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 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 review under applicable information quality 

guidelines. It has not been formally disseminated by EPA. It does not represent and should not 

be construed to represent any Agency determination or policy. It is being circulated for review of 

its technical accuracy and science policy implications.

 U.S. Environmental Protection Agency 

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

IV-ii DRAFT—DO NOT CITE OR QUOTE 

DISCLAIMER

 This document is a preliminary draft for review purposes only. This information is 

distributed solely for the purpose of pre-dissemination peer review under applicable information 

quality guidelines. It has not been formally disseminated by EPA. It does not represent and 

should not be construed to represent any Agency determination or policy. Mention of trade 

names or commercial products does not constitute endorsement or recommendation for use. 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-iii DRAFT—DO NOT CITE OR QUOTE 

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 Production............................. 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 Animal 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 Effects at the 

Portal of Entry ................................................................................ 3-10 

3.2.3.2. Variability in the Nasal Dosimetry of Formaldehyde 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: Differentiating Covalent 

Binding and Metabolic Incorporation............................................. 3-16 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-iv DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

3.4. METABOLISM .......................................................................................................... 3-20 

3.4.1. In Vitro and In Vivo Characterization of Formaldehyde Metabolism............ 3-20 

3.4.2. Formaldehyde Exposure and Perturbation of Metabolic 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 Breath...................................... 3-27 

3.5.3. Formaldehyde Excretion in Human Urine...................................................... 3-31 

3.6. MODELING THE TOXICOKINETICS OF FORMALDEHYDE AND DPX.......... 3-32 

3.6.1. Motivation ...................................................................................................... 3-32 

3.6.2. Species Differences in Anatomy: Consequences for Gas Transport and 

Risk................................................................................................................ 3-34 

3.6.3. Modeling Formaldehyde Uptake in Nasal Passages....................................... 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 Respiratory Tract.................. 3-42 

3.6.5. Uncertainties in Formaldehyde Dosimetry Modeling .................................... 3-44 

3.6.5.1. Verification of Predicted Flow Profiles........................................... 3-44 

3.6.5.2. Level of Confidence in Formaldehyde Uptake Simulations............ 3-45 

3.6.6. PBPK Modeling of DNA Protein Cross-Links (DPXs) 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 Rhesus DPX Data.............. 3-51 

3.6.7. Uncertainty in Prediction of Human DPX Concentrations ............................ 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 Irritation)................................ 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.................................................................. 4-42 

4.1.1.7. Developmental and Reproductive Toxicity..................................... 4-45 

4.1.1.8. Oral Exposure Effects on the Gastrointestinal Tract ....................... 4-56 

4.1.1.9. Summary: Noncarcinogenic Hazard in Humans ............................. 4-56 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-v DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

4.1.2. Cancer Health Effects..................................................................................... 4-57 

4.1.2.1. Respiratory Tract Cancer................................................................. 4-57 

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 Toxicity................................ 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................................................... 4-324 

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 ..................................................................... 4-344 

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 Systems............................ 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 NONCARCINOGENIC

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 Inform MOAs ..................... 4-385 

4.4.6. Noncancer Modes of Actions ....................................................................... 4-387 

4.4.7. Immunotoxicity ............................................................................................ 4-389 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-vi DRAFT—DO NOT CITE OR QUOTE 

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 Mechanistic 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 (Developmental 

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......................................................................... 4-419 

4.5.2.6. Solid Tumors of Lymphoid Origin................................................ 4-421 

4.5.2.7. Supporting Evidence from Animal Bio-Assays for 

Formaldehyde-Induced Lymphohematopoietic Malignancies...... 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 Respiratory 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 Carcinogenicity...................... 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 ...................................................................................... 4-462 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-vii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

4.6.6. Co-Exposures ............................................................................................... 4-464 

4.6.6.1. Cumulative Risk ............................................................................ 4-464 

4.6.6.2. Aggregate Exposure....................................................................... 4-465 

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 RfCs.......................................... 5-33 

5.1.2.1. Selection of Studies for Candidate RfC Derivation......................... 5-33 

5.1.2.2. Derivation of Candidate RfCs from Key Studies ............................ 5-40 

5.1.2.3. Evaluation of the Study-Specific Candidate RfC ............................ 5-66 

5.1.3. Database Uncertainties in the RfC Derivation ............................................... 5-69 

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 NATIONAL 

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 National Cancer 

Institute Cohort............................................................................... 5-76 

5.2.2.2. Prediction of Lifetime Extra Risk of Nasopharyngeal Cancer 

Mortality......................................................................................... 5-79

5.2.2.3. Prediction of Lifetime Extra Risk of Nasopharyngeal 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 National Cancer 

Institute Cohort............................................................................... 5-88 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-viii DRAFT—DO NOT CITE OR QUOTE 

CONTENTS (continued) 

5.2.3.2. Prediction of Lifetime Extra Risks for Hodgkin Lymphoma 

and Leukemia Mortality ................................................................. 5-91 

5.2.3.3. Prediction of Lifetime Extra Risks for Hodgkin Lymphoma 

and Leukemia Incidence................................................................. 5-93 

5.2.3.4. Sources of Uncertainty .................................................................... 5-95 

5.2.4. Conclusions on Cancer Unit Risk Estimates Based on Human Data............. 5-99 

5.3. DOSE-RESPONSE MODELING OF RISK OF SQUAMOUS CELL 

CARCINOMA IN THE RESPIRATORY TRACT USING ANIMAL DATA ...... 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 Modeling.............................. 5-106 

5.3.2.1. Major Results of the CIIT Modeling Effort................................... 5-111 

5.3.3. This Assessment’s Conclusions from Evaluation of Dose-Response 

Models of DPX Cell-Replication and Genomics Data, and of BBDR 

Models for Risk Estimation......................................................................... 5-111 

5.3.4. Benchmark Dose Approaches to Rat Nasal Tumor Data ............................. 5-118 

5.3.4.1. Benchmark Dose Derived from BBDR Rat Model and Flux 

as Dosimeter ................................................................................. 5-118 

5.3.4.2. Comparison with Other Benchmark Dose Modeling 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 INHALATION... 5-133 

5.4.1. Inhalation Unit Risk Estimates Based on Human Data................................ 5-133 

5.4.2. Inhalation Unit Risk Estimates Based on Rodent Data ................................ 5-133 

5.4.3. Summary of Inhalation Unit Risk Estimates ................................................ 5-135

5.4.4. Application of Age-Dependent Adjustment Factors (ADAFs) .................... 5-136 

5.4.5. Conclusions: Cancer Inhalation Unit Risk Estimates................................... 5-137 

6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD 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 Excretion ..................................... 6-1 

6.1.3. Noncancer Health Effects in Humans and Laboratory 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 ....................................... 6-12 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-ix DRAFT—DO NOT CITE OR QUOTE 

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 Animals...................................... 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 Concentrations ........................ 6-25 

6.2.1.3. Adequacy of Overall Data Base for RfC Derivation ....................... 6-26 

6.2.1.4. Uncertainties in the Reference Concentration (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 Estimates ............................ 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 PUBLIC 

COMMENTS AND DISPOSITIONS.............................................................. A-1 

APPENDIX B: SIMULATIONS OF INTERINDIVIDUAL AND ADULT-TO-CHILD 

VARIABILITY IN REACTIVE GAS UPTAKE IN A SMALL SAMPLE 

OF PEOPLE (Garcia et al., 2009).....................................................................B-1 

APPENDIX C: LIFETABLE ANALYSIS .................................................................................C-1 

APPENDIX D: MODEL STRUCTURE & CALIBRATION IN CONOLLY ET AL. 

(2003, 2004)..................................................................................................... D-1 

APPENDIX E: EVALUATION OF BBDR MODELING OF NASAL CANCER 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-1 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-x DRAFT—DO NOT CITE OR QUOTE 

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 QUANTITATIVE 

EVALUATION OF ANIMAL TOXICOLOGY DATA FOR 

ANALYZING CANCER RISK DUE TO INHALED FORMALDEHYDE... H-1 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xi DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES 

Table 2-1. Physicochemical properties of formaldehyde............................................................ 2-2 

Table 2-2. Ambient air levels by land use category.................................................................... 2-6 

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

Table 3-1. Endogenous formaldehyde levels in animal 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 populations ............................................... 3-25 

Table 3-4. Percent distribution of airborne [14C]-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 levels 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 concentrations in the mouth and nose, 

and in the oral cavity after breath holding in three healthy male laboratory 

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

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

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

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

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

nasopharyngeal cancer) ..........................................................................................4-78 

Table 4-4. Epidemiologic studies of formaldehyde and lymphohematopoietic cancers...........4-98 

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

strains of mice ......................................................................................................4-112 

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

strains of rats ........................................................................................................4-113 

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

mice exposed to 15 ppm.......................................................................................4-116 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xii DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

Table 4-8. Exposure regimen for cross-tolerance study..........................................................4-117 

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

respiratory tract.....................................................................................................4-127 

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

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

Table 4-11. Enzymatic activities in nasal respiratory 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 parameters 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 severity) on histopathologic changes in 

the noses and larynxes of male and female albino SPF Wistar rats exposed to 

formaldehyde 6 hours/day for 13 weeks...............................................................4-148 

Table 4-18. Formaldehyde-induced nonneoplastic histopathologic 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 exposure..............................4-149 

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

exposed to formaldehyde (6 hours/day, 5 days/week for 13 weeks) and 

examined at the end of 130 weeks inclusive of exposure ....................................4-150 

Table 4-20. Formaldehyde effects on nasal epithelium for various concentration-bytime products in male albino Wistar rats..............................................................4-153 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xiii DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

Table 4-21. Rhinitis observed in formaldehyde-treated 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 pooled for males and 

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

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

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

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

cerebral cortex of male Wistar rats exposed to formaldehyde 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 measurements 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 exposed to inhaled 

formaldehyde and wood dust for 104 weeks .......................................................4-166 

Table 4-30. Histopathologic changes (including tumors) 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 nasal 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 Kerns et al. (1983) 

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

Table 4-33. Incidence and location of nasal squamous 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...................................................................................................4-183 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xiv DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

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

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

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

cell count, basal membrane length, proliferation cells, and two measures of 

cell proliferation, LI and ULLI, in male F344 rats ..............................................4-196 

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

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

Table 4-38. Cell population and surface area estimates 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 cell proliferation in the upper 

respiratory tract....................................................................................................4-202 

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

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

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

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

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

MNNG and NaCl in male Wistar rats exposed to formaldehyde (0.5% 

formalin) in drinking water for 8 weeks..............................................................4-212 

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

reported in male and female Sprague-Dawley rats exposed to 

formaldehyde in drinking water at different ages................................................4-214 

Table 4-44. Incidence of hemolymphoreticular neoplasia 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 7001) .................................4-215 

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

B6C3F1 mice after a 3 week, 15-ppm formaldehyde exposure ..........................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 determining the effects of 

formaldehyde exposure on pulmonary S. aureus infection .................................4-221 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xv DRAFT—DO NOT CITE OR QUOTE 

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 to formaldehyde through different 

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

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

inhalation, topical application, or footpad injection............................................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 swelling responses and skin mRNA 

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

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

sensitization due to formaldehyde in experimental animals................................4-251 

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

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

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

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

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

male and female LEW.1K rats ............................................................................4-263 

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

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

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

dams exposed via inhalation from mating through gestation..............................4-289 

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

male Wistar rats...................................................................................................4-298 

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

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

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

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

epithelial height in Wistar rats following 18 weeks of exposure ........................4-302 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xvi DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

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

formaldehyde-treated mice ..................................................................................4-302 

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

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

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

abnormal sperm after oral administration of formaldehyde to male 

Wistar rats............................................................................................................4-305 

Table 4-66. Effect of formaldehyde on spermatogenic 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 exposure of male rats to formaldehyde...........4-308 

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

exposure studies ..................................................................................................4-311 

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

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

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

exposure studies ..................................................................................................4-318 

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

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

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

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

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

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

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

Table 4-76. Formaldehyde-DNA reactions (DPX formation) ...............................................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 formaldehyde..............................................................4-348 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xvii DRAFT—DO NOT CITE OR QUOTE 

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 science 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 studies which evaluated the 

incidence of all LHP cancers in formaldehyde-exposed populations 

(ICD-8 Codes: 200-209) and all leukemias (ICD-8 Codes: 204-207).................4-412 

Table 4-91. Secondary analysis of published mortality statistics to explore alternative 

disease groupings within the broad category of all lymphohematopoetic 

malignancies ........................................................................................................4-419 

Table 4-92. Summary of studies which provide mortality statistics for myeloid 

leukemia sub-types ..............................................................................................4-420 

Table 4-93. Summary of mortality statistics for Hodgkin’s lymphoma, lymphoma and 

multiple myeloma from cohort analyses of formaldehyde exposed workers ......4-422 

Table 4-94. Summary of chronic bioassays which address rodent leukemia and 

lymphoma ............................................................................................................4-424 

Table 4-95. Incidence of lymphoblastic leukemia and lymphosarcoma orally dosed in 

Sprague-Dawley rats............................................................................................4-425 

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

exposure...............................................................................................................4-469 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xviii DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

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

animal studies .......................................................................................................5-19 

Table 5-2. Effects of formaldehyde exposure on completion 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 formaldehyde RfC development by 

health endpoint category........................................................................................5-36 

Table 5-5. Adjustment for nonoccupational exposures 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 from 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-linear 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. EC0005, LEC0005, and inhalation unit risk estimates for NPC mortality from 

formaldehyde exposure based on the Hauptmann et al. (2004) log-linear 

trend analyses for cumulative exposure.................................................................5-81 

Table 5-11. EC0005, LEC0005, and inhalation unit risk estimates for NPC incidence 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 metrics.........................................5-90 

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

from NCI trend test models ...................................................................................5-90 

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

of continuous exposure to formaldehyde...............................................................5-91 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xix DRAFT—DO NOT CITE OR QUOTE 

LIST OF TABLES (continued) 

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

continuous exposure to formaldehyde...................................................................5-91 

Table 5-16. EC0005, LEC0005, and inhalation unit risk estimates for Hodgkin lymphoma 

mortality from formaldehyde exposure based on Beane Freeman et al. 

(2009) log-linear trend analyses for cumulative exposure.....................................5-93 

Table 5-17. EC005, LEC005, and inhalation unit risk estimates for leukemia mortality 

from formaldehyde exposure based on Beane Freeman et al. (2009) 

log-linear trend analyses for cumulative exposure ................................................5-93 

Table 5-18. EC0005, LEC0005, and inhalation unit risk estimates for Hodgkin 

lymphoma incidence from formaldehyde exposure, based on Beane 

Freeman et al. (2009) log-linear trend analyses for cumulative exposure.............5-94 

Table 5-19. EC005, LEC005, and inhalation unit risk estimates for leukemia incidence 

from formaldehyde exposure based on Beane Freeman et al. (2009) 

log-linear trend analyses for cumulative exposure ................................................5-94 

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

Table 5-21. Calculation of combined cancer incidence unit risk estimate at 0.1 ppm ..........5-100 

Table 5-22. Summary of tumor incidence in long-term bioassays on F344 rats....................5-105 

Table 5-23. BMD modeling of unit risk of SCC in the human respiratory tract ...................5-125 

Table 5-24. Formaldehyde-induced rat tumor incidences......................................................5-128 

Table 5-25. Human benchmark extrapolations of nasal tumors in rats using 

formaldehyde flux and DPX................................................................................5-135 

Table 5-26. Summary of inhalation unit risk estimates .........................................................5-136 

Table 5-27. Total cancer risk from exposure to a constant formaldehyde exposure 

level of 1 μg/m3

 from ages 0–70 years ................................................................5-137 

Table 6-1. Summary of candidate Reference Concentrations (RfC) for co-critical studies.....6-27 

Table 6-2. Effective concentrations (lifetime continuous 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.............................................................................................................6-42 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xx DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES 

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

Figure 2-2. Locations of hazardous air pollutant monitors........................................................2-5 

Figure 2-3. Modeled ambient air concentrations based on 1999 emissions ..............................2-7 

Figure 2-4. Range of formaldehyde air concentrations (ppb) in different environments...........2-9 

Figure 3-1. Formaldehyde-mediated protein modifications.......................................................3-2 

Figure 3-2. 3H/14C ratios in macromolecular extracts from rat tissues following exposure 

to 14C and 3H-labeled formaldehyde (0.3, 2, 6, 10, 15 ppm)..............................3-18 

Figure 3-3. Formaldehyde clearance by ALDH2 (GSH-independent) and ADH3 

(GSH-dependent)................................................................................................3-20 

Figure 3-4. Metabolism of formate..........................................................................................3-22 

Figure 3-5. Scatter plot of formaldehyde concentrations 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 rat, rhesus monkey, and human................3-36 

Figure 3-7. Illustration of interspecies differences in airflow and verification of CFD 

simulations with water-dye studies ....................................................................3-37 

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

the F344 rat, rhesus monkey, and human ...........................................................3-38 

Figure 3-9. CFD simulations of formaldehyde flux to human nasal lining at different 

inspiratory flow rates..........................................................................................3-39 

Figure 3-10. Single-path model simulations of surface 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 et al., 1990) or in rats 

in vivo.................................................................................................................3-45 

Figure 3-12. Formaldehyde-DPX dosimetry in the F344 rat ...................................................3-47 

Figure 4-1. Delayed asthmatic reaction following the inhalation of formaldehyde after 

“painting” 100% formalin for 20 minutes..........................................................4-20 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxi DRAFT—DO NOT CITE OR QUOTE 

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 oriented to the left)......................................4-121 

Figure 4-4. Main components of the nasal respiratory 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 section levels chosen for morphometry 

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

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

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

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

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

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

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

Figure 4-10. Effect of formaldehyde exposure on cell 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/m3 carbon black, or both ...............................................4-223 

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

following formaldehyde exposure in guinea pigs after OVA sensitization and 

OVA challenge .................................................................................................4-235 

Figure 4-13. OVA-specific IgG1 (1B) in formaldehyde-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 week 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 Wistar rats............................4-239 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxii DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

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

infiltration (Panel A) and goblet cell proliferation (Panel B) after Der f 

challenge in the nasal mucosa of male ICR mice after sensitization and 

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

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

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

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

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

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

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

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

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

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

after termination of 10-minute formaldehyde exposure ...................................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 the 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 formaldehyde-exposed mice .........................4-274 

Figure 4-27. Mortality corrected cumulative incidences 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 rats exposed to formaldehyde for 

24 months .........................................................................................................4-332 

Figure 4-30. Cumulative leukemia incidence in female 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......................................4-334 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxiii DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

Figure 4-32. DNA-protein cross-links (DPX) and thymidine 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 formaldehyde exposure and the risk of 

lymphohematopoietic malignancy....................................................................4-415 

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

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

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

human colon carcinoma cells and in (B) human umbilical 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 lymphocytes 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 exposed to formaldehyde in the high 

exposure index category with 8-hour time weighted average 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 concentrations. ..............................5-41 

Figure 5.5. Odds ratios for physician-diagnosed asthma in children associated with inhome formaldehyde levels in air .......................................................................5-45 

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

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

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

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

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

formaldehyde exposures and sensory irritation (eye irritation) ..........................5-53 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxiv DRAFT—DO NOT CITE OR QUOTE 

LIST OF FIGURES (continued) 

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

formaldehyde exposures and sensory irritation (burning 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 pharmacokinetic and pharmacodynamic 

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

Figure 5-12. Fit to the rat tumor incidence data using 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, resulting in 20 flux bins ..........5-120 

Figure 5-14. Distribution of cells at risk across flux bins in the F344 rat nasal lining..........5-120 

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

two BBDR models............................................................................................5-124 

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

Monticello et al. (1996) data on tumor incidence showing BMC10 and 

BMCL10 ............................................................................................................5-127 

Figure 5-17. EPA multistate Weibull modeling: nasal 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................................................5-132 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxv DRAFT—DO NOT CITE OR QUOTE 

LIST OF ABBREVIATIONS AND ACRONYMS 

ACGIH American Conference of Governmental Industrial 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 Registry 

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 concentration 

BMCR binuclated micronucleated cell ratefluoresce 

BMD benchmark dose 

BMDL 95% lower bound on the benchmark dose 
This document is a draft for review purposes only and does not constitute Agency policy.

IV-xxvi DRAFT—DO NOT CITE OR QUOTE 

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 Prevention 

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 

CO2 carbon dioxide 

COEHHA California Office of Environmental Health Hazard Assessment 

CREB cyclic AMP responsive element binding proteins 

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

IV-xxvii DRAFT—DO NOT CITE OR QUOTE 

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 factor 

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 transferase 

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 Cancer 
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IV-xxviii DRAFT—DO NOT CITE OR QUOTE 

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 

Km Michaels-Menton constant 

KM Kaplan-Meier 

LD50 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 level 
This document is a draft for review purposes only and does not constitute Agency policy.

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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 Research Council/Agriculture and Resource 

Management Council of Australia and New Zealand 

NNK nitrosamine nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-butanone 

N

6

-hmdA N6-hydroxymethyldeoxyadenosine 

N

4

-hmdC N4-hydroxymethylcytidine 

N

2

-hmdG N2-hydroxymethyldeoxyguanosine 

NICNAS National Industrial Chemicals Notification and Assessment Scheme 

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

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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 index 

RA reflex apnea 

RANTES regulated upon activation, normal T–cell expressed and secreted 

RB reflex bradypnea 

RBC red blood cells 

RD50 exposure concentration that results in a 50% reduction 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 dehydrogenase 

SEER Surveillance, Epidemiology, and End Results 

SEM standard error of the mean 

SEN sensitizer 

SH sulfhydryl 

SHE Syrian hamster embryo 

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

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LIST OF ABBREVIATIONS AND ACRONYMS (continued) 

SMR standardized mortality ratio 

SNP single nucleotide polymorphism 

SOD superoxide dismutase 

SOMedA N6

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

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

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APPENDIX A 

1 

2 SUMMARY OF EXTERNAL PEER REVIEW 

3 AND PUBLIC COMMENTS AND DISPOSITIONS 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

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

15 following External Peer review and receipt of public comments.]

16 
Appendix B
This document is a draft for review purposes only and does not constitute Agency policy.

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1 APPENDIX B 

2 

3 SIMULATIONS OF INTERINDIVIDUAL AND ADULT-TO-CHILD VARIABILITY IN 

4 REACTIVE GAS UPTAKE IN A SMALL SAMPLE OF PEOPLE (Garcia et al., 2009) 

5 

6 Garcia et al. (2009) used computational fluid dynamics to study human variability in the 

7 nasal dosimetry of reactive, water-soluble gases in 5 adults and 2 children, aged 7 and 8 years 

8 old. They considered two model categories of gases, corresponding to maximal and moderate 

9 absorption at the nasal lining. The nasal airway (including the nasopharynx) geometries of these 

10 individuals were mapped out using magnetic resonance imaging or computed tomography scans. 

