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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IV-ii DRAFT—DO NOT CITE OR QUOTE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IV-iii DRAFT—DO NOT CITE OR QUOTE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.

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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.

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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. 3

H/14C ratios in macromolecular extracts from rat tissues following exposure 

to 14C and 3

H-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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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
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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

N6

-hmdA N6-hydroxymethyldeoxyadenosine

N4-hmdC N4-hydroxymethylcytidine

N2

-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
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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
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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
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Appendix A
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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

1
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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 

12 no 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 

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

17 adult 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). 
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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 

16 localized regions of hot spots (see chapter 3), and thus may not be a proper measure of inter17 individual variability in flux distribution patterns over the whole nose. Furthermore, numerical 

18 error in the calculation (such as mass balance and irregularly shaped elements of the finite19 element mesh) is likely to be most pronounced when estimates are considered over extremely 

20 small regions.

21

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

23

% 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)

24

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

26 Source: Garcia et al. (2009).

27
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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 

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

5 : 

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)
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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 

8 this 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.
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Appendix C

1
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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
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not constitute Agency policy

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Table C-1. Extra-risk calculationa for environmental exposure to 0.0461 ppm formaldehyde (the LEC0005 for 

NPC incidence)b using a log-linear exposure-response model based on the cumulative exposure trend results of 

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

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

Interval 

number 

(i)

Age 

interval

All cause 

mortality 

(×105

/yr)

NPC 

incidence 

(×105

/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)

Exposed 

prob of 

surviving 

interval 

(qx)

Exposed 

prob of 

surviving 

up to 

interval 

(Sx) 

Exposed 

cond 

prob of 

NPC in 

interval 

(Rx)

1 <1 728.7 0 0.0073 0.9927 1.0000 0.00000 0.000000 0 0.0000 0.0000 0.0073 0.9927 1.0000 0.00000 

2 1-4 32.9 0.05 0.0013 0.9987 0.9927 0.00000 0.000002 0 0.0000 0.0000 0.0013 0.9987 0.9927 0.00000 

3 5-9 16.4 0.03 0.0008 0.9992 0.9914 0.00000 0.000001 0 0.0000 0.0000 0.0008 0.9992 0.9914 0.00000 

4 10-14 20.9 0.09 0.0010 0.9990 0.9906 0.00000 0.000004 0 0.0000 0.0000 0.0010 0.9990 0.9906 0.00000 

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 0.9966 0.9896 0.00001 

6 20-24 96 0.16 0.0048 0.9952 0.9862 0.00001 0.000008 7.5 1.0517 0.0000 0.0048 0.9952 0.9862 0.00001 

7 25-29 99 0.23 0.0050 0.9951 0.9815 0.00001 0.000011 12.5 1.7528 0.0000 0.0050 0.9951 0.9815 0.00001 

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 0.9942 0.9766 0.00003 

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 0.9919 0.9710 0.00003 

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 0.9882 0.9631 0.00008 

11 45-49 356 1.3 0.0178 0.9824 0.9518 0.00007 0.000061 32.5 4.5572 0.0001 0.0178 0.9823 0.9517 0.00009 

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 0.9744 0.9349 0.00012 

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 0.9607 0.9110 0.00012 

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 0.9390 0.8751 0.00014 

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 0.9080 0.8217 0.00021 

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 0.8620 0.7461 0.00014 

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 0.7962 0.6431 0.00013 

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 0.6907 0.5120 0.00009 

Ro = 0.000725 Rx = 0.001225 

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 (× 105

/year) (2000 data from NCHS).

Column D: NPC incidence rate for interval i (× 105

/year) (1996-2000 SEER data).

Column E: all-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of years 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 up to interval i without 

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

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 × 20/10 converts continuous 

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 × 0.01915) = 0.08333) [0.05183 

per ppm × year is the regression coefficient obtained, along with its SE of 0.01915, from Dr. Hauptmann (see Section 5.2.2.1). To estimate the 

LEC0005, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 0.05%, the 95% upper bound on the regression coefficient is 

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 = Sxi−1 × qxi-1, for i>1).

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

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 or being diagnosed with 

the specific cancer during the interval, i.e., (the all-cause mortality rate for the interval + the cancer-specific incidence rate for the interval—the cancer-specific 

mortality rate for the interval [so that a cancer case isn’t counted twice, i.e., upon diagnosis and upon death]) × number of years in interval. This adjustment 

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

1
This document is a draft for review purposes only and does not constitute Agency policy.

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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, 

8 2000; 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 

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

36 model for an anatomically realistic representation of the human upper airways and an idealized 
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1 representation of the lower airways. However, the model does not incorporate any data on 

2 human responses to formaldehyde exposure. The rat and human formaldehyde models are 

3 detailed further below.

4

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

6 N cell, normal cell

7 I cell, initiated cell

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

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

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

αN, division rate of normal cells (hours–1 11 )

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

13 of normal cells)

αI, division rate of an initiated cell (hours–1 14 )

βI, death rate of an initiated cell (hours–1 15 )

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

17 division of initiated cells)

18

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

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

tissue expressed as pmol/mm2 21 -hour. This dosimetry is predicted by computational fluid 

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

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

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

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

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

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

28

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

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

31 Subramaniam et al., 1998)

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

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

34 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/mm2 15 -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

21 μN = μI = μNbasal + KMU × DPX (1)

22

23 The unknown constants μNbasal 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 

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

17 at 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/mm2 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 
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maximum flux of 39,300 pmol/mm2 1 -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/mm2 2 -

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 

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

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

20 assumed βI = αN at all flux values.

21

22 Source: Subramaniam et al. (2008).

23
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, 

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

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

8

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

10

11 where αN ≡ αN(flux), αN(basal) 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

Flux (pmole/mm2/h)

0 10000 20000 30000 40000

αI

/

α

N

0.96

1.00

1.04

1.08

 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.

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).

24

 

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). 
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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

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

35 British doctors (Doll and Peto, 1978). The two other parameters in the rat model that 
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D-9 DRAFT—DO NOT CITE OR QUOTE

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

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

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

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

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

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

7 KMU/μbasal is invariant across species. Thus,

8

( ) ( )

( )

Nbasal(rat)

Nbasal human KMU human KMU rat

μ

μ 9 = × (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, 

7 2000; Conolly, 2002; Kimbell et al., 2001a, b; Overton et al., 2001; CIIT, 1999). The model 

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

9 evaluation of the uncertainties of this model and alternative approaches based on its conceptual 

10 framework was presented in a summary form. This Appendix provides the relevant details of that 

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

12 into the following major sections. First, an overview of all the issues evaluated is provided in 

13 tabular form. The rest of the Appendix then presents only those issues which have a significant 

14 impact on model predictions. These issues pertain to the use of history controls, the uncertainty 

15 and variability in the dose-response for normal cell-replication rates, and sensitivity of model 

16 results to uncertainty in the kinetics of initiated cells. The issues have significant impact on mode

17 of action inferences, 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

E-3 DRAFT

—DO 

Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344 rat

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation

Further 

elaborationa

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-dependent 

parameters. 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.

