DRAFT - DO NOT CITE OR QUOTE EPA/635/R-10/002C

www.ep a.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, DCThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- ii 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 appl icable 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 end orsement or recommendation for use.This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- iii CONTENTS

— TOXICOLOGICAL REVIEW OF FORMALDEHYDE (CAS No. 50

- 00

- 0)

LIST OF TABLES

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

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

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

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

................................ ............................ xxxiii

VOLUME I

1. I NTRODUCTION

................................ ................................ ................................ .................. 1

- 1

2. BACKGROUND

................................ ................................ ................................ .................... 2

- 1
2.1. PHYSICOCHEMICA L PROPERTIES OF FORM ALDEHYDE

................................ 2

- 1 2.2. PRODUCTION, US ES, AND SOURCES OF F ORMALDEHYDE

........................... 2

- 1
2.3. ENVIRONMENTAL LEVELS AND HUMAN EXP OSURE

................................ ..... 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 PROPE RTIES 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 Metabolis m (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 Formal dehyde 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 Form aldehyde 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

- 16This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- iv CONTENTS (continued)

3.4. METABOLISM

................................ ................................ ................................ .......... 3

- 20 3.4.1. In Vitro and In Vivo Characterization of 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. EXCRETI ON

................................ ................................ ................................ .............. 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 T OXICOKINETICS OF FOR MALDEHYDE 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 Formald ehyde 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 Modelin g 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 R at 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 Irritat ion)

............................... 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 Expo sure Effects on the Gastrointestinal Tract

....................... 4

- 56 4.1.1.9. Summary: Noncarcinogenic Hazard in Humans

............................. 4

- 56This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- v CONTENTS (continued)

4.1.2. Cancer Health Effects

................................ ................................ ..................... 4

- 57 4.1.2.1. Respiratory Tract Cancer

................................ ................................ . 4

- 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. M utagenicity 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

- 38 2 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

- 389This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- vi CONTENTS (continued)

4.4.8. Effects on the Nervous System

................................ ................................ .... 4

- 390
4.4.8.1. Irritant Threshold Detection

................................ .......................... 4

- 390
4.4.8.2. Behavioral Effects

................................ ................................ ......... 4

- 391 4.4.8.3. Neurochemistry, Neuropathology, and 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 Reproduc tive 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 CARCIN OGENICITY

............................ 4

- 402 4.5.1. Cancers of the Respiratory Tract

................................ ................................ .. 4

- 402 4.5.2. Lymphohematopoietic Mal ignancies

................................ ........................... 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 fro m 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 (Naso pharyngeal 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 PO PULATIONS

................................ ................................ ............ 4

- 454 4.6.1. Life Stages

................................ ................................ ................................ .... 4

- 454 4.6.1.1. Early Life Stages

................................ ................................ ........... 4

- 455 4.6.1.2. Later Life Stages

................................ ................................ ............ 4

- 459
4.6.1.3. Conclusions on Life

- Stage Susceptibility

................................ ..... 4

- 459
4.6.2. Health/Disease Status

................................ ................................ ................... 4

- 460
4.6.3. Nutritional Status

................................ ................................ .......................... 4

- 461
4.6.4. Gender Differences

................................ ................................ ....................... 4

- 462
4.6.5. Genetic Differences

................................ ................................ ...................... 4

- 462This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- vii CONTENTS (continued)

4.6.6. Co

- Exposures

................................ ................................ ............................... 4

- 464
4.6.6.1. Cumulative Risk

................................ ................................ ............ 4

- 464 4.6.6.2. Aggregate Exposure

................................ ................................ ...... 4

- 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 Potenti al Susceptibility

................................ ............................ 4

- 467

VOLUME III

5. QUANTITATIVE ASSESSMENT: INHALATION EXPOSURE

................................ ....... 5

- 1
5.1. INHALATION REF ERENCE CONCENTRATION (R f C)

................................ ........ 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. De velopmental 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 C ANCER ASSESSMENT BAS ED ON THE NATIONAL
CANCER INST ITUTE 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

Mort ality

................................ ................................ ......................... 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

- 88This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- viii CONTENTS (continued)

5.2.3.2. Prediction of Lifetime Extra Risks for 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 CEL L
CARCINOMA IN THE RES PIRATORY TRACT USING ANIMAL DATA

...... 5

- 102 5.3.1. Long

- Term Bioassays in Laboratory Animals

................................ ............. 5

- 104
5.3.1.1. Nasal Tum or 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
Model s 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 FR OM THE QUANTITATIVE ASSESSMENT OF
CANCER RISK FROM FOR MALDEHYDE EXPOSURE B Y 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

- 13 6
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 HUM AN 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 Ato pic Symptoms

.................... 6

- 9 6.1.3.5. Effects on the Immune System

................................ ........................ 6

- 10 6.1.3.6. Neurological Effects

................................ ................................ ........ 6

- 11 6.1.3.7. Reproductive and Developmental Effects

................................ ....... 6

- 12This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- ix CONTENTS (continued)

6.1.3.8. Effects on General Systemic Toxicity

................................ ............. 6

- 13 6.1.3.9. Summary

................................ ................................ .......................... 6

- 14
6.1.4. Carcinogenicity in Human and Laboratory 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

- RESPONS E 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 CO NCLUSI ONS

................................ ................................ ......... 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 (Gar cia 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

IMPLEM ENTATIONS

................................ ................................ ..................... E

- 1
APPENDIX F: SENSITIVITY ANALYSIS OF BBDR MODEL FOR FORMALDEHYDE
INDUCED RESPIRATORY CANCER IN HUMANS

................................ .... F

- 1This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- x CONTENTS (continued)

APPENDIX G: EVALUATION OF THE CANCER DOSE

- RESPONSE MODELING
OF GENOMIC DATA FOR FORMALDEHYDE RISK ASSESSMENT

..... G

- 1 APPENDIX H: EXPERT PANEL CONSULTATION ON QUANTITATIVE
EVALUATION OF ANIMAL TOXICOLOGY DATA FOR
ANALYZING CANCER RISK DUE TO INHALED FORMALDEHYDE

.. H

- 1This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xi LIST OF TABLES

Table 2

- 1. Physicochemical properties of formaldehyde

................................ ........................... 2

- 2

Table 2

- 2. Ambient air l evels 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 [ 14 C]

- formaldehyde in F344 rats

.......................... 3

- 26

Table 3

- 5. Apparent formaldehyde levels in exhaled breath of individuals atten ding 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 concentra tions 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

Tabl e 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 brad ypnea 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

- 116This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xii LIST OF TABLES (continued)

Table 4

- 8. Exposure regimen for cross

- tolerance study

................................ ......................... 4

- 117

Table 4

- 9. Summ ary 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 epithe lium 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 bioch emical 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

Tabl e 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 hi stopathologic 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 forma ldehyde (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

- by

time products in male albino Wistar rats

................................ .............................. 4

- 153This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xiii LIST OF T ABLES (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 Wis tar 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 forma ldehyde

treated 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. His topathologic 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 fo rmaldehyde for 2 years

................................ ............... 4

- 175

Table 4

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

................................ ................................ ................................ ... 4

- 183This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xiv LIST OF TABLES (continued)

Table 4

- 35. Cell proliferation in nasal mucosa, trachea, and free lung cells isolated
fro m 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 a nd 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 rat s 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 exposu re 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 b enign 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 Sp rague

- 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 ex posure

.......................... 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

- 221This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xv LIST OF TABLES (continued)

Table 4

- 48. Summary of immune function changes due to inhaled formaldehyde exposure in experimental animals

................................ ................................ ....... 4

- 226

Table 4

- 49 . Study design for guinea pigs exposed 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 f ootpad 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 behavi oral 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

- 2 59

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. F ormaldehyde 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 semin iferous tubule diameter and epithelial height in Wistar rats following 18 weeks of exposure

........................ 4

- 302This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xvi LIST OF TABLES (continued)

Table 4

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

- treated mice

................................ ................................ .................. 4

- 302

Table 4

- 64. Body w eights 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 rat s 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 re ported 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 clastogenic ity of formaldehyde

................................ ............................. 4

- 348This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xvii LIST OF TABLES (continued)

Table 4

- 81 Summary of mutagenicity of formaldehyde in bacterial systems

....................... 4

- 350

Table 4

- 82. Mutagenicity in mammalian cell systems

................................ .......................... 4

- 355

Table 4

- 83. Genotoxicity in laboratory animals

................................ ................................ .... 4

- 361

Table 4

- 84. MN frequencies in buccal mucosa cells of volunteers exposed to formaldehyde

................................ ................................ ................................ ....... 4

- 364

Table 4

- 85. MN and SCE formation in mortuary science students exposed to formaldehyde for 85 days

................................ ................................ .................... 4

- 364

Table 4

- 86. Incidence of MN formation in mortuary students expos ed 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 worker s 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 le ukemias (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 p rovide 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 formaldehyd e
exposure

................................ ................................ ................................ ............... 4

- 469This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xviii LIST OF TABLES (continued)

Table 5

- 1. Points of departure (POD) for nervous 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. EC 0005 , LEC 0005 , and inhalation unit risk estimates for NPC mortality from formaldehyde exposure based on the Hauptmann et al. (2004) log

- linear trend analyses fo r cumulative exposure

................................ ................................ 5

- 81

Table 5

- 11. EC 0005 , LEC 0005 , 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 f or 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

- 91This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xix LIST OF TABLES (continued)

Table 5

- 15. Extra risk estimates for leukemia mortality from various levels of continuous exposure to formaldehyde

................................ ................................ ... 5

- 91

Table 5

- 16. EC 0005 , LEC 0005 , 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

- 9 3

Table 5

- 17. EC 005 , LEC 005 , 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. EC 0005 , LEC 0005 , and inhalation un it 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. EC 005 , LEC 005 , 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 i ncidences

................................ ..................... 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/m 3 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 can cer risk levels for selected formaldehyde

- related

cancers

................................ ................................ ................................ ................... 6

- 36

Table 6

- 3. Inhalation unit risk estimates based on epidemiological and experimental
animal data

................................ ................................ ................................ ............. 6

- 42This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xx LIST OF FIGURES

Figure 2

- 1. Chemical structure of formaldehyde

................................ ................................ ....... 2

- 1

Figure 2

- 2. Locat ions 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 mo difications

................................ ...................... 3

- 2

Figure 3

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

to 14 C and 3 H

- labeled formaldehyde (0.3, 2, 6, 10, 15 ppm)

.............................. 3

- 18

Figure 3

- 3. Formaldehyde clearance by ALDH2 (GSH

- independent) and ADH3 (GSH

- depende nt)

................................ ................................ ................................ 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

- 20This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxi LIST OF FIGURES (continued)

Figure 4

- 2. Formaldehyde effects on minute volume in naïve and formaldehyde

pretreated 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 epit helium

................................ ........ 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 i n 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/m 3 carbon black, or both

................................ ............... 4

- 223

Figure 4

- 12. Compressed air in milliliters as parameter for airway obstruction following formaldehyd e 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 anta gonists on formaldehyde

- induced vascular permeability in the trachea and bronchi of male Wistar rats

............................ 4

- 239This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxii LIST OF FIGURES (continued)

Figure 4

- 17. The effects of formaldehyde inhalation exposures on eosinophil infiltration (Panel A) and goblet cell pro liferation (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. Pla sma 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

- 2 56

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 ter mination 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

Figu re 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 a nimals for lymphoma in female mice exposed to formaldehyde for 24 months

................................ ...... 4

- 334This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxiii LIST OF FIGURES (continued)

Figure 4

- 32. DNA

- protein cross

- links (DPX) and thymidine kinase ( tk ) mutants in TK6 human lymphoblasts exposed to formaldehyde for 2 hours

.................... 4

- 357

F igure 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 o f 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

Fi gure 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 form aldehyde 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 formaldehy de 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 in

home formaldehyde levels in air

................................ ................................ ....... 5

- 45

Figure 5

- 6. Prevalence of asthma and respiratory symptom scores in children
associated with in

- home formald ehyde 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 ir ritation (eye irritation)

......................... 5

- 53This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxiv 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 ra tes 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
Conoll y 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

Figur e 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 BMC 10 and
BMCL 10

................................ ................................ ................................ ............ 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 Weib ull model fit of tumor incidence data compared with KM estimates of spontaneous tumor incidence

................................ ................ 5

- 132This document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxv 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 mea tus
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 es terase
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 bra in

- 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 binucla ted micronucleated cell ratefluoresce

BMD benchmark dose

BMDL 95% lower bound on the benchmark doseThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxvi LIST OF ABBREVIATIONS AND ACRONYMS (continued)

BMR benchmark response

BN Brown

- Norway
BrdU bromodeoxyuridine

BUN blood urea nitrogen

BW body weight
CA chromosomal aberrations
CalEPA California Environmental Protection Agency
CAP College of American Pathologists
CASRN Chemical Abstracts Service Registry Number
CAT catalase
CBMA cytokinesis

- blocked micronucleus assay
CBMN cytokinesis

- blocked micron ucleus
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 To xicology
CLL chronic lymphocytic leukemia
CML chronic myelogenous leukemia
CNS central nervous system
CO 2 carbon dioxide
COEHHA California Office of Environmental Health Hazard Assessment
CREB cyclic AMP responsive element binding proteins
CS conditi oned stimulus
C × t concentration times time

DA Daltons

DAF dosimetric adjustment factor

DDX DNA

- DNA cross

- links

DEI daily exposure index

DEN diethylnitrosamine
Der f common dust mite allergen
DMG dimethylglycine
DMGDH dimethylglycine dehydrogenase
DNA deoxyribonucleic acid
DOPAC 3,4

- dihydroxyphenylacetic acid
DPC / DPX DNA

- protein cross

- links

EBV Epstein

- Barr virus
EC effective concentration

ED effective dose
EHC Environmental Health Committee
ELISA enzyme

- linked immunosorbent assayThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxvii LIST OF ABBREVIATIONS AND ACRONYMS (continued)

EPA U.S. Environmental Protection Agency
ERPG emergency response planning guideline

ET ethmoid turbinates

FALDH formaldehyde dehydrogenase

FDA U.S. Food and Drug Administration

FDR fecundability density ratio

FE F 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 g as 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

- hydroxy methylglutathione

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 Substan ces Data Bank Hsp heat shock protein

HWE healthy worker effect
I cell initiated cell
IARC International Agency for Research on CancerThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxviii LIST OF ABBREVIATIONS AND ACRONYMS (continued)

ICD International Classification of Diseases

IF interfacial

IFN int erferon Ig immunoglobulin

IL interleukin

I.P. intraperitoneal
IPCS International Programme on Chemical Safety
IRIS Integrated Risk Information System

K
m Michaels

- Menton constant

KM Kaplan

- Meier

LD 50 median lethal dose

LDH lactate dehydrogenase
LEC 95% lower bound on the effective concentration

LED 95% lower bound on the effective dose

LHP lymphohematopoietic

LI labeling index

LM Listeria monocytogenes
LMS linearized multistage
LLNA local lymph node assay
LOAEL lowest

- observed

- adverse

- effect level
LPS lipopolysaccharide

LR low responders

LRT lower respiratory tract

MA methylamine

MALT mucus

- associated lymph tissues
MCH mean corpuscular hemoglobin
MCHC mean corpuscular hemoglobin concentration
MCS multiple chemical sensitivity
MCV mean corpuscular volume

MDA malondialdehyde

MEF maximal expiratory flow

ML myeloid leukemia

MLE maximum likelihood estimate
MMS methyl methane sulfonate

MMT medial maxilloturbinate

MN micronucleus, micronuclei
MNNG N

- methyl

- N

- nitro

- N

- nitrosoguanidine
MO A mode of action
MoDC monocyte

- derived dendritic cell

MP macrophage

MPD multistage polynomial degree
MPS mononuclear phagocyte system

MRL minimum risk levelThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxix LIST OF ABBREVIATIONS AND ACRONYMS (continued)

mRNA messenger ribonucleic acid

MVE

- 2 Murray Valley encephalitis virus

MVK Moolgavkar, Venzon, and Knudson
N cell normal cell
NaCl sodium choride
NAD+ nicotinamide adenine dinucleotide
NADH reduced nicotinamide adenine dinucleotide
NALT nasally associated lymphoid tissue
NATA National

- Scale A ir Toxics Assessment
NCEA National Center for Environmental Assessment
NCHS National Center for Health Statistics
NCI National Cancer Institute
NEG Nordic Expert Group

NER nucleotide excision repair
NGF nerve growth factor

NHL non

- Hodgkin’s lymphoma
NHMRC/ARMCANZ National Health and Medical Research Council/Agriculture and Resource Management Council of Australia and New Zealand

NNK nitrosamine nitrosamine 4

- (methylnitrosamino)

- 1

- (3

- pyridyl)

- butanone

N 6

- hmdA N 6

- hydroxymethyldeoxyadenosine

N 4

- hmdC N 4

- hydroxymethylcytidine

N 2

- hmdG N 2

- 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

- aspartat e 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 Administrat ion
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 polymeras e chain reaction
PCV packed cell volumeThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxx LIST OF ABBREVIATIONS AND ACRONYMS (continued)

PECAM platelet endothelial cell adhesion molecule

PEF peak expiratory flow

PEFR peak expiratory flow rates

PEL permissible exposure limit
PFC plaque

- forming cell

P G 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 depart ure 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 b lood cells

RD 50 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
SE M standard error of the mean
SEN sensitizer
SH sulfhydryl
SHE Syrian hamster embryo
SLMA spontaneous locomotor activityThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxxi LIST OF ABBREVIATIONS AND ACRONYMS (continued)

SMR standardized mortality ratio
SNP single nucleotide polymorphism
SOD superoxi de dismutase
SOMedA N 6

- sulfomethyldeoxyadenosine Sp1 specificity protein
SPIR standardized proportionate incidence ratio
SSAO semicarbozole

- sensitive amine oxidase
SSB single strand breaks
STEL short

- term exposure limit

TBA tumor bearing animal

TH T

- lymphocyte helper

THF tetrahydrofolate

TK toxicokinetics

TL tail length

TLV threshold limit value

TNF tumor necrosis factor

TP total protein

TRI Toxic Release Inventory

TRPV transient receptor potential vanilloid
TWA time

- weighted average
TZCA t hiazolidine

- 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 EuropeThis document is a draft for review pu rposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE IV

- xxxii 

### This page intentionally left blank.

# Appendix AThis document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE A

- 2 AP PENDIX A

1
SUMMARY OF EXTERNAL PEER REVIEW 2
AND PUBLIC COMMENTS AND DISPOSITIONS 3
4

5

6

7

8

9

10

11

12

13 

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


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

16

# Appendix B

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

— DO NOT CITE OR QUOTE B

- 2 APPENDIX B 1

2
SIMULATIONS OF INTERINDIVIDUAL AND ADULT

- TO

- CHILD VARIABILITY IN 3 REACTIVE GAS UPTAKE IN A SMALL SAMPLE OF PEOPLE (Garcia et al., 2009) 4
5 Garcia et al. (2009) used computational fluid dynamics to study human variability in the 6
nasal dosimetry of reactive, water

- soluble gases in 5 adults and 2 children, aged 7 and 8 years 7
old. They considered two model categories of gases, corresponding to maximal and moderate 8
absorption at the nasal lining. The nasal airway (including the nasopharynx) geometries of these 9

in dividuals were mapped out using magnetic resonance imaging or computed tomography scans. 10
The scans chosen for the analysis were from individuals who had normal nasal anatomies with 11
no pathology (as per a review carried out by a ear

- nose

- throat surgeon). Th e minute volumes of 12
these individuals were estimated to range from 6.8 to 9.0 L/min (adults) and 5.5 to 5.8 L/min 13
(children). The sample size in this study is too small to consider the results representative of the 14
population as a whole (as also recognized by the authors). Nonetheless, various comparisons 15
with the characteristics of other study populations add to the strength of this study. The range of 16
adult minute volumes in this study is reported by the authors to be in good agreement with that 17

obtained in many other studies in the literature; minute volumes for the children in the study 18
were found to be similar to the average minute volume of 6.1 ± 1.7 L/min obtained by Bennett 19
and Zeman (2004) in a study of 36 children aged 6 to 13 years; the range of n asal surface area 20
values for the adults agreed well with that obtained by Guilmette et al. (1997) for 45 adults; and 21
the range of values for the surface area to volume ratio is in good agreement with that obtained 22
for 40 adult Caucasians studied by Yokley (2006). The surface area to volume ratio is useful for 23
comparing the rate of diffusional transport of a gas out of different cavities; however in the case 24
of the highly non

