IS
K
Harvey Clewell and Rusty Thomas
CIIT Centers for Health Research
1

Overview
• Background
• Alternative Cancer Risk Assessment Approaches
• Applications of Genomics in Risk Assessment
• Examples
• Arsenic
• Issue: location of key dose-dependent transition(s) below observed tumor range
• Formaldehyde
• Issue: relative contribution of genotoxicity vs. cytotoxicity/proliferation
• Genomic Dose-Response Methodology
2

• Linear (default)
– Assumes linear relationship of cancer risk to dose from ED10 (dose associated with 10% increase in tumor incidence) to zero
– Regulation typically based on 1/10,000 to
1/1,000,000 risk
– Most appropriate for directly mutagenic carcinogens with no effect on cell proliferation
– Can greatly overestimate risks for chemicals with a mode of action dominated by cytotoxicity and increased cell proliferation
3

• Biologically Based Dose-Response (BBDR) Model
– Preferred approach in EPA guidelines
– Supports risk estimates below range of observation of tumors
– Requires quantitative data on dose-response for key elements in the mode of action
– Complexity of detailed BBDR description (e.g. formaldehyde) leads to agency concerns about potential uncertainties
4

Biologically Based Cancer Dose Response Model

N

1

1

I


2
A Biologically Motivated Model for Cancer
Goal: capture dose-dependence of critical, rate-limiting processes -- despite a lack of information on the specific biological details
M
Key Capability:
Describes the
Interaction of
Mutation and
Cell Division
- requires data on D/R for
- cytotoxicity/apoptosis
- cell proliferation
- DNA damage
- supports investigation of interactions between key processes
- DNA damage/repair
- cell cycle control
- cytotoxicity/apoptosis

• Margin of Exposure (MoE)
– Point of departure can based on LED10 for tumors or obligatory precursor events
– MoE selected to address human inter-individual variability and uncertainties in the underlying data
– Does not provide quantitative risk estimate
– Requires evidence of nonlinear mode of action (the hard part)
• Issues:
– Alternative / Multiple modes of action
– “Lurking” Genotoxicity
6

• MoE Approach Using Genomic Dose-Response
– It may be a very long time before a fully-developed BBDR model gains acceptance
– Simplified dose-response descriptions that maintain a biological basis may be useful in the near term to inform both mode of action and dose-response
– Basis of simplified approach: nonlinear dose-response analysis of data on genomic alterations in key cell signal pathways
– Combined with quantitative modeling of cell signal pathways, may pave the way to a more detailed BBDR model
7

• The conduct of quantitative cancer risk assessment that has minimal reliance on default factors requires knowledge of the key events leading to tumor induction.
• The same sorts of information can lead to the development of informative bioindicators of tumor response.
• Whole-genome approaches appear to offer the best chance for success.
• Computational approaches have to be developed in parallel with the experimental methods.
• These key events can be used for the purpose of extrapolations thereby reducing much of the uncertainty currently handicapping the process.
8

Mode of Action from a Systems Biology Perspective:
Chemical Perturbation of Biological Processes
Exposure
Tissue Dose
Biological Interaction
Perturbation
Systems
Inputs
Biological
Function
Molecular Target(s)
(Chemical Mode of Action Link)
Impaired
Function
Adaptation Disease
Morbidity &
Mortality
9

Hazard Identification – Use of pattern recognition analysis to identify similarity of gene changes from uncharacterized compound with changes produced by compounds with known effects
- can provide insights into key elements in mode of action
- essentially qualitative
- typically, little consideration given to tissue dosimetry
10

Functional Genomics – Characterize interactions of compound with gene regulatory network using temporal analysis and iterative gene over-expression / inhibition
Input
Pulse
Increasing
Stimulus
Growth factor
MAPKKK
MAPKK
MAPK
(Conolly 2004)
can elucidate key elements of cellular dose-response
(e.g., switch-like behaviors)
- time-consuming, requires sophisticated analyses
- modeling of gene regulation is in its infancy
11

Dose-Response – Collection of data on genomic responses to a compound over a range of cell/tissue exposure concentrations to identify dose-response for key genomic bio-indicators of response
- provides support for mode of action hypothesis
- requires characterization of tissue dosimetry or phenotypic anchoring
400
(Snow et al. 2002)
300
APE/Ref-1 mRNA
200
100
0
0
Ligase I
5
Pol 
10 15
µM As III
Trx mRNA
20
12
25