11 The scans chosen for the analysis were from individuals who had normal nasal anatomies with no 

12 pathology (as per a review carried out by a ear-nose-throat surgeon). The minute volumes of 

13 these individuals were estimated to range from 6.8 to 9.0 L/min (adults) and 5.5 to 5.8 L/min 

14 (children). The sample size in this study is too small to consider the results representative of the 

15 population as a whole (as also recognized by the authors). Nonetheless, various comparisons with 

16 the characteristics of other study populations add to the strength of this study. The range of adult 

17 minute volumes in this study is reported by the authors to be in good agreement with that 

18 obtained in many other studies in the literature; minute volumes for the children in the study 

19 were found to be similar to the average minute volume of 6.1 ± 1.7 L/min obtained by Bennett 

20 and Zeman (2004) in a study of 36 children aged 6 to 13 years; the range of nasal surface area 

21 values for the adults agreed well with that obtained by Guilmette et al. (1997) for 45 adults; and 

22 the range of values for the surface area to volume ratio is in good agreement with that obtained 

23 for 40 adult Caucasians studied by Yokley (2006). The surface area to volume ratio is useful for 

24 comparing the rate of diffusional transport of a gas out of different cavities; however in the case 

25 of the highly non-homogenously shaped nasal lumen, this might at best be considered a gross 

26 indicator. 

27 We focus here only on the “maximum uptake” simulations in Garcia et al. (2009). In this 

28 case, the gas was considered so highly reactive and soluble that it was reasonable to assume an 

29 infinitely fast reaction of the absorbed gas with compounds in the airway lining. Although such a 

30 gas could be reasonably considered to represent formaldehyde, these results cannot be fully 

31 utilized to inform quantitative estimates of formaldehyde dosimetry (and that does not appear to 

32 have been the intent of the authors either). This is because the same boundary condition 

33 corresponding to maximal uptake was applied on the vestibular section as well as on the 

34 transitional and transitional epithelial lining of the nasal cavity. This is not appropriate for 

35 formaldehyde as the lining on the nasal vestibule is made of keratinized epithelium which is 

36 considerably less absorbing than the transitional or respiratory epithelium (Kimbell et al. 2001). 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Table B-1 provides results obtained by Garcia et al. (2009) for five adults and two 

2 children for uptake for the maximal uptake scenario. Although the nasal cavities of the children 

3 were smaller in surface area, volume and length, the surface-area-to-volume ratios were similar 

4 in the two age groups. Overall uptake efficiency, average flux (rate of gas absorbed per unit 

5 surface area of the nasal lining) and maximum flux levels over the entire nasal lining did not 

6 vary substantially between adults (1.6-fold difference in average flux and much less in maximum 

7 flux), and the mean values of these quantities were comparable between adults and children. 

8 These results are in agreement with conclusions reached by Ginsberg et al. (2005) that overall 

9 extrathoracic absorption of highly and moderately reactive and soluble gases (corresponding to 

10 category 1 and 2 reactive gases as per the scheme in USEPA [1994]) is similar in adults and 

11 children. However, none of these three quantities should be considered as reasonable indicators 

12 of variability in the interaction of the gas with the nasal lining. For a very reactive and soluble 

13 gas, regional absorption of the gas is highly nonhomogenously distributed; therefore 

14 interindividual variability in this distribution will be washed out when averaged over the whole 

15 nose. Estimates of maximum gas flux, on the other hand, correspond to extremely small localized 

16 regions of hot spots (see chapter 3), and thus may not be a proper measure of inter-individual 

17 variability in flux distribution patterns over the whole nose. Furthermore, numerical error in the 

18 calculation (such as mass balance and irregularly shaped elements of the finite-element mesh) is 

19 likely to be most pronounced when estimates are considered over extremely small regions. 

20 

21 Table B-1: Variations in overall nasal uptake, whole nose flux, and key parameters 

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.m2) 10-8 kg/(s.m2)

23 

24 MV = minute volume, SA=nasal surface area, V=nasal volume 

25 Source: Garcia et al. (2009). 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 On the other hand, Figure 6A of Garcia et al. (2009), reproduced here as Figure B-1, 

2 shows significant interhuman variability in flux values at specific points on the nasal walls. The 

3 local flux of formaldehyde varies among individuals by a factor of 3 to 5 at various distances 

4 along the septal axis of the nose. However, interpretation of the values in this plot is problematic 

5 for reasons explained in the paper1: 

6 The greater variability among individuals seen for wall fluxes at specific sites of the nasal 

7 passages (Figure 6) in comparison to the minimal variability in total uptake 

8 (Table 2) and whole-nose dose (Tables 3 and Tables 4) indicates that fluxes of equal 

9 magnitude do not exactly overlay the same anatomical regions of the nasal cavity in each 

10 individual. This implies that specific anatomical regions subtended by maximum flux 

11 could be offset from one individual to another. 

12 Notwithstanding this difficulty in interpretion, we believe the quantities plotted in Figure B-1 

13 provide a better perspective of the inter-individual (adult) variability in local flux than the 

14 variation in whole nose average or in maximum flux presented in Table B-1. 

15 

16 

17 Figure B-1: Gas flux across the nasal lining for the case of a “maximum 

18 uptake” gas in Garcia et al. (2009) as a function of axial distance from the 

19 nostril. The vertical bars show range of variation. See the paper for further 

20 details. Figure is reproduced from Garcia et al. (2009). 

21 

22 

23 Clearly, multiple measures of variability in dose can be developed depending on the 

24 adverse response. The advantage of models such as that developed by Garcia et al. is that they 

25 make it possible to carry out these calculations. For example, if deficit in pulmonary function is 

 

1 The Figures and Tables in the cited text refer those in Garcia et al. (2009) 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 the adverse response, then variation in average whole nose flux or overall nasal uptake efficiency 

2 would be most useful since one is then interested in inter-individual variability in the overall 

3 dose delivered to the lung. It is possible to conceive of allergic or irritation responses being 

4 triggered by some threshold value of local flux. In such a case it may be preferable to calculate 

5 the variability associated with the net surface area receiving flux values greater than that 

6 threshold. On the other hand, the probability of developing a tumor at a nasal site may be non7 linearly related to the flux at that site and linearly related to the number of cells at that site. In this 

8 case, the appropriate metric may be the nasal surface area associated with some intermediate 

9 levels of local flux (see Appendix in Subramaniam et al. 2008). 

10 Various caveats presented by the authors as limitations of their study should be noted: 

11 Possible nonuniform distribution of epithelial types, enzymes, glands and other cellular 

12 metabolic or clearance machinery were not considered in the model; only effects pertaning to 

13 resting breathing were considered; the study sample size was small; children younger than 7 

14 years old were not studied; and, the model assumed a rigid nasal geometry. 

15 

16 Garcia et al. (2009) conclude their paper as follows: 

17 “..., our simulations predicted no differences in the nasal dosimetry of reactive, water18 soluble gases between children and adults, suggesting that the risk factor of 10 typically 

19 used to accommodate inter-human variability is adequate.” 

20 

21 In addition to the obvious caveat already recognized by the authors in regards extrapolating from 

22 a study involving just two children, this conclusion needs further qualification. Firstly, the safety 

23 factor of 10 that is typically applied for interhuman variability does not specifically include 

24 children. Instead, EPA practice is that unless there is reasonable evidence that childhood forms a 

25 more susceptible lifestage, no additional factors are applied for this population. Second, it is 

26 important not to confuse dosimetric differences across lifestages−which actually contribute to 

27 intra-individual differences−with inter-individual differences that may contribute towards the 10-

28 fold uncertainty factor. This interhuman variability in susceptibility−for a given life-stage−may 

29 be considered to arise from both pharmacokinetic and pharmacodynamic factors, and the practice 

30 in the US has been to split these into factors of 3.3 each. Then, the roughly 3 to 5 -fold variation 

31 estimated for adults (and also between the two children) in Figure B-1 suggest that a factor of 10 

32 may not be adequate to accommodate inter-human variability for those formaldehyde-induced 

33 adverse responses for which the localized nature of formaldehyde flux, and therefore the inter34 individual differences in regional dosimetry, play a role. 
This document is a draft for review purposes only and does not constitute Agency policy.

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

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1 APPENDIX C 

2 

3 LIFETABLE ANALYSIS 

4 

5 A spreadsheet illustrating the extra-risk calculation for the derivation of the lower 95% 

6 bound on the effective concentration associated with a 0.05% extra risk (LEC0005) for 

7 nasopharyngeal carcinoma (NPC) incidence is presented in Table C-1. 

8 
This document is a draft for review purposes only and does not constitute Agency policy

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Table C-1. Extra-risk calculation

a for environmental exposure to 0.0461 ppm formaldehyde (the L

NPC incidence)

b using a log-linear exposure-response model based on the cumulative exposure tren

Hauptmann et al. (2004), as described in Section 5.2.2.

A B C D E F G H I J K L M 

Interval 

number 

(i) 

Age 

interval

All cause 

mortality 

(×10

5/yr)

NPC 

incidence 

(×10

5/yr)

All 

cause 

hazard 

rate (h*)

Prob of 

surviving 

interval 

(q) 

Prob of 

surviving 

up to 

interval 

(S) 

NPC 

cancer 

hazard 

rate (h) 

Cond 

prob of 

NPC 

incidence 

in 

interval 

(Ro) 

Exp 

duration 

mid 

interval 

(xtime)

Cum 

exp mid 

interval 

(xdose)

Exposed 

NPC 

hazard 

rate (hx)

Exposed 

all cause 

hazard 

rate 

(h*x) 

1 <1 728.7 0 0.0073 0.9927 1.0000 0.00000 0.000000 0 0.0000 0.0000 0.0073 2 1-4 32.9 0.05 0.0013 0.9987 0.9927 0.00000 0.000002 0 0.0000 0.0000 0.0013 3 5-9 16.4 0.03 0.0008 0.9992 0.9914 0.00000 0.000001 0 0.0000 0.0000 0.0008 4 10-14 20.9 0.09 0.0010 0.9990 0.9906 0.00000 0.000004 0 0.0000 0.0000 0.0010 5 15-19 68.2 0.12 0.0034 0.9966 0.9896 0.00001 0.000006 2.5 0.3506 0.0000 0.0034 6 20-24 96 0.16 0.0048 0.9952 0.9862 0.00001 0.000008 7.5 1.0517 0.0000 0.0048 7 25-29 99 0.23 0.0050 0.9951 0.9815 0.00001 0.000011 12.5 1.7528 0.0000 0.0050 8 30-34 116.3 0.48 0.0058 0.9942 0.9766 0.00002 0.000023 17.5 2.4539 0.0000 0.0058 9 35-39 162.2 0.55 0.0081 0.9919 0.9710 0.00003 0.000027 22.5 3.1550 0.0000 0.0081 10 40-44 237.3 1.14 0.0119 0.9882 0.9631 0.00006 0.000055 27.5 3.8561 0.0001 0.0119 11 45-49 356 1.3 0.0178 0.9824 0.9518 0.00007 0.000061 32.5 4.5572 0.0001 0.0178 12 50-54 518.6 1.72 0.0259 0.9744 0.9350 0.00009 0.000079 37.5 5.2583 0.0001 0.0260 13 55-59 801.8 1.69 0.0401 0.9607 0.9111 0.00008 0.000075 42.5 5.9594 0.0001 0.0401 14 60-64 1257.9 1.9 0.0629 0.9390 0.8753 0.00010 0.000081 47.5 6.6605 0.0002 0.0630 15 65-69 1928.2 2.87 0.0964 0.9081 0.8219 0.00014 0.000112 52.5 7.3616 0.0003 0.0965 16 70-74 2968.1 2.1 0.1484 0.8621 0.7464 0.00011 0.000073 57.5 8.0627 0.0002 0.1485 17 75-59 4556.6 2.19 0.2278 0.7963 0.6434 0.00011 0.000063 62.5 8.7638 0.0002 0.2279 18 80-84 7399.6 1.98 0.3700 0.6907 0.5123 0.00010 0.000042 67.5 9.4649 0.0002 0.3701 

Ro = 0.000725 

Extra Risk = (Rx−Ro)/(1−Ro) = 0.0005 
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Column 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 (× 10

5/year) (2000 data from NCHS). 

Column D: NPC incidence rate for interval i (× 10

5/year) (1996-2000 SEER data). 

Column E: all-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of years in age interval).

c

Column F: probability of surviving interval i without being diagnosed with NPC (qi) (= exp(−h*i)). 

Column G: probability of surviving up to interval i without 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 u

having been diagnosed with NPC [Ro, the background lifetime probability of being diagnosed with NPC = the sum of the c

across 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 × 20/1

environmental exposures to corresponding occupational exposures]. 

Column L: NPC incidence hazard rate in exposed people for interval i (hxi) (= hi × (1 + β × xdose), where β = 0.05183 + (1.645 × 0.01

per ppm × year is the regression coefficient obtained, along with its SE of 0.01915, from Dr. Hauptmann (see Section 5.2.2LEC0005, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 0.05%, the 95% upper bound on the 

used, 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 i without having been diagnosed with NPC for exposed people (Sxi) (Sx1 = 1; Sxi = S

Column P: conditional probability of being diagnosed with NPC in interval i for exposed people (= (hxi/h*xi) × Sxi × (1−qxi)) [Rx, the 

being diagnosed with NPC for exposed people = the sum of the conditional probabilities across the intervals]. 

a Using the methodology of BEIR IV (1988). b The estimated 95% lower bound on the continuous exposure level of TCE that gives a 0.05% extra lifetime risk of NPC. c For the cancer incidence calculation, the all-cause hazard rate for interval i should technically be the rate of either dying of any cause 

the specific cancer during the interval, i.e., (the all-cause mortality rate for the interval + the cancer-specific incidence rate for the intemortality rate for the interval [so that a cancer case isn’t counted twice, i.e., upon diagnosis and upon death]) × number of years in int

was ignored here because the NPC incidence rates are small compared with the all-cause mortality rates. 

MLE = maximum likelihood estimate, SE = standard error 
Appendix D
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1 APPENDIX D 

2 

3 MODEL STRUCTURE & CALIBRATION IN CONOLLY ET AL. (2003, 2004) 

4 

5 The various studies indicated in Section 5.4.1 were followed by the development of a 

6 biologically motivated dose-response model for formaldehyde-induced cancer as represented in a 

7 series of papers and in a health assessment report (CIIT model) (Conolly et al., 2004, 2003, 2000; 

8 Conolly, 2002; Kimbell et al., 2001a, b; Overton et al., 2001; CIIT, 1999). EPA’s cancer 

9 guidelines (U.S. EPA, 2005a) suggest using a BBDR model for extrapolation when data permits 

10 since it facilitates the incorporation of MOA in risk assessment. The CIIT modeling and 

11 available data were evaluated in a series of peer-reviewed papers (Klein et al., 2009; Crump et 

12 al., 2008; Subramaniam et al., 2008, 2007) and debated further in the literature (Conolly et al., 

13 2009; Crump et al., 2009). In addition, alternatives to the CIIT biological modeling (but based 

14 on that original model) are further explored and evaluated here. 

15 In Conolly et al. (2003), tumor incidence data in the above long-term bioassays were 

16 modeled by using an approximation of the two-stage clonal growth model (Moolgavkar et al., 

17 1988) and allowing formaldehyde to have directly mutagenic action. Conolly et al. (2003) 

18 combined these data with historical control data on 7,684 animals obtained from National 

19 Toxicology Program (NTP) bioassays. These models are based on the Moolgavkar, Venzon, and 

20 Knudson (MVK) stochastic two-stage model of cancer (Moolgavkar et al., 1988; Moolgavkar 

21 and Knudson, 1981; Moolgavkar and Venzon, 1979), which accounts for growth of a pool of 

22 normal cells, mutation of normal cells to initiated cells, clonal expansion and death of initiated 

23 cells, and mutation of initiated cells to fully malignant cells. 

24 The MVK model for formaldehyde accounted for two MOAs that may be relevant to 

25 formaldehyde carcinogenicity: 

26 

27 o An indirect MOA in which the regenerative cell proliferation in response to formaldehyde 

28 cytotoxicity increased the probability of errors in DNA replication. This MOA was modeled 

29 by using labeling data on normal cells in nasal mucosa of rats exposed to formaldehyde. 

30 o A possible direct mutagenic MOA, based on information indicating that formaldehyde is 

31 mutagenic (Speit and Merk, 2002; Heck et al., 1990; Grafstrom et al., 1985), was modeled by 

32 using rat data on formaldehyde production of DPXs (Monticello et al., 1996, 1991). 

33 

34 The human model for formaldehyde carcinogenicity (Conolly et al., 2004) is conceptually 

35 very similar to the rat model. The model uses, as input, results from a dosimetry model for an 

36 anatomically realistic representation of the human upper airways and an idealized representation 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 of the lower airways. However, the model does not incorporate any data on human responses to 

2 formaldehyde exposure. The rat and human formaldehyde models are detailed further below. 

3 

4 The following notations are used in the rest of this chapter: 

5 N cell, normal cell 

6 I cell, initiated cell 

7 LI, labeling index (number of labeled cells/(number labeled + unlabeled cells) 

8 ULLI, unit length labeling index (number labeled cells/length of basement membrane) 

9 N, number of normal cells that are eligible for progression to malignancy 

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

11 μN, rate at which an initiated cell is formed by mutation of a normal cell (per cell division 

12 of normal cells) 

αI, division rate of an initiated cell (hours–1 13 ) 

βI, death rate of an initiated cell (hours–1 14 ) 

μI, 15 rate at which a malignant cell is formed by mutation of an initiated cell (per cell 

16 division of initiated cells) 

17 

18 A novel contribution of the CIIT model is that cell replication rates and DPX 

19 concentrations are driven by local dose, which is formaldehyde flux to each region of nasal tissue 

expressed as pmol/mm2

20 -hour. This dosimetry is predicted by computational fluid dynamics 

21 (CFD) modeling using anatomically accurate representations of the nasal passages (see 

22 Chapter 3). Such a feature is important to incorporating site-specific toxicity in the case of a 

23 highly reactive gas like formaldehyde, for which uptake patterns are spatially localized and 

24 significantly different across species (see Chapter 3). In the CIIT model, each of these 

25 parameters is characterized by local flux. The inputs to the two-stage cancer modeling consisted 

26 of results from other model predictions as well as empirical data as follows: 

27 

28 ● Regional uptake of formaldehyde in the respiratory tract predicted by using CFD 

29 modeling in the F344 rat and human (Kimbell et al., 2001a, b; Overton et al., 2001; 

30 Subramaniam et al., 1998) 

31 ● Concentrations of DPXs predicted by a PBPK model (Conolly et al., 2000) calibrated to 

32 fit the DPX data in F344 rat and rhesus monkey (Casanova et al., 1994, 1991) and 

33 subsequently scaled up to humans 
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1 ● αN inferred from LI data on rats exposed to formaldehyde (Monticello et al., 1996, 1991, 

2 1990) 

3 D.1. DPX AND MUTATIONAL ACTION 

4 Formaldehyde interacts with DNA to form DPXs. These cross-links are considered to 

5 induce mutagenic as well as clastogenic effects. Casanova et al. (1994, 1989) carried out two 

6 studies of DPX measurements in F344 rats. In the first study, rats were exposed to 

7 concentrations of 0.3, 0.7, 2, 6, and 10 ppm for 6 hours and DPX measurements were made over 

8 the whole respiratory mucosa of the rat, while, in the second study, the exposure was to 0.7, 2, 6, 

9 or 15 ppm formaldehyde for 3 hours and measurements were made at “high” and “low” tumor 

10 sites. Overall, these studies showed statistically significantly elevated levels of DPXs at 

11 concentrations ≥2 ppm, with the trend also indicating elevated DPXs at 0.7 ppm. In Conolly et 

12 al. (2003), DPX formation is considered proportional to the intracellular dose that induces 

13 mutations. Conolly et al. (2000) used data from the second study to develop a PBPK model that 

14 predicted the time course of DPX concentrations as a function of regional formaldehyde flux 

(estimated in the CFD modeling and expressed as pmol/mm215 -hour). In Conolly et al. (2003), this 

16 PBPK model was then used to predict regional DPX concentrations (that is, as a function of 

17 regional formaldehyde flux) (Figure 5-11, Chapter 5). These data were incorporated into the 

18 two-stage clonal expansion model by defining the mutation rate of normal and initiated cells as 

19 the same linear function of DPX concentration as follows: 

20 

μN = μI = μNbasal 21 + KMU × DPX (1) 

22 

The unknown constants μNbasal 23 and KMU were estimated by fitting model predictions to the 

24 tumor bioassay data. 

25 

26 D.2. CALIBRATION OF MODEL 

27 The rat model in Conolly et al. (2003) involved six unknown statistical parameters that 

28 were estimated by fitting the model to the rat formaldehyde bioassay data shown in Table 5-24 in 

29 Chapter 5 (Monticello et al., 1996; Kerns et al., 1983) plus data from several thousand control 

30 animals from all the rat bioassays conducted by the NTP. These NTP bioassays were conducted 

31 from 1976 through 1999 and included 7,684 animals with an incidence of 13 SCCs (i.e., 0.17% 

32 incidence). The resulting model predicts the probability of a nasal SCC in the F344 rat as a 

33 function of age and exposure to formaldehyde. The fit to the tumor incidence data is shown in 

34 Figure 5-12, Chapter 5. (Note: This figure also shows the fit to the data obtained by the 
This document is a draft for review purposes only and does not constitute Agency policy.

D-5 DRAFT—DO NOT CITE OR QUOTE 

1 implementation of this model in Subramaniam et al. [2007], which is discussed later in this 

2 chapter.) 

3 

4 D.3. FLUX BINS

5 The spatial distribution of formaldehyde over the nasal lining was characterized by 

6 partitioning the nasal surface by formaldehyde flux to the tissue, resulting in 20 “flux bins” 

7 (Figure 5-13, Chapter 5). Each bin is comprised of elements (not necessarily contiguous) of the 

8 nasal surface that receive a particular interval of formaldehyde flux per ppm of exposure 

9 concentration (Kimbell et al., 2001a). The spatial coordinates of elements comprising a 

10 particular flux bin are fixed for all exposure concentrations, with formaldehyde flux in a bin 

11 scaling linearly with exposure concentration (ppm). The number of cells at risk varies across the 

12 bins, as shown in Figure 5-14, Chapter 5. 

13 

14 D.4. USE OF LABELING DATA 

15 Cell replication rates in Conolly et al. (2003) were obtained by pooling labeling data from 

16 two phases of a labeling study in which male F344 rats were exposed to formaldehyde gas at 

17 similar concentrations (0, 0.7, 2.0, 6.0, 10.0, or 15.0 ppm). The first phase employed injection 

18 labeling with a 2-hour pulse labeling time, and animals were exposed to formaldehyde for early 

19 exposure periods of 1, 4, and 9 days and 6 weeks (Monticello et al., 1991). The second phase 

20 used osmotic minipumps for labeling with a 120-hour labeling time to quantify labeling in 

21 animals exposed for 13, 26, 52, and 78 weeks (Monticello et al., 1996). The combined pulse and 

22 continuous labeling data were expressed as one exposure TWA over all sites for each exposure 

23 concentration. αN was calculated from these labeling data by using an approximation from 

24 Moolgavkar and Luebeck (1992). A dose-response curve for normal cell replication rates (i.e., 

25 αN as a function of formaldehyde flux) was then calculated as shown in Figure D-1. These steps 

26 are carefully detailed and evaluated in Subramaniam et al. (2008), and discussion of the data will 

27 continue in the section on uncertainties in characterizing cell replication rates. 