- homogenously shaped nasal lumen, this might at best be considered a gross 25

indicato r. 26
We focus here only on the “maximum uptake” simulations in Garcia et al. (2009). In this 27
case, the gas was considered so highly reactive and soluble that it was reasonable to assume an 28
infinitely fast reaction of the absorbed gas with compounds in the a irway lining. Although such a 29
gas could be reasonably considered to represent formaldehyde, these results cannot be fully 30
utilized to inform quantitative estimates of formaldehyde dosimetry (and that does not appear to 31

have been the intent of the authors e ither). This is because the same boundary condition 32
corresponding to maximal uptake was applied on the vestibular section as well as on the 33
transitional and transitional epithelial lining of the nasal cavity. This is not appropriate for 34
formaldehyde as the lining on the nasal vestibule is made of keratinized epithelium which is 35
considerably less absorbing than the transitional or respiratory epithelium (Kimbell et al. 2001). 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE B

- 3 Table B

- 1 provides results obtained by Garcia et al. (2009) for five adults and tw o 1
children for uptake for the maximal uptake scenario. Although the nasal cavities of the children 2
were smaller in surface area, volume and length, the surface

- area

- to

- volume ratios were similar 3
in the two age groups. Overall uptake efficiency, average flu x (rate of gas absorbed per unit 4
surface area of the nasal lining) and maximum flux levels over the entire nasal lining did not 5
vary substantially between adults (1.6

- fold difference in average flux and much less in maximum 6

flux), and the mean values of t hese quantities were comparable between adults and children. 7
These results are in agreement with conclusions reached by Ginsberg et al. (2005) that overall 8
extrathoracic absorption of highly and moderately reactive and soluble gases (corresponding to 9

categ ory 1 and 2 reactive gases as per the scheme in USEPA [1994]) is similar in adults and 10
children. However, none of these three quantities should be considered as reasonable indicators 11
of variability in the interaction of the gas with the nasal lining. For a very reactive and soluble 12
gas, regional absorption of the gas is highly nonhomogenously distributed; therefore 13
interindividual variability in this distribution will be washed out when averaged over the whole 14
nose. Estimates of maximum gas flux, on the oth er hand, correspond to extremely small 15
localized regions of hot spots (see chapter 3), and thus may not be a proper measure of inter

16
individual variability in flux distribution patterns over the whole nose. Furthermore, numerical 17

error in the calculation ( such as mass balance and irregularly shaped elements of the finite

18
element mesh) is likely to be most pronounced when estimates are considered over extremely 19
small regions. 20

21
Table B

- 1: Variations in overall nasal uptake, whole nose flux, and key parameters 22
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.m 2 ) 10 -8 kg/(s.m 2 )

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

Source: Garcia et al. (2009). 26
27This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE B

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

- 1, 1
shows significant interhuman variability in flux valu es at specific points on the nasal walls. The 2
local flux of formaldehyde varies among individuals by a factor of 3 to 5 at various distances 3
along the septal axis of the nose. However, interpretation of the values in this plot is problematic 4

for reasons ex plained in the paper 1
The greater variability among individuals seen for wall fluxes at specific sites of the nasal 6
passages (Figure 6) in comparison to the minimal variability in total uptake 7 : 5

(Table 2) and whole

- nose dose (Tables 3 and Tables 4) indicat es that fluxes of equal 8
magnitude do not exactly overlay the same anatomical regions of the nasal cavity in each 9
individual. This implies that specific anatomical regions subtended by maximum flux 10

could be offset from one individual to another. 11

Notwithstan ding this difficulty in interpretion, we believe the quantities plotted in Figure B

- 1 12
provide a better perspective of the inter

- individual (adult) variability in local flux than the 13
variation in whole nose average or in maximum flux presented in Table B

- 1. 14

15

16 Figure B

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

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

— DO NOT CITE OR QUOTE B

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

would be most useful since one is then interested in inter

- individual variability in the overall 2

dos e delivered to the lung. It is possible to conceive of allergic or irritation responses being 3
triggered by some threshold value of local flux. In such a case it may be preferable to calculate 4
the variability associated with the net surface area receiving f lux values greater than that 5
threshold. On the other hand, the probability of developing a tumor at a nasal site may be non

6
linearly related to the flux at that site and linearly related to the number of cells at that site. In 7
this case, the appropriate me tric may be the nasal surface area associated with some intermediate 8
levels of local flux (see Appendix in Subramaniam et al. 2008). 9
Various caveats presented by the authors as limitations of their study should be noted: 10

Possible nonuniform distribution o f epithelial types, enzymes, glands and other cellular 11
metabolic or clearance machinery were not considered in the model; only effects pertaning to 12
resting breathing were considered; the study sample size was small; children younger than 7 13
years old were n ot studied; and, the model assumed a rigid nasal geometry. 14

15
Garcia et al. (2009) conclude their paper as follows: 16
“..., our simulations predicted no differences in the nasal dosimetry of reactive, water

17
soluble gases between children and adults, suggesting t hat the risk factor of 10 typically 18

used to accommodate inter

- human variability is adequate.” 19

20
In addition to the obvious caveat already recognized by the authors in regards extrapolating from 21
a study involving just two children, this conclusion needs furt her qualification. Firstly, the safety 22
factor of 10 that is typically applied for interhuman variability does not specifically include 23
children. Instead, EPA practice is that unless there is reasonable evidence that childhood forms a 24
more susceptible lifes tage, no additional factors are applied for this population. Second, it is 25

important not to confuse dosimetric differences across lifestages

**−** which actually contribute to 26

intra

- individual differences

**−** with inter

- individual differences that may contribute tow ards the 10

- 27

fold uncertainty factor. This interhuman variability in susceptibility

**−** for a given life

- stage

**−** may 28
be considered to arise from both pharmacokinetic and pharmacodynamic factors, and the practice 29
in the US has been to split these into factors of 3 .3 each. Then, the roughly 3 to 5

- fold variation 30
estimated for adults (and also between the two children) in Figure B

- 1 suggest that a factor of 10 31
may not be adequate to accommodate inter

- human variability for those formaldehyde

- induced 32
adverse responses for which the localized nature of formaldehyde flux, and therefore the inter

33
individual differences in regional dosimetry, play a role. 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE B

- 6 

### This page intentionally left blank.

# Appendix C

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

— DO NOT CITE OR QUOTE C

- 2 APPENDIX C 1

2
LIFETABLE ANALYSIS 3
4 A spreadsheet i llustrating the extra

- risk calculation for the derivation of the lower 95% 5

bound on the effective concentration associated with a 0.05% extra risk (LEC 0005 ) for 6
nasopharyngeal carcinoma (NPC) incidence is presented in Table C

- 1. 7

8This document is a draft forreview purposes only and doesnot constitute Agency policyC-3DRAFT—DO Tab le C -1. Extra- risk calculation a for environmental exposure to 0.0461 ppm formaldehyde (the LEC 0005 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 (×10 5 /yr) NPC
incidence (×10 5 /yr) All

cause hazard rate (h*) Prob of surviving
interval (q) Prob of surviving

up to
interval (S) NPC

cancer hazard rate (h) Cond prob of

NPC
incidence
in
interval

(Ro) Exp
duration
mid
interval (xtime) Cum exp mid
interval (xdose) Exposed
NPC
hazard rate (hx) Exposed
all cause
hazard

rate (h*x) 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) = 0.0005This document is a draft forreview purposes only and doesnot constitute Agency policyC-4DRAFT—DO Column A: interval index number (i).
Column B: 5

- year age interval (except <1 and 1 −4) up to age 85.
Column C: all

- cause mortality rate for interval i (× 10 5 /year) (2000 data from NCHS).
Column D: NPC incidence rate for interval i (× 10 5 /year) (1996

- 2000 SEER data).
Column E: all

- cause hazard rate for interval i (h* i ) (= all

- cause mortality rate × number of years in age interval). c

Column F: probability of surviving interval i without being diagnosed with NPC (q i ) (= exp( −h* i )).
Column G: probability of surviving up to interval i without having been diagnosed with NPC (S i ) (S 1 = 1; S i = S i −1 × q i −1 , for i>1).
Column H: NPC incidence hazard rate for interval i (h i ) (= NPC incidence rate × number of years in interval).
C olumn I: conditional probability of being diagnosed with NPC in interval i (= (h i /h* i ) × S i × (1 −q i )), i.e., conditional upon surviving up to interval i without having been diagnosed with NPC [Ro, the background lifetime probability o f 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 (hx i ) (= h i × (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
LEC 0005 , 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*x i ) (= h* i + (hx i − h i )).
Column N: probabili ty of surviving interval i without being diagnosed with NPC for exposed people (qx i ) (= exp( −h*x i )).
Column O: probability of surviving up to interval i without having been diagnosed with NPC for exposed people (Sx i ) (Sx 1 = 1; Sx i = Sx i −1 × qx i

- 1 , for i>1)

. Column P: conditional probability of being diagnosed with NPC in interval i for exposed people (= (hx i /h*x i ) × Sx i × (1 −qx i )) [Rx, the lifetime probability of being diagnosed with NPC for exposed people = the sum of the conditional probabilities across t he 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

m ortality 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 mortalit y rates. MLE = maximum likelihood estimate, SE = standard error

# Appendix D

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

— DO NOT CITE OR QUOTE D

- 2 APPENDIX D 1

2
MODEL STRUCTURE & CA LIBRATION IN CONOLLY ET AL. (2003, 2004) 3 4
The various studies indicated in Section 5.4.1 were followed by the development of a 5

biol ogically motivated dose

- response model for formaldehyde

- induced cancer as represented 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; CII T, 1999). EPA’s cancer 8
guidelines (U.S. EPA, 2005a) suggest using a BBDR model for extrapolation when data permits 9

since it facilitates the incorporation of MOA in risk assessment . The CIIT modeling and 10
available data were evaluated in a series of peer

- re viewed papers (Klein et al., 2009; Crump et 11
al., 2008; Subramaniam et al., 2008, 2007) and debated further in the literature (Conolly et al., 12
2009; Crump et al., 2009). In addition, alternatives to the CIIT biological modeling (but based 13
on that original model) are further explored and evaluated here. 14
In Conolly et al. (2003), tumor incidence data in the above long

- term bioassays were 15
modeled by using an approximation of the two

- stage clonal growth model (Moolgavkar et al., 16
1988) and allowing formaldehyde to have directly mutagenic action. Conolly et al. (2003) 17

combined these data with historical control data on 7,684 animals obtained from National 18
Toxicology Program (NTP) bioassays. These models are based on the Moolgavkar, Venzon, and 19

Knudson (MVK) sto chastic two

- stage model of cancer (Moolgavkar et al., 1988; Moolgavkar 20
and Knudson, 1981; Moolgavkar and Venzon, 1979), which accounts for growth of a pool of 21
normal cells, mutation of normal cells to initiated cells, clonal expansion and death of initiate d 22
cells, and mutation of initiated cells to fully malignant cells. 23
The MVK model for formaldehyde accounted for two MOAs that may be relevant to 24
formaldehyde carcinogenicity: 25

26

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

o A possible direct mutagenic MOA, based on information indicating that formaldehyde is 30
mutagenic (Speit and Merk, 2002; Heck et al., 1990; Grafstrom et al., 1985), was modeled by 31
using rat data on formaldehyde production of DPXs (Monticello et al., 1996, 1991). 32

33
The human model for formaldehyde carcinogenicity (Conoll y et al., 2004) is 34
conceptually very similar to the rat model. The model uses, as input, results from a dosimetry 35
model for an anatomically realistic representation of the human upper airways and an idealized 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE D

- 3 representation of the lower airways. However, the model does not incorporate any data on 1
human responses to formaldehyde exposure. The rat and human formaldehyde models are 2

detailed further below. 3

4
The following notations are used in the rest of this chapter: 5

N cell, normal cell 6

I cell, initiated ce ll 7
LI, labeling index (number of labeled cells/(number labeled + unlabeled cells) 8
ULLI, unit length labeling index (number labeled cells/length of basement membrane) 9
N, number of normal cells that are eligible for progression to malignancy 10

**α** N , division rate of normal cells (hours

– 1 ) 11

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

of normal cells) 13

**α** I, division rate of an initiated cell (hours

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

– 1 ) 15

μ I, ra te at which a malignant cell is formed by mutation of an initiated cell (per cell 16

division of initiated cells) 17

18
A novel contribution of the CIIT model is that cell replication rates and DPX 19
concentrations are driven by local dose, which is formaldehyde flu x to each region of nasal 20
tissue expressed as pmol/mm 2

- hour . This dosimetry is predicted by computational fluid 21
dynamics (CFD) modeling using anatomically accurate representations of the nasal passages (see 22
Chapter 3). Such a feature is important to inco rporating site

- specific toxicity in the case of a 23
highly reactive gas like formaldehyde, for which uptake patterns are spatially localized and 24
significantly different across species (see Chapter 3) . In the CIIT model, each of these 25
parameters is character ized by local flux. The inputs to the two

- stage cancer modeling consisted 26
of results from other model predictions as well as empirical data as follows: 27

28

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

Subramaniam et al., 1998) 31

● Concentrations of DPXs predicted by a PBPK model (Conolly et al., 2000) calibrated to 32
fit the DPX data in F344 rat and rhesus monkey (Casanova et al., 1994, 1991) and 33

s ubsequently scaled up to humans 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE D

- 4 ● α N inferred from LI data on rats exposed to formaldehyde (Monticello et al., 1996, 1991, 1

1990) 2

D.1. DPX AND MUTATIONAL ACTION 3
Formaldehyde interacts with DNA to form DPXs. These cross

- links are considered to 4
induce mutagenic as well as clastogenic effect s. Casanova et al. (1994, 1989) carried out two 5
studies of DPX measurements in F344 rats. In the first study, rats were exposed to 6
concentrations of 0.3, 0.7, 2, 6, and 10 ppm for 6 hours and DPX measurements were made over 7
the whole respiratory mucosa o f the rat, while, in the second study, the exposure was to 0.7, 2, 6, 8

or 15 ppm formaldehyde for 3 hours and measurements were made at “high” and “low” tumor 9
sites. Overall, these studies showed statistically significantly elevated levels of DPXs at 10

conce ntrations ≥2 ppm, with the trend also indicating elevated DPXs at 0.7 ppm. In Conolly et 11
al. (2003), DPX formation is considered proportional to the intracellular dose that induces 12
mutations. Conolly et al. (2000) used data from the second study to devel op a PBPK model that 13
predicted the time course of DPX concentrations as a function of regional formaldehyde flux 14
(estimated in the CFD modeling and expressed as pmol/mm 2

- hour). In Conolly et al. (2003), this 15
PBPK model was then used to predict regional DP X concentrations (that is, as a function of 16
regional formaldehyde flux) (Figure 5

- 11, Chapter 5). These data were incorporated into the 17

two

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

the same linear functio n of DPX concentration as follows: 19

20

μ N = μ I = μ Nbasal + KMU × DPX (1) 21

22
The unknown constants μ Nbasal and KMU were estimated by fitting model predictions to the 23
tumor bioassay data. 24

25

D.2. CALIBRATION OF MODEL 26
The rat model in Conolly et al. (2003) in volved six unknown statistical parameters that 27
were estimated by fitting the model to the rat formaldehyde bioassay data shown in Table 5

- 24 in 28
Chapter 5 ( Monticello et al., 1996; Kerns et al., 1983 ) plus data from several thousand control 29

animals from all the rat bioassays conducted by the NTP. These NTP bioassays were conducted 30
from 1976 through 1999 and included 7,684 animals with an incidence of 13 SCCs (i.e., 0.17% 31

incidence). The resulting model predicts the probability of a nasal SCC in the F344 ra t as a 32
function of age and exposure to formaldehyde. The fit to the tumor incidence data is shown in 33
Figure 5

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

— DO NOT CITE OR QUOTE D

- 5 implementation of this model in Subramaniam et al. [2007], whic h is discussed later in this 1
chapter.) 2

3

D.3. FLUX BINS 4
The spatial distribution of formaldehyde over the nasal lining was characterized by 5
partitioning the nasal surface by formaldehyde flux to the tissue, resulting in 20 “flux bins” 6
(Figure 5

- 13, Chapter 5). Each bin is comprised of elements (not necessarily contiguous) of the 7
nasal surface that receive a particular interval of formaldehyde flux per ppm of exposure 8
concentration (Kimbell et al., 2001a). The spatial coordinates of elements comprising a 9

p articular flux bin are fixed for all exposure concentrations, with formaldehyde flux in a bin 10
scaling linearly with exposure concentration (ppm). The number of cells at risk varies across the 11
bins, as shown in Figure 5

- 14 , Chapter 5. 12

13
D.4. USE OF LABELIN G DATA 14
Cell replication rates in Conolly et al. (2003) were obtained by pooling labeling data 15
from two phases of a labeling study in which male F344 rats were exposed to formaldehyde gas 16
at similar concentrations (0, 0.7, 2.0, 6.0, 10.0, or 15.0 ppm). The first phase employed injection 17
labeling with a 2

- hour pulse labeling time, and animals were exposed to formaldehyde for early 18
exposure periods of 1, 4, and 9 days and 6 weeks (Monticello et al., 1991). The second phase 19
used osmotic minipumps for labeling with a 120

- hour labeling time to quantify labeling in 20
animals exposed for 13, 26, 52, and 78 weeks (Monticello et al., 1996). The combined pulse and 21
continuous labeling data were expressed as one exposure TWA over all sites for each exposure 22

concentratio n. α N was calculated from these labeling data by using an approximation from 23
Moolgavkar and Luebeck (1992). A dose

- response curve for normal cell replication rates (i.e., 24

**α** N as a function of formaldehyde flux) was then calculated as shown in Figure D

- 1 . The se steps 25
are carefully detailed and evaluated in Subramaniam et al. (2008), and discussion of the data will 26
continue in the section on uncertainties in characterizing cell replication rates. 27

28

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

The exte nsive labeling data collected by Monticello et al. (1996, 1991) present an 30
opportunity to use precursor data in assessing cancer risk. However, these empirical data could 31

be used to determine **α Ν**  **(** flux) only for the lower flux range, 0

– 9,340 pmol/mm 2

- hour (see 32
Subramaniam et al. [2008] for the reasons), as shown by the solid line in Figure D

- 1, whereas the 33
highest computed flux at 15.0 ppm exposure was 39,300 pmol/mm 2

- hour. Therefore Conolly et 34

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

— DO NOT CITE OR QUOTE D

- 6 maximum flux of 39,300 pmol/mm 2

- hour. **α** max was estimated by maximizing the likelihood of 1

the two

- stage model fit to the tumor incidence data. For 9,340 < f lux ≤ 39,300 pmol/mm 2

- 2

hour, **α Ν**  **(** flux) was determined by linear interpolation from **α Ν**  (9,340) to **α** max , as shown by the 3

dashed line in Figure D

- 1

. 4

5

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

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

Estimated **α** N
Estimated **α Ι** 

0.000 0.001 0.002

0 2000 4000 6000 0.000 0.001 0.002 J-shape

Hockey 

### α max

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

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

Estimated **α** N
Estimated **α Ι** 

0.000 0.001 0.002

0 2000 4000 6000 0.000 0.001 0.002 J-shape

Hockey 

### α max

6 7 Figure D

- 1. Dose response of normal ( **α** N ) and initiated ( **α** I ) cell division rate 8 in Conolly et al. (2003). 9
10 Note: Empirically derived values of **α** N (TWA over six sites) from Table 1 in 11 Conolly et al. (2003) and optimized parameter values from their Table 4 were 12 used. The main panel is for the J

- shape dose response. Insets show J

- shape and 13 hockey

- stick shape rep resentations at the low end of the flux range. The long 14 arrow denotes the upper end of the flux range for which the empirical unit

- length 15 labeling data are available for use in the clonal growth model

. **α** max is the value of 16

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

assumed **β** I = **α** N at all flux values. 20
21
Source: Subra maniam et al. (2008). 22
23This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE D

- 7 D.6. INITIATED CELL DIVISION AND DEATH RATES 1
The pool of cells used for obtaining the LI data in Monticello et al. (1996, 1991) consists 2
of largely normal cells with perhaps increasing numbers of initiated cells at higher exposure 3

c oncentrations. Since the division rates of initiated cells in the nasal epithelium, either 4
background or formaldehyde exposed, could not be inferred from the available empirical data, 5
Conolly et al. (2003) made what they perceived to be a biologically rea sonable assumption for 6