Heirarchical Model for Cellular Responses to Stressors (A. Nel)
Stressors (heat, pH change, reactive compounds, etc.)
Normal
Epithelial
Cell
Adaptive
State
Stressed
State
Pathology
Necrosis
Atrophy
Biochemical effects
GSH/GSSG ratio
Interactions with MM
Genomic alterations
HSP proteins
Anti-Apoptotic
Inflammation
Toxicity
DNA-Repair
Proliferative
Apoptotic
Goal of Genomic Dose-Response Modeling:To identify key elements of each state and the points of transition
13

Example of Heirarchical Response:
Effects of Diesel Exhaust Particles on Cells:
(Gilmour et al., 2006, EHP)
14

PENTAVALENT
SPECIES:
Metabolism of Inorganic Arsenic
O O
H O
As
OH
OH H
3
C
As
OH
OH
O
H
3
C
As OH
CH
3
ARSENATE
METHYL
ARSONIC ACID
DIMETHYL
ARSINIC ACID
TRIVALENT
SPECIES:
OH
H O
As
OH
ARSENITE
MMA(III)
OH
As
H
3
C
OH
METHYL
ARSONOUS ACID
H
3
C
CH
3
As
OH
DIMETHYL
ARSINOUS ACID
15

• Epidemiology: cancer in multiple tissues
• Most common: bladder and lung
• Animal bioassays: equivocal
• Co-carcinogenic
• Mutagenicity:
• Arsenite: clastogenic, co-mutagenic
• MMA(III): genotoxic(?)
• Noncancer toxicity: dermal, vascular
• Proliferation
• Chemical activity: binding to vicinal dithiols
• arsenite, MMA(III)
16

• Tumors from inorganic arsenic observed in human populations at around 500 ppb, but animal bioassays at much higher concentrations have been negative
• Increased tumor risk from inorganic arsenic in drinking water correlates with MMA/DMA ratio
• Suggests role for MMA(III)
• Humans exposed to inorganic arsenic in drinking water have higher concentrations of MMA in urine than rodents
• Rodents: higher DMA
• No evidence of endocrine related tumors in chronically exposed human populations
17

Inorganic
Arsenic
Exposures
Target Tissue
Concentrations of Arsenite
(and Trivalent
MMA)
Biochemical
Targets of
Arsenic
Increased
Mutation
Frequency and Tumors
Dosimetry Modeling Tissue Response Modeling
Putative Mode of Action:
As III / MMA III interactions with key cellular proteins
18

Proposed Model for Bladder Cancer Progression p53 -
TCCs
Papillary Low-Grade
Non-Invasive
Normal Urothelium
9 -
Papillary High-Grade
Non-Invasive
Carcinoma in situ
9 -
CIS p53 -
Lamina Propria Invasive
Muscle Invasive
Metastases
19

Arsenite Effects and Biological Responses
(+)
(+)
Oxidative
Stress
(+)
Oxidative
Stress
Response
(-)
Proteotoxicity
Arsenite
(+/-)
(+)
DNA repair
Proliferative signaling
Co-exposure to Mutagens
(+/-)
Cell cycle control
Transition thru cycle
Cancer
20

• PUBMED literature search concentrating on genomic response in in vitro and in vivo studies
• Prioritization and review of over 300 articles identified
• Population and development of inorganic arsenic genomic database
21

• The database contains information from 161 unique studies evaluating 354 specific genes or proteins.
• 960 specific entries pertaining to specific genes or proteins
• 167 entries pertain to miscellaneous endpoints such as apoptosis, cytotoxicity, or changes in mitotic indexes.
• 1127 total database entries.
22

Summary of the Types of Data Describing Changes in
Gene/Protein Levels Following Arsenic Exposure
Type of Data
Information for specific genes and/or proteins
In vitro data entries

In vitro gene/protein specific information measured in immortalized/cancer cell lines

In vitro gene/protein specific information measured in normal cells
In vivo data entries

In vivo gene/protein specific information measured in immortalized/cancer cell lines

In vivo gene/protein specific information measured in normal cells
Total Number Percentage of Total
354 31%
700
230
470
427
44
383
62%
33%
67%
38%
10%
90%
23

