 50 times 1 larger than the Conolly et al. (2004) estimates. I f only concurrent controls are used, both the 2 MLE estimates and those based on the 95% upper boun d on KMU / μ basal are unboundedly large. 3
4

1E-3 0.01 0.1 1

1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 0.01 0.1Additional Risk

Formaldehyde Exposure (ppm) Hockey, Concurrent Controls, MLE and 95% UB; J-Shape, Concurrent Controls, 95% UB

J-Shape, All NTP Controls, MLE and 95% UB; J-Shape, Inh. NTP Controls, MLE and 95% UB; Conolly et al. (2004) J-Shape UB Hockey, All NTP Controls, MLE Hockey, All NTP Controls, 95% UB Hockey, Inh. NTP Controls, 95% UB J-Shape, Concurrent Controls, MLE

Conolly et al . (2004), Hockey UB Hockey, Inh. NTP Controls, MLE

1E-3 0.01 0.1 1 1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 0.01 0.1Additional Risk

Formaldehyde Exposure (ppm) Hockey, Concurrent Controls, MLE and 95% UB; J-Shape, Concurrent Controls, 95% UB

J-Shape, All NTP Controls, MLE and 95% UB; J-Shape, Inh. NTP Controls, MLE and 95% UB; Conolly et al. (2004) J-Shape UB Hockey, All NTP Controls, MLE Hockey, All NTP Controls, 95% UB Hockey, Inh. NTP Controls, 95% UB J-Shape, Concurrent Controls, MLE

Conolly et al . (2004), Hockey UB Hockey, Inh. NTP Controls, MLE

5 6 Figure F-1. Effect of choice of NTP bioassays for historical controls on 7 human risk. 8 9 Note: Estimates of additional human risk of respira tory cancer by age 80 from 10 lifetime exposure to formaldehyde are obtained by u sing different control groups 11 of rats. 12 13 Source: Crump et al. (2008). 14 15 16
F.2.2. Alternative Assumptions Regarding the Rate of Repli cation of Initiated Cells 17 For the human model, Conolly et al. (2004) made the same assumptions for relating 18

α I (flux) and β I (flux) to α N (flux) as in their rat model (Conolly et al., 2003) . That is, these 19 quantities were related by using eq 2 and eq 3. As discussed in the context of the rat modeling, 20 by extending the shape of these curves to humans, t he authors’ model brings the cytotoxic action 21 of formaldehyde to bear strongly on the parameteriz ation of the human model as well. 22This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-7 In the sensitivity analyses of the rat modeling, it was concluded that other biologically 1 plausible assumptions for α I and β I resulted in several orders of magnitude variations in the low 2 dose risk relative to those obtained by models base d on the assumptions in Conolly et al. (2003) 3 but that the highest risks were nonetheless of the order of 10 –6 at the 10 ppb level. This section 4 examines how these uncertainties in the rat model p ropagate to the human model. 5 Crump et al. (2008) made minor modifications to the assumed division rates of initiated 6 cells in Conolly et al. (2004), while all other asp ects of the model and input data were kept 7 unchanged. Two alternatives were considered for ea ch of the J-shape and hockey-stick models. 8 Figure F-2 shows the hockey-stick model for initiat ed cells in rats. In the first modification to the 9 hockey-stick model (hockey-stick Mod 1), rather tha n having a threshold at a flux of 10 1,240 pmol/m 2 -hour, the division rate increases linearly with in creasing flux until the graph 11 intersects the original curve at 4,500 pmol/m 2 -hour, where it then assumes the same value as in 12 the original curve for larger values of flux. The second modification (hockey-stick Mod 2) is 13 similar, except the modified curve intersects the o riginal curve at a flux of 3,000 pmol/m 2 -hour. 14
15

0 10000 20000 30000 40000 0.00 0.01 0.02 0.03 0.04 0.05

0 1000 2000 3000 4000 5000

0.0000 0.0005 0.0010alphaI

Formaldehyde Flux [pmol/(m 2 -h)] Mod 1

Mod 2

0 10000 20000 30000 40000 0.00 0.01 0.02 0.03 0.04 0.05

0 1000 2000 3000 4000 5000

0.0000 0.0005 0.0010alphaI

Formaldehyde Flux [pmol/(m 2 -h)] Mod 1

Mod 2

16 Figure F-2. Conolly et al. (2003) hockey-stick mod el for division rates of 17 initiated cells in rats and two modified models. 18 19 Source: Crump et al. (2008). 20 21 22 Figure F-3 shows the rat J-shape model for initiate d cells. In the first modification to this 23 dose response (J-shape Mod 1), rather than having a J shape, the division rate of initiated cells 24This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-8 remains constant at the basal value until the origi nal curve rises above the basal value and has the 1 same value as the original curve for larger values of flux. In the second modification (J-shape 2 Mod 2), the J shape is retained but somewhat mitiga ted. In this modification, the division rate 3 initially decreases in a linear manner similar to t hat of the original model but with a less negative 4 slope until it intersects the original curve at a f lux of 1,240 μm/m 2 -hour, where it then follows 5 the original curve for higher values of flux. 6
7

0 10000 20000 30000 40000 0.00 0.01 0.02 0.03 0.04 0.05

0 1000 2000 3000 4000 5000

0.0000 0.0005 0.0010alphaI

Formaldehyde Flux [pmol/(m 2 -h)]

Mod 1

Mod 2

0 10000 20000 30000 40000 0.00 0.01 0.02 0.03 0.04 0.05

0 1000 2000 3000 4000 5000

0.0000 0.0005 0.0010alphaI

Formaldehyde Flux [pmol/(m 2 -h)]

Mod 1

Mod 2

8 9 Figure F-3. Conolly et al. (2003) J-shape model fo r division rates of initiated 10 cells in rats and two modified models. 11 12 Source: Crump et al. (2008). 13 14 15 Since the first constraint on the variation in α Iwas in concordance with the rat time-to16 tumor incidence data, Crump et al. (2008) applied e ach of the modified models in Figures F-2 17 and F-3 to the version of the formaldehyde models i n Subramaniam et al. (2007) that employed 18 all NTP controls and the hockey-stick curve for α N . These authors restricted their analysis to this 19 case since their stated purpose was only a sensitiv ity analysis as opposed to developing alternate 20 credible risk estimates. Figure F-4 reproduces (fr om Crump et al. [2008]) curves of the 21 cumulative probability of a rat dying from a nasal SCC by a given age for bioassay exposure 22 groups of 6, 10, and 15 ppm. For comparison purpos es, the corresponding KM (nonparametric) 23 estimates of the probability of death from a nasal tumor are also shown. Three sets of 24 probabilities are graphed: the original unmodified one and the ones obtained by using hockey25 stick Mod 1 and Mod 2. Crump et al. (2008) state t hat the changes in the tumor probability 26 resulting from these modifications are so slight th at the three models cannot be readily 27This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-9 distinguished in this graph. 4 Thus, the modifications considered to the models for the division 1 rates of initiated cells caused an inconsequential change in the fit of the model-predicted tumor 2
incidence to the animal tumor data. 3
4