**α** I , assuming **α** I to be linked to **α** N via a two

- parameter function: 7

8

α I = α N ×{multb

– multc × max[ α N

– α N(basal) , 0]} (2) 9 10
where **α** N ≡ **α** N (flux), **α** N ( basal ) is the estimated average cell division rate in unexposed normal 11

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

the tumor data. 2



### Flux (pmole/mm 2 /h) 0 10000 20000 30000 40000**α**I**/ α** N

0.96 1.00 1.04 1.08 The value of **α** N(basal) was equal to 3.39 × 10

– 4 hours

– 1 as determined by Conolly 13

et al. (2003) from the raw averaged LI data. The ratio **α** I / **α** N is plotted a gainst flux in Figure D

- 2, 14

and **α** I (flux) is shown by the dotted line in Figure D

- 1. 15

16
17 Figure D

- 2. Flux dependence of ratio of initiated and normal cell replication 18 rates ( **α** I / **α** N ) in CIIT model. 19 Note: Cell replication rate of initiated cells is less than no rmal cell replication rate 20 at flux exceeding the value denoted by the arrow. By assumption, the y

- axis also 21 represents ( **α** I / **β** I ). 22 Source: Subramaniam et al. (2008). 23
24

2 multb and multc were equal to 1.072 and 2.583, respectively (J

- shaped α N )

, , and 1.070 and 2.515, respectively (hockey

- stick shaped α N )

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

— DO NOT CITE OR QUOTE D

- 8 Death rates of initiated cells ( β I ) are assumed to equal the division rates of normal cell s 1
for all formaldehyde flux values, that is 2

3
β I (flux) = **α** N (flux) (3) 4

5

Conolly et al. (2003) stated that this formulation for **α** I and β I provided the best fit of the 6

model to the tumor data. 7

8

D.7. STRUCTURE OF THE CIIT HUMAN MODEL 9
Subsequent to the BBDR model for modeling rat cancer, Conolly et al. (2004) developed 10
a corresponding model for humans for the purpose of extrapolating the risk to humans estimat ed 11
by the rat model. Also, rather than considering only nasal tumors, it is used to predict the risk of 12
all human respiratory tumors. DPXs observed at proximal portions of the rhesus monkey LRT 13
(Casanova et al., 1991) suggested that the LRT may be at ris k in addition to the URT. In 14
addition, some epidemiologic studies (Gardner et al., 1993; Blair et al., 1990, 1986) reported an 15
increase in lung cancer associated with formaldehyde exposure, while others reported no such 16

increases (Collins et al., 1997; St ayner et al., 1988). The human model for formaldehyde 17
carcinogenicity (Conolly et al., 2004) is conceptually very similar to the rat model and follows 18
the schematic in Figure 5

- 11, Chapter 5 . The following points need to be noted: 19

20

**•** The model does not inc orporate any data on human responses to formaldehyde exposure. 21

**•** The model is based on an anatomically realistic representation of the human nasal 22
passages (in a single individual) and an idealized representation of the LRT. Local 23
formaldehyde flux to resp iratory tissue is estimated by a CFD model for humans 24

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

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

same in humans as in rats. 27

**•** The concentra tion of formaldehyde

- induced DPXs in humans is estimated by scaling up 28
from values obtained from experiments in the F344 rat and rhesus monkey. This scaling 29
up was discussed in chapter 3. 30

**•** The statistical parameters for the human model are either estimated by fitting the model 31
to the human background data, assumed to have the same value as obtained in the rat 32
model, or, in one case, fixed at a value suggested by the epidemiologic literature. The 33
delay, D, is fixed at 3.5 years, based on a fit to the incide nce of lung cancer in a cohort of 34

British doctors (Doll and Peto, 1978). The two other parameters in the rat model that 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE D

- 9 affect the background rate of cancer (multb and μ basal ) are estimated by fitting to U.S. 1
cancer incidence or mortality data. These par ameters affect the baseline values for the 2

human **α** I, μ N , and μ I. Since **α** max , multfc, and KMU do not affect the background cancer 3
rate, they cannot be estimated from the ( baseline) U.S. cancer incidence rates. Therefore, 4

in Conolly et al. (2004, 2003), **α** max and multfc are assumed to have the same values in 5
humans as in rats, and the human value for KMU is obtained by assuming that the ratio 6
KMU/μ basal is invariant across species. Thus, 7

8

**( )**  **( )**  **( )** 

**( )**  rat Nbasal human Nbasal

rat human 

### KMU KMU
**μ μ** 
**× =**  (4) 9This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE D

- 10 

### This page intentionally left blank.

# Appendix EThis document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 2 APPENDIX E

1
EVA LUATION OF BBDR MODELING OF NASAL CANCER IN THE F344 RAT: 2 CONOLLY ET AL. (2003) AND ALTERNATIVE IMPLEMENTATIONS 3

4
A biologically based dose

- response model for formaldehyde

- induced cancer was in a 5
series of papers and in a health assessment report (CIIT mode l) (Conolly et al., 2004, 2003, 6
2000; Conolly, 2002; Kimbell et al., 2001a, b; Overton et al., 2001; CIIT, 1999). The model 7
structure, notations, and calibration have been described in Appendix D. In Chapter 5, an 8

evaluation of the uncertainties of this mo del and alternative approaches based on its conceptual 9
framework was presented in a summary form. This Appendix provides the relevant details of that 10
evaluation and presents a range of dose

- response curves for tumor risk in the rat. It is divided 11

into the following major sections. First, an overview of all the issues evaluated is provided in 12
tabular form. The rest of the Appendix then presents only those issues which have a significant 13
impact on model predictions. These issues pertain to the use of history controls, the uncertainty 14
and variability in the dose

- response for normal cell

- replication rates, and sensitivity of model 15
results to uncertainty in the kinetics of initiated cells. The issues have significant impact on mode 16

of action inferences, and this is discussed in some detail. 17

18

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

Table E

- 1 summarizes model uncertainties and their impact as evaluated by EPA. The 20
key uncertainties are discussed in considerably more detail in additional sections in this 21
Appendix and in published manuscripts as denoted in the tables. 22

23

E.2. Statistical Methods Used in Evaluation 24
Parameters of the alternate models shown here were estimated by maximizing the 25
likelihood function defined by the data (Cox and Hinkley, 1 974). Such estimates are referred to 26
as maximum likelihood estimates (MLEs). Statistical confidence bounds were computed by 27
using the profile likelihood method (Crump, 2002; Cox and Oakes, 1984; Cox and Hinkley, 28
1974). In this approach, an asymptotic 10 0(1

– α )% upper (lower) statistical confidence bound 29
for a parameter, β , in the animal cancer model is calculated as the largest (smallest) value of β 30

that satisfies 31

32

2[L
max – L*(β)] = x 1

- 2α (5) 33

34This document is a draft forreview purposes only and doesnot constitute Agency policyE-3DRAFT—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
elaboration a
Hoogenveen et al. (1999) solution method, which is valid only for time

independent parameters, is

ac curate 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 a ssuming 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

o ccur 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 i mplementation 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 fr om 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

me asured 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 forreview purposes only and doesnot constitute Agency policyE-4DRAFT—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
elaboration a

Pulse and continuous labeling data combined in deriving α N from L I. Continuous LI normalized by ratio of pulse to continuous
values for control data. Formula used for deriving α N 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 int o S

- phase; not enough information to derive α N 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 α N , labeling data were weighted by exposure time (t) and averaged ove r 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
int erspecies extrapolation. 1) TWA assigns low weight to early time LI, but α N 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 α N derived from continuous ULLI does not significantly impact model prediction s. 3) Site

- to

- site variation of α N 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 prob lematic. 5) Histologic changes, thickening of epithelium and metaplasia occur at later times for the higher dose and would affect replication rate. Figures E

- 1, E

2 , E

- 3 ; Subramaniam et al. (2008);
Sec E.3.2.3This document is a draft forreview purposes only and doesnot constitute Agency policyE-5DRAFT—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
elaboration a
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 high er 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) Calibra tion of PBPK model for DPXs seen to be highly
sensitive to tissue thickness. Kimbell et al (1997);
Subramaniam
et al. (2008)

TWA α N (flux) rises above baseline levels only at cytolethal dose. Above such dose, α N (flux) rises sharply due to regenerative proliferation. Variability in α N (flux) rate is represented by also considering hockey

- stick (threshold in dose) when TWA
indicates J

- shape (inhibition of cell division) description of

α N (flux). 1) Uncertainty and variability in α N quantitatively evaluated to be large. In addition, several qualitative uncertainties in
characterization of α N (flux) from LI.
2) Several dose

- response shape s, 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, E

2 , E

- 3, E

- 4, E

5;
Subramaniam et al. (2008);
Sec E.3.2This document is a draft forreview purposes only and doesnot constitute Agency policyE-6DRAFT—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
elaboration a
Dose response for α I was obtained from α N , assuming ratio ( α I / α N ) to
be a two

- parameter function of flux (see Figures 5

- 7, 5

- 9).
Parameters were estimated by optimizing model predictions against tumor
incidence data. ( α I / α N ) was >1.0 in line
with the notion of I cells po ssessing a growth advantage over
N cells.

Satisfies Occam’s razor principle (Conolly et al., 2009). 1) Estimated ( α I / α N ) in CIIT modeling is <1.0 (growth
dis advantage) 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 α I , sensitivity of risk
estimates to various functional forms was evaluated. Risk estimates for the rat were extremely sensitive to alternate biologically plausible assumptions for α I (flux) and varied by

many o rders 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, E

5, E

- 6 ; Subramaniam et al. (2008); Crump et al. (2009, 2008);
Sec E.3.3

Death rate of I cells **β** I (flux) assumed = division
rate of N cells α N (flux). Based on homeostasis ( α N = **β** N ) and assumption that formaldehyde is equally cytotoxic to N cells and
I cells. Satisfies
Occam’s Razor principle (Conolly et al., 2009). 1) In general, data indicat e I cells are more resistant to cytolethality and that ADH3 clearance capacity is greater in transformed cells. Therefore, plausibility of assumption ( **β** I =

α N ) is tenuous.
2) Alternate assumption, **β** I proportional to α I , was examined.

Risk estimates extre mely sensitive to assumptions on **β** I ( 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). H alf

- 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 3This document is a draft forreview purposes only and doesnot constitute Agency policyE-7DRAFT—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
elaboration a
Formaldehyde mutagenic action takes place only while DPXs are in place. DNA lesions remain after DP X 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 seco ndary 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. DRAFT

— DO NOT CITE OR QUOTE E

- 8 where L indicates the likelihood of the rat bioassay data, L
max is its maximum value, L*(β) is, for 1

a fixed value of β , the maximum value of the log

- likelihood with respect to all of the remaining 2
parameters, and x 1

- 2α is the 100(1

– 2α ) percentage point of the chi

- square distribution with one 3
degree of freedom. The required bound for a para meter, β , was determined via a numerical 4

search for a value of β that satisfies this equation. 5
The additional risk is defined as the probability of an animal dying from an SCC by the 6
age of 790 days, in the absence of other competing risks of death, while exposed throughout life 7
to a prescribed constant air concentration of formaldehyde, minus the corresponding probability 8
in an animal not exposed to formaldehyde. The MLE of additional risk is the additional risk 9
computed using MLEs of the model parameters

. 10
The method described above for computing profile likelihood confidence bounds cannot 11
be used with additional risk because additional risk is not a parameter in the cancer model. 12
Instead, an asymptotic 100(1

– α )% upper (lower) statistical confidence bound for additional risk 13
was computed by finding the parameter values that presented the largest (smallest) value of 14
additional risk, subject to the inequality 15

16

2[L
max – L] ≤ x 1

- 2α (6) 17

18
being satisfied, with the resulting value of additional risk being the required bound. This 19
procedure was implemented through use of penalty functions (Smith and Coit, 1995). For 20
example, the profile upper bound on additional risk was computed by maximizing the “penalized 21

ad ded risk,” defined as ( additional risk

– penalty ), where 22

23
penalty = W × {[(L

max – L)

- x 1

- 2α /2] + } 2 (7) 24

25

and [] + equals the quantity in the brackets whenever it is positive and zero otherwise. The 26
multiplicative weight, W , was selected by trial and error so that the final solution satisfied the 27
following equation sufficiently well. 28

29

2(L
ma x – L) = x 1

- 2α (8) 30

31
The computer code was written in Microsoft Excel® 2002 SP3 Visual Basic. Either the 32
regular Excel Solver or the Frontline Systems Premium Solver® was used to make the required 33
function optimizations. Computation of confidence b ounds was highly computationally 34
intensive, and, consequently, confidence bounds were computed only for selected parameters in 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

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

bounds in order to confirm their acc uracy. Values so calculated were found to be in agreement 2
with those calculated by using the likelihood method. 3

4

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

The evaluation of the CIIT model and alternatives to this model that were im plemented 6
for the F344 rat will be discussed first. Of the issues tabulated above, the following uncertainties 7
in the modeling of the F344 rat data will be discussed in considerably more detail: use of 8
historical controls, uncertainty and variability in c haracterizing cell replication rates from the 9
labeling data, and uncertainty in model specification of initiated cell kinetics. 10
In their evaluation, Subramaniam et al. (2007) first attempted to reproduce the Conolly et 11

al. (2003) results under similar co nditions and assumptions as employed in their paper, which 12
included the assumption that tumors were rapidly fatal. Figure 5

- 12 in Chapter 5 shows the 13
results for this case. The predicted probabilities shown in this figure were obtained by 14

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

- fitting model and plotted against the Kaplan

- Meier (KM) probabilities

. 16
Although the results are largely similar, there are some differences (Subramaniam e t al., 2007). 17
Given the scope of issues to examine for the uncertainty analyses, the evaluation 18
proceeded in stages. First, the Hoogenveen et al. (1999) solution was replaced by one that is 19
valid for a model with time varying parameters (first entry in T able E

- 1), and tumors found at 20
scheduled sacrifices were assumed to be incidental rather than fatal (second entry in Table E

- 1 ). 21
Second, weekly averaged solutions for DPX concentration levels were used instead of hourly 22
varying solutions (predicted by a P BPK model). The log

- likelihood values and tumor 23
probabilities remained essentially unchanged. Upon quantitative evaluation, these factors, 24
although important from a methodological point of view, were not found to be major 25

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

et al., 2007). 27
Subramaniam et al. (2007), as in Georgieva et al. (2003), used the DPX clearance rate 28

constant obtained from in vitro data instead of the assumption in Conolly et al. (2003) th at all 29
DPXs cleared within 18 hours (Subramaniam et al., 2007). With this revision, weekly average 30
DPX concentrations were larger than those in Conolly et al. (2003) by essentially a constant ratio 31
equal to 4.21 (range of 4.12

– 4.36) when averaged over flu x bin and exposure concentrations. 32
Accordingly, cancer model fits to the rat tumor incidence data using the two sets of DPX 33
concentrations (everything else remaining the same) provided very similar parameter estimates, 34
except that the parameter KMU rat in equation 1 (and equation 4) was 4.23 times larger with the 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 10 Conolly et al. (2003) DPX concentrations. In other words, the product KMU × DPX remained 1
substantially unchanged. However, the different clearance rate does significantly impact the 2

scale

- up of the two

- stage clonal growth model to the human. 3
After making the above modifications, the impact of the following uncertainties, which 4
had a large impact on modeling, was examined sequentially. 5

6
E.3.1. Sensitivity to Use of Historical Controls 7

E.3.1.1. Use of Historical Controls 8
Conolly et al. (2003) combined the historical controls arising from the entire NTP 9
database of bioassays. Tumor and survival rates in control groups from different NTP studies 10
are known to vary due to genetic drift in animals ov er time and differences in laboratory 11
procedures, such as diet, housing, and pathological procedures (Haseman, 1995; Rao et al., 12
1987). In order to minimize extra variability when historical control data are used, the current 13
NTP practice is to limit the historical control data, as far as possible, to studies involving the 14
same route of exposure and to use historical control data from the most recent studies (Peddada 15

et al., 2007)

. 16
Bickis and Krewski (1989) analyzed 49 NTP long

- term rodent cancer bioassays and 17
found a large difference in determinations of carcinogenicity, depending on the use of historical 18
controls with concurrent control animals. The historical controls used in the CIIT modeling 19
controls came from different rat colonies and from experiments conducted in different 20

lab oratories over a wide span of years, so it is clearly problematic to assume that background 21
rates in these historical control animals are the same as those in the concurrent control group. 22
There are considerable differences among the background tumor rate s of SCCs in all NTP 23

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

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

inhalation controls ( p = 0.01, Fisher’s exact test) . Given these differences, the inclusion of any 26
type of historical controls is problematic and is thought to have limited value if these factors are 27

not controlled for (Haseman, 1995). 28

29

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

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

in Subramaniam et al. (2007) were conducted by using the following sets of data for controls (the 32
fraction of animals with SCCs is denoted in parentheses): only concurrent con trols (0/341), 33
concurrent controls plus all the NTP historical control data used by Conolly et al.(2003) 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 11 (13/8,031), and concurrent controls plus data from historical controls obtained from NTP 1

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

The results of th e evaluation are shown in Table E

- 2. For these analyses, the same 3
normal cell replication rates and the same relationship (see eq 2) between initiated cell and 4
normal cell replication rates as used in Conolly et al. (2003) were used. In all cases, weekly 5
averaged values of DPX concentrations were used. Model fits to the tumor incidence data were 6

similar in all cases (see Figure 5

- 12 in Chapter 5 and Subramaniam et al. [2007] for a more 7
complete discussion ). The biggest influence of the control data was seen to be on the estimated 8

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

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

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

2

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

et al. (2004) assumed i ts invariance as given by eq 4 (Appendix D). Now, μ Nbasal in the human is 13
estimated independently by fitting a scaled

- up version of the two

- stage model to human baseline 14
rates of tumor incidence. Thus, a decrease in the value of μ Nbasal estimated in the rat modeling 15
increases the formaldehyde

- induced mutational effect in the human. 16
While the MLE of KMU/μ Nbasal is zero in the CIIT animal model (Conolly e t al., 2003) , it 17
takes a range of values from 0 to 0.9 mm 3 /pmol and undefined (or infinite, when μ Nbasal = 0) in 18
the various cases examined in this paper. The 95% upper confidence bound on this ratio ranges 19

from 0.25

– 6.2 (these values would be four times larger had the Conolly et al. [2003] DPX 20
concentrations been used) to infinite. Thus, the extrapolation to human risk by using the 21
approach in Conolly et al. (2004) becomes particularly problematic when only concurrent 22

controls are used, because then the mutational contribution to formaldehyde

- induced risk in 23
humans becomes unbounded. This issue will be discussed again toward the end of the 24

discussion on historical controls. 25

26

3 Three animals in the inhalation historical controls we re 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 therefor e not included on the advice of Dr. Morgan. See Subramaniam et al. (2007) for more details.This document is a draft forreview purposes only and doesnot constitute Agency policyE-12DRAFT—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
historical a All NTP
historical a NTP
inhalation
historical a NTP
inhalation
historical a Concurrent only a Concurrent only a Cell replication dose

response J

- sh aped 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

μ Nbasal 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/μ Nbasal 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, ∞)

α max 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: A dapted from Subramaniam et al. (2007).This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 13 It may be noted, however, that absence of tumors in the limited number of concurrent 1
animals does not imply that the calculation will necessarily predict a zero background 2
probability of tumor (i.e., a parameter estim ate of μ Nbasal = 0). We observed such a 3
counterexample estimate for μ Nbasal in simulations involving the alternate dose

- response curves 4

for **α** N and **α** I that are discussed later. Nonetheless, when μ Nbasal = 0, an upper bound for μ Nbasal 5
using the concurrent controls can be inferred. Accordingly, the 90% statistical lower confidence 6
bound on the ratio KMU/μ Nbasal is also reported in Table E

- 2. Such a value would of course 7
provide a lower bound on risk by using this model and would therefore not be conservat ive. 8
Conolly et al. (2003) estimated KMU to be zero for both the hockey

- stick and J

- shape 9
cell replication models. However, the estimate for the coefficient K MU (obtained using the 10
solution of Crump et al. [ 2005]) is zero only for the case of the model with the hockey

- stick 11

curve for cell replication and with control data as used by Conolly et al. (2003). It is positive in 12
all other cases and statistically significantly so in all cases in which either inhalation control data 13
or concurrent controls were used. With concurrent controls only and the J

- shape cell replication 14

model, the MLE estimate for KMU (1.2 × 10

– 6 ) is larger than the statistical upper bound 15
obtained by Conolly et al. (2003) (8. 2 × 10

– 7 ). It should also be kept in mind that the estimate 16
would be about 4.2 times larger still had the Conolly et al. (2003) DPX model been used. 17