Dose-Response for the In-Vitro Effects of Arsenic in Normal Cells
0.01 uM 0.1 uM 1.0 uM 10 uM 100 uM
Oxidative Stress
Inflammation
Proteotoxicity
Proliferation
DNA Repair
Cell Cycle Control
Apoptosis
Gene Expression:
Trx
Trx Reductase
SOD1
COX-2
HSP-32
AP-1
FGFR4
DDB2 p53
EGR-1 p105 p65
Increase
Fos
Jun
Pol beta
Ligase I
P53
NF-kB p53
HSP-70
VEGF
Myc p70
Erk
PARP-1 Ligase I
CDC25A p21
CDC25B
CDC25C
Casp3
Casp8
Casp9
HSP-60
HSP-27
ERK-1
ERK-2
EGFR
GADD153
SRC
JNK
JNK3
Decrease
HO-1
GSR
TPX-11
IL-8
MT-1
MT-2
NRF-2
Acute increase, chronic decrease
24

High-Concentration (1-100 uM) Arsenic Effects on Cells:
“Apoptosis” (Anti-Neoplastic Agent)
Arsenite
Oxidative Stress
Response
Non-specific Binding to Thiols
Depletion of NPSH
Specific Binding to Vicinal Di-thiols
Ubiquitization
Of key proteins
Inhibition of
DNA Repair Enzymes
(Ligase I)
Proteotoxicity
Response
Inflammatory Response
Proliferative Signaling
Cell Cycle Stasis
Induction of Apoptosis 25

Mid-Concentration (0.1-10 uM) Arsenic Effects on Cells:
“Toxicity” (Cancer, Blackfoot Disease)
Arsenite
Oxidative Stress
Response
Non-specific Binding to Thiols
Depletion of NPSH
Specific Binding to Vicinal Di-thiols
Ubiquitization
Of key proteins
Inhibition of
DNA Repair Enzymes
(Ligase I)
Proteotoxicity
Response
Inflammatory Response
Proliferative Signaling
Cell Cycle Delay
Induction of Apoptosis 26

Low Concentration (0.01-1 uM) Arsenic Effects on Cells:
“Adaptive Response”
Arsenite
Oxidative Stress
Response
Non-specific Binding to Thiols
Depletion of NPSH
Specific Binding to Vicinal Di-thiols
Ubiquitization
Of key proteins
Inhibition of
DNA Repair Enzymes
(PARP-1)
Proteotoxicity
Response
Delay of Apoptosis
Pre-Inflammatory Response
Growth Factor Elaboration
27

• In vivo: Drinking water exposures
– Female C57Bl/J mouse (bioassay strain)
– 4 concentrations arsenate plus controls (0.05-50 ppm As)
– Genomic analysis of bladder tissues at 1 and 12 weeks
– Concentrations of all relevant arsenic species
• In vitro: Bladder epithelial cell incubations
– Primary bladder epithelial cells
– Multiple concentrations, time-points
– Concentrations of all relevant arsenic species
– Compare mouse and human cells
28

29

Biological Process Categories
Up-regulated by 50 ppm Arsenite
Function Name Unique Gene Total positive regulation of apoptosis anterior/posterior pattern formation regulation of transcription, DNA-dependent
6
5
47 nuclear mRNA splicing, via spliceosome cell cycle intracellular protein transport protein folding mRNA processing ubiquitin cycle transcription mitosis carbohydrate metabolism protein modification cytokinesis response to DNA damage stimulus
10
14
13
34
13
14
11
7
7
6
6
7
30

Biological Process Categories
Down-Regulated by 50 ppm
Arsenite
Unique Input
Total Function Name cell-substrate junction assembly collagen catabolism proteolysis and peptidolysis cell-matrix adhesion cell adhesion regulation of cell growth integrin-mediated signaling pathway signal transduction cell differentiation
G-protein coupled receptor protein signaling pathway development transcription regulation of transcription, DNA-dependent
13
5
11
5
5
13
6
5
5
11
13
7
7
31

• Develop and test dose-response approach with animal data
– PK: predict or measure tissue concentrations of active moieties in both short-term exposures and bioassays
– PD: link tissue concentration to cellular responses using in
vitro and iv vivo genomic data
• Apply dose-response approach in human
– PK: predict or measure tissue concentrations of active moieties in exposed populations
– PD: link tissue concentrations to signal pathway alterations using in vitro genomic data from human cells
32