28 

29 D.5. UPWARD EXTRAPOLATION OF NORMAL CELL DIVISION RATE 

30 The extensive labeling data collected by Monticello et al. (1996, 1991) present an 

31 opportunity to use precursor data in assessing cancer risk. However, these empirical data could 

be used to determine αΝ(flux) only for the lower flux range, 0–9,340 pmol/mm

2

32 -hour (see 

33 Subramaniam et al. [2008] for the reasons), as shown by the solid line in Figure D-1, whereas the 

highest computed flux at 15.0 ppm exposure was 39,300 pmol/mm2

34 -hour. Therefore Conolly et 

35 al. (2003) introduced an adjustable parameter, αmax, that represented the value of αΝ(flux) at the 
This document is a draft for review purposes only and does not constitute Agency policy.

D-6 DRAFT—DO NOT CITE OR QUOTE 

maximum flux of 39,300 pmol/mm21 -hour. αmax was estimated by maximizing the likelihood of 

the two-stage model fit to the tumor incidence data. For 9,340 < flux ≤ 39,300 pmol/mm22 -

3 hour, αΝ(flux) was determined by linear interpolation from αΝ(9,340) to αmax, as shown by the 

4 dashed line in Figure D-1. 

5 

Flux (pmole/mm2/h)

0 10000 20000 30000 40000

Cell Division Rate, 

α (1/h)

0.00

0.01

0.02

0.03

0.04

0.05

Empirical αN (from ULLI data)

Estimated αN 

Estimated αΙ 

0.000

0.001

0.002

0 2000 4000 6000

0.000

0.001

0.002

J-shape

Hockey

αmax

Flux (pmole/mm2/h)

0 10000 20000 30000 40000

Cell Division Rate, 

α (1/h)

0.00

0.01

0.02

0.03

0.04

0.05

Empirical αN (from ULLI data)

Estimated αN 

Estimated αΙ 

0.000

0.001

0.002

0 2000 4000 6000

0.000

0.001

0.002

J-shape

Hockey

αmax

6 7 

8 Figure D-1. Dose response of normal (αN) and initiated (αI) cell division rate 

9 in Conolly et al. (2003). 

10 

11 Note: Empirically derived values of αN (TWA over six sites) from Table 1 in 

12 Conolly et al. (2003) and optimized parameter values from their Table 4 were 

13 used. The main panel is for the J-shape dose response. Insets show J-shape and 

14 hockey-stick shape representations at the low end of the flux range. The long 

15 arrow denotes the upper end of the flux range for which the empirical unit-length 

16 labeling data are available for use in the clonal growth model. αmax is the value of 

17 αN at the maximum formaldehyde flux delivered at 15 ppm exposure and 

estimated by optimizing against the tumor incidence data. αI 18 < αN for flux greater 

19 than the value indicated by the small vertical arrow. Conolly et al. (2004, 2003) 

assumed βI 20 = αN at all flux values. 

21 

22 Source: Subramaniam et al. (2008). 
This document is a draft for review purposes only and does not constitute Agency policy.

D-7 DRAFT—DO NOT CITE OR QUOTE 

1 D.6. INITIATED CELL DIVISION AND DEATH RATES 

2 The pool of cells used for obtaining the LI data in Monticello et al. (1996, 1991) consists 

3 of largely normal cells with perhaps increasing numbers of initiated cells at higher exposure 

4 concentrations. Since the division rates of initiated cells in the nasal epithelium, either 

5 background or formaldehyde exposed, could not be inferred from the available empirical data, 

Conolly et al. (2003) made what they perceived to be a biologically reasonable assumption for αI 6 , 

assuming αI 7 to be linked to αN via a two-parameter function: 

8 

αI = αN ×{multb – multc × max[αN – αN(basal) 9 , 0]} (2) 

10 

where αN ≡ αN(flux), αN(basal) 11 is the estimated average cell division rate in unexposed normal 

12 cells, and multb and multc are unknown parameters estimated by likelihood optimization against 

the tumor data.2

 The value of αN(basal) was equal to 3.39 × 10–4 hours–1 13 as determined by Conolly 

14 et al. (2003) from the raw averaged LI data. The ratio αI/αN is plotted against flux in Figure D-2, 

15 and αI(flux) is shown by the dotted line in Figure D-1. 

Flux (pmole/mm2/h)

0 10000 20000 30000 40000

αI

/α

N

0.96

1.00

1.04

1.08

16 

17 

18 Figure D-2. Flux dependence of ratio of initiated and normal cell replication 

19 rates (αI/αN) in CIIT model. 

20 Note: Cell replication rate of initiated cells is less than normal cell replication rate 

21 at flux exceeding the value denoted by the arrow. By assumption, the y-axis also 

22 represents (αI/βI). 

23 Source: Subramaniam et al. (2008). 

 

2 multb and multc were equal to 1.072 and 2.583, respectively (J-shaped αN),, and 1.070 and 2.515, respectively 

(hockey-stick shaped αN).  
This document is a draft for review purposes only and does not constitute Agency policy.

D-8 DRAFT—DO NOT CITE OR QUOTE 

1 Death rates of initiated cells (βI) are assumed to equal the division rates of normal cells 

2 for all formaldehyde flux values, that is 

3 

4 βI(flux) = αN(flux) (3) 

5 

Conolly et al. (2003) stated that this formulation for αI

 and βI 6 provided the best fit of the 

7 model to the tumor data. 

8 

9 D.7. STRUCTURE OF THE CIIT HUMAN MODEL

10 Subsequent to the BBDR model for modeling rat cancer, Conolly et al. (2004) developed 

11 a corresponding model for humans for the purpose of extrapolating the risk to humans estimated 

12 by the rat model. Also, rather than considering only nasal tumors, it is used to predict the risk of 

13 all human respiratory tumors. DPXs observed at proximal portions of the rhesus monkey LRT 

14 (Casanova et al., 1991) suggested that the LRT may be at risk in addition to the URT. In 

15 addition, some epidemiologic studies (Gardner et al., 1993; Blair et al., 1990, 1986) reported an 

16 increase in lung cancer associated with formaldehyde exposure, while others reported no such 

17 increases (Collins et al., 1997; Stayner et al., 1988). The human model for formaldehyde 

18 carcinogenicity (Conolly et al., 2004) is conceptually very similar to the rat model and follows 

19 the schematic in Figure 5-11, Chapter 5. The following points need to be noted: 

20 

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

22 • The model is based on an anatomically realistic representation of the human nasal 

23 passages (in a single individual) and an idealized representation of the LRT. Local 

24 formaldehyde flux to respiratory tissue is estimated by a CFD model for humans 

25 (Subramaniam et al., 1998; Kimbell et al., 2001a; Overton et al., 2001). 

26 • Rates of cell division and cell death are, with a minor modification, assumed to be the 

27 same in humans as in rats. 

28 • The concentration of formaldehyde-induced DPXs in humans is estimated by scaling up 

29 from values obtained from experiments in the F344 rat and rhesus monkey. This scaling 

30 up was discussed in chapter 3. 

31 • The statistical parameters for the human model are either estimated by fitting the model 

32 to the human background data, assumed to have the same value as obtained in the rat 

33 model, or, in one case, fixed at a value suggested by the epidemiologic literature. The 

34 delay, D, is fixed at 3.5 years, based on a fit to the incidence of lung cancer in a cohort of 
This document is a draft for review purposes only and does not constitute Agency policy.

D-9 DRAFT—DO NOT CITE OR QUOTE 

1 British doctors (Doll and Peto, 1978). The two other parameters in the rat model that 

2 affect the background rate of cancer (multb and μbasal) are estimated by fitting to U.S. 

3 cancer incidence or mortality data. These parameters affect the baseline values for the 

4 human αI, μN, and μI. Since αmax, multfc, and KMU do not affect the background cancer 

5 rate, they cannot be estimated from the (baseline) U.S. cancer incidence rates. Therefore, 

6 in Conolly et al. (2004, 2003), αmax and multfc are assumed to have the same values in 

7 humans as in rats, and the human value for KMU is obtained by assuming that the ratio 

8 KMU/μbasal is invariant across species. Thus, 

9 

( ) ( )

( )

Nbasal( ) rat

Nbasal human KMU human KMU rat

μ

μ

10 = × (4) 
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Appendix E
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APPENDIX E 

1 

2 EVALUATION OF BBDR MODELING OF NASAL CANCER IN THE F344 RAT: 

3 CONOLLY ET AL. (2003) AND ALTERNATIVE IMPLEMENTATIONS 

4 

5 A biologically based dose-response model for formaldehyde-induced cancer was 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). The model structure, 

8 notations, and calibration have been described in Appendix D. In Chapter 5, an evaluation of the 

9 uncertainties of this model and alternative approaches based on its conceptual framework was 

10 presented in a summary form. This Appendix provides the relevant details of that evaluation and 

11 presents a range of dose-response curves for tumor risk in the rat. It is divided into the following 

12 major sections. First, an overview of all the issues evaluated is provided in tabular form. The rest 

13 of the Appendix then presents only those issues which have a significant impact on model 

14 predictions. These issues pertain to the use of history controls, the uncertainty and variability in 

15 the dose-response for normal cell-replication rates, and sensitivity of model results to uncertainty 

16 in the kinetics of initiated cells. The issues have significant impact on mode of action inferences, 

17 and this is discussed in some detail. 

18 

19 E.1. TABULATION OF ALL ISSUES EVALUATED IN THE RAT MODELS 

20 Table E-1 summarizes model uncertainties and their impact as evaluated by EPA. The 

21 key uncertainties are discussed in considerably more detail in additional sections in this 

22 Appendix and in published manuscripts as denoted in the tables. 

23 

24 E.2. Statistical Methods Used in Evaluation 

25 Parameters of the alternate models shown here were estimated by maximizing the 

26 likelihood function defined by the data (Cox and Hinkley, 1974). Such estimates are referred to 

27 as maximum likelihood estimates (MLEs). Statistical confidence bounds were computed by 

28 using the profile likelihood method (Crump, 2002; Cox and Oakes, 1984; Cox and Hinkley, 

29 1974). In this approach, an asymptotic 100(1 – α)% upper (lower) statistical confidence bound 

30 for a parameter, β, in the animal cancer model is calculated as the largest (smallest) value of β

31 that satisfies 

32 

33 2[Lmax – L*(β)] = x1-2α (5) 

34 
This document is a draft for review purposes only and does not constitute Agency policy

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation 

Hoogenveen et al. (1999) solution method, which is valid only for timeindependent parameters, is accurate enough. 

Errors due to this assumption thought to be significant only at high concentration and not at human exposures. 

EPA implemented a solution method valid for time-depenparameters. Results did not differ significantly from those obtained assuming Hoogenveen et al.(1999) solutions. 

Caveat: Impact not evaluated for the case where cell replication rates vary in time. 

All SCC tumors are rapidly fatal; no incidental tumors. 

Death is expected to occur typically within 1–2 weeks of observed tumor (personal communication with R. Conolly). 

1) Overall, assumption does not impact model calibration prediction. 2) However, since 57 animals were observed to have tum

at interim sacrifice times, EPA implementation distinguisbetween incidental and fatal tumors. Time lag between observable tumor and time of death was significant comp

to time lag between first malignant cell and observable tu

Historical controls from entire NTP database were lumped with concurrent controls in studies. 

Data is on control animals, and number is large (7,684). Therefore, intercurrent mortality was not expected to be substantial. 

1) Tumor incidence in “all NTP” 10-fold higher than in “

inhalation NTP” controls. Including all NTP controls is 

considered inappropriate. 2) Low-dose response curve sensitive to use of historical controls. 3) Model inference on relevance of formaldehyde’s mutagpotential to its carcinogenicity varies from “insignificant” “highly significant,” depending on controls used. (See Appendix F for impact on human risk.) 

LI was derived from experimentally measured ULLI. 

Derived from correlating ULLI to LI measured in same experiment. 

Significant variation in number of cells per unit length of 

basement membrane. Spread in ULLI/LI ~25%. Impact orisk not evaluated. 
This document is a draft for review purposes only and does not constitute Agency policy

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation 

Pulse and continuous labeling data combined in deriving 

α

N from LI. 

Continuous LI normalized by ratio of pulse to continuous values for control data. 

Formula used for deriving 

α

N from LI is not applicable fo

pulse labeling data. Pulse labeling is measure of number cells in S-phase, not of their recruitment rate into S-phaseenough information to derive 

α

N from pulse data. Impact 

risk predictions could not be evaluated. 

To construct dose response for 

α

N, labeling data were 

weighted by exposure time (t) and averaged over all nasal sites (TWA). Flux at an exposure concentration was averaged over all nasal sites. 

Site-to-site variation in LI large and did not vary consistently with flux. No reasonable approach available for incorporating time variation in labeling in interspecies extrapolation. 

1) TWA assigns low weight to early time LI, but 

α

N for ea

is very important to the cancer process. Since pulse ULLused for t < 13 weeks, impact of these ULLIs on risk coulbe evaluated. 2) Time dependence in 

α

N derived from continuous ULLI 

does not significantly impact model predictions. 3) Site-to-site variation of 

α

N at least 10-fold and has maj

impact on model calibration. 10-fold variation in tumor incidence 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 dose and woaffect replication rate. 
This document is a draft for review purposes only and does not constitute Agency policy

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation 

Steady-state flux estimates not affected by airway and tissue reconfiguration due to long-term dosing. 

Histopathologic changes not likely to be rate-limiting factors in dosimetry. 

1) Thickening of epithelium and squamous metaplasiaoccurring at later times for the higher dose will reduce tisflux. Not incorporated in model. 2) These effects will push regions of higher flux to more posterior regions of respiratory tract. Likely to affect calibration of rat model. Uncertainty not evaluated quantitatively. 3) Calibration of PBPK model for DPXs seen to be highly sensitive to tissue thickness. 

TWA 

α

N(flux) rises above 

baseline levels only at cytolethal dose. Above such dose, 

α

N(flux) rises 

sharply due to regenerative proliferation. 

Variability in 

α

N(flux) 

rate is represented by also considering hockey-stick (threshold in dose) when TWA indicates J-shape (inhibition of cell division) description of 

α

N(flux). 

1) Uncertainty and variability in 

α

N quantitatively evaluat

be large. In addition, several qualitative uncertainties in characterization of 

α

N(flux) from LI. 

2) Several dose-response shapes, including a monotonic increasing curve without a threshold, were considered in oto adequately describe highly dispersed cell replication daSubstantial impact on low dose risk, including negative arisk. 
This document is a draft for review purposes only and does not constitute Agency policy

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation 

Dose response for 

αI was 

obtained from 

α

N, 

assuming ratio (

αI /

α

N) to 

be a two-parameter function of flux (see Figures 5-7, 5-9). Parameters were estimated by optimizing model predictions against tumor incidence data. 

(αI /

α

N) was >1.0 in line 

with the notion of I cells possessing a growth advantage over N cells. Satisfies Occam’s razor principle (Conolly et al., 2009). 

1) Estimated (

αI /

α

N) in CIIT modeling is <1.0 (growth 

disadvantage) for higher flux values and is >1.0 only at loend of flux range in model (see Figure 5-9). 2) Since there are no data to inform 

αI, sensitivity of risk 

estimates to various functional forms was evaluated. Risk estimates for the rat were extremely sensitive to alternate biologically plausible assumptions for 

αI(flux) and varied 

many orders of magnitude at 

≤1 ppm, including negative 

 All these models described tumor incidence data and cell replication and DPX data equally well. 

Death rate of I cells βI(flux) assumed = division 

rate of N cells 

α

N(flux). 

Based on homeostasis (α

N = 

β

N) and 

assumption that formaldehyde is equally cytotoxic to N cells and I cells. Satisfies Occam’s Razor principle (Conolly et al., 2009). 

1) In general, data indicate I cells are more resistant to 

cytolethality and that ADH3 clearance capacity is greater transformed cells. Therefore, plausibility of assumption (

α

N) is tenuous. 

2) Alternate assumption, 

βI proportional to 

αI, was examin

Risk estimates extremely sensitive to assumptions on 

βI (

Figure 5-12). 

DPX is dose surrogate for formaldehyde mutagenic potential. DPX clearance is rapid and complete in 18 hours. 

Casanova et al. (1994). Half-life for DPX clearance in in vitro experiments on 

transformed cell lines was sevenfold longer than estimateConolly et al. (2004, 2003) and perhaps 14-fold longer winormal (non-transformed) human cells. Some DPX accumulation therefore likely. However, model calibratioand dose response in rat is insensitive to this uncertainty. 
This document is a draft for review purposes only and does not constitute Agency policy

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation 

Formaldehyde mutagenic action takes place only while DPXs are in place. 

 DNA lesions remain after DPX removal. DPX induces

further DNA and protein damage. Potential for formaldehyde-induced mutation after DPX clearance. Thformaldehyde mutagenicity may be underrepresented. Conot quantitatively evaluate uncertainty (no data on clearanof secondary lesions). 

aReferences stated here are in addition to Conolly et al. (2004, 2003). 

Note: Risk estimates discussed in this table are for the F344 rat.
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1 where L indicates the likelihood of the rat bioassay data, Lmax is its maximum value, L*(β) is, for 

2 a fixed value of β, the maximum value of the log-likelihood with respect to all of the remaining 

3 parameters, and x1-2α is the 100(1–2α) percentage point of the chi-square distribution with one 

4 degree of freedom. The required bound for a parameter, β, was determined via a numerical 

5 search for a value of β that satisfies this equation. 

6 The additional risk is defined as the probability of an animal dying from an SCC by the 

7 age of 790 days, in the absence of other competing risks of death, while exposed throughout life 

8 to a prescribed constant air concentration of formaldehyde, minus the corresponding probability 

9 in an animal not exposed to formaldehyde. The MLE of additional risk is the additional risk 

10 computed using MLEs of the model parameters. 

11 The method described above for computing profile likelihood confidence bounds cannot 

12 be used with additional risk because additional risk is not a parameter in the cancer model. 

13 Instead, an asymptotic 100(1 – α)% upper (lower) statistical confidence bound for additional risk 

14 was computed by finding the parameter values that presented the largest (smallest) value of 

15 additional risk, subject to the inequality 

16 

17 2[Lmax – L] ≤ x1-2α (6) 

18 

19 being satisfied, with the resulting value of additional risk being the required bound. This 

20 procedure was implemented through use of penalty functions (Smith and Coit, 1995). For 

21 example, the profile upper bound on additional risk was computed by maximizing the “penalized 

22 added risk,” defined as (additional risk – penalty), where 

23 

penalty = W × {[(Lmax – L) - x1-2α/2]+}

2

24 (7) 

25 

and []+26 equals the quantity in the brackets whenever it is positive and zero otherwise. The 

27 multiplicative weight, W, was selected by trial and error so that the final solution satisfied the 

28 following equation sufficiently well. 

29 

30 2(Lmax – L) = x1-2α (8) 

31 

32 The computer code was written in Microsoft Excel® 2002 SP3 Visual Basic. Either the 

33 regular Excel Solver or the Frontline Systems Premium Solver® was used to make the required 

34 function optimizations. Computation of confidence bounds was highly computationally 

35 intensive, and, consequently, confidence bounds were computed only for selected parameters in 
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E-9 DRAFT—DO NOT CITE OR QUOTE 

1 selected runs. For select cases, the bootstrap method was also used to calculate confidence 

2 bounds in order to confirm their accuracy. Values so calculated were found to be in agreement 

3 with those calculated by using the likelihood method. 

4 

5 E.3. PRIMARY UNCERTAINTIES IN BBDR MODELING OF THE F344 RAT DATA 

6 The evaluation of the CIIT model and alternatives to this model that were implemented 

7 for the F344 rat will be discussed first. Of the issues tabulated above, the following uncertainties 

8 in the modeling of the F344 rat data will be discussed in considerably more detail: use of 

9 historical controls, uncertainty and variability in characterizing cell replication rates from the 

10 labeling data, and uncertainty in model specification of initiated cell kinetics. 

11 In their evaluation, Subramaniam et al. (2007) first attempted to reproduce the Conolly et 

12 al. (2003) results under similar conditions and assumptions as employed in their paper, which 

13 included the assumption that tumors were rapidly fatal. Figure 5-12 in Chapter 5 shows the 

14 results for this case. The predicted probabilities shown in this figure were obtained by 

15 Subramaniam et al. (2007) by using the source code made available by Dr. Conolly. These are 

16 compared with the best-fitting model and plotted against the Kaplan-Meier (KM) probabilities. 

17 Although the results are largely similar, there are some differences (Subramaniam et al., 2007). 

18 Given the scope of issues to examine for the uncertainty analyses, the evaluation 

19 proceeded in stages. First, the Hoogenveen et al. (1999) solution was replaced by one that is 

20 valid for a model with time varying parameters (first entry in Table E-1), and tumors found at 

21 scheduled sacrifices were assumed to be incidental rather than fatal (second entry in Table E-1 ). 

22 Second, weekly averaged solutions for DPX concentration levels were used instead of hourly 

23 varying solutions (predicted by a PBPK model). The log-likelihood values and tumor 

24 probabilities remained essentially unchanged. Upon quantitative evaluation, these factors, 

25 although important from a methodological point of view, were not found to be major 

26 determinants of either calibration or prediction of the model for the F344 rat data (Subramaniam 

27 et al., 2007). 

28 Subramaniam et al. (2007), as in Georgieva et al. (2003), used the DPX clearance rate 

29 constant obtained from in vitro data instead of the assumption in Conolly et al. (2003) that all 

30 DPXs cleared within 18 hours (Subramaniam et al., 2007). With this revision, weekly average 

31 DPX concentrations were larger than those in Conolly et al. (2003) by essentially a constant ratio 

32 equal to 4.21 (range of 4.12–4.36) when averaged over flux bin and exposure concentrations. 

33 Accordingly, cancer model fits to the rat tumor incidence data using the two sets of DPX 

34 concentrations (everything else remaining the same) provided very similar parameter estimates, 

except that the parameter KMUrat 35 in equation 1 (and equation 4) was 4.23 times larger with the 
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1 Conolly et al. (2003) DPX concentrations. In other words, the product KMU × DPX remained 

2 substantially unchanged. However, the different clearance rate does significantly impact the 

3 scale-up of the two-stage clonal growth model to the human. 

4 After making the above modifications, the impact of the following uncertainties, which 

5 had a large impact on modeling, was examined sequentially. 

6 

7 E.3.1. Sensitivity to Use of Historical Controls 

8 E.3.1.1. Use of Historical Controls

9 Conolly et al. (2003) combined the historical controls arising from the entire NTP 

10 database of bioassays. Tumor and survival rates in control groups from different NTP studies are 

11 known to vary due to genetic drift in animals over time and differences in laboratory procedures, 

12 such as diet, housing, and pathological procedures (Haseman, 1995; Rao et al., 1987). In order to 

13 minimize extra variability when historical control data are used, the current NTP practice is to 

14 limit the historical control data, as far as possible, to studies involving the same route of 

15 exposure and to use historical control data from the most recent studies (Peddada et al., 2007). 

16 Bickis and Krewski (1989) analyzed 49 NTP long-term rodent cancer bioassays and 

17 found a large difference in determinations of carcinogenicity, depending on the use of historical 

18 controls with concurrent control animals. The historical controls used in the CIIT modeling 

19 controls came from different rat colonies and from experiments conducted in different 

20 laboratories over a wide span of years, so it is clearly problematic to assume that background 

21 rates in these historical control animals are the same as those in the concurrent control group. 