18

E.3.1.3. Influence on Dose

- Response Curve 19

Subramaniam et al. (2007) showed that inclusion of historica l controls had a strong 20
impact on the tumor probability curve below the range of exposures over which tumors were 21
observed in the formaldehyde bioassays. As shown there, the MLE probabilities for occurrence 22
of a fatal tumor at exposure concentrations belo w 6 ppm were roughly an order of magnitude 23
higher when all the NTP historical controls were used, compared with MLE probabilities 24
predicted when historical controls were drawn only from inhalation bioassays, and many orders 25
of magnitude higher than MLE pro babilities predicted when only concurrent controls were used 26
in the analysis. (Note that this comparison should not be inferred to apply to upper bound risk 27
estimates since there were many fewer concurrent than historical controls, so error bounds could 28

b e much larger in the case where concurrent controls were used.) 29
However, as shown by these authors, model fits to the tumor data in the 6

– 15 ppm 30
exposure concentration range were qualitatively indifferent to which of these control data sets 31

was used. This observation emphasizes the statistical aspect of the CIIT modeling

— that 32
significant interplay among the various adjustable parameters allows the model to achieve a 33
good fit to the tumor incidence data independent of the control data used. On the other ha nd, the 34
results in Subramaniam et al. (2007) show that changes in the control data affect parameter 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 14 KMU, resulting in significantly different tumor predictions at lower exposure concentrations. 1
Therefore, the strong influence of using all the NTP historic al controls on the low

- dose region of 2

the time

- to

- tumor curves presented in Subramaniam et al. (2007) suggests that large 3
uncertainties may arise in extrapolating to both human and rat (in the low

- dose region) from 4

such considerations alone. 5

6

E.3.1.4. Pro blem Including 1976 Study for Inhalation Historical Control 7
A crucial point needs to be noted with regard to the use of inhalation NTP historical 8

controls (i.e., cases B and E) in the two

- stage clonal growth modeling. The single relevant tumor 9

in the NTP inhalation studies came from the very first NTP inhalation study, dated 1976, and the 10
animals in this study were from Hazelton Laboratories, whereas the concurrent animals were all 11
from Charles River Laboratories. Similar problems arise with inclusion of several other NTP 12
inhalation studies. As mentioned before, genetic and other time

- related variation can lead to 13
different tumor and survival rates, and in general it is recommended that use of historical 14
controls be restricted to the same kind of bioassay s and to studies within a 5

– 7 year span of the 15
concurrent animals (Peddada et al., 2007). Thus, it is problematic to assume that the tumor in 16
the 1976 NTP study is representative of the risk of SCCs in the formaldehyde bioassays. Even if 17
it were appropri ate to consider the 1976 study, this leads to the unstable situation in which, 18
despite all of the “upstream” mechanistic information used to construct the BBDR model, the 19
only piece of data that might keep the model predictions of human risk bounded is a s ingle tumor 20
found among several thousand rats from NTP bioassays (Crump et al., 2008). In summary, 21
although it can be argued that the rate of SCCs among the controls in the rat bioassay is probably 22
not zero, it is also problematic to assume that this rate can be adequately represented by the 23
background rate in NTP historical controls or even in NTP inhalation historical controls. 24

25

E.3.1.5. Inference on MOA 26

Subramaniam et al. (2007) also examined the contribution of the DPX component (which 27
represents the directly mutagenic potential of formaldehyde in the model) to the calculated tumor 28
probability, choosing for their case study the optimized models that use the NTP inhalation 29
control data. In the range of exposures where tumors were observed (6.0

– 15.0 ppm ), the DPX 30
term was found to be responsible for 58

– 74% of the added tumor probability. Below 6.0 ppm 31
the estimated DPX contribution was extremely sensitive to the shape of apha_n and varied 32

between 2 and 80%. 33
The CIIT BBDR cancer modeling has contributed to the weight

- of

- evidence process in 34
various formaldehyde risk assessment efforts and papers by lending weight to the argument that 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 15 the direct mutations induced by formaldehyde are relatively irrelevant compared to the 1
importance of cytotoxicity

- induced ce ll proliferation in explaining the observed tumorigenicity 2
in rodent bioassays and in projecting those observations to human exposures ( Conolly et al., 3

2004, 2003; Slikker et al., 2004; Bogdanffy et al., 2001, 1999; Conolly, 1995)

. The reanalyses in 4
Subramaniam et al. (2007) (in particular, the results in the above paragraph) indicate that, if the 5
CIIT mathematical modeling were utilized to inform this debate, it would in fact indicate the 6

contrary

— that a large contribution from formaldehyde’s mutagenic potential may be needed to 7
explain formaldehyde carcinogenicity. This discussion is resumed in the context of uncertainties 8
in model spe cification for initiated cells. 9

10
E.3.2. Characterization of Uncertainty

- Variability in cell Replication Rates 11

E.3.2.1. Dose

- Response for **α** N as Used in the CIIT Clonal Growth Modeling 12
Monticello et al. (1996, 1991) used ULLI to quantify cell replication within the respiratory 13
epithelium. ULLI is a ratio between a count of labeled cells and the corresponding length (in 14

millimeters) of basal membrane examined, whereas the per

- cell LI is the ratio of labeled cells to 15
all epithelial cells, in this case, along some length of basal membrane and its associated layer of 16
epithelial cells. Monticello et al. (1996, 1991) published ULLI values averaged over replicate 17
animals for each combination of exposure concentration, exposure time, and nasal site. These 18
values are plotted in Figure E

- 1

. Conolly et al. (2003) adopted the following procedure in using 19

these values (Subramaniam et al., 2008): 20

1. The in jection labeled ULLI data were first normalized by the ratio of the average 21 minipump ULLI for controls to the average injection labeled ULLI for controls. 22

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

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

nose. 28

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

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

6. Correspondingly, for a given exposure concentration, the steady

- state formaldehyde flux 34 into tissue, computed by CFD modeling, was averaged over all nasal si tes. Thus, the 35

α N (flux) constructed by Conolly et al. (2003) consisted of a single α N and a single 36 average flux for each of six exposures. 37
38This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 16 1

Figure E

- 1. ULLI data for pulse and continuous labeling studies. 2 Note: Data are from pulse labeling study, left

- hand side, at 1

– 42 days of exposure 3
and from the continuous

- labeling study, right

- hand side, at 13

– 78 weeks of 4 exposure for five nasal sites ALM, AMS, MMT, PLM, and posterior mid septum 5 [PMS]). Within each graph, lines with more breaks correspond to short er 6 exposure times. Data source: Monticello et al. (1996, 1991). 7
8 0 5 10 150.55.050.0 Exposure < 12 wk (pulse labeling)

ALM

0 5 10 150.55.050.0 Exposure > 12 wk (continuous labeling)

ALM

0 5 10 150.55.050.0

AMS

0 5 10 150.55.050.0

AMS

0 5 10 150.55.050.0

MMT

0 5 10 150.55.050.0

MMT

0 5 10 150.55.050.0

PLM

0 5 10 150.55.050.0

PLM

0 5 10 150.55.050.0

PMS

0 5 10 150.55.050.0

PMS

Formaldehyde exposure concentration, ppmMean values of ULLI (log scale)This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 17 This yielded a J

- shaped dose

- response curve for cell replication (when viewed on a non

1

transformed scale for **α** N ), as shown in Figure D

- 1 (Appendix D) for the full range of flux values 2
used in their modeling. The authors also considered a hockey

- stick threshold representation of 3

their J

- shaped curve for α N in order to make a health

- protective choice, and the diff erences 4

between the two can be seen from the insets in Figure D

- 1 . In these curves, the cell replication 5
rate is less than or the same as the baseline cell replication rate at low formaldehyde flux values. 6
The shape of the dose

- response curve for cell re plication as characterized in Conolly et al. 7
(2003) is seen as representing regenerative cell proliferation secondary to the cytotoxicity of 8
formaldehyde (Conolly

, 2 002). Considerable uncertainty and variability, both quantitative and 9
qualitative, exist i n the use and interpretation of these labeling data for characterizing a dose 10
response for cell replication rates. The primary issues are discussed here. Unlike the preceding 11
sections, these have largely not been published elsewhere, so more details are provided. 12

13

E.3.2.2. Uncertainty in the Use of Pulse Labeling Data, and Short

- Time Exposure Effects on 14
Cell Replication 15

The formula used for obtaining **α** N from LI in Conolly et al. (2003) was due to 16
Moolgavkar and Luebeck (1992) who derived this formula for continuous LI, cautioning that it 17
is not applicable for pulse labeled data. However, Conolly et al. (2003) applied this formula to 18
the injection (pul se) labeled data also. Such an application is problematic because 2

- hour pulse 19
labeled data represent the pool of cells in S

- phase rather than the rate at which cells are recruited 20

to the pool and because the baseline values of **α** N obtained in this manner from both data sets 21
differ considerably. As such, we are not aware of any reasonable manner to derive cell 22
replication rates from these pulse data without acquisition of data at additional time points. 23
Therefore, the quantitative analysis of cell replica tion rates is restricted in this document to the 24

continuous labeled data (Monticello et al., 1996), which does not include measurements made 25
before 13 weeks of exposure. 26
It is unfortunate that the continuous labeled data do not include any early measurem ents 27
because, as indicated by Figure E

- 1 , the temporal variation in the unit

- length LI (ULLI, the raw 28
data) is quite different between the “early time” (left panel) and “later time” (right panel) and 29
these early

- time effects may be quite important to the c ancer modeling. At the earliest times in 30
the left panel, the data show an increased trend in labeling at 2 ppm for the sites anterior lateral 31
meatus (ALM), anterior medial septum (AMS), posterior lateral meatus (PLM), and medial 32

maxilloturbinate (MMT) rel ative to control. (Also see the dose

- response plotted as a function of 33

flux in Figure E

- 4 for the 13

- week exposure time, where such an increase is generally indicated 34

for low flux values.) 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 18 The early times would be important if, say, repeated episodic ex posures were considered, 1
where adequate time has not elapsed for adaptive effects to take place. Such an exposure 2
scenario may be the norm in the human context. However, the contribution of the early

- time 3
labeling data is minimized in the CIIT cancer mod eling since the LI was weighted by exposure 4
time. Because of the problems described above in incorporating the pulse

- labeled data, the 5
sensitivity analysis will be restricted to only the continuous labeling data. 6

7

E.3.2.3. Site and Time Variability 8

In th e remainder of this section, the factors that are considered in order to represent the 9
uncertainty and variability in the cell replication data when developing alternate dose

- response 10

curves for **α** N (flux) will be elaborated. Figure E

- 2 (from Subramaniam et al., 2008) shows the 11
variability due to replicated animals, exposure times, and nasal sites in the continuous labeled 12

data obtained by Monticello et al. (1996)

. The ULLI data for individual ani mals were provided 13

by CIIT. In this figure, log **α** N versus site

- specific flux are plotted for six sites and four exposure 14
times for four to six replicate animals in each case. (The mean ULLI over these replicates were 15
shown in Figure E

- 1 for each site and time as a function of exposure concentration.) It needs to 16
be noted that these nasal sites differ considerably in the number of cells estimated at these 17

locations as shown in Table E

- 3. Each point in Figure E

- 2 represents data from a single site for a 18

si ngle animal at a given time. For comparison, the **α** N (flux) in Conolly et al. (2003) is also 19
plotted in this figure at their averaged flux values (filled circles). For flux >9,340 pmol/mm 2

- 20

hour, Conolly et al. (2003) extrapolated this empirically derived **α** N (flux) by using a scheme 21
discussed in Appendix D (section D.5) on the upward extrapolation of cell replication rate. The 22
curves shown connecting the filled circles in the figure represent their linear interpolation (long 23
dashes) between the six points. Their linear extrapolation for flux value >9,340 pm ol/mm 2

- hour 24
is also shown (short dashes). Note that the linear interpolation/extrapolation is shown 25
transformed to a logarithmic scale. 26

In Figures E

- 3, fitted dose

- response curves are plotted for log 10 ( α N ) versus flux with 27
simultaneous confidence limits separately for each time point for two of the largest sites in Table 28

E

- 3 (ALM and PLM). Note that flux levels are different at each site. Simple polynomial models 29
in flux (as a continuous predictor), wi th time included as a factor (i.e., a class or indicator 30

variable, **τ** i representing the effect of the i th time) were used as follows: 31

32
log( α N ) = a + b × flux + c × flux 2 + d × flux 3 + **τ** i (9) 33

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

— DO NOT CITE OR QUOTE E

- 19 0 2000 4000 6000 8000 10000 12000-5-4-3-2 ADS
ALM AMS
MMT PLM PMS

1

2 Figure E

- 2. Logarithm of normal cell replication rate **α** N versus 3 formaldehyde flux (in units of pmol/mm 2

- hour) for the F344 rat nasal 4 epithelium. 5

6 Note: Values were derived from continuous unit length labeled data obtained by 7 Monticello et al. (1996) for four to six individual animals at all six nasal sites 8 (legend, si tes as denoted in original paper) and four exposure durations (13, 26, 9 52, 78 weeks)

. Each point represents a measurement for one rat, at one nasal site, 10 and at a given exposure time. Filled red circles: **α** N (flux) used in Conolly et al. 11 (2003) plotted at their averaged flux values (see text for details). Long dashed 12 lines: their linear interpolation between points. Short dashed line: their linear 13 extrapolation for flux value >9,340 pmol/mm 2

- hour (see Figu re D

- 1 for full range 14 of extrapolation). Linear interpolation/extrapolation is shown with y

- axis 15 transformed to logarithmic scale. 16
17 Source: Subramaniam et al. (2008). 18
19This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 20 Table E

- 3. Variation in number of cells across nasal sites in the F344 rat 1

2

Nasal sit e No. of cells

Anterior lateral meatus 976,000

Posterior lateral meatus 508,000 Anterior mid septum 184,000 Posterior mid septum 190,000 Anterior dorsal septum 128,000

Anterior medial maxilloturbinate 104,000
3

Note: Mean number of cells in each sid e of the nose of control animals. 4 5 Source: Monticello et al. (1996). 6
7This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 21 1



### Fluxlog10(alpha)



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

### 13 weeks

Fluxlog10(alpha)



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

### 26 weeks

Fluxlog10(alpha)



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

### 52 weeks

Fluxlog10(alpha)



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

### 78 weekslog10**α**log10**α**log10**α**log10**α** 

### Fluxlog10(alpha)



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

### 13 weeks

Fluxlog10(alpha)



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

### 26 weeks

Fluxlog10(alpha)



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

### 52 weeks

Fluxlog10(alpha)



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

### 78 weekslog10**α**log10**α**log10**α**log10**α**

2 3 Figure E

- 3A. Logarithm of normal cell replication rate versus formaldehyde 4
flux with simultaneous confidence limits for the ALM. 5

6 Source: Subramaniam et al. (2008). 7This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 22 

### Fluxlog10(alpha)



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

### 13 weeks

Fluxlog10(alpha)



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

### 26 weeks

Fluxlog10(alpha)



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

### 52 weeks

Fluxlog10(alpha)



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

### 78 weekslog10**α**log10**α**log10**α**log10**α**log10**α** 

### Fluxlog10(alpha)



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

### 13 weeks

Fluxlog10(alpha)



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

### 26 weeks

Fluxlog10(alpha)



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

### 52 weeks

Fluxlog10(alpha)



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

### 78 weekslog10**α**log10**α**log10**α**log10**α**log10**α**

1 Figure E

- 3 B. Logarithm of normal cell replication rate versus formaldehyde 2
flux with simultaneous confidence limits for the PLM. 3
4 Source: Subramaniam et al. (2008). 5

6

7 The variability considered is that among animals and any measurement error as well as 8
any other d esign

- related components of error. Simultaneous 95% confidence limits for log( α N ) 9
were produced using Scheffe’s method (Snedecor and Cochran, 1980). These 95% confidence 10
limits span a range of 0.96 in log10( α N ) , or nearly a 10

- fold range in median α N

. T here is 11
additional dispersion in these data that does not appear in Figures E

- 2 and E

- 3; d ue to variation 12
in the number of cells per mm basement membrane, the ratio of ULLI/LI had a spread of 13
approximately ±25% (0.45 to 0.71, mean 0.60) among the eight obs ervations considered in 14

Monticello et al. (1990). Thus: 15This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 23 1. As suggested by Table E

- 3, and Figures E

- 2 and E

- 3 , the shape of α N (flux) in Conolly et 1
al. (2003) is therefore likely to be very sensitive to how α N is weighted and averaged over 2

site and time. 3

2. Averaging of sites could significantly affect model calibration because of substantial 4

nonlinearity in model dependence on **α** N at the 10 and 15 ppm doses associated with high 5

cancer incidence. 6

3. Monticello et al. (1996) found a high correlation between tumor rate and the ULLI 7
weighted by the number of cells at a site. Therefore, considering these factors while 8

regressing **α** N aga inst tissue dose would be important in the context of site differences in 9

tumor response. 10

4. A further complexity arises because of histologic changes and thickening that occurs in 11
the nasal epithelium over time in the higher dose groups (Morgan, 1997), fac tors that are 12
likely to affect estimates of local formaldehyde flux, uptake, and replication rates 13

(Subramaniam et al., 2008). 14

15
Figure E

- 1 indicates that the time dependence in ULLI is significant. It would also be 16
useful to examine if the time dependence affects the results of the time

- to

- tumor modeling and if 17
early temporal changes in replication rate are important to consider because of the generally 18
cumulative nature of cancer risk. The time window over which formaldehyde

- induced cancer 19

risk is most i nfluenced is not known, but the time weighting used by Conolly et al. (2003) 20
assigns a relatively low weight to labeling observed at early times compared with those observed 21
at later time points. Finally, initiated cells are likely to be replicating at hi gher rates than normal 22
cells as evidenced in several studies on premalignant lesions (Coste et al., 199 6; Dragan et al., 23

199 5; Rotstein et al.