0 200 400 600 800 1000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Probability of Response

Time (days) 15 ppm

10 ppm

6 ppm

0 200 400 600 800 1000 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Probability of Response

Time (days) 15 ppm

10 ppm

6 ppm

5 6 Figure F-4. Very similar model estimates of probab ility of fatal tumor in rats 7 for three models in Figure F-2. 8 9 Note: The differences are visually indistinguishable. Mo dels were derived from 10 the implementation of Conolly et al. (2003) with th e hockey-stick curves for 11

α I (flux) and α N (flux) and variants derived from modifications (Mod 1 and Mod 2, 12 Figure F-2) to α I (flux). Model probabilities are compared to KM est imates. The 13 three sets of model estimates are so similar that t hey cannot be distinguished on 14 this graph. 15 16 Source: Crump et al. (2008). 17

4 The largest change in the tumor probability result ing from this modification for any dose group and a ny age up through 900 days was found to be less than 0.002, a change so small that it would be impossible to det ect, even in the largest bioassays ever conducted. The changes in t umor probability resulting from the other modificat ions described earlier were found to be even smaller. These compa risons were made in Crump et al. (2008) without re- optimizing the likelihood. The authors note that re-optimizati on of the model subsequent to the variations would have made the fit of modified models even better.This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-10 The above modifications did not affect the basal ra te of cell division in the model and 1
likewise had no effect on the fit to the human back ground data (Crump et al., 2008). 2 Crump et al. (2008) noted that, although the thresh old model for initiated cells in Conolly 3 et al. (2003) was replaced with a model that had a small positive slope at the origin, the resulting 4 curves, hockey-stick Mod 1 and hockey-stick Mod 2, could have been shifted slightly to the right 5 along the flux axis in order to introduce a thresho ld for α I without materially affecting the risk 6 estimates resulting from these modified curves. Th us, “the assumption of a linear no-threshold 7 response is not an essential feature of the modific ations to the hockey-stick model; clearly 8 threshold models exist that would produce essential ly the same effect” (Crump et al. 2008). 9
10
F.2.3. Biological Plausibility of Alternate Assumptions 11 These very small variations made to the α I in Conolly et al. (2003) are seen to be 12 consistent with the tumor-incidence data (just demo nstrated above); small compared with the 13 variability and uncertainty in the cell replication rates characterized from the available empirical 14 data (at the formaldehyde flux where α I was varied); supported (qualitatively) by limited data, 15 suggesting increased cell proliferation at doses be low cytotoxic; perturbations that one should 16 expect on any dose response derived from laboratory animal data because of human population 17 variability in cell replication; and biologically p lausible because cell cycle control in initiated 18 cells is likely to be disrupted. 19 The averaged cell replication rate constants as tab ulated in Table 1 of Conolly et al. 20 (2003) and shown by the red curve in Figure E-2 of Appendix E (for various exposure 21 concentrations and corresponding average formaldehy de flux values in the F344 rat nose) 22 demonstrate an increase over baseline values only a t exposure concentrations of 6 ppm and 23 higher. Increased cell proliferation at these conc entrations of formaldehyde, whether transient or 24 sustained, have been associated in the literature w ith epithelial response to the cytotoxic 25 properties of formaldehyde (Conolly, 2002; Monticel lo and Morgan, 1997; Monticello et al., 26 1996, 1991). The labeling data are considered to s how a lack of cytotoxicity and regenerative 27 cell proliferation in the F344 rat at exposures of 2 ppm and below (Conolly, 2002). In the 28 Conolly et al. (2003) modeling, it is further assum ed that the formaldehyde flux levels at which 29 cell replication exceeds baseline rates remain esse ntially unchanged when extrapolated to the 30
human and for initiated cells for the rat as well a s the human. These assumptions need to be first 31 viewed in the context of the uncertainty and variab ility in the data on normal cells discussed 32
earlier. 33This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-11 Arguments for a hockey-stick or J shape over the ba ckground have been made in the 1 literature for sustained and chronic cell replicati on rates; the analyses of the cell replication data 2 show that the data are not consistently (over each site and time) indicative of a hockey-stick or 3 J shape as the best representation of the data. Th is uncertainty is particularly prominent when 4 examining the cell replication data at the 13-week exposure time and the pooled data from the 5 PLM nasal site from Monticello et al. (1996) (Figur es E-1 [dotted curve], E-3B, E-4 of Appendix 6 E). The earliest exposure time in this experiment was at 13 weeks, and the 13-week cell 7 replication data appear to be more representative o f a monotonic increasing dose response 8 without a threshold. It is possible that early tim es are of more relevance to the carcinogenesis as 9 well as for considering typical (short duration) hu man exposures. 10 For initiated cells, there are no data on which to evaluate the modifications made to these 11 rates. However, some perspective can be gained by comparing them to the variability in the 12
division rates obtained from the data on normal cel ls used to construct the formaldehyde model. 13 As shown in Figure E-2 and discussed further in Sub ramaniam et al. (2008), these data show 14 roughly an order of magnitude variation in the cell replication rate at a given flux. As part of a 15 statistical evaluation of these data, a standard de viation of 0.32 was calculated for the log16
transforms of individual measurements of division r ates of normal cells. By comparison, the 17 maximum change in the log-transform division rate o f initiated cells resulting from hockey-stick 18 Mod 2 was only 0.20, and the average change would b e considerably smaller. Thus, although 19 there are no data for initiated cells, it can be sa id that the modifications introduced in Crump et 20 al. (2008) for initiated cells are extremely small in comparison to the dispersion in the data for 21
normal cells. 22 Subramaniam et al. (2008) also point to some additi onal, albeit limited, data, suggesting 23 that exposure to formaldehyde could result in incre ased cell replication at doses far below those 24 that are considered to be cytotoxic. Tyihak et al. (2001) treated different human cell lines in 25 culture to various doses (0.1–10 mM) of formaldehyd e and found that the mitotic index increased 26 at the lowest dose of 0.1 mM. These findings consi dered along with human population 27 variability and susceptibility (for example, polymo rphisms in ADH3 [Hedberg et al., 2001]) 28 indicate that it is necessary to consider the possi bility of small increases in the human α I over 29 baseline levels at exposures well below those at wh ich cytotoxicity-driven proliferative response 30 is thought to occur. 31 Heck and Casanova (1999) have provided arguments to explain that the formation of 32 DPXs by formaldehyde leads to inhibition of cell re plication (i.e., if this effect alone is 33 considered, normal cell replication rate of the exp osed cells would be less than the baseline rate). 34 However, this hypothesis was posed for normal cells . Subramaniam et al. (2008) argue that if an 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-12 initiated cell is created by a specific mutation th at impairs cell cycle control, the effect would be 1 to mitigate the DPX-induced inhibition in cell repl ication, either partially or fully, depending on 2 the extent to which the cell cycle control has been disrupted. In the absence of data on initiated 3 cells, the above argument provided biological motiv ation to the modification applied to the 4 J-shape model for cell division (Crump et al. 2008)