Crump et al. 

(2006); 

Subramaniam 

et al. (2007)

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 or 

prediction. 

2) However, since 57 animals were observed to have tumors 

at interim sacrifice times, EPA implementation distinguished 

between incidental and fatal tumors. Time lag between 

observable tumor and time of death was significant compared 

to time lag between first malignant cell and observable tumor. 

Subramaniam 

et al. (2007)

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 “all 

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 

mutagenic potential to its carcinogenicity varies from 

“insignificant” to “highly significant,” depending on controls 

used. (See Appendix F for impact on human risk.)

Table E-2; 

Subramaniam 

et al. (2007); 

Sec E.3.1

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 on 

risk not evaluated.

Subramaniam 

et al. (2008); 
This document is a draft for 

review purposes only and does 

not constitute Agency policy

E-4 DRAFT

—DO 

Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344 rat

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation

Further 

elaborationa

Pulse and continuous 

labeling data combined in 

deriving αN from LI.

Continuous LI 

normalized by ratio of 

pulse to continuous 

values for control data.

Formula used for deriving αN from LI is not applicable for 

pulse labeling data. Pulse labeling is measure of number of 

cells in S-phase, not of their recruitment rate into S-phase; not 

enough information to derive αN from pulse data. Impact on 

risk predictions could not be evaluated.

Subramaniam 

et al. (2008); 

Sec E.3.2.2

To construct dose response 

for αN, 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 αN for early t 

is very important to the cancer process. Since pulse ULLI 

was used for t < 13 weeks, impact of these ULLIs on risk 

could not be evaluated. 

2) Time dependence in αN derived from continuous ULLI 

does not significantly impact model predictions. 

3) Site-to-site variation of αN at least 10-fold and has major 

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 and 

metaplasia occur at later times for the higher dose and would 

affect replication rate.

Figures E-1, E2, E-3;

Subramaniam 

et al. (2008); 

Sec E.3.2.3
This document is a draft for 

review purposes only and does 

not constitute Agency policy

E-5 DRAFT

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Table E-1. Evaluation of assumptions and uncertainties in the CIIT model for nasal tumors in the F344 rat

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation

Further 

elaborationa

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 metaplasia 

occurring at later times for the higher dose will reduce tissue 

flux. 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.

Kimbell et al 

(1997); 

Subramaniam 

et al. (2008)

TWA αN(flux) rises above 

baseline levels only at 

cytolethal dose. Above 

such dose, αN(flux) rises 

sharply due to regenerative 

proliferation. 

Variability in αN(flux) 

rate is represented by 

also considering 

hockey-stick (threshold 

in dose) when TWA 

indicates J-shape 

(inhibition of cell 

division) description of 

αN(flux).

1) Uncertainty and variability in αN quantitatively evaluated 

to be large. In addition, several qualitative uncertainties in 

characterization of αN(flux) from LI.

2) Several dose-response shapes, including a monotonic 

increasing curve without a threshold, were considered in order 

to adequately describe highly dispersed cell replication data. 

Substantial impact on low dose risk, including negative added 

risk.

Figures E-1, E2, E-3, E-4, E5; 

Subramaniam 

et al. (2008); 

Sec E.3.2
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 F344 rat

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation

Further 

elaborationa

Dose response for αI was 

obtained from αN,

assuming ratio (αI /αN) 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 /αN) 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 /αN) in CIIT modeling is <1.0 (growth 

disadvantage) for higher flux values and is >1.0 only at lower 

end 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 by 

many orders of magnitude at ≤1 ppm, including negative risk. 

All these models described tumor incidence data and cell 

replication and DPX data equally well.

Figures D-2, E5, E-6;

Subramaniam 

et al. (2008); 

Crump et al. 

(2009, 2008); 

Sec E.3.3

Death rate of I cells 

βI(flux) assumed = division 

rate of N cells αN(flux).

Based on homeostasis 

(αN = βN) 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 in 

transformed cells. Therefore, plausibility of assumption (βI = 

αN) is tenuous.

2) Alternate assumption, βI proportional to αI, was examined. 

Risk estimates extremely sensitive to assumptions on βI (see 

Figure 5-12).

Subramaniam 

et al. (2008); 

Crump et al. 

(2009, 2008); 

Sec E.3.3

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 estimated by 

Conolly et al. (2004, 2003) and perhaps 14-fold longer with 

normal (non-transformed) human cells. Some DPX 

accumulation therefore likely. However, model calibration 

and dose response in rat is insensitive to this uncertainty.

Quievryn and 

Zhitkovich, 

(2000); 

Subramaniam 

et al. (2007); 

Chap 3
This document is a draft for 

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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 F344 rat

Assumptions, approach, 

and characterization of 

input data Rationale EPA evaluation

Further 

elaborationa

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. Thus, 

formaldehyde mutagenicity may be underrepresented. Could 

not quantitatively evaluate uncertainty (no data on clearance 

of secondary lesions).

Barker et al. 

(2005); Speit 

and Schmid 

(2006); 

Subramaniam 

et al. (2008); 

Chap 4

a

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

Note: Risk estimates discussed in this table are for the F344 rat.
This document is a draft for review purposes only and does not constitute Agency policy.

E-8 DRAFT—DO NOT CITE OR QUOTE

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

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, 

35 except that the parameter KMUrat in equation 1 (and equation 4) was 4.23 times larger with the 
This document is a draft for review purposes only and does not constitute Agency policy.

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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 

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

12 procedures, such as diet, housing, and pathological procedures (Haseman, 1995; Rao et al., 

13 1987). In order to minimize extra variability when historical control data are used, the current 

14 NTP practice is to limit the historical control data, as far as possible, to studies involving the 

15 same route of exposure and to use historical control data from the most recent studies (Peddada

16 et al., 2007).

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

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

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

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

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

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

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

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

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

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

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

28 not controlled for (Haseman, 1995).

29

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

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

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

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

34 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.

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

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

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 

9 basal mutation rate in rats, μNbasal(rat), 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.