, 1986). Therefore, LI data as an estimator of normal cell replication rate 24
would be most reliable at early times when the mix of cells sampled include fewer preneoplastic 25
or neoplastic cells. 26

The more relevant question, therefore, is whether **α** N (flux) derived by a TWA over all 27
sites (as carried out by Conolly et al. [2003]) has an effect on low

- dose risk estimates. Given the 28
above uncertainties and variability not characterized in CIIT (1999) or in Conolly et al. (2003), it 29
is important to exa mine whether additional dose

- response curves that fit the cell replication data 30
reasonably well have an impact on estimated risk . Such sensitivity analyses are carried out in 31

the sections that follow. Clearly, a large number of alternative **α** N (flux) can b e developed. In 32
conjunction with the other uncertainties , mainly the use of control data and alternative model 33
structures for initiated cell kinetics, the number of plausible clonal growth models to be 34
exercised soon require a prohibitively large investme nt of time. Therefore, detailed analyses 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 24 were restricted to a select set of biologically plausible choices of curves for **α** N (flux), which 1
would allow the identification of a range of plausible risk estimates (MLEs and statistical 2

bounds). 3

4

E.3.2.4. Alternate dose

- response curves for cell replication

. 5
Six alternative equations for α N were developed by regression analysis of the Monticello 6
et al. (1996) ULLI data. The replicate data corresponding to the summary data presented in this 7
paper were kindly provided to EPA by CIIT for further analyses. In each of these equations, α N 8
is expressed as a function of formaldehyde flux t o nasal tissue (pmol/mm 2

- hour) and, in one 9
equation (eq 15) that explored time

- dependence, the duration of exposure to formaldehyde in 10

weeks. All the graphs use flux/10,000 for the x

- axis, and the y

- axis expresses log 10 **α** N

. 11
One source of uncertainty in t he cell proliferation dose response in Conolly et al. (2003) 12
is the large value of α
max in the upward extrapolation (the cell replication rate corresponding to 13

the upper end of the flux range at 15 ppm exposure). The optimal value of **α** max was found by 14

Con olly et al. (2003) to be 0.0435 hour

– 1 . As noted by the authors, an argument in support of 15
this value is that it corresponds to the inverse of the fastest cell cycle times found in the 16
literature. Since the model treats the induced replication rates as b eing time invariant, this means 17
that cells in the high

- flux region(s) divide at the highest cell turnover rate ever observed 18
throughout most of an animal’s life. This does not seem to be biologically plausible 19

(Subramaniam et al., 2008). 20
In the analysis, it was found that a 20% increase or decrease in the estimated value for 21

**α** max degraded the fit to the tumor incidence data considerably. Because of the interplay between 22

the parameters estimated by optimization, this sensitivity of the model to **α** max indica tes that it is 23

necessary to examine if other plausible values of **α** max are also indicated by the data and to what 24
extent low dose estimates of risk are influenced by the uncertainty in its value. The need for 25

such an analysis is also indicated by Figure E

- 2 . The value of **α** max (log 10 **α** max = −1.37) in the 26
modeling of Conolly et al. (2003) is roughly an order of magnitude greater than the values of 27

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

be used f or **α** N (flux), then, based solely on the trend in **α** N (flux) in Figure E

- 2, it appears unlikely 29

that **α** N (flux) could increase up to this value of **α** max . Visually, these empirically derived data 30

collectively suggest that **α** N versus flux could be leveling off rath er than increasing 10

- fold. 31

Therefore, as an alternative to the approach taken in Conolly et al. (2003) of estimating **α** max via 32
likelihood optimization against the tumor data, regressions of the empirical cell replication data 33

were used to extrapolate **α** N (f lux) outside the range of observation (recognizing the uncertainty 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 25 and model dependence that still results from extrapolating well outside the range of observed 1

data). 2
In fitting dose

- response curves to the cell replication data, a functional form was used 3
that was flexible to allow a variety of monotonic and non

- monotonic shapes, with a parameter 4
that determined the asymptotic behavior of the dose

- response function. This allowed the 5
extrapolation by only relying on the empirical cell replication data with out using an adjustable 6
parameter estimated by fitting to the tumor data. However, the plausible asymptotes obtained 7
spanned a large range. In one case below, the asymptote suggested by the fit was judged to be 8

abnormally high. In this case, the **α** N vers us flux curve was followed until the biological 9

maximum of **α** max (as given in Conolly et al. [2003]) was reached. 10
In three of the regression models below, the data were restricted to the earliest exposure 11

time (13 weeks) in Monticello et al. (1996) for whic h the cell proliferation rate ( α N ) could be 12
calculated. The interest in using only the 13

- week exposure time arises from observations 13
(Monticello et al., 1996, 1991) that at later times there were more frequent and severe histologic 14
changes, which may hav e altered formaldehyde uptake and cell proliferation response. 15
Consequently, given that the data in Monticello et al. (1991) for times earlier than 13 weeks 16
could not be utilized as explained earlier, the 13

- week responses might better represent 17
prolifera tion rates for use in a two

- stage model of the cancer process than the rest of the 18

Monticello et al.(1996) data. 19
Second, the LI data showed considerable variation among nasal sites, which may be 20
related to the variation in tumor response among sites. Sinc e the cell replication dose

- response 21
curves used in the cancer model represent all of the sites, it was attempted to include this 22
variation by weighting the regression by the relative cell populations at risk at each of the sites. 23

This was carried out for some of the models as stated below. The following models (denoted 24

N1

– N6), shown in Figure E

- 4, have been included in addition to using the hockey stick

- and J

25
shaped curves in Conolly et al. (2003). Applicable equations are as follows: 26

27

N1 : Quadratic; m onotone increasing in flux, derived from fit to all of the Monticello et al. (1996) 28

ULLI data. 29

30

α N = Exp{

– 2.015

– 6.513 × Exp[

– (6.735×10

–

4 × flux) 2 ]} (10) 31

32

N2 : Linear

- quadratic; decreasing in flux for small values of flux, derived from fit to all of th e 33

Monticello et al. (1996) ULLI data. 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 26 xy

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

1 Figure E

- 4, N1. Various dose

- response modeling of normal cell replication 2

rate. 3
Note: See text for definitions of N1

– N6. N1: Quadratic; monotone increasing in 4 flux, derived from fit to all of the Monticello et a l. (1996) ULLI data. 5

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

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

6 Figure E

- 4, N2: Various dose

- response modeling of normal cell replication 7

rate. 8
Note: See text for definitions of N1

– N6. N2: Linear

- quadratic; decreasing in flux 9 for small values of flux, derived from fit to all of the Monticello e t al. (1996) 10
ULLI data. 11

12This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 27 weighted mean flux/10,000weighted mean alpha

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

1 Figure E

- 4, N3. Various dose

- response modeling of normal cell replication 2

rate. 3

4
Note: See text for definitions of N1

– N6. N3: Linear

- quadratic; decreasing in flux 5 for small values of flux, derived from fit to the 13

- week Montic ello et al. (1996) 6 ULLI data, using average flux over all sites for a given ppm exposure and 7 weighting regression by estimates of the numbers of cells at each of five sites. 8

flux/10,000log(alpha)

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

9 Figure E

- 4, N4. Various dose

- response modeling of normal cell replication 10

rate. 11

12
Note: See text for definitions of N1

– N6. N4: Quadratic; monotone increasing in 13 flux, derived from unweighted fit to 13

- week Monticello et al. (1996) ULLI data. 14This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 28 weighted mean flux/10,000weighted mean alpha

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

1 Figure E

- 4, N5. Various dose

- response modeling of normal cell replication 2

rate. 3

4
Note: S ee text for definitions of N1

– N6. N5: Linear

- quadratic

- cubic; initially 5 increasing slightly with increasing flux, then decreasing slightly, and finally 6 increasing, derived from fit to 13

- week Monticello et al. (1996) ULLI data, using 7 average flux over all sites for a given ppm exposure and weighting regression by 8
estimates of the numbers of cells at each of five sites. 9
10

11This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 29 1 Figure E

- 4, N6. Various dose

- response modeling of normal cell replication 2

rate. 3

4
Note: See text for definitions of N1

– N6. N6: Linear

- quadratic

- cubic; initially 5 increasing slightly with increasing flux, then decreasing slightly, and finally 6 increasing, derived from fit to all Monticello et al. (1996) ULLI data, using weeks 7 of exposure as a covariate. In this model, time was a regression (continuous) 8 predictor, not a class variable, and its coefficient represents the decrease in log 10 9

**α** N per week of exposure time. 10
11
12

α N = Exp{

– 5.906

– 2.272 × Exp[2.188×10

–

4 × flux

– (2.162×10

–

4 × flux ) 2 ]} (11) 13

14

N3 : Linear

- quadratic; decreasing in f lux for small values of flux, derived from fit to the 13

- week 15
Monticello et al. (1996) ULLI data, using average flux over all sites for a given ppm exposure 16
and weighting regression by estimates of the numbers of cells at each of five sites. 17

. α N = Exp{

– 5. 274

– 2.792 × Exp[1.407×10

–

4 × flux

– (1.986×10

–

4 × flux) 2 ]} (12) 18 xy

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

xy

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

xy

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

xy

0.0 0.2 0.4 0.6 0.8 1.0 1.2-12-8-6-4 Time = 78 All Sites, ~ Time + 2nd order in FluxThis document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 30 1

N4 : Quadratic; monotone increasing in flux, derived from unweighted fit to 13

- week Monticello 2

et al. (1996) ULLI data. 3

4

α N = Exp{

– 3.858

– 4.809 × Exp[

– (9.293×10

– 5 × flux) 2 ]} (13) 5

6

N5 : Linear

- quadratic

- cubic; initially increasing slightly with increasing flux, then decreasing 7
slightly, and finally increasing, derived from fit to 13

- week Monticello et al. (1996) ULLI data, 8
using average flux over all sites for a given ppm exposu re and weighting regression by estimates 9

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

11

α N = Exp{

– 5.488

– 2.755 × Exp[

– 7.808×10

– 5 × flux + (2.349×10

–

4 × flux) 2 12

– (2.166×10

–4 × flux) 3 ]} (14) 13

14

N6 : Linear

- quadratic

- cubic; initially increasing slightly with increasing flux, then decreasing 15
slightly, and finally increasing, derived from fit to all Monticello et al. (1996) ULLI data, using 16
weeks of exposure as a covariate. In this model, time was a regression (continuous) predictor, 17
not a class variable, and its coefficient represents the decrease in log 10 α N per week of expo sure 18

time. 19

20

α N = Exp{7.785×10

– 3 × (weeks)

– 5.722

– 2.501 × Exp[1.103×10

–

4 × flux 21

– (7.223×10

– 5 × flux) 2

– (1.575×10

–

4 × flux) 3 ]} (15) 22

23
Further details on the above regressions are provided in the appendix. These regressions 24
of the cell rep lication data as well as the hockey

- stick and J

- shaped curves used by Conolly et al. 25
(2003) (shown in Figure D

- 1, Appendix D) are used next as inputs to the clonal growth model 26

for cancer. 27

28
E.3.3. Uncertainty in Model Specification of Initiated Cell Repli cation and Death 29
E.3.3.1. Biological Inferences of Assumptions in Conolly et al. (2003) 30

The results of a two

- stage MVK model are extremely sensitive to the values for initiated 31

cell division ( **α** I ) and death ( **β** I ) rates, particularly in the case of a sharply rising dose

- response 32
curve as in the case of formaldehyde. The pool of cells used for obtaining the available LI data 33
(Monticello et al., 1996, 1991) consists of largely normal cells with per haps increasing numbers 34
of initiated cells at higher exposure concentrations. As such there is no way of inferring the 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 31 division rates of initiated cells in the nasal epithelium, either spontaneous (baseline) or induced 1
by exposure to formaldehyde, from th e available empirical data. Conolly et al. (2003) 2

considered **α** I (flux) as a function of **α** N (flux) as given by eq 2 in Appendix D. As shown in 3

Figure D

- 1 (Appendix D), **α** I is estimated in Conolly et al. (2003) to be very similar to **α** N . That 4

is, with eq 2 assumed to relate **α** I (flux) to **α** N (flux), a J

- or hockey

- shaped do se

- response curve 5

for **α** N (flux) results in a J or hockey shape for **α** I (flux). 6

The J shape for the TWA **α** N (flux) in Conolly et al. (2003) could plausibly be explained, 7
as suggested by the examples in Conolly and Lutz (2004), by a mathematical superposition of 8

dose

- response curves describing the effects of the inhibition of cell replication by the formation 9
of DPXs (Heck and Casanova, 1999) and cytotoxicity

- induced regenerative replication (Conolly

, 10
2002). However, as explained earlier, there is considerable uncertainty and variability, both 11
qualitative and quantitative, in the interpretation of the LI data and in the derivation of normal 12
cell replication rates from the ULLI data. While the TWA values of ULLI indicate a J

- shaped 13
dose response for some sites, as also concluded by Gaylor et al. (2004), this is not consistently 14
the case for all exposure times and sites as discussed earlier. Notwithstanding this uncertainty 15
variability, and in the absence of data, the following essential questions have a signifi cant impact 16
on risk predictions and need resolution if the model structure in eq 2 is to be used in a 17
biologically based (or motivated) sense to predict risk outside observable data: 18

19

● Should mechanisms that might explain a J

- shaped dose response for normal cell 20
replication or a cytotoxicity

- driven threshold in dose response (as indicated by a hockey

21

stick

- shaped curve) be expected to prevail also for initiated cells? 22

● Would the formaldehyde flux at which the cell replication dose

- response curve rises 23

above i ts baseline be similar in value for both normal and initiated cells as inferred by the 24
CIIT model in Figure D

- 1? 25

26

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

namely, **β** I ( flux) = α N (flux) (eq 3). In Subramaniam et al. (2008), the rationale for this 28
assumption in Conolly et al. (2003) is explained by assuming formaldehyde to be equally 29
cytotoxic to initiated and normal cells (since the mechanism is presumed to be via its ge neral 30
chemical reactivity). In essence, this assumption brings the cytotoxic action of formaldehyde to 31
bear strongly on the parameterization of the CIIT model. 32
There are no data to evaluate the strength of these assumptions, so Subramaniam et al. 33

(2008 ) studied the plausibility of various inferences that arise as a result of these assumptions. 34
These inferences are only briefly listed here (see the paper for further discussion). 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 32 1

● For flux <27,975 pmol/mm 2

- hour, α I > α N (Figures D

- 1 & D

- 2 of Appendix D). 2
Qualitatively, this is in line with data on epithelial and other tissue types with or without 3
exposure to specific chemicals. 4

● For higher flux levels, however, the model indicates α I < α N (Figure D

- 2). Ther e are no 5
data to shed further light on this inference. 6

● At these higher flux levels, initiated cells in the model die at a faster rate than they 7
divide, indicating the extinction of initiated cell clones in regions subject to these flux 8

levels. There are n o data indicating formaldehyde to have this effect. 9

10
In evaluating these inferences, Subramaniam et al. (2008) point to various data that 11
indicate that initiated cells represent distinctly different cell populations (from that of normal 12
cells) with regard to proliferation response (Ceder et al.

, 2007; Bull

, 2000; Schulte

- Hermann et 13
al., 1997; Coste et al., 1996; Dragan et al., 1995), have excess capacity to clear formaldehyde 14
and, in general, are considerably more resistant to cytotoxicity ( such a resistan ce is manifested 15
variably as decreased ability of the toxicant to induce cell death or to inhibit cell proliferation 16
compared to corresponding effects in normal cells), and may already have altered cell cycle 17
control; thus, the influence of formaldehyde on apoptosis likely differs between normal and 18

initiated cells. 19
As concluded in Subramaniam et al. (2008), taken together, there is much data to suggest 20

that inferring **α** I < **α** N at cytotoxic formaldehyde flux levels is problematic and that death rates of 21

ini tiated cells are likely to be very different from those of normal cells. In the absence of data to 22
indicate that eq 2 and eq 3 (in Appendix D) are biologically reasonable approaches to link the 23

kinetics of initiated cells with those of normal cells, alter nate model structures other than those 24
represented by these relationships considered by Conolly et al. (2003) need to be explored, given 25

that the two

- stage model is extremely sensitive to **α** I and **β** I . Such an evaluation needs to 26
primarily explore if the assumptions in eq 2 and eq 3 significantly impact the intended use of the 27
model, namely extrapolation to low

- dose human cancer risk and the calculation of an upper 28
bound on human risk. Any s uch alternate model structure needs to provide a good fit to the 29

time

- to

- tumor data. 30

31This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 33 E.3.3.2. Plausible Alternative Assumptions for **α** I and **β** I 1
Therefore, in the additional sensitivity analysis presented here, initiated cell kinetics are 2

considered to be i ndependent of normal cells, and initiated cell proliferation cannot take a J 3
shape (motivated by the consideration that lower

- than

- baseline turnover rate represents an 4
increased amount of DNA repair taking place, which may not be consistent with impaired D NA 5
repair in initiated cells). 6
Thus, two alternatives were considered to eq 2 for α I (flux): 7

8

I1: α I = γ 1 × [1 + exp( γ 2 / γ 3 )] / {1 + exp[

– (flux

– γ 2 ) / γ 3 ]} (16) 9

10

I2: α I = max[ α I (eq. I1), α NBasal ] (17) 11

12

Here γ 1 , γ 2 , and γ 3 are parameters estimated by fitting the cancer model to the rat bioassay 13
data. In eq 16, α I increases m onotonically with flux from a background level of γ 1 asymptotically 14
up to a maximum value of γ 1 × [1 + Exp( γ 2 / γ 3 )]. The choice of this functional form in eq 16 15
and eq 17 was considered in order to be parsimonious while at the same time allowing for a 16

fl exible shape to the dose

- response curve. The sigmoidal curve allows for the possibility of a 17
slow rise in the curve at low dose and an asymptote. 18
Equation 17 is a modification of equation 16 that restricts the rate of division of initiated 19

cells to be at least as large as the spontaneous division rate of unexposed normal cells. There is 20
evidence to suggest (e.g., in the case of liver foci) that initiated cells have a growth advantage 21
over normal cells, with or without exposure to specific chemicals (Cede r et al., 2007; Grasl

22
Kraupp et al., 2000; Schulte

- Hermann et al., 1999; Coste et al., 1996; Dragan et al., 1995). 23
In addition, in most runs, an upper bound ( α high ) is selected for both α N and α I . This value 24
is assumed to represent the largest biologica lly plausible rate of cell division. Following Conolly 25

et al. (2003), in most cases α high is set equal to 0.045 hours

– 1 . If a value of α I or α N computed 26
using one of the above formulas exceeded α high , the value of α high was used in the computation 27

rather than the value obtained by using the formula. 28
As noted above, Conolly et al. (2003) set the rate of death for intermediate cells, β I , equal 29

to the division rate of normal cells, β I = α N . On the other hand, apoptotic rates and cell 30
proliferation rates are thought to be coupled (Schulte

- Hermann, 1999; Moolgavkar, 1994), so 31
that death rates of initiated cells would rise concomitantly with an increase in their division rates 32

(Grasl

- Kraupp et al., 2000; Schulte

- Hermann et al., 1999). Therefore, as an alternat ive to the 33
Conolly et al. (2003) formulation, it is assumed that the death rate of intermediate cells is 34
proportional to the division rate of intermediate cells. 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 34 β

I = Κ β × α I (18) 1

2
where the constant of proportionality, Κ β , is an additional parameter to be estimated by 3
optimization against the tumor incidence data. Such an assumption has also been made by other 4
authors (Luebeck et al., 2000, 1995; Moolgavk ar et al., 1993). 5
Since most of the SCCs in the rat bioassays occurred in rats exposed to the highest 6
formaldehyde concentration (15 ppm), the data from this exposure level have a big impact on the 7
estimated model parameters. In most runs that incorporate d the 15 ppm data, the model 8
appeared, based on inspection of the KM plots, to fit the 15 ppm data quite well but to fit the 9
lower exposure data less well. Because of the high level of necrosis occurring at 15 ppm, it is 10
possible that the data at this exp osure may not be particularly relevant to modeling the sharp 11
upward rise in the dose response at 6 ppm. Furthermore, the principal interest is in the 12
predictions of the model at lower levels to which human populations may be exposed. 13
Consequently, in ord er to improve the fit of the model at lower exposures, some of the 14
alternative models were constructed with the 15 ppm data omitted. 15

16

E.3.4. Results of Sensitivity Analyses on **α** N , **α** I , and **β** I 17

E.3.4.1. Further Constraints 18
The number of models that might be constructed if all the possibilities listed above for 19

**α** N

, **α** I , and **β** I are to be tried in a systematic manner clearly become exponential and daunting. 20
(Optimally, it would have been desirable to elucidate the role of a specific modification while 21
keeping others unchanged to determine risk.) Therefore, in order to carry out a viable sensitivity 22
analysis while at the same time examining the plausible range of risks resulting from variations 23
in parameters and model structures, various uncertainties were combined in any given 24

simulation. By using the constraints described above (eq 10

– 17 and associated text) for **α** I , **β** I , 25

and **α** N , 19 models were obtained that provided similarly good fits to the time

- to

- tumor data 26
(which in some cases contained only five dose groups). 27

However, for many of these models, the optimal **α** I (flux) displayed a threshold in flux 28

even when the model utilized for **α** N (flux) was a monotonic increasing curve without a threshold 29

(i.e., model N4 for **α** N in Figure E

- 4). Indeed, if a thresholded dose

- response curve was 30

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

**α** N , and such models are removed from consideration. 32
Secondly, t he basal value of α I was required to be at least as large as the basal value of 33

α N

. Another constraint was placed on the baseline initiated cell replication rate. In the absenc e 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 35 of formaldehyde exposure, **α** I was not allowed to be greater than two or four times **α** N , even if 1
such models described the tumor data, including the control data, very well. There are some data 2
that suggest that baseline initiated cells have a small growth advantage over normal cells, so a 3
huge advantage was thought to be biologically less plausible. 4

5

E.3.4.2. Sensitivity of Risk Estimates for the F344 Rat 6
Figure E

- 5 contains plots of the MLE of additional risk computed for the F344 rat at 7
formaldehyde e xposures of 0.001, 0.01, 0.1, and 1 ppm for eight models. Two log

- log plots are 8
provided. For those models for which the estimates of additional risk are all positive, the 9
additional risks are plotted (panel A), and, for those for which estimates of addi tional risk are 10
negative, the negatives of additional risks are plotted (panel B). Only five dose groups were 11
considered (i.e., 15 ppm data omitted) for models 8, 5, 15, and 16. Figure E

- 6 shows the dose

12

response curves for **α** N and **α** I for these eight case s (panels A and B corresponding to those in 13
Figure E

- 5). The primary results are as follows: 14

15

1. Among the models considered, negative values for additional risk can arise only in 16
models in which the dose response for normal cells is J shaped. Thus, all of the models 17
with negative dose responses for risk have J

- shaped dose responses for normal cells. 18
However, the converse is not necessarily true as may be noted from model 8. This model 19
has both a positive dose response for risk and a J

- shaped dose response for normal cells. 20
In this case, the strong positive increase in response of initiated cells at low dose was 21
sufficient to counteract the negative response of normal cells. 22