. 5 Thus, the previous paragraphs suggest that the chan ges made in the analysis in Crump et 6 al. (2008) to the assumption by Conolly et al. (200 3) regarding the dose response for the division 7 rate of initiated cells are not implausible. 8
9
F.2.4. Effect of Alternate Assumptions for Initiated Cell Kinetics on Human Risk Estimates 10 Figure F-5 contains graphs of the additional human risks estimated (in Crump et al. 11 [2008]) by applying these modified models for α I and using all NTP controls, compared with 12 those obtained by using the original Conolly et al. (2004) model. Each of the four modified 13 models presents a very different picture from that of Conolly et al. (2004). At low exposures, 14 these risks are three to four orders of magnitude l arger than the largest estimates obtained by 15 Conolly et al. (2004). 16 These results have been criticized by Conolly et al . (2009) as being unrealistically large 17 and above the realm of any epidemiologic estimate f or formaldehyde SCC. Thus, they argue that 18 the parameter adjustments made in Crump et al. (200 8) are inappropriate. Crump et al. (2009) 19 rebutted these points by arguing that the purpose o f their work was not to provide a more reliable 20 or plausible model but to carry out a sensitivity a nalysis. They argued that the changes made to 21 the model (in their analyses) were reasonable since they did not violate any biological constraints 22 or the available data. Further, they pointed out t hat “by appropriately mitigating the small 23 modifications [they] made to the division rates of initiated cells, the model [would] provide any 24 desired risk ranging from that estimated by the ori ginal model up to risks 1,000-fold larger than 25 the conservative estimate in Conolly et al. (2004). ” 26 Crump et al. (2008) also evaluated the assumption i n eq 3 of the CIIT modeling 27 pertaining to initiated cell death rates ( β I ) by making small changes to β I . They report that they 28 obtained similarly large values for estimates of ad ditional human risk at low exposures. 29 Obtaining reliable data on cell death rates in the nasal epithelium appears to be an unusually 30 difficult proposition (Hester et al., 2003; Montice llo and Morgan, 1997), and, even if data are 31 obtained, they are likely to be extremely variable. 32This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-13 1E-3 0.01 0.1 1 1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 0.01 0.1 1Additional Risk

Formaldehyde Exposure (ppm) Unmodified Hockey Stick Model Hockey Mod 2

Hockey Mod 1 J-Shape Mod 1

Conolly et al . (2004) “Risk Conservative” Hockey Stick Model J-Shape Mod 2

1E-3 0.01 0.1 1 1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 0.01 0.1 1Additional Risk

Formaldehyde Exposure (ppm) Unmodified Hockey Stick Model Hockey Mod 2

Hockey Mod 1 J-Shape Mod 1

Conolly et al . (2004) “Risk Conservative” Hockey Stick Model J-Shape Mod 2

1 2 Figure F-5. Graphs of the additional human risks e stimated by applying 3 these modified models for α I , using all NTP controls, compared to those 4 obtained using the original Conolly et al. (2004) m odel. 5 6 Source: Crump et al. (2008). 7This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE F-14 

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# Appendix GThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-2 APPENDIX G 1 2 EVALUATION OF THE CANCER DOSE-RESPONSE MODELING 3 OF GENOMIC DATA FOR FORMALDEHYDE RISK ASSESSMENT 4 5 G.1. MAJOR CONCLUSIONS IN ANDERSEN ET AL. (2008) 6 In Chapter 4, the gene microarray data from animal studies on formaldehyde (Andersen et 7 al., 2008; Thomas et al., 2007) were described. Th e analysis of these animal high throughput 8 data and the conclusions reached in these two groun dbreaking papers were closely examined for 9 use in this assessment. Studies on high throughput animal data provide a wealth of information 10 that helps further understanding of the relevant me chanisms. However, such studies have 11 generally not made quantitative bottom-line inferen ces that inform low dose human risk. The 12 above-mentioned studies are a notable exception due to the breadth of their conclusions on low 13 dose MOAs, their pioneering application of the benc hmark dose (BMD) methodology to 14 genomic data, their use of BMD-response analysis th at identified dose estimates at which specific 15 cellular processes were significantly altered, the fact that they were accompanied by 16 recommendation in the literature urging use of thes e results in setting exposure standards for 17 formaldehyde (Daston, 2008). 18 We focus here on the conclusions in these papers wi th regard to modeling the cancer 19 dose-response for formaldehyde. In addition to sup porting our disposition of these analyses for 20 this assessment, this write-up serves the purpose o f exemplifying critical issues that need to be 21
considered for the future. 22 The overall BMD determined in Andersen et al. (200 8) for all genes with significant 23 dose-response averaged 6.4 ppm. These analyses ind icated a general progression with the lowest 24 BMD values (i.e., the most sensitive epithelial res ponses) for extracellular and cell membrane 25 components and higher BMD values for intracellular processes. Overall, these authors 26
concluded that 27 28

 Genomic changes, including those suggestive of muta genic effects, did not temporally 29 precede or occur at lower doses than phenotypic cha nges in the tissue 30

 Genomic changes were no more sensitive than tissue responses 31

 Formaldehyde, being an endogenous chemical, is well handled until some threshold is 32 achieved. Above these doses, toxicity rapidly ensue s with concomitant genomic and 33 histologic changes. 34