Human in vivo
Mouse in vivo
Predict
Urothelial cells
Extend dose-response
Human in vitro
Bladder
Validate ability to predict in vivo
Mouse in vitro
Bladder cells Compare to understand difference in response
Bladder cells
33

Anchor in vitro dose-response to in vivo tumor incidence:
• Validate in vitro genomic assays by comparison with data from exposed population in Mongolia (study being conducted by Judy Mumford, EPA)
• Apply human genomic dose response to extend tumor doseresponse below the region of observation
• Proposed approach:
– Point of departure based on lowest LED10 for genomic response associated with non-adaptive response
– MoE selected to consider uncertainty in genomic data and human interindividual variability
34

Hypothetical Impact of Population Variability on Cancer
Dose-Response for Arsenic in Drinking Water
0.01
Average Individual Dose-Response
Sensitive / Resistant Individual Dose-Response
Population Dose-Response
0.001
Linear
0.0001
Extrapolation
0.00001
0.001
Susceptibility
Factors:
- Dietary intake
- Nutritional status
- Other exposures
- selenium
- mutagens
- Genetic factors
- metabolism (GST)
- cell control (P53)
0.01
0.1
Concentration in Drinking Water (mg/L) 35
1.0

Formaldehyde bioassay results: rat nasal tumors
Kerns et al., 1983
Monticello et al., 1990
0 0.7
2 6 10
Exposure Concentration (ppm)
15
30
20
10
0
60
50
40
36

Increased cell proliferation
Secondary to Cytotoxicity
Tumor
Dosimetry
DNA interactions
DNA-protein cross-links
Adduct formation
Modes of action Effects
37

DPX dose-response for Rhesus monkey
10
-1
10
-2
10
-3
10
-4
1 2 3
Vmax: 91.02. pmol/mm
3
/min
Km: 6.69 pmol/mm
3 kf: 1.0878 1/min
Tissue thickness
ALWS: 0.5401 mm
MT: 0.3120 mm
NP: 0.2719 mm
5 6
PPM
4 7
95% UCL on KMU
7.00E-04
6.00E-04
5.00E-04
4.00E-04
3.00E-04
2.00E-04
0 1 2 3 4 5 6 7
38

DPX dose-response for Rhesus monkey
10
-1
10
-2
10
-3
10
-4
1 2 3
Vmax: 91.02. pmol/mm
3
/min
Km: 6.69 pmol/mm
3 kf: 1.0878 1/min
Tissue thickness
ALWS: 0.5401 mm
MT: 0.3120 mm
NP: 0.2719 mm
PPM
4 5 6 7
95% UCL on KMU
5.5000E-04
5.0000E-04
4.5000E-04
4.0000E-04
3.5000E-04
3.0000E-04
2.5000E-04
2.0000E-04
0 1 2 3 4 5 6 7
39

Mechanistic Dose Response Model with Genomic Data
(1)
Dosimetry
Inhaled Formaldehyde
(2) (3)
Tissue Phase Reactions
Cl
2
HOCl + HCl
Normal
Epithelial
Cell
Adaptive
State
Stressed
State
Pathology
Necrosis
Atrophy
Use specific in vivo studies to develop a dose response model for activation of proteotoxic response pathways following formaldehyde exposure and differentiate dose regions that activate cell homeostasis pathways vs. DNA-repair delays and proliferative pressure
40

Formaldehyde Genomics Study Design
•
Expose F344 rats to 0, 0.7, 2.0, and 6.00 ppm formaldehyde for 3 weeks
•
Assess dose- and time-dependent genomic changes using rat gene chips from Affymetrix
•
Evaluate gene family changes for heat shock response
(proteotoxic), oxidative stress, DNA-repair, cell cycling, apoptosis, etc.
•
Develop qualitative and quantitative models to link genomic changes with cell behaviors
•
• account for J-shaped cell-proliferation response incorporate dose response of DNA-damage sensors
41

Hybridized to a Affymetrix Rat Genome 230 2.0 array with over 30,000 probe sets
42

