United States
Environmental Protection
M lk Agency
September 2012
EPA/600/R-12/044
Advances in Inhalation Gas Dosimetry
for Derivation of a Reference Concentration
(RfC) and Use in Risk Assessment
September 2012
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This report has been reviewed in accordance with U.S. Environmental Protection Agency Policy and approved for
publication. Mention of trade names or commercial products does not constitute endorsement or recommendation
for use.
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TABLE OF CONTENTS
AUTHORS, CONTRIBUTORS, AND REVIEWERS VI
GLOSSARY IX
ABBREVIATIONS AND ACRONYMS XII
EXECUTIVE SUMMARY XIV
1 INTRODUCTION AND PURPOSE 1-1
2 REVIEW OF THE 1994 RFC METHODS FOR GAS DOSIMETRY 2-1
2.1 Gas Categorizations - General 2-1
2.1.1 The RfC Methods Gas Categorization Scheme 2-1
Figure 2-1 Gas categorization scheme based on water solubility and reactivity as
major determinants of gas uptake. 2-2
Table 2-1 Gas categorization characteristics and examples according to RfC Methods
classification scheme 2-3
2.2 Conceptual and Historical Basis for Comparative Dosimetry of Inspired Gases in RfC Methods -
Minute Ventilation/Surface Area of the Respiratory Tract (Ve/SArt) 2-3
2.2.1 Factors Controlling Comparative Inhaled Dose 2-3
2.2.1.1 Comparative Respiratory Anatomy and Physiology 2-4
2.2.1.2 Regions of the Respiratory Tract Common among Species 2-4
Table 2-2 Respiratory tract regions 2-5
Figure 2-2 Diagrammatic representation of the three respiratory tract regions
designated in humans. 2-6
Table 2-3 Default surface areas for the extrathoracic (ET),tracheobronchial (TB), and
pulmonary (PU) regions of the respiratory tract in various species 2-7
Table 2-4 Intercept (bo) and coefficient (b-i) values used to calculate default ventilation
rates based on body weight3 2-8
Table 2-5 Default ventilation rate and body weights for multiple species 2-9
2.3 Normalization of Inhaled Concentration to Surface Area of Respiratory Tract Regions 2-9
2.3.1 Dose-Response in Respiratory Tract Tissues is Based on External Exposure
Concentration 2-9
2.3.2 Normalization of External Exposure Concentration to Surface Areas 2-10
2.4 Interspecies Gas Dosimetry in the RfC - Application of Ve/SA in Calculation of the Human
Equivalent Concentration, HEC: The Default Approach for Inspired Gases 2-10
Table 2-6 Hierarchy of model structures for dosimetry and interspecies extrapolation 2-11
2.4.1 The Dosimetric Adjustment Factor (DAF) 2-11
2.4.2 The DAF for POE Effects; the Regional Gas Dose Ratio, RGDRr 2-12
2.4.3 Assumptions in the Application of Ve /SA 2-13
2.5 Current Applications Using the Default DAFs - RGDRet, RGDRjb, RGDRpu, and Hb/g 2-14
2.5.1 The RGDR for the Extrathoracic Region - RGDRet 2-14
2.5.2 The RGDR forthe Tracheobronchial (TB) Region - RGDRjb 2-15
2.5.3 The RGDR forthe Pulmonary (PU) Region - RGDRpu 2-16
2.5.4 Limitations in the Assumptions and Application of Ve /SA 2-17
Figure 2-3 Representation of the assumptions of uniformity following from Ve /SA as
applied to comparative gas dosimetry. 2-18
2.6 The DAF for Systemic (SYS) Sites - Hb/g 2-19
Table 2-7 Some example blood:air partition coefficients (Hb/g) in humans and rats
expressed as a ratio, A/H 2-21
2.7 Children's Dosimetry 2-21
Table 2-8 Human lifestages and corresponding age ranges through adolescence 2-22
3 ADVANCES 3-1
3.1 A Modified Gas Scheme: Descriptors versus Categories 3-1
Figure 3-1 A schematic representation of the physicochemical properties of reactivity
and water solubility overlaid with descriptors of their practical limits. 3-2
3.2 Major Scientific Advances Related to Inhalation Gas Dosimetry in the ET Region 3-3
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3.2.1 Tracer Dye-Flow in Cast Models 3-3
3.2.2 Computational Fluid Dynamic Modeling 3-4
3.2.2.1 CFD Air Flow Models of the Rat ET Region 3-4
3.2.2.2 CFD Air Flow Models of the Human ET Region 3-4
Figure 3-2 The coronal sections are divided into sub-sections which are indicated by
the letters. 3-5
Table 3-1 Summary of CFD simulated flow apportionment (as a % of total at 15
L/min) on the coronal cross-sectional area in the middle turbinate (as mm2)
of the ET region in selected human models as analyzed by Wen et al.
(2008). 3-6
3.2.2.3 CFD Air Flow Models - Predictions of Reactive Gas Distribution in the ET Region 3-6
3.2.2.4 Interspecies CFD Air Flow Models Predictions of Gas Distribution in the ET
Region 3-7
Figure 3-3 Nasal wall flux spectra of inhaled formaldehyde simulated in rats, monkey
and humans at normal inspiratory flow rates. 3-8
Table 3-2 Estimates of formaldehyde flux to ET surface walls for various species 3-8
3.2.3 Range and Distribution of Flux in ET Regions for Various Species 3-9
3.2.4 Correlation of High Flux with Lesions in the ET Region 3-9
Figure 3-4 Graphs showing (A) the incidence of formaldehyde-induced squamous
metaplasias and (B) modeled formaldehyde flux values along regions
assigned to the perimeter of a transected nasal airway of rats. 3-11
Figure 3-5 Schematic diagram of the transverse nasal section through the ethmoid
turbinates (top left, Section 2 of the nasal cavity) with plot of lesion
incidence at 30 and 80 ppm (top right). 3-12
Figure 3-6 Representation of application of the state of the science to the assumptions
and outcome of the RfC Methods basic default procedures for comparative
gas dosimetry in the ET region. 3-14
3.2.5 Evaluation and Use of Models in Interspecies Inhalation Dosimetry - ET Region 3-14
3.2.5.1 Overview of CFD-PBPK Hybrid Modeling - Combination of Gas Transport in the
Air Phase into the Liquid/Tissue Phase 3-15
3.2.5.2 CFD-PBPK Hybrid Modeling and the Overall Mass Transport Coefficient - Kg 3-16
3.2.5.3 Results and Analysis of Interspecies Inhalation Dosimetry Modeling - ET Region 3-18
Table 3-3 Primary toxicological endpoint(s), uptake, properties, and physicochemical
descriptor for representative gases—ranked by percentage of uptake in rats_ 3-19
Table 3-4 Comparison of approaches for calculating the DAF for representative gases
in determining the HEC - portal of entry ET or nasal effects 3-22
3.3 Major Scientific Advances Related to Inhalation Gas Dosimetry in the TB and PU Regions 3-24
3.3.1 Air Flow and Deposition Modeling in the TB Region 3-25
Figure 3-7 Distributions of deposition enhancement factor (DEF) for MTBE vapor with
Qin = 30 L/min in the bifurcation airway models. 3-28
Figure 3-8 Distributions of deposition enhancement factor (DEF) for ethanol vapor with
Qin = 30 L/min in the bifurcation airway models. 3-28
Figure 3-9. The simulated local deposition patterns of napthalene vapor for concurrent
nasal and oral breathing for (A) K=7.3 cm"1 and (B) perfect wall absorption.
This figure shows nonuniform deposition patterns and deposition in the
upper airways is more uniformly distributed with lower wall absorption. The
locations of enhanced deposition may not change; however, the maximum
DEF value increases with increasing absorption. 3-30
Figure 3-10. Total deposition fraction is independent of breathing mode at the larynx
and beyond. 3-31
3.3.2 Advances in TB Inhalation Dosimetry Modeling 3-32
Table 3-5 Modeled predictions of amount of O3 and SO2 absorbed at various sites in
the airways of three species 3-33
3.3.3 Air Flow and Deposition Modeling in the PU Region 3-35
Figure 3-11 Dynamic ventilation He MRI after inhalation of hyperpolarized JHe gas. 3-36
Figure 3-12 Simulated flow velocities from CFD solutions in an alveolar sac model. 3-37
3.3.4 Advances in PU Inhalation Dosimetry Modeling 3-38
3.4 Advances in the Measurement of Ve and Airway Geometry 3-39
3.4.1 Lung Geometry and Surface Area 3-39
Table 3-6 Estimates of right, left, and total lung volumes in male wistar rats 3-40
Table 3-7 Summary data on human lung alveolar number and volume 3-41
Table 3-8 Summary table of measures from right lungs of human cadavers 3-42
Table 3-9 Functional and morphological features of the developing male rat lung 3-43
3.5 Major Scientific Advances in Inhalation Gas Dosimetry Related to Systemic (SYS) Sites 3-43
3.5.1 Methods and Advances for Estimating Blood:Gas (Air) Partition Coefficients 3-43
3.5.2 Quantitation using Inhalation PBPK Models for Systemic Sites 3-44
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Table 3-10 Compilation of blood:gas (air) partition coefficients used in Inhalation PBPK
models for animal to human interspecies extrapolation 3-45
3.5.3 Results and Analysis of Systemic Interspecies Inhalation Dosimetry Modeling 3-46
Table 3-11 Estimations from inhalation PBPK models of human equivalent
concentrations (HECs) from effect levels and internal dose measures in
laboratory animals 3-47
Table 3-12 Comparison of approaches for calculating human equivalent concentrations
(HECs) for several gases with systemic (SYS) effects 3-49
3.6 Current Science Related to Children's Inhalation Dosimetry 3-50
3.6.1 Introduction and Focus 3-50
3.6.2 Results and Analysis of Inhalation Dosimetry Modeling Considering Children 3-52
Table 3-13 Human kinetic adjustment factors (UFh-tk) obtained for inhalation exposure
in each population group using a dose surrogate of 24 hour AUCpc 3-54
Table 3-14 Air concentration of chloroform at various ages and genders corresponding
to threshold of damage in human liver and kidney 3-56
Table 3-15 Age-dependent and gender-specific dose metric comparison of inhaled
isopropanol 3-57
Table 3-16 Tissue concentrations in various compartments expressed as adult/child (1
to 2 years old) ratios for 8 different gases 3-59
3.6.3 Respiratory Tract Air Flow Models Considering Children 3-61
Table 3-17 Summary listing of findings on morphometry and gas flow/uptake
simulations for human nasal cavities 3-62
Table 3-18 Selected morphologic and simulated modeling results of hydrogen sulfide
dosimetry in casts of human nasal cavities 3-63
3.6.4 Respiratory Tract Growth 3-63
Figure 3-13 Alveoli count per lung as a function of age 3-68
Table 3-19 Lung weights (right and left) of males and females from birth to adulthood 3-70
4 FINDINGS AND CONCLUSIONS 4-1
Figure 4-1 A revised schematic representation of the outcomes for interspecies
inhalation dosimetry of gases for the ET region following from the advances
presented. 4-3
Table 4-1 Overview of major findings related to the state of the science for inhalation
dosimetry of gases 4-6
Table 4-2 Summary of major finding related to state of the science of children's
inhalation dosimetry 4-7
5 REFERENCES 5-1
APPENDIX A. SUMMARY AND DISPOSITION OF INDEPENDENT EXTERNAL PEER
REVIEW COMMENTS A-1
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
AUTHORS
John J. Stanek
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
Eva D. McLanahan
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
REVIEWERS
This document has been reviewed by EPA scientists and has undergone a peer review
performed by independent scientists external to EPA. A summary of significant
comments made by the external peer reviewers, and EPA responses, is included in
Appendix A.
INTERNAL EPA SCIENTIFIC CONTRIBUTORS AND REVIEWERS
Robert Benson
Region 8
U.S. Environmental Protection Agency
Denver, CO
Lyle Burgoon
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
Jeff Dawson
Office of Pesticide Programs
U.S. Environmental Protection Agency
Washington, DC
Rebecca Dzubow
Office of Children's Health Protection
U.S. Environmental Protection Agency
Washington, DC
Lynn Flowers
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
Brenda Foos
Office of Children's Health Protection
U.S. Environmental Protection Agency
Washington, DC
Suril Mehta
Office of Children's Health Protection
U.S. Environmental Protection Agency
Washington, DC
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Elizabeth Mendez
Office of Pesticide Programs
U.S. Environmental Protection Agency
Washington, DC
Deirdre Murphy
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Paul Schlosser
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
Ravi Subramanian
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
John Vandenberg
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
John Whalan
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
INTERNAL EPA TECHNICAL REVIEW AND SUPPORT
Ellen Lorang
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
J. Sawyer Lucy
Student Services Authority
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
Connie Meacham
National Center for Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC
EXTERNAL REVIEWERS
Bahman Asgharian, Ph.D.
Principal Scientist
Applied Research Associates
Raleigh, NC
Donald E. Gardner, Ph.D.
President
Inhalation Toxicology Associates, Inc.
Savannah, GA
Panos G. Georgopoulos, Ph.D.
Professor, Department of Environmental and Occupational Medicine
University of Medicine and Dentistry of New Jersey
Robert Wood Johnson Medical School
Piscataway, NJ
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Rogene F. Henderson, Ph.D., DABT
Senior Scientist (Retired)
Lovelace Respiratory Research Institute
Albuquerque, NM 87108
Robert F. Phalen, Ph.D.
Professor, Department of Medicine
University of California-Irvine
Irvine, CA
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GLOSSARY
Aerosol - A suspension of liquid or solid particles in air.
Chronic Exposure - Multiple exposures occurring over an extended period of time, or a significant fraction of the
animal's or the individual's lifetime.
Computational fluid dynamics (CFD) - (Three-dimensional) - A branch of fluid mechanics that uses numerical
methods and algorithms to solve and analyze problems of fluid flows. Flows may apply to liquid and gases,
including inspired and expired air, and are thus applicable to solving flows within the respiratory tract. The
fundamental bases of any CFD problem are the Navier-Stokes equations, which define any single-phase fluid flow.
These equations can be simplified by removing terms describing viscosity to yield the Euler equations.
Critical Effect - The first adverse effect, or its known precursor, that occurs as the dose rate increases. Designation
is based on evaluation of overall data base.
Diffusion (gas) or Diffusivity (gas) - The transport of matter from one point to another by random molecular
motions to become equalized with respect to concentration. For gases, rates of diffusion increase with the
temperature and are inversely proportional to the pressure. The interdiffusion coefficients of gas mixtures are almost
independent of the composition. Kinetic theory shows that diffusion of a pure gas is inversely proportional to both
the square root of the molecular weight and the square of the molecular diameter.
Dorsal - On or near the upper surface (of the nasal tract).
Dosimetric Adjustment Factor (DAF) - A multiplicative factor used to adjust observed experimental or
epidemiological data to human equivalent concentration (HEC) for assumed ambient scenario. See also regional gas
dose ratio (RGDR).
Extrarespiratory (ER) - see Systemic.
Extrathoracic or Upper Respiratory Tract (ET/URT) - The region of the respiratory tract that extends from just
posterior to the external nares to just anterior to the trachea.
Flux - The rate of flow of energy, gas or particles across a given surface.
Gas - Term referring to a compressible fluid phase of a substance. Fixed gases are gases for which no liquid or solid
can form at the temperature of the gas, such as air at ambient temperatures.
Identical Path Model - (One- or two-dimensional) - An anatomical mathematical model where all paths from the
nose or mouth entrance to the alveolar sacs are treated as being identical.
Inhalation Reference Concentration (RfC) - An estimate (with uncertainty spanning perhaps an order of
magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is
likely to be without an appreciable risk of deleterious noncancer health effects during a lifetime. The inhalation
reference concentration is for continuous inhalation exposures and is appropriately expressed in units of mg/m3.
Henry's Law Constant - The law can be expressed in several equivalent forms, a convenient form being: Cg =
HxCi where Cg and Ci are the gas-(g) and liquid-(l) phase concentrations. The constant (H) is the ratio at
equilibrium of the gas phase concentration to the liquid-phase concentration of the gas (i.e., moles per liter in
air/moles per liter in solution).
Kg - The overall mass transfer coefficient describing movement of gas from the air phase into the liquid phase of the
respiratory tract (see also MTC).
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kg - The gas phase mass transfer coefficient describing movement of gas from the gas phase to liquid/tissue
boundary (see also MTC).
Lowest-Observed-Adverse-Effect Level (LOAEL) - The lowest exposure level at which there are statistically
and/or biologically significant increases in frequency or severity of adverse effects between the exposed population
and its appropriate control group.
Mass Transfer Coefficient (MTC) - A diffusion rate constant that relates the mass transfer rate, mass transfer area,
and concentration gradient as driving force between and through phases. These coefficients may also be viewed in
terms of resistance to flow and movement. For purposes of this report (with phases of gas and solid) MTC requires
units of mass, time, distance, and concentration: mol/(s m2), mol/m3, or m/s. Examples of MTCs used in this report
relate to movement of gases in the respiratory tract. They include the MTC designated for the gas phase only, kg, and
an overall MTC inclusive of both the gas and liquid phases, Kg.
No-Observed-Adverse-Effect Level (NOAEL) - An exposure level at which there are no statistically and/or
biologically significant increases in the frequency or severity of adverse effects between the exposed population and
its appropriate control. Some effects may be produced at this level, but they are neither considered adverse nor
immediate precursors to specific adverse effects. In an experiment with several NOAELs, the assessment focus is
primarily on the highest one for a given critical effect, leading to the common usage of the term NOAEL as the
highest exposure without adverse effect.
Physiologically-Based Pharmacokinetic (PBPK) Modeling - (Zero-dimensional) - A mathematical modeling
technique for predicting the absorption, distribution, metabolism and excretion of a compound in humans and other
animal species. PBPK models strive to be mechanistic by mathematically transcribing anatomical, physiological,
physical, and chemical descriptions of the phenomena involved in complex pharmacokinetic processes. These
models have an extended domain of applicability compared to that of classical, empirical function based,
compartmental pharmacokinetic models.
Portal-of-Entry (POE) Effect - A local effect produced at the tissue or organ of first contact between the biological
system and the toxicant.
Pulmonary (PU) - The region of the respiratory tract which includes the terminal bronchioles and alveolar sacs.
Regional Gas Dose (RGDr) - The gas dose per respiratory tract surface area per minute (mg/cm2-min) calculated
for the respiratory tract region of interest (r) as related to the observed toxicity (e.g., calculated for the
tracheobronchial region for an adverse effect in the conducting airways). Regions of interest may be the
extrathoracic (ET), tracheobronchial (TB), or pulmonary (PU).
Regional Gas Dose Ratio (RGDRr) - The ratio of the deposited gas dose in a respiratory tract region (r) for the
laboratory animal species of interest to that of humans. This ratio is used to adjust the observed gas exposure level
for interspecies dosimetric differences.
Sherwood Number (Sh) - A dimensionless term for the ratio of convective to diffusive forces. The air-phase mass
transfer coefficient can be defined in terms of the Sherwood number.
Systemic (SYS) - Regions and organs of the body remote to the respiratory tract. Also Extrarespiratory (ER).
Tracheobronchial (TB) - The region of the respiratory tract defined as the trachea to the terminal bronchioles
where proximal mucociliary transport begins.
Uncertainty Factors (UF) - Generally 3- or 10-fold factors, used in deriving the inhalation reference concentration
(RfC) from experimental data. UFs are intended to account for (1) the variation in sensitivity among the members of
the human population, (2) the uncertainty in extrapolating laboratory animal data to humans, (3) the uncertainty in
extrapolating from data obtained in a study that is of less-than-lifetime exposure, (4) the uncertainty in using
LOAEL data rather than NOAEL data, and (5) an incomplete characterization of the chemical's toxicity that could
result in a lower reference concentration if additional data were available.
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Vapor - A term referring to a gas phase at a temperature below the critical temperature of the substance where the
same substance can also exist in the liquid or solid state. If the gas is in contact with the liquid or solid phase, the
two phases will be in a state of equilibrium. This report is intended to consider those agents present as gaseous
vapors at ambient temperatures.