2 

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

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

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

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

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

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

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

25 discussion 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 

Table E-2. Influence of control data in modeling formaldehyde-induced cancer in the F344 rat

Case A D B E C F

Control animals (combined 

with concurrent controls)

All NTP

historicala

All NTP 

historicala

NTP 

inhalation 

historicala

NTP 

inhalation 

historicala

Concurrent 

onlya

Concurrent 

onlya

Cell replication dose 

response

J-shaped Hockey stick J-shaped Hockey stick J-shaped Hockey stick

Log-likelihood −1692.65 −1693.68 −1,493.21 −1,493.35 −1,474.29 −1,474.29

μNbasal 1.87 × 10–6 2.12 × 10–6 7.32 × 10–7 9.32 × 10–7 0.0 0.0

KMU 1.12 × 10–7 0.0 6.84 × 10–7 6.18 × 10–7 1.20 × 10–6 1.20 × 10–6

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, ∞)

∞

(0.41, ∞)

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, 0.045)

0.045

(0.027, 0.045)

a

Values in parentheses denote lower and upper 90% confidence bounds.

Source: Adapted from Subramaniam et al. (2007).
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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 

3 probability of tumor (i.e., a parameter estimate of μNbasal = 0). We observed such a 

4 counterexample estimate for μNbasal in simulations involving the alternate dose-response curves 

5 for αN and αI that are discussed later. Nonetheless, when μNbasal = 0, an upper bound for μNbasal

6 using the concurrent controls can be inferred. Accordingly, the 90% statistical lower confidence 

7 bound on the ratio KMU/μNbasal is also reported in Table E-2. Such a value would of course 

8 provide a lower bound 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 

17 would be 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 

34 good 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 
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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 

4 uncertainties may arise in extrapolating to both human and rat (in the low-dose region) from 

5 such 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 

15 controls be restricted to the same kind of bioassays and to studies within a 5–7 year span of the 

16 concurrent animals (Peddada et al., 2007). Thus, it is problematic to assume that the tumor in 

17 the 1976 NTP study is representative of the risk of SCCs in the formaldehyde bioassays. Even if 

18 it were appropriate to consider the 1976 study, this leads to the unstable situation in which, 

19 despite all of the “upstream” mechanistic information used to construct the BBDR model, the 

20 only piece of data that might keep the model predictions of human risk bounded is a single tumor 

21 found among several thousand rats from NTP bioassays (Crump et al., 2008). In summary, 

22 although it can be argued that the rate of SCCs among the controls in the rat bioassay is probably 

23 not zero, it is also problematic to assume that this rate can be adequately represented by the 

24 background rate in NTP 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 

32 the estimated DPX contribution was extremely sensitive to the shape of apha_n and varied 

33 between 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 
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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 

3 in rodent bioassays and in projecting those observations to human exposures (Conolly et al., 

4 2004, 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 

24 al. (1996, 1991) and averaged over the nasal sites. Thus, the data were combined into 

25 one 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 

37 average flux for each of six exposures.

38
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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).

8

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)
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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. 

8 (2003) 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 

18 is not applicable for pulse labeled data. However, Conolly et al. (2003) applied this formula to 

19 the 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 

34 flux in Figure E-4 for the 13-week exposure time, where such an increase is generally indicated 

35 for low flux values.) 
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E-18 DRAFT—DO NOT CITE OR QUOTE

1 The early times would be important if, say, repeated episodic exposures were considered, 

2 where adequate time has not elapsed for adaptive effects to take place. Such an exposure

3 scenario may be the norm in the human context. However, the contribution of the early-time 

4 labeling data is minimized in the CIIT cancer modeling since the LI was weighted by exposure 

5 time. Because of the problems described above in incorporating the pulse-labeled data, the 

6 sensitivity analysis will be restricted to only the continuous labeling data.

7

8 E.3.2.3. Site and Time Variability

9 In the remainder of this section, the factors that are considered in order to represent the 

10 uncertainty and variability in the cell replication data when developing alternate dose-response 

11 curves for αN(flux) will be elaborated. Figure E-2 (from Subramaniam et al., 2008) shows the 

12 variability due to replicated animals, exposure times, and nasal sites in the continuous labeled 

13 data obtained by Monticello et al. (1996). The ULLI data for individual animals were provided 

14 by CIIT. In this figure, log αN versus site-specific flux are plotted for six sites and four exposure 

15 times for four to six replicate animals in each case. (The mean ULLI over these replicates were 

16 shown in Figure E-1 for each site and time as a function of exposure concentration.) It needs to 

17 be noted that these nasal sites differ considerably in the number of cells estimated at these 

18 locations as shown in Table E-3. Each point in Figure E-2 represents data from a single site for a 

19 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/mm2 20 -

21 hour, Conolly et al. (2003) extrapolated this empirically derived αN(flux) by using a scheme 

22 discussed in Appendix D (section D.5) on the upward extrapolation of cell replication rate. The 

23 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 24 -hour

25 is also shown (short dashes). Note that the linear interpolation/extrapolation is shown 

26 transformed to a logarithmic scale. 

27 In Figures E-3, fitted dose-response curves are plotted for log10(αN) versus flux with 

28 simultaneous confidence limits separately for each time point for two of the largest sites in Table 

29 E-3 (ALM and PLM). Note that flux levels are different at each site. Simple polynomial models 

30 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 31 time) were used as follows:

32

log(αN) = a + b×flux + c×flux2 + d×flux3 33 + τi (9)

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

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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).

19
This document is a draft for review purposes only and does not constitute Agency policy.

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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).

7
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.

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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: 
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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) 

21 assigns a relatively low weight to labeling observed at early times compared with those observed 

22 at later time points. Finally, initiated cells are likely to be replicating at higher rates than normal 

23 cells as evidenced in several studies on premalignant lesions (Coste et al., 1996; Dragan et al., 

24 1995; Rotstein et al., 1986). Therefore, LI data as an estimator of normal cell replication rate 

25 would be most reliable at early times when the mix of cells sampled include fewer preneoplastic 

26 or 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 

32 the 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 

35 exercised soon require a prohibitively large investment of time. Therefore, detailed analyses 
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1 were restricted to a select set of biologically plausible choices of curves for αN(flux), which 

2 would allow the identification of a range of plausible risk estimates (MLEs and statistical 

3 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/mm2 9 -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 

16 this value is that it corresponds to the inverse of the fastest cell cycle times found in the 

17 literature. Since the model treats the induced replication rates as being time invariant, this means 

18 that cells in the high-flux region(s) divide at the highest cell turnover rate ever observed 

19 throughout most of an animal’s life. This does not seem to be biologically plausible 

20 (Subramaniam et al., 2008).