2. The risk estimates predicted by the different models span a very large range for doses 23
below which no tumors were observed. This result points to large uncertainties in model 24
specification (how to relate the kinetics of normal and initiated cells) as well as in 25
parameter values. As mentioned above, the analysis does not attempt to s eparate the 26
influence of the different sources of uncertainty, so t his range also incorporates the 27

uncertainty arising from the use of different control data and that due to **α** max . 28

3. At the 10 ppb (0.01 ppm) concentration, MLE risks range from −4.0 ×10

– 6 to +1.3 ×10

– 7

. 29
At this dose, models that gave only positive risks resulted in a five orders of magnitude 30
risk range from 1.2 ×10

– 12 to 1.3 ×10

– 7 , while narrowing to a four orders of magnitude 31
risk range from 1.2 ×10

– 10 to 1.3 ×10

– 6 at the 0.1 ppm level. Thi s narrowing continues as 32
exposure concentration increases, and the curves coalesce to substantially similar values 33

at 6 ppm and above (not shown). 34This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 36 1 Figure E

- 5A. BBDR models for the rat

— models with positive added risk. 2
3

Note: All four models provide “s imilar” fits to tumor data (see text). 4
5 As in Conolly et al. (2003), Hockey
stick **α** N and **α** I 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 17This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 37 1 Figure E

- 5B. BBDR rat models resulting in negative added risk. 2
3

Note: All four models provide “similar” fits to tumor data (see text). 4
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 1NEGATIVE Added risk

model 3

model 4

model 5

model 13 Exposure conc ( ppm )

As in Conolly et al. (2003), J

- shape **α** N and **α** I but using Inhalation NTP controls
added to concurrent All 8 models in A & B provide similar fits to tumor dataThis document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 38 1

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 8

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 15

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 16

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

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

2 Figure E

- 6A. Models resulting in positive added rat risk: Dose

- response for 3 normal and initiated cell replication 4
5

6This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 39 1

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 3

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 4

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 5

0 10000 20000 30000 40000 1E-4 1E-3 0.01 0.1

1**α**(divisions/hr)

Flux (pmole/mm 2 /hr) **α**

N

**α**

I

Model 13 J

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

2 Figure E

- 6B. Models resulting in negative added rat risk: Dose

- response for 3 normal and initiated cell replication 4
5

6
4. There does not seem to be any systematic effect on additional risk that depends on 7

whether t he 15 ppm data are included in the analysis. 8

5. For all of the models except models 13 and 17 in Figures E

- 5 & E

- 6, the additional risk 9
varies substantially linearly with exposure at low exposures between 0.001 and 1.0 ppm 10
(departing only to a small extent fr om linearity between 0.1 and 1.0 ppm). Models 13 11
and 17 (the models in Conolly et al. [2003] except for different control data being used) 12
show a quadratic dependence. 13

14
The various model choices presented in Figure E

- 5 all provided equally good fits to t he 15

time

- to

- tumor data although within the context of a significant qualification. It was not possible 16This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 40 to simply use the log

- likelihood values as a means of comparing the goodness

- of

- fit to the tumor 1

incidence data across these model choices. This is beca use many of the model choices differed 2

in the number of doses or in the number of control animals that were used, so the fits were 3
compared across such models only visually. Within model choices where such a comparison did 4
not pose a problem, the log

- like lihood values did not differ statistically significantly. 5
Wherever results from the BBDR modeling are discussed, values of added risk, as 6
opposed to extra risk, are reported. This is purely for convenience in interpretation. Because of 7
the low backgrou nd incidence, these values are only negligibly different from the corresponding 8
extra risk estimate. The final risk (or unit risk) estimates provided in this document are based on 9

extra risk estimates. 10

11

E.3.4.3. MOA Inferences Revisited 12

The ratio KMU/ μ Nb asal represents the added fractional probability of mutation per cell 13
generation ( μ N

– μ Nbasal )/ μ Nbasal due to unit concentration of DPXs. As discussed earlier, this 14
parameter has a critical impact on the extrapolation as well as on inferring whether the 15
mutagenic action of formaldehyde is relevant to the quantitative risk characterization. In that 16
prior discussion, this ratio was found to be extremely sensitive to the choice of historical control 17
data. The analysis indicates that, for a given set of con trol data that is used, uncertainties 18

associated with **α** N and **α** I also have a large impact on this ratio. In that discussion, this ratio was 19

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

zero. The use of thes e concurrent controls, however, does not necessarily imply that μ Nbasal will 21
be determined to be zero. In one of the scenarios examined in the sensitivity analysis, where 22

concurrent controls were used along with the combination of dose

- response curves eq 13 for **α** N 23
(Figure E

- 4) and eq 17 for α I , the optimal value of the ratio KMU/ μ Nbasal was equal to 0.25. For 24
the models in Figure 5

- 13A, this ratio was 0 for all except model 17 for which it was infinite. 25
For the models in Figure 5

- 13B with negative added risk, t he ratio ranged from 0

– 4.5. For some 26

of those models where KMU/ μ Nbasal was finite, the upper confidence bound on this ratio was 27
found to increase by an order of magnitude from the MLE value. Thus, we conclude that the 28
modeling does not help resolve the d ebate as to the relevance of formaldehyde’s mutagenic 29
potential to its carcinogenicity. 30

31

E.3.4.4. Confidence Bounds: Model Uncertainty Versus Statistical Uncertainty 32
For models 15 and 17 in Figures E

- 5A and E

- 6A, 90% CIs for additional risk were 33

calculate d by using the profile likelihood method. Table E

- 4 compares the lower and upper 34
confidence bounds for these models for 0.001 ppm, 0.1 ppm (doses well below the range where 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 41 tumors were observed), and 6 ppm (the lowest dose where tumors were observed) with the MLE 1
risk estimates at these doses. In both cases, these intervals were quite narrow compared with the 2
differences in risk predicted by different models in Figure E

- 5. This suggests that model 3
uncertainty is of more consequence in the formaldehyde an imal model than is statistical 4
uncertainty. We also estimated confidence bounds using the bootstrap method for select models, 5
and determined that these estimates were in agreement with the bounds calculated using the 6
profile likelihood method. These resu lts are not presented here. We return to the calculation of 7
confidence limits when determining points of departure (PODs). 8

9
Table E

- 4. Comparison of statistical confidence bounds on added risk for 10
two models 11
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
In conclusion , it is demonstrated that the different formaldehyde clonal growth models 15
can fit the data about equally well and still produce considerable variation in additional risk and 16
biological inferences at low exposures. Ho wever, even with these large variations, the highest 17
MLE added risk for the F344 rat is only of the order of 10

– 6 at 0.1 ppm. Thus, with regard to 18
calculating a reasonable upper bound that includes model and statistical uncertainty, the relevant 19
question is whether the range arising out of uncertainties in the rat model amplifies when 20
extrapolated to the human. Thus, in Appendix F, the human model in Conolly et al. (2004) will 21

be examined. 22This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE E

- 42 

### This page intentionally left blank.

# Append ix F

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

— DO NOT CITE OR QUOTE F

- 2 APPENDIX F 1

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

F.1. MAJOR UNCERTAINTIES IN THE FORMALDEHYDE HUMAN BBDR MODEL 6

Subsequent to the BBDR model for modeling rat cancer, Conolly et al. (2004) developed 7

a corresponding model for humans for the purpose of extrapolating the risk to humans estimated 8

by the rat model. Also, rather than considering only nasal tumors, it is used to predict the risk of 9

all human respiratory tumors. The human model for formalde hyde carcinogenicity (Conolly et 10

al., 2004) is conceptually very similar to the rat model and follows the schematic in Figure 5

- 11 11

in Chapter 5. The model structure, notations, and calibration are described in Appendix D. 12

Unlike the sensitivity analysis of the rat modeling where a number of issues were examined, a 13

much more restricted analysis will be presented here for the sake of brevity. A more extensive 14

analysis was carried out initially that carried forward several of the rat models to the human, and 15

the lessons learned from those exercises are in agreement with the more restricted presentation 16

that follows. Table F

- 1 lists the major uncertainties and assumptions in the human extrapolation 17

model in Conolly et al. (2004). 18This document is a draft forreview purposes only and doesnot constitute Agency policyF-3DRAFT—DO Table F

- 1. Summary of evaluati on of major uncertainties in CIIT human BBDR model

Assumptions, approach, and characterization of input data a Rationale in Conolly et al. (2003) or CIIT (1999) EPA uncertainty evaluation 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

resp iratory 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 averag ing 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); Subr amaniam et
al. (2008, 1998)

KMU/μ Nbasal is species invariant (used to estimate human). Human cells are more difficult to transform than rodent, both spontaneously and by exposure to formaldehyde. μ Nbasal is 0 when concurrent controls or inhalation NTP

con trols 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) Hock ey

- stick dose

- response for α N was included even though TWA indicated

J

- shape. 2) Overall respiratory tract cancer
incidence data for human baseline rates
were used. 3) Risk was evaluated at statistical upper bound of the proportionality parameter
relat ing 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 polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 4 F.2. SENSITIVITY ANALYSIS OF HUMAN BBDR MODELING 1
Crump et al. (2008) carried out a limited sensitivity analysis of the Conolly et al. (2 004) 2
human model. This analysis was limited to evaluating the effect on the human model of the 3
following. These evaluations have been the subject of some debate in the literature and in 4
various conferences (Conolly, 2009; Conolly et al., 2009, 2008; Crum p et al. 2009). 5

6

1. The use of the alternative sets of control data for the rat bioassay data that were 7
considered in the sensitivity analysis of the rat model (Subramaniam et al., 2007). 8

2. Minor perturbations in model assumptions regarding the effect of formal dehyde on the 9

division and death rates of initiated cells ( **α** I , **β** I ) . Now, recall from the description of the 10
structure of the human model that one (of the two) adjustable parameter in the expression 11

for the human **α** I was determined from the model fit to the rat tumor incidence data while 12
the second parameter was determined from background rates of cancer incidence in the 13

human. Therefore, variations considered in **α** I were constrained to only those that (a) did 14
not meaningfully degrade the fit of the model to the rat tumor incidence data and (b) were 15
in concordance with background rates in the human. Crump et al. (2008) also evaluated 16
these variations with respect to their biological plausibility. The sensitivity analysis on 17

assumed initiated cell kinetics w as thought to be particularly important since there were 18
no data to even crudely inform the kinetics of initiated cells for use in the models, even in 19

rats, and the two

- stage clonal expansion model is very sensitive to initiated cell kinetics 20
(Gaylor and Z heng, 1996; Crump, 1994a, b). 21

22
Crump et al. (2008) note that, since the purpose of their analysis was to carry out a 23
sensitivity analysis, in order to illustrate certain points, only risks to the general U.S. population 24
from constant lifetime exposure t o various levels of formaldehyde under the Conolly et al. 25
(2004) environmental scenario (8 hours/day sleeping, 8 hours/day sitting, and 8 hours/day 26
engaged in light activity) are considered. Fits based on the hockey

- stick and J

- shape models 27

were identical , and, of the three estimated parameters (μbasal, multb, and D), only the estimate 28
of μbasal differed between the two models. 29

30

F.2.1

. Effect of background Rates of Nasal Tumors in Rats on Human Risk Estimates 31
Crump et al. (2008) quantitatively evaluated t he impact of different control groups on 32

estimates of additional human risk as follows: 33

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

— DO NOT CITE OR QUOTE F

- 5 1. Concurrent controls plus all NTP controls:, the same as used by Conolly et al. (2004); 1

2. Concurrent controls plus controls from NTP inhalation studies; 2

3. Only concurrent controls; 3

4. Each set of control data was applied with both the J shape and hockey

- stick models in 4
Conolly et al. (2004) for α N (flux) and α I (flux) for a total of six analyses;. 5

5. Uncertainties associated with α N or α I are not addressed. Parameters α
max , mult fc, and 6
KMU were estimated in exactly the same manner as in Conolly et al. (2004). 7

8
Crump et al. (2008) present the following dose

- response predictions of additional risk in 9
humans from constant lifetime exposure to various levels of formaldehyde arisi ng from 10
exercising the above six cases. Their plots are reproduced in Figure F

- 1
, where the 11
corresponding curves based on Conolly et al. (2004) are also shown for comparison. 12
The lowest dotted curve in Figure F

- 1 represents the highest estimates of human risk 13
developed by Conolly et al. (2004). This resulted from use of the hockey

- stick model for cell 14
division rates in conjunction with the statistical upper bound for the parameter KMU. As 15
indicated by the downward block arrows in the figure, their corres ponding estimates based on 16

the J

- shape model were all negative for exposures below 1 ppm. 17
Consider next the solid curves in the figure, which show predicted MLE added risks that 18
were positive and less than 0.5. Crump et al. (2008) next examined the added risk obtained 19

when the MLE estimate of ( KMU / μ basal ) in these cases is replaced by the 95% upper bound of 20
this parameter ratio. The upper bound risk estimates in Conolly et al. (2004) were calculated in a 21
similar manner (but using all NTP historical contro ls). Except for minor differences, risk 22
estimates corresponding to such an upper bound and using all NTP controls were very similar in 23
the two efforts (Crump et al., 2008; Conolly et al., 2004). 24
Figure F

- 1 shows that the choice of controls to include in t he rat model can make an 25

enormous difference in estimates of additional human risk. For the J

- shaped model for cell 26
replication rate both estimates based on the MLE and those based on the 95% upper bound on 27

KMU / μ basal are negative for formaldehyde exposur es below 1 ppm. However, when only 28
concurrent controls are used in the model in Crump et al. (2008), the MLE from the J

- shape 29
model is positive and is more than three orders of magnitude higher than the highest estimates 30
obtained by Conolly et al. (2004). Using only concurrent controls, estimates based on the 95% 31
upper bound on KMU / μ basal are unboundedly large (block arrows at the top of the figure). For 32
the hockey

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

estimate s based on the MLEs are zero for exposures less than about 0.5 ppm. If only inhalation 34
controls are added, the MLEs are about seven times larger than the Conolly et al. (2004) upper 35This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 6 bound estimates, and the estimates based on the 95% upper bound on KMU / μ b asal are about 50 1
times larger than the Conolly et al. (2004) estimates. If only concurrent controls are used, both 2
the MLE estimates and those based on the 95% upper bound on KMU / μ basal are unboundedly 3
large. 4

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 Figure F

- 1. Effect of choice of NTP bioa ssays for historical controls on 8
human risk. 9
10 Note: Estimates of additional human risk of respiratory cancer by age 80 from 11 lifetime exposure to formaldehyde are obtained by using different control groups 12
of rats. 13
14 Source: Crump et al. (2008). 15
16
17

F.2.2

. A lternative Assumptions Regarding the Rate of Replication of Initiated Cells 18
For the human model, Conolly et al. (2004) made the same assumptions for relating 19

**α** I (flux) and **β** I (flux) to **α** N (flux) as in their rat model (Conolly et al., 2003). That is, these 20

qu antities were related by using eq 2 and eq 3. As discussed in the context of the rat modeling, 21
by extending the shape of these curves to humans, the authors’ model brings the cytotoxic action 22
of formaldehyde to bear strongly on the parameterization of the human model as well. 23This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 7 In the sensitivity analyses of the rat modeling, it was concluded that other biologically 1

plausible assumptions for **α** I and **β** I resulted in several orders of magnitude variations in the low 2
dose risk relative to those obtained by models based on the assumptions in Conolly et al. (2003) 3
but that the highest risks were nonetheless of the order of 10

– 6 at the 10 ppb level. This section 4

examines how these uncertainties in the rat model propagate to the human model. 5
Crump et al. (2008) made minor modifications to the assumed division rates of initiated 6
cells in Conolly et al. (2004), while all other aspects of the model and in put data were kept 7
unchanged. Two alternatives were considered for each of the J

- shape and hockey

- stick models. 8
Figure F

- 2 shows the hockey

- stick model for initiated cells in rats. In the first modification to 9
the hockey

- stick model (hockey

- stick Mod 1) , rather than having a threshold at a flux of 10

1,240 pmol/m 2

- hour, the division rate increases linearly with increasing flux until the graph 11
intersects the original curve at 4,500 pmol/m 2

- hour, where it then assumes the same value as in 12
the original curve f or larger values of flux. The second modification (hockey

- stick Mod 2) is 13
similar, except the modified curve intersects the original curve at a flux of 3,000 pmol/m 2

- hour. 14

15

Mod 1

Mod 2

16 Figure F

- 2. Conolly et al. (2003) hockey

- stick model for division rates of 17
init iated cells in rats and two modified models. 18
19 Source: Crump et al. (2008). 20
21
22 Figure F

- 3 shows the rat J

- shape model for initiated cells. In the first modification to this 23
dose response (J

- shape Mod 1), rather than having a J shape, the division rate of i nitiated cells 24
remains constant at the basal value until the original curve rises above the basal value and has 25This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

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

(J

- shape Mod 2), the J shape is retained but somewha t mitigated. In this modification, the 2
division rate initially decreases in a linear manner similar to that of the original model but with a 3
less negative slope until it intersects the original curve at a flux of 1,240 μm/m 2

- hour, where it 4

then follows th e original curve for higher values of flux. 5

6

Mod 1

Mod 2

7 8 Figure F

- 3. Conolly et al. (2003) J

- shape model for division rates of initiated 9
cells in rats and two modified models. 10
11 Source: Crump et al. (2008). 12
13
14

Since the first constraint on the variation in **α** I was in concordance with the rat time

- to

15
tumor incidence data, Crump et al. (2008) applied each of the modified models in Figures F

- 2 16

and F

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

all NTP controls and the ho ckey

- stick curve for **α** N . These authors restricted their analysis to 18
this case since their stated purpose was only a sensitivity analysis as opposed to developing 19
alternate credible risk estimates. Figure F

- 4 reproduces (from Crump et al. [2008]) curves o f the 20
cumulative probability of a rat dying from a nasal SCC by a given age for bioassay exposure 21
groups of 6, 10, and 15 ppm. For comparison purposes, the corresponding KM (nonparametric) 22
estimates of the probability of death from a nasal tumor are also shown. Three sets of 23
probabilities are graphed: the original unmodified one and the ones obtained by using hockey

24

stick Mod 1 and Mod 2. Crump et al. (2008) state that the changes in the tumor probability 25
resulting from these modifications are so slight that the three models cannot be readily 26This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 9 distinguished in this graph. 4

4 Thus, the modifications considered to the models for the division 1
rates of initiated cells caused an inconsequential change in the fit of the model

- predicted tumor 2

incidence to the ani mal tumor data. 3



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



### Time (days) 15 ppm

10 ppm

6 ppm



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



### Time (days) 15 ppm

10 ppm

6 ppm

5 6 Figure F

- 4. Very similar model estimates of probability of fatal tumor in 7 rats for three models in Figure F

- 2. 8
9
Note: The differences are visually indistinguishable. Models were derived from 10 the implementation of Conolly et al. (2003) with the hockey

- stick curves for 11

**α** I (flux) and **α** N (flux) and variants derived from modifications (Mod 1 and Mod 2, 12 Figure F

- 2) to **α** I (flux). Model probabilities are compared to KM estimates. The 13 three sets of model estimates are so similar that they cannot be distinguished on 14 this graph. 15
16 Source: Crump et al. (2008). 17
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 re

optimizing the likelihood. The authors note that re

- optimization of the model subsequent to the variations would
have made the fit of modified models even better.This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 10 The above modifications did not affect the basal rate of cell division in the model and 1
likewise had no effect on the fit to the human background data (Crump et al., 2008). 2
Crump et al. (20 08) noted that, although the threshold model for initiated cells in Conolly 3
et al. (2003) was replaced with a model that had a small positive slope at the origin, the resulting 4
curves, hockey

- stick Mod 1 and hockey

- stick Mod 2, could have been shifted slig htly to the right 5
along the flux axis in order to introduce a threshold for α I without materially affecting the risk 6
estimates resulting from these modified curves. Thus, “the assumption of a linear no

- threshold 7
response is not an essential feature of the modifications to the hockey

- stick model; clearly 8

threshold models exist tha t would produce essentially the same effect” (Crump et al. 2008). 9

10

F.2.3. Biological Plausibility of Alternate Assumptions 11

These very small variations made to the **α** I in Conolly et al. (2003) are seen to be 12

consistent with the tumor

- incidence data (just de monstrated above); small compared with the 13
variability and uncertainty in the cell replication rates characterized from the available empirical 14

data (at the formaldehyde flux where **α** I was varied ); supported (qualitatively) by limited data, 15
suggesting incre ased cell proliferation at doses below cytotoxic; perturbations that one should 16
expect on any dose response derived from laboratory animal data because of human population 17
variability in cell replication; and biologically plausible because cell cycle contr ol in initiated 18
cells is likely to be disrupted. 19
The averaged cell replication rate constants as tabulated in Table 1 of Conolly et al. 20
(2003) and shown by the red curve in Figure E