Ventral - On or near the lower surface (of the nasal tract).
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ABBREVIATIONS AND ACRONYMS
1,1,1-TCE
1,1,1 -trichloroethane
GSH
glutathione
1,2,4-TMB
1,2,4-trimethylbenzene
3He
hyperpolarized helium-3
2-BE
2-butoxyethanol
Hb/g
blood:air or blood:gas partition
2-ME
ethylene glycol monomethyl ether
coefficient
2D
two dimensional
(Hb/g)A
animal blood:gas (air) partition
3D
three dimensional
coefficient
A
overall or summation hydrogen bond
(Hb/g)H
human blood:gas (air) partition
acidity
coefficient
ADAM
aerosol-derived airway morphometry
Ht/g
tissue:gas partition coefficient
ADC
apparent diffusion coefficient
HEC
human equivalent concentration
ADH
alcohol dehydrogenase
HP
hyperpolarized
AMET
amount metabolized per 24 hour period
hr
hour
ASPM
axisymmetric single path model
K
absorption parameter
AUC
area under the curve
kg
kilogram
AUCpe
area under the parent compound's
kg
gas-phase mass-transport coefficient
arterial blood concentration vs. time
Kg
overall mass transfer coefficient
curve
ki
liquid/tissue phase mass transport
AV
alveolar volumes
coefficient
B
overall or summation hydrogen bond
L
log of the gas-hexadecane partition
basicity
coefficient (unitless) at 25 °C
bb
bronchioles
LBGK
Lattice Boltzmann variant
BB
tracheobronchial
LBM
Lattice Boltzmann method
BW
body weight
LFER
linear free energy relationship
C xt
concentration times time
LRT
lower respiratory tract
CA
arterial blood concentration
Mel
methyl iodide
CATE
carbon tetrachloride
mM
millimolar
CFD
computational fluid dynamic
MR
magnetic resonance
CFDM
computational fluid dynamic modeling
MRI
magnetic resonance imaging
Cmax
maximum concentration
MTBE
methyl tertiary butyl ether
co2
carbon dioxide
NAS
National Academy of Science
CT
computed tomography
o2
oxygen
CV
venous blood concentration
o3
ozone
d
day
PBPK
physiologically-based pharmacokinetic
D
diffusivity
PC
partition coefficient
d2o
deuterium oxide
PCE
perchloroethylene
DAF
dosimetric adjustment factor
PD
pharmacodynamic
DEF
deposition enhancement factor
PDIR
physiological daily inhalation rate
DF
deposition fraction
PGME
propylene glycol methyl ether
DLCO
diffusion capacity of carbon monoxide
PGMEA
propylene glycol methyl ether acetate
DLW
doubly labeled water
PK
pharmacokinetic
E
solute excess molar refractivity with
POD
point of departure
units of (dm3 mol"1 )/10
PODadj
point of departure duration adjusted
Ehr
hepatic extraction ratio
PPb
parts-per-billion
EAD
effective air space dimension
ppm
parts-per-million
EBZ
ethylbenzene
PU
pulmonary
ECG
energy cost of growth
Qalv
alveolar ventilation rate
EPA
Environmental Protection Agency
Qb
regional blood flow
ER
extrarespiratory
R
radius of the airway
ET
extrathoracic
RfC
reference concentration
F
flux fraction
RGDR
regional gas dose ratio
fp
fractional penetration
S
solute dipolarity/ polarizability
FQPA
Food Quality Protection Act
SA
surface area
FVC
forced vital capacity
Sh
Sherwood number
g
gram
so2
sulfur dioxide
GCMS
gas chromatography mass spectrometry
Sp
available surface area
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STP
standard temperature and pressure
URT
SYS
systemic
V
tl/2
half-life
Vd
TAV
time-activity-ventilation
vE
TB
tracheobronchial
TCE
trichloroethylene
VLD,
TDEE
total daily energy expenditure
TLC
total lung capacity
VQ
UBA
upper bronchial airway
wk
UFh
uncertainty factor for interindividual
XYL
human variability
yr
upper respiratory tract
viscosity
volume of distribution
ventilation rate or minute volume
(L/min)
volume of gas required to reach
transitional bronchioles into the lung
ventilator equivalent ratio
week
m-xylene
year
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EXECUTIVE SUMMARY
1 The purpose of this report is to present the findings and conclusions of new scientific
2 developments and advancements in inhalation gas dosimetry for the extrathoracic (ET) or
3 upper respiratory tract (URT), tracheobronchial (TB), pulmonary (PU), and
4 extrarespiratory (systemic, SYS) regions related to the U.S. EPA's 1994 Methods for
5 Derivation of Inhalation Reference Concentrations and Applications of Inhalation
6 Dosimetry (U.S. EPA. 1994) (hereafter RfCMethods). With few exceptions, the studies
7 that contribute to the overall findings and conclusions presented herein were detailed
8 previously in either the 2009 Status Report: Advances in Inhalation Dosimetry of Gases
9 and Vapors with Portal of Entry Effects in the Upper Respiratory Tract (U.S. EPA.
10 2009b) (hereafter Status I Report) or the 2011 Status Report: Advances in Inhalation
11 Dosimetry for Gases with Lower Respiratory Tract and Systemic Effects (U.S. EPA.
12 201 lb) (hereafter Status II Report) and serve as the basis for this final report. A few
13 additional studies were identified since these reports were completed and have been
14 included in this report where appropriate.
15 In this report, as in the prior Status Reports, particular emphasis is placed on the kinetic
16 component of the animal to human dosimetric extrapolation in derivation of a chronic
17 reference concentration (RfC) for gases. In addition, as related to the derivation of a
18 chronic RfC, this report summarizes information on inhalation dosimetry throughout the
19 respiratory tract of children (i.e. early lifestages). The primary results from this multi-
20 year review include empirical information related to the assumptions underlying the
21 default approaches described in RfC Methods, as well as how the advanced dosimetry
22 modeling techniques and state of the science inform these assumptions. This series of
23 reports, and the conclusions summarized here, provides the scientific foundation
24 necessary for ensuring that methods and guidance used and implemented by EPA in
25 chronic inhalation risk assessment of gases reflect the state of the science. For the most
26 recent information pertaining to the derviation of acute reference concentrations, refer to
27 the Organization for Economic and Co-operation and Development's (OECD) Guidance
28 Document for the Derivation of an Acute Reference Concentration (ARfC) (OECD,
29 2011). For information on the state of the science regarding particulate dosimetry, refer to
30 Chapter 6 of the 2004 particulate matter air quality criteria document (U.S. EPA. 2004)
31 and Chapter 4 and Annex B of the 2009 Particulate Matter Integrated Science
32 Assessment (U.S. EPA. 2009c. a).
33 One of the principal findings from these reviews is that internal dose equivalency in
34 the ET region for rats and humans is achieved through similar external exposure
35 concentrations. This finding and the underlying evaluation of internal dose equivalency
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relates to EPA's methods for interspecies extrapolation and not to potential differences in
dosimetry across the human population. Overall, the scientific advances support and, in
some cases, build further upon the approaches of the current default methodology, as
described in RfCMethods, for gas dosimetry in the TB, PU, and SYS regions. An
additional overarching finding of this review is the general compatibility of the
evidence specific to gas dosimetry during early lifestages with the default approach
for derivation of a chronic RfC as described in RfCMethods. An additional
observation from this review suggests that in some cases, chemical-specific
information may indicate alternative chemical-specific approaches for shorter-term
reference values for some specific lifestages. It is anticipated that information will
continue to become available to further inform this issue.
Comparative (animal to human) dosimetry is critical to all inhalation assessment
activities that relate effects observed in animals to humans. The basic principle involved
in comparative dosimetry is the determination of the internal target-tissue dose. This
principle, in turn, is founded on the fundamentals of risk, as is stated by the NRC in its
1994 publication "Science and Judgment in Risk Assessment" and discussed further in its
2009 publication "Science and Decisions: Advancing Risk Assessment":
"... the target-site dose is the ultimate determinant of risk... " .
The goal of comparative inhalation dosimetry is to characterize the steps leading from (1)
estimation of the internal target-tissue dose in an animal resulting from a given external
air concentration followed by (2) estimation of the external air concentration to which
humans would be exposed to attain that same internal target-tissue dose. The external
concentration of a human exposure scenario that produces the equivalent internal target-
tissue dose is termed a human equivalent concentration (HEC) in the RfC Methods.
For gases producing portal-of-entry (POE) effects, the default approximation of the
internal target-tissue dose from the external exposure concentration presented in the 1994
RfC Methods uses species-specific overall minute ventilation (VE) and the overall surface
area (SA) for the respiratory tract region of interest. In the default procedures for gas
dosimetry, the RfC Methods uses ratios (animal: human) of these measures as a dosimetric
adjustment factor (DAF) that is then applied to the animal external exposure
concentration to estimate a HEC. The application of the default approach typically results
in DAFs of 0.2-0.3 for the ET region and 2-3 for the TB and PU regions. The main
assumptions underlying the procedures on gas dosimetry currently in use by the Agency
that follow from the application of overall VE /SA relationships are that there is
uniformity of airflow and uniformity of deposition on surfaces.
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For the ET region, the state of the science presented in the Status I Report indicates
extensive nonuniformity associated with these measures. This is supported by detailed
state of the science estimations of target-tissue dose based on the quantitatively linked
airflow and tissue kinetic models. Overall, these advances both provide more information
related to airflow and gas deposition as included in the RJCMethods and present
solutions to accommodate nonuniformity. A primary finding for gas deposition in the
ET region is that the internal target-tissue dose equivalency between humans and
rats is achieved through equivalency at the level of the externally applied
concentration, i.e., for both rats and humans, the same external air concentration,
rather than one adjusted by VE/SA, leads to the similar internal target-tissue dose to
the URT.
In contrast, the studies identified in the Status IIReport addressing overall concepts
and approaches for POE gas dosimetry in the TB and PU regions of the airways
support the principles and default procedures in RfCMethods. In some cases these
studies suggest and provide examples of further refinement within the existing dosimetry
modeling framework of the RJC Methods through development and application of mass
transfer coefficients as regional measures of gas uptake. Alternative gas dosimetry
procedures published using simplified airway models inclusive of the TB and PU regions
arrive at tissue metrics that support the default approach of the RJC Methods. In addition,
recent advances in understanding the airflow to the TB and PU regions have been made.
Models and measurements of airflow and deposition in the human PU region generally
support the assumption of uniformity as methodological advances and increased
resolution of several in vivo imaging techniques indicate highly uniform and
homogenous flows in the alveolar regions. On the other hand, examination of the TB
regions with human models and advanced dynamic fluid flow programs reveal a degree
of nonuniformity of flow for this region although apparently not to the extent that has
been documented for the upper airway. As recently demonstrated by Corley et al. Corlev
et al. 0. the methods for extension of state of the science flow models to the TB and PU
areas promise further refinement and resolution for inhalation gas dosimetry.
Recently, refined methods for measurement of inhalation rates in humans have been
developed. The advent of the doubly labeled water (DLW) technique in estimation of
physiological daily inhalation rates (PDIR) has provided resolutions to concerns
regarding inhalation patterns of free-living individuals across all age groups including
children. These results were summarized in detail in the Status IIReport (U.S. EPA.
201 lb). DLW-based PDIR values are currently included in the Child-Specific Exposure
Factors Handbook (U.S. EPA. 2008). and are being proposed for inclusion in other key
Agency documents, including the updated Exposure Factors Handbook (201 la), for all
ages including children.
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Marked advances in our understanding of the morphometry of upper respiratory tract
regions in both animals and humans are being achieved with the development and
application of stereology. These techniques, described as the estimation of higher
dimensional information from lower dimensional samples, have and continue to provide
more accurate estimates of flow to regions of the respiratory tract. Most of these
advancements, however, apply to humans and comparable information in the laboratory
animal, the critical comparative component of interspecies extrapolation, lags. The
currently available information in this area supports improvements in dosimetry
modeling.
As recognized by RfC Methods, with regard to dosimetry beyond the respiratory tract, the
principal determinative component for dosimetry is the highly chemical-specific
blood:gas (air) partition coefficient (Hb/g). The Hb/g is also a key parameter of
physiologically-based pharmacokinetic (PBPK) models, models that are of ever
increasing utility to the risk assessment community. Different techniques and approaches
have been proposed to derive these values for both human and laboratory animals. A set
of key reviews (Abraham et al.. 2005; Payne and Kenny. 2002). compiling and analyzing
results from several of these approaches, makes several conclusions relevant to dosimetry
and risk assessment, including that there appears to be no difference between human and
laboratory animal values for a prominent subgroup of toxic gases, the volatile organics.
Examination and compilation of Hb/gs in published inhalation PBPK models configured
for interspecies comparisons was also undertaken. These findings also provide evidence
that the current default dosimetry approach of RfC Methods that uses Hb/gs as a
basis of dosimetry for systemic toxicity remains valid.
As presented in the Status II Report, recent research relevant to inhalation gas dosimetry
in children was found to closely follow the recommendations and guidance of the
National Academy of Sciences (NAS) on children's risk (NRC. 1993). These
recommendations include use of PBPK models to explore and evaluate potential child
susceptibility as well as the related effort to generate accurate measurements and
parameters to be used in these models. A number of studies were reviewed that followed
from these activities including development of physiological-based daily inhalation rates,
morphometry of conducting airways and lung tissue using advanced state of the science
techniques, as well as respiratory tract function using new highly refined in vivo analyses
of airway function. Sophisticated flow models that use these refined measures and that
are capable of examining uptake differences of gases in the upper airways of both adults
and children are also presented and discussed. Several PBPK models have been
configured and parameterized with results from these newer techniques to consider child
versus adult dosimetry. Although few datasets and models pertaining to gas dosimetry in
children exist, the spectrum of methods and approaches is robust. In several cases, the
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available methods and modeling approaches are fairly uniform in their indications
of potential higher inhaled doses in young children (3 mo), which may be 2- to 3-fold
more than in adults. Individual instances exceeding this range are also found but no
apparent pattern appears to be associated with these occurrences. This range is within that
built into RfC Methods using the human interindividual uncertainty factor (UFH) to
accommodate pharmacokinetic and pharmacodynamic variability and for consideration of
potential sensitive population and lifestages including children. It should be noted that
this finding is very similar to that of the NAS (NRC. 1993).
This review also provides a gas characterization scheme that differs fundamentally from
the categories that guide selection of a default dosimetric adjustment approach in RfC
Methods. The RfC Methods gas scheme related physicochemical properties of gases to a
numerical category; this category was then related to the observed toxicity, including that
of the target tissue. In its implementation, however, complexities associated with
categorizing gases often placed greater emphasis on physicochemical properties (rather
than target tissue observations) when identifying the default dosimetric adjustment
approach. The scheme featured in this report may provide the basis for constructing an
improved and simplified descriptor approach for characterizing gases that relates the
properties of the gas directly to the site of the observed toxicity without the need for
categorization. An improved scheme would also need to properly account for and
incorporate the potential role of metabolism.
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1
INTRODUCTION AND PURPOSE
The purpose of this report is to evaluate and summarize the pertinent scientific
developments and advancements in gas dosimetry focusing on extrathoracic (ET) or
upper respiratory tract (URT), tracheobronchial (TB), pulmonary (PU), and
extrarespiratory (systemic, SYS) inhalation dosimetry related to the current methodology
used by EPA. Particular emphasis is placed on animal to human dosimetric extrapolation
performed in derivation of a chronic reference concentration (RfC). An RfC is defined as
an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous
inhalation exposure for a chronic duration (up to a lifetime) to the human population
(including sensitive subgroups) that is likely to be without an appreciable risk of
deleterious effects during a lifetime. In addition, this report summarizes available data
pertaining to inhalation dosimetry throughout the respiratory tract of children as it relates
to derivation of an RfC. This report provides the scientific foundation necessary for
ensuring that methods and guidance used and implemented by EPA in inhalation risk
assessment of gases reflects the state of the science.
The current guidance, Methods for Derivation of Inhalation Reference Concentrations
and Application of Inhalation Dosimetry (U.S. EPA. 1994) | hereafter RfC Methods |. was
made publicly available in 1994. RfC Methods is used by EPA in developing RfCs for the
Agency's IRIS (Integrated Risk Information System) public database. RfC Methods
addresses broad areas of risk assessment but focuses especially on inhalation dosimetry
and provides methods for converting inhalation exposures in laboratory animals to human
equivalent exposure concentrations (HECs). Sections devoted to inhalation dosimetry are
extensive including information on respiratory tract function and anatomy, physiology,
and pathology in humans and typical laboratory animals. Other sections explore the
properties of inhaled agents (e.g., particles and gases). In critical areas where important
observations and application processes were not yet available, reasoned approaches based
on scientific theory were given. These data, theories, and empirical observations were
then synthesized into methods applicable to RfC derivation. These methods are also
discussed in A Review of the Reference Dose and Reference Concentration Processes
(U.S. EPA. 2002).
Since 1994, significant advancements have occurred throughout risk assessment sciences;
in particular, interspecies comparative dosimetry of gases eliciting effects in the upper
respiratory tract (URT). Since many of these advancements impact core components of
the RfC Methods a need was recognized to assess the state of the science in this area.
In 2009, the document Status Report: Advances in Inhalation Dosimetry of Gases and
Vapors with Portal of Entry Effects in the Upper Respiratory Tract (U.S. EPA. 2009b).
hereafter Status I Report, was completed. The purpose of the Status I Report was to
evaluate scientific developments and advancements since 1994 in the area of gas
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dosimetry, focusing on the ET region, and to determine how this information might
inform our approach to gas dosimetry. In 2011, a similar report titled Advances in
Inhalation Dosimetry for Gases with Lower Respiratory Tract and Systemic Effects (U.S.
EPA. 201 lb), hereafter Status II Report, was completed. The Status II Report focused on
the remaining regions comprising the lower respiratory tract or thoracic (TH) region as
designated by RfCMethods, the TB and PU regions. The Status IIReport also included
new information to inform inhalation dosimetry for systemic effects of gases. The focus
of the evaluations in the Status I and II Reports were based on the results from an expert
panel assembled in 2005 and tasked with reviewing the state of the science of inhalation
gas dosimetry in relationship to the RfC Methods.
The Status II Report also evaluated new data and approaches for inhalation dosimetry of
gases in children (birth through adolescence). This area was included in recognition of
the Agency's commitment to ensuring that EPA actions are protective of children, given
the potential for sensitivity of childhood lifestages to some environmental exposures. RfC
Methods currently considers children within the intraspecies uncertainty factor intended
to account for intrahuman variability in response among sensitive populations and
lifestages within the population but devotes no further analysis to the matter.
Furthermore, U.S. EPA (2002) additionally considers children within the database
uncertainty factor.
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2 REVIEW OF THE 1994 RFC METHODS FOR GAS
DOSIMETRY
This section provides a brief overview of the important concepts governing the current
default method for inhalation gas dosimetry and its application as outlined in the 1994
RfCMethods. In addition, an overview is provided regarding how the RfCMethods
account for inhalation dosimetry in children. These concepts serve as the basis for
comparison and examination with the state of the science provided in Section 3.
2.1 Gas Categorizations - General
Numerous model structures have been used for describing aspects of toxicant uptake,
including gases and particles, in the respiratory tract. Common uptake modeling schemes
are often founded on the physicochemical characteristics of the gases to which they are
applied. These uptake schemes are frequently based on the chemical-specific
physicochemical characteristics (e.g., solubility and reactivity) of the subject gases, and
described in terms of a qualitative continuum (e.g., low, moderate, or high). Therefore,
any model scheme comprised of discrete categories has limited application to the broad
range of gases that exist and that the RfC methodology must evaluate.
2.1.1 The RfC Methods Gas Categorization Scheme
The three category gas scheme currently in RfC Methods was constructed based on
physicochemical characteristics as determinants of gas uptake as shown in Figure 2-1.