21 In the analysis, it was found that a 20% increase or decrease in the estimated value for 

22 αmax degraded the fit to the tumor incidence data considerably. Because of the interplay between 

23 the parameters estimated by optimization, this sensitivity of the model to αmax indicates that it is 

24 necessary to examine if other plausible values of αmax are also indicated by the data and to what 

25 extent low dose estimates of risk are influenced by the uncertainty in its value. The need for 

26 such an analysis is also indicated by Figure E-2. The value of αmax (log10αmax = −1.37) in the 

27 modeling of Conolly et al. (2003) is roughly an order of magnitude greater than the values of 

28 αN(flux) at the highest flux levels in this figure. If the data pooled over all sites and times are to 

29 be used for αN(flux), then, based solely on the trend in αN(flux) in Figure E-2, it appears unlikely 

30 that αN(flux) could increase up to this value of αmax. Visually, these empirically derived data 

31 collectively suggest that αN versus flux could be leveling off rather than increasing 10-fold. 

32 Therefore, as an alternative to the approach taken in Conolly et al. (2003) of estimating αmax via 

33 likelihood optimization against the tumor data, regressions of the empirical cell replication data 

34 were used to extrapolate αN(flux) outside the range of observation (recognizing the uncertainty 
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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

4 that was flexible to allow a variety of monotonic and non-monotonic shapes, with a parameter 

5 that 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)2 31 ]} (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.
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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.

12
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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, 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.
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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

11
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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 )2 13 ]} (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.

. αN = Exp{–5.274 – 2.792 × Exp[1.407×10–4 × flux – (1.986×10–4 × flux)2 18 ]} (12)

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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1

2 N4: Quadratic; monotone increasing in flux, derived from unweighted fit to 13-week Monticello 

3 et al. (1996) ULLI data.

4

αN = Exp{–3.858 – 4.809 × Exp[– (9.293×10–5 × flux)2 5 ]} (13)

6

7 N5: Linear-quadratic-cubic; initially increasing slightly with increasing flux, then decreasing 

8 slightly, and finally increasing, derived from fit to 13-week Monticello et al. (1996) ULLI data, 

9 using average flux over all sites for a given ppm exposure and weighting regression by estimates 

10 of the numbers of cells at each of five sites.

11

αN = Exp{–5.488 – 2.755 × Exp[–7.808×10–5 × flux + (2.349×10–4 × flux)2 12

– (2.166×10–4 × flux)3 13 ]} (14)

14

15 N6: Linear-quadratic-cubic; initially increasing slightly with increasing flux, then decreasing 

16 slightly, and finally increasing, derived from fit to all Monticello et al. (1996) ULLI data, using 

17 weeks of exposure as a covariate. In this model, time was a regression (continuous) predictor, 

18 not a class variable, and its coefficient represents the decrease in log10 αN per week of exposure 

19 time.

20

αN = Exp{7.785×10–3 × (weeks) – 5.722 – 2.501 × Exp[1.103×10–4 21 × flux

– (7.223×10–5 × flux)2 – (1.575×10–4 × flux)3 22 ]} (15)

23

24 Further details on the above regressions are provided in the appendix. These regressions 

25 of the cell replication data as well as the hockey-stick and J-shaped curves used by Conolly et al. 

26 (2003) (shown in Figure D-1, Appendix D) are used next as inputs to the clonal growth model 

27 for cancer.

28

29 E.3.3. Uncertainty in Model Specification of Initiated Cell Replication and Death

30 E.3.3.1. Biological Inferences of Assumptions in Conolly et al. (2003)

31 The results of a two-stage MVK model are extremely sensitive to the values for initiated 

32 cell division (αI) and death (βI) rates, particularly in the case of a sharply rising dose-response 

33 curve as in the case of formaldehyde. The pool of cells used for obtaining the available LI data 

34 (Monticello et al., 1996, 1991) consists of largely normal cells with perhaps increasing numbers 

35 of initiated cells at higher exposure concentrations. As such there is no way of inferring the 
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1 division rates of initiated cells in the nasal epithelium, either spontaneous (baseline) or induced 

2 by exposure to formaldehyde, from the available empirical data. Conolly et al. (2003) 

3 considered αI(flux) as a function of αN(flux) as given by eq 2 in Appendix D. As shown in 

4 Figure D-1 (Appendix D), αI is estimated in Conolly et al. (2003) to be very similar to αN. That 

5 is, with eq 2 assumed to relate αI(flux) to αN(flux), a J- or hockey-shaped dose-response curve 

6 for αN(flux) results in a J or hockey shape for αI(flux). 

7 The J shape for the TWA αN(flux) in Conolly et al. (2003) could plausibly be explained, 

8 as suggested by the examples in Conolly and Lutz (2004), by a mathematical superposition of 

9 dose-response curves describing the effects of the inhibition of cell replication by the formation 

10 of DPXs (Heck and Casanova, 1999) and cytotoxicity-induced regenerative replication (Conolly, 

11 2002). However, as explained earlier, there is considerable uncertainty and variability, both 

12 qualitative and quantitative, in the interpretation of the LI data and in the derivation of normal

13 cell replication rates from the ULLI data. While the TWA values of ULLI indicate a J-shaped 

14 dose response for some sites, as also concluded by Gaylor et al. (2004), this is not consistently 

15 the case for all exposure times and sites as discussed earlier. Notwithstanding this uncertainty 

16 variability, and in the absence of data, the following essential questions have a significant impact 

17 on risk predictions and need resolution if the model structure in eq 2 is to be used in a 

18 biologically based (or motivated) sense to predict risk outside observable data:

19

20 ● Should mechanisms that might explain a J-shaped dose response for normal cell 

21 replication or a cytotoxicity-driven threshold in dose response (as indicated by a hockey22 stick-shaped curve) be expected to prevail also for initiated cells?

23 ● Would the formaldehyde flux at which the cell replication dose-response curve rises 

24 above its baseline be similar in value for both normal and initiated cells as inferred by the 

25 CIIT model in Figure D-1?

26

27 The next critical assumption was that made for βI (the death rate of initiated cells), 

28 namely, βI(flux) = αN(flux) (eq 3). In Subramaniam et al. (2008), the rationale for this 

29 assumption in Conolly et al. (2003) is explained by assuming formaldehyde to be equally 

30 cytotoxic to initiated and normal cells (since the mechanism is presumed to be via its general 

31 chemical reactivity). In essence, this assumption brings the cytotoxic action of formaldehyde to 

32 bear strongly on the parameterization of the CIIT model. 

33 There are no data to evaluate the strength of these assumptions, so Subramaniam et al. 

34 (2008) studied the plausibility of various inferences that arise as a result of these assumptions. 