22 There are considerable differences among the background tumor rates of SCCs in all NTP 

23 controls (13/7,684 = 0.0017), NTP inhalation controls (1/4,551 = 0.0002), and concurrent 

24 controls (0/341 = 0.0). The rate in all NTP controls is significantly higher than that in NTP 

25 inhalation controls (p = 0.01, Fisher’s exact test). Given these differences, the inclusion of any 

26 type of historical controls is problematic and is thought to have limited value if these factors are 

27 not controlled for (Haseman, 1995). 

28 

29 E.3.1.2. Influence on Model Calibration and on Human Model 

30 To investigate the effect of including historical controls in the CIIT model, the analyses in 

31 Subramaniam et al. (2007) were conducted by using the following sets of data for controls (the 

32 fraction of animals with SCCs is denoted in parentheses): only concurrent controls (0/341), 

33 concurrent controls plus all the NTP historical control data used by Conolly et al.(2003) 
This document is a draft for review purposes only and does not constitute Agency policy.

E-11 DRAFT—DO NOT CITE OR QUOTE 

1 (13/8,031), and concurrent controls plus data from historical controls obtained from NTP 

inhalation studies (1/4,949) (NTP, 2005).3

2 

3 The results of the evaluation are shown in Table E-2. For these analyses, the same 

4 normal cell replication rates and the same relationship (see eq 2) between initiated cell and 

5 normal cell replication rates as used in Conolly et al. (2003) were used. In all cases, weekly 

6 averaged values of DPX concentrations were used. Model fits to the tumor incidence data were 

7 similar in all cases (see Figure 5-12 in Chapter 5 and Subramaniam et al. [2007] for a more 

8 complete discussion). The biggest influence of the control data was seen to be on the estimated 

basal mutation rate in rats, μNbasal(rat) 9 , which, in turn, influences the estimated mutation effect in 

10 humans through eq 4 (Appendix D). αmax was also seen to be a sensitive parameter and is 

11 discussed later. See Subramaniam et al. (2007) for other parameters in the calibration. 

The ratio KMU/μNbasal 12 is of particular interest because extrapolation to human in Conolly 

et al. (2004) assumed its invariance as given by eq 4 (Appendix D). Now, μNbasal 13 in the human is 

14 estimated independently by fitting a scaled-up version of the two-stage model to human baseline 

rates of tumor incidence. Thus, a decrease in the value of μNbasal 15 estimated in the rat modeling 

16 increases the formaldehyde-induced mutational effect in the human. 

While the MLE of KMU/μNbasal 17 is zero in the CIIT animal model (Conolly et al., 2003), it 

takes a range of values from 0 to 0.9 mm3/pmol and undefined (or infinite, when μNbasal 18 = 0) in 

19 the various cases examined in this paper. The 95% upper confidence bound on this ratio ranges 

20 from 0.25–6.2 (these values would be four times larger had the Conolly et al. [2003] DPX 

21 concentrations been used) to infinite. Thus, the extrapolation to human risk by using the 

22 approach in Conolly et al. (2004) becomes particularly problematic when only concurrent 

23 controls are used, because then the mutational contribution to formaldehyde-induced risk in 

24 humans becomes unbounded. This issue will be discussed again toward the end of the discussion 

25 on historical controls. 

26 

 

3

 Three animals in the inhalation historical controls 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. Kevin Morgan and 

Ms. Betsy Gross Bermudez, personal communication). These two tumors were therefore not included on the advice 

of Dr. Morgan. See Subramaniam et al. (2007) for more details. 
This document is a draft for review purposes only and does not constitute Agency policy

E-12 DRAFT—DO NOT CITE OR QUOTE

Table E-2. Influence of control data in modeling formaldehyde-induced cancer in the F344 rat 

Case A D B E C 

Control animals (combined 

with concurrent controls) 

All NTP 

historical

a

All NTP 

historical

a

NTP 

inhalation 

historical

a

NTP 

inhalation 

historical

a

Concurre

only

a

Cell replication dose 

response J-shaped Hockey stick J-shaped Hockey stick J-shape

Log-likelihood −1692.65 −1693.68 −1,493.21 −1,493.35 −1,474.2

μNbasal 1.87 × 10–6 2.12 × 10–6 7.32 × 10–7 9.32 × 10–7 0.0 

KMU 1.12 × 10–7 0.0 6.84 × 10–7 6.18 × 10–7 1.20 × 10

KMU/μNbasal 0.06 

(0.0, 0.40) 

0.0 

(0.0, 0.25) 

0.94 

(0.26, 6.20) 

0.66 

(0.2, 5.20) 

∞

(0.42, 

∞

αmax

0.045 

(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, 0.0

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 and does not constitute Agency policy.

E-13 DRAFT—DO NOT CITE OR QUOTE 

1 It may be noted, however, that absence of tumors in the limited number of concurrent 

2 animals does not imply that the calculation will necessarily predict a zero background probability 

of tumor (i.e., a parameter estimate of μNbasal 3 = 0). We observed such a counterexample estimate 

for μNbasal in simulations involving the alternate dose-response curves for αN and αI 4 that are 

discussed later. Nonetheless, when μNbasal = 0, an upper bound for μNbasal 5 using the concurrent 

6 controls can be inferred. Accordingly, the 90% statistical lower confidence bound on the ratio 

7 KMU/μNbasal is also reported in Table E-2. Such a value would of course provide a lower bound 

8 on risk by using this model and would therefore not be conservative. 

9 Conolly et al. (2003) estimated KMU to be zero for both the hockey-stick and J-shape 

10 cell replication models. However, the estimate for the coefficient KMU (obtained using the 

11 solution of Crump et al. [2005]) is zero only for the case of the model with the hockey-stick 

12 curve for cell replication and with control data as used by Conolly et al. (2003). It is positive in 

13 all other cases and statistically significantly so in all cases in which either inhalation control data 

14 or concurrent controls were used. With concurrent controls only and the J-shape cell replication 

model, the MLE estimate for KMU (1.2 × 10–6 15 ) is larger than the statistical upper bound obtained 

by Conolly et al. (2003) (8.2 × 10–7 16 ). It should also be kept in mind that the estimate would be 

17 about 4.2 times larger still had the Conolly et al. (2003) DPX model been used. 

18 

19 E.3.1.3. Influence on Dose-Response Curve

20 Subramaniam et al. (2007) showed that inclusion of historical controls had a strong 

21 impact on the tumor probability curve below the range of exposures over which tumors were 

22 observed in the formaldehyde bioassays. As shown there, the MLE probabilities for occurrence 

23 of a fatal tumor at exposure concentrations below 6 ppm were roughly an order of magnitude 

24 higher when all the NTP historical controls were used, compared with MLE probabilities 

25 predicted when historical controls were drawn only from inhalation bioassays, and many orders 

26 of magnitude higher than MLE probabilities predicted when only concurrent controls were used 

27 in the analysis. (Note that this comparison should not be inferred to apply to upper bound risk 

28 estimates since there were many fewer concurrent than historical controls, so error bounds could 

29 be much larger in the case where concurrent controls were used.) 

30 However, as shown by these authors, model fits to the tumor data in the 6–15 ppm 

31 exposure concentration range were qualitatively indifferent to which of these control data sets 

32 was used. This observation emphasizes the statistical aspect of the CIIT modeling—that 

33 significant interplay among the various adjustable parameters allows the model to achieve a good 

34 fit to the tumor incidence data independent of the control data used. On the other hand, the 

35 results in Subramaniam et al. (2007) show that changes in the control data affect parameter 
This document is a draft for review purposes only and does not constitute Agency policy.

E-14 DRAFT—DO NOT CITE OR QUOTE 

1 KMU, resulting in significantly different tumor predictions at lower exposure concentrations. 

2 Therefore, the strong influence of using all the NTP historical controls on the low-dose region of 

3 the time-to-tumor curves presented in Subramaniam et al. (2007) suggests that large uncertainties 

4 may arise in extrapolating to both human and rat (in the low-dose region) from such 

5 considerations alone. 

6 

7 E.3.1.4. Problem Including 1976 Study for Inhalation Historical Control 

8 A crucial point needs to be noted with regard to the use of inhalation NTP historical 

9 controls (i.e., cases B and E) in the two-stage clonal growth modeling. The single relevant tumor 

10 in the NTP inhalation studies came from the very first NTP inhalation study, dated 1976, and the 

11 animals in this study were from Hazelton Laboratories, whereas the concurrent animals were all 

12 from Charles River Laboratories. Similar problems arise with inclusion of several other NTP 

13 inhalation studies. As mentioned before, genetic and other time-related variation can lead to 

14 different tumor and survival rates, and in general it is recommended that use of historical controls 

15 be restricted to the same kind of bioassays and to studies within a 5–7 year span of the concurrent 

16 animals (Peddada et al., 2007). Thus, it is problematic to assume that the tumor in the 1976 NTP 

17 study is representative of the risk of SCCs in the formaldehyde bioassays. Even if it were 

18 appropriate to consider the 1976 study, this leads to the unstable situation in which, despite all of 

19 the “upstream” mechanistic information used to construct the BBDR model, the only piece of 

20 data that might keep the model predictions of human risk bounded is a single tumor found among 

21 several thousand rats from NTP bioassays (Crump et al., 2008). In summary, although it can be 

22 argued that the rate of SCCs among the controls in the rat bioassay is probably not zero, it is also 

23 problematic to assume that this rate can be adequately represented by the background rate in NTP 

24 historical controls or even in NTP inhalation historical controls. 

25 

26 E.3.1.5. Inference on MOA

27 Subramaniam et al. (2007) also examined the contribution of the DPX component (which 

28 represents the directly mutagenic potential of formaldehyde in the model) to the calculated tumor 

29 probability, choosing for their case study the optimized models that use the NTP inhalation 

30 control data. In the range of exposures where tumors were observed (6.0–15.0 ppm), the DPX 

31 term was found to be responsible for 58–74% of the added tumor probability. Below 6.0 ppm the 

32 estimated DPX contribution was extremely sensitive to the shape of apha_n and varied between 

33 2 and 80%. 

34 The CIIT BBDR cancer modeling has contributed to the weight-of-evidence process in 

35 various formaldehyde risk assessment efforts and papers by lending weight to the argument that 
This document is a draft for review purposes only and does not constitute Agency policy.

E-15 DRAFT—DO NOT CITE OR QUOTE 

1 the direct mutations induced by formaldehyde are relatively irrelevant compared to the 

2 importance of cytotoxicity-induced cell proliferation in explaining the observed tumorigenicity in 

3 rodent bioassays and in projecting those observations to human exposures (Conolly et al., 2004, 

4 2003; Slikker et al., 2004; Bogdanffy et al., 2001, 1999; Conolly, 1995). The reanalyses in 

5 Subramaniam et al. (2007) (in particular, the results in the above paragraph) indicate that, if the 

6 CIIT mathematical modeling were utilized to inform this debate, it would in fact indicate the 

7 contrary—that a large contribution from formaldehyde’s mutagenic potential may be needed to 

8 explain formaldehyde carcinogenicity. This discussion is resumed in the context of uncertainties 

9 in model specification for initiated cells. 

10 

11 E.3.2. Characterization of Uncertainty-Variability in cell Replication Rates

12 E.3.2.1. Dose-Response for αN as Used in the CIIT Clonal Growth Modeling

13 Monticello et al. (1996, 1991) used ULLI to quantify cell replication within the respiratory 

14 epithelium. ULLI is a ratio between a count of labeled cells and the corresponding length (in 

15 millimeters) of basal membrane examined, whereas the per-cell LI is the ratio of labeled cells to 

16 all epithelial cells, in this case, along some length of basal membrane and its associated layer of 

17 epithelial cells. Monticello et al. (1996, 1991) published ULLI values averaged over replicate 

18 animals for each combination of exposure concentration, exposure time, and nasal site. These 

19 values are plotted in Figure E-1. Conolly et al. (2003) adopted the following procedure in using 

20 these values (Subramaniam et al., 2008): 

21 1. The injection labeled ULLI data were first normalized by the ratio of the average 

22 minipump ULLI for controls to the average injection labeled ULLI for controls. 

23 2. Next, these ULLI average values were weighted by the exposure times in Monticello et al. 

24 (1996, 1991) and averaged over the nasal sites. Thus, the data were combined into one 

25 TWA for each exposure concentration. 

26 3. LI was linearly related to the measured ULLI by using data from a different experiment 

27 (Monticello et al., 1990) where both quantities had been measured for two sites in the 

28 nose. 

29 4. Cell replication rates of normal cells (αN) were then calculated as αN = (−0.5/t)log(1 − LI) 

30 (Moolgavkar and Luebeck, 1992), where LI is the labeling index and t is the period of 

31 labeling. 

32 5. This was repeated for each exposure concentration of formaldehyde, resulting in one 

33 value of αN for each exposure concentration. 

34 6. Correspondingly, for a given exposure concentration, the steady-state formaldehyde flux 

35 into tissue, computed by CFD modeling, was averaged over all nasal sites. Thus, the 

36 αN(flux) constructed by Conolly et al. (2003) consisted of a single αN and a single average 

37 flux for each of six exposures. 
This document is a draft for review purposes only and does not constitute Agency policy.

E-16 DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure E-1. ULLI data for pulse and continuous labeling studies. 

3 Note: Data are from pulse labeling study, left-hand side, at 1–42 days of exposure 

4 and from the continuous-labeling study, right-hand side, at 13–78 weeks of 

5 exposure for five nasal sites ALM, AMS, MMT, PLM, and posterior mid septum 

6 [PMS]). Within each graph, lines with more breaks correspond to shorter 

7 exposure times. Data source: Monticello et al. (1996, 1991). 

0 5 10 15

0.5 5.0 50.0

Exposure < 12 wk (pulse labeling)

ALM

0 5 10 15

0.5 5.0 50.0

Exposure > 12 wk (continuous labeling)

ALM

0 5 10 15

0.5 5.0 50.0

AMS

0 5 10 15

0.5 5.0 50.0

AMS

0 5 10 15

0.5 5.0 50.0

MMT

0 5 10 15

0.5 5.0 50.0

MMT

0 5 10 15

0.5 5.0 50.0

PLM

0 5 10 15

0.5 5.0 50.0

PLM

0 5 10 15

0.5 5.0 50.0

PMS

0 5 10 15

0.5 5.0 50.0

PMS

Formaldehyde exposure concentration, ppm

Mean values of ULLI (log scale)
This document is a draft for review purposes only and does not constitute Agency policy.

E-17 DRAFT—DO NOT CITE OR QUOTE 

1 This yielded a J-shaped dose-response curve for cell replication (when viewed on a non2 transformed scale for αN), as shown in Figure D-1 (Appendix D) for the full range of flux values 

3 used in their modeling. The authors also considered a hockey-stick threshold representation of 

4 their J-shaped curve for αN in order to make a health-protective choice, and the differences 

5 between the two can be seen from the insets in Figure D-1. In these curves, the cell replication 

6 rate is less than or the same as the baseline cell replication rate at low formaldehyde flux values. 

7 The shape of the dose-response curve for cell replication as characterized in Conolly et al. (2003) 

8 is seen as representing regenerative cell proliferation secondary to the cytotoxicity of 

9 formaldehyde (Conolly, 2002). Considerable uncertainty and variability, both quantitative and 

10 qualitative, exist in the use and interpretation of these labeling data for characterizing a dose 

11 response for cell replication rates. The primary issues are discussed here. Unlike the preceding 

12 sections, these have largely not been published elsewhere, so more details are provided. 

13 

14 E.3.2.2. Uncertainty in the Use of Pulse Labeling Data, and Short-Time Exposure Effects on 

15 Cell Replication

16 The formula used for obtaining αN from LI in Conolly et al. (2003) was due to 

17 Moolgavkar and Luebeck (1992) who derived this formula for continuous LI, cautioning that it is 

18 not applicable for pulse labeled data. However, Conolly et al. (2003) applied this formula to the 

19 injection (pulse) labeled data also. Such an application is problematic because 2-hour pulse 

20 labeled data represent the pool of cells in S-phase rather than the rate at which cells are recruited 

21 to the pool and because the baseline values of αN obtained in this manner from both data sets 

22 differ considerably. As such, we are not aware of any reasonable manner to derive cell 

23 replication rates from these pulse data without acquisition of data at additional time points. 

24 Therefore, the quantitative analysis of cell replication rates is restricted in this document to the 

25 continuous labeled data (Monticello et al., 1996), which does not include measurements made 

26 before 13 weeks of exposure. 

27 It is unfortunate that the continuous labeled data do not include any early measurements 

28 because, as indicated by Figure E-1, the temporal variation in the unit-length LI (ULLI, the raw 

29 data) is quite different between the “early time” (left panel) and “later time” (right panel) and 

30 these early-time effects may be quite important to the cancer modeling. At the earliest times in 

31 the left panel, the data show an increased trend in labeling at 2 ppm for the sites anterior lateral 

32 meatus (ALM), anterior medial septum (AMS), posterior lateral meatus (PLM), and medial 

33 maxilloturbinate (MMT) relative to control. (Also see the dose-response plotted as a function of 
This document is a draft for review purposes only and does not constitute Agency policy.

E-18 DRAFT—DO NOT CITE OR QUOTE 

1 flux in Figure E-4 for the 13-week exposure time, where such an increase is generally indicated 

2 for low flux values.) 

3 The early times would be important if, say, repeated episodic exposures were considered, 

4 where adequate time has not elapsed for adaptive effects to take place. Such an exposure 

5 scenario may be the norm in the human context. However, the contribution of the early-time 

6 labeling data is minimized in the CIIT cancer modeling since the LI was weighted by exposure 

7 time. Because of the problems described above in incorporating the pulse-labeled data, the 

8 sensitivity analysis will be restricted to only the continuous labeling data. 

9 

10 E.3.2.3. Site and Time Variability

11 In the remainder of this section, the factors that are considered in order to represent the 

12 uncertainty and variability in the cell replication data when developing alternate dose-response 

13 curves for αN(flux) will be elaborated. Figure E-2 (from Subramaniam et al., 2008) shows the 

14 variability due to replicated animals, exposure times, and nasal sites in the continuous labeled 

15 data obtained by Monticello et al. (1996). The ULLI data for individual animals were provided 

16 by CIIT. In this figure, log αN versus site-specific flux are plotted for six sites and four exposure 

17 times for four to six replicate animals in each case. (The mean ULLI over these replicates were 

18 shown in Figure E-1 for each site and time as a function of exposure concentration.) It needs to 

19 be noted that these nasal sites differ considerably in the number of cells estimated at these 

20 locations as shown in Table E-3. Each point in Figure E-2 represents data from a single site for a 

21 single animal at a given time. For comparison, the αN(flux) in Conolly et al. (2003) is also 

plotted in this figure at their averaged flux values (filled circles). For flux >9,340 pmol/mm222 -

23 hour, Conolly et al. (2003) extrapolated this empirically derived αN(flux) by using a scheme 

24 discussed in Appendix D (section D.5) on the upward extrapolation of cell replication rate. The 

25 curves shown connecting the filled circles in the figure represent their linear interpolation (long 

dashes) between the six points. Their linear extrapolation for flux value >9,340 pmol/mm2

26 -hour 

27 is also shown (short dashes). Note that the linear interpolation/extrapolation is shown 

28 transformed to a logarithmic scale. 

29 In Figures E-3, fitted dose-response curves are plotted for log10(αN) versus flux with 

30 simultaneous confidence limits separately for each time point for two of the largest sites in Table 

31 E-3 (ALM and PLM). Note that flux levels are different at each site. Simple polynomial models 

32 in flux (as a continuous predictor), with time included as a factor (i.e., a class or indicator 

variable, τi

 representing the effect of the ith 33 time) were used as follows: 

34 

log(αN) = a + b×flux + c×flux2 + d×flux335 + τi (9) 
This document is a draft for review purposes only and does not constitute Agency policy.

E-19 DRAFT—DO NOT CITE OR QUOTE 

0 2000 4000 6000 8000 10000 12000

-5 -4 -3 -2

ADS

ALM

AMS

MMT

PLM

PMS

1 

2 

3 Figure E-2. Logarithm of normal cell replication rate αN versus 

formaldehyde flux (in units of pmol/mm2

4 -hour) for the F344 rat nasal 

5 epithelium. 

6 

7 Note: Values were derived from continuous unit length labeled data obtained by 

8 Monticello et al. (1996) for four to six individual animals at all six nasal sites 

9 (legend, sites as denoted in original paper) and four exposure durations (13, 26, 

10 52, 78 weeks). Each point represents a measurement for one rat, at one nasal site, 

11 and at a given exposure time. Filled red circles: αN(flux) used in Conolly et al. 

12 (2003) plotted at their averaged flux values (see text for details). Long dashed 

13 lines: their linear interpolation between points. Short dashed line: their linear 

extrapolation for flux value >9,340 pmol/mm2

14 -hour (see Figure D-1 for full range 

15 of extrapolation). Linear interpolation/extrapolation is shown with y-axis 

16 transformed to logarithmic scale. 

17 

18 Source: Subramaniam et al. (2008). 
This document is a draft for review purposes only and does not constitute Agency policy.

E-20 DRAFT—DO NOT CITE OR QUOTE 

1 Table E-3. Variation in number of cells across nasal sites in the F344 rat 

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 

4 Note: Mean number of cells in each side of the nose of control animals. 

5 

6 Source: Monticello et al. (1996). 
This document is a draft for review purposes only and does not constitute Agency policy.

E-21 DRAFT—DO NOT CITE OR QUOTE 

1 

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

13 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

26 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

52 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

78 weeks

log10

α

log10

α

log10

α

log10

α

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

13 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

26 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

52 weeks

Flux

log10(alpha)

0 4000 8000 12000

-5 -4 -3 -2

78 weeks

log10

α

log10

α

log10

α

log10

α

2 

3 

4 Figure E-3A. Logarithm of normal cell replication rate versus formaldehyde 

5 flux with simultaneous confidence limits for the ALM. 

6 

7 Source: Subramaniam et al. (2008). 
This document is a draft for review purposes only and does not constitute Agency policy.

E-22 DRAFT—DO NOT CITE OR QUOTE 

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

13 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

26 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

52 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

78 weeks

log10

α log10

α

log10

α log10

α

log10

α

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

13 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

26 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

52 weeks

Flux

log10(alpha)

0 2000 6000

-5 -4 -3 -2

78 weeks

log10

α log10

α

log10

α log10

α

log10

α

1 

2 Figure E-3B. Logarithm of normal cell replication rate versus formaldehyde 

3 flux with simultaneous confidence limits for the PLM. 

4 

5 Source: Subramaniam et al. (2008). 

6 

7 

8 The variability considered is that among animals and any measurement error as well as 

9 any other design-related components of error. Simultaneous 95% confidence limits for log(αN) 

10 were produced using Scheffe’s method (Snedecor and Cochran, 1980). These 95% confidence 

11 limits span a range of 0.96 in log10(αN), or nearly a 10-fold range in median αN. There is 

12 additional dispersion in these data that does not appear in Figures E-2 and E-3; due to variation 

13 in the number of cells per mm basement membrane, the ratio of ULLI/LI had a spread of 

14 approximately ±25% (0.45 to 0.71, mean 0.60) among the eight observations considered in 

15 Monticello et al. (1990). Thus: 
This document is a draft for review purposes only and does not constitute Agency policy.