A similar scheme has been developed by the International Commission on Radiological
Protection (ICRP. 1993). The numerical gas categories are placed on this scheme relative
to their character of these determinants; Category 1 in the upper right hand corner
corresponding to high reactivity and high water solubility; Category 3 in the lower left
hand corner corresponding to low reactivity and low water solubility; and Category 2
occupying the area intermediate to the other two categories. Category 1 gases are
indicated to be absorbed in the ET region which corresponds generally to the nasal
cavity. Category 3 gases are indicated to be absorbed in the deeper pulmonary region,
distal to the ET region, whereas Category 2 gases are indicated to be absorbed throughout
the entire respiratory tract. Detail describing the regions of the respiratory tract is
included in Section 2.2.1.2 of this report.
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¦Q
O
(/)
<1>
ro
g
• • • •••••
•• w:
• * *(2)* * ••
©
Reactivity
Gas Category Scheme
Category 1: Do not penetrate to blood
(e.g., highly water soluble/
rapidly reactive)
Category 2: Water soluble/Blood
accumulation
Category 3: Water insoluble/
Perfusion limited
Note: Reactivity is defined to include both the propensity for dissociation as well as the ability to serve as a substrate for metabolism
in the respiratory tract. Definitive characteristics of each category and anticipated location (region) for respiratory tract uptake are
shown.
Source: U.S. EPA (1994).
Figure 2-1 Gas categorization scheme based on water solubility and reactivity as major
determinants of gas uptake.
Table 2-1 summarizes the characteristics of these categories and provides examples in
accordance with the RfC Methods. The definition of reactivity includes both the
propensity for dissociation as well as the ability to serve as a substrate for metabolism in
the respiratory tract.
~
Location
¦ Extrathoracic absorption
II Entire tract absorption
~ Predominantly pulmonary
absorption
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Table 2-1 Gas categorization characteristics and examples according to RfC Methods
classification scheme
Category
Water
solubility
Characteristics
Accumulation in
Reactivity blood
Site of
Toxicity
Examples
1
High
Rapidly irreversibly
reactive
Not significant
Portal of
entry
Hydrogen fluoride,
chlorine, formaldehyde,
volatile organic acids and
esters
2
Moderate
Rapidly reversibly reactive,
or moderately to slowly
irreversibly metabolized in
respiratory tract tissue
Potential
Portal of
entry,
maybe
systemic
Ozone, sulfur dioxide,
xylene, propanol, isoamyl
alcohol
3
Low
Unreactive in surface liquid
and tissue
Yes
Systemic
toxicity
Styrene
2.2 Conceptual and Historical Basis for Comparative Dosimetry of
Inspired Gases in RfC Methods- Minute Ventilation/Surface Area
of the Respiratory Tract (VE/SART)
The RfC Methods presents an in-depth consideration of what was known about dosimetry
of inspired gases in different species i at the time of publication, with expansive
commentary on the fundamental underlying determinants of dosimetry. These included
species differences in anatomical and physiological characteristics of the respiratory
tracts, the wide range of physicochemical properties associated with inhaled chemicals,
the diversity of cell types that may be affected throughout the respiratory tract, as well as
the many mechanistic and metabolic differences, all combining to make characterization
of dosimetry particularly complex.
This section briefly reviews and summarizes knowledge of several of the most critical
determinants of inhalation dosimetry that define and control the inhaled gas dose as well
as presenting the underlying basis for the RfC Methods interspecies normalization of the
gas dose. Scientific and technical advances informing these critical determinants are
described in Section 3.
2.2.1 Factors Controlling Comparative Inhaled Dose
Factors that determine inhaled gas dose are related to (1) respiratory anatomy and
physiology and (2) the physicochemical characteristics of the inhaled gas. The health
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effect or response that results from an inhaled gas dose is directly related to the target-
tissue dose. However, any description of the continuum defined by exposure, dose, and
response requires integration of quantitative knowledge of determinants of chemical
disposition, toxicant-target interactions, and tissue responses into an overall model of
pathogenesis. Among other things, this process would involve determining the dose
delivered to the target organ of various species as well as determining the sensitivity of
the target organ to that dose. Once such aspects of dosimetry have been established and
species sensitivity has been accounted for, the effective chemical concentration in
laboratory animals can be quantitatively related to dose responses in humans. Models
employed to perform this interspecies extrapolation would incorporate parameters such as
species-specific anatomical and ventilatory differences, metabolic processes, as well as
the physicochemical properties of the pollutant and should be based upon the
physiological factors that govern transport and removal of the pollutant.
2.2.1.1 Comparative Respiratory Anatomy and Physiology
The respiratory systems of humans and various experimental animals, especially rodents
which are the most frequently studied experimental species, differ markedly in numerous
quantitative and qualitative aspects of anatomy and physiology. These differences affect
critical aspects such as air flow patterns in the respiratory tract and thus deposition and
retention of the agent. New information on measurements of anatomical and
physiological parameters (i.e. VE and SA) were discussed in the Status IIReport
according to respiratory regions and branching patterns as it related to derivation of an
RfC. A summary of these findings are provided in Section 3.4 of this report.
2.2.1.2 Regions of the Respiratory Tract Common among Species
The respiratory tract in both humans and experimental animals including the commonly
used murine species (i.e., rats and mice) can be divided into three similar regions on the
basis of structure and function: the extrathoracic region (ET) that extends from just
posterior to the external nares to just anterior to the trachea, the tracheobronchial region
(TB) defined as the trachea to the terminal bronchioles where proximal mucociliary
transport begins, and the pulmonary region (PU) including the terminal bronchioles and
alveolar sacs. The thoracic (TH) region or lower airways is defined as the TB and PU
regions combined. The anatomic structures included in each of these respiratory tract
regions are listed in Table 2-2 and Figure 2-2 provides a diagrammatic representation of
these regions in humans.
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Table 2-2 Respiratory tract regions
Region
Anatomic Structures
Other Terminology
Extrathoracic (ET)
Nose
Mouth
Nasopharynx
Oropharynx
Laryngopharynx
Larynx
Head airways region
Nasopharynx (NP)
Upper respiratory tract (URT)
Upper airways
Thoracic (TH)
Tracheobronchial
(TB)
Trachea
Bronchi
Bronchioles (to terminal
bronchioles)
Pulmonary (PU)
Respiratory bronchioles
(not found in rodents)
Alveolar ducts and sacs
Alveoli
Conducting airways
Gas exchange region
Alveolar region
Parenchyma
Source: Adapted from Phalen et al. (1988).
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Extrathoracic
Region
Tracheobronchial
Region
Pulmonary
Region
Source: U.S. EPA (1994).
Figure 2-2 Diagrammatic representation of the three respiratory tract regions designated
in humans.
These interspecies similarities occur only at this very general level of organization, as
analysis at any more refined level begins to reveal marked differences. A more complete
listing and analysis of comparative airway anatomy differences between humans and
murine species is available in the RfCMethods.
2.2.1.2.1 Surface Areas (SA) of Common Respiratory Regions
The existence of these general functional regions, the ET, TB and the PU, within the
respiratory tracts of humans and murine laboratory species has been thoroughly
documented. It is through, within, around, and over these regions that inspiratory and
expiratory air flows.
Considerable effort in the scientific community has been expended on estimating the
surface areas (SA) for each of these respiratory tract regions for both humans and a
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number of the common laboratory test species. Some accepted values for the SA of these
various regions are given in the RfCMethods and reproduced below, complete with
sources listed in the RfC Methods in Table 2-3.
Table 2-3 Default surface areas for the extrathoracic (ET),tracheobronchial (TB), and
pulmonary (PU) regions of the respiratory tract in various species
Species
ET (cm2)
Source
TB (cm2)
Source
PU (cm2)
Source
Human
200.0
Guilmette
etal. (1989)
3,200
Mercer et al.
(1994b)
540,000
Mercer et al.
(1994a)
Mouse
3.0
Gross et al.
(1982)
3.5
Mercer et al.
(1994b)
500
Geelhaar and
Weibel (1971);
Mercer et al.
(1994a)
Hamster
14.0a
20.0
Yu and Xu
(1987)
3,000
Lechner (1978)
Rat
15.0b
Gross et al.
(1982)
22.5
Mercer et al.
(1994b)
3,400
Mercer et al.
(1994a)
Guinea pig
30.0
Schreider
and
Hutchens
(1980)
200.0
Schreider and
Hutchens (1980)
9,000
Tenney and
Remmers (1963)
Rabbit
30.0
Kliment
(1973)
300.0
Kliment (1973)
59,000
Gehr et al.
(1981)
aNo measurements of hamster ET surface area were found in the literature. This value is estimated based on similarity of the other
regional surface areas to the rat.
bAdditional unpublished measurements of the surface area beyond the ethmoid turbinates are included.
Source: U.S. EPA (1994)
2.2.1.2.2 Comparative Respiratory Ventilation Rates (VE)
The means by which exposures of any respiratory surface would occur from agents in the
air is most likely and logically via the agent concentration present within the inspired and
expired air ~ the ventilation rate (i.e., minute volume), VE.
The RfC Methods provides procedures and parameters for calculating typical ventilation
rates, VE, expressed as total volume of air inspired in a minute, both for laboratory test
animals and for humans. The default values for ventilation rate [VE = tidal volume (VT) x
breathing frequency (f)] are based on accepted body weight allometric scaling equations
provided in the literature.
The basis of interspecies allometric scaling is to account for disproportionalities between
species. It should be kept in mind that these disproportionalities manifest because smaller
species have proportionally greater VE per unit body weight than larger species. VE also
varies with age, activity, and disease.
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RfCMethods provides species-specific values or algorithms to generate values for VE
required to derive the RGDR for the ET, TB, and PU regions. The listing of the
coefficients used to generate VE given in RfC Methods are presented below in Table 2-4.
In addition, the typical default body weight and calculated VE for several species and
strains of laboratory animals used in chronic studies are shown in Table 2-5.
For purposes of interspecies scaling, the RfC Methods specifies a default body weight for
the human of 70 kg, and a corresponding VE of 13.8 L/min or 20 m3/day.
Table 2-4 Intercept (b0) and coefficient (b^ values used to calculate default ventilation
rates based on body weight3
Species
b0
bi
Rat
-0.578
0.821
Mouse
0.326
1.050
Hamster
-1.054
0.902
Guinea pig
-1.191
0.516
Rabbit
-0.783
0.830
Calculation of default ventilation rate based on body weight is conducted using the following algorithm:
((In VE[L/min]) = b0 + b-, * In (BW[kg]))
Source: U.S. EPA (1994)
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Table 2-5 Default ventilation rate and body weights for multiple species
Ve (L/min)
BW (kg)
Species & Strain
Male
Female
Male
Female
Rats
Fisher 344
0.254
0.167
0.380
0.229
Sprague-Dawley
0.445
0.381
0.523
0.338
Long-Evans
0.429
0.383
0.472
0.344
Osborne-Mendel
0.443
0.401
0.514
0.389
Wistar
0.426
0.364
0.462
0.297
Mice
B6C3F1
0.043
0.041
0.037
0.035
BAF1
0.036
0.030
0.026
0.022
Hamsters
Syrian
0.159
0.164
0.134
0.145
Chinese
0.100
0.097
0.041
0.038
Guinea pigs
[Not specified]
0.296
0.294
0.890
0.860
Rabbits
New Zealand
0.737
0.749
3.76
3.93
Source: U.S. EPA (1994)
2.3 Normalization of Inhaled Concentration to Surface Area of
Respiratory Tract Regions
2.3.1 Dose-Response in Respiratory Tract Tissues is Based on External
Exposure Concentration
A central consistent observation from inhalation exposure has been the capacity of the
external exposure concentration to establish and explain the response in the respiratory
tract. Pathology in laboratory animals from inhalation exposure to a variety of agents has
characterized responses in the ET, TB and PU regions of the respiratory tract. Response
characterization based on external exposure concentration has been thorough, extending
from the basis of structure and cell biology, from various mechanisms of toxic action, as
well as response to injury. Some studies and responses to inhalation exposures have been
characterized by inclusion of other variables, e.g. that some responses may follow a
concentration x duration (Cx t) relationship. Even with advanced dose-response
modeling approaches for inhalation exposures, the inception of any analysis is the
external exposure concentration.
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Transformation of the external exposure concentration in air from typical units of ppm or
mass chemical per unit volume air to other units, such as total mg/day, does not provide a
metric with a direct relationship to response, especially when the response being
characterized is a POE response involving one of the surfaces in the respiratory tract. For
extremely reactive gases, deposition may only occur in the first few centimeters of the
ET, thus invalidating even those metrics based on the overall surface area of the ET.
2.3.2 Normalization of External Exposure Concentration to Surface Areas
The basic dosimetry scheme put forth by the RfC Methods was based largely on
conclusions of work by Chang and coworkers (1983) such that normalizing the dosimetry
to nasal surface area could lead to better understanding of species differences in nasal
toxicity. In the RfC Methods, inhaled dose to the respiratory tract is based on species -
specific relationships of minute ventilation and surface areas associated with the target
regions of the respiratory tract:
Ve /SAet,tb or pu
2.4 Interspecies Gas Dosimetry in the RfC - Application of VE/SA in
Calculation of the Human Equivalent Concentration, HEC: The
Default Approach for Inspired Gases
The overall goal of the RfC procedures is to estimate toxicokinetically equivalent doses
to target tissues in laboratory animals to those of humans. RfC Methods gives application
procedures for these various components to produce an estimate of a human equivalent
concentration (HEC). RfC Methods elaborates upon and outlines practices and data
requirements for other procedures that are arranged in a hierarchy of approaches for
estimations of human equivalent concentrations for gases (see Table 2-6). The approach
discussed and analyzed in this report relative to new information is that of the "default"
approach only, the most generalized and data limited situation where information is
limited to that discussed thus far in this report, principally species VE and SA for the
various regions of the respiratory tract. As indicated in the hierarchy scheme, the default
approach would be bypassed when more sophisticated or chemical-specific models are
available (e.g. PBPK, CFD, and CFD-PBPK hybrid).
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Table 2-6 Hierarchy of model structures for dosimetry and interspecies extrapolation
Optimal model structure
Structure describes all significant mechanistic determinants of chemical disposition, toxicant-
target interaction, and tissue response
Uses chemical-specific and species-specific parameters
Dose metric described at level of detail commensurate to toxicity data
Default model structure
Limited or default description of mechanistic determinants of chemical disposition, toxicant-
target interaction, and tissue response
Uses categorical or default values for chemical and species parameters
Dose metric at generic level of detail
Source: U.S. EPA (1994)
2.4.1 The Dosimetric Adjustment Factor (DAF)
The purpose of dosimetry is to calculate an internal dose metric (e.g., target tissue dose,
steady-state blood concentration) that results from an experimentally applied laboratory
animal dose (or concentration) and estimate a human exposure dose (or concentration)
that would result in an equivalent dose metric. Below, the steps currently used, according
to the 1994 RfCMethods, for the dosimetric adjustment procedure for deposition in the
TB and PU regions, as well as the SYS adjustment, are reviewed.
Equation 2-1 is a general equation that may be applied to estimate a human equivalent
concentration (HEC) from an animal point of departure (POD). The POD corresponds to
an exposure concentration at which a particular effect is observed (or not observed in the
case of a NOAEL) in response to a particular exposure scenario of interest (duration and
frequency). The subscript "ADJ" refers to a duration adjustment (i.e., C x t) that converts
the POD from the actual exposure concentration to an average daily exposure
concentration for a continuous exposure scenario. This adjustment will not be considered
further as it is not a focus of this report.
PODhec (mg/m3) = PODadj (mg/m3) x DAFr
Equation 2-1
The DAFr is the dosimetric adjustment factor for a respiratory tract region, where r in this
report refers to ET, TB, PU, or SYS. As can be seen here, the DAF is a factor used to
convert an average exposure concentration for a particular laboratory species to an
2-11
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estimate of a constant exposure concentration for humans that would result in the same
delivered dose, the HEC. When evaluating toxicity following inhalation exposure, in
particular, dose refers to the mass of toxicant absorbed across an airway surface per unit
surface area. Also, for such POE (e.g., ET, TB and PU) effects, the DAF is termed the
regional gas dose ratio (RGDR) and depends on animal to human ratios of two important
parameters: minute volume or ventilation rate (Ve), and surface area (SA) of the target
region. When evaluating SYS effects, the DAF depends on the ratio of animal and human
blood:gas partition coefficients (Hb/g).
2.4.2 The DAF for POE Effects; the Regional Gas Dose Ratio, RGDRr
The equations to derive the regional gas dose ratio (RGDRr) for different gas categories
and for the various regions of the respiratory tract (versus remote sites) are provided and
described further in RfC Methods. As outlined in more detail in the Status I Report, the
basic default (or reduced) equation given in RfC Methods used to calculate the RGDR,
i.e., the DAF, for gases incorporates basic determinants of inhaled dose — species-
specific minute ventilation and surface areas:
RGDRr =
(Ve/SA)h
Equation 2-2
where:
VE = ventilation rate (L/min),
SAr = surface area of the exposed respiratory tract region (cm2), and
A, H = subscripts denoting laboratory animal and human, respectively.
Basically, the RGDRr is used as the DAF in Equation 2-1 to dosimetrically adjust the
experimental POD (duration adjusted or not) to estimate an HEC POD (PODhec) as
follows:
POD(hec) (mg/m3) = POD(Adj) (mg/m3) x RGDRr
Equation 2-3
RGDR, can be seen to be equal to the ratio of the RGD in laboratory animal species to
that of humans (RGDr)A/(RGDr)H.
2-12
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2.4.3 Assumptions in the Application of VE ISA
Application of the species specific values of Ve/SA for dosimetric adjustment has
resulted in considerable scientific debate. This debate has led to the identification and
clarification of assumptions made, either explicitly or implicitly, underlying the default
dosimetric procedures for interspecies extrapolation. It is these assumptions, the most
critical and significant of which are listed below, that provide a basis for evaluation and
refinement of the dosimetric procedures used by the Agency as they related to the state of
the science.
Assumption 1 - Since VE is the parameter used to describe the inspiratory flow, the flow
of the gas through the respiratory tract region of interest is assumed to be uniform. At the
time of the RfC Methods development, nonuniformity was suspected although there was
no substantial basis to quantitatively evaluate its extent.
Assumption 2 - The SAs of the respiratory tract regions exposed to the inspired gas are
uniform and equivalent, i.e., the cell types, relative amount, and distributions are
equivalent in human and animal species. Although considered valid for the general
regions of the respiratory tract, the available SAs incorporate no further refinement
regarding tissue- or cell-types within any region. It represents an average for surface
areas that usually contain nonuniform, sometimes widely divergent cell types. Under the
RfCMethods the most refinement that can be achieved with VE /SA is essentially limited
to the SA term in the denominator. As discussed in the Status I Report, this report, and
the RfC Methods, such an assumption may be most problematic for the ET, a region that
may be considered the most anatomically complex, divergent, and varied in tissue type of
all regions in the respiratory tract.
Assumption 3 - The inspired gas is uniformly distributed (deposited) over the entire
surface of the respiratory tract region in question. A further assumption for gases with
POE effects is that the deposition/uptake is complete or 100% in the region in question
and is the same in animals and humans as deposition/uptake information for humans is
frequently lacking. Together these assumptions allow for the application of VE/SA. In
addition, inspection of the VE /SA relationship reveals that modulation of either VE or SA
would directly influence the "intensity" or flux of the gas deposited to the SA. For
example, increasing VE and decreasing the SA would increase the estimated flux at the
SA; decreasing VE and increasing the SA would decrease the flux at the SA. Perhaps the
most obvious inconsistency of this assumption is the empirically demonstrable proximal
to distal, high to low concentration response gradient is known to occur for gases that
produce respiratory tract lesions. Also, for gases extensively scrubbed from the upper
airways (the ET region) such as formaldehyde, the assumption of 100%
uptake/absorption may be valid. However, this assumption cannot be valid for a great
many other gases. These issues as well as many others critical to the practice of
2-13
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inhalation interspecies dosimetry as outlined in RfCMethods are evaluated in the Status I
and II Reports and briefly reviewed in Section 3.
2.5 Current Applications Using the Default DAFs - RGDRETj RGDRTBj
RGDRpu, and Hb/g
2.5.1 The RGDR for the Extrathoracic Region - RGDREt
The DAF for the ET region is the "regional gas dose, extrathoracic" ratio (RGDRET). It is
constructed with species-specific ventilation rates (or minute volumes) and surface areas
for the ET region (U.S. EPA. 1994).