- 2 of Appendix E (for various exposure 21
concentrations and corresponding ave rage formaldehyde flux values in the F344 rat nose) 22
demonstrate an increase over baseline values only at exposure concentrations of 6 ppm and 23
higher. Increased cell proliferation at these concentrations of formaldehyde, whether transient or 24

sustained, hav e been associated in the literature with epithelial response to the cytotoxic 25
properties of formaldehyde (Conolly, 2002; Monticello and Morgan, 1997; Monticello et al., 26
1996, 1991). The labeling data are considered to show a lack of cytotoxicity and regen erative 27
cell proliferation in the F344 rat at exposures of 2 ppm and below (Conolly, 2002). In the 28
Conolly et al. (2003) modeling, it is further assumed that the formaldehyde flux levels at which 29
cell replication exceeds baseline rates remain essentially unchanged when extrapolated to the 30
human and for initiated cells for the rat as well as the human. These assumptions need to be first 31
viewed in the context of the uncertainty and variability in the data on normal cells discussed 32

earlier. 33
Arguments for a hockey

- stick or J shape over the background have been made in the 34
literature for sustained and chronic cell replication rates; the analyses of the cell replication data 35This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 11 show that the data are not consistently (over each site and time) indicative of a hocke y

- stick or 1

J shape as the best representation of the data. This uncertainty is particularly prominent when 2
examining the cell replication data at the 13

- week exposure time and the pooled data from the 3

PLM nasal site from Monticello et al. (1996) (Figures E

- 1 [dotted curve], E

- 3B, E

- 4 of Appendix 4

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

- week cell 5
replication data appear to be more representative of a monotonic increasing dose response 6
without a threshold. It is possibl e that early times are of more relevance to the carcinogenesis as 7
well as for considering typical (short duration) human exposures. 8

For initiated cells, there are no data on which to evaluate the modifications made to these 9
rates. However, some perspectiv e can be gained by comparing them to the variability in the 10
division rates obtained from the data on normal cells used to construct the formaldehyde model. 11
As shown in Figure E

- 2 and discussed further in Subramaniam et al. (2008), these data show 12
roughly an order of magnitude variation in the cell replication rate at a given flux. As part of a 13
statistical evaluation of these data, a standard deviation of 0.32 was calculated for the log

14

transforms of individual measurements of division rates of normal cell s. By comparison, the 15
maximum change in the log

- transform division rate of initiated cells resulting from hockey

- stick 16
Mod 2 was only 0.20, and the average change would be considerably smaller. Thus, although 17

there are no data for initiated cells, it can be said that the modifications introduced in Crump et 18
al. (2008) for initiated cells are extremely small in comparison to the dispersion in the data for 19

normal cells. 20
Subramaniam et al. (2008) also point to some additional, albeit limited, data, suggestin g 21
that exposure to formaldehyde could result in increased cell replication at doses far below those 22
that are considered to be cytotoxic. Tyihak et al. (2001) treated different human cell lines in 23

culture to various doses (0.1

– 10 mM) of formaldehyde and fo und that the mitotic index 24
increased at the lowest dose of 0.1 mM. These findings considered along with human population 25
variability and susceptibility (for example, polymorphisms in ADH3 [Hedberg et al., 2001]) 26
indicate that it is necessary to consider t he possibility of small increases in the human α I over 27
baseline levels at exposures well below those at which cytotoxicity

- driven proliferative response 28
is thought to occur. 29
Heck and Casanova (1999) have provided arguments to explain that the formation o f 30
DPXs by formaldehyde leads to inhibition of cell replication (i.e., if this effect alone is 31
considered, normal cell replication rate of the exposed cells would be less than the baseline rate). 32
However, this hypothesis was posed for normal cells. Subrama niam et al. (2008) argue that if an 33
initiated cell is created by a specific mutation that impairs cell cycle control, the effect would be 34
to mitigate the DPX

- induced inhibition in cell replication, either partially or fully, depending on 35This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 12 the extent to whic h the cell cycle control has been disrupted. In the absence of data on initiated 1
cells, the above argument provided biological motivation to the modification applied to the 2

J

- shape model for cell division (Crump et al. 2008). 3
Thus, the previous paragraphs suggest that the changes made in the analysis in Crump et 4
al. (2008) to the assumption by Conolly et al. (2003) regarding the dose response for the division 5
rate of initiated cells are not implausible. 6

7

F.2.4. Effect of Alternate Assumptions for Initiate d Cell Kinetics on Human Risk Estimates 8
Figure F

- 5 contains graphs of the additional human risks estimated (in Crump et al. 9
[2008]) by applying these modified models for α I and using all NTP controls, compared with 10
those obtained by using the original Cono lly et al. (2004) model. Each of the four modified 11
models presents a very different picture from that of Conolly et al. (2004). At low exposures, 12
these risks are three to four orders of magnitude larger than the largest estimates obtained by 13
Conolly et a l. (2004). 14
These results have been criticized by Conolly et al. (2009) as being unrealistically large 15
and above the realm of any epidemiologic estimate for formaldehyde SCC. Thus, they argue that 16
the parameter adjustments made in Crump et al. (2008) are inappropriate. Crump et al. (2009) 17
rebutted these points by arguing that the purpose of their work was not to provide a more reliable 18
or plausible model but to carry out a sensitivity analysis. They argued that the changes made to 19

the model (in their an alyses) were reasonable since they did not violate any biological 20
constraints or the available data. Further, they pointed out that “by appropriately mitigating the 21
small modifications [they] made to the division rates of initiated cells, the model [woul d] 22
provide any desired risk ranging from that estimated by the original model up to risks 1,000

- fold 23
larger than the conservative estimate in Conolly et al. (2004).” 24
Crump et al. (2008) also evaluated the assumption in eq 3 of the CIIT modeling 25
pertaining to initiated cell death rates ( β

I ) by making small changes to β I

. They report that they 26
obtained similarly large values for estimates of additional human risk at low exposures. 27
Obtaining reliable data on cell death rates in the nasal epithelium appears to be an unusually 28
difficult pro position (Hester et al., 2003; Monticello and Morgan, 1997), and, even if data are 29
obtained, they are likely to be extremely variable. 30This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 13 Unmodified Hockey Stick
Model Hockey Mod 2 Hockey Mod 1 J

- Shape Mod 1

Conolly et al. (2004)
“Risk Conservative” Hockey Stick Model J

- Shape Mod 2

Unmodified Hockey Stick
Model Hockey Mod 2 Hockey Mod 1 J

- Shape Mod 1

Conolly et al. (2004)
“Risk Conservative” Hockey Stick Model J

- Shape Mod 2

1 2 Figure F

- 5. Graphs of the additional human risks estimated by applying 3
these modified models for α I , using all NTP controls, compared to those 4 obtained using the original Conolly et al. (2004) model. 5

6 Source: Crump et al. (2008). 7This document is a draft for review purposes only and does not constitute Agency polic y. DRAFT

— DO NOT CITE OR QUOTE F

- 14 

### This page intentionally left blank.

# Appendix G

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

— DO NOT CITE OR QUOTE G

- 2 APPENDIX G 1

2
EVALUATION OF THE CANCER DOSE

- RESPONSE MODELING 3
OF GENOMI C DATA FOR FORMALDEHYDE RISK ASSESSMENT 4
5

G.1. MAJOR CONCLUSIONS IN ANDERSEN ET AL. (2008) 6
In Chapter 4, the gene microarray data from animal studies on formaldehyde (Andersen 7
et al., 2008; Thomas et al., 2007) were described. The analysis of these anima l high throughput 8
data and the conclusions reached in these two groundbreaking papers were closely examined for 9
use in this assessment. Studies on high throughput animal data provide a wealth of information 10
that helps further understanding of the relevant mechanisms. However, such studies have 11
generally not made quantitative bottom

- line inferences that inform low dose human risk. The 12

above

- mentioned studies are a notable exception due to the breadth of their conclusions on low 13
dose MOAs, their pioneering application of the benchmark dose (BMD) methodology to 14
genomic data, their use of BMD

- response analysis that identified dose estimates at which 15
specific cellular processes were significantly altered, the fact that they were accompanied by 16

recommendation i n the literature urging use of these results in setting exposure standards for 17
formaldehyde (Daston, 2008). 18
We focus here on the conclusions in these papers with regard to modeling the cancer 19

dose

- response for formaldehyde. In addition to supporting our disposition of these analyses for 20

this assessment, this write

- up serves the purpose of exemplifying critical issues that need to be 21

considered for the future. 22
The overall BMD determined in Andersen et al. (2008) for all genes with significant 23

dose

- respon se averaged 6.4 ppm. These analyses indicated a general progression with the lowest 24
BMD values (i.e., the most sensitive epithelial responses) for extracellular and cell membrane 25
components and higher BMD values for intracellular processes. Overall, thes e authors 26

concluded that 27

28

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

 Genomic changes were no more sensitive than tissue responses 31

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

 Linear extrapolations, or extrapolations that specify similar MOAs at high and low doses 35
would be inappropriate. 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 3 These findings were judged to have significant implications on the debated MOA for 1
formaldehyde carcinogenicity, confirming results from earli er bioassays and dose

- response 2
modeling that the mutagenicity of formaldehyde was too weak to be of relevance to its 3
carcinogenicity. Daston (2008) judged the method in these efforts to be extremely sensitive and 4
therefore suited to examining whether resp onses at the molecular level take place at doses below 5
which frank adverse effects occur. Daston (2008) argued that “... if there are pleiotropic effects 6
at lower exposure levels that would elicit a different profile of gene expression, those genes 7

would no t go unnoticed” and thus concluded that “the gene expression data confirm that the 8
responses are not linear at low doses.” 9
In the analyses that follow, we point to some significant quantitative factors that impact 10

on these conclusions. 11

12

G.2. USE OF MULTIP LE FILTERS ON THE DATA 13
The analyses in these papers involved the following sequence of data filters. 14

15

1. Gene probe sets that differed in expression in response to treatment were identified by 16

one

- way analysis of variance. Probability values were adjusted for multiple comparisons 17
by using a false discovery rate of 5%. 18

2. Next, in addition to the above statistical filter, the output was further screened by 19
selecting only those genes that exhibited a change from the control group that was greater 20
than or equal to 1.5

- fold (logarithmic). 21

3. The gene probe sets that demonstrated significant dose

- response behavior were then 22
matched to their corresponding biological process and molecular function gene ontology 23
(GO) categories (considering only those involving more t han three genes) and grouped 24
into process categories such as cell division, DNA repair, cellular proliferation, 25
apoptosis, and related molecular function categories. 26

27
A large number of genes are expressed in these studies; therefore, clearly some 28
appropri ate filter needs to be used for meaningful interpretation of the vast database. Tissue 29
pathology served as a phenotypic anchor for the interpretation of microarray results, and the 30
genomic study confirmed (and improved on) the qualitative and quantitative understanding 31
derived from the histopathology and observation of frank effects. It is possible that the 32
combination of filters used by these authors is adequate for an inquiry into some mechanisms 33
associated with the specific phenotypic effects. However , the studies reached bottom

- line 34
conclusions with regard to the low

- dose MOA and approach to be considered for quantitative 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 4 extrapolation. These conclusions necessarily involve questions as to whether there were gene 1
expression changes at low dose and at early exposure times that may be relevant to initiating 2
carcinogenesis and finally as to whether there is a threshold in dose associated with 3
formaldehyde carcinogenesis. However, collectively, the three filters employed in these studies 4
likely constitut e overly stringent criteria, taking away the resolution needed to observe critical 5
gene changes needed to delineate low dose effects. An indication that this may indeed be the 6
case can be seen by examining the correlations in their findings with the obser ved trend in the 7
data on DPXs formed by formaldehyde. This is detailed in the following section. 8

9

G.3. DATA FOR LOW

- DOSE CANCER RESPONSE 10
A significant finding in Thomas et al. (2007) is that BMD estimates for the GO 11
categories applicable to cell prolife ration and DNA damage were similar to values obtained for 12
cell labeling indices and DPXs in earlier studies and to BMD estimates obtained for the onset of 13
nasal tumors. The mean BMD for the GO category of “positive regulation of cell proliferation” 14

was 5.7 ppm; in comparison, Schlosser et al. (2003) obtained a 10% BMD of 4.9 ppm for the cell 15
labeling index. The GO category associated with “response to DNA damage stimulus,” seen as a 16
genomic correlate to a mutagenic effect, had a mean BMD of 6.31 ppm. Thom as et al. (2007) 17
compare this finding with significant increase at 6 ppm of DPXs following a 3

- hour exposure in 18
the study by Casanova et al. (1994). The formation and repair of DPXs have been considered to 19
be one of the potential mechanisms associated wit h the genotoxic action of formaldehyde 20
(Conolly et al., 2003, 2000). Based on earlier work in the same laboratory (Conolly et al., 2004, 21
2003; Conolly, 2002), Slikker et al. (2004) concluded that there is a dose threshold (at about 22

6 ppm) to formaldehyde carcinogenicity and that the putative mutagenic action of formaldehyde 23
is not relevant to its carcinogenicity. Therefore, the finding that a significant genomic response 24
(e.g., induction of DNA repair genes) is not observed at doses lower than those that induce 25
tumors in rodent bioassays is seen by these authors (Andersen et al., 2008; Daston, 2008; 26

Thomas et al., 2007) to further buttress the above conclusions related to the mode of action for 27
formaldehyde

- induced respiratory cancer. 28
However, phenotypic anchoring to the DPX data drawn only from Casanova et al. (1994) 29

misses critical low

- dose data that informs mode of action. In an earlier study, Casanova et al. 30
(1989) observed statistically significantly elevated (over controls) levels of DPXs at 2 ppm a nd a 31
trend towards elevated DPXs at 0.7 ppm. In analysis of low

- dose data, the trend in the dose

32
response is critically important because data inherently lack the power to establish statistical 33
significance. Furthermore, the two studies by Casanova and co workers are different in some 34
respects. The earlier study was a 6

- hour exposure, while the later study was a 3

- hour study; thus, 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 5 on this account alone, it appears more relevant to compare with the older study. Exposures in 1
the earlier study were addition ally at 0.3 and 10 ppm, thus affording a lower exposure 2
concentration. In the earlier study, tissue from the whole nose was analyzed, whereas in the later 3
study tissue from two specific regions was obtained from the “high” tumor (Level II) and “low” 4

tumor regions. Together, these data suggest that DPXs occur at exposure concentrations 5
considerably lower than those that elicited transcriptional changes. One possible explanation is 6
that the increase in DPXs was not sufficient to induce DNA repair genes. A lternatively, these 7
discrepancies may be due to the stringent filters and the low statistical power of the Andersen et 8
al. (2008) study. These disparities between the gene array study and the DPXs question the 9
ability of the studies in Andersen et al. (20 08) and Thomas et al. (2007) to inform the presence or 10
absence of a mutational MOA for formaldehyde, and in essence, to inform the low

- dose response 11
curve for formaldehyde

- induced cancer. 12
In another instance, Andersen et al. (2008) clearly stated that no g enes were significantly 13
altered by exposure to 0.7 ppm, yet they state that there was “a trend toward altered expression at 14
0.7 ppm” in some genes with U and inverted U shape dose

- responses (Figures 4 and 5 of their 15
paper). While these changes may not be s tatistically significant, they could be biologically 16
significant. 17

18

G.4. DIFFICULTIES IN INTERPRETING THE BENCHMARK MODELING 19
The benchmark analyses are summarized in Thomas et al. (2007) as average BMD 20
estimates for genes in a given GO that were statistic ally significantly dose related. The 21
benchmark modeling was then used by the authors to identify that the dose below individual 22
cellular processes was judged to be “not altered.” 23
The BMD definition used by these authors is quite stringent: it defines an effect so that 24
only 0.005 of controls will be considered affected and sets the BMR corresponding to this dose 25

at 0.105. The net effect is that the BMD is the air level, such that the increase in the mean 26
response is 1.349 × standard deviation. This is e ssentially an arbitrary definition. For 27
comparison, if 0.05 of controls are considered affected and the BMR is set at 0.1 (common 28
values that are applied to whole animal data), the BMD is the air level such that the increase in 29
the mean response is 0.608 × standard deviation. Thus, if this definition had been used (as is 30
traditionally the case), the BMD estimates would all be 2.2 times smaller than those obtained by 31
Schlosser et al. (2003). Furthermore, the analysis assumes equal variance in all dose gro ups. 32
Thus, further consideration of these issues with regard to interpretation of the BMR obtained 33
from these studies is needed before it can be used in regulatory exposure setting. Secondly, 34

lower confidence limits on the BMDs need to be derived for the data in Andersen et al. (2008). 35This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 6 G.5. STATISTICAL SENSITIVITY OF THE DATA FOR DOSE

- RESPONSE 1
Another cautionary note pertains to the qualification of gene array studies as being 2
extremely sensitive. Such a qualification should actually refer to the fact that only tiny amounts 3
of mRNA are needed, that is, the sensitivity of the assay per se for measuring gene expression. 4
However, this should not be confused with the sensitivity needed to identify the very small dose

5
related changes at low dose. Andersen et al. (2008) reports on results of studies that involve 6
small numbers of animals in each dose group (five or eight). Despite the limited power in such 7
studies, the paper equates the absence of a statistically significant effect with no effect. This 8

limit ation is generally true of studies of the dose responses of changes in gene expression 9
conducted to date; they have generally relied on very few animals ( ≤10 per dose group). Since 10
there will likely always be background amounts of gene expression, quantifying the dose 11
response requires statistically significant changes in gene expression as a function of dose. If the 12
genomic data inv olve even fewer animals per group than the histopathological data, they have 13
even less power to delineate the dose response; in particular, whether there is a threshold at low 14
exposures. This is illustrated by the example in Figure G

- 1 of the dose respons es for epithelial 15
hyperplasia (Andersen et al. 2008, lesion 2). These appear equally consistent with both a 16
threshold at around 1 ppm and a linear response down to zero. 17

18

G.6. LENGTH OF THE STUDY AND STOCHASTIC EVENTS 19
Another significant consideration wi th regard to MOA conclusions that are pertinent to 20
the disease process is the length of the study, 15 days. If formaldehyde

- induced tumor formation 21
is a stochastic process (e.g., genotoxicity), then exposure of a small number of animals to low 22

concentratio ns for 15 days may not be long enough to detect changes that might occur under 23
long

- term exposure scenarios. 24
Relatedly, it has been suggested that gene (and protein) expression is a stochastic process 25
whereby steady state gene expression obeys Poisson st atistics (i.e. distribution of rare events), 26
and that events of interest may occur in a single cell or small number of cells in which larger 27
tissue samples can average out such stochastic events and prevent the detection of non

- average 28

behavior (Quakenbush , 2007). Given the implied difficulty in such an analysis, duration of 29
exposure may be one of the most tenable ways of addressing whether a chemical increases the 30
probability of an adverse response. 31

32This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 7 1
Figure G

- 1: Graphs of epithelial hyperplasia (Lesion 2 ) versus formaldehyde 2 concentration (ppm) with 95% confidence intervals (with linear fit by eye) 3
4
5
G.7. OVERALL CONCLUSION 6
We believe our analyses of the presentations in Andersen et al. (2008) and Daston (2008) 7
are generally useful with regard to future developments in quantitative analyses of genomic data 8
if they are to be of relevance to risk assessment. For risk assessment, rather than focusing on 9
what responses are statistically significant, an analysis should focus on 1) what range of values 10

of crit ical parameters (e.g., gene expression) are consistent with the data, and 2) what these 11
values imply for whole animal risk. This is of course, an extremely difficult proposition because 12 0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 1

0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 1 Recovery

0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 5 0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 6

0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 6 Recovery

0 0.2 0.4 0.6 0.8

1

0 1 2 3 4 5 6 7 Day 15This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE G

- 8 we do not know nearly enough about how changes in genes quantitativel y affect whole animal 1
risk, or even which genes are important. 2

# Appendix H

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

— DO NOT CITE OR QUOTE H

- 2 APPENDIX H 1

2 EXPERT PANEL CONSULTATION ON QUANTITATIVE EVALUATION OF ANIMAL 3
TOXICOLOGY DATA FOR ANALYZING CANCER RISK DUE TO INHALED 4
FORMALDEHYDE 5