35 These inferences are only briefly listed here (see the paper for further discussion).
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1

● For flux <27,975 pmol/mm2 2 -hour, αI > αN (Figures D-1 & D-2 of Appendix D). 

3 Qualitatively, this is in line with data on epithelial and other tissue types with or without 

4 exposure to specific chemicals.

5 ● For higher flux levels, however, the model indicates αI < αN (Figure D-2). There are no 

6 data to shed further light on this inference.

7 ● At these higher flux levels, initiated cells in the model die at a faster rate than they 

8 divide, indicating the extinction of initiated cell clones in regions subject to these flux 

9 levels. There are no data indicating formaldehyde to have this effect.

10

11 In evaluating these inferences, Subramaniam et al. (2008) point to various data that 

12 indicate that initiated cells represent distinctly different cell populations (from that of normal 

13 cells) with regard to proliferation response (Ceder et al., 2007; Bull, 2000; Schulte-Hermann et 

14 al., 1997; Coste et al., 1996; Dragan et al., 1995), have excess capacity to clear formaldehyde 

15 and, in general, are considerably more resistant to cytotoxicity (such a resistance is manifested 

16 variably as decreased ability of the toxicant to induce cell death or to inhibit cell proliferation 

17 compared to corresponding effects in normal cells), and may already have altered cell cycle 

18 control; thus, the influence of formaldehyde on apoptosis likely differs between normal and 

19 initiated cells. 

20 As concluded in Subramaniam et al. (2008), taken together, there is much data to suggest 

21 that inferring αI < αN at cytotoxic formaldehyde flux levels is problematic and that death rates of 

22 initiated cells are likely to be very different from those of normal cells. In the absence of data to 

23 indicate that eq 2 and eq 3 (in Appendix D) are biologically reasonable approaches to link the 

24 kinetics of initiated cells with those of normal cells, alternate model structures other than those 

25 represented by these relationships considered by Conolly et al. (2003) need to be explored, given 

26 that the two-stage model is extremely sensitive to αI and βI. Such an evaluation needs to 

27 primarily explore if the assumptions in eq 2 and eq 3 significantly impact the intended use of the 

28 model, namely extrapolation to low-dose human cancer risk and the calculation of an upper 

29 bound on human risk. Any such alternate model structure needs to provide a good fit to the 

30 time-to-tumor data.

31
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1 E.3.3.2. Plausible Alternative Assumptions for αI 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 

4 shape (motivated by the consideration that lower-than-baseline turnover rate represents an 

5 increased amount of DNA repair taking place, which may not be consistent with impaired DNA 

6 repair in initiated cells).

7 Thus, two alternatives were considered to eq 2 for αI(flux):

8

9 I1: αI = γ1 × [1 + exp(γ2 / γ3)] / {1 + exp[–(flux – γ2) / γ3]} (16)

10

11 I2: αI = 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 

14 data. In eq 16, αI 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). 

24 In addition, in most runs, an upper bound (αhigh) is selected for both αN and αI. 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 26 . If a value of αI 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.

29 As noted above, Conolly et al. (2003) set the rate of death for intermediate cells, βI, equal 

30 to the division rate of normal cells, βI = αN. On the other hand, apoptotic rates and cell 

31 proliferation rates are thought to be coupled (Schulte-Hermann, 1999; Moolgavkar, 1994), so 

32 that 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.
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1 βI = Κβ × αI (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 

15 alternative models were constructed with the 15 ppm data omitted.

16

17 E.3.4. Results of Sensitivity Analyses on αN, αI, and βI

18 E.3.4.1. Further Constraints

19 The number of models that might be constructed if all the possibilities listed above for 

20 αN, αI, and βI 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 

25 simulation. By using the constraints described above (eq 10–17 and associated text) for αI , βI, 

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 

31 plausible for αI based on arguments of cytotoxicity, then a threshold is all the more plausible for 

32 αN, and such models are removed from consideration.

33 Secondly, the basal value of αI was required to be at least as large as the basal value of 

34 αN. Another constraint was placed on the baseline initiated cell replication rate. In the absence 
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1 of formaldehyde exposure, αI was not allowed to be greater than two or four times αN, even if 

2 such models described the tumor data, including the control data, very well. There are some data 

3 that suggest that baseline initiated cells have a small growth advantage over normal cells, so a 

4 huge 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 dose13 response curves for αN and αI 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).
This document is a draft for review purposes only and does not constitute Agency policy.

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).

5

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

6

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
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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/mm2

/hr)

αN

 αI

Model 8

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 15

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 16

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 17

Hockey stick αN and 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

6
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/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 3

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 4

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2

/hr)

αN

 αI

Model 5

0 10000 20000 30000 40000

1E-4

1E-3

0.01

0.1

1

α(divisions/hr)

Flux (pmole/mm2/hr)

αN

 αI

Model 13

J-shape αN, αI in 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 

12 and 17 (the models in Conolly et al. [2003] except for different control data being used) 

13 show 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

13 The ratio KMU/μNbasal represents the added fractional probability of mutation per cell 

14 generation (μN – μNbasal)/μNbasal 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 

16 mutagenic action of formaldehyde is relevant to the quantitative risk characterization. In that 

17 prior discussion, this ratio was found to be extremely sensitive to the choice of historical control 

18 data. The analysis indicates that, for a given set of control data that is used, uncertainties 

19 associated with αN and αI also have a large impact on this ratio. In that discussion, this ratio was 

20 infinite when concurrent controls were used because the MLE value for μNbasal was found to be 

21 zero. The use of these concurrent controls, however, does not necessarily imply that μNbasal will 

22 be 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

24 (Figure E-4) and eq 17 for αI, the optimal value of the ratio KMU/μNbasal 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 

35 confidence bounds for these models for 0.001 ppm, 0.1 ppm (doses well below the range where 
This document is a draft for review purposes only and does not constitute Agency policy.

E-41 DRAFT—DO NOT CITE OR QUOTE

1 tumors were observed), and 6 ppm (the lowest dose where tumors were observed) with the MLE 

2 risk estimates at these doses. In both cases, these intervals were quite narrow compared with the 

3 differences in risk predicted by different models in Figure E-5. This suggests that model 

4 uncertainty is of more consequence in the formaldehyde animal model than is statistical 

5 uncertainty. We also estimated confidence bounds using the bootstrap method for select models, 

6 and determined that these estimates were in agreement with the bounds calculated using the 

7 profile likelihood method. These results are not presented here. We return to the calculation of 

8 confidence limits when determining points of departure (PODs).