E-23 DRAFT—DO NOT CITE OR QUOTE 

1 1. As suggested by Table E-3, and Figures E-2 and E-3, the shape of αN(flux) in Conolly et 

2 al. (2003) is therefore likely to be very sensitive to how αN is weighted and averaged over 

3 site and time. 

4 2. Averaging of sites could significantly affect model calibration because of substantial 

5 nonlinearity in model dependence on αN at the 10 and 15 ppm doses associated with high 

6 cancer incidence. 

7 3. Monticello et al. (1996) found a high correlation between tumor rate and the ULLI 

8 weighted by the number of cells at a site. Therefore, considering these factors while 

9 regressing αN against tissue dose would be important in the context of site differences in 

10 tumor response. 

11 4. A further complexity arises because of histologic changes and thickening that occurs in 

12 the nasal epithelium over time in the higher dose groups (Morgan, 1997), factors that are 

13 likely to affect estimates of local formaldehyde flux, uptake, and replication rates 

14 (Subramaniam et al., 2008). 

15 

16 Figure E-1 indicates that the time dependence in ULLI is significant. It would also be 

17 useful to examine if the time dependence affects the results of the time-to-tumor modeling and if 

18 early temporal changes in replication rate are important to consider because of the generally 

19 cumulative nature of cancer risk. The time window over which formaldehyde-induced cancer 

20 risk is most influenced is not known, but the time weighting used by Conolly et al. (2003) assigns 

21 a relatively low weight to labeling observed at early times compared with those observed at later 

22 time points. Finally, initiated cells are likely to be replicating at higher rates than normal cells as 

23 evidenced in several studies on premalignant lesions (Coste et al., 1996; Dragan et al., 1995; 

24 Rotstein et al., 1986). Therefore, LI data as an estimator of normal cell replication rate would be 

25 most reliable at early times when the mix of cells sampled include fewer preneoplastic or 

26 neoplastic cells. 

27 The more relevant question, therefore, is whether αN(flux) derived by a TWA over all 

28 sites (as carried out by Conolly et al. [2003]) has an effect on low-dose risk estimates. Given the 

29 above uncertainties and variability not characterized in CIIT (1999) or in Conolly et al. (2003), it 

30 is important to examine whether additional dose-response curves that fit the cell replication data 

31 reasonably well have an impact on estimated risk. Such sensitivity analyses are carried out in the 

32 sections that follow. Clearly, a large number of alternative αN(flux) can be developed. In 

33 conjunction with the other uncertainties, mainly the use of control data and alternative model 

34 structures for initiated cell kinetics, the number of plausible clonal growth models to be exercised 
This document is a draft for review purposes only and does not constitute Agency policy.

E-24 DRAFT—DO NOT CITE OR QUOTE 

1 soon require a prohibitively large investment of time. Therefore, detailed analyses were 

2 restricted to a select set of biologically plausible choices of curves for αN(flux), which would 

3 allow the identification of a range of plausible risk estimates (MLEs and statistical bounds). 

4 

5 E.3.2.4. Alternate dose-response curves for cell replication. 

6 Six alternative equations for αN were developed by regression analysis of the Monticello 

7 et al. (1996) ULLI data. The replicate data corresponding to the summary data presented in this 

8 paper were kindly provided to EPA by CIIT for further analyses. In each of these equations, αN is 

expressed as a function of formaldehyde flux to nasal tissue (pmol/mm29 -hour) and, in one 

10 equation (eq 15) that explored time-dependence, the duration of exposure to formaldehyde in 

11 weeks. All the graphs use flux/10,000 for the x-axis, and the y-axis expresses log10 αN. 

12 One source of uncertainty in the cell proliferation dose response in Conolly et al. (2003) 

13 is the large value of αmax in the upward extrapolation (the cell replication rate corresponding to 

14 the upper end of the flux range at 15 ppm exposure). The optimal value of αmax was found by 

Conolly et al. (2003) to be 0.0435 hour–1 15 . As noted by the authors, an argument in support of this 

16 value is that it corresponds to the inverse of the fastest cell cycle times found in the literature. 

17 Since the model treats the induced replication rates as being time invariant, this means that cells 

18 in the high-flux region(s) divide at the highest cell turnover rate ever observed throughout most 

19 of an animal’s life. This does not seem to be biologically plausible (Subramaniam et al., 2008). 

20 In the analysis, it was found that a 20% increase or decrease in the estimated value for 

21 αmax degraded the fit to the tumor incidence data considerably. Because of the interplay between 

22 the parameters estimated by optimization, this sensitivity of the model to αmax indicates that it is 

23 necessary to examine if other plausible values of αmax are also indicated by the data and to what 

24 extent low dose estimates of risk are influenced by the uncertainty in its value. The need for such 

25 an analysis is also indicated by Figure E-2. The value of αmax (log10αmax = −1.37) in the 

26 modeling of Conolly et al. (2003) is roughly an order of magnitude greater than the values of 

27 αN(flux) at the highest flux levels in this figure. If the data pooled over all sites and times are to 

28 be used for αN(flux), then, based solely on the trend in αN(flux) in Figure E-2, it appears unlikely 

29 that αN(flux) could increase up to this value of αmax. Visually, these empirically derived data 

30 collectively suggest that αN versus flux could be leveling off rather than increasing 10-fold. 

31 Therefore, as an alternative to the approach taken in Conolly et al. (2003) of estimating αmax via 

32 likelihood optimization against the tumor data, regressions of the empirical cell replication data 

33 were used to extrapolate αN(flux) outside the range of observation (recognizing the uncertainty 
This document is a draft for review purposes only and does not constitute Agency policy.

E-25 DRAFT—DO NOT CITE OR QUOTE 

1 and model dependence that still results from extrapolating well outside the range of observed 

2 data). 

3 In fitting dose-response curves to the cell replication data, a functional form was used that 

4 was flexible to allow a variety of monotonic and non-monotonic shapes, with a parameter that 

5 determined the asymptotic behavior of the dose-response function. This allowed the 

6 extrapolation by only relying on the empirical cell replication data without using an adjustable 

7 parameter estimated by fitting to the tumor data. However, the plausible asymptotes obtained 

8 spanned a large range. In one case below, the asymptote suggested by the fit was judged to be 

9 abnormally high. In this case, the αN versus flux curve was followed until the biological 

10 maximum of αmax (as given in Conolly et al. [2003]) was reached. 

11 In three of the regression models below, the data were restricted to the earliest exposure 

12 time (13 weeks) in Monticello et al. (1996) for which the cell proliferation rate (αN) could be 

13 calculated. The interest in using only the 13-week exposure time arises from observations 

14 (Monticello et al., 1996, 1991) that at later times there were more frequent and severe histologic 

15 changes, which may have altered formaldehyde uptake and cell proliferation response. 

16 Consequently, given that the data in Monticello et al. (1991) for times earlier than 13 weeks 

17 could not be utilized as explained earlier, the 13-week responses might better represent 

18 proliferation rates for use in a two-stage model of the cancer process than the rest of the 

19 Monticello et al.(1996) data. 

20 Second, the LI data showed considerable variation among nasal sites, which may be 

21 related to the variation in tumor response among sites. Since the cell replication dose-response 

22 curves used in the cancer model represent all of the sites, it was attempted to include this 

23 variation by weighting the regression by the relative cell populations at risk at each of the sites. 

24 This was carried out for some of the models as stated below. The following models (denoted 

25 N1–N6), shown in Figure E-4, have been included in addition to using the hockey stick- and J26 shaped curves in Conolly et al. (2003). Applicable equations are as follows: 

27 

28 N1: Quadratic; monotone increasing in flux, derived from fit to all of the Monticello et al. (1996) 

29 ULLI data. 

30 

αN = Exp{–2.015 – 6.513 × Exp[– (6.735×10–4 × flux)231 ]} (10) 

32 

33 N2: Linear-quadratic; decreasing in flux for small values of flux, derived from fit to all of the 

34 Monticello et al. (1996) ULLI data. 
This document is a draft for review purposes only and does not constitute Agency policy.

E-26 DRAFT—DO NOT CITE OR QUOTE 

x

y

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 

2 Figure E-4, N1. Various dose-response modeling of normal cell replication 

3 rate. 

4 Note: See text for definitions of N1–N6. N1: Quadratic; monotone increasing in 

5 flux, derived from fit to all of the Monticello et al. (1996) ULLI data.

weighted mean flux/10,000

weighted 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 

7 Figure E-4, N2: Various dose-response modeling of normal cell replication 

8 rate. 

9 Note: See text for definitions of N1–N6. N2: Linear-quadratic; decreasing in flux 

10 for small values of flux, derived from fit to all of the Monticello et al. (1996) 

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

E-27 DRAFT—DO NOT CITE OR QUOTE 

weighted mean flu x/10 ,000

weig hted m ean alpha

0.0 0.2 0 .4 0. 6 0.8 1.0

-8 -7 -6

Time = 13 weeks

1 

2 Figure E-4, N3. Various dose-response modeling of normal cell replication 

3 rate. 

4 

5 Note: See text for definitions of N1–N6. N3: Linear-quadratic; decreasing in flux 

6 for small values of flux, derived from fit to the 13-week Monticello et al. (1996) 

7 ULLI data, using average flux over all sites for a given ppm exposure and 

8 weighting regression by estimates of the numbers of cells at each of five sites.

flux/10,000

log(alpha)

0.0 0.2 0.4 0.6 0.8 1.0 1.2

-10 -8 -6 -4

Time = 13 weeks

9 

10 Figure E-4, N4. Various dose-response modeling of normal cell replication 

11 rate. 

12 

13 Note: See text for definitions of N1–N6. N4: Quadratic; monotone increasing in 

14 flux, derived from unweighted fit to 13-week Monticello et al. (1996) ULLI data. 
This document is a draft for review purposes only and does not constitute Agency policy.

E-28 DRAFT—DO NOT CITE OR QUOTE 

weighted mean flux/10,000

weighted mean alpha

0.0 0.2 0.4 0.6 0.8 1.0

-8 -7 -6

Time = 13 weeks

1 

2 Figure E-4, N5. Various dose-response modeling of normal cell replication 

3 rate. 

4 

5 Note: See text for definitions of N1–N6. N5: Linear-quadratic-cubic; initially 

6 increasing slightly with increasing flux, then decreasing slightly, and finally 

7 increasing, derived from fit to 13-week Monticello et al. (1996) ULLI data, using 

8 average flux over all sites for a given ppm exposure and weighting regression by 

9 estimates of the numbers of cells at each of five sites. 

10 
This document is a draft for review purposes only and does not constitute Agency policy.

E-29 DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure E-4, N6. Various dose-response modeling of normal cell replication 

3 rate. 

4 

5 Note: See text for definitions of N1–N6. N6: Linear-quadratic-cubic; initially 

6 increasing slightly with increasing flux, then decreasing slightly, and finally 

7 increasing, derived from fit to all Monticello et al. (1996) ULLI data, using weeks 

8 of exposure as a covariate. In this model, time was a regression (continuous) 

9 predictor, not a class variable, and its coefficient represents the decrease in log10

10 αN per week of exposure time. 

11 

12 

αN = Exp{–5.906 – 2.272 × Exp[2.188×10–4 × flux – (2.162×10–4 × flux )213 ]} (11) 

14 

15 N3: Linear-quadratic; decreasing in flux for small values of flux, derived from fit to the 13-week 

16 Monticello et al. (1996) ULLI data, using average flux over all sites for a given ppm exposure 

17 and weighting regression by estimates of the numbers of cells at each of five sites. 

x

y

0.0 0.2 0.4 0.6 0.8 1.0 1.2

-12 -8 -6 -4

Time = 13

x

y

0.0 0.2 0.4 0.6 0.8 1.0 1.2

-12 -8 -6 -4

Time = 26

x

y

0.0 0.2 0.4 0.6 0.8 1.0 1.2

-12 -8 -6 -4

Time = 52

x

y

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 Flux
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E-30 DRAFT—DO NOT CITE OR QUOTE 

. αN = Exp{–5.274 – 2.792 × Exp[1.407×10–4 × flux – (1.986×10–4 × flux)21 ]} (12) 

2 

3 N4: Quadratic; monotone increasing in flux, derived from unweighted fit to 13-week Monticello 

4 et al. (1996) ULLI data. 

5 

αN = Exp{–3.858 – 4.809 × Exp[– (9.293×10–5 × flux)26 ]} (13) 

7 

8 N5: Linear-quadratic-cubic; initially increasing slightly with increasing flux, then decreasing 

9 slightly, and finally increasing, derived from fit to 13-week Monticello et al. (1996) ULLI data, 

10 using average flux over all sites for a given ppm exposure and weighting regression by estimates 

11 of the numbers of cells at each of five sites. 

12 

αN = Exp{–5.488 – 2.755 × Exp[–7.808×10–5 × flux + (2.349×10–4 × flux)2 13 

– (2.166×10–4 × flux)314 ]} (14)

15 

16 N6: Linear-quadratic-cubic; initially increasing slightly with increasing flux, then decreasing 

17 slightly, and finally increasing, derived from fit to all Monticello et al. (1996) ULLI data, using 

18 weeks of exposure as a covariate. In this model, time was a regression (continuous) predictor, 

19 not a class variable, and its coefficient represents the decrease in log10 αN per week of exposure 

20 time. 

21 

αN = Exp{7.785×10–3 × (weeks) – 5.722 – 2.501 × Exp[1.103×10–4 22 × flux 

– (7.223×10–5 × flux)2

 – (1.575×10–4 × flux)323 ]} (15) 

24 

25 Further details on the above regressions are provided in the appendix. These regressions 

26 of the cell replication data as well as the hockey-stick and J-shaped curves used by Conolly et al. 

27 (2003) (shown in Figure D-1, Appendix D) are used next as inputs to the clonal growth model for 

28 cancer. 

29 

30 E.3.3. Uncertainty in Model Specification of Initiated Cell Replication and Death 

31 E.3.3.1. Biological Inferences of Assumptions in Conolly et al. (2003) 

32 The results of a two-stage MVK model are extremely sensitive to the values for initiated 

33 cell division (αI) and death (βI) rates, particularly in the case of a sharply rising dose-response 

34 curve as in the case of formaldehyde. The pool of cells used for obtaining the available LI data 

35 (Monticello et al., 1996, 1991) consists of largely normal cells with perhaps increasing numbers 
This document is a draft for review purposes only and does not constitute Agency policy.

E-31 DRAFT—DO NOT CITE OR QUOTE 

1 of initiated cells at higher exposure concentrations. As such there is no way of inferring the 

2 division rates of initiated cells in the nasal epithelium, either spontaneous (baseline) or induced 

3 by exposure to formaldehyde, from the available empirical data. Conolly et al. (2003) considered 

4 αI(flux) as a function of αN(flux) as given by eq 2 in Appendix D. As shown in Figure D-1 

5 (Appendix D), αI is estimated in Conolly et al. (2003) to be very similar to αN. That is, with eq 2 

6 assumed to relate αI(flux) to αN(flux), a J- or hockey-shaped dose-response curve for αN(flux) 

7 results in a J or hockey shape for αI(flux). 

8 The J shape for the TWA αN(flux) in Conolly et al. (2003) could plausibly be explained, 

9 as suggested by the examples in Conolly and Lutz (2004), by a mathematical superposition of 

10 dose-response curves describing the effects of the inhibition of cell replication by the formation 

11 of DPXs (Heck and Casanova, 1999) and cytotoxicity-induced regenerative replication (Conolly, 

12 2002). However, as explained earlier, there is considerable uncertainty and variability, both 

13 qualitative and quantitative, in the interpretation of the LI data and in the derivation of normal

14 cell replication rates from the ULLI data. While the TWA values of ULLI indicate a J-shaped 

15 dose response for some sites, as also concluded by Gaylor et al. (2004), this is not consistently 

16 the case for all exposure times and sites as discussed earlier. Notwithstanding this uncertainty 

17 variability, and in the absence of data, the following essential questions have a significant impact 

18 on risk predictions and need resolution if the model structure in eq 2 is to be used in a 

19 biologically based (or motivated) sense to predict risk outside observable data: 

20 

21 ● Should mechanisms that might explain a J-shaped dose response for normal cell 

22 replication or a cytotoxicity-driven threshold in dose response (as indicated by a hockey23 stick-shaped curve) be expected to prevail also for initiated cells? 

24 ● Would the formaldehyde flux at which the cell replication dose-response curve rises 

25 above its baseline be similar in value for both normal and initiated cells as inferred by the 

26 CIIT model in Figure D-1? 

27 

 The next critical assumption was that made for βI 28 (the death rate of initiated cells), 

29 namely, βI(flux) = αN(flux) (eq 3). In Subramaniam et al. (2008), the rationale for this 

30 assumption in Conolly et al. (2003) is explained by assuming formaldehyde to be equally 

31 cytotoxic to initiated and normal cells (since the mechanism is presumed to be via its general 

32 chemical reactivity). In essence, this assumption brings the cytotoxic action of formaldehyde to 

33 bear strongly on the parameterization of the CIIT model. 
This document is a draft for review purposes only and does not constitute Agency policy.

E-32 DRAFT—DO NOT CITE OR QUOTE 

1 There are no data to evaluate the strength of these assumptions, so Subramaniam et al. 

2 (2008) studied the plausibility of various inferences that arise as a result of these assumptions. 

3 These inferences are only briefly listed here (see the paper for further discussion). 

4 

● For flux <27,975 pmol/mm2-hour, αI 5 > αN (Figures D-1 & D-2 of Appendix D). 

6 Qualitatively, this is in line with data on epithelial and other tissue types with or without 

7 exposure to specific chemicals. 

● For higher flux levels, however, the model indicates αI 8 < αN (Figure D-2). There are no 

9 data to shed further light on this inference. 

10 ● At these higher flux levels, initiated cells in the model die at a faster rate than they divide, 

11 indicating the extinction of initiated cell clones in regions subject to these flux levels. 

12 There are no data indicating formaldehyde to have this effect. 

13 

14 In evaluating these inferences, Subramaniam et al. (2008) point to various data that 

15 indicate that initiated cells represent distinctly different cell populations (from that of normal 

16 cells) with regard to proliferation response (Ceder et al., 2007; Bull, 2000; Schulte-Hermann et 

17 al., 1997; Coste et al., 1996; Dragan et al., 1995), have excess capacity to clear formaldehyde 

18 and, in general, are considerably more resistant to cytotoxicity (such a resistance is manifested 

19 variably as decreased ability of the toxicant to induce cell death or to inhibit cell proliferation 

20 compared to corresponding effects in normal cells), and may already have altered cell cycle 

21 control; thus, the influence of formaldehyde on apoptosis likely differs between normal and 

22 initiated cells. 

23 As concluded in Subramaniam et al. (2008), taken together, there is much data to suggest 

that inferring αI 24 < αN at cytotoxic formaldehyde flux levels is problematic and that death rates of 

25 initiated cells are likely to be very different from those of normal cells. In the absence of data to

26 indicate that eq 2 and eq 3 (in Appendix D) are biologically reasonable approaches to link the 

27 kinetics of initiated cells with those of normal cells, alternate model structures other than those 

28 represented by these relationships considered by Conolly et al. (2003) need to be explored, given 

that the two-stage model is extremely sensitive to αI and βI 29 . Such an evaluation needs to 

30 primarily explore if the assumptions in eq 2 and eq 3 significantly impact the intended use of the 

31 model, namely extrapolation to low-dose human cancer risk and the calculation of an upper 

32 bound on human risk. Any such alternate model structure needs to provide a good fit to the time33 to-tumor data. 

34 
This document is a draft for review purposes only and does not constitute Agency policy.

E-33 DRAFT—DO NOT CITE OR QUOTE 

E.3.3.2. Plausible Alternative Assumptions for αI 1 and βI

2 Therefore, in the additional sensitivity analysis presented here, initiated cell kinetics are 

3 considered to be independent of normal cells, and initiated cell proliferation cannot take a J shape 

4 (motivated by the consideration that lower-than-baseline turnover rate represents an increased 

5 amount of DNA repair taking place, which may not be consistent with impaired DNA repair in 

6 initiated cells). 

7 Thus, two alternatives were considered to eq 2 for αI(flux): 

8 

I1: αI 9 = γ1 × [1 + exp(γ2 / γ3)] / {1 + exp[–(flux – γ2) / γ3]} (16) 

10 

I2: αI 11 = max[αI(eq. I1), αNBasal] (17) 

12 

13 Here γ1,γ2, and γ3 are parameters estimated by fitting the cancer model to the rat bioassay 

data. In eq 16, αI 14 increases monotonically with flux from a background level of γ1 asymptotically 

15 up to a maximum value of γ1 × [1 + Exp(γ2 / γ3)]. The choice of this functional form in eq 16 

16 and eq 17 was considered in order to be parsimonious while at the same time allowing for a 

17 flexible shape to the dose-response curve. The sigmoidal curve allows for the possibility of a 

18 slow rise in the curve at low dose and an asymptote. 

19 Equation 17 is a modification of equation 16 that restricts the rate of division of initiated 

20 cells to be at least as large as the spontaneous division rate of unexposed normal cells. There is 

21 evidence to suggest (e.g., in the case of liver foci) that initiated cells have a growth advantage 

22 over normal cells, with or without exposure to specific chemicals (Ceder et al., 2007; Grasl23 Kraupp et al., 2000; Schulte-Hermann et al., 1999; Coste et al., 1996; Dragan et al., 1995). 

In addition, in most runs, an upper bound (αhigh) is selected for both αN and αI 24 . This value 

25 is assumed to represent the largest biologically plausible rate of cell division. Following Conolly 

et al. (2003), in most cases αhigh is set equal to 0.045 hours–1. If a value of αI 26 or αN computed 

27 using one of the above formulas exceeded αhigh, the value of αhigh was used in the computation 

28 rather than the value obtained by using the formula. 

As noted above, Conolly et al. (2003) set the rate of death for intermediate cells, βI 29 , equal 

to the division rate of normal cells, βI 30 = αN. On the other hand, apoptotic rates and cell 

31 proliferation rates are thought to be coupled (Schulte-Hermann, 1999; Moolgavkar, 1994), so that 

32 death rates of initiated cells would rise concomitantly with an increase in their division rates 

33 (Grasl-Kraupp et al., 2000; Schulte-Hermann et al., 1999). Therefore, as an alternative to the 

34 Conolly et al. (2003) formulation, it is assumed that the death rate of intermediate cells is 

35 proportional to the division rate of intermediate cells. 
This document is a draft for review purposes only and does not constitute Agency policy.

E-34 DRAFT—DO NOT CITE OR QUOTE 

βI = Κβ × αI 1 (18) 

2 

3 where the constant of proportionality, Κβ, is an additional parameter to be estimated by 

4 optimization against the tumor incidence data. Such an assumption has also been made by other 

5 authors (Luebeck et al., 2000, 1995; Moolgavkar et al., 1993). 