The equation for deriving a default RGDRET for reactive and water soluble (e.g.,
Category 1) gases as it appears in RfC Methods (U.S. EPA. 1994) (Equation 4-18 and
Appendix I, Equation 1-19) is shown in Equation 2-4, where VE is the ventilation rate
(L/min) and SAET the surface area (cm2) of the ET region for laboratory animals (A) or
humans (H).
Shown below is an example calculation of the DAF for the ET region using Equation 2-4
for a rat to human extrapolation assuming a rat VE of 0.250 L/min and SA of 15 cm2 and
a human VE of 13.8 L/min and SA of 200 cm2.
RGDRet—
Equation 2-4
2-14
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RGDRgT
\
13,8 L/min i
/•
0.25 L/rnin y
' 15cm2
.200 cm'
A
H
= 0.24
Equation 2-4 (example)
The calculation using these default parameters (U.S. EPA. 1994) results in a RGDRET of
0.24 indicating an assumption that humans would receive approximately 4 times
(1/0.24 = 4.17-fold) more dose in the ET region on a per SA unit basis than rats.
2.5.2 The RGDR for the Tracheobronchial (TB) Region - RGDRjb
The DAF for the TB region is the "regional gas dose, tracheobronchial" ratio (RGDRTB).
It is constructed with species-specific ventilation rates (or minute volumes) and surface
areas for the TB region (U.S. EPA. 1994).
The equation for deriving a default RGDRTB for reactive and water soluble (e.g.,
Category 1) gases as it appears in RfCMethods (U.S. EPA. 1994) (Equation 4-22 and
Appendix I, Equation 1-24) is shown in Equation 2-5, where VE is the ventilation rate
(L/min) and SATB the surface area (cm2) of the TB region for laboratory animals (A) or
humans (H). More detailed information on the derivation of the default equation is
provided in the Status II Report.
Shown below is an example calculation of the DAF for the TB region using Equation 2-5
for a rat to human extrapolation assuming a rat VE of 0.250 L/min and SA of 22.5 cm2
and a human VE of 13.8 L/min and SA of 3,200 cm2.
RGDR
Equation 2-5
2-15
-------
0,25 L/min
A
RGDRET
(13.8 L/min j
\
= 2.6
3,200 cm2
H
Equation 2-5 (example)
The calculation using these default parameters (U.S. EPA. 1994) results in a RGDRTB of
2.6 indicating the assumption that rats would receive nearly 3 times more dose in the TB
region on a per SA unit basis than humans.
The DAF for the pulmonary region is the "regional gas dose ratio, pulmonary" ratio
(RGDRpu). It is constructed with species-specific ventilation values and surface areas for
the PU region.
The equation for deriving a default RGDRPU for reactive and water soluble
(e.g., Category 1) gases as it appears in RfCMethods (U.S. EPA. 1994) (Equations 4-23,
4-25 and 4-28 and Appendix I Equations 1-35,1-43 and 1-46) is shown below as
Equation 2-6 where Qaiv is the alveolar ventilation rate (L/min) and SAPU the surface area
of the pulmonary region for laboratory animals (A) or humans (H) (m2). More detailed
information on the derivation of the default equation is provided in the Status II Report.
Alveolar ventilation (Qaiv) in the RGDRPU equations, refers to the gas that reaches the
alveoli and takes part in gas exchange and excludes that which does not, often referred to
as alveolar dead space or residual volume. However, Qah, is often not measured or
reported in laboratory animal inhalation studies, whereas VE is readily measured and
typically reported in epidemiological and laboratory animal studies. Thus, the equation to
determine the RGDRPU has been simplified through usage to the form presented in
Equation 2-7:
2.5.3 The RGDR for the Pulmonary (PU) Region - RGDRPU
RGDR
Equation 2-6
2-16
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A
RGDRpu —
H
Equation 2-7
Shown below is an example calculation of the DAF for the PU region using Equation 2-7
for a rat to human extrapolation assuming a rat VE of 0.250 L/min and SA of 0.34 m2 and
a human VE of 13.8 L/min and SA of 54 m2.
The calculation using these default parameters (U.S. EPA. 1994) results in a RGDRPU of
2.9 indicating the assumption that rats would receive nearly 3 times more dose to the PU
region on a per SA unit basis than humans.
2.5.4 Limitations in the Assumptions and Application of VE ISA
Although inhalation dosimetry based on the measure of "dose" estimated through
VE /SAET TB) pu or Total has been demonstrated to be more explanatory of inhaled dose and
responses in the respiratory tract and POE effects than alternatives discussed in the Status
I Report, such an approach is put forth on assumptions, either explicit or implicit, whose
existence, limitations and caveats need to be considered.
Perhaps the most debated aspect of this default dosimetric approach outlined in the RfC
Methods with VE /SA regarded as dose normalized to surface area concerns the outcome
of the example above using the ET region. As demonstrated in Section 2.5.1, the default
dosimetric adjustment for gas exposures in this region estimates that human ET tissues
receive a three- to fivefold higher dose than do rat tissues. One primary reason the VE
/SAr relationship is debated is its marked divergence from the closely related relationship
of VE /BW. Parallel construction of an RGDR based on body weight instead of SAET
would yield a value of 3 ([(rat VE /0.3 kg)/(human VE /70 kg)] = 0.6/0.2 = 3) indicating
that human tissues would receive threefold less dose than rat tissues. This outcome as
RGDR
= 2.9
Equation 2-7 (example)
2-17
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related to the underlying assumptions made in the application of the basic element of
default dosimetry, i.e., VE/SA is illustrated in Figure 2-3.
1 - assumption of uniform gas flow throughout the respiratory tract region of interest
2 - assumption of uniform tissue >Sl-tv in the respiratory tract region exposed to the inspiratory flow
3 - assumption of uniformgas deposition over the SA of the respiratory tract region of interest
Note: The surface area (SA) of the various regions (r) of respiratory tract are exposed to inspired gas (VE). The right side of this
figure shows the comparative results of applying VE ISA to the ET regions of rats and humans.
Figure 2-3 Representation of the assumptions of uniformity following from VE ISA as
applied to comparative gas dosimetry.
Figure 2-3 illustrates a major outcome following from the interspecies extrapolation for
the ET region with these attendant VE /SAET assumptions. With the accompanying
assumption that all of the gas is absorbed in the region defined by SA, the approximate
fivefold higher value for VE /SAET in humans compared to rats indicates that the surface
of the human ET would receive a fivefold higher dose than the rat ET. Another way to
view the relationship is that surface area per unit of ventilation is five-folder lower in
humans, because humans have less complex nasal passages, so there is more airflow
delivery per unit surface area in humans.
This interspecies difference is reflected in the application of VE /SA in actual calculation
of an HEC where the DAF (the RGDRET) applied to the animal point of departure
approximates the inverse of this ratio, i.e., 0.2-0.3 (i.e., 1/5 - 1/3). Thus, human
equivalent exposures based on responses in the ET from rat exposures are adjusted
downward by this fraction. This adjustment lowers the overall estimate for a POD in
C
o
1 i
Hum hi V r/SA
2-18
-------
humans by a factor of 3 to 5 even before consideration of uncertainty factors. In contrast,
application of the DAFs for the TB and PU region would raise the overall estimate for
POD concentration in humans by a factor of 2 to 3 before consideration of uncertainty
factors.
2.6 The DAF for Systemic (SYS) Sites - Hb/g
Gases with physicochemical properties that lessen their potential for effects in the
respiratory tract (e.g., nonreactivity and higher lipid versus water solubility) may at the
same time exhibit potential for significant uptake and accumulation in the blood where
they can cause toxicity at systemic or remote sites. Based on these properties and other
kinetic properties governing how such gases may be expected to distribute in the body,
RfCMethods posits a fundamentally different DAF for gases that have little or no
potential for reactivity in the respiratory tract.
This DAF is based on assumptions of dose-response that are consistent with basic
principles of kinetics and toxicity applied to the scenario of systemic toxicity from an
inhaled toxicant:
• toxicity is directly related to the concentration of the agent at the target site,
• the concentration of the agent at the target site is related to the concentration of
the agent in the arterial blood at equilibrium and the duration of exposure1;
• arterial blood concentration at equilibrium is related to its concentration in the
inspired air.
The last link in this process, the partitioning of the agent from the inspired air into the
blood at the alveolar endothelial interface, is determined by the blood:gas (air) partition
coefficient, Hb/g. Further, it is reasonably anticipated that as properties of blood differ
between species so will the partition coefficient itself. In application of this DAF, the RfC
Methods outlines a number of additional assumptions. In making the assumption that
differences will exist between species for the basic biological component of Hb/g, blood,
assumptions also are made that similarities will exist between species. These assumptions
include:
• chronic laboratory animal exposure scenarios are equivalent to human lifetime
exposures,
• the human toxic effects observed will be the same as in the animal when the
time-integrated arterial blood concentration (i.e., area under the curve or AUC) in
humans is equal to that of the exposed laboratory animal
1 The gas or its concentration multiplied by time (C x t)
2-19
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It is also emphasized in RfCMethods that the equilibrium referred to here is that which
occurs during the portion of the exposure period that is under conditions of "periodicity",
i.e., when the periodic steady state concentration versus time profile is the same for every
week. RfC Methods further states that conditions of periodicity should be met during
"most" (elsewhere indicated as 90%) of the exposure duration.
Thus, the DAF for SYS sites is based upon the species-specific (animal / human) ratio of
the blood:gas (air) partition coefficient (Hb/g) at equilibrium shown here in Equation 2-8:
_Wa
""¦rSyg— ,
V b/gj„
Equation 2-8
Appendix J of the RfC Methods provides a mathematical derivation and application of
this procedure as well as a case study employing a physiologically-based
pharmacokinetic (PBPK) model parameterized for interspecies extrapolation.
In the RfC Methods, the DAF derivation for SYS effects is based more on science policy
than on an empirical procedure. Further, this policy is bi-level; (1) where if Hb/g values
are unknown the default value for (Hb/g)A / (Hb/g)H = 1; (2) if (Hb/g)A is greater than (Hb/g)H
then a default value of 1 is also used. These procedures are justified by RfC Methods on
the animal human datasets that were available at the time (Gargas et al.. 1989). Gargas et
al. (1989). reported that for an appreciable number of volatile and nonvolatile agents the
(Hb/g)A was greater than the corresponding (Hb/g)H. These values as well as their A/H ratio
are also shown below in Table 2-7.
2-20
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Table 2-7 Some example bloockair partition coefficients (Hb/g) in humans and rats
expressed as a ratio, A/H
Chemical
Human (Hb/g)
Animal (rat, Hb/g)
Animal/Human
Chloroform
6.85
20.8
3.0
Dichloromethane
8.94
19.4
2.2
Carbon tetrachloride
2.73
4.52
1.7
Chlorodibromomethane
52.7
116
2.2
Chloroethane
2.69
4.08
1.5
1,1-Dichloroethane
4.94
11.2
2.3
1,2-Dichloroethane
19.5
30.4
1.6
1,1,1-Trichloroethane
2.53
5.67
2.2
1,1,2-Trichloroethane
35.7
58
1.6
1,1,1,2-T etrachloroethane
30.2
41.7
1.4
1,1,2,2-Tetrachloroethane
116
142
1.2
Hexachloroethane
52.4
62.7
1.2
Methylchloride
2.48
2.47
1.0
Source: Reprinted with permission of Elsevier; Gargas et al. (1989)
2.7 Children's Dosimetry
Consideration in RfC Methods of dosimetry for various human conditions or lifestages,
including childhood is discussed as a component of the intraspecies uncertainty factor
(UFh) that accounts for unknown pharmacokinetic and pharmacodynamic differences.
The default value of this UF is 10 and is applied where appropriate to the underlying
evidence to account for uncertainty and potential variations in susceptibility within the
human population (interhuman variability) and the possibility that the available database
is not representative of the population groups that may be most sensitive to the health
hazards. Early lifestages (including (1) embryo, fetus, and neonate and (2) young children
- ages 1 to 4) are also listed in Table 2-4 of the RfC Methods as 2 of 5 sensitive
populations and lifestages who, based on empirical observations or compromised
physiological functions, are assumed susceptible to toxicity elicited by certain groups of
chemicals. It is discussed further that certain populations and lifestages may be
differentially susceptible, e.g., elderly individuals could be more susceptible to some
chemicals and children to others. RfC Methods acknowledged that very little is known
about this important area of population sensitivity and that guidance should be developed
concerning the prevalence of sensitive populations and lifestages and the range of
sensitivities in the general population exposed to inhaled toxicants.
2-21
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Two subsequent reports (U.S. EPA) further defined and outlined the various lifestages -
including children - that could be considered when assessing potential health risks from
exposure. For this purpose, lifestages are defined as periods of life with distinct
anatomical, physiological, and behavioral or functional characteristics that contribute to
potential differences in susceptibility to environmental exposures. These lifestages and
their corresponding age ranges are shown in Table 2-8.
Table 2-8 Human lifestages and corresponding age ranges through adolescence
U.S. EPA (2002)
U.S. EPA (2006a)
Lifestage
Age
Lifestage
Age
Embryonic
GD 0-58
Prenatal
Conception to birth
(includes embryonic and
fetal stages)
Fetal
GD 58-267
Neonate
PND 0-30
Infant
Child
Birth—1 yr
1 yr—11 yrs
Infant
PND 30-1 yr
Toddler
2-3 yrs
Preschool
3-6 yrs
Elementary School Age 6-12 yrs
Adolescent
12-21 yrs
Adolescent
11-21 yrs
The Food Quality Protection Act (FQPA) of 1996 contains several requirements (directed
primarily toward the evaluation of pesticides) related to its standard described as
"reasonable certainty of no harm." One of the specific requirements identified was that
the EPA considers the specific risk pesticides might have for infants and children. In
general, the manner in which this was to be accomplished was through application of
uncertainty factors based on an evaluation of information relevant to children. This
requirement engendered considerable interest, including interest in inhalation dosimetry
in children. On the whole, these evaluations, including conclusions by the NAS (1993).
indicate that for most chemicals the very large majority of people, including children,
respond sufficiently similarly so that the 10-fold intraspecies uncertainty factor is
adequate to cover any variability that may exist in the human population. However, there
are some chemicals for which some humans may display a greater range of variability
and sometimes that variability appears age-related, with children exhibiting a greater
degree of sensitivity than adults. U.S. EPA (U.S. EPA. 2002) also considers potential
children's sensitivity within the database uncertainty factor. Further considerations of
these matters are included in the section on children's dosimetry in the Status IIReport
and Section 3.6 of this report.
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3 ADVANCES
Section 2 briefly summarized the origins, underlying principles and concepts, and
demonstrated the application of the default procedure in the RfC Methods for performing
inhalation dosimetry of gases. RfC Methods was a state-of-the-art document for
inhalation dosimetry of gases in 1994. This Section summarizes the major scientific
advances in inhalation gas dosimetry, originally presented in detail in the Status I and II
Reports, related to the default approaches presented in the RfC Methods with the primary
focus on interspecies extrapolation. Information evaluated related to the gas
categorization scheme and the assumptions underlying the current approach as discussed
in Section 2 is also presented. New information for measures of critical parameters such
as VE and SA are included where appropriate. Furthermore, a section devoted to
summarizing the current state of the science for inhalation dosimetry in children is
provided. A few additional studies that have recently been identified and were not
presented in the earlier reports are also included in the appropriate sections. These
findings will provide the scientific foundation necessary for ensuring that methods and
guidance used and implemented by EPA in inhalation risk assessment of gases reflects
the state of the science.
3.1 A Modified Gas Scheme: Descriptors versus Categories
Two physicochemical properties, water solubility and reactivity, have repeatedly been
used as predictors of the site of gas uptake in the respiratory tract and/or absorption into
blood, as well as the potential toxic actions in both inhalation POE sites and in sites
remote from the inhalation POE.
Medinsky and Bond (2001) proposed a descriptor scheme based on water solubility and
reactivity that differs from the RfC Methods. In the Medinsky and Bond (2001) scheme,
water soluble gases are defined as gases that readily dissolve in the mucus lining of the
upper respiratory tract followed by diffusion into the underlying epithelial cells and,
potentially, into the blood for systemic distribution. Generally, water-insoluble gases
penetrate the mucus lining more slowly and are transported to the lower respiratory tract
where they may be absorbed into the blood. The other principal determinant, reactivity,
defined in this scheme as the tendency of a gas to undergo chemical reaction, is simple to
understand. However, reactivity at the level of organization relating to tissue dosimetry is
complex. In the distance from the airway to the blood, reactive gases may undergo
interactions with components in the air, mucus, and tissue. These reactions lead to rapid
and substantive decreases in the concentration of the reactive gas across this distance.
Chemical reactivity of the gas controls its molecular interactions with respiratory tissues
and influences its penetration to the blood.
3-1
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Rather than assigning specific numerical categories to gases, these descriptors are placed
on a chart that represents reactivity and water solubility as continuous variables. This
scheme, along with the descriptors for the boundary conditions of the variables, is shown
in Figure 3-1. It is important to note that this scheme provides examples of gases that fit
these discrete descriptors, but that the majority of gases may not fit one particular
descriptor. Also depicted is the primary site(s) of toxicity associated with these gases.
Just as gases may not fit a specific descriptor, the expected site of effect may not fit as
well. For example, chloroprene is water-insoluble and nonreactive but has been found to
induce POE and systemic effects at the same exposure concentration (IRIS). In addition,
the potential role of metabolism and its influence on uptake and toxicity is not directly
accounted for in this scheme. Therefore, this scheme also has its limitations. However,
examination of these examples at the extremes should help facilitate understanding of the
behavior of other gases.
O
CD
(D
Od
Water-insoluble,
reactive
Ozone:
Nose and lung
Water-insoluble,
nonreactive
Butadiene:
Lung
Chloroprene:
Nose, lung, systemic
Water-soluble,
reactive
Formaldehyde:
Nose
Water-soluble,
nonreactive
Diabetic esters:
Nose
Water Solubility
Note: Examples of specific chemicals with their primary site(s) of toxicity are also presented.
Source: Adapted from Medinsky and Bond (2001).
Figure 3-1 A schematic representation of the physicochemical properties of reactivity
and water solubility overlaid with descriptors of their practical limits.
Utilization of such a scheme yields information about the nature and site of toxicity that
is based on the determinative variables of water solubility and reactivity. Such a scheme
3-2
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would have best applicability in situations where gas exposure levels are relatively low
and the effects observed are identified in the most sensitive target tissues. Information on
the nature and site of toxicity is crucial to formulating an approach to inhalation
dosimetry. This approach and scheme contrasts with the current RfC Methods scheme
where the gas was often first placed in a numerical category, often irrespective of the
sites and conditions of the observed toxicity. The current RfC Methods scheme has
resulted in some confusion in that an unintended emphasis was placed on the numerical
category and its associated dosimetric approach and outcome as opposed to the site of the
effect of the toxicant.
3.2 Major Scientific Advances Related to Inhalation Gas Dosimetry in
the ET Region
The following sections summarize the major scientific findings related to the current
default procedure for interspecies inhalation dosimetric extrapolation for gases in the ET
region. The information evaluated includes results and observations from anatomically
based airflow and fluid dynamics modeling as well as from chemical specific interspecies
physiologically based pharmacokinetic (PBPK), computational fluid dynamics (CFD),
and hybrid CFD-PBPK models.
3.2.1 Tracer Dye-Flow in Cast Models
Morgan and co-workers (1991) laid the foundation for studying airflow distribution
patterns in the ET region, - the nasal tract, using solid acrylic casts through which water
was pulled with tracer dye streaks introduced, allowing for direct observation of the flow
field. Consequences of these (and other similar) observations are significant on several
levels. First, the complex but generally consistent and orderly streamlines revealed by the
cast method show a sensitive dependence of nasal airflow patterns on nostril geometry
throughout the ET region. Second, all observations indicate overwhelmingly that flow
into the nasal area, either liquid or air, is in no way uniform but has discernible patterns
that could only result in highly nonuniform deposition onto surfaces (i.e., nasal epithelial
surfaces).