 Linear extrapolations, or extrapolations that speci fy similar MOAs at high and low doses 35 would be inappropriate. 36This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-3 These findings were judged to have significant impl ications on the debated MOA for 1 formaldehyde carcinogenicity, confirming results fr om earlier bioassays and dose-response 2 modeling that the mutagenicity of formaldehyde was too weak to be of relevance to its 3 carcinogenicity. Daston (2008) judged the method i n these efforts to be extremely sensitive and 4 therefore suited to examining whether responses at the molecular level take place at doses below 5 which frank adverse effects occur. Daston (2008) a rgued that “... if there are pleiotropic effects 6 at lower exposure levels that would elicit a differ ent profile of gene expression, those genes 7 would not go unnoticed” and thus concluded that “th e gene expression data confirm that the 8 responses are not linear at low doses.” 9 In the analyses that follow, we point to some signi ficant quantitative factors that impact 10
on these conclusions. 11
12
G.2. USE OF MULTIPLE FILTERS ON THE DATA 13 The analyses in these papers involved the following sequence of data filters. 14
15
1. Gene probe sets that differed in expression in resp onse to treatment were identified by 16 one-way analysis of variance. Probability values w ere adjusted for multiple comparisons 17 by using a false discovery rate of 5%. 18
2. Next, in addition to the above statistical filter, the output was further screened by 19 selecting only those genes that exhibited a change from the control group that was greater 20 than or equal to 1.5-fold (logarithmic). 21
3. The gene probe sets that demonstrated significant d ose-response behavior were then 22 matched to their corresponding biological process a nd molecular function gene ontology 23 (GO) categories (considering only those involving m ore than three genes) and grouped 24 into process categories such as cell division, DNA repair, cellular proliferation, apoptosis, 25 and related molecular function categories. 26
27 A large number of genes are expressed in these stu dies; therefore, clearly some 28 appropriate filter needs to be used for meaningful interpretation of the vast database. Tissue 29 pathology served as a phenotypic anchor for the int erpretation of microarray results, and the 30 genomic study confirmed (and improved on) the quali tative and quantitative understanding 31 derived from the histopathology and observation of frank effects. It is possible that the 32 combination of filters used by these authors is ade quate for an inquiry into some mechanisms 33 associated with the specific phenotypic effects. H owever, the studies reached bottom-line 34 conclusions with regard to the low-dose MOA and app roach to be considered for quantitative 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-4 extrapolation. These conclusions necessarily invol ve questions as to whether there were gene 1 expression changes at low dose and at early exposur e times that may be relevant to initiating 2 carcinogenesis and finally as to whether there is a threshold in dose associated with 3 formaldehyde carcinogenesis. However, collectively , the three filters employed in these studies 4 likely constitute overly stringent criteria, taking away the resolution needed to observe critical 5 gene changes needed to delineate low dose effects. An indication that this may indeed be the 6 case can be seen by examining the correlations in t heir findings with the observed trend in the 7 data on DPXs formed by formaldehyde. This is detail ed in the following section. 8
9
G.3. DATA FOR LOW -DOSE CANCER RESPONSE 10 A significant finding in Thomas et al. (2007) is th at BMD estimates for the GO categories 11 applicable to cell proliferation and DNA damage wer e similar to values obtained for cell labeling 12
indices and DPXs in earlier studies and to BMD esti mates obtained for the onset of nasal tumors. 13 The mean BMD for the GO category of “positive regul ation of cell proliferation” was 5.7 ppm; 14 in comparison, Schlosser et al. (2003) obtained a 1 0% BMD of 4.9 ppm for the cell labeling 15 index. The GO category associated with “response t o DNA damage stimulus,” seen as a 16 genomic correlate to a mutagenic effect, had a mean BMD of 6.31 ppm. Thomas et al. (2007) 17 compare this finding with significant increase at 6 ppm of DPXs following a 3-hour exposure in 18 the study by Casanova et al. (1994). The formation and repair of DPXs have been considered to 19 be one of the potential mechanisms associated with the genotoxic action of formaldehyde 20 (Conolly et al., 2003, 2000). Based on earlier wor k in the same laboratory (Conolly et al., 2004, 21 2003; Conolly, 2002), Slikker et al. (2004) conclud ed that there is a dose threshold (at about 22 6 ppm) to formaldehyde carcinogenicity and that the putative mutagenic action of formaldehyde 23 is not relevant to its carcinogenicity. Therefore, the finding that a significant genomic response 24 (e.g., induction of DNA repair genes) is not observ ed at doses lower than those that induce 25 tumors in rodent bioassays is seen by these authors (Andersen et al., 2008; Daston, 2008; 26 Thomas et al., 2007) to further buttress the above conclusions related to the mode of action for 27 formaldehyde-induced respiratory cancer. 28 However, phenotypic anchoring to the DPX data drawn only from Casanova et al. (1994) 29
misses critical low-dose data that informs mode of action. In an earlier study, Casanova et al. 30 (1989) observed statistically significantly elevate d (over controls) levels of DPXs at 2 ppm and a 31 trend towards elevated DPXs at 0.7 ppm. In analysi s of low-dose data, the trend in the dose32 response is critically important because data inher ently lack the power to establish statistical 33 significance. Furthermore, the two studies by Casan ova and coworkers are different in some 34 respects. The earlier study was a 6-hour exposure, while the later study was a 3-hour study; thus, 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-5 on this account alone, it appears more relevant to compare with the older study. Exposures in the 1 earlier study were additionally at 0.3 and 10 ppm, thus affording a lower exposure concentration. 2 In the earlier study, tissue from the whole nose w as analyzed, whereas in the later study tissue 3 from two specific regions was obtained from the “hi gh” tumor (Level II) and “low” tumor 4 regions. Together, these data suggest that DPXs oc cur at exposure concentrations considerably 5 lower than those that elicited transcriptional chan ges. One possible explanation is that the 6
increase in DPXs was not sufficient to induce DNA r epair genes. Alternatively, these 7 discrepancies may be due to the stringent filters a nd the low statistical power of the Andersen et 8 al. (2008) study. These disparities between the ge ne array study and the DPXs question the 9 ability of the studies in Andersen et al. (2008) an d Thomas et al. (2007) to inform the presence or 10 absence of a mutational MOA for formaldehyde, and i n essence, to inform the low-dose response 11 curve for formaldehyde-induced cancer. 12 In another instance, Andersen et al. (2008) clearly stated that no genes were significantly 13 altered by exposure to 0.7 ppm, yet they state that there was “a trend toward altered expression at 14 0.7 ppm” in some genes with U and inverted U shape dose-responses (Figures 4 and 5 of their 15 paper). While these changes may not be statisticall y significant, they could be biologically 16 significant. 17
18
G.4. DIFFICULTIES IN INTERPRETING THE BENCHMARK MO DELING 19 The benchmark analyses are summarized in Thomas et al. (2007) as average BMD 20 estimates for genes in a given GO that were statist ically significantly dose related. The 21 benchmark modeling was then used by the authors to identify that the dose below individual 22 cellular processes was judged to be “not altered.” 23 The BMD definition used by these authors is quite stringent: it defines an effect so that 24 only 0.005 of controls will be considered affected and sets the BMR corresponding to this dose at 25
0.105. The net effect is that the BMD is the air l evel, such that the increase in the mean response 26 is 1.349 × standard deviation. This is essentially an arbitrary definition. For comparison, if 0.05 27
of controls are considered affected and the BMR is set at 0.1 (common values that are applied to 28 whole animal data), the BMD is the air level such t hat the increase in the mean response is 29 0.608 × standard deviation. Thus, if this definiti on had been used (as is traditionally the case), 30
the BMD estimates would all be 2.2 times smaller th an those obtained by Schlosser et al. (2003). 31 Furthermore, the analysis assumes equal variance i n all dose groups. Thus, further consideration 32 of these issues with regard to interpretation of th e BMR obtained from these studies is needed 33 before it can be used in regulatory exposure settin g. Secondly, lower confidence limits on the 34
BMDs need to be derived for the data in Andersen et al. (2008). 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-6 G.5. STATISTICAL SENSITIVITY OF THE DATA FOR DOSE- RESPONSE 1 Another cautionary note pertains to the qualificat ion of gene array studies as being 2 extremely sensitive. Such a qualification should a ctually refer to the fact that only tiny amounts 3 of mRNA are needed, that is, the sensitivity of the assay per se for measuring gene expression. 4 However, this should not be confused with the sensi tivity needed to identify the very small dose5 related changes at low dose. Andersen et al. (2008 ) reports on results of studies that involve 6 small numbers of animals in each dose group (five o r eight). Despite the limited power in such 7 studies, the paper equates the absence of a statist ically significant effect with no effect. This 8 limitation is generally true of studies of the dose responses of changes in gene expression 9 conducted to date; they have generally relied on ve ry few animals ( ≤ 10 per dose group). Since 10 there will likely always be background amounts of g ene expression, quantifying the dose 11 response requires statistically significant changes in gene expression as a function of dose. If the 12 genomic data involve even fewer animals per group t han the histopathological data, they have 13 even less power to delineate the dose response; in particular, whether there is a threshold at low 14 exposures. This is illustrated by the example in F igure G-1 of the dose responses for epithelial 15 hyperplasia (Andersen et al. 2008, lesion 2). These appear equally consistent with both a 16 threshold at around 1 ppm and a linear response dow n to zero. 17 18 G.6. LENGTH OF THE STUDY AND STOCHASTIC EVENTS 19 Another significant consideration with regard to MO A conclusions that are pertinent to 20 the disease process is the length of the study, 15 days. If formaldehyde-induced tumor formation 21 is a stochastic process (e.g., genotoxicity), then exposure of a small number of animals to low 22 concentrations for 15 days may not be long enough t o detect changes that might occur under 23 long-term exposure scenarios. 24 Relatedly, it has been suggested that gene (and pro tein) expression is a stochastic process 25 whereby steady state gene expression obeys Poisson statistics (i.e. distribution of rare events), 26 and that events of interest may occur in a single c ell or small number of cells in which larger 27 tissue samples can average out such stochastic even ts and prevent the detection of non-average 28 behavior (Quakenbush, 2007). Given the implied diff iculty in such an analysis, duration of 29 exposure may be one of the most tenable ways of add ressing whether a chemical increases the 30 probability of an adverse response. 31This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-7 1 Figure G-1: Graphs of epithelial hyperplasia (Lesio n 2) versus formaldehyde 2 concentration (ppm) with 95% confidence intervals ( with linear fit by eye) 3 4 5 G.7. OVERALL CONCLUSION 6 We believe our analyses of the presentations in And ersen et al. (2008) and Daston (2008) 7 are generally useful with regard to future developm ents in quantitative analyses of genomic data 8 if they are to be of relevance to risk assessment. For risk assessment, rather than focusing on 9 what responses are statistically significant, an an alysis should focus on 1) what range of values of 10 critical parameters (e.g., gene expression) are con sistent with the data, and 2) what these values 11 imply for whole animal risk. This is of course, an extremely difficult proposition because we do 12 0