6 Since most of the SCCs in the rat bioassays occurred in rats exposed to the highest 

7 formaldehyde concentration (15 ppm), the data from this exposure level have a big impact on the 

8 estimated model parameters. In most runs that incorporated the 15 ppm data, the model 

9 appeared, based on inspection of the KM plots, to fit the 15 ppm data quite well but to fit the 

10 lower exposure data less well. Because of the high level of necrosis occurring at 15 ppm, it is 

11 possible that the data at this exposure may not be particularly relevant to modeling the sharp 

12 upward rise in the dose response at 6 ppm. Furthermore, the principal interest is in the 

13 predictions of the model at lower levels to which human populations may be exposed. 

14 Consequently, in order to improve the fit of the model at lower exposures, some of the alternative 

15 models were constructed with the 15 ppm data omitted. 

16 

E.3.4. Results of Sensitivity Analyses on αN, αI, and βI 17 

18 E.3.4.1. Further Constraints 

19 The number of models that might be constructed if all the possibilities listed above for 

αN, αI, and βI 20 are to be tried in a systematic manner clearly become exponential and daunting. 

21 (Optimally, it would have been desirable to elucidate the role of a specific modification while 

22 keeping others unchanged to determine risk.) Therefore, in order to carry out a viable sensitivity 

23 analysis while at the same time examining the plausible range of risks resulting from variations 

24 in parameters and model structures, various uncertainties were combined in any given 

simulation. By using the constraints described above (eq 10–17 and associated text) for αI , βI 25 , 

26 and αN, 19 models were obtained that provided similarly good fits to the time-to-tumor data 

27 (which in some cases contained only five dose groups). 

28 However, for many of these models, the optimal αI(flux) displayed a threshold in flux 

29 even when the model utilized for αN(flux) was a monotonic increasing curve without a threshold 

30 (i.e., model N4 for αN in Figure E-4). Indeed, if a thresholded dose-response curve was plausible 

for αI 31 based on arguments of cytotoxicity, then a threshold is all the more plausible for αN, and 

32 such models are removed from consideration. 

 Secondly, the basal value of αI 33 was required to be at least as large as the basal value of αN.

34 Another constraint was placed on the baseline initiated cell replication rate. In the absence of 
This document is a draft for review purposes only and does not constitute Agency policy.

E-35 DRAFT—DO NOT CITE OR QUOTE 

formaldehyde exposure, αI 1 was not allowed to be greater than two or four times αN, even if such 

2 models described the tumor data, including the control data, very well. There are some data that 

3 suggest that baseline initiated cells have a small growth advantage over normal cells, so a huge 

4 advantage was thought to be biologically less plausible. 

5 

6 E.3.4.2. Sensitivity of Risk Estimates for the F344 Rat

7 Figure E-5 contains plots of the MLE of additional risk computed for the F344 rat at 

8 formaldehyde exposures of 0.001, 0.01, 0.1, and 1 ppm for eight models. Two log-log plots are 

9 provided. For those models for which the estimates of additional risk are all positive, the 

10 additional risks are plotted (panel A), and, for those for which estimates of additional risk are 

11 negative, the negatives of additional risks are plotted (panel B). Only five dose groups were 

12 considered (i.e., 15 ppm data omitted) for models 8, 5, 15, and 16. Figure E-6 shows the doseresponse curves for αN and αI 13 for these eight cases (panels A and B corresponding to those in 

14 Figure E-5). The primary results are as follows: 

15 

16 1. Among the models considered, negative values for additional risk can arise only in 

17 models in which the dose response for normal cells is J shaped. Thus, all of the models 

18 with negative dose responses for risk have J-shaped dose responses for normal cells. 

19 However, the converse is not necessarily true as may be noted from model 8. This model 

20 has both a positive dose response for risk and a J-shaped dose response for normal cells. 

21 In this case, the strong positive increase in response of initiated cells at low dose was 

22 sufficient to counteract the negative response of normal cells. 

23 2. The risk estimates predicted by the different models span a very large range for doses 

24 below which no tumors were observed. This result points to large uncertainties in model 

25 specification (how to relate the kinetics of normal and initiated cells) as well as in 

26 parameter values. As mentioned above, the analysis does not attempt to separate the 

27 influence of the different sources of uncertainty, so this range also incorporates the 

28 uncertainty arising from the use of different control data and that due to αmax. 

3. At the 10 ppb (0.01 ppm) concentration, MLE risks range from −4.0 ×10–6 to +1.3 ×10–7 29 .

30 At this dose, models that gave only positive risks resulted in a five orders of magnitude 

risk range from 1.2 ×10–12 to 1.3 ×10–7 31 , while narrowing to a four orders of magnitude 

risk range from 1.2 ×10–10 to 1.3 ×10–6 32 at the 0.1 ppm level. This narrowing continues as

33 exposure concentration increases, and the curves coalesce to substantially similar values 

34 at 6 ppm and above (not shown). 
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E-36 DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure E-5A. BBDR models for the rat—models with positive added risk. 

3 

4 Note: All four models provide “similar” fits to tumor data (see text). 

As in Conolly et al. (2003), Hockey 

stick αN and αI but using concurrent 

controls

All 8 models in A & B provide similar fits to tumor data

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 17
This document is a draft for review purposes only and does not constitute Agency policy.

E-37 DRAFT—DO NOT CITE OR QUOTE 

1 

2 Figure E-5B. BBDR rat models resulting in negative added risk. 

3 

4 Note: All four models provide “similar” fits to tumor data (see text). 

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 1

NEGATIVE Added risk

model 3

model 4

model 5

model 13

Exposure conc (ppm)

As in Conolly et al. (2003), J-shape αN

and αI but using Inhalation NTP controls 

added to concurrent

All 8 models in A & B provide similar fits to tumor data
This document is a draft for review purposes only and does not constitute Agency policy.

E-38 DRAFT—DO NOT CITE OR QUOTE 

1 

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α (divisions/hr)

Flux (pmole/m m2/hr)

αN

 αI

Model 8

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α (divisions/hr)

Flux (p mole/m m2/ hr)

αN

 αI

Model 15

0 10000 20000 30000 40000

1E- 4

1E- 3

0.01

0.1

1

α(divisions/hr)

Flux (pm ole/mm 2/hr)

αN

 αI

Model 16

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α (divisio ns/hr)

Flux (pmole/m m2/hr)

αN

 αI

Model 17

Hockey stick αNand αI with concurrent 

controls 

All 8 models in A & B provide 

similar fits to tumor data

2 

3 Figure E-6A. Models resulting in positive added rat risk: Dose-response for 

4 normal and initiated cell replication 

5 
This document is a draft for review purposes only and does not constitute Agency policy.

E-39 DRAFT—DO NOT CITE OR QUOTE 

1 

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/h r)

Flux (pmole/m m2/ hr)

αN

 αI

Model 3

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α (divisions/hr)

Flux (pm ole /mm2/hr)

αN

 αI

Model 4

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pm ole/mm 2/hr)

αN

 αI

Model 5

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α (divisions/hr)

Flu x (pm ole/mm 2/hr)

αN

 αI

Model 13

J-shape αN, αIin Conolly (2003) with 

Inhalation NTP + concurrent controls 

All 8 models in A & B provide 

similar fits to tumor data

2 

3 Figure E-6B. Models resulting in negative added rat risk: Dose-response for 

4 normal and initiated cell replication 

5 

6 

7 4. There does not seem to be any systematic effect on additional risk that depends on 

8 whether the 15 ppm data are included in the analysis. 

9 5. For all of the models except models 13 and 17 in Figures E-5 & E-6, the additional risk 

10 varies substantially linearly with exposure at low exposures between 0.001 and 1.0 ppm 

11 (departing only to a small extent from linearity between 0.1 and 1.0 ppm). Models 13 and 

12 17 (the models in Conolly et al. [2003] except for different control data being used) show 

13 a quadratic dependence. 

14 

15 The various model choices presented in Figure E-5 all provided equally good fits to the 

16 time-to-tumor data although within the context of a significant qualification. It was not possible 
This document is a draft for review purposes only and does not constitute Agency policy.

E-40 DRAFT—DO NOT CITE OR QUOTE 

1 to simply use the log-likelihood values as a means of comparing the goodness-of-fit to the tumor 

2 incidence data across these model choices. This is because many of the model choices differed 

3 in the number of doses or in the number of control animals that were used, so the fits were 

4 compared across such models only visually. Within model choices where such a comparison did 

5 not pose a problem, the log-likelihood values did not differ statistically significantly. 

6 Wherever results from the BBDR modeling are discussed, values of added risk, as 

7 opposed to extra risk, are reported. This is purely for convenience in interpretation. Because of 

8 the low background incidence, these values are only negligibly different from the corresponding 

9 extra risk estimate. The final risk (or unit risk) estimates provided in this document are based on 

10 extra risk estimates. 

11 

12 E.3.4.3. MOA Inferences Revisited

The ratio KMU/μNbasal 13 represents the added fractional probability of mutation per cell 

generation (μN – μNbasal)/μNbasal 14 due to unit concentration of DPXs. As discussed earlier, this 

15 parameter has a critical impact on the extrapolation as well as on inferring whether the mutagenic 

16 action of formaldehyde is relevant to the quantitative risk characterization. In that prior 

17 discussion, this ratio was found to be extremely sensitive to the choice of historical control data. 

18 The analysis indicates that, for a given set of control data that is used, uncertainties associated 

with αN and αI 19 also have a large impact on this ratio. In that discussion, this ratio was infinite 

when concurrent controls were used because the MLE value for μNbasal 20 was found to be zero. The 

use of these concurrent controls, however, does not necessarily imply that μNbasal 21 will be 

22 determined to be zero. In one of the scenarios examined in the sensitivity analysis, where 

23 concurrent controls were used along with the combination of dose-response curves eq 13 for αN

(Figure E-4) and eq 17 for αI, the optimal value of the ratio KMU/μNbasal 24 was equal to 0.25. For 

25 the models in Figure 5-13A, this ratio was 0 for all except model 17 for which it was infinite. 

26 For the models in Figure 5-13B with negative added risk, the ratio ranged from 0–4.5. For some 

27 of those models where KMU/μNbasal was finite, the upper confidence bound on this ratio was 

28 found to increase by an order of magnitude from the MLE value. Thus, we conclude that the 

29 modeling does not help resolve the debate as to the relevance of formaldehyde’s mutagenic 

30 potential to its carcinogenicity. 

31 

32 E.3.4.4. Confidence Bounds: Model Uncertainty Versus Statistical Uncertainty

33 For models 15 and 17 in Figures E-5A and E-6A, 90% CIs for additional risk were 

34 calculated by using the profile likelihood method. Table E-4 compares the lower and upper 
This document is a draft for review purposes only and does not constitute Agency policy.

E-41 DRAFT—DO NOT CITE OR QUOTE 

1 confidence bounds for these models for 0.001 ppm, 0.1 ppm (doses well below the range where 

2 tumors were observed), and 6 ppm (the lowest dose where tumors were observed) with the MLE 

3 risk estimates at these doses. In both cases, these intervals were quite narrow compared with the 

4 differences in risk predicted by different models in Figure E-5. This suggests that model 

5 uncertainty is of more consequence in the formaldehyde animal model than is statistical 

6 uncertainty. We also estimated confidence bounds using the bootstrap method for select models, 

7 and determined that these estimates were in agreement with the bounds calculated using the 

8 profile likelihood method. These results are not presented here. We return to the calculation of 

9 confidence limits when determining points of departure (PODs). 

10 

11 Table E-4. Comparison of statistical confidence bounds on added risk for 

12 two models 

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 

16 In conclusion, it is demonstrated that the different formaldehyde clonal growth models 

17 can fit the data about equally well and still produce considerable variation in additional risk and 

18 biological inferences at low exposures. However, even with these large variations, the highest 

MLE added risk for the F344 rat is only of the order of 10–6 19 at 0.1 ppm. Thus, with regard to 

20 calculating a reasonable upper bound that includes model and statistical uncertainty, the relevant 

21 question is whether the range arising out of uncertainties in the rat model amplifies when 

22 extrapolated to the human. Thus, in Appendix F, the human model in Conolly et al. (2004) will 

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

E-42 DRAFT—DO NOT CITE OR QUOTE 

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

F-2 DRAFT—DO NOT CITE OR QUOTE 

1 APPENDIX F 

2 

3 SENSITIVITY ANALYSIS OF BBDR MODEL FOR FORMALDEHYDE INDUCED 

4 RESPIRATORY CANCER IN HUMANS 

5 

6 F.1. MAJOR UNCERTAINTIES IN THE FORMALDEHYDE HUMAN BBDR MODEL 

7 Subsequent to the BBDR model for modeling rat cancer, Conolly et al. (2004) developed 

8 a corresponding model for humans for the purpose of extrapolating the risk to humans estimated 

9 by the rat model. Also, rather than considering only nasal tumors, it is used to predict the risk of 

10 all human respiratory tumors. The human model for formaldehyde carcinogenicity (Conolly et 

11 al., 2004) is conceptually very similar to the rat model and follows the schematic in Figure 5-11 

12 in Chapter 5. The model structure, notations, and calibration are described in Appendix D. 

13 Unlike the sensitivity analysis of the rat modeling where a number of issues were examined, a 

14 much more restricted analysis will be presented here for the sake of brevity. A more extensive 

15 analysis was carried out initially that carried forward several of the rat models to the human, and 

16 the lessons learned from those exercises are in agreement with the more restricted presentation 

17 that follows. Table F-1 lists the major uncertainties and assumptions in the human extrapolation 

18 model in Conolly et al. (2004).
This document is a draft for review purposes only and does not constitute Agency policy

F-3 DRAFT—DO NOT CITE OR QUOTE

Table F-1. Summary of evaluation of major uncertainties in CIIT human BBDR model 

Assumptions, approach, and 

characterization of input data

a

Rationale in Conolly et al. (2003) or 

CIIT (1999) EPA uncertainty evaluation 

Cell division rates derived from rat labeling data assumed applicable for human (except for assuming different fraction of cells with replicative potential). 

No equivalent LI data for human or guidance in extrapolating cell division rate across species. 

Enzymatic metabolism plays a role in mitosis. Therefore, we expect interspecies difference in cel

division rate. Basal cell division rates in humans

expected to be much more variable than in laborato

animals. 

Development of PBPK model for DPX concentration in human respiratory lining. 

See text (Chap 3) See text (Chap 3) 

Anatomically realistic representation of nasal passages. 

Reduces uncertainty (over default calculation carried out by averaging dose over entire nasal surface). 

Computer representation pertains to that of one individual (Caucasian male adult). Considerable interindividual variability in nasal anatomy. Susceptible individuals even more variable. 

KMU/μNbasal is species invariant 

(used to estimate human). 

Human cells are more difficult to transform than rodent, both spontaneously and by exposure to formaldehyde. 

μNbasal is 0 when concurrent controls or inhalation N

controls in time frame of concurrent bioassays are Leads to infinitely large KMU for human. 

Conservative assumptions were made. Results are conservative in the face of model uncertainties. 

1) Hockey-stick dose-response for 

α

N was 

included even though TWA indicated J-shape. 2) Overall respiratory tract cancer incidence data for human baseline rates were used. 3) Risk was evaluated at statistical upper bound of the proportionality parameter relating DPXs to the probability of mutation. 

CIIT result cannot be characterized as conservative the face of model uncertainties and as a plausible ubound on human risk. Human model is unstable. 

aAssumptions in this table are in addition to those listed for the BBDR model for the F344 rat.
This document is a draft for review purposes only and does not constitute Agency policy.

F-4 DRAFT—DO NOT CITE OR QUOTE 

1 F.2. SENSITIVITY ANALYSIS OF HUMAN BBDR MODELING 

2 Crump et al. (2008) carried out a limited sensitivity analysis of the Conolly et al. (2004) 

3 human model. This analysis was limited to evaluating the effect on the human model of the 

4 following. These evaluations have been the subject of some debate in the literature and in 

5 various conferences (Conolly, 2009; Conolly et al., 2009, 2008; Crump et al. 2009). 

6 

7 1. The use of the alternative sets of control data for the rat bioassay data that were 

8 considered in the sensitivity analysis of the rat model (Subramaniam et al., 2007). 

9 2. Minor perturbations in model assumptions regarding the effect of formaldehyde on the 

division and death rates of initiated cells (αI 10 , βI). Now, recall from the description of the 

11 structure of the human model that one (of the two) adjustable parameter in the expression 

for the human αI 12 was determined from the model fit to the rat tumor incidence data while 

13 the second parameter was determined from background rates of cancer incidence in the 

human. Therefore, variations considered in αI 14 were constrained to only those that (a) did 

15 not meaningfully degrade the fit of the model to the rat tumor incidence data and (b) were 

16 in concordance with background rates in the human. Crump et al. (2008) also evaluated 

17 these variations with respect to their biological plausibility. The sensitivity analysis on 

18 assumed initiated cell kinetics was thought to be particularly important since there were 

19 no data to even crudely inform the kinetics of initiated cells for use in the models, even in 

20 rats, and the two-stage clonal expansion model is very sensitive to initiated cell kinetics 

21 (Gaylor and Zheng, 1996; Crump, 1994a, b). 

22 

23 Crump et al. (2008) note that, since the purpose of their analysis was to carry out a 

24 sensitivity analysis, in order to illustrate certain points, only risks to the general U.S. population

25 from constant lifetime exposure to various levels of formaldehyde under the Conolly et al. (2004) 

26 environmental scenario (8 hours/day sleeping, 8 hours/day sitting, and 8 hours/day engaged in 

27 light activity) are considered. Fits based on the hockey-stick and J-shape models were identical, 

28 and, of the three estimated parameters (μbasal, multb, and D), only the estimate of μbasal 

29 differed between the two models. 

30 

31 F.2.1. Effect of background Rates of Nasal Tumors in Rats on Human Risk Estimates

32 Crump et al. (2008) quantitatively evaluated the impact of different control groups on 

33 estimates of additional human risk as follows: 

34 
This document is a draft for review purposes only and does not constitute Agency policy.

F-5 DRAFT—DO NOT CITE OR QUOTE 

1 1. Concurrent controls plus all NTP controls:, the same as used by Conolly et al. (2004); 

2 2. Concurrent controls plus controls from NTP inhalation studies; 

3 3. Only concurrent controls; 

4 4. Each set of control data was applied with both the J shape and hockey-stick models in 

5 Conolly et al. (2004) for αN(flux) and αI(flux) for a total of six analyses;. 

5. Uncertainties associated with αN or αI 6 are not addressed. Parameters αmax, multfc, and 

7 KMU were estimated in exactly the same manner as in Conolly et al. (2004). 

8 

9 Crump et al. (2008) present the following dose-response predictions of additional risk in 

10 humans from constant lifetime exposure to various levels of formaldehyde arising from 

11 exercising the above six cases. Their plots are reproduced in Figure F-1, where the 

12 corresponding curves based on Conolly et al. (2004) are also shown for comparison. 

13 The lowest dotted curve in Figure F-1 represents the highest estimates of human risk 

14 developed by Conolly et al. (2004). This resulted from use of the hockey-stick model for cell 

15 division rates in conjunction with the statistical upper bound for the parameter KMU. As 

16 indicated by the downward block arrows in the figure, their corresponding estimates based on the 

17 J-shape model were all negative for exposures below 1 ppm. 

18 Consider next the solid curves in the figure, which show predicted MLE added risks that 

19 were positive and less than 0.5. Crump et al. (2008) next examined the added risk obtained when 

20 the MLE estimate of (KMU/μbasal) in these cases is replaced by the 95% upper bound of this 

21 parameter ratio. The upper bound risk estimates in Conolly et al. (2004) were calculated in a 

22 similar manner (but using all NTP historical controls). Except for minor differences, risk 

23 estimates corresponding to such an upper bound and using all NTP controls were very similar in 

24 the two efforts (Crump et al., 2008; Conolly et al., 2004). 

25 Figure F-1 shows that the choice of controls to include in the rat model can make an 

26 enormous difference in estimates of additional human risk. For the J-shaped model for cell 

27 replication rate both estimates based on the MLE and those based on the 95% upper bound on 

KMU/μbasal 28 are negative for formaldehyde exposures below 1 ppm. However, when only 

29 concurrent controls are used in the model in Crump et al. (2008), the MLE from the J-shape 

30 model is positive and is more than three orders of magnitude higher than the highest estimates 

31 obtained by Conolly et al. (2004). Using only concurrent controls, estimates based on the 95% 

upper bound on KMU/μbasal 32 are unboundedly large (block arrows at the top of the figure). For the 

33 hockey-stick shaped model for cell replication rate, when all NTP controls are used, the estimates 

34 based on the MLEs are zero for exposures less than about 0.5 ppm. If only inhalation controls 

35 are added, the MLEs are about seven times larger than the Conolly et al. (2004) upper bound 
This document is a draft for review purposes only and does not constitute Agency policy.

F-6 DRAFT—DO NOT CITE OR QUOTE 

estimates, and the estimates based on the 95% upper bound on KMU/μbasal 1 are about 50 times 

2 larger than the Conolly et al. (2004) estimates. If only concurrent controls are used, both the 

MLE estimates and those based on the 95% upper bound on KMU/μbasal 3 are unboundedly large. 

4 

1E-3 0.01 0.1 1

1E-8

1E-7

1E-6

1E-5

1E-4

1E-3

0.01

0.1

Additional 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

0.1

Additional 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 

7 Figure F-1. Effect of choice of NTP bioassays for historical controls on 

8 human risk. 

9 

10 Note: Estimates of additional human risk of respiratory cancer by age 80 from 

11 lifetime exposure to formaldehyde are obtained by using different control groups 

12 of rats. 

13 

14 Source: Crump et al. (2008). 

15 

16 

17 F.2.2. Alternative Assumptions Regarding the Rate of Replication of Initiated Cells

18 For the human model, Conolly et al. (2004) made the same assumptions for relating 

19 αI(flux) and βI(flux) to αN(flux) as in their rat model (Conolly et al., 2003). That is, these 

20 quantities were related by using eq 2 and eq 3. As discussed in the context of the rat modeling, 

21 by extending the shape of these curves to humans, the authors’ model brings the cytotoxic action 

22 of formaldehyde to bear strongly on the parameterization of the human model as well. 
This document is a draft for review purposes only and does not constitute Agency policy.

F-7 DRAFT—DO NOT CITE OR QUOTE 

1 In the sensitivity analyses of the rat modeling, it was concluded that other biologically 

plausible assumptions for αI

 and βI 2 resulted in several orders of magnitude variations in the low 

3 dose risk relative to those obtained by models based on the assumptions in Conolly et al. (2003) 

but that the highest risks were nonetheless of the order of 10–6 4 at the 10 ppb level. This section 

5 examines how these uncertainties in the rat model propagate to the human model. 

6 Crump et al. (2008) made minor modifications to the assumed division rates of initiated 

7 cells in Conolly et al. (2004), while all other aspects of the model and input data were kept 

8 unchanged. Two alternatives were considered for each of the J-shape and hockey-stick models. 

9 Figure F-2 shows the hockey-stick model for initiated cells in rats. In the first modification to the

10 hockey-stick model (hockey-stick Mod 1), rather than having a threshold at a flux of 

1,240 pmol/m2

11 -hour, the division rate increases linearly with increasing flux until the graph 

intersects the original curve at 4,500 pmol/m212 -hour, where it then assumes the same value as in 

13 the original curve for larger values of flux. The second modification (hockey-stick Mod 2) is 

similar, except the modified curve intersects the original curve at a flux of 3,000 pmol/m214 -hour. 