9

10 Table E-4. Comparison of statistical confidence bounds on added risk for 

11 two models

12

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

13

14

15 In conclusion, it is demonstrated that the different formaldehyde clonal growth models 

16 can fit the data about equally well and still produce considerable variation in additional risk and 

17 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 18 at 0.1 ppm. Thus, with regard to 

19 calculating a reasonable upper bound that includes model and statistical uncertainty, the relevant 

20 question is whether the range arising out of uncertainties in the rat model amplifies when 

21 extrapolated to the human. Thus, in Appendix F, the human model in Conolly et al. (2004) will 

22 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

1
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 

Table F-1. Summary of evaluation of major uncertainties in CIIT human BBDR model 

Assumptions, approach, and 

characterization of input dataa

Rationale in Conolly et al. (2003) or 

CIIT (1999) EPA uncertainty evaluation Further elaboration

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 cell 

division rate. Basal cell division rates in humans 

expected to be much more variable than in laboratory 

animals. 

Subramaniam et al. 

(2008)

Development of PBPK model for 

DPX concentration in human 

respiratory lining.

See text (Chap 3) See text (Chap 3) Chap 3; Conolly et 

al. (2000); 

Subramaniam et al. 

(2008); Klein et al. 

(2009)

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.

Kimbell et al. (2001a, 

b); Subramaniam et 

al. (2008, 1998)

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 NTP 

controls in time frame of concurrent bioassays are 

used. Leads to infinitely large KMU for human.

Subramaniam et al. 

(2007); Crump et al. 

(2009, 2008). 

Conservative assumptions were 

made. Results are conservative in 

the face of model uncertainties.

1) Hockey-stick dose-response for αN 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 in 

the face of model uncertainties and as a plausible upper 

bound on human risk. Human model is unstable.

Conolly et al. (2004); 

Subramaniam et al. 

(2007); Crump et al. 

(2009, 2008).

a

Assumptions 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 

10 division and death rates of initiated cells (αI, βI). Now, recall from the description of the 

11 structure of the human model that one (of the two) adjustable parameter in the expression 

12 for the human αI 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 

14 human. Therefore, variations considered in αI 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. 

26 (2004) environmental scenario (8 hours/day sleeping, 8 hours/day sitting, and 8 hours/day 

27 engaged in light activity) are considered. Fits based on the hockey-stick and J-shape models 

28 were identical, and, of the three estimated parameters (μbasal, multb, and D), only the estimate 

29 of μbasal 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;. 

6 5. Uncertainties associated with αN or αI 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 

17 the 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 

20 when the MLE estimate of (KMU/μbasal) in these cases is replaced by the 95% upper bound of 

21 this 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 

28 KMU/μbasal 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% 

32 upper bound on KMU/μbasal are unboundedly large (block arrows at the top of the figure). For 

33 the hockey-stick shaped model for cell replication rate, when all NTP controls are used, the 

34 estimates based on the MLEs are zero for exposures less than about 0.5 ppm. If only inhalation 

35 controls are added, the MLEs are about seven times larger than the Conolly et al. (2004) upper 
This document is a draft for review purposes only and does not constitute Agency policy.

F-6 DRAFT—DO NOT CITE OR QUOTE

1 bound estimates, and the estimates based on the 95% upper bound on KMU/μbasal are about 50 

2 times larger than the Conolly et al. (2004) estimates. If only concurrent controls are used, both 

3 the MLE estimates and those based on the 95% upper bound on KMU/μbasal are unboundedly 

4 large.

5

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

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

6

7

8 Figure F-1. Effect of choice of NTP bioassays for historical controls on 

9 human risk.

10

11 Note: Estimates of additional human risk of respiratory cancer by age 80 from 

12 lifetime exposure to formaldehyde are obtained by using different control groups 

13 of rats.

14

15 Source: Crump et al. (2008).

16

17

18 F.2.2. Alternative Assumptions Regarding the Rate of Replication of Initiated Cells

19 For the human model, Conolly et al. (2004) made the same assumptions for relating 

20 αI(flux) and βI(flux) to αN(flux) as in their rat model (Conolly et al., 2003). That is, these 

21 quantities were related by using eq 2 and eq 3. As discussed in the context of the rat modeling, 

22 by extending the shape of these curves to humans, the authors’ model brings the cytotoxic action 

23 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 

2 plausible assumptions for αI and βI 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 

10 the 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/m2 12 -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/m2 14 -hour.

15

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 

25 remains constant at the basal value until the original curve rises above the basal value and has 
This document is a draft for review purposes only and does not constitute Agency policy.

F-8 DRAFT—DO NOT CITE OR QUOTE

1 the same value as the original curve for larger values of flux. In the second modification 

2 (J-shape Mod 2), the J shape is retained but somewhat mitigated. In this modification, the 

3 division rate 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/m2 4 -hour, where it 

5 then follows the original curve for higher values of flux.

6

Mod 1

Mod 2

7

8

9 Figure F-3. Conolly et al. (2003) J-shape model for division rates of initiated 

10 cells in rats and two modified models.

11

12 Source: Crump et al. (2008).

13

14

15 Since the first constraint on the variation in αI was in concordance with the rat time-to16 tumor incidence data, Crump et al. (2008) applied each of the modified models in Figures F-2 

17 and F-3 to the version of the formaldehyde models in Subramaniam et al. (2007) that employed 

18 all NTP controls and the hockey-stick curve for αN. These authors restricted their analysis to 

19 this case since their stated purpose was only a sensitivity analysis as opposed to developing 

20 alternate credible risk estimates. Figure F-4 reproduces (from Crump et al. [2008]) curves of the 

21 cumulative probability of a rat dying from a nasal SCC by a given age for bioassay exposure 

22 groups of 6, 10, and 15 ppm. For comparison purposes, the corresponding KM (nonparametric) 

23 estimates of the probability of death from a nasal tumor are also shown. Three sets of 

24 probabilities are graphed: the original unmodified one and the ones obtained by using hockey25 stick Mod 1 and Mod 2. Crump et al. (2008) state that the changes in the tumor probability 

26 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.4

4

1 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.

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 

8 rats 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).

18

 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 reoptimizing 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 

6 along the flux axis in order to introduce a threshold for αI 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

12 These very small variations made to the αI 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 

15 data (at the formaldehyde flux where αI 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. 

34 Arguments for a hockey-stick or J shape over the background have been made in the 

35 literature for sustained and chronic cell replication rates; the analyses of the cell replication data 
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1 show that the data are not consistently (over each site and time) indicative of a hockey-stick or 

2 J shape as the best representation of the data. This uncertainty is particularly prominent when 

3 examining the cell replication data at the 13-week exposure time and the pooled data from the 

4 PLM nasal site from Monticello et al. (1996) (Figures E-1 [dotted curve], E-3B, E-4 of Appendix 

5 E). The earliest exposure time in this experiment was at 13 weeks, and the 13-week cell 

6 replication data appear to be more representative of a monotonic increasing dose response 

7 without a threshold. It is possible that early times are of more relevance to the carcinogenesis as 

8 well as for considering typical (short duration) human exposures.