0.2 0.4 0.6 0.8 1

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Day 15This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE G-8 not know nearly enough about how changes in genes q uantitatively affect whole animal risk, or 1 even which genes are important. 2

# Appendix HThis document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-2 APPENDIX H 1 2 EXPERT PANEL CONSULTATION ON QUANTITATIVE EVALUATIO N OF ANIMAL 3 TOXICOLOGY DATA FOR ANALYZING CANCER RISK DUE TO IN HALED 4 FORMALDEHYDE 5 6 The National Center for Environmental Assessment co nvened an expert panel of 7 scientists for advice on evaluating available appro aches for incorporating biological information 8 in analyzing animal tumor data for assessing cancer risk due to inhaled formaldehyde. This 9 Appendix pertains to the major deliberations and re sults of that meeting and is divided into three 10
sections. 11
12
A. Scope and Agenda of Meeting on Quantitative Evaluat ion of Animal Toxicology Data for 13 Analyzing Cancer Risk due to Inhaled Formaldehyde. October 28 & 29, 2004. 14
15
B. Summary of Consultative Meeting on CIIT Formaldehyd e Model. October 28 & 29, 16
2004. 17
18
C. Meeting Report from Dr. Rory B. Conolly 19This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-3 A. Scope and Agenda of Meeting on Quantitative Eval uation of Animal Toxicology 1 Data for Analyzing Cancer Risk due to Inhaled Forma ldehyde 2 October 28 & 29, 2004. Washington, DC. 3 4 This meeting is to assist EPA in evaluating availab le approaches for incorporating biological 5 information in analyzing animal tumor data for asse ssing cancer risk due to inhaled 6 formaldehyde. The CIIT Centers for Health Research (CIIT) has published a novel risk 7 assessment that links site-specific predictions of flux using computational fluid dynamics (CFD) 8 modeling with a two-stage clonal growth model of ca ncer to analyze nasal tumor incidence in 9 two rodent bioassays. The rodent models are used w ith corresponding human models for low10 dose extrapolation of cancer risk to people. 11 12 Key predictions of the CIIT effort are a zero maxim um likelihood estimate of the probability of 13 formaldehyde-induced mutation per cell generation i n the rat and a de minimus additional 14 lifetime risk in non-smokers due to continuous envi ronmental exposure below 0.2 ppm. The 15 National Center for Environmental Assessment is car rying out sensitivity analyses and examining 16 variations of the CIIT model in order to understand the implications of the model structure and 17 parameters on model predictions. In this meeting, we wish to focus on the strengths and key 18 uncertainties of this model, the extent to which as sumptions in the CIIT model are supported by 19 biological data, and examine the impact of uncertai nty and variability on the overall quantitative 20 risk characterization. 21 22 Broadly, the discussions will focus on the followin g areas: 23 24