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

0.0010

alphaI

Formaldehyde Flux [pmol/(m2-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

0.0010

alphaI

Formaldehyde Flux [pmol/(m2-h)]

Mod 1

Mod 2

16 

17 Figure F-2. Conolly et al. (2003) hockey-stick model for division rates of 

18 initiated cells in rats and two modified models. 

19 

20 Source: Crump et al. (2008). 

21 

22 

23 Figure F-3 shows the rat J-shape model for initiated cells. In the first modification to this 

24 dose response (J-shape Mod 1), rather than having a J shape, the division rate of initiated cells 
This document is a draft for review purposes only and does not constitute Agency policy.

F-8 DRAFT—DO NOT CITE OR QUOTE 

1 remains constant at the basal value until the original curve rises above the basal value and has the 

2 same value as the original curve for larger values of flux. In the second modification (J-shape 

3 Mod 2), the J shape is retained but somewhat mitigated. In this modification, the division rate 

4 initially decreases in a linear manner similar to that of the original model but with a less negative 

slope until it intersects the original curve at a flux of 1,240 μm/m25 -hour, where it then follows 

6 the original curve for higher values of flux. 

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

0.0010

alphaI

Formaldehyde Flux [pmol/(m2-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

0.0010

alphaI

Formaldehyde Flux [pmol/(m2-h)]

Mod 1

Mod 2

8 

9 

10 Figure F-3. Conolly et al. (2003) J-shape model for division rates of initiated 

11 cells in rats and two modified models. 

12 

13 Source: Crump et al. (2008). 

14 

15 

Since the first constraint on the variation in αI 16 was in concordance with the rat time-to17 tumor incidence data, Crump et al. (2008) applied each of the modified models in Figures F-2 

18 and F-3 to the version of the formaldehyde models in Subramaniam et al. (2007) that employed 

19 all NTP controls and the hockey-stick curve for αN. These authors restricted their analysis to this 

20 case since their stated purpose was only a sensitivity analysis as opposed to developing alternate 

21 credible risk estimates. Figure F-4 reproduces (from Crump et al. [2008]) curves of the 

22 cumulative probability of a rat dying from a nasal SCC by a given age for bioassay exposure 

23 groups of 6, 10, and 15 ppm. For comparison purposes, the corresponding KM (nonparametric) 

24 estimates of the probability of death from a nasal tumor are also shown. Three sets of 

25 probabilities are graphed: the original unmodified one and the ones obtained by using hockey26 stick Mod 1 and Mod 2. Crump et al. (2008) state that the changes in the tumor probability 

27 resulting from these modifications are so slight that the three models cannot be readily 
This document is a draft for review purposes only and does not constitute Agency policy.

F-9 DRAFT—DO NOT CITE OR QUOTE 

distinguished in this graph.41 Thus, the modifications considered to the models for the division 

2 rates of initiated cells caused an inconsequential change in the fit of the model-predicted tumor 

3 incidence to the animal tumor data. 

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

0.9

Probability 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

0.9

Probability of Response

Time (days)

15 ppm

10 ppm

6 ppm

5 

6 

7 Figure F-4. Very similar model estimates of probability of fatal tumor in rats 

8 for three models in Figure F-2.

9 

10 Note: The differences are visually indistinguishable. Models were derived from 

11 the implementation of Conolly et al. (2003) with the hockey-stick curves for 

12 αI(flux) and αN(flux) and variants derived from modifications (Mod 1 and Mod 2, 

13 Figure F-2) to αI(flux). Model probabilities are compared to KM estimates. The 

14 three sets of model estimates are so similar that they cannot be distinguished on 

15 this graph. 

16 

17 Source: Crump et al. (2008). 

 

4

 The largest change in the tumor probability resulting from this modification for any dose group and any age up 

through 900 days was found to be less than 0.002, a change so small that it would be impossible to detect, even in the 

largest bioassays ever conducted. The changes in tumor probability resulting from the other modifications described 

earlier were found to be even smaller. These comparisons were made in Crump et al. (2008) without re-optimizing 

the likelihood. The authors note that re-optimization 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 and does not constitute Agency policy.

F-10 DRAFT—DO NOT CITE OR QUOTE 

1 The above modifications did not affect the basal rate of cell division in the model and 

2 likewise had no effect on the fit to the human background data (Crump et al., 2008). 

3 Crump et al. (2008) noted that, although the threshold model for initiated cells in Conolly 

4 et al. (2003) was replaced with a model that had a small positive slope at the origin, the resulting 

5 curves, hockey-stick Mod 1 and hockey-stick Mod 2, could have been shifted slightly to the right 

along the flux axis in order to introduce a threshold for αI 6 without materially affecting the risk 

7 estimates resulting from these modified curves. Thus, “the assumption of a linear no-threshold 

8 response is not an essential feature of the modifications to the hockey-stick model; clearly 

9 threshold models exist that would produce essentially the same effect” (Crump et al. 2008). 

10 

11 F.2.3. Biological Plausibility of Alternate Assumptions 

These very small variations made to the αI 12 in Conolly et al. (2003) are seen to be 

13 consistent with the tumor-incidence data (just demonstrated above); small compared with the 

14 variability and uncertainty in the cell replication rates characterized from the available empirical 

data (at the formaldehyde flux where αI 15 was varied); supported (qualitatively) by limited data, 

16 suggesting increased cell proliferation at doses below cytotoxic; perturbations that one should 

17 expect on any dose response derived from laboratory animal data because of human population 

18 variability in cell replication; and biologically plausible because cell cycle control in initiated 

19 cells is likely to be disrupted. 

20 The averaged cell replication rate constants as tabulated in Table 1 of Conolly et al. 

21 (2003) and shown by the red curve in Figure E-2 of Appendix E (for various exposure 

22 concentrations and corresponding average formaldehyde flux values in the F344 rat nose) 

23 demonstrate an increase over baseline values only at exposure concentrations of 6 ppm and 

24 higher. Increased cell proliferation at these concentrations of formaldehyde, whether transient or 

25 sustained, have been associated in the literature with epithelial response to the cytotoxic 

26 properties of formaldehyde (Conolly, 2002; Monticello and Morgan, 1997; Monticello et al., 

27 1996, 1991). The labeling data are considered to show a lack of cytotoxicity and regenerative 

28 cell proliferation in the F344 rat at exposures of 2 ppm and below (Conolly, 2002). In the 

29 Conolly et al. (2003) modeling, it is further assumed that the formaldehyde flux levels at which 

30 cell replication exceeds baseline rates remain essentially unchanged when extrapolated to the 

31 human and for initiated cells for the rat as well as the human. These assumptions need to be first 

32 viewed in the context of the uncertainty and variability in the data on normal cells discussed 

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

F-11 DRAFT—DO NOT CITE OR QUOTE 

1 Arguments for a hockey-stick or J shape over the background have been made in the 

2 literature for sustained and chronic cell replication rates; the analyses of the cell replication data

3 show that the data are not consistently (over each site and time) indicative of a hockey-stick or 

4 J shape as the best representation of the data. This uncertainty is particularly prominent when 

5 examining the cell replication data at the 13-week exposure time and the pooled data from the 

6 PLM nasal site from Monticello et al. (1996) (Figures E-1 [dotted curve], E-3B, E-4 of Appendix 

7 E). The earliest exposure time in this experiment was at 13 weeks, and the 13-week cell 

8 replication data appear to be more representative of a monotonic increasing dose response 

9 without a threshold. It is possible that early times are of more relevance to the carcinogenesis as 

10 well as for considering typical (short duration) human exposures. 

11 For initiated cells, there are no data on which to evaluate the modifications made to these 

12 rates. However, some perspective can be gained by comparing them to the variability in the 

13 division rates obtained from the data on normal cells used to construct the formaldehyde model. 

14 As shown in Figure E-2 and discussed further in Subramaniam et al. (2008), these data show 

15 roughly an order of magnitude variation in the cell replication rate at a given flux. As part of a 

16 statistical evaluation of these data, a standard deviation of 0.32 was calculated for the log17 transforms of individual measurements of division rates of normal cells. By comparison, the 

18 maximum change in the log-transform division rate of initiated cells resulting from hockey-stick 

19 Mod 2 was only 0.20, and the average change would be considerably smaller. Thus, although 

20 there are no data for initiated cells, it can be said that the modifications introduced in Crump et 

21 al. (2008) for initiated cells are extremely small in comparison to the dispersion in the data for 

22 normal cells. 

23 Subramaniam et al. (2008) also point to some additional, albeit limited, data, suggesting 

24 that exposure to formaldehyde could result in increased cell replication at doses far below those 

25 that are considered to be cytotoxic. Tyihak et al. (2001) treated different human cell lines in 

26 culture to various doses (0.1–10 mM) of formaldehyde and found that the mitotic index increased 

27 at the lowest dose of 0.1 mM. These findings considered along with human population 

28 variability and susceptibility (for example, polymorphisms in ADH3 [Hedberg et al., 2001]) 

indicate that it is necessary to consider the possibility of small increases in the human αI 29 over 

30 baseline levels at exposures well below those at which cytotoxicity-driven proliferative response 

31 is thought to occur. 

32 Heck and Casanova (1999) have provided arguments to explain that the formation of 

33 DPXs by formaldehyde leads to inhibition of cell replication (i.e., if this effect alone is 

34 considered, normal cell replication rate of the exposed cells would be less than the baseline rate). 

35 However, this hypothesis was posed for normal cells. Subramaniam et al. (2008) argue that if an 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 initiated cell is created by a specific mutation that impairs cell cycle control, the effect would be 

2 to mitigate the DPX-induced inhibition in cell replication, either partially or fully, depending on 

3 the extent to which the cell cycle control has been disrupted. In the absence of data on initiated 

4 cells, the above argument provided biological motivation to the modification applied to the 

5 J-shape model for cell division (Crump et al. 2008).

6 Thus, the previous paragraphs suggest that the changes made in the analysis in Crump et 

7 al. (2008) to the assumption by Conolly et al. (2003) regarding the dose response for the division 

8 rate of initiated cells are not implausible. 

9 

10 F.2.4. Effect of Alternate Assumptions for Initiated Cell Kinetics on Human Risk Estimates 

11 Figure F-5 contains graphs of the additional human risks estimated (in Crump et al. 

[2008]) by applying these modified models for αI 12 and using all NTP controls, compared with 

13 those obtained by using the original Conolly et al. (2004) model. Each of the four modified 

14 models presents a very different picture from that of Conolly et al. (2004). At low exposures, 

15 these risks are three to four orders of magnitude larger than the largest estimates obtained by 

16 Conolly et al. (2004). 

17 These results have been criticized by Conolly et al. (2009) as being unrealistically large 

18 and above the realm of any epidemiologic estimate for formaldehyde SCC. Thus, they argue that 

19 the parameter adjustments made in Crump et al. (2008) are inappropriate. Crump et al. (2009) 

20 rebutted these points by arguing that the purpose of their work was not to provide a more reliable 

21 or plausible model but to carry out a sensitivity analysis. They argued that the changes made to 

22 the model (in their analyses) were reasonable since they did not violate any biological constraints 

23 or the available data. Further, they pointed out that “by appropriately mitigating the small 

24 modifications [they] made to the division rates of initiated cells, the model [would] provide any 

25 desired risk ranging from that estimated by the original model up to risks 1,000-fold larger than 

26 the conservative estimate in Conolly et al. (2004).” 

27 Crump et al. (2008) also evaluated the assumption in eq 3 of the CIIT modeling 

pertaining to initiated cell death rates (βI) by making small changes to βI 28 . They report that they 

29 obtained similarly large values for estimates of additional human risk at low exposures. 

30 Obtaining reliable data on cell death rates in the nasal epithelium appears to be an unusually 

31 difficult proposition (Hester et al., 2003; Monticello and Morgan, 1997), and, even if data are 

32 obtained, they are likely to be extremely variable.
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1E-3 0.01 0.1 1

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Hockey Stick Model

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Hockey Stick Model

J-Shape Mod 2

1 

2 

3 Figure F-5. Graphs of the additional human risks estimated by applying 

these modified models for αI 4 , using all NTP controls, compared to those 

5 obtained using the original Conolly et al. (2004) model. 

6 

7 Source: Crump et al. (2008). 
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Appendix G
This document is a draft for review purposes only and does not constitute Agency policy.

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1 APPENDIX G 

2 

3 EVALUATION OF THE CANCER DOSE-RESPONSE MODELING 

4 OF GENOMIC DATA FOR FORMALDEHYDE RISK ASSESSMENT 

5 

6 G.1. MAJOR CONCLUSIONS IN ANDERSEN ET AL. (2008) 

7 In Chapter 4, the gene microarray data from animal studies on formaldehyde (Andersen et 

8 al., 2008; Thomas et al., 2007) were described. The analysis of these animal high throughput 

9 data and the conclusions reached in these two groundbreaking papers were closely examined for 

10 use in this assessment. Studies on high throughput animal data provide a wealth of information 

11 that helps further understanding of the relevant mechanisms. However, such studies have 

12 generally not made quantitative bottom-line inferences that inform low dose human risk. The 

13 above-mentioned studies are a notable exception due to the breadth of their conclusions on low 

14 dose MOAs, their pioneering application of the benchmark dose (BMD) methodology to 

15 genomic data, their use of BMD-response analysis that identified dose estimates at which specific 

16 cellular processes were significantly altered, the fact that they were accompanied by 

17 recommendation in the literature urging use of these results in setting exposure standards for 

18 formaldehyde (Daston, 2008). 

19 We focus here on the conclusions in these papers with regard to modeling the cancer 

20 dose-response for formaldehyde. In addition to supporting our disposition of these analyses for 

21 this assessment, this write-up serves the purpose of exemplifying critical issues that need to be 

22 considered for the future. 

23 The overall BMD determined in Andersen et al. (2008) for all genes with significant 

24 dose-response averaged 6.4 ppm. These analyses indicated a general progression with the lowest 

25 BMD values (i.e., the most sensitive epithelial responses) for extracellular and cell membrane 

26 components and higher BMD values for intracellular processes. Overall, these authors 

27 concluded that 

28 

29  Genomic changes, including those suggestive of mutagenic effects, did not temporally 

30 precede or occur at lower doses than phenotypic changes in the tissue 

31  Genomic changes were no more sensitive than tissue responses 

32  Formaldehyde, being an endogenous chemical, is well handled until some threshold is 

33 achieved. Above these doses, toxicity rapidly ensues with concomitant genomic and 

34 histologic changes. 

35  Linear extrapolations, or extrapolations that specify similar MOAs at high and low doses 

36 would be inappropriate. 
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1 These findings were judged to have significant implications on the debated MOA for 

2 formaldehyde carcinogenicity, confirming results from earlier bioassays and dose-response 

3 modeling that the mutagenicity of formaldehyde was too weak to be of relevance to its 

4 carcinogenicity. Daston (2008) judged the method in these efforts to be extremely sensitive and 

5 therefore suited to examining whether responses at the molecular level take place at doses below 

6 which frank adverse effects occur. Daston (2008) argued that “... if there are pleiotropic effects 

7 at lower exposure levels that would elicit a different profile of gene expression, those genes 

8 would not go unnoticed” and thus concluded that “the gene expression data confirm that the 

9 responses are not linear at low doses.” 

10 In the analyses that follow, we point to some significant quantitative factors that impact 

11 on these conclusions. 

12 

13 G.2. USE OF MULTIPLE FILTERS ON THE DATA

14 The analyses in these papers involved the following sequence of data filters. 

15 

16 1. Gene probe sets that differed in expression in response to treatment were identified by 

17 one-way analysis of variance. Probability values were adjusted for multiple comparisons 

18 by using a false discovery rate of 5%. 

19 2. Next, in addition to the above statistical filter, the output was further screened by 

20 selecting only those genes that exhibited a change from the control group that was greater 

21 than or equal to 1.5-fold (logarithmic). 

22 3. The gene probe sets that demonstrated significant dose-response behavior were then 

23 matched to their corresponding biological process and molecular function gene ontology 

24 (GO) categories (considering only those involving more than three genes) and grouped 

25 into process categories such as cell division, DNA repair, cellular proliferation, apoptosis, 

26 and related molecular function categories. 

27 

28 A large number of genes are expressed in these studies; therefore, clearly some 

29 appropriate filter needs to be used for meaningful interpretation of the vast database. Tissue 

30 pathology served as a phenotypic anchor for the interpretation of microarray results, and the 

31 genomic study confirmed (and improved on) the qualitative and quantitative understanding 

32 derived from the histopathology and observation of frank effects. It is possible that the 

33 combination of filters used by these authors is adequate for an inquiry into some mechanisms 

34 associated with the specific phenotypic effects. However, the studies reached bottom-line 

35 conclusions with regard to the low-dose MOA and approach to be considered for quantitative 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 extrapolation. These conclusions necessarily involve questions as to whether there were gene 

2 expression changes at low dose and at early exposure times that may be relevant to initiating 

3 carcinogenesis and finally as to whether there is a threshold in dose associated with 

4 formaldehyde carcinogenesis. However, collectively, the three filters employed in these studies 

5 likely constitute overly stringent criteria, taking away the resolution needed to observe critical 

6 gene changes needed to delineate low dose effects. An indication that this may indeed be the 

7 case can be seen by examining the correlations in their findings with the observed trend in the 

8 data on DPXs formed by formaldehyde. This is detailed in the following section. 

9 

10 G.3. DATA FOR LOW-DOSE CANCER RESPONSE 

11 A significant finding in Thomas et al. (2007) is that BMD estimates for the GO categories 

12 applicable to cell proliferation and DNA damage were similar to values obtained for cell labeling 

13 indices and DPXs in earlier studies and to BMD estimates obtained for the onset of nasal tumors. 

14 The mean BMD for the GO category of “positive regulation of cell proliferation” was 5.7 ppm; 

15 in comparison, Schlosser et al. (2003) obtained a 10% BMD of 4.9 ppm for the cell labeling 

16 index. The GO category associated with “response to DNA damage stimulus,” seen as a 

17 genomic correlate to a mutagenic effect, had a mean BMD of 6.31 ppm. Thomas et al. (2007) 

18 compare this finding with significant increase at 6 ppm of DPXs following a 3-hour exposure in 

19 the study by Casanova et al. (1994). The formation and repair of DPXs have been considered to 

20 be one of the potential mechanisms associated with the genotoxic action of formaldehyde 

21 (Conolly et al., 2003, 2000). Based on earlier work in the same laboratory (Conolly et al., 2004, 

22 2003; Conolly, 2002), Slikker et al. (2004) concluded that there is a dose threshold (at about 

23 6 ppm) to formaldehyde carcinogenicity and that the putative mutagenic action of formaldehyde 

24 is not relevant to its carcinogenicity. Therefore, the finding that a significant genomic response 

25 (e.g., induction of DNA repair genes) is not observed at doses lower than those that induce 

26 tumors in rodent bioassays is seen by these authors (Andersen et al., 2008; Daston, 2008; 

27 Thomas et al., 2007) to further buttress the above conclusions related to the mode of action for 

28 formaldehyde-induced respiratory cancer. 

29 However, phenotypic anchoring to the DPX data drawn only from Casanova et al. (1994) 

30 misses critical low-dose data that informs mode of action. In an earlier study, Casanova et al. 

31 (1989) observed statistically significantly elevated (over controls) levels of DPXs at 2 ppm and a 

32 trend towards elevated DPXs at 0.7 ppm. In analysis of low-dose data, the trend in the dose33 response is critically important because data inherently lack the power to establish statistical 

34 significance. Furthermore, the two studies by Casanova and coworkers are different in some 

35 respects. The earlier study was a 6-hour exposure, while the later study was a 3-hour study; thus, 
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1 on this account alone, it appears more relevant to compare with the older study. Exposures in the 

2 earlier study were additionally at 0.3 and 10 ppm, thus affording a lower exposure concentration. 

3 In the earlier study, tissue from the whole nose was analyzed, whereas in the later study tissue 

4 from two specific regions was obtained from the “high” tumor (Level II) and “low” tumor 

5 regions. Together, these data suggest that DPXs occur at exposure concentrations considerably 

6 lower than those that elicited transcriptional changes. One possible explanation is that the 

7 increase in DPXs was not sufficient to induce DNA repair genes. Alternatively, these 

8 discrepancies may be due to the stringent filters and the low statistical power of the Andersen et 

9 al. (2008) study. These disparities between the gene array study and the DPXs question the 

10 ability of the studies in Andersen et al. (2008) and Thomas et al. (2007) to inform the presence or 

11 absence of a mutational MOA for formaldehyde, and in essence, to inform the low-dose response 

12 curve for formaldehyde-induced cancer. 

13 In another instance, Andersen et al. (2008) clearly stated that no genes were significantly 

14 altered by exposure to 0.7 ppm, yet they state that there was “a trend toward altered expression at 

15 0.7 ppm” in some genes with U and inverted U shape dose-responses (Figures 4 and 5 of their 

16 paper). While these changes may not be statistically significant, they could be biologically 

17 significant. 

18 

19 G.4. DIFFICULTIES IN INTERPRETING THE BENCHMARK MODELING

20 The benchmark analyses are summarized in Thomas et al. (2007) as average BMD 

21 estimates for genes in a given GO that were statistically significantly dose related. The 

22 benchmark modeling was then used by the authors to identify that the dose below individual 

23 cellular processes was judged to be “not altered.” 

24 The BMD definition used by these authors is quite stringent: it defines an effect so that 

25 only 0.005 of controls will be considered affected and sets the BMR corresponding to this dose at 

26 0.105. The net effect is that the BMD is the air level, such that the increase in the mean response 

27 is 1.349 × standard deviation. This is essentially an arbitrary definition. For comparison, if 0.05 

28 of controls are considered affected and the BMR is set at 0.1 (common values that are applied to 

29 whole animal data), the BMD is the air level such that the increase in the mean response is 

30 0.608 × standard deviation. Thus, if this definition had been used (as is traditionally the case), 

31 the BMD estimates would all be 2.2 times smaller than those obtained by Schlosser et al. (2003). 

32 Furthermore, the analysis assumes equal variance in all dose groups. Thus, further consideration 

33 of these issues with regard to interpretation of the BMR obtained from these studies is needed 

34 before it can be used in regulatory exposure setting. Secondly, lower confidence limits on the 

35 BMDs need to be derived for the data in Andersen et al. (2008). 
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1 G.5. STATISTICAL SENSITIVITY OF THE DATA FOR DOSE-RESPONSE

2 Another cautionary note pertains to the qualification of gene array studies as being 

3 extremely sensitive. Such a qualification should actually refer to the fact that only tiny amounts 

4 of mRNA are needed, that is, the sensitivity of the assay per se for measuring gene expression. 

5 However, this should not be confused with the sensitivity needed to identify the very small dose6 related changes at low dose. Andersen et al. (2008) reports on results of studies that involve 

7 small numbers of animals in each dose group (five or eight). Despite the limited power in such 

8 studies, the paper equates the absence of a statistically significant effect with no effect. This 

9 limitation is generally true of studies of the dose responses of changes in gene expression 

10 conducted to date; they have generally relied on very few animals (≤10 per dose group). Since 

11 there will likely always be background amounts of gene expression, quantifying the dose 

12 response requires statistically significant changes in gene expression as a function of dose. If the 

13 genomic data involve even fewer animals per group than the histopathological data, they have 

14 even less power to delineate the dose response; in particular, whether there is a threshold at low 

15 exposures. This is illustrated by the example in Figure G-1 of the dose responses for epithelial 

16 hyperplasia (Andersen et al. 2008, lesion 2). These appear equally consistent with both a 

17 threshold at around 1 ppm and a linear response down to zero. 