Time
Point
6 hours
Results of Formaldehyde Genomics Study blue: pathology , red: cell proliferation , green: genomics
Controls 0.7 ppm 2 ppm 6 ppm
1 day
5 days no pathology no gene changes at levels 2 to 3
Inflammation
(minimal) at level 1 no gene changes at levels 2 to 3 no pathology no gene changes at levels 2 to 3
Inflammation
(minimal) at level 1 no gene changes at levels 2 to 3 no pathology no increase in
ULLI for any sites at levels 2 or 3 no gene changes at levels 2 to 3 no pathology no increase in ULLI for any sites at levels 2 or 3 no gene changes at levels 2 to 3
Inflammation (minimal) at level 1 some gene changes at levels 2 to 3
Epithelial hyperplasia at level 1 no gene changes at levels 2 to 3
Inflammation and epithelial hyperplasia at level 1
Increased ULLI on lateral wall at level 3 many gene changes at levels 2 to 3
Inflammation (mild) at level 1
Some epithelial hyperplasia of lateral wall at level 2 many gene changes at levels 2 to 3
Inflammation at level 1
Widespread epithelial hyperplasia of lateral wall at level 2 no gene changes at levels 2 to 3
Inflammation, hyperplasia, and squamous metaplasia at level 1
Widespread epithelial hyperplasia of lateral wall at level 2
Increased ULLI for all sites at levels 2 and 3 some gene changes at levels 2 to 3
43

Results of Formaldehyde Genomics Study blue: pathology , red: cell proliferation , green: genomics
Time
Point
Controls 0.7 ppm 2 ppm 6 ppm
8 days Inflammation
(minimal) at level 1
Inflammation
(minimal) at level 1
Inflammation (minimal) at level 1
Inflammation at level 1
Widespread epithelial hyperplasia of lateral wall at level 2
9 days Inflammation
(minimal) at level 1 no pathology Some inflammation and epithelial hyperplasia of lateral wall at level 2
Inflammation at level 1
Widespread epithelial hyperplasia of lateral wall at level 2
19 days Inflammation
(minimal) at level 1 no increase in
ULLI for any sites at levels 2 or 3 no gene changes at levels 2 to 3 no pathology no increase in ULLI for any sites at levels
2 or 3 no gene changes at levels 2 to 3
Some epithelial hyperplasia of lateral wall at level 2 no increase in ULLI for any sites at levels 2 or 3 no gene changes at levels 2 to 3
Inflammation at level 1
Widespread epithelial hyperplasia of lateral wall at level 2 no increase in ULLI for any sites at levels 2 or 3 many gene changes at levels 2 to 3
44

Principal Components Analysis: 0, 0.7, 2 and 6 ppm

•
No genes were significantly altered at 0.7 ppm in any of the exposures, nor were there any differences in pathology in the noses of the 0.7 ppm exposed rats.
•
Transient gene changes at 2 ppm (at 5 days of exposure only)
Most not altered at the higher concentrations – including circadian rhythm related genes;
•
A consistent pattern of genes changed at 6 ppm over time
Most of these genes were part of the group of genes altered immediately after the first 15 ppm, 6 hour exposure.
•
Although only a small number genes were affected by the 6 ppm exposure, the GO categories for the longer exposure 6 ppm include gene families related to apoptosis.
46

•
Exposures: 6 hours/day, 5 days/week, 13 weeks
•
Concentrations: 0, 0.7, 2, 6, 10, and 15 ppm
(Same as in cancer bioassay)
•
Endpoints for which dose-responses will be
•
•
•
•
Genomics
Pathology
Cell proliferation rates
P53 mutation frequency (NCTR)
•
Goal: determination of dominant factors in mode of action for carcinogenicity
•
•
•
• genomic alterations cytotoxicity proliferative pressure mutagenicity 47

Applications of Results
•
Differentiate dose regions for adaptive (survival) responses and overt DNA damage responses
•
Evaluate hypothesis of enhanced mutagenic potency at toxic concentrations as compared to lower concentrations
•
Provide mechanistic basis for U-shaped proliferation doseresponse noted in bioassay studies
•
Consider possible implications for low concentration
‘hormesis’ with formaldehyde and other irritants
48

Experimental Methods: Integrating Genomic
Data with Dose-Response Analysis
Gene Expression Dose Response
Data
One-Way Analysis of Variance to
Identify Genes Changing with Dose
Power
Model
Linear
Model
Polynomial
Model (2 ° )
Nested test to Select Best
Polynomial Model
Select Best Model
Remove Genes with BMD > Highest Dose
Group Genes by Gene Ontology
Category
Estimate BMD and BMDL for each
Gene Ontology Category
Polynomial
Model (3 ° )
CIIT
Centers For Health Research