Use of this approach for dosimetric comparisons is limited. These limitations include the
accuracy, representativeness and resolution of the casting process itself and the inability
to quantitatively evaluate any of the flows and flow rates observed. These limitations
remained to be addressed with quantitative mathematical airflow models which are
presented and discussed in the following sections.
3-3
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3.2.2 Computational Fluid Dynamic Modeling
Computational fluid dynamics (CFD), allows for quantitative prediction of all variables
of fluid flow (e.g., pressure and velocity) based on the mathematical laws governing fluid
behavior and, with proper software, offers a three dimensional visualization of the
predicted flow. CFD and its predictions are applied to quantitatively evaluate airflow and
the distribution of dilute gases and vapors (toxicants) by that airflow in the airways of the
respiratory tract.
The goal of inhalation dosimetry is to estimate dose or concentration in target tissues.
Models of air flow, either dye-flow or CFD, visualize or estimate the movement of
materials to surfaces. For the regions of the respiratory tract, flow models give estimates
of the flux of materials present or entrained in the inhaled air to discrete areas. The rate of
transfer across the boundary/surface (i.e. level of flux) may be regarded as an exposure to
the agent or toxicant of concern in air having units of mass flow to a unit area. Thus,
these model outputs may yield a quantitative estimate of materials flowing to the
boundaries of their meshes (i.e., to the tissue surface) but to date do not afford an
estimate of an actual dose to the tissue, whose units are typically mass to a volume or
weight.
3.2.2.1 CFD Air Flow Models of the Rat ET Region
As summarized in detail in the Status I Report, the observations reported by Kimbell et
al. (1997a) clearly illustrate the applicability of CFD modeling techniques for resolution
of flow occurring in the ET region of the rat. Two independent approaches, dye-
visualized and computer-simulated, consistently revealed a high degree of complexity
and nonuniformity of airflow patterns in the ET region. Furthermore, simulations
revealed marked differences in flow rates. Flow in the ethmoid substructure was more
than an order of magnitude slower than flow in interior and ventral portions of the nasal
airway.
3.2.2.2 CFD Air Flow Models of the Human ET Region
Wen et al. (2008) reviewed the work of several investigators (Subramaniam et al.. 1998;
Kevhani et al.. 1995; Schreck et al.. 1993) who developed intricate mesh models based on
highly refined human MRI and CAT scans and to simulate and characterize flow in,
through, and around the ET region. Results analogous to those observed in rat analyses
are obvious with medial, ventral, lateral, and dorsal airflow streams being observed from
the simulations. These simulations of flow in human ET regions also predict low flow
apportionments to the dorsal regions with accompanying vortices. Increased airflow
3-4
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resulted in increased complexity, especially in the dorsal regions where larger and
multiple vortices were simulated.
Localized volumetric flows and their apportionments were also determined in a number
of these studies. These results indicate a wide range of flow values to these subsections of
the ET. Also, simplistic apportionment of percent flow per mm2 surface area of these
various sections can be seen to result in a range of values; e.g., from 0.12%/mm2
(1.2%/9.7 mm2) in section B as reported by Wen (2008) to 1.03%/mm2 (28.7%/27.9
mm2) in section E as reported by Keyhani et al. (1995) (Figure 3-2 and Table 3-1). These
composite simulated results clearly indicate a high degree of nonuniformity of flow
within the human ET region and variability between models.
Note: The section is located at (a) 6.1 cm from the anterior tip of the nose used in Wen (2008). (b) 6.2 cm from the anterior tip of the
nose used by Keyhani et al. (1995) and (c) 6.0 cm from the anterior end of the nose used by Subramaniam et al. (1998).
Source: Wen (2008).
Figure 3-2 The coronal sections are divided into sub-sections which are indicated by the
letters.
3-5
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Table 3-1 Summary of CFD simulated flow apportionment (as a % of total at 15 L/min)
on the coronal cross-sectional area in the middle turbinate (as mm2) of the ET
region in selected human models as analyzed by Wen et al. (2008).
Sections: Dorsal (A)
to Ventral (E,F)
Wen et al. (2008) (left)
Subramaniam et al. (1998)
Kevhani et al. (1995)
Cross-sectional
area
% Flow
Cross-
sectional area
% Flow
Cross-
sectional area
% Flow
A
13.7
11.6
7.9
1.9
15.6
11.4
B
9.7
1.2
15.4
1.9
6.0
3.0
C
23.2
21.4
20.8
11.3
35.5
27.3
D
21.6
20.3
54.8
46.7
27.9
18.3
E
50.3
43.7
20.5
24.4
27.9
28.7
F
42.8
1.8
28.9
13.9
26.5
11.3
Total
161.3
100
148.3
100
139.4
100
Source: Wen et al. (2008)
These results indicate that inspired airflow to the various areas of the ET region is highly
nonuniform. Some reasons for the nonuniformity of flow have their basis in the extensive
departures from unimpeded flow that airway morphology in different species impose on
the incoming airstream.
Finck et al. (2007) described an approach to describe nasal flows in an artificial model of
the nose using a variant of the lattice Boltzmann method (LBGK), an alternative to
Navier-Stokes solvers. This approach provided several advantages over the conventional
Navier-Stokes approach, such that lattice-BGK enabled higher resolution, faster grid
generation, and easy implementation of boundary conditions. Using this novel method,
Finck et al. (2007) performed computations for steady flows at the inspiration and
expiration phase of nose breathing. More recently, Mosges (2010) used the lattice
Boltzmann method (LBM) and a computed tomography (CT) dataset to describe nasal
cavity flow in a human. The CT allowed for visualization of the flow, while LBM
provided higher resolution of this flow field (compared to Navier-Stokes solutions).
3.2.2.3 CFD Air Flow Models - Predictions of Reactive Gas Distribution
in the ET Region
The Status I Report presented detailed information on the application of CFD to address
the fate of inspired gases within the upper respiratory tract. Briefly, Kimbell and co-
workers (Kimbell et al.. 2001a; Kimbell et al.. 2001b; Kimbell and Subramanian. 2001;
Kimbell etal.. 1997a; Kimbell et al.. 1997b; Kimbell et al.. 1993) used CFD modeling of
airflow in the ET regions of laboratory animals and humans as a basis to describe
3-6
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deposition of inhaled gases using formaldehyde, a highly water soluble and reactive gas,
as an example.
The results of the study by Kimbell et al. (1993) were among the first to demonstrate the
application of CFD to regional dosimetry of inhaled gases in predicting quantitative mass
flux patterns to surface (mesh) walls, which acted as a sink. Consistent with other
advances related to predictive dosimetry for the ET region, these results also give
indications that considerable levels of nonuniformity exists across the surfaces of the ET
region, in this instance for mass flux.
3.2.2.4 Interspecies CFD Air Flow Models Predictions of Gas
Distribution in the ET Region
Subsequent to the initial studies of Kimbell et al. (1993). a number of investigators
developed and published similar sophisticated models for various species and gases of
different solublilites and reactivities. A listing of these studies is provided in
3-7
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Table 3-3 of the Status I Report.
Kimbell et al. (2001b) constructed anatomically accurate, 3-dimensional computational
fluid dynamics models of nasal passages of F344 rat, Rhesus monkey, and humans for the
purposes of modeling inhaled formaldehyde (see the Status 1 Report for details).
Simulations configured for uptake of formaldehyde were run for all three of the ET
models, the results of which are shown in Figure 3-3. Despite the difference in size, with
the rat ET being 13-fold smaller than the human ET based on surface area, comparative
aspects regarding flux are apparent. Visual inspection of Figure 3-3 shows clearly, for
example, that relative proportions of area for the highest formaldehyde flux in the ET
region ("red" in color) is highest in rat, with the rank order following rat > monkey >
human. The authors estimated both maximum and average formaldehyde flux over the
whole ET in each species. Tire difference between the maximum:average flux ranged
from 3- to 10-fold among these three species (Table 3-2).
F344 Rat
Key
pmol/fmn^-hr-ppm)
Rhesus Monkey
Note : Nostrils are to the right.
Source: Kimbeil et ai. (2001b).
Figure 3-3 Nasal wall flux spectra of inhaled formaldehyde simulated in rats, monkey and
humans at normal inspiratory flow rates.Table 3-2 Estimates of formaldehyde
flux to ET surface walls for various species
3-8
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Formaldehyde flux estimate (pmol/[mm2-hr-ppm])a,b
Area
Rat
Monkey
Human
Whole nose: maximum
3210
4492
2082
Whole nose: average
336
508
568
Maximum/Average
10
9
4
Nonsquamous: maximum
2620
4492
2082
Nonsquamous: average
284
535
611
Maximum/Average
9
8
3
Simulations conducted at inhaled concentration of 1 ppm formaldehyde.
bSimulations conducted at flow rates of twice the minute ventilation for rat (576 mL/min), monkey (4.8 L/min), and human (15.0
L/min).
Source: Adapted from Kimbell et al. (2001b).
3.2.3 Range and Distribution of Flux in ET Regions for Various Species
The general ranges of rat and human flux in the ET region estimated from visual
inspection in Figure 3-2 and from the average and maximum ranges in Table 3-2 were
further analyzed by Kimbell et al. (^OOlbV The ET regions for each species were first
partitioned into 20 evenly spaced flux levels between zero and the maximum predicted
flux value for each species; 2620 pmol/(mm2 -hr-ppm) at a flow rate of 576 mL/min in
the rat and 2082 pmol/(mm2 -hr-ppm) at a flow rate of 15 L/min in the human. Surface
areas of the ET found to be within these flux levels were then assigned or "binned"
accordingly. This strategy allowed for estimating the distribution of flux levels over the
surface area of the ET, each species being "normalized" to its respective range of flux.
The binning revealed that flux values higher than half the maximum flux value (flux
median) were predicted for nearly 20% of human ET surfaces whereas only 5% of rat ET
surfaces were associated with fluxes higher than flux medians. This relationship was
maintained for flux levels higher than 75% of the maximum flux value with
approximately 1.8% of human but only approximately 0.6% of rat ET surfaces were
exposed to this higher level of flux. Distribution within the ET region of what may be
considered "high" flux will be examined in the next section in relation to actual
occurrence of lesions in the ET region.
3.2.4 Correlation of High Flux with Lesions in the ET Region
Airflow and CFD modeling approaches have given very similar and internally consistent
results concerning patterns and distributions of airflow and gases in the ET region of
various species, including humans. Supporting empirical observations would, however,
3-9
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provide a more robust basis for these modeling results. One logical strategy that could
provide support and reinforcement for the modeling results would be to examine the
extent of correlation between flux and lesions. There are currently several examples of
such a correlation analysis in the current literature. Results from two of these studies, -
formaldehyde (Kimbcll etal.. 1997b) and hydrogen sulfide (Moulin et al.. 2002) - are
summarized here.
Kimbell and colleagues (1997b) investigated the relationship between squamous
metaplasia and areas of high formaldehyde flux in the nasal tissues of adult rats. They
first performed a pathology analysis on the incidence of squamous metaplasia in specific
areas of the nasal tracts of rats that had been exposed via inhalation to formaldehyde at 0,
0.7, 2, 6, 10 or 15 ppm for 6 hr/d, 5 d/wk for 6 months. These specific target tissue areas
were then divided into 20 regions based on anatomical landmarks and the location of
major airflow streams. Transport of formaldehyde through the air and into the nasal
epithelium was assumed by the model to occur by convective forces and molecular
diffusion. Incidence of squamous metaplasia was then calculated for each region and flux
values modeled for each region, ranked high to low and statistically analyzed for
correlation. The results, shown in Figure 3-4, provide clear evidence that, at high flux
levels (and high exposures) of formaldehyde, the distribution of squamous metaplasia is
closely related to the location of regions of high formaldehyde flux into airway walls.
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Region number
squam nonabsorbing
scuam absorbing
Region number
Source: Kimbell et al. (1997b).
Figure 3-4 Graphs showing (A) the incidence of formaldehyde-induced squamous
metaplasias and (B) modeled formaldehyde flux values along regions
assigned to the perimeter of a transected nasal airway of rats.
Moulin et al. (2002) used a similar approach to investigate the relationship between
lesions in olfactory epithelia and areas of high hydrogen sulfide flux in the nasal cavity of
adult rats. They also performed a pathology analysis on the incidence of lesions
(olfactory neuronal loss and basal cell hyperplasia) in adult male rats (n =
12/concentration) that had been exposed to hydrogen sulfide at either 0, 10, 30 or 80 ppm
for 6 hr/d for 70 days. The CFD modeled flux predictions at 80 ppm hydrogen sulfide
were made at the same level of the transverse nasal section (through the ethmoid
turbinates) that had been divided into 39 regions. Transport of hydrogen sulfide through
the air and to the nasal epithelium was assumed by the model to occur by convective
forces and molecular diffusion. Rank correlations between lesion incidence and flux were
then carried out. Distinct hot spots of regional flux occurred in the ethmoid turbinate
section at those regions corresponding to high airstream flow. These results are presented
in Figure 3-5. These regions of high flux were closely associated with hydrogen sulfide-
induced nasal lesions if that region was lined by olfactory epithelium. An additional
3-11
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observation made regarding high hydrogen sulfide flux was that lesions were not
observed in those regions lined with respiratory tissue other than olfactory epithelium.
This lack of correlation between high flux and non-olfactory epithelium lesions is
apparently due to resistance of this tissue to hydrogen-sulflde toxicity.
Section 2
100
80
o
w
TJ
60
O
40
C
o
(A
20
-I
0
30 ppm
80 ppm
" .¦
mm~ ~'
10
•••
20
¦¦¦¦¦¦¦¦
30 40
Nasal Region Number
300.0
» 200.0
1
E
I
r 100.0
0.0
*
f I"
n
ii 4
' * '17\
*
i
11 m
Hi fjjr
« J f
II
Medium Uptake
- Low Uptake
- High Uptake
40
Nasal Region Number
Note: coding of surface areas in schematic to the x-axis of plot. Riot of predicted hydrogen sulfide flux under different assumptions
of uptake: low - 20%, medium - 40%, and high - 80% (bottom right). Diagram of same section (bottom left) under intermediate
uptake conditions at 80 ppm where red corresponds to 320 pg/(mm -s) and blue corresponds to 0 pg/(mm2-s). Plot of predicted flux
at 80 ppm (bottom right) on designated regions of Section 2.
Source: Moulin et al. (2002).
Figure 3-5 Schematic diagram of the transverse nasal section through the ethmoid
turbinates (top left, Section 2 of the nasal cavity) with plot of lesion incidence
at 30 and 80 ppm (top right).
Similar correlations between lesion incidence and tissue dose were also observed for
acrolein (Schroctcr et al.. 2008) and diacetyl (Morns and Hubbs. 2009). For acrolein,
predicted aintissue flux from the rat nasal CFD model compared well with the
distribution of nasal lesions observed in a subchronic inhalation study. In the case of
diacetyl, a strong correlation of injury location and pathology severity scores with CFD-
PBPK hybnd model predicted tissue concentrations was observed in nasal tissues.
These examples provide strong support for a direct relationship between flux and
responses in tissues in four independent cases. The hydrogen sulfide case offers
resolution sufficient to demonstrate expected target tissue specificity as responses were
3-12
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only seen in olfactory epithelium despite equivalent flux levels in more proximal
respiratory epithelium. In the case of formaldehyde, the relationship between flux
intensity and response appears to be one in which high flux is more predictive of lesions
than are low flux levels (i.e., either appreciably above or below the median flux level).
Similar relationships between flux intensity and response were observed with acrolein
and diacetyl. These additional observations indicate the flux-response relationship to
have a high degree of resolution as well as providing predictability of a dose-response
relationship. Thus, results from these four examples may be regarded further as providing
compelling support for a close correlation between flux and tissue-specific responses.
However, the weaker correlation for low flux areas suggests that other factors also impact
site specificity at low exposure levels.
In summary, the intent of this portion of the report is to identify and discuss the findings
that inform the shortcomings of the basic assumptions following from the use of VE/SAr,
and in particular Ve/SAet, as the basis for default interspecies dosimetry of inspired
gases. Section 2 presented the reasoning behind the use of VE as a surrogate for gas dose
and use of SAr for normalization of dose along with acknowledgment of the general
advancement this concept made to inhalation dosimetry. Figure 3-6 provides an
illustrative summary and perspective of what airflow and CFD models have presented
regarding the default procedure. This figure illustrates the assumptions of uniformity
underlying VE/SA originally shown in Figure 2-3. The composite evidence and results
from this information indicate nonuniform air flow, surface areas, and deposition for
gases in the ET region. In addition, this information indicates that humans do not receive
5 times the "dose" to the ET region compared to rats.
Thus, more extensive quantitative models are needed to integrate newer information that
may lead to more informed estimates of interspecies dosimetry. These models and the
results from their application are the principal subject of Section 3.2.5 of this report.
3-13
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3
O
3
1
1 o^5
I //
sl Ł/
X-i //$
V I
' 1 Jt
n \ j
Human ^
•
jy 2
1 - nonuniform gas flow throughout the respiratory tract region of interest
2-nonuniform tissue SAs in the respiratory tract region exposed to the inspiratory flow
3 - nonuniformgas deposition over the S A of the respiratory tract region of interest
Geometry
&
Anatomv
Dve-flow
Lesions &
Flux-lesion
correlation
CFD ET
models
Note: The text-containing arrows below the figure represent the information presented in this chapter that has addressed the
assumptions of uniformity. The arrows and labels extending to the right demonstrate the outcome of applying this information to the
overall process introduced in Figure 2-3.
Figure 3-6 Representation of application of the state of the science to the assumptions
and outcome of the RfC Methods basic default procedures for comparative
gas dosimetry in the ET region.
3.2.5 Evaluation and Use of Models in Interspecies Inhalation Dosimetry -
ET Region
As discussed previously, the default RfC method for interspecis dose-extrapolation for
the ET region is determined by the ratio VE/SAET of animals to humans. When applied for
an interspecies comparison with rats, for example, the calculation results in a RGDR of
approximately 0.2 - 0.3, indicating a default assumption that the inhalation delivered
dose to humans is up to fivefold greater than to the rat.
Current evidence indicates that flow and distribution of VEin the respiratory tract is not
uniform, and that CFD modeling offers refined and characterized disposition of airflow
and of the gases present in VE. However, CFD offers resolution only to the surface of the
tissues in contact with the airflow. It does not and cannot offer resolution of interspecies
tissue dosimetry, that is, the concentration of the gas in the target tissues of the
respiratory tract.
3-14
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The purpose of this section is to summarize new information that relates these basic
issues of dosimetry in the airways, i.e., flux of gas to the tissue surfaces and the
concentration of gas in the airway tissues. The focus of this section is on the results and
insights gained from the combination of modeling approaches of CFD on airflow and gas
disposition in the airways with the state of the science developments from
physiologically-based pharmacokinetic or PBPK models describing pharmacokinetics in
respiratory tract tissues.
Various models have been developed and utilized to examine and quantitatively estimate
dose to target tissues via the inhalation route of exposure in both animals and humans. As
discussed above, 3-D, anatomically accurate CFD models were developed to model
inspiratory airflow and estimate regional uptake and amount of inhaled gas reaching sites
in the nose, with formaldehyde serving as the vanguard example for this application
(Kimbcll et al.. 2001a; Kimbell et al.. 2001b; Kimbell and Subramanian. 2001; Kimbell
etal.. 1997a; Kimbell et al.. 1997b; Kimbell et al.. 1993). One output of these simulations
is an estimate of flux which is the rate of mass transport in the direction perpendicular to
the nasal wall, typically with units of pmol/(mm2-h-ppm). In this regard, the CFD model
estimates a "dose" of gas delivered to the tissue boundary but not into the tissue itself.
CFD modeling simulations can also be utilized to estimate the surface area and volume of
specific anatomical features, the allocation of inspired air to specific flow streams, and
gas phase mass transfer coefficients (Kimbell and Subramanian. 2001). While
representing a major step forward in describing and refining interspecies inhalation
dosimetry compared to the current default RfC methods, the nature of CFD model output
(in terms of flux) does not provide a definitive measure of target tissue dose.