• Impact of uncertainties in dosimetry on human risk estimates 25

• Uncertainties in the use of experimental data on la beling index 26

• The model structure related to initiated cells and DNA protein cross-links 27

• Considerations of time-to-tumor in the clonal growt h modeling 28

• Inferences and information on the role of mutation and cytotoxicity in estimating human risk 29

• Relative merits of benchmark dose modeling vs. the 2-stage clonal growth model 30 31 Discussions on Mode of Action are expected to be an integral part of several of the sessions. 32 Therefore a specific time-slot is not set aside for this purpose. 33 34 The meeting will have a panel discussion format. T here will be no formal presentations unless 35 necessary to elucidate an issue. Various attachmen ts referred to in the Agenda below, as well as 36 the relevant manuscripts will be sent separately. 37 38 Specifically, we suggest the following issues upon which to focus the discussion in the above 39 areas, and approximate time frames and discussion l eads, although discussants should feel free to 40 bring up other critical issues. 41 42This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-4 I. Introduction and purpose of discussion 1 Peter Preuss. 2 9:00 AM, Oct 28 3 4 II. Impact of uncertainties in dosimetry on risk es timates 5 Lead discussant: Linda Hanna 6 9:15 - 11 AM Oct 28 7 8 Boundary conditions 9 The CFD modeling specified a mass transfer coeffici ent as a boundary condition on the 10 nasal lining, adjusting the value of this coefficie nt on the “absorbing” portion of the 11 lining so as to match simulated overall uptake in t he rat nose to the experimentally 12 determined average overall uptake. This value was t hen used for the corresponding 13 human nasal lining. Are these boundary conditions appropriate surrogates for the 14 underlying pharmacokinetics, including saturation i n metabolism and mucociliary 15 clearance, particularly with reference to humans? 16 17 Turbulence 18 Turbulent flow has been seen to occur in experiment al models of the human nose at some 19 of the higher flow rates at which the CFD models we re used in CIIT = s assessment. It is 20 not likely that the CIIT CFD model can reliably ide ntify signatures of transition to 21 turbulent behavior. Turbulent flow can significantl y alter regional uptake patterns. 22 Additionally, significant mass balance errors were seen at the higher flow rates in the 23 human flow models. Discuss if these are likely to impact significantly on risk estimates. 24 25 Interindividual variability 26 The CIIT assessment has focused on the nasal anatom y of a single individual. Discuss 27 the implications of interindividual variations in n asal anatomy on the population 28 distribution in risk. 29 30 III

. Uncertainties in the use of experimental data on la beling index 31 Lead discussant: George Lucier 32 11AM – 11:45 AM, 1:00 - 3:15 PM Oct 28 33 34 Cell-replication rate and its relationship to flux is a critical determinant of risk. Therefore 35 uncertainties and variability in measurement of the unit length labeling index and its use in the 36 CIIT clonal growth modeling need to be characterize d. 37 38 1. Discuss the strengths, uncertainties and limitation s associated with estimating cell 39 replication rates from the unit length labeling ind ex (ULLI). 40

a. For example, a constant ratio of the measured ULLI to the labeling index (LI) that 41 is used in the model is assumed. Is it valid to as sume this ratio to be constant 42 across nasal sites, dose and exposure time. 43 b. How uncertain is this ratio? 44 45This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-5 2. Considering the large patterns of variability in th e ULLI data, discuss the validity of using 1 ULLI averaged over site and exposure times. 2

a. The averaging loses information on the sequential e ffect of change with 3 time, and on significant differences among sites. 4 b. How sensitive is the clonal growth modeling result to these variations in the dose5 response function for cell replication rates vs. fl ux to the tissue? A discussion of 6 this question in this session is intended to serve as input to later deliberations on 7 the issue. 8 9 3. Discuss the validity of combining data collected in different experiments using different 10 labeling methods, and the validity of estimating ce ll replication rates from LI or ULLI 11 measured in a single pulse labeling experiment. 12 13 See attachment C: “ULLI Dose-Response Modeling and Statistical Analysis” for a 14 discussion of these issues, and Moolgavkar and Lueb eck (1992). 15 16 IV

. Model Structure: Birth and death rates for Initiate d cells, Role of DPX 17 Lead discussant: Kenny Crump 18 3:30 - 6:00 PM Oct 28. 19 20 Parameters for initiated cells 21
22 1. The CIIT analysis of ULLI data allows for a virtual threshold in dose in the replication 23 rate of normal cells. Discuss the validity of ascr ibing such a behavior to initiated cells 24 considering the sensitivity of 2-stage model result s to the initiated cell replication rates. 25 26 2. Discuss the treatment of death rate for initiated c ells in the model (set equal to birth rate 27 of normal cells in Conolly et al., 2003) and implic ations for confidence in model 28 predictions. 29 30 Also see Attachment A (memo from Rory Conolly) and Attachment D (EPA discussion of 31 CIIT clonal growth modeling and some sensitivity an alyses. . .) 32
33 Treatment of DNA protein cross-links (DPX) in clona l expansion model 34
35 3. FORMALDEHDYE-INDUCED MUTATION IS MODELED AS TAKING 36 PLACE ONLY WHILE DPX ARE IN PLACE WITH DPX UNDERGOI NG 37 RAPID REPAIR. DISCUSS THE POSSIBILITY OF PERSISTEN T GENETIC 38 DAMAGE THAT EXTENDS BEYOND THE DPX HALF-LIFE AND 39 ENHANCES MUTATION. HOW MIGHT THIS ISSUE BE INCLUDE D IN 40 THE MODEL STRUCTURE? 41 42This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-6 V. Considerations of time-to-tumor in the CIIT clon al growth modeling 1 Lead discussant: Christopher Portier 2 8:30 – 11:00 AM, Oct 29. 3 4 1. A number of issues affect likelihood values and the model fit to the time-to-tumor data. 5 Discuss assumptions in the treatment of time-to-tum or in the CIIT clonal expansion 6 model, and their impact on parameter estimates. Fo r example, 7 8

a. Results in Conolly et al. (2003, 2004) are derived considering all tumors to be 9 fatal. Note in this context that serially sacrifice d animals have been combined 10 with those experiencing mortality B the effect of this is visible as irregularities in 11 the time-to-tumor curve. 12 13 b. How is the time variability in ULLI likely to impac t on the time-to14 tumor predictions? 15 16 2. Long delay times are predicted by the model for obs ervation of detectable tumor. Is this 17 compatible with the assumption of rapidly fatal tum ors? 18 19 3. Discuss the weight to be given to differences in li kelihood when comparing with 20 variations on the Conolly et al (2003) model struct ure such as in Attachment A or D. 21 22 VI