80
60
40
20
Experimental Results: Benchmark Models
Goodness-of-Fit to Transcriptomic Data
120 120.00%
Frequency
Cumulative %
100 100.00%
80.00%
60.00% p > 0.05 for 85% of genes
40.00%
20.00%
0
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
1
Probability Value of Model Fit
0.00%
50

ID: 1383471_at
Model: Linear
Fit p-value: 0.0102
ID: 1368215_at
Model: Polynomial 3°
Fit p-value: 0.0502
ID: 1370317_at
Model: Polynomial 2°
Fit p-value: 0.1002
ID: 1371736_at
Model: Power
Fit p-value: 0.4804
51

Experimental Methods: Integrating Genomic
Data with Dose-Response Analysis
Gene Expression Dose Response
Data
One-Way Analysis of Variance to
Identify Genes Changing with Dose
Power
Model
Linear
Model
Polynomial
Model (2 ° )
Nested test to Select Best
Polynomial Model
Select Best Model
Remove Genes with BMD > Highest Dose
Group Genes by Gene Ontology
Category
Estimate BMD and BMDL for each
Gene Ontology Category
Polynomial
Model (3 ° )
52

1.349*
σ
μ
0.5%
0.5%
11%
μ
0 BMD
Dose (ppm)
53
> BMD

Experimental Results: Benchmark Doses by Gene Ontology Category
Biological process GO Categories with the lowest mean BMD and other selected categories
Biological Process GO Category
Regulation of cell size
Cell growth
Cell division
Taxis
Chemotaxis
Sensory perception
Locomotory behavior
Pattern specification
Wound healing
Chromatin modification
M phase
Monovalent inorganic cation transport
Protein import
Neurophysiological process
Cellular morphogenesis
Negative regulation of transcription,
DNA-dependent
Cell migration
Cellular macromolecule catabolism
Establishment and/or maintenance of chromatin architecture
DNA packaging
Gene
Count
15
14
11
10
10
11
11
10
12
12
12
15
12
28
35
19
34
22
13
13
Mean BMD
4.12
4.38
4.46
4.54
4.54
4.71
4.78
4.89
5.05
5.07
5.08
5.15
5.22
5.27
5.29
5.33
5.33
5.38
5.43
5.43
Standard
Deviation BMD Mean BMDL
2.64
2.52
4.51
1.84
1.84
3.53
3.20
4.26
4.84
2.90
5.53
3.41
2.89
3.15
3.28
4.59
3.45
3.54
3.53
3.53
2.78
2.95
3.02
3.23
3.23
3.36
3.26
3.46
3.50
3.39
3.68
3.43
3.72
3.67
3.72
4.02
3.66
3.60
3.78
3.78
Other Selected GO Categories
DNA repair
Response to DNA damage stimulus
Cell proliferation
Apoptosis
Inflammatory response
Response to unfolded protein
12
24
73
71
16
10
6.81
5.99
7.12
7.21
7.47
7.67
4.21
4.17
4.39
4.29
3.75
3.56
5.22
4.54
4.96
5.12
4.97
5.75
BMD for cell labeling index: 4.9 ppm (Schlosser, Risk Anal., 2003)
BMD for tumors: 6.4 ppm (Schlosser, Risk Anal., 2003)
54

• Merging genomic tools with BMD analysis allows BMDs to be estimated for individual functional categories.
• Preliminary analysis suggests that the BMD estimates for the genomic effects are similar to those observed for cell labeling and tumor incidence.
• The use of genomic data together with BMD analysis may reduce the need for expensive animal bioassays.
• Challenges will be determining which functional categories represent adverse versus adaptive effects.
• Future and ongoing analyses are being performed on arsenic, chloroform, and a receptor-mediated toxicant.
55

CIIT
Mel Andersen
Andy Nong
Cecilia Tan
Ed Bermudez
Linda Pluta
Dana Stanley
Chris Learn
Frank Boellmann
Longlong Yang
Todd Page
Tom Halsey
R
IS
K
56
ENVIRON
Robinan Gentry
Bruce Allen
Annette Shipp
EPA
Elaina Kenyon
Mike Hughes
Rory Conolly
Mike Devito
Jeff Gift
Other Collaborators
Jan Yager (EPRI)
Tom O’Connell (UNC)
Russ Wolfinger (SAS)
Funding
EPRI
Formaldehyde Council, Inc.
American Chemistry Council