18 

19 G.6. LENGTH OF THE STUDY AND STOCHASTIC EVENTS

20 Another significant consideration with regard to MOA conclusions that are pertinent to 

21 the disease process is the length of the study, 15 days. If formaldehyde-induced tumor formation 

22 is a stochastic process (e.g., genotoxicity), then exposure of a small number of animals to low 

23 concentrations for 15 days may not be long enough to detect changes that might occur under 

24 long-term exposure scenarios. 

25 Relatedly, it has been suggested that gene (and protein) expression is a stochastic process 

26 whereby steady state gene expression obeys Poisson statistics (i.e. distribution of rare events), 

27 and that events of interest may occur in a single cell or small number of cells in which larger 

28 tissue samples can average out such stochastic events and prevent the detection of non-average 

29 behavior (Quakenbush, 2007). Given the implied difficulty in such an analysis, duration of 

30 exposure may be one of the most tenable ways of addressing whether a chemical increases the 

31 probability of an adverse response. 
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1 

2 Figure G-1: Graphs of epithelial hyperplasia (Lesion 2) versus formaldehyde 

3 concentration (ppm) with 95% confidence intervals (with linear fit by eye) 

4 

5 

6 G.7. OVERALL CONCLUSION 

7 We believe our analyses of the presentations in Andersen et al. (2008) and Daston (2008) 

8 are generally useful with regard to future developments in quantitative analyses of genomic data 

9 if they are to be of relevance to risk assessment. For risk assessment, rather than focusing on 

10 what responses are statistically significant, an analysis should focus on 1) what range of values of 

11 critical parameters (e.g., gene expression) are consistent with the data, and 2) what these values 

12 imply for whole animal risk. This is of course, an extremely difficult proposition because we do 

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1 not know nearly enough about how changes in genes quantitatively affect whole animal risk, or 

2 even which genes are important. 
Appendix H
This document is a draft for review purposes only and does not constitute Agency policy.

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1 APPENDIX H 

2 

3 EXPERT PANEL CONSULTATION ON QUANTITATIVE EVALUATION OF ANIMAL 

4 TOXICOLOGY DATA FOR ANALYZING CANCER RISK DUE TO INHALED 

5 FORMALDEHYDE 

6 

7 The National Center for Environmental Assessment convened an expert panel of 

8 scientists for advice on evaluating available approaches for incorporating biological information 

9 in analyzing animal tumor data for assessing cancer risk due to inhaled formaldehyde. This 

10 Appendix pertains to the major deliberations and results of that meeting and is divided into three 

11 sections. 

12 

13 A. Scope and Agenda of Meeting on Quantitative Evaluation of Animal Toxicology Data for 

14 Analyzing Cancer Risk due to Inhaled Formaldehyde. October 28 & 29, 2004. 

15 

16 B. Summary of Consultative Meeting on CIIT Formaldehyde Model. October 28 & 29, 

17 2004. 

18 

19 C. Meeting Report from Dr. Rory B. Conolly
This document is a draft for review purposes only and does not constitute Agency policy.

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1 A. Scope and Agenda of Meeting on Quantitative Evaluation of Animal Toxicology 

2 Data for Analyzing Cancer Risk due to Inhaled Formaldehyde 

3 October 28 & 29, 2004. Washington, DC. 

4 

5 This meeting is to assist EPA in evaluating available approaches for incorporating biological 

6 information in analyzing animal tumor data for assessing cancer risk due to inhaled 

7 formaldehyde. The CIIT Centers for Health Research (CIIT) has published a novel risk 

8 assessment that links site-specific predictions of flux using computational fluid dynamics (CFD) 

9 modeling with a two-stage clonal growth model of cancer to analyze nasal tumor incidence in 

10 two rodent bioassays. The rodent models are used with corresponding human models for low11 dose extrapolation of cancer risk to people. 

12 

13 Key predictions of the CIIT effort are a zero maximum likelihood estimate of the probability of 

14 formaldehyde-induced mutation per cell generation in the rat and a de minimus additional 

15 lifetime risk in non-smokers due to continuous environmental exposure below 0.2 ppm. The 

16 National Center for Environmental Assessment is carrying out sensitivity analyses and examining 

17 variations of the CIIT model in order to understand the implications of the model structure and 

18 parameters on model predictions. In this meeting, we wish to focus on the strengths and key 

19 uncertainties of this model, the extent to which assumptions in the CIIT model are supported by 

20 biological data, and examine the impact of uncertainty and variability on the overall quantitative 

21 risk characterization. 

22 

23 Broadly, the discussions will focus on the following areas: 

24 

25 • Impact of uncertainties in dosimetry on human risk estimates

26 • Uncertainties in the use of experimental data on labeling index

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

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

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

30 • Relative merits of benchmark dose modeling vs. the 2-stage clonal growth model

31 

32 Discussions on Mode of Action are expected to be an integral part of several of the sessions. 

33 Therefore a specific time-slot is not set aside for this purpose. 

34 

35 The meeting will have a panel discussion format. There will be no formal presentations unless 

36 necessary to elucidate an issue. Various attachments referred to in the Agenda below, as well as 

37 the relevant manuscripts will be sent separately. 

38 

39 Specifically, we suggest the following issues upon which to focus the discussion in the above 

40 areas, and approximate time frames and discussion leads, although discussants should feel free to 

41 bring up other critical issues. 

42 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 I. Introduction and purpose of discussion 

2 Peter Preuss. 

3 9:00 AM, Oct 28 

4 

5 II. Impact of uncertainties in dosimetry on risk estimates

6 Lead discussant: Linda Hanna 

7 9:15 - 11 AM Oct 28

8 

9 Boundary conditions

10 The CFD modeling specified a mass transfer coefficient as a boundary condition on the 

11 nasal lining, adjusting the value of this coefficient on the “absorbing” portion of the 

12 lining so as to match simulated overall uptake in the rat nose to the experimentally 

13 determined average overall uptake. This value was then used for the corresponding 

14 human nasal lining. Are these boundary conditions appropriate surrogates for the 

15 underlying pharmacokinetics, including saturation in metabolism and mucociliary 

16 clearance, particularly with reference to humans? 

17 

18 Turbulence

19 Turbulent flow has been seen to occur in experimental models of the human nose at some 

20 of the higher flow rates at which the CFD models were used in CIIT=s assessment. It is 

21 not likely that the CIIT CFD model can reliably identify signatures of transition to 

22 turbulent behavior. Turbulent flow can significantly alter regional uptake patterns. 

23 Additionally, significant mass balance errors were seen at the higher flow rates in the 

24 human flow models. Discuss if these are likely to impact significantly on risk estimates. 

25 

26 Interindividual variability

27 The CIIT assessment has focused on the nasal anatomy of a single individual. Discuss 

28 the implications of interindividual variations in nasal anatomy on the population 

29 distribution in risk. 

30 

31 III. Uncertainties in the use of experimental data on labeling index

32 Lead discussant: George Lucier

33 11AM – 11:45 AM, 1:00 - 3:15 PM Oct 28 

34 

35 Cell-replication rate and its relationship to flux is a critical determinant of risk. Therefore 

36 uncertainties and variability in measurement of the unit length labeling index and its use in the 

37 CIIT clonal growth modeling need to be characterized. 

38 

39 1. Discuss the strengths, uncertainties and limitations associated with estimating cell 

40 replication rates from the unit length labeling index (ULLI). 

41 a. For example, a constant ratio of the measured ULLI to the labeling index (LI) that 

42 is used in the model is assumed. Is it valid to assume this ratio to be constant 

43 across nasal sites, dose and exposure time. 

44 b. How uncertain is this ratio? 

45 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 2. Considering the large patterns of variability in the ULLI data, discuss the validity of using 

2 ULLI averaged over site and exposure times. 

3 a. The averaging loses information on the sequential effect of change with 

4 time, and on significant differences among sites. 

5 b. How sensitive is the clonal growth modeling result to these variations in the dose6 response function for cell replication rates vs. flux to the tissue? A discussion of 

7 this question in this session is intended to serve as input to later deliberations on 

8 the issue. 

9 

10 3. Discuss the validity of combining data collected in different experiments using different 

11 labeling methods, and the validity of estimating cell replication rates from LI or ULLI 

12 measured in a single pulse labeling experiment. 

13 

14 See attachment C: “ULLI Dose-Response Modeling and Statistical Analysis” for a 

15 discussion of these issues, and Moolgavkar and Luebeck (1992). 

16 

17 IV. Model Structure: Birth and death rates for Initiated cells, Role of DPX 

18 Lead discussant: Kenny Crump

19 3:30 - 6:00 PM Oct 28.

20 

21 Parameters for initiated cells 

22 

23 1. The CIIT analysis of ULLI data allows for a virtual threshold in dose in the replication 

24 rate of normal cells. Discuss the validity of ascribing such a behavior to initiated cells 

25 considering the sensitivity of 2-stage model results to the initiated cell replication rates. 

26 

27 2. Discuss the treatment of death rate for initiated cells in the model (set equal to birth rate 

28 of normal cells in Conolly et al., 2003) and implications for confidence in model 

29 predictions. 

30 

31 Also see Attachment A (memo from Rory Conolly) and Attachment D (EPA discussion of 

32 CIIT clonal growth modeling and some sensitivity analyses. . .) 

33 

34 Treatment of DNA protein cross-links (DPX) in clonal expansion model 

35 

36 3. FORMALDEHDYE-INDUCED MUTATION IS MODELED AS TAKING 

37 PLACE ONLY WHILE DPX ARE IN PLACE WITH DPX UNDERGOING 

38 RAPID REPAIR. DISCUSS THE POSSIBILITY OF PERSISTENT GENETIC 

39 DAMAGE THAT EXTENDS BEYOND THE DPX HALF-LIFE AND 

40 ENHANCES MUTATION. HOW MIGHT THIS ISSUE BE INCLUDED IN 

41 THE MODEL STRUCTURE? 

42 
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1 V. Considerations of time-to-tumor in the CIIT clonal growth modeling 

2 Lead discussant: Christopher Portier 

3 8:30 – 11:00 AM, Oct 29. 

4 

5 1. A number of issues affect likelihood values and the model fit to the time-to-tumor data. 

6 Discuss assumptions in the treatment of time-to-tumor in the CIIT clonal expansion 

7 model, and their impact on parameter estimates. For example, 

8 

9 a. Results in Conolly et al. (2003, 2004) are derived considering all tumors to be 

10 fatal. Note in this context that serially sacrificed animals have been combined 

11 with those experiencing mortalityBthe effect of this is visible as irregularities in 

12 the time-to-tumor curve. 

13 

14 b. How is the time variability in ULLI likely to impact on the time-to15 tumor predictions? 

16 

17 2. Long delay times are predicted by the model for observation of detectable tumor. Is this 

18 compatible with the assumption of rapidly fatal tumors? 

19 

20 3. Discuss the weight to be given to differences in likelihood when comparing with 

21 variations on the Conolly et al (2003) model structure such as in Attachment A or D. 

22 

23 VI. Inferences on the role of formaldehyde-induced mutation and cell proliferation

24 Lead discussant: Dale Hattis

25 11:15 – 12:00 PM, 1:00 – 4:00 PM, Oct 29. 

26 

27 1. The model structure in Conolly et al. (2003) predicts a zero maximum likelihood estimate 

28 for the constant of proportionality (KMU) linking DPX to the probability of 

29 formaldehyde-induced mutation per cell generation. Examine the strength of this 

30 conclusion, and the extent to which an insignificant probability of formaldehyde-induced 

31 mutation per cell generation is supported by data. 

32 

33 2. Discuss the biological relevance and validity of model-estimated parameters, particularly 

34 in the context of low-dose predictions. 

35 a. Discuss possible avenues to validate CIIT cancer model predictions. 

36 

37 3. Discuss the validity of using cell replication rates determined for the rat to predict human 

38 risk in a population. 

39 

40 4. In the face of uncertainties, are the results in Conolly et al. (2003, 2004) conservative in 

41 the sense of overpredicting risk? 

42 a. Discuss the extent to which sensitivity analyses have addressed this issue and the 

43 extent to which sensitivity analyses can speak to the strength of the model. [See 

44 Attachments A: Memo from Conolly, and D: EPA discussion of CIIT clonal 

45 growth modeling and some sensitivity analyses . . .].
This document is a draft for review purposes only and does not constitute Agency policy.

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1 VII. Benchmark Dose Modeling 

2 Lead discussant: Kenny Crump 

3 4:15 – 5:30 PM, Oct 29. 

4 

5 Discuss the relative merits of using a benchmark dose approach that incorporates 

6 biological modeling (such as estimating flux to tissue or DPX levels) as compared with 

7 the CIIT 2-stage model for cancer. (See attachment E and Schlosser et al., 2003.) 

8 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 B. Summary of Consultative Meeting on CIIT Formaldehyde Model 

2 October 28 & 29 2004, NCEA, Washington, DC 

3 

4 Date: November 10, 2004 

5 Ravi P. Subramaniam, Ph.D. 

6 Quantitative Risk Methods Group 

7 National Center for Environmental Assessment, ORD, US EPA 

8 

9 This is a broad summary of the most important issues at the formaldehyde meeting. 

10 It was generally felt by consultants that the broad framework of the approach adopted by 

11 CIIT, namely the use of a two-stage model for cancer, the linking of localized flux to cell 

12 replication rates and DPX concentration, and the expression of formaldehyde-induced mutation 

13 as a linear function of DPX, was reasonable. 

14 Potential errors in the dosimetry modeling were seen not to have a significant effect on 

15 risk estimates. The boundary conditions used were discussed to be a reasonable representation of 

16 the pharmacokinetics for both rats and humans. The discussion on the impact of interindividual 

17 variability of nasal anatomy was not particularly conclusive. It was determined that there was 

18 likely to be much less variability in reactive gas uptake than that seen in particulates. 

19 Crucial errors were however identified on several fronts in the manner in which the clonal 

20 growth model had been implemented in the CIIT effort. Dr. Portier felt that the calculation of 

21 probability was seriously flawed on account of lumping serially-sacrificed animals and animals 

22 that died of tumor together, while at the same time assuming rapid fatality of all tumors. This 

23 was seen to significantly alter the calculation of tumor probability (the shape of the dose24 response curve), and his insight was that a correction was likely to allow for a substantially 

25 higher value for the probability of formaldehyde-induced mutation at low-dose. The best 

26 estimate for this probability is now zero in the model. Drs. Crump, Portier and Hattis argued that 

27 replacing this estimate by an upper confidence bound on KMU (the coefficient determining the 

28 role of DPX in the probability of mutation per cell generation), keeping other structural problems 

29 in the model unexplored, or other parameters fixed, would not be enough. There was a 

30 discussion on the need to provide confidence bounds on risk determined by allowing all the 

31 parameters to vary. Drs. Crump and Hattis (and Portier?) felt such an estimate would be very 

32 different from that calculated based on individual parameters. 

33 Drs. Crump, Hattis and Portier urged us not to be constrained by the optimal likelihood 

34 values of a single plausible model, and underscored the need to explore a variety of biologically 

35 reasonable model structures as a requisite for utilizing such a model in risk assessment. 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 Likelihood was seen to be an inadequate expression of what is to be considered an optimal model 

2 (okay only for comparing models that were nested, etc.). These models should allow the 

3 expression of variability and uncertainty in the data, as well as in underlying assumptions in 

4 model specification. Dr. Crump (and Hattis also?) felt that alternate model structures, if 

5 explored, could potentially lead to risk estimates, for the range below the observed data, that 

6 were higher by several thousands. 

7 Dr. Crump cautioned that extrapolating to human using the hockey or J-shaped cell 

8 replication curve used in the rodent carried with it a large uncertainty that had not been 

9 characterized in the Conolly modeling. 

10 Dr. Portier expressed concern over the manner in which historical and concurrent controls 

11 were lumped together. The thrust of Portier’s comments was that such a combination of controls 

12 was generally not done. The large number of historical controls was likely to significantly bias 

13 the impact of the bioassay data in determining the time-to-tumor fits. 

14 There were various discussions about the pros and cons of constructing a joint likelihood 

15 of the cell replication data and the tumor data, and the weights to be assigned to the separate 

16 likelihoods. This was considered to be problematic by Dr. Portier. 

17 Dr. Crump’s opinion was that the Conolly model, and those explored by EPA, fit the 

18 tumor data poorly, and that an improved description of the tumor data was needed before the 

19 model could be used for low-dose and inter-species extrapolation. 

20 Drs. Lucier and Hattis placed emphasis on including the early-time cell replication data 

21 instead of constructing a time-weighted average. It was felt that the two Monticello experiments 

22 could not be combined together as in Conolly et al. Dr. Lucier felt that the early-time data would 

23 have a greater impact in the progression of carcinogenesis. In general, the effect of “time” was 

24 considered to have significant effects on the time-to-tumor modeling, and they urged us to 

25 incorporate time-dependent terms in the modeling. CIIT expressed willingness to provide the 

26 original cell replication data to us for further analysis. (Further discussion on this matter did not

27 take place in the open forum.) 

28 Preliminary indications are, particularly based on Dr. Portier’s insight, that the currently29 held “de-minimus” picture of low-dose risk, as expressed in Conolly et al. (2004), is not likely to 

30 be the case if these various suggestions are incorporated in the modeling. 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 C. Meeting Report from Dr. Rory B. Conolly 

2 

3 Rory B. Conolly, Sc.D., D.A.B.T. 

4 106 Michael’s Way 

5 Chapel Hill, NC 27516 

6 Voice: 919.929.2258

7 

8 July 24, 2005 

9 

10 Dr. Bobette Norse 

11 ORAU Procurement - MS-04 

12 P.O. Box 117 

13 Oak Ridge, TN 37831-0117 

14 Phone: 865-576-3051 

15 Fax: 865-576-9385 

16 

17 Dear Dr. Nourse, 

18 

19 The following is my final written report on the formaldehyde review meeting held at the 

20 U.S. EPA in Washington, D.C. on 28-29 October, 2004. 

21 EPA provided no guiding philosophical statement about the criteria being used to 

22 evaluate the CIIT assessment. The new Guidelines for Carcinogen Assessment state that the 

23 preferred default approach is to use a biologically based model. Since the key components of the 

24 CIIT assessment have been published in the peer-reviewed literature and have undergone several 

25 peer reviews other than the current NCEA effort, one has to wonder just how high the bar is set 

26 for acceptance of biologically based assessments. Given the time and resources expended on the 

27 CIIT assessment and the richness of the supporting data base, I find it difficult to imagine what 

28 an acceptable biologically-based assessment might look like if in the end the CIIT assessment is 

29 deemed not acceptable by NCEA. If this is in fact the outcome it will have major implications 

30 for the likelihood that anyone will be willing to commit the significant resources needed to 

31 develop of these kinds of risk assessment models. 

32 The documents provided in advance of the October 2004 review meeting were 

33 collectively a discussion of uncertainty about the CIIT work. With respect to the clonal growth 

34 model, however, no new risk predictions were provided, so there was no way to judge how the 

35 uncertainties that NCEA identified might impact predicted risk. Evaluation of the significance of 

36 "uncertainties" when the impact of the uncertainties on the predicted risk is not known is itself an 

37 uncertain process. 

38 A related concern is that there did not seem to be any consideration of the historical 

39 context of the CIIT assessment. EPA developed formaldehyde assessments in 1987 and 1991. 

40 The 1987 assessment used ppm as the input and the LMS model for the dose-response prediction. 

41 The 1991 assessment used DPX as a dosimeter and the LMS model. BMD assessments have 

42 since become available from other sources such as Paul Schlosser's work. The risk predictions of 

43 the BMD models are similar to the 1991 LMS assessment. Both the DPX-LMS and BMD 

44 assessments predicted somewhat less risk than the 1987 assessment, establishing the trend of less 

45 risk with increased incorporation of relevant data. I have always argued (probably initially at the 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 1998 Ottawa review) that the historical context is the appropriate context for evaluating the CIIT 

2 clonal growth model. For a "level playing field" the uncertainties of the 1987 and 1991 

3 assessments, and of the more recent BMD models, should be analyzed to the same degree as the 

4 clonal growth model. Does NCEA think that, because the LMS and BMD approaches used 

5 structurally simpler dose-response models and much more limited data inputs, they are less 

6 uncertain? The NCEA analysis seemed to be implying that use of more data and of a biologically 

7 more realistic model structure actually makes the CIIT approach more uncertain than the LMS 

8 and BMD approaches. I encourage NCEA to consider how uncertainties that can be evaluated 

9 explicitly in the structurally rich CIIT model compare to hidden uncertainties in the simpler 

10 models, where the hidden uncertainties encompass, for example: 

11 

12 1. Missing or incomplete descriptions of the regional dosimetry of formaldehyde. 

13 2. Lack of simultaneous incorporation of the directly mutagenic and 

14 cytolethal/regenerative proliferation modes of action. 

15 3. Lack of explicit consideration of the multistage nature of cancer. 

16 4. Lack of consideration of the growth kinetics of initiated cell populations 

17 5. Lack of evaluation of the measured J-shaped dose response for regenerative cellular 

18 proliferation. 

19 

20 A careful, balanced comparison of the CIIT assessment with the previous assessments along 

21 these lines would be informative with respect to the suitability of the CIIT assessment as the 

22 basis for a new IRIS listing for formaldehyde. 

23 A further concern involves the peer-review of the CIIT formaldehyde assessment held in 

24 Ottawa in 1998. This review was sponsored by the U.S. EPA and Health Canada and involved 

25 what was arguably a world-class review panel. The CIIT assessment was not in its final form at 

26 that time, though we did provide a detailed description of the overall approach and the specific 

27 methods we were using to generate dose-response predictions. The 1999 CIIT document and the 

28 subsequent peer-reviewed publications are responsive to the comments and suggestions raised by 

29 the reviewers. My concern is that no information was provided on the role that Ottawa review 

30 plays in the ongoing review of the CIIT formaldehyde assessment by NCEA. Should the October 

31 2004 review be viewed as standing on the shoulders of the 1998 review or as being in parallel to 

32 it? It was not at all clear to me that the October 2004 review in any way utilized the judgments 

33 of the 1998 review. It seems that the 2004 review was more of a parallel effort and that the 1998 

34 review was ignored and was effectively a waste of time and money. I would like to have some 

35 clear understanding of how the 2004 review effort should be viewed relative to that of 1998. 

36 In closing, let me reiterate that while the detailed examination of the CIIT formaldehyde 

37 assessment is laudable, this examination should be conduced with an eye to the historical context 

38 of formaldehyde risk assessment on the one had and, on the other hand, to a concern for 

39 encouraging, and not discouraging, development of biologically based risk assessment models. 

40 

41 Sincerely yours, 

42 

43 Rory B. Conolly, Sc.D., D.A.B.T. 
This document is a draft for review purposes only and does not constitute Agency policy.

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- End of Volume IV -