. Inferences on the role of formaldehyde-induced muta tion and cell proliferation 23 Lead discussant: Dale Hattis 24 11:15 – 12:00 PM, 1:00 – 4:00 PM, Oct 29. 25 26 1. The model structure in Conolly et al. (2003) predic ts a zero maximum likelihood estimate 27 for the constant of proportionality (KMU) linking D PX to the probability of 28 formaldehyde-induced mutation per cell generation. Examine the strength of this 29 conclusion, and the extent to which an insignifican t probability of formaldehyde-induced 30 mutation per cell generation is supported by data. 31 32 2. Discuss the biological relevance and validity of mo del-estimated parameters, particularly 33 in the context of low-dose predictions. 34

a. Discuss possible avenues to validate CIIT cancer mo del predictions. 35 36 3. Discuss the validity of using cell replication rate s determined for the rat to predict human 37 risk in a population. 38 39 4. In the face of uncertainties, are the results in Co nolly et al. (2003, 2004) conservative in 40 the sense of overpredicting risk? 41

a. Discuss the extent to which sensitivity analyses ha ve addressed this issue and the 42 extent to which sensitivity analyses can speak to t he strength of the model. [See 43 Attachments A: Memo from Conolly, and D: EPA discus sion of CIIT clonal 44 growth modeling and some sensitivity analyses . . . ]. 45This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-7 VII. Benchmark Dose Modeling 1 Lead discussant: Kenny Crump 2 4:15 – 5:30 PM, Oct 29. 3 4 Discuss the relative merits of using a benchmark do se approach that incorporates 5 biological modeling (such as estimating flux to tis sue or DPX levels) as compared with 6 the CIIT 2-stage model for cancer. ( See attachment E and Schlosser et al., 2003.) 7 8This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-8 B. Summary of Consultative Meeting on CIIT Formalde hyde Model 1 October 28 & 29 2004, NCEA, Washington, DC 2
3 Date: November 10, 2004 4 Ravi P. Subramaniam, Ph.D. 5 Quantitative Risk Methods Group 6 National Center for Environmental Assessment, ORD, US EPA 7
8 This is a broad summary of the most important issue s at the formaldehyde meeting. 9 It was generally felt by consultants that the broad framework of the approach adopted by 10 CIIT, namely the use of a two-stage model for cance r, the linking of localized flux to cell 11 replication rates and DPX concentration, and the ex pression of formaldehyde-induced mutation 12 as a linear function of DPX, was reasonable. 13 Potential errors in the dosimetry modeling were see n not to have a significant effect on 14 risk estimates. The boundary conditions used were discussed to be a reasonable representation of 15 the pharmacokinetics for both rats and humans. The discussion on the impact of interindividual 16 variability of nasal anatomy was not particularly c onclusive. It was determined that there was 17 likely to be much less variability in reactive gas uptake than that seen in particulates. 18
Crucial errors were however identified on several f ronts in the manner in which the clonal 19 growth model had been implemented in the CIIT effor t. Dr. Portier felt that the calculation of 20 probability was seriously flawed on account of lump ing serially-sacrificed animals and animals 21 that died of tumor together, while at the same time assuming rapid fatality of all tumors. This 22 was seen to significantly alter the calculation of tumor probability (the shape of the dose23 response curve), and his insight was that a correct ion was likely to allow for a substantially 24 higher value for the probability of formaldehyde-in duced mutation at low-dose. The best 25 estimate for this probability is now zero in the mo del. Drs. Crump, Portier and Hattis argued that 26 replacing this estimate by an upper confidence boun d on KMU (the coefficient determining the 27 role of DPX in the probability of mutation per cell generation), keeping other structural problems 28 in the model unexplored, or other parameters fixed, would not be enough. There was a 29 discussion on the need to provide confidence bounds on risk determined by allowing all the 30 parameters to vary. Drs. Crump and Hattis (and Por tier?) felt such an estimate would be very 31
different from that calculated based on individual parameters. 32 Drs. Crump, Hattis and Portier urged us not to be c onstrained by the optimal likelihood 33 values of a single plausible model, and underscored the need to explore a variety of biologically 34 reasonable model structures as a requisite for util izing such a model in risk assessment. 35This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-9 Likelihood was seen to be an inadequate expression of what is to be considered an optimal model 1 (okay only for comparing models that were nested, e tc.). These models should allow the 2 expression of variability and uncertainty in the da ta, as well as in underlying assumptions in 3 model specification. Dr. Crump (and Hattis also?) felt that alternate model structures, if 4 explored, could potentially lead to risk estimates, for the range below the observed data, that 5 were higher by several thousands. 6 Dr. Crump cautioned that extrapolating to human usi ng the hockey or J-shaped cell 7 replication curve used in the rodent carried with i t a large uncertainty that had not been 8 characterized in the Conolly modeling. 9 Dr. Portier expressed concern over the manner in wh ich historical and concurrent controls 10 were lumped together. The thrust of Portier’s comm ents was that such a combination of controls 11 was generally not done. The large number of histor ical controls was likely to significantly bias 12 the impact of the bioassay data in determining the time-to-tumor fits. 13 There were various discussions about the pros and c ons of constructing a joint likelihood 14 of the cell replication data and the tumor data, an d the weights to be assigned to the separate 15 likelihoods. This was considered to be problematic by Dr. Portier. 16 Dr. Crump’s opinion was that the Conolly model, and those explored by EPA, fit the 17 tumor data poorly, and that an improved description of the tumor data was needed before the 18 model could be used for low-dose and inter-species extrapolation. 19 Drs. Lucier and Hattis placed emphasis on including the early-time cell replication data 20 instead of constructing a time-weighted average. I t was felt that the two Monticello experiments 21 could not be combined together as in Conolly et al. Dr. Lucier felt that the early-time data would 22 have a greater impact in the progression of carcino genesis. In general, the effect of “time” was 23 considered to have significant effects on the time- to-tumor modeling, and they urged us to 24 incorporate time-dependent terms in the modeling. CIIT expressed willingness to provide the 25 original cell replication data to us for further an alysis. (Further discussion on this matter did not 26 take place in the open forum.) 27 Preliminary indications are, particularly based on Dr. Portier’s insight, that the currently28 held “de-minimus” picture of low-dose risk, as expr essed in Conolly et al. (2004), is not likely to 29 be the case if these various suggestions are incorp orated in the modeling. 30This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-10 C. Meeting Report from Dr. Rory B. Conolly 1 2 Rory B. Conolly, Sc.D., D.A.B.T. 3 106 Michael’s Way 4 Chapel Hill, NC 27516 5 Voice: 919.929.2258 6 7 July 24, 2005 8 9 Dr. Bobette Norse 10 ORAU Procurement - MS-04 11 P.O. Box 117 12 Oak Ridge, TN 37831-0117 13 Phone: 865-576-3051 14 Fax: 865-576-9385 15 16 Dear Dr. Nourse, 17 18 The following is my final written report on the fo rmaldehyde review meeting held at the 19 U.S. EPA in Washington, D.C. on 28-29 October, 2004