Developments in tissue physiology and kinetic models, however, have provided this
information and allow for more refined and accurate dosimetry in and between species.
Combination of these two modeling approaches led to the development of CFD-PBPK
hybrid models allowing for the most highly refined and accurate estimates of target tissue
dose currently available. More detail on these modeling approaches is provided in the
Status I Report.
3.2.5.1 Overview of CFD-PBPK Hybrid Modeling - Combination of Gas
Transport in the Air Phase into the Liquid/Tissue Phase
CFD-PBPK hybrid modeling represents the state-of-the-art science for examining
inhalation dosimetry. As discussed in Bush et al. (1998). combined CFD and PBPK
models were developed to help address how factors related to airway anatomy might be a
reason that other models assuming uniformity, such as the ventilation-perfusion model,
failed to fully explain the effects of gas flow on total vapor uptake in different animal
species. Consequently, Bush and colleagues (1998) developed a hybrid model based on
combining these two aspects, the CFD model for consideration of gas disposition in the
3-15
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air phase and the PBPK models for consideration of gas disposition into the liquid and
tissue phases within the rat nose. This was accomplished by coupling PBPK and CFD
models at the gas-tissue phase interface with a permeability coefficient - termed Kgm -
that incorporated the gas phase mass transfer coefficient (kg) with a mucus phase
diffusion parameter.
The model and its predictions were then validated by using overall uptake data from rat
inhalation studies for three nonreactive vapors that were either completely inert (acetone),
reversibly ionized in aqueous media (acrylic acid), or prevented from being metabolized
by an enzyme inhibitor (isoamyl alcohol). This CFD-PBPK hybrid model was thus
parameterized and validated with empirical observational data to model actual uptake
into tissues such that actual tissue concentration of the test case vapors could be
predicted. The results of this modeling work showed variation of surface area, cross-
sectional area, and values of kg along the differing flow paths among the regions,
reflecting the convoluted nature and complexity of the rat nasal geometry.
3.2.5.2 CFD-PBPK Hybrid Modeling and the Overall Mass Transport
Coefficient - Kg
In the hybrid modeling approach described by Bush et al. (1998). the PBPK and CFD
models were coupled at the gas-tissue phase interface by Kgm. The aim of this approach
was to determine the regional dose within the respiratory tract by characterizing the
transport of gases between the air phase, the intervening surface liquid and tissue, and the
blood. Kgm is also referred to as the overall mass transport coefficient or Kg.
As described in Appendix I of the RJC Methods, the concept of Kg (the overall mass
transport coefficient, MTC) is used to describe transport through several different phases
including air and liquid. The basic structure of this approach, which relies on Kg, was
used in the CFD-PBPK hybrid models developed by Bush et al. (1998) and subsequently
by Frederick et al. (1998). both of which incorporate the output from CFD simulations to
describe anatomy, airflow, kg, and flux of inhaled gas in the POE (air phase) linked to a
PBPK model describing the systemic compartments (tissue phase).
Bush et al. (1998) and Frederick et al. (1998) provide an updated and modified version of
the approach presented in Appendix I to describe gas phase mass transport in which the
estimate of the overall transport or flux, N, across and airliquid interface is expressed by
N = Kg (Cg - Q/PC)
Equation 3-1
3-16
-------
where Kg (cm/min) is the overall mass transfer coefficient, Cg (|_imol/cm3) is the air phase
gas concentration, and Ct ((imol/cm3) is the concentration in the liquid/tissue phase, and
PC (unitless) is the surface liquid/tissue:air partition coefficient.
In general, the overall mass transport coefficient, Kg, from the air phase into the liquid
phase may be determined from the transport coefficients of each individual phase, such
that
1/ Kg = 1/ kg + l/(PC*kt)
Equation 3-2
where kg (cm/min) is the gas phase mass transfer coefficient as defined above, and kt
(cm/min) is the liquid phase mass transfer coefficient. Contextually, Kg may be
considered analogous to a tissue clearance term used in compartmental pharmacokinetic
studies as similar principles apply (Frederick et al.. 1998). In the case where the surface
liquid and tissue cannot be assumed to be a single compartment, a separate partition
coefficient and transport coefficient would need to be incorporated to account for
additional compartments. For example, in cases where gas diffuses through the tissue into
the blood and contributes to overall absorption, additional mass transport resistances must
be considered to describe this additional compartment. This is important because
significant accumulation and recirculation of gas in the bloodstream may reduce the
concentration driving force (and thereby reduce the absorption rate) and contribute to the
development of a "back pressure", which may result in desorption during exhalation due
to the reversal in the concentration gradient between the air and tissue.
An initial difficulty identified in the RfCMethods in using such approaches, i.e., to
determine or decompose an empirically founded Kg, was lack of kg values in airways of
laboratory animals (and humans), and the lack of a data base in which Pk( could be
determined. However, much of this difficulty has been overcome and resolved by the
advancement and validation of CFD models to obtain estimates of kg as the gas phase
term is dependent on flow rate, flow geometry, and the gas phase diffusivity. In cross-
species comparisons, the flow geometry differences of the species are likely to
predominately determine kg. For both CFD and PBPK models, the increase in the amount
of data available from various sources defining the gas and water diffusivities, and
partition coefficients for many compounds, as well as parameters such as tissue surface
area, thickness, volume, air flow, etc., used to describe the anatomical and physiological
"compartments" in both animals and humans has aided in advancing these approaches.
Several of the models represented below use kg values determined specifically for that
study or the compartmental (or regional) kg values determined by the CFD simulations
conducted by Frederick et al. (1998) for both animals and humans for the purpose of
describing interspecies inhalation dosimetry.
3-17
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3.2.5.3 Results and Analysis of Interspecies Inhalation Dosimetry
Modeling - ET Region
The purpose of this section is to provide an example-based overview of concepts related
to the various aspects of inhalation gas dosimetry discussed in the preceding sections and
as presented in more detail in the Status I Report.
3-18
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Table 3-3 provides information for various gases including primary toxicological
endpoint(s), measured nasal uptake in the rat (if available), as well as water solubilities
and partition coefficients. This information was used to provide a physicochemical
descriptor for each gas based on the scheme outlined by Medinsky and Bond (2001).
Table 3-4 compares the various methods for interspecies dose-extrapolation (i.e.,
determination of the HEC) between animals and humans for the ET region based on
CFD, CFD-PBPK hybrid, or PBPK modeling for these gases to the default RfC method
based on VE /SA.
In comparing these physicochemical properties, reactivity, and measured uptake for the
representative gases shown in
3-19
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Table 3-3, a general pattern emerges. Those gases with high uptake (>90% -
formaldehyde, acrylic acid) in the ET region are reactive, have higher liquid/tissue:gas
(air) partition coefficients (PC), and water solubility. Reactive gases with moderate
uptake (acrolein, acetaldehyde, and diacetyl) in the ET region have moderate to low
liquid/tissue:gas (air) PC values and water solubility. Gases with low uptake (<25% -
hydrogen sulfide, ethyl acrylate and propylene oxide) have among the lowest
liquid/tissue:gas (air) PC values in this group of gases. However, these gases also exhibit
a range of water solubilities and reactivities. In general, high ET uptake gases tend to be
more reactive and scrubbed more efficiently in nasal tissues with little penetration to the
lower respiratory tract and less potential for systemic distribution. Likewise, low ET
uptake gases have the potential to reach the lower respiratory tract and produce an effect
and/or be more readily distributed systemically via the gas exchange area of the lung. An
important consideration in examining the relationship among these properties and uptake
is that they are substantiated by experimental observations of uptake in the rat.
3-20
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Table 3-3 Primary toxicological endpoint(s), uptake, properties, and physicochemical
descriptor for representative gases—ranked by percentage of uptake in rats
Endpoint/
Effect
Uptake in
Rat3
Liquid/tissue
:gas (air) PC
values
Water
Solubility
Physico-
chemical
Descriptor13
References
Formaldehyde
RE Tumors and
squamous
metaplasia0
> 90%
72,000
(calculated)
400 g/L
soluble-
reactive
Kimbell et al.
(2001b)
Acrylic Acid
OE degeneration
> 90%
6,100
1,000 g/L
soluble-
reactive
Frederick
et al. (1998);
Andersen
et al. (2000)
Acrolein
OE degeneration
and atrophy
80 - 60%
88 (or 200)
212 g/L
soluble-
reactive
Schroeter
et al. (2008);
Morris (1998);
Corley et al.
Acetaldehyde
RE and OE
degeneration
80 - 40%
140
1,000 g/L
soluble-
reactive
Teegaurden
et al. (2008);
Dorman
et al. (2008);
Morris et al.
(1997)
Di acetyl
Nasal, tracheal,
bronchial toxicity
76 - 36%
550
200 g/L
soluble-
reactive
Morris and
Hubbs (2009)
Vinyl Acetate
OE degeneration
93 - 40%
29
20 g/L
nonsoluble-
nonreactive
Bogdanffy
et al. (1999)
Hydrogen
Sulfide
OE degeneration
and necrosis
26-18%
2.8
4-5 g/L
nonsoluble-
nonreactive
Schroeter
et al. (2006)
Ethyl Acrylate
OE toxicity
25-18%
86
15 g/L
nonsoluble-
nonreactive
Sweeney
et al. (2004);
Frederick
et al. (2002)
Dimethyl
Sulfate
Nasal tissue
tumors
NAd
100
28 g/L
moderately
soluble-
reactive
Sarangapani
et al. (2004)
Propylene
Oxide
RE hyperplasia
and OE
degeneration
23-11%
68
590 g/L
moderately
soluble-
reactive
Csanady et al.
(2007); Morris et
al. (2004)
aUptake - measured percent of inspired vapor that is retained or deposited in the URT of the rat
bPhysicochemical Descriptor from Medinsky and Bond (2001) (see text for details)
°RE = respiratory epithelium; OE = olfactory epithelium
dNA = not available
However, these generalizations do not always hold. For example, propylene oxide has
less nasal uptake than might be predicted based solely on its water solubility and/or
reactivity and comparable liquid/tissue:gas (air) PC to that of acrolein, thus highlighting
its hazard categorization. Conversely, the nasal uptake of vinyl acetate is greater than
might be predicted based on those same two properties as well as its relatively low
3-21
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liquid/tissue:gas (air) PC. In the case of vinyl acetate, nasal metabolism via
carboxylesterase greatly enhances its uptake into nasal tissues and also plays a role in its
toxicity. Yet, the primary toxicity induced by both of these gases is damage to the nasal
epithelium. In addition, it is critical to note the potential that systemic circulation can
contribute to effects in regions remote to the site of deposition. For example, some gases
can be distributed to remote sites after absorption in nasal tissues, while other gases may
be absorbed primarily in the lungs and result in POE effects. These observations highlight
the complexity of interspecies dose extrapolation for inhalation as well as the limitations
in the application of a strict categorization or descriptor scheme. Therefore, it is
important that dosimetry extrapolations/calculations should be based on the effect and the
target tissue, and not based solely on physicochemical properties.
Using the physicochemical descriptors scheme proposed by Medinsky and Bond (2001).
an attempt was made to characterize or classify the selected gases based on their criteria
as discussed in Section 3.1. For the majority of the chemicals shown in
3-22
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Table 3-3, application of these descriptors appears straightforward. For example,
formaldehyde and acrylic acid are highly soluble and/or reactive with tissue components
with little potential for systemic distribution, have high liquid/tissue:gas (air) PC values,
and high uptake in the nasal cavity. Acrolein, acetaldehyde, and diacetyl are also best
described as soluble and/or reactive with some potential for systemic distribution because
of their water solubility, moderate to high liquid/tissue: gas (air) PC values, and moderate
to high uptake in the nasal cavity. On the other hand, hydrogen sulfide is best described
as non-soluble based on its low water solubility, low liquid/tissue:gas (air) PC and low
uptake, and nonreactive based on its hypothesized mode of action - inhibition of
cytochrome oxidase due to competitive binding. Also, ethyl acrylate is best described as
non-soluble based on its low water solubility, moderate liquid/tissue:gas (air) PC, and
low uptake, and nonreactive as its toxicity is mediated via metabolism to acrylic acid.
However, similar to the 1994 RfC Methods, a set of qualitative descriptors cannot capture
the impact of multiple, interacting quantitative properties; once again highlighting the
limitations in applying a strict categorization or descriptor scheme. As discussed above,
vinyl acetate exemplifies these limitations for a number of reasons. It is relatively non-
soluble as a result of its low solubility and partition coefficient and is non-reactive. Both
its toxicity mediated by being a reactive aldehyde and its acid metabolites and higher than
expected uptake is due to the presence of carboxylesterase in nasal tissues. Further adding
to the complexity and limitations of applying such a scheme in a strict manner is the
consideration of metabolism. Metabolism may also be considered a component of
reactivity in characterizing gas transport in the tissue and predicting site of gas uptake
and effect, but based on the examples provided by Medinsky and Bond (2001).
metabolism appears to be excluded in designating the parent gas as "reactive".
As discussed in the previous section, two parameters integral to these models shown are
Kg and one of its components kg. Kg, the overall mass transfer coefficient, describes the
movement of gas from the air phase into the liquid or tissue phase by combining kg, the
gas phase mass transfer coefficient, with a liquid or mucus phase transfer coefficient, kŁ
or kmc. The CFD-PBPK model developed by Bush et al. (1998) and expanded upon by
Frederick et al. (1998) describes and highlights the importance of these parameters in the
basic model structure for estimating target tissue dose for a wide range of inhaled gases
in regions of the nasal cavity for different exposure scenarios. The model was initially
used to evaluate the rodent nasal deposition of several poorly metabolized gases, but was
further validated using the physicochemical and toxicity properties information for
acrylic acid. As a result, several of the examples presented in Table 3-4 also used this
same approach, employing the CFD derived kg values from Frederick et al. (1998).
The modeling results for acrylic acid demonstrated several important findings regarding
interspecies differences in inhalation target tissue dosimetry (Frederick et al.. 1998).
First, the CFD simulations provided estimates of the volume of the airflow through the
various regions of the rat and human nasal cavities at various flow rates. These data
3-23
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confirmed the results observed in other studies showing that a relatively small fraction of
inspired air ventilates the human olfactory region compared to the rat. Second, the CFD
simulations also showed that where the data can be compared, the regional kg values for
the rat are higher (up to one to two orders of magnitude in the respiratory epithelium)
than those for the human. This difference in kg values indicates that rat nasal cavity is
much more efficient in scrubbing gas from inspired air than the human. On a regional
basis in the nasal cavity, this interspecies difference in the delivery of inspired gases in
the overall nasal cavity is significant due to differences in air flow patterns and
distribution of target epithelium.
Table 3-4 shows the DAFs (and HECs) calculated from various state of the science
inhalation dosimetry models compared to the default RfC Method. The most critical
observation is that the default DAF for the ET region is approximately 0.2 - 0.3 for each
of the gases whereas DAF values based on modeling are different for nearly every gas,
over a range of sevenfold in this group. These differences are indications of the models'
capacity to employ and integrate numerous critical gas- and species-specific parameters
and variables in characterizing gas transport through the air and tissue phases for the ET
region. For example, the models for ethyl acrylate and dimethyl sulfate indicate that a
DAF of 3 or 7, respectively, be applied to the rat POD to determine the HEC. The model
predicted DAF for each gas is based on more detailed dose metrics: for ethyl acrylate,
internal metabolite concentration; and for dimethyl sulfate, specific DNA adduct
concentration. The fact that quantitative differences exist in the DAF values estimated for
the ET region is an indication that the comparative dosimetry is sensitive to some
combination of these parameters and variables. On the other hand, the default method is
c/e facto restricted in its use of the relationship between just two general parameters, VE
and SAet, to characterize gas transport through the air and tissues phases. Another
observation from Table 3-4 is that the DAF values from modeling are all one or greater
despite the wide range of gas descriptions and characteristics shown in
3-24
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Table 3-3, including uptake (11 - >90%), water solubility (5 - 1,000 g/L), and tissue:air
partition coefficient values (<3 to >6000). Additionally, these modeled outputs of DAF >
1 were achieved through a similarly wide range of dose metrics including those based on
maximum target tissue flux and/or maximum target tissue flux at the rat NOAEL, target
tissue concentration, target tissue metabolite concentration, adduct concentration, and
even changes in intracellular pH. Despite these wide ranges of sensitive parameters and
variables, and gas descriptors ranging from "soluble-reactive", to "nonsoluble-reactive"
to "nonsoluble-nonreactive", the gases in Table 3-4 all achieved the same internal target
tissue dose in both rats and humans at either similar (DAF ~ 1) or greater (DAF >1)
external concentration. It is important to note that molecular markers are emerging as
useful dosimeters (Ostcrm an-Golkar et al.. 2003; Rios-Blanco et al.. 2003) and as noted
for dimethyl sulfate (Sarangapani et al.. 2004).
Table 3-4 Comparison of approaches for calculating the DAF for representative gases in
determining the HEC - portal of entry ET or nasal effects
V/SAet3 CFDb CFD-PBPK PBPKC References
hybrid0
Formaldehyde HEC = 0.2 * DAF = 1.26 (based Kimbell et al.
AEL on the target tissue (2001b)
max flux of 2620 in
Rat and 2082 in
Human at 1 ppm)
Acrylic Acid
HEC = 0.2*
AEL
DAF = 1.36
(based on target
tissue dose at
the Rat NOAEL
of 25 ppm)
Frederick et al.
(1998):
Andersen et al.
(2000)
Acrolein
HEC = 0.2*
AEL
DAF = 1.4
(based on Rat OE°
NOAEL =
0.6 ppm: highest
flux of 682 in Rat
and 476 in Human)
HEC = 0.2*
AEL
DAF = 2.1
(based on 0.6 ppm
model results of
max flux rates in
the anterior nasal
airways of 1,400 in
Rat and 660 in
Human)
Schroeter et al.
(2008):
Morris et al.
(1998)
Corley et al.
Acetaldehyde HEC = 0.2 * DAF = 1.4 Teegaurden
AEL (based on et al. (2008):
steady-state Dorman et al.
tissue (2008): Morris
concentrations e* a'- (1SHZ.)
at the Rat
NOAEL =
50 ppm
3-25
-------
V/SAet3 CFDb
CFD-PBPK PBPKC
hybrid0
References
Di acetyl
HEC = 0.2*
AEL
DAF = 1 (based
on nasal and
tracheal
target tissue
concentration)
Morris and
Hubss (2009)
Vinyl Acetate
HEC = 0.2*
AEL
DAF = 1.14
(based on
equivalent
change in OE
intracellular pH
at the Rat
NOAEL)
Bogdanffy et al.
(1999)
Hydrogen
Sulfide
HEC = 0.2*
AEL
DAF = 2.6 (based
on Rat OE NOAEL
10 ppm: highest
flux of 34 in Rat
and 13 In Human)
Schroeter et al.
(2006)
EthylAcrylate
HEC = 0.2*
AEL
DAF = 3 (based
on target
internal
metabolite
concentration
Sweeney et al.
(2004);
Frederick et al.
(2002)
Propylene
Oxide
HEC = 0.2*
AEL
DAF = 1 (based Csanady et al.
on eauivalent (2007): Morris
concentrations in (2004)
RE and venous
blood at <
50ppm)
Dimethyl
Sulfate
HEC = 0.2*
AEL
RE: DAF = 7;
OE: DAF = 2
(based on tissue
N7mG adduct
concentration)
Sarangapani
et al. (2004)
Calculated based on procedures in U.S. EPA (1994) RfC Methodology where:
HEC = DAF (RGDR) x Adjusted Exposure Level (AEL - based on NOAEL, LOAEL, or BMCLx)
DAF or RGDR = /SAET-anima|A/E /SAET-human
DAFet = (0.18 L/min/15 cm2)/(13.8 L/min/200 cm2) = 0.18 or 0.2
(SAET-animai = 15 cm2; VE = 0.18 L/min for a 250 g rat; SAET-human = 200 cm2; VE = 13.8 L/min for a 70 kg human)
bResults from CFD simulation modeling - DAF based on comparative animal (rat): human flux values as indicated
°Results from CFD-PBPK hybrid or PBPK modeling - DAF based on modeled target tissue dose or dose metric as indicated
dRE = respiratory epithelium; OE = olfactory epithelium
As is the case with all "state of the science" techniques and approaches, limitations and
restrictions need be considered, including those of the CFD-PBPK hybrid models
presented and described here. These hybrid models may be considered somewhat limited
in their refinement of typical PBPK models relative to the linked CFD model. The
surface area characterized by the PBPK tissue "stack" is much less refined and defined
than the flux values to that same area characterized by the linked CFD model. The gas
3-26
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flux portion of the hybrid model for this area is represented by a localized kg into which
some of the flux has been collapsed and incorporated. Therefore, small localized areas of
very high flux may be diluted or not sufficiently characterized especially for gases
exhibiting high flux, i.e., gases that are highly reactive and that have high uptake.