6
The National Center for Environmental Assessment convened an expert panel of 7
scientists for advice on evaluating available approaches for incorporating biological information 8
in analyzing animal tumor data for assessing cancer risk due to inhaled formaldehyde. This 9
Appendix pertains to the major deliberations and results of that meeting and is divided into three 10

sections. 11

12

A. Scope and Agenda of Meeting on Quantitative Evaluation of Animal Toxicology Data for 13
Analyzing Cancer Risk due to Inhaled Formaldehyde. October 28 & 29, 2 004. 14

15

B. Summary of Consultative Meeting on CIIT Formaldehyde Model. October 28 & 29, 16

2004. 17

18

C. Meeting Report from Dr. Rory B. Conolly 19This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 3 A. Scope and Agenda of Meeting on Quantitative Evaluation of Animal Toxicology 1
Data for Analyzing Cance r Risk due to Inhaled Formaldehyde 2
October 28 & 29, 2004. Washington, DC. 3
4 This meeting is to assist EPA in evaluating available approaches for incorporating biological 5 information in analyzing animal tumor data for assessing cancer risk due to inhaled 6
for maldehyde. The CIIT Centers for Health Research (CIIT) has published a novel risk 7
assessment that links site

- specific predictions of flux using computational fluid dynamics (CFD) 8 modeling with a two

- stage clonal growth model of cancer to analyze nasal tum or incidence in 9 two rodent bioassays. The rodent models are used with corresponding human models for low

10 dose extrapolation of cancer risk to people. 11
12 Key predictions of the CIIT effort are a zero maximum likelihood estimate of the probability of 13
formal dehyde

- induced mutation per cell generation in the rat and a de minimus additional 14
lifetime risk in non

- smokers due to continuous environmental exposure below 0.2 ppm. The 15 National Center for Environmental Assessment is carrying out sensitivity analyses a nd 16 examining variations of the CIIT model in order to understand the implications of the model 17 structure and parameters on model predictions. In this meeting, we wish to focus on the 18 strengths and key uncertainties of this model, the extent to which assum ptions in the CIIT model 19 are supported by biological data, and examine the impact of uncertainty and variability on the 20 overall quantitative risk characterization. 21
22 Broadly, the discussions will focus on the following areas: 23
24

**•** Impact of uncertainties in dosimetry on human risk estimates 25

**•** Uncertainties in the use of experimental data on labeling index 26

**•** The model structure related to initiated cells and DNA protein cross

- links 27

**•** Considerations of time

- to

- tumor in the clonal growth modeling 28

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

**•** Relative merits of benchmark dose modeling vs. the 2

- stage clonal growth model 30
31 Discussions on Mode of Action are expected to be an integral part of several of the sessions. 32
Ther efore a specific time

- slot is not set aside for this purpose. 33
34 The meeting will have a panel discussion format. There will be no formal presentations unless 35 necessary to elucidate an issue. Various attachments referred to in the Agenda below, as well as 36 the relevant manuscripts will be sent separately. 37
38 Specifically, we suggest the following issues upon which to focus the discussion in the above 39 areas, and approximate time frames and discussion leads, although discussants should feel free to 40 bring up o ther critical issues. 41

42
43This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 4 I. Introduction and purpose of discussion 1

Peter Preuss. 2 9:00 AM, Oct 28 3
4 II. Impact of uncertainties in dosimetry on risk estimates 5
Lead discussant: Linda Hanna 6
9:15

- 11 AM Oct 28 7
8 Boundary conditions 9 The CFD modeling specified a mass transfer coefficient as a boundary condition on the 10 nasal lining, adjusting the value of this coefficient on the “absorbing” portion of the 11 lining so as to match simulated overall uptake in the rat nose to the experimentally 12 determined average over all uptake. This value was then used for the corresponding 13 human nasal lining. Are these boundary conditions appropriate surrogates for the 14 underlying pharmacokinetics, including saturation in metabolism and mucociliary 15 clearance, particularly with refere nce to humans? 16
17
Turbulence 18 Turbulent flow has been seen to occur in experimental models of the human nose at some 19 of the higher flow rates at which the CFD models were used in CIIT = s assessment. It is 20 not likely that the CIIT CFD model can reliably identif y signatures of transition to 21 turbulent behavior. Turbulent flow can significantly alter regional uptake patterns. 22 Additionally, significant mass balance errors were seen at the higher flow rates in the 23 human flow models. Discuss if these are likely to i mpact significantly on risk estimates. 24
25 Interindividual variability 26 The CIIT assessment has focused on the nasal anatomy of a single individual. Discuss 27 the implications of interindividual variations in nasal anatomy on the population 28
distribution in risk

. 29
30

III

. Uncertainties in the use of experimental data on labeling index 31 Lead discussant: George Lucier 32
11AM

– 11:45 AM, 1:00

- 3:15 PM Oct 28 33
34
Cell

- replication rate and its relationship to flux is a critical determinant of risk. Therefore 35
uncertainties a nd variability in measurement of the unit length labeling index and its use in the 36 CIIT clonal growth modeling need to be characterized. 37
38
1. Discuss the strengths, uncertainties and limitations associated with estimating cell 39 replication rates from the unit l ength labeling index (ULLI). 40

a. For example, a constant ratio of the measured ULLI to the labeling index (LI) that 41
is used in the model is assumed. Is it valid to assume this ratio to be constant 42 across nasal sites, dose and exposure time. 43
b. How uncertain is this ratio? 44
45This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 5 2. Considering the large patterns of variability in the ULLI data, discuss the validity of 1 using ULLI averaged over site and exposure times. 2

a. The averaging loses information on the sequential effect of change with 3 time, and on significant dif ferences among sites. 4
b. How sensitive is the clonal growth modeling result to these variations in the dose

5 response function for cell replication rates vs. flux to the tissue? A discussion of 6 this question in this session is intended to serve as input to later deliberations on 7
the issue. 8
9
3. Discuss the validity of combining data collected in different experiments using different 10 labeling methods, and the validity of estimating cell replication rates from LI or ULLI 11 measured in a single pulse labeling experim ent. 12
13
See attachment C: “ULLI Dose

- Response Modeling and Statistical Analysis” for a 14 discussion of these issues, and Moolgavkar and Luebeck (1992). 15
16
IV

. Model Structure: Birth and death rates for Initiated cells, Role of DPX 17 Lead discussant: Kenny Crump 18
3:30

- 6:00 PM Oct 28. 19
20
Parameters for initiated cells 21

22
1. The CIIT analysis of ULLI data allows for a virtual threshold in dose in the replication 23 rate of normal cells. Discuss the validity of ascribing such a behavior to initiated cells 24
consider ing the sensitivity of 2

- stage model results to the initiated cell replication rates. 25
26
2. Discuss the treatment of death rate for initiated cells in the model (set equal to birth rate 27 of normal cells in Conolly et al., 2003) and implications for confidence in model 28 predictions. 29
30 Also see Attachment A (memo from Rory Conolly) and Attachment D (EPA discussion of 31
CIIT clonal growth modeling and some sensitivity analyses. . .) 32

33
Treatment of DNA protein cross

- links (DPX) in clonal expansion model 34

35
3. FORMAL DEHDYE

- INDUCED MUTATION IS MODELED AS TAKING 36
PLACE ONLY WHILE DPX ARE IN PLACE WITH DP X UNDERGOING 37
RAPID REPAIR. DISCU SS THE POSSIBILITY O F PERSISTENT GENETIC 38
DAMAGE THAT EXTENDS BEYOND THE DPX HALF

- LIFE AND 39
ENHANCES MUTATION. HOW MIGHT THIS ISSUE BE INC LUDED IN 40
THE MODEL STRUCTURE? 41
42
43This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 6 V. Considerations of time

- to

- tumor in the CIIT clonal growth modeling 1 Lead discussant: Christopher Portier 2
8:30

– 11:00 AM, Oct 29. 3

4
1. A number of issues affect likelihood values and the model fit to the time

- to

- tumor data. 5

D iscuss assumptions in the treatment of time

- to

- tumor in the CIIT clonal expansion 6 model, and their impact on parameter estimates. For example, 7
8

a. Results in Conolly et al. (2003, 2004) are derived considering all tumors to be 9
fatal. Note in this context tha t serially sacrificed animals have been combined 10 with those experiencing mortality B the effect of this is visible as irregularities in 11
the time

- to

- tumor curve. 12
13
b. How is the time variability in ULLI likely to impact on the time

- to

14 tumor predictions? 15
16
2. Long de lay times are predicted by the model for observation of detectable tumor. Is this 17 compatible with the assumption of rapidly fatal tumors? 18
19
3. Discuss the weight to be given to differences in likelihood when comparing with 20 variations on the Conolly et al (2003 ) model structure such as in Attachment A or D. 21
22
VI

. Inferences on the role of formaldehyde

- induced mutation and cell proliferation 23
Lead discussant: Dale Hattis 24
11:15

– 12:00 PM, 1:00

– 4:00 PM, Oct 29. 25
26
1. The model structure in Conolly et al. (2003) predi cts a zero maximum likelihood estimate 27 for the constant of proportionality (KMU) linking DPX to the probability of 28 formaldehyde

- induced mutation per cell generation. Examine the strength of this 29 conclusion, and the extent to which an insignificant probabi lity of formaldehyde

- induced 30 mutation per cell generation is supported by data. 31
32
2. Discuss the biological relevance and validity of model

- estimated parameters, particularly 33
in the context of low

- dose predictions. 34

a. Discuss possible avenues to validate CIIT c ancer model predictions. 35
36
3. Discuss the validity of using cell replication rates determined for the rat to predict human 37 risk in a population. 38
39
4. In the face of uncertainties, are the results in Conolly et al. (2003, 2004) conservative in 40 the sense of overpred icting risk? 41

a. Discuss the extent to which sensitivity analyses have addressed this issue and the 42 extent to which sensitivity analyses can speak to the strength of the model. [See 43 Attachments A: Memo from Conolly, and D: EPA discussion of CIIT clonal 44
growt h modeling and some sensitivity analyses . . .]. 45
46This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 7 VII. Benchmark Dose Modeling 1
Lead discussant: Kenny Crump 2

4:15

– 5:30 PM, Oct 29. 3
4 Discuss the relative merits of using a benchmark dose approach that incorporates 5 biological modeling (such as e stimating flux to tissue or DPX levels) as compared with 6
the CIIT 2

- stage model for cancer. ( See attachment E and Schlosser et al., 2003.) 7
8
9This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 8 B. Summary of Consultative Meeting on CIIT Formaldehyde Model 1 October 28 & 29 2004, NCEA, Washington, DC 2

3

Date: Nov ember 10, 2004 4

Ravi P. Subramaniam, Ph.D. 5
Quantitative Risk Methods Group 6

National Center for Environmental Assessment, ORD, US EPA 7

8
This is a broad summary of the most important issues at the formaldehyde meeting. 9
It was generally felt by consultants th at the broad framework of the approach adopted by 10
CIIT, namely the use of a two

- stage model for cancer, the linking of localized flux to cell 11
replication rates and DPX concentration, and the expression of formaldehyde

- induced mutation 12

as a linear function of DPX, was reasonable. 13
Potential errors in the dosimetry modeling were seen not to have a significant effect on 14
risk estimates. The boundary conditions used were discussed to be a reasonable representation 15
of the pharmacokinetics for both rats and huma ns. The discussion on the impact of 16
interindividual variability of nasal anatomy was not particularly conclusive. It was determined 17
that there was likely to be much less variability in reactive gas uptake than that seen in 18
particulates. 19

Crucial errors we re however identified on several fronts in the manner in which the 20
clonal growth model had been implemented in the CIIT effort. Dr. Portier felt that the 21
calculation of probability was seriously flawed on account of lumping serially

- sacrificed animals 22

and animals that died of tumor together, while at the same time assuming rapid fatality of all 23
tumors. This was seen to significantly alter the calculation of tumor probability (the shape of the 24

dose

- response curve), and his insight was that a correction was likely to allow for a substantially 25
higher value for the probability of formaldehyde

- induced mutation at low

- dose. The best 26
estimate for this probability is now zero in the model. Drs. Crump, Portier and Hattis argued that 27
replacing this estimate by an upper confidence bound on KMU (the coefficient determining the 28
role of DPX in the probability of mutation per cell generation), keeping other structural problems 29
in the model unexplored, or other parameters fixed, would not be enough. There was a 30

discussi on on the need to provide confidence bounds on risk determined by allowing all the 31
parameters to vary. Drs. Crump and Hattis (and Portier?) felt such an estimate would be very 32
different from that calculated based on individual parameters. 33
Drs. Crump, Hatt is and Portier urged us not to be constrained by the optimal likelihood 34
values of a single plausible model, and underscored the need to explore a variety of biologically 35
reasonable model structures as a requisite for utilizing such a model in risk assessme nt. 36This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 9 Likelihood was seen to be an inadequate expression of what is to be considered an optimal 1
model (okay only for comparing models that were nested, etc.). These models should allow the 2
expression of variability and uncertainty in the data, as well as i n underlying assumptions in 3
model specification. Dr. Crump (and Hattis also?) felt that alternate model structures, if 4
explored, could potentially lead to risk estimates, for the range below the observed data, that 5
were higher by several thousands. 6

Dr. Cr ump cautioned that extrapolating to human using the hockey or J

- shaped cell 7
replication curve used in the rodent carried with it a large uncertainty that had not been 8
characterized in the Conolly modeling. 9
Dr. Portier expressed concern over the manner in w hich historical and concurrent 10
controls were lumped together. The thrust of Portier’s comments was that such a combination of 11
controls was generally not done. The large number of historical controls was likely to 12
significantly bias the impact of the bioa ssay data in determining the time

- to

- tumor fits. 13
There were various discussions about the pros and cons of constructing a joint likelihood 14
of the cell replication data and the tumor data, and the weights to be assigned to the separate 15

likelihoods. This was considered to be problematic by Dr. Portier. 16
Dr. Crump’s opinion was that the Conolly model, and those explored by EPA, fit the 17
tumor data poorly, and that an improved description of the tumor data was needed before the 18

model could be used for low

- dose and inter

- species extrapolation. 19
Drs. Lucier and Hattis placed emphasis on including the early

- time cell replication data 20
instead of constructing a time

- weighted average. It was felt that the two Monticello experiments 21
could not be combined together as i n Conolly et al. Dr. Lucier felt that the early

- time data would 22
have a greater impact in the progression of carcinogenesis. In general, the effect of “time” was 23
considered to have significant effects on the time

- to

- tumor modeling, and they urged us to 24

in corporate time

- dependent terms in the modeling. CIIT expressed willingness to provide the 25
original cell replication data to us for further analysis. (Further discussion on this matter did not 26
take place in the open forum.) 27
Preliminary indications are, particularly based on Dr. Portier’s insight, that the currently

28

held “de

- minimus” picture of low

- dose risk, as expressed in Conolly et al. (2004), is not likely to 29
be the case if these various suggestions are incorporated in the modeling. 30
31This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 10 C. Meeting Report from Dr. Rory B. Conolly 1

2 Rory B. Conolly, Sc.D., D.A.B.T. 3 106 Michael’s Way 4 Chapel Hill, NC 27516 5
Voice: 919.929.2258 6

7 July 24, 2005 8
9
Dr. Bobette Norse 10
ORAU Procurement

- MS

- 04 11
P.O. Box 117 12 Oak Ridge, TN 37831

- 0117 13
Phone: 865

- 576

- 3051 14

Fax: 865

- 576

- 938 5 15
16 Dear Dr. Nourse, 17
18 The following is my final written report on the formaldehyde review meeting held at the 19 U.S. EPA in Washington, D.C. on 28

- 29 October, 2004. 20 EPA provided no guiding philosophical statement about the criteria being used to 21
evaluate the CIIT assessment. The new Guidelines for Carcinogen Assessment state that the 22 preferred default approach is to use a biologically based model. Since the key components of the 23 CIIT assessment have been published in the peer

- reviewed literature and have un dergone several 24 peer reviews other than the current NCEA effort, one has to wonder just how high the bar is set 25 for acceptance of biologically based assessments. Given the time and resources expended on the 26 CIIT assessment and the richness of the supporti ng data base, I find it difficult to imagine what 27 an acceptable biologically

- based assessment might look like if in the end the CIIT assessment is 28 deemed not acceptable by NCEA. If this is in fact the outcome it will have major implications 29
for the likeli hood that anyone will be willing to commit the significant resources needed to 30 develop of these kinds of risk assessment models. 31 The documents provided in advance of the October 2004 review meeting were 32 collectively a discussion of uncertainty about the CI IT work. With respect to the clonal growth 33 model, however, no new risk predictions were provided, so there was no way to judge how the 34 uncertainties that NCEA identified might impact predicted risk. Evaluation of the significance 35
of "uncertainties" when the impact of the uncertainties on the predicted risk is not known is itself 36 an uncertain process. 37 A related concern is that there did not seem to be any consideration of the historical 38 context of the CIIT assessment. EPA developed formaldehyde assessment s in 1987 and 1991. 39 The 1987 assessment used ppm as the input and the LMS model for the dose

- response 40 prediction. The 1991 assessment used DPX as a dosimeter and the LMS model. BMD 41
assessments have since become available from other sources such as Paul Schlosser's work. The 42 risk predictions of the BMD models are similar to the 1991 LMS assessment. Both the DPX

43 LMS and BMD assessments predicted somewhat less risk than the 1987 assessment, establishing 44 the trend of less risk with increased incorporation of relevant data. I have always argued 45This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 11 (probably initially at the 1998 Ottawa review) that the historical context is the appropriate 1 context for evaluating the CIIT clonal growth model. For a "level playing field" the 2
uncertainties of the 1987 and 1991 a ssessments, and of the more recent BMD models, should be 3 analyzed to the same degree as the clonal growth model. Does NCEA think that, because the 4 LMS and BMD approaches used structurally simpler dose

- response models and much more 5 limited data inputs, the y are less uncertain? The NCEA analysis seemed to be implying that use 6 of more data and of a biologically more realistic model structure actually makes the CIIT 7 approach more uncertain than the LMS and BMD approaches. I encourage NCEA to consider 8
how unc ertainties that can be evaluated explicitly in the structurally rich CIIT model compare to 9 hidden uncertainties in the simpler models, where the hidden uncertainties encompass, for 10 example: 11
12
1. Missing or incomplete descriptions of the regional dosimetry of f ormaldehyde. 13
2. Lack of simultaneous incorporation of the directly mutagenic and 14 cytolethal/regenerative proliferation modes of action. 15
3. Lack of explicit consideration of the multistage nature of cancer. 16
4. Lack of consideration of the growth kinetics of initiate d cell populations 17
5. Lack of evaluation of the measured J

- shaped dose response for regenerative cellular 18 proliferation. 19
20 A careful, balanced comparison of the CIIT assessment with the previous assessments along 21 these lines would be informative with respect t o the suitability of the CIIT assessment as the 22 basis for a new IRIS listing for formaldehyde. 23 A further concern involves the peer

- review of the CIIT formaldehyde assessment held in 24 Ottawa in 1998. This review was sponsored by the U.S. EPA and Health Cana da and involved 25 what was arguably a world

- class review panel. The CIIT assessment was not in its final form at 26 that time, though we did provide a detailed description of the overall approach and the specific 27 methods we were using to generate dose

- response predictions. The 1999 CIIT document and the 28 subsequent peer

- reviewed publications are responsive to the comments and suggestions raised by 29 the reviewers. My concern is that no information was provided on the role that Ottawa review 30 plays in the ongoing review of the CIIT formaldehyde assessment by NCEA. Should the 31 October 2004 review be viewed as standing on the shoulders of the 1998 review or as being in 32 parallel to it? It was not at all clear to me that the October 2004 review in any way utilized the 33 judgments of the 1998 review. It seems that the 2004 review was more of a parallel effort and 34 that the 1998 review was ignored and was effectively a waste of time and money. I would like to 35 have some clear understanding of how the 2004 review effort sho uld be viewed relative to that of 36
1998. 37 In closing, let me reiterate that while the detailed examination of the CIIT formaldehyde 38 assessment is laudable, this examination should be conduced with an eye to the historical context 39 of formaldehyde risk assessm ent on the one had and, on the other hand, to a concern for 40 encouraging, and not discouraging, development of biologically based risk assessment models. 41
42 Sincerely yours, 43
44 Rory B. Conolly, Sc.D., D.A.B.T. 45This document is a draft for review purposes only and does not constitute Agency policy. DRAFT

— DO NOT CITE OR QUOTE H

- 12 

# - End of Volume IV

-