9 For initiated cells, there are no data on which to evaluate the modifications made to these 

10 rates. However, some perspective can be gained by comparing them to the variability in the 

11 division rates obtained from the data on normal cells used to construct the formaldehyde model. 

12 As shown in Figure E-2 and discussed further in Subramaniam et al. (2008), these data show 

13 roughly an order of magnitude variation in the cell replication rate at a given flux. As part of a 

14 statistical evaluation of these data, a standard deviation of 0.32 was calculated for the log15 transforms of individual measurements of division rates of normal cells. By comparison, the 

16 maximum change in the log-transform division rate of initiated cells resulting from hockey-stick 

17 Mod 2 was only 0.20, and the average change would be considerably smaller. Thus, although 

18 there are no data for initiated cells, it can be said that the modifications introduced in Crump et 

19 al. (2008) for initiated cells are extremely small in comparison to the dispersion in the data for 

20 normal cells.

21 Subramaniam et al. (2008) also point to some additional, albeit limited, data, suggesting 

22 that exposure to formaldehyde could result in increased cell replication at doses far below those 

23 that are considered to be cytotoxic. Tyihak et al. (2001) treated different human cell lines in 

24 culture to various doses (0.1–10 mM) of formaldehyde and found that the mitotic index 

25 increased at the lowest dose of 0.1 mM. These findings considered along with human population 

26 variability and susceptibility (for example, polymorphisms in ADH3 [Hedberg et al., 2001]) 

27 indicate that it is necessary to consider the possibility of small increases in the human αI over 

28 baseline levels at exposures well below those at which cytotoxicity-driven proliferative response 

29 is thought to occur. 

30 Heck and Casanova (1999) have provided arguments to explain that the formation of 

31 DPXs by formaldehyde leads to inhibition of cell replication (i.e., if this effect alone is 

32 considered, normal cell replication rate of the exposed cells would be less than the baseline rate). 

33 However, this hypothesis was posed for normal cells. Subramaniam et al. (2008) argue that if an 

34 initiated cell is created by a specific mutation that impairs cell cycle control, the effect would be 

35 to mitigate the DPX-induced inhibition in cell replication, either partially or fully, depending on 
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1 the extent to which the cell cycle control has been disrupted. In the absence of data on initiated 

2 cells, the above argument provided biological motivation to the modification applied to the 

3 J-shape model for cell division (Crump et al. 2008).

4 Thus, the previous paragraphs suggest that the changes made in the analysis in Crump et 

5 al. (2008) to the assumption by Conolly et al. (2003) regarding the dose response for the division 

6 rate of initiated cells are not implausible.

7

8 F.2.4. Effect of Alternate Assumptions for Initiated Cell Kinetics on Human Risk Estimates

9 Figure F-5 contains graphs of the additional human risks estimated (in Crump et al. 

10 [2008]) by applying these modified models for αI and using all NTP controls, compared with 

11 those obtained by using the original Conolly et al. (2004) model. Each of the four modified 

12 models presents a very different picture from that of Conolly et al. (2004). At low exposures, 

13 these risks are three to four orders of magnitude larger than the largest estimates obtained by 

14 Conolly et al. (2004). 

15 These results have been criticized by Conolly et al. (2009) as being unrealistically large 

16 and above the realm of any epidemiologic estimate for formaldehyde SCC. Thus, they argue that 

17 the parameter adjustments made in Crump et al. (2008) are inappropriate. Crump et al. (2009) 

18 rebutted these points by arguing that the purpose of their work was not to provide a more reliable 

19 or plausible model but to carry out a sensitivity analysis. They argued that the changes made to 

20 the model (in their analyses) were reasonable since they did not violate any biological 

21 constraints or the available data. Further, they pointed out that “by appropriately mitigating the 

22 small modifications [they] made to the division rates of initiated cells, the model [would] 

23 provide any desired risk ranging from that estimated by the original model up to risks 1,000-fold 

24 larger than the conservative estimate in Conolly et al. (2004).” 

25 Crump et al. (2008) also evaluated the assumption in eq 3 of the CIIT modeling 

26 pertaining to initiated cell death rates (βI) by making small changes to βI. They report that they 

27 obtained similarly large values for estimates of additional human risk at low exposures. 

28 Obtaining reliable data on cell death rates in the nasal epithelium appears to be an unusually 

29 difficult proposition (Hester et al., 2003; Monticello and Morgan, 1997), and, even if data are 

30 obtained, they are likely to be extremely variable.
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Unmodified

Hockey Stick

Model

Hockey Mod 2

J-Shape Mod 1 Hockey Mod 1

Conolly et al. (2004)

“Risk Conservative”

Hockey Stick Model

J-Shape Mod 2

Unmodified

Hockey Stick

Model

Hockey Mod 2

J-Shape Mod 1 Hockey Mod 1

Conolly et al. (2004)

“Risk Conservative”

Hockey Stick Model

J-Shape Mod 2

1

2

3 Figure F-5. Graphs of the additional human risks estimated by applying 

4 these modified models for αI, 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

1
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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 

8 et 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 

16 specific 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, 

26 apoptosis, 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 
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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 

12 categories applicable to cell proliferation and DNA damage were similar to values obtained for 

13 cell labeling indices and DPXs in earlier studies and to BMD estimates obtained for the onset of 

14 nasal tumors. The mean BMD for the GO category of “positive regulation of cell proliferation” 

15 was 5.7 ppm; in comparison, Schlosser et al. (2003) obtained a 10% BMD of 4.9 ppm for the cell 

16 labeling 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 

2 the earlier study were additionally at 0.3 and 10 ppm, thus affording a lower exposure 

3 concentration. In the earlier study, tissue from the whole nose was analyzed, whereas in the later 

4 study tissue from two specific regions was obtained from the “high” tumor (Level II) and “low” 

5 tumor regions. Together, these data suggest that DPXs occur at exposure concentrations 

6 considerably lower than those that elicited transcriptional changes. One possible explanation is 

7 that the 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 

26 at 0.105. The net effect is that the BMD is the air level, such that the increase in the mean 

27 response is 1.349 × standard deviation. This is essentially an arbitrary definition. For 

28 comparison, if 0.05 of controls are considered affected and the BMR is set at 0.1 (common 

29 values that are applied to whole animal data), the BMD is the air level such that the increase in 

30 the mean response is 0.608 × standard deviation. Thus, if this definition had been used (as is 

31 traditionally the case), the BMD estimates would all be 2.2 times smaller than those obtained by 

32 Schlosser et al. (2003). Furthermore, the analysis assumes equal variance in all dose groups. 

33 Thus, further consideration of these issues with regard to interpretation of the BMR obtained 

34 from these studies is needed before it can be used in regulatory exposure setting. Secondly, 

35 lower confidence limits on the 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.