Conversely, this refinement limitation would be less applicable to gases that are not
highly reactive and that have lower uptake. In addition, due to the common practice of
having and utilizing a single constant value for partition coefficients, these models are
most appropriately used under exposure conditions that do not approach the limits of
solubility and the concomitant establishment of biphasic conditions. In general, these
conditions would be those within the linear range of solubility that also allow for
maximization of all clearance processes, i.e. low level, long term exposures. Thus,
although the currently available CFD-PBPK models are considered to provide better
estimates of target tissue dose compared to conventional default methods, they may also
be characterized as providing more certainty for relatively nonreactive gases versus
highly reactive gases and for lower rather than higher concentrations of reactive gases.
3.3 Major Scientific Advances Related to Inhalation Gas Dosimetry in
the TB and PU Regions
Complexity of airway structure, large variations in the geometry of lung airways, multi-
scale dimensions of airway parameters (length, diameter, etc.), lack of measurements of
all airways including the alveoli, uncertainty regarding airflow distribution among
pathways of the lung, and inter-subject variability (also true for the upper airways)
combine to make modeling in these regions a challenge. In order of preference, the
approaches for lung uptake modeling are (1) CFD modeling, (2) hybring whole lung-
CFD modeling, and (3) whole-lung modeling. The use of CFD for a region and entire
lung should be attempted when pertinent information is available. In the hybrid model,
MRI- or CT-based images of the upper airway (including nasal and first few generations)
are reconstructed computationally and are attached by whole-lung models at the distal
ends to create a computational domain for the entire lung. The advantage of hybrid model
is that it uses state-of-the-art in computational resources and fills in the missing
information with mechanistically-based whole-lung models. For the whole-lung
modeling, representative geometry of the entire respiratory tract is used in the area-
averaged mass balance (convective-diffusion) equation for gases to find regional gas
uptake.
The following sections summarize the major scientific findings related to the current
default procedure for interspecies inhalation dosimetric extrapolation for gases in the TB
and PU regions. The information evaluated includes results and observations from
anatomically based airflow, deposition, and fluid dynamics modeling as well as from
chemical specific interspecies physiologically based pharmacokinetic (PBPK),
3-27
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computational fluid dynamics (CFD), CFD-PBPK hybrid models, and whole-lung
modeling.
3.3.1 Air Flow and Deposition Modeling in the TB Region
A number of conceptual and simulation modeling approaches for both the TB and PU
region are under investigation. However, many of these approaches are being examined
using only human model structures thus limiting their utility in directly informing
interspecies dosimetric extrapolation for risk assessment purposes. Nonetheless, several
studies provide useful qualitative information relative to the current default approach of
Ve/SA.
Taylor et al. (2007) examined the pattern of lung injury resulting from exposure to ozone.
The distribution of ozone uptake was studied in a single, symmetrically branched airway
bifurcation using CFD. Separate simulations for inspiratory and expiratory flows were
conducted at laminar flow conditions to examine the effect of flow rate on uptake. The
simulations demonstrated the total rate of ozone uptake increased with increasing flow
rate during both inspiration and expiration and that flux progressively decreased along the
parent branch. In addition, hotspots of ozone flux were observed at the carina of the
bifurcation for all simulated flow rates. Compared to a straight tube with a similar surface
area, the presence of branching resulted in a enhancement of overall uptake.
Padaki et al. (2009) used CFD modeling to simulate the transport and uptake of ozone for
comparison between an idealized model of the larynx, trachea, and first bifurcation and a
"control" model in which the larynx was replaced by an equivalent, cylindrical tube
segment. This comparison was performed in order to examine the effect of laryngeal
geometry on flow behavior. The results revealed a strong laryngeal jet with a
reattachment point in the proximal trachea indicated by an increase in flow velocity and
abrupt geometry change in flow. Jet turbulence occurred only at the high Reynolds
numbers and was attenuated by the first bifurcation. Hotspots previously reported at the
first carina were confirmed by the local fractional uptake data; additional hotspots at the
glottis and proximal trachea were also observed. Maximal laryngeal effects (-15%
enhancement of uptake efficiency) occurred at the highest flow rate. Although the
increase in regional uptake subsided by the end of the model (i.e. the first bifurcation),
the effect of the larynx on cumulative uptake persisted further downstream. Together,
these results suggested that with prolonged exposure to a reactive gas entire regions of
the larynx and proximal trachea could show effects of tissue exposure.
Zhang et al. (2006) employed a representative human upper airway model to describe
uptake and deposition of MTBE and ethanol vapors. This description was accomplished
using CFD approach. Model simulations were done under varying conditions, including 3
inspiratory flow rates (Qm = 15,30, and 60 L/min). The airway model utilized was
3-28
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created from a human cast consisting of two parts: the oral airway, including oral cavity,
pharynx, larynx and trachea; and a symmetric triple bifurcation representing generations
GO (trachea) to G3 (referred to in their report as the "upper bronchial airway" or UBA).
To attain representative modeling of airflow in such a model, a low-Reynolds-number
model was selected (to assure laminar flow and constant fluid motion) and adapted to the
laminar-to-turbulent flow regimes that are likely to occur in the human airway during
inhalation at the flow rates employed in the simulations. The deposition of vapors in each
airway segment was described by the deposition fraction (DF), which was calculated with
the regional mass balance and the sum of local wall mass flux. An uptake parameter (K)
was also calculated for both ethanol and MTBE using available values of diffusivity of
vapor in air and liquid mucus phase and equilibrium partition coefficients in gas and
liquid interfaces. The respiratory mass transfer coefficient (called hm by the authors) was
also estimated.
The simulations showed that flow rate had a strong effect on vapor deposition; the lower
the flow rate, the higher the deposition fraction due to the extended vapor residence
times. Results showed that as the flow rate decreased from 60 L/min to 15 L/min, DF for
MTBE increased from 2.5% to 7.7% in the UBA. The simulation showed further that the
DFs increased in a nearly linear fashion with the distance into the airway, indicating
consistent deposition efficiency along the airway passage. Compared with MTBE, DF
values of ethanol were approximately three to six times greater in the oral airways and
two to five times greater in the UBA in the range of flow rates used. The higher
deposition of ethanol vapor may be attributed not only to its higher diffusivity but, more
importantly, to its higher solubility in the mucus layer as indicated by the value of K for
ethanol (413) compared to MTBE (11). Vapors that pass through the upper airway may
further penetrate into and deposit partly in the lower airway and alveolar regions.
Compared to ethanol, this suggests MTBE may penetrate further and thus deposit in the
lower airways.
Simulations based on the mesh were analyzed by the authors on a more refined scale.
Local vapor deposition patterns were quantified in terms of a deposition enhancement
factor (DEF), which is defined as the ratio of local to average deposition densities, DEF
therefore being an indication and representation of vapor deposition "hotspots" in a given
region. Figure 3-7 and Figure 3-8 show the distributions of these DEFs in the airway
components of the model. These deposition patterns were clearly not homogeneous and
were nonuniform for ethanol, which is relatively highly absorbed in the UBA, and for
MTBE, which is not highly absorbed. The maximum DEF was ~1.5 for MTBE in the
UBA with the value reaching 7.8 in the UBA model for ethanol. The low maximum DEF
values for MTBE indicated that deposition of MTBE vapor was relatively uniformly
distributed in the upper airways with relatively little absorbed by the airway walls
whereas the opposite appears to be the case for ethanol with the greater overall absorption
allowing for more contrasting differences and higher DEF "hotspots."
3-29
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In the bifurcation airway model, enhanced deposition occurred mainly at the carina
ridges and the inside walls around the carina ridges, due to the complicated airflows and
large concentration gradients in these regions. When the absorption parameter (K)
increases above the typical value, however, deposition of MTBE increases but with
deposition patterns being about the same. With increasing absorption, however, the
locations of enhanced deposition receive even greater deposition and the maximum DEF
values increase.
3-30
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GO-3
Source: Reprinted with permission of Informa Healthcare; Zhang et al. (2006)
Figure 3-7 Distributions of deposition enhancement factor (DEF) for MTBE vapor with
Qin = 30 L/min in the bifurcation airway models.
Source: Reprinted with permission of Informa Healthcare; Zhang et al. (2006)
Figure 3-8 Distributions of deposition enhancement factor (DEF) for ethanol vapor with
Qin = 30 L/min in the bifurcation airway models.
These simulations utilized a three dimensional computational fluid dynamic simulation
method and provided detailed local deposition patterns for both MTBE and ethanol,
agents widely disparate in uptake, transport and deposition. These deposition patterns
showed clearly that tissue burdens at local sites may exceed by many times the average
dose of the airways, i.e. they are highly nonuniform. Whereas flow rates greatly affected
deposition fractions, deposition patterns were not much altered. The localized deposition
pattern suggested that the uptake pathway may have a preferential route along which
local tissues are subjected to heavy exposure of vapors much the same as has been
3-31
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demonstrated for formaldehyde in the ET region of rats (e.g.. Kimbell etal.. 1997b).
Thus, this enhanced deposition at local sites in this lower region of the respiratory tract
may also result in tissue damage or other adverse biological responses at local sites in the
first four generations of the human tracheobronchial tree.
In more recent work, Zhang et al. (2011) used a human CFD model to estimate local and
regional uptake of napthalene and tetradecane, considering three different breathing
scenarios (nose-only, oral-only, and a combination). This upper airway CFD model
consisting of the oral airways, nasal airways, and trachea (asymmetric bifurcations, GO-
GS) was developed based on previously published geometries. The authors found the
airway wall absorption is a key determinant of deposition in the respiratory system, while
other parameters (e.g., diffusivity, airway geometry, breathing patterns, inspiratory flow
rates) are also key factors. The representative absorption parameter, K, was 4 orders of
magnitude greater for napthalane (7.3 cm"1) compared to tetradecane (7.4 x 10"4 cm"1)
because of physical properties of the chemicals and variations in the thickness of the
mucus layer. Thus, tetradecane was found to have a DF <1% in the ET and TB regions,
due to its low solubility in the mucus layer, while DFs in the alveolar region ranged from
7-24% depending on inhalation rate and mucus thickness. However, the opposite was true
for napthalene which deposits mostly in the ET (DF of 12-34%) and TB (DF of 66-87%)
regions. Zhang et al. (2011) also investigated the local deposition patterns assuming
perfect wall absorption (K —> qo) (Figure 3-9. The simulated local deposition patterns of
napthalene vapor for concurrent nasal and oral breathing for (A) K=7.3 cm"1 and (B)
perfect wall absorption. This figure shows nonuniform deposition patterns and
deposition in the upper airways is more uniformly distributed with lower wall absorption.
The locations of enhanced deposition may not change; however, the maximum DEF
value increases with increasing absorption.). This showed nonuniform deposition
patterns and that the vapor deposition in the upper airways is more uniformly distributed
with lower wall absorption. Their results also showed that the variation in breathing
route (nasal vs. oral) for both chemicals does not substantially impact vapor deposition
beyond the larynx (Figure 3-10. Total deposition fraction is independent of breathing
mode at the larynx and beyond.).
3-32
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(a)
DEF
I
2.20
1.98
1.76
1.54
1.32
1.10
0.88
0.66
0.44
0.22
0.00
-------
0.3
0.25
0.2
•fc
c
a
"8
Cl
0.15
0.1 +
0.05 {
0
~ Half nasal inhalation and
half oral inhalation
n Nasal inhalation only
~ Oral inhalation only
rl-i fl nn fH ITI m rrn ril
^ ^
^ J? & & &
-------
coefficients at each bifurcation unit was also closely predicted, and the average
concentration variation axially was qualitatively the same in both the predictions from the
CFDM and ASPM models with quantitative differences observed likely due to the
differences in flow characteristics in the branches. The authors concluded that these
results indicated that the "simplified" ASPM was very useful in predicting mass transfer
coefficients, flux at the walls, and hence injury sites as accurately as the "complex"
CFDM in symmetric lung systems where it was not possible to measure them. Similar
observations were made by Madasu et al. (2008).
3.3.2 Advances in TB Inhalation Dosimetry Modeling
Recently, Morris and Hubbs (2009) characterized the inhalation dosimetry of diacetyl, a
component of butter flavoring vapors, through development of a CFD-PBPK hybrid
model. Upper respiratory tract (URT) uptake of diacetyl was measured experimentally
and used to validate the model. Model simulations were then performed to estimate tissue
(anterior and posterior) and airborne concentrations of diacetyl for the URT (i.e. nasal)
and trachea in rats and humans. At an exposure concentration of 100 ppm, tissue
concentrations in the nose were estimated to be 1.6 and 1.4 mM in rats and 1.4 and 1.2
mM in humans, and in the trachea were estimated to be 1.2 and 1.1 mM in rats and 1.2
mM in humans. The air exiting the URT was estimated to be 67 ppm in rats and 82 ppm
in humans, and air exiting the trachea was estimated to be 61 ppm in rats and 79 ppm in
humans. When the human model was run for mouth breathing only, the tissue
concentrations in the trachea were predicted to be 1.5 mM and the air exiting this region
to be 96 ppm. These results demonstrated that target tissue concentrations of diacetyl in
the trachea were highly similar in rats and humans and that diacetyl may penetrate to the
lower airways of humans to a greater degree than in rats. The authors concluded that
based on these dosimetric relationships and differences in regional uptake efficiencies,
upper airway injury in the rat may be predictive of lower airway injury in humans.
Tsujino et al. (2005) developed a simplified mathematical airway model to simulate the
transport of gases (ozone [03] and sulfur dioxide [S02]) in airways of laboratory animals
(rats and dogs) and humans. The aim of the study was to examine through model
simulations how interspecies anatomical and physiological differences influence the
transport of the inhaled gases throughout the airways and alveoli. This comparison could
potentially provide an interspecies comparison of gas dosimetry in airways. The authors
acknowledge and document that nearly all input parameters used were assumed or scaled,
albeit with reasonable assumptions and allometry. Gas absorption at the surface of the
airways was determined by mathematical formulations incorporating the basic elements
of diffusivity and absorption constants (which included the absorption rate at the airway
surface) that were scaled to each gas. The basis for this scaling was actual absorption data
and concentration differences for these gases obtained by direct measurements in dog
3-35
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airways. Real-time changes in gas concentrations were simulated at three airway sites in
each species: (1) the upper airway, (2) the lower airways consisting of the 5th or 10th
bronchial generation and (3) the alveolar region. The amount of 03 and S02 absorbed
(modeled assuming a 10% concentration) at the airway surface was then calculated.
Interspecies comparison was also performed for the amount of gas absorbed per body
weight (g/BW), and for the corrected amount of gas absorbed per unit of airway surface
area (g/cm2). The results obtained for 03 and S02 are shown in Table 3-5 below.
Table 3-5 Modeled predictions of amount of 03 and S02 absorbed at various sites in the
airways of three species
Parameter
Rats
Dogs
Humans
Ozone
Total absorbed amount (g/kg BW)
1.1 x 10"7
1.46 x 10-7
0.847 x 10-7
Upper airways (% of total)
73.9
80.7
34.4
Lower airways (% of total)
23.4
16.3
60.7
Alveolar region (% of total)
2.7
3.0
4.9
Absorbed amount per SA/unit time
Upper airways (g/cm2/ min)
1.76 x 10"7
0.89 x 10-7
1.31 x 10"7
Lower airways (g/cm2/ min )
3.52 x 10"8
1.29 x 10-8
7.58 x 10-8
Alveolar region (g/cm2/ min)
1.56 x 10 13
1.23 x 10 13
1.40 x 10 13
Sulfur dioxide
Total absorbed amount (g/kg BW)
1.77 x 10-7
3.24 x 10"7
1.61 x 10"7
Upper airways (% of total)
98.6
99.4
96.5
Lower airways (% of total)
1.4
0.6
3.5
Alveolar region (% of total)
0.0
0.0
0.0
Source: Reprinted with permission of Informa Healthcare; Tsujino et al. (2005)
These simulations indicate that the amount of 03 absorbed per body weight throughout
the airways was lowest in humans (Table 3-5). However, the amount of absorbed 03 per
surface area in each airway were fairly equivalent in the upper airways and alveolar
regions, and were higher in humans in the lower airways - over 2 times that of rats. This
trend was noted also for S02. Concentrations of S02 in the lower airways and alveoli
were low in all species, which reflects the predicted rapid absorption of the gas in the
upper airway. Also, these simulations were for short periods of inhalation and relatively
high concentrations of these agents. It should be noted that many simplifications and
assumptions were necessary in order to accomplish the simulations. Some of these were
application of a simple three-compartment model of the airways and alveoli, without
specific consideration of the effects of different branching patterns on the airway surface
areas. Coaxial diffusion of gas molecules was not taken into account in the simulations,
3-36
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as it is well known that gas molecules in airways are transported by both bulk flow and
diffusion. Thus the modeled gas concentrations might not accurately reflect actual
concentrations, particularly in the peripheral airways and in the alveoli. Nonetheless this
study is of considerable value for further hypothesis testing regarding the variations in the
kinetics of inhaled gases among experimental animals and humans. It numerically
demonstrated that interspecies variations in anatomy and respiratory patterns cause
significant differences in gas transport in the airways and alveoli of rats, dogs, and
humans.
The results of Morris and Hubbs (2009) and Tsujino et al. (2005) estimate similar target
tissue doses in the TB and PU regions between rats and humans albeit by different
modeling approaches (i.e. CFD-PBPK hybrid vs. simplified mathematical model). These
indicate and support an approximate DAF of 1 when extrapolating from rats to humans.
This is in contrast to the results obtained when applying the default RfC Method using
Ve/SA which gives rat to human DAFs of approximately 2.6 for the TB region and 3 for
the PU region.
Flux-based dosimetry estimates for formaldehyde gas to the TB and PU regions were
developed by Overton and coworkers (2001). These estimates were inclusive of
calculations for overall mass transport coefficients for the lower respiratory tract.
Formaldehyde transport and uptake for the generations comprising the TB and PU
regions were all approximated by a one-dimensional (ID) convection-dispersion equation
that accounted principally for molecular diffusion and absorption at the air-liquid surface.
The mass transfer coefficients in the nasal cavity were estimated by matching (within
0.2%) the percent uptake predicted by an existing CFD model of transport during
inspiratory flow through an anatomically accurate reconstruction of the nasal passages of
an adult human male. The resulting overall identical-path nasal airway mass transfer
coefficients multiplied by the nasal surface area, corresponding to minute volumes of 7.5,
9.0, 25, and 50 L/min (nasal steady-state inspired flows rates of 15, 18, 50, and 46 L/min)
were 1.68, 1.78, 2.98, and 2.83 cm/s, respectively. The Kg for the lower airways was
calculated with extensive consideration given to the kg component. The ID equation of
mass transport was then applied to each generation airway and airway passage of a
symmetric, bifurcating respiratory tract anatomical model to provide predictions of local
formaldehyde surface fluxes (dose). The results obtained included the following:
• More than 95% of inhaled formaldehyde is predicted to be retained by the
respiratory tract for all activity states simulated (for a total of 4 different minute
volumes).
• In the lower respiratory tract, surface flux (dose) is predicted to increase for
several generations and then decrease rapidly.
3-37
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• Compared to first pulmonary generation fluxes, the first few tracheobronchial
generation fluxes are over 1,000 times larger.