. 20 EPA provided no guiding philosophical statement abo ut the criteria being used to 21 evaluate the CIIT assessment. The new Guidelines f or Carcinogen Assessment state that the 22 preferred default approach is to use a biologically based model. Since the key components of the 23 CIIT assessment have been published in the peer-rev iewed literature and have undergone several 24 peer reviews other than the current NCEA effort, on e has to wonder just how high the bar is set 25 for acceptance of biologically based assessments. Given the time and resources expended on the 26 CIIT assessment and the richness of the supporting data base, I find it difficult to imagine what 27 an acceptable biologically-based assessment might l ook like if in the end the CIIT assessment is 28 deemed not acceptable by NCEA. If this is in fact the outcome it will have major implications 29 for the likelihood that anyone will be willing to c ommit the significant resources needed to 30 develop of these kinds of risk assessment models. 31 The documents provided in advance of the October 20 04 review meeting were 32 collectively a discussion of uncertainty about the CIIT work. With respect to the clonal growth 33 model, however, no new risk predictions were provid ed, so there was no way to judge how the 34 uncertainties that NCEA identified might impact pre dicted risk. Evaluation of the significance of 35 "uncertainties" when the impact of the uncertaintie s on the predicted risk is not known is itself an 36 uncertain process. 37 A related concern is that there did not seem to be any consideration of the historical 38 context of the CIIT assessment. EPA developed form aldehyde assessments in 1987 and 1991. 39 The 1987 assessment used ppm as the input and the L MS model for the dose-response prediction. 40 The 1991 assessment used DPX as a dosimeter and th e LMS model. BMD assessments have 41 since become available from other sources such as P aul Schlosser's work. The risk predictions of 42 the BMD models are similar to the 1991 LMS assessme nt. Both the DPX-LMS and BMD 43 assessments predicted somewhat less risk than the 1 987 assessment, establishing the trend of less 44 risk with increased incorporation of relevant data. I have always argued (probably initially at the 45This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-11 1998 Ottawa review) that the historical context is the appropriate context for evaluating the CIIT 1 clonal growth model. For a "level playing field" t he uncertainties of the 1987 and 1991 2 assessments, and of the more recent BMD models, sho uld be analyzed to the same degree as the 3 clonal growth model. Does NCEA think that, because the LMS and BMD approaches used 4 structurally simpler dose-response models and much more limited data inputs, they are less 5 uncertain? The NCEA analysis seemed to be implying that use of more data and of a biologically 6 more realistic model structure actually makes the C IIT approach more uncertain than the LMS 7 and BMD approaches. I encourage NCEA to consider h ow uncertainties that can be evaluated 8 explicitly in the structurally rich CIIT model comp are to hidden uncertainties in the simpler 9 models, where the hidden uncertainties encompass, f or example: 10 11 1. Missing or incomplete descriptions of the regional dosimetry of formaldehyde. 12 2. Lack of simultaneous incorporation of the directly mutagenic and 13 cytolethal/regenerative proliferation modes of acti on. 14 3. Lack of explicit consideration of the multistage na ture of cancer. 15 4. Lack of consideration of the growth kinetics of ini tiated cell populations 16 5. Lack of evaluation of the measured J-shaped dose re sponse for regenerative cellular 17 proliferation. 18 19 A careful, balanced comparison of the CIIT assessme nt with the previous assessments along 20 these lines would be informative with respect to th e suitability of the CIIT assessment as the 21 basis for a new IRIS listing for formaldehyde. 22 A further concern involves the peer-review of the C IIT formaldehyde assessment held in 23 Ottawa in 1998. This review was sponsored by the U .S. EPA and Health Canada and involved 24 what was arguably a world-class review panel. The CIIT assessment was not in its final form at 25 that time, though we did provide a detailed descrip tion of the overall approach and the specific 26 methods we were using to generate dose-response pre dictions. The 1999 CIIT document and the 27 subsequent peer-reviewed publications are responsiv e to the comments and suggestions raised by 28 the reviewers. My concern is that no information w as provided on the role that Ottawa review 29 plays in the ongoing review of the CIIT formaldehyd e assessment by NCEA. Should the October 30 2004 review be viewed as standing on the shoulders of the 1998 review or as being in parallel to 31 it? It was not at all clear to me that the October 2004 review in any way utilized the judgments 32 of the 1998 review. It seems that the 2004 review was more of a parallel effort and that the 1998 33 review was ignored and was effectively a waste of t ime and money. I would like to have some 34 clear understanding of how the 2004 review effort s hould be viewed relative to that of 1998. 35 In closing, let me reiterate that while the detaile d examination of the CIIT formaldehyde 36 assessment is laudable, this examination should be conduced with an eye to the historical context 37 of formaldehyde risk assessment on the one had and, on the other hand, to a concern for 38 encouraging, and not discouraging, development of b iologically based risk assessment models. 39 40 Sincerely yours, 41 42 Rory B. Conolly, Sc.D., D.A.B.T. 43This document is a draft for review purposes only a nd does not constitute Agency policy. DRAFT—DO NOT CITE OR QUOTE H-12 

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