32
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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 

11 of critical parameters (e.g., gene expression) are consistent with the data, and 2) what these 

12 values imply for whole animal risk. This is of course, an extremely difficult proposition because 

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1 we do not know nearly enough about how changes in genes quantitatively affect whole animal 

2 risk, or even which genes are important. 
Appendix H

1
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
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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 

17 examining variations of the CIIT model in order to understand the implications of the model 

18 structure and parameters on model predictions. In this meeting, we wish to focus on the 

19 strengths and key uncertainties of this model, the extent to which assumptions in the CIIT model 

20 are supported by biological data, and examine the impact of uncertainty and variability on the 

21 overall quantitative 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

43
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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
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1 2. Considering the large patterns of variability in the ULLI data, discuss the validity of 

2 using 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

43
This document is a draft for review purposes only and does not constitute Agency policy.

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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 . . .].

46
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

9
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 

16 of the pharmacokinetics for both rats and humans. The discussion on the impact of 

17 interindividual variability of nasal anatomy was not particularly conclusive. It was determined 

18 that there was likely to be much less variability in reactive gas uptake than that seen in 

19 particulates. 

20 Crucial errors were however identified on several fronts in the manner in which the 

21 clonal growth model had been implemented in the CIIT effort. Dr. Portier felt that the 

22 calculation of probability was seriously flawed on account of lumping serially-sacrificed animals 

23 and animals that died of tumor together, while at the same time assuming rapid fatality of all 

24 tumors. This was seen to significantly alter the calculation of tumor probability (the shape of the 

25 dose-response curve), and his insight was that a correction was likely to allow for a substantially 

26 higher value for the probability of formaldehyde-induced mutation at low-dose. The best 

27 estimate for this probability is now zero in the model. Drs. Crump, Portier and Hattis argued that 

28 replacing this estimate by an upper confidence bound on KMU (the coefficient determining the 

29 role of DPX in the probability of mutation per cell generation), keeping other structural problems 

30 in the model unexplored, or other parameters fixed, would not be enough. There was a 

31 discussion on the need to provide confidence bounds on risk determined by allowing all the 

32 parameters to vary. Drs. Crump and Hattis (and Portier?) felt such an estimate would be very 

33 different from that calculated based on individual parameters.

34 Drs. Crump, Hattis and Portier urged us not to be constrained by the optimal likelihood 

35 values of a single plausible model, and underscored the need to explore a variety of biologically 

36 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 

2 model (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 

11 controls were lumped together. The thrust of Portier’s comments was that such a combination of 

12 controls was generally not done. The large number of historical controls was likely to 

13 significantly bias 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. 

31
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

36 of "uncertainties" when the impact of the uncertainties on the predicted risk is not known is itself 

37 an 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 

41 prediction. The 1991 assessment used DPX as a dosimeter and the LMS model. BMD 

42 assessments have since become available from other sources such as Paul Schlosser's work. The 

43 risk predictions of the BMD models are similar to the 1991 LMS assessment. Both the DPX44 LMS and BMD assessments predicted somewhat less risk than the 1987 assessment, establishing 

45 the trend of less risk with increased incorporation of relevant data. I have always argued 
This document is a draft for review purposes only and does not constitute Agency policy.

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1 (probably initially at the 1998 Ottawa review) that the historical context is the appropriate 

2 context for evaluating the CIIT clonal growth model. For a "level playing field" the 

3 uncertainties of the 1987 and 1991 assessments, and of the more recent BMD models, should be 

4 analyzed to the same degree as the clonal growth model. Does NCEA think that, because the 

5 LMS and BMD approaches used structurally simpler dose-response models and much more 

6 limited data inputs, they are less uncertain? The NCEA analysis seemed to be implying that use 

7 of more data and of a biologically more realistic model structure actually makes the CIIT 

8 approach more uncertain than the LMS and BMD approaches. I encourage NCEA to consider 

9 how uncertainties that can be evaluated explicitly in the structurally rich CIIT model compare to 

10 hidden uncertainties in the simpler models, where the hidden uncertainties encompass, for 

11 example:

12

13 1. Missing or incomplete descriptions of the regional dosimetry of formaldehyde.

14 2. Lack of simultaneous incorporation of the directly mutagenic and 

15 cytolethal/regenerative proliferation modes of action.

16 3. Lack of explicit consideration of the multistage nature of cancer.

17 4. Lack of consideration of the growth kinetics of initiated cell populations

18 5. Lack of evaluation of the measured J-shaped dose response for regenerative cellular 

19 proliferation.

20

21 A careful, balanced comparison of the CIIT assessment with the previous assessments along 

22 these lines would be informative with respect to the suitability of the CIIT assessment as the 

23 basis for a new IRIS listing for formaldehyde.

24 A further concern involves the peer-review of the CIIT formaldehyde assessment held in 

25 Ottawa in 1998. This review was sponsored by the U.S. EPA and Health Canada and involved 

26 what was arguably a world-class review panel. The CIIT assessment was not in its final form at 

27 that time, though we did provide a detailed description of the overall approach and the specific 

28 methods we were using to generate dose-response predictions. The 1999 CIIT document and the 

29 subsequent peer-reviewed publications are responsive to the comments and suggestions raised by 

30 the reviewers. My concern is that no information was provided on the role that Ottawa review 

31 plays in the ongoing review of the CIIT formaldehyde assessment by NCEA. Should the 

32 October 2004 review be viewed as standing on the shoulders of the 1998 review or as being in 

33 parallel to it? It was not at all clear to me that the October 2004 review in any way utilized the

34 judgments of the 1998 review. It seems that the 2004 review was more of a parallel effort and 

35 that the 1998 review was ignored and was effectively a waste of time and money. I would like to 

36 have some clear understanding of how the 2004 review effort should be viewed relative to that of 

37 1998.

38 In closing, let me reiterate that while the detailed examination of the CIIT formaldehyde 

39 assessment is laudable, this examination should be conduced with an eye to the historical context 

40 of formaldehyde risk assessment on the one had and, on the other hand, to a concern for 

41 encouraging, and not discouraging, development of biologically based risk assessment models.

42

43 Sincerely yours,

44

45 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 -