• There is essentially no flux in the alveolar sacs.
The authors stated the predicted fluxes based on the ID model for those lower regions of
the respiratory tract can be used in dose-response modeling. This work provided
information on mass transfer coefficients for the PU and TB regions including their
derivation, and demonstrated their use in a dosimetry model for these regions.
3.3.3 Air Flow and Deposition Modeling in the PU Region
3He MRI has been especially used to visualize dynamic ventilation during both
inspiration and expiration of ventilation in normal individuals (Kauczor et al.. 2002).
Application of this technique indicates that normal ventilation in healthy lungs is
represented by a completely homogeneous distribution at the level of resolution of 3He
signal. Figure 3- illustrates the in-life rapid and homogenous filling of the airspaces
bilaterally (the numbers correspond to the sequence imaging times). In volunteers the
inflow of 3He was shown to be very rapid with the discernible signal appearing almost
simultaneously in the upper, middle and lower portions of the lung with a uniform
wash-in and wash-out of the gas also observed. Further advances, involving echo-planar
imaging of axial slices having rapid temporal resolution times of 122 ms, are able to
demonstrate preferential ventilation of the posterior lung zones in supine individuals,
again through visualization of areas of nonhomogenous flow in the lung. Further
demonstrations of the resolution of the 3He- imaging is the capacity to observe even small
(2 cm) transient ventilation defects in the lungs of smokers that appear as
nonhomogeneous flow and distribution. In clinically healthy smokers even markedly
smaller ventilation defects leading to nonhomogenous flow, such as those thought to
correspond to chronic inflammation and obstruction of small airways caused by smoking,
can be detected with 3He MRI. Thus, these techniques provide an approach to acquire
regional information on lung morphology and pulmonary function.
3-38
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Source: Reprinted with permission of Springer Berlin/Heidelberg; Kauczoret al. (2002)
Figure 3-11 Dynamic ventilation He MRI after inhalation of hyperpolarized sHe gas.
Whole-lung dosimetry models do not account for the flow field to the level of inside the
alveoli and therefore may not accurately describe alveolar flow or deposition. To better
understand the fluid characteristics at this level of the lung. Harding and Robinson (2010)
employed CFD to a model of a terminal air sac much in the manner that it has been
applied to other respiratory tract regions, notably the extrathoracic (ET) region. An
expanding terminal alveolar sac using truncated spheres to represent individual alveoli
was modeled numerically, based on dimensions from human lung casts. The flow field is
quantified for a breathing cycle derived from pulmonary function test measurements. The
alveolar sac model was considered representative of a terminal air unit in humans that
could be present in Weibel generations 19 and below based upon dimensions from
literature. The wall motion of the alveolar sac model (full expansion of 15.6% over the
initial volume or functional residual capacity - FRC) was obtained in vivo using a
spirometer for a 21-year-old female breathing normally in the sitting position. Model
output was obtained for detailed regional flow rates, alveolar mouth to depth flow rate
ratio, and penetration depth of residual air. Figure 3- demonstrates the directionality and
range of regional flow velocities as well as their extent of incursion into the sac, all
obtained from the model (Harding and Robinson. 2010). Examination of the flow field in
the alveoli revealed no recirculation during any point in the breathing cycle. Other
parameters addressed with the model included the flow rate ratios of alveolar mouth to
duct flow that were noted in the range of 0.18-0.36. Penetration depths were less than
33% into the air sac during inhalation, decreasing in length for air inside the sac to zero
3-39
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near the wall. These results indicated dominance of diffusive motion over convective
motion and flow at the level of the alveoli. However, more studies are needed before
quantification of flow fields in the alveolar region can be clearly understood as the ratios
that are present in vivo are also unknown.
Velocity Magnitude
(m/s)
B1.2E-04
1.1E-04
1.0E-04
9.6E-05
8.8E-05
8.0E-05
7.2E-05
6.4E-05
5.6E-05
4.8E-05
4.0E-05
3.2E-05
2.4E-05
1.6E-05
8.0E-06
fvA -r'
ff* i iA
v-. 71
T X
0.00°
-0.200
-0.400
-0.600
Point A
-0.200-0.100 0.000 0.100 0.200
X (mm)
Source: Reprinted with permission of Informa Healthcare; Harding and Robinson (Harding and Robinson. 2010).
Figure 3-9 Simulated flow velocities from CFD solutions in an alveolar sac model.
In an earlier study, Tsuda et al. (2002) observed flow patterns of different colored
polymerizable fluids, representing tidal and residual air, injected into rat lungs in a
manner to simulate inhalation of tidal air. These authors concluded that the swirls seen in
the solidified cast in the large, medium, and alveolar airways were characteristic of
chaotic flow. They observed swirl patterns in alveoli that became more intense with
increasing number of cycles, which were not seen by Harding and Robinson (2010). who
utilized a model of terminal air sacs. Although these authors did not indicate whether
their observations were from a terminal sac or a respiratory bronchiole, it is possible that
the patterns observed by Tsuda et al. (2002) occurred higher up in respiratory bronchioles
where the flow rate ratio was large enough to cause irreversibility.
It is clear that more studies are needed on pulmonary fluid flow to better understand the
nature of tidal and residual air mixing and the conditions under which mixing occur. It is
apparent from these disparate results that more corroborating evidence is needed before
actual flow fields in the terminal air sacs are understood. In addition, the occurrence of
significant localized deposition cannot be excluded without additional studies.
3-40
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3.3.4 Advances in PU Inhalation Dosimetry Modeling
Following from the work of Morris and Hubbs (2009). Gloede et al. (2011) developed a
CFD-PBPK model for inhalation dosimetry of diacetyl in rats and humans to compare
respiratory tract vapor absorption focused on the lower respiratory tract. The CFD-PBPK
modeling approach that had been utilized for URT dosimetry was expanded to include
the lower respiratory tract (LRT) based on anatomical models reported elsewhere in the
literature. Using this detailed model, they estimated human and rat bronchiolar tissue
concentrations of diacetyl. The difference between the human and the rat were more
marked under light exercise and mouth breathing. The largest difference was in diacetyl
bronchiolar concentrations for the mouth breathing-lightly exercising human that
exceeded the nose breathing rat by 20- to 40-fold. Diacetyl bronchiolar concentrations in
other human scenarios (human nose breathing at rest and human mouth breathing at rest)
were only 3-7 times greater than the nose breathing rat for a 10 minute simulation at
lppm (Gloede etal.. 2011). These differences may in large part be due to the greater
distal penetration of inspired diacetyl in the human than in the rat as also shown in Morris
and Hubbs (2009). This CFD-PBPK model provided a method to predict diacetyl
concentrations in tissues under multiple scenarios that are not easily obtainable via
controlled laboratory experiments.
More recently, Asgharian et al. (2012) published a mechanistic model of vapor uptake for
inhalaed soluble, reactive vapors (formaldehyde, acrolein, and acetaldehyde) in the
human lung for a single breath. Since formaldehyde is highly reactive and soluble, it was
mostly absorbed in the trachea with 97% of the inhaled dose estimated to be absorbed.
On the other hand, acrolein and acetaldehyde are moderately soluble, thus they were
absorbed deeper in the lung, with acetaldehyde being absorbed more distally than
acrolein. However, overall uptake for acrolein was slightly greater (84%) than for
acetaldehyde (80%). Tissue concentrations, following inhalation of 1 mg/m3, of each
chemical were calculated during the inhalation, pause, and exhalation phase of one
breathing cycle. This mechanistic lung dosimetry model is the first to provide a
prediction of the transient tissue concentrations over the entire breathing cycle and how
the tissue concentrations impact the absorption from the airway. The model showed that
flux patterns do not always correspond to tissue dose and concentration and confirmed
that tissue thickness is a critical determinant for absorption into systemic circulation.
The Asgharian et al. (2012) model lacks description of the URT which does impact the
dose delivered to the lung; however, Corley et al. (2012) report a more complete CFD-
PBPK model for acrolein that extended from the nasal cavity to on average 9, 17, or 19
generations in the pulmonary region for the human, rat, and monkey, respectively. This
model found flux values in ET region to be greatest in the rat compared to the monkey
followed by the human; comparisons were not made for the TB or PU regions. In the
LRT flux rates were low in comparison with those observed in the URT. Hot spots were
identified in areas with changing airflow directions and velocities and potential sites of
3-41
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metabolism. When the model was run at twice the VE, peak concentrations were 258%
higher. These results are similar to what was found in acrolein nasal extraction studies by
Morris and Hubbs (2009) and the light-exercising human diacetyl nasal extraction
estimations of Gloede et al. (2011) in which an increase in flow rate increased distal
penetration.
3.4 Advances in the Measurement of VE and Airway Geometry
Within the past decade, advancements have been made in the methods used to measure
ventilation rate (VE) and determination of airway geometry. The advancements in
determination of VE were presented in detail in the Status IIReport (U.S. EPA. 201 lb).
Briefly, two prominent approaches for inhalation rate measurement in a nonclinical
setting include (1) activity pattern questionnaires where oxygen consumption is
calculated from daily activity patterns/energy intake and (2) differential dilution of
isotopes in water administered orally as a bolus, usually over a two-week period. This
latter method, the doubly labeled water (DLW) method, measures oxygen lost through
carbon dioxide production. The DLW method was used to calculate the physiological
daily inhalation rates (PDIR) for 2,210 individuals aged 3 weeks to 96 years (Brochuet
al.. 2006b). These were considered state of the science and included in EPA's recently
updated Exposure Factors Handbook (U.S. EPA. 201 la) and evaluated in the Status II
Report (U.S. EPA. 201 lb). More recently, Brochu et al. (2011) improved upon their
initial calculations of PDIRs by including both daytime and nighttime respiratory
parameters (oxygen uptake factors and ventilatory equivalents) in the calculation, thereby
providing more precise PDIRs for use in risk assessment. In general, these values were
within 10% of the values reported earlier by Brochu (2006b. a; 2006c). Kawahara et al.
(In Pressln Press. 2011) also calculated PDIR values for 5-6 year old Japanese children
for an average of weekday and weekend activities. Overall, these results were slightly
less than the values determined by Brochu et al. for both genders.
In addition, methods to determine airway geometry and surface area have evolved,
providing more reliable values for use in inhalation gas dosimetry. These are summarized
below and more detail is also provided in the Status IIReport (U.S. EPA. 201 lb).
3.4.1 Lung Geometry and Surface Area
The estimation of alveolar number in the lung has traditionally been done by assuming a
specific geometric shape. These geometries are then applied to small sampled volumes of
pulmonary tissue. However, the realizations that there exists a diversity of alveolar
shapes and that statistical error from small sample size and bias may be considerable,
have led to alternative approaches. Hyde et al. (2004) synthesized recent approaches and
3-42
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technologies that were designed to be less prone to error and bias and therefore produce
more reliable counts. These authors employed the following for the counting of alveoli in
the lungs of monkeys and rats: a fractionator which allows for systematic random
sampling from blocks of variable slab thickness (thereby minimizing the inaccuracy
inherent in using section sampling fractions based on the average thickness of sections of
variable thicknesses); use of the Euler characteristic of the net of alveolar openings to
estimate alveolar number; the disector principle (usually a counting probe for isolated
objects) as a sampling probe of the Euler characteristic. The Euler characteristic of
structure (an integer)2 applies to any level of topological complexity and is not biased
toward any specific geometry (as have other attempts to count alveoli).
Lung tissues from four male and one female rhesus macaques (Macaca mulatto) ranging
in age from 28 to 157 months and in body weight from 3.4 to 11.6 kg, as well as tissue
from five male Wistar rats with age not given and varying in body weight from 503 to
625g were used for this study. Using this approach on these tissues indicated the number
of alveoli in the two left lung lobes in the monkey ranged from 48.8 x 106 to 67.1 x 106
with a mean of 57.7 x 106. The average number of alveoli in the rat lung ranged from
17.3 x 106 to 24.6 x 106, with a mean of 20.1 x 106. With age (2-13 years) the alveolar
volume increased 3-fold (as did parenchymal volume) in monkeys, but the alveolar
number was unchanged. The lung volumes as estimated in rats are presented in Table 3-6.
Table 3-6 Estimates of right, left, and total lung volumes in male wistar rats
Lung Volumes (cm3)
Animal #
Body Weight (g)
Right Lung
Left Lung
Total Lung
R5
503
10.6
00
CO
19.4
R3
528
8.0
4.2
12.2
R4
573
11.4
5.5
16.9
R1
595
10.2
5.3
15.5
R2
625
12.3
6.1
18.4
Mean
565; (CV 0.09)
10.5; (CV 0.15)
6.0; (CV 0.29)
16.5; (CV 0.17)
CV = coefficient of variation
Source: Reprinted with permission of John Wiley and Sons; Hyde et al. (2004)
Ochs et al. (2004) performed advanced stereologic analysis of human lungs for the
purpose of evaluating the number of alveoli present in the total lung (Table 3-7). The
2 The Euler characteristic is a number that describes a shape or structure regardless of its orientation or the manner
in which it may be bent. For simple structures it may be determined from the formula % = V -E + F, where x is
the Euler characteristic, V the vertices, E the edges, and F the faces of a polyhedron shape. For a tetrahedron, for
example, the Euler characteristic from this formula is 4 - 6 + 4 = 2.
3-43
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stereologic method for the estimation of alveoli utilized the Euler number as the basis for
quantification, eliminating assumptions and the resultant bias about the shape, the size, or
the spatial orientation or distribution of alveoli. Alveolar number was estimated using
light microscopic sections and concentrating on alveolar lumens, using their appearance
or disappearance in a physical disector as counting events. Lungs for analysis were
obtained from six cases of single lung transplantation, four females and two males. In six
adult human lungs, the mean alveolar number determined by these procedures was 480
million (240 million x 2 to account for both right and left lungs), with a range of 274-790
million and the coefficient of variation 37% (Table 3-7). Alveolar number was observed
to be closely related to total lung volume, with larger lungs having considerably more
alveoli. The mean size of a single alveolus was rather constant with 4.2 * 106 (.inr1 (range:
3.3 x 106to 4.8 x 106 |_im3: coefficient of variation 10%), irrespective of the lung size. The
authors calculated that one cubic millimeter of lung parenchyma contains around 170
alveoli. No further attempts were made by the authors to obtain estimates for other
parameters including surface areas, although such calculations were feasible.
Table 3-7 Summary data on human lung alveolar number and volume
Parameter
Lung 1
Lung 2
Lung 3
Lung 4
Lung 5
Lung 6
Mean Value
Gender
(age)
Female
(31)
Female
(41)
Female
(18)
Female
(37)
Male
(24)
Male
(20)
Lung
analyzed
Left
Right
Right
Left
Right
Left
N (alv), 10s
137
226
220
185
275
395
240 ± 89
V (lung), cm3
1,031
1,273
1,509
1,103
1,917
2,317
1,534 ± 521
N/V (mm3)
132
178
146
168
143
170
156
Source: Reprinted with permission of American Thoracic Society; Ochs et al. (2004'
Wiebe and Laursen (1995) compared a stereological morphometric method with a
standard fluid displacement method for determination of volume of right human lungs
obtained from 4 cadavers. Comparison showed that the two methods were in very close
agreement (Table 3-8). These authors then completed a stereological estimation of
alveolar surface area of these same lungs. Specifically sampled sections of lung tissue
(vertical and isotropic uniform random, IUR) were evaluated by specific counting
techniques related to a test line in a reference space whereas the volume of the section
was evaluated with the Cavalieri principle3. The authors also estimated that of the total
variation encountered in the processes only approximately 2% was due to the
3 For a 3-dimensional case, the Cavalieri principle is: suppose two regions in three-space (solids) are included
between two parallel planes. If every plane parallel to these two planes intersects both regions in cross-sections of
equal area, then the two regions have equal volumes. This provides an unbiased and efficient estimate of the volume
of a solid object of arbitrary shape using systematic stereologic sectioning.
3-44
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stereological variation. In evaluating their estimates of lung surface areas by these
techniques, the authors compared their results with other known determinations of lung
surface area (Table 3-8).
Table 3-8 Summary table of measures from right lungs of human cadavers
Lung Measure
Case #
Mean ± SD
Reference
1
2
3
4
Volume (L)
Fluid
1.9
1.7
1.9
2.0
1.9 ± 0.13
Wiebe and Laursen
(1995)
Cavalieri
2.2
1.7
2.2
2.2
2.1 ± 0.25
Wiebe and Laursen
(1995)
Capillary length
(m x 10s)
Vertical slices
12.3
5.6
7.5
6.3
7.9 ± 3.0
Wiebe and Laursen
(1995)
IUR
11.6
6.1
9.6
6.6
8.5 ± 2.6
Wiebe and Laursen
(1995)
SA (m2)
Vertical section
50.3
35.0
49.4
38.5
43.3 ± 7.7
Wiebe and Laursen
(1995)
IUR section
49.9
32.0
49.1
35.3
41.6± 9.3
Wiebe and Laursen
(1995)
Total SA (m2)
40-97a
Thurlbeck (1967)
78.4-81.6b
Wiebe and Laursen
(1995)
internal surface area range for 25 pairs of lungs, free from acute or chronic disease, from patients ranging from 25 to 70 years of age.
bCalculated by authors using right lung SA mean measurements of Vertical section 43.3/0.53 = 81.6 m2 and of IUR section
41.6/0.53 = 78.4 m2.
Source: Reprinted with permission of John Wiley and Sons; Wiebe and Laursen (1995)
Knust et al. (2009) employed advanced stereological morphometric techniques in
measuring lung parameters in adult female CL57B6 mice (20.6 g average weight; no N
given). Capillary length was measured using the harmonic mean of the surface weighted
diameter. The Euler characteristic was applied in the physical fractionator with varying
but known sampling fractions and enabled the estimation of alveolar number. The
estimation of volume fractions of different lung compartments was carried out by point
counting. All values were corrected for tissue shrinkage. The following measures were
obtained for adult mice lungs (mean, CV):
• total values for alveolar number of 2.31 * 106 (0.23)
• alveolar surface area of 82.2 cm2 (0.17)
• alveolar air spaces of 138 mm3 (0.29)
• capillary surface area of 124 cm2 (0.13)
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• capillary length of 1.13 km (0.13)
Bolle et al. (2008) examined functional and morphological characteristics in the
developing rat lung. Groups of specific pathogen-free Wistar-Kyoto (WKY) rats were
used for the examinations. Measures recorded included lung volume, respiratory
mechanics (intrapulmonary gas mixing, and gas exchange) and structural (alveolar
surface area, mean linear intercept length, and alveolar septal thickness) at 7-90 days.
Four males were sacrificed at each age for analysis. A selected set of measurements are
presented from this report in Table 3-9.
Table 3-9 Functional and morphological features of the developing male rat lung
Parameter (n = 4) 7 Days 14 Days 21 Days 35 Days 90 Days
Body weight (g)
22 ± 1.4
34 ± 6.5
76 ± 8.5
165 ± 13.3
417 ± 22.6
Surface area (cm2)
744 ± 20
1,175 ± 114
1,648 ± 188
3,571 ±490
6,536 ± 488
Total lung capacity (mL)
1.54 ± 0.07
1.9 ± 0.46
4.6 ± 2.6
7.8 ± 0.83
16.7 ±2.46
Alveolar wall thickness (|jm)
13.4 ±1.8
8.1 ± 0.6
5.4 ± 0.4
5.5 ± 0.8
6.4 ± 1.0
Source: Reprinted with permission of The American Physiology Society; Bolle et al. (2008)
3.5 Major Scientific Advances in Inhalation Gas Dosimetry Related to
Systemic (SYS) Sites
3.5.1 Methods and Advances for Estimating Blood:Gas (Air) Partition
Coefficients
The importance of blood:gas (air) partition coefficients (Hb/g) for PBPK models, and lack
thereof, prompted several approaches and strategies to enhance their development and
availability. Payne and Kenny (2002) reviewed, evaluated, and conducted a comparative
analysis of several predictive methods and models utilized to calculate Hb/g. As a first step
in their analysis, these authors gathered principal resources and approaches to derive Hb/g
(Meulenberg and Viiverberg. 2000; DeJongh et al.. 1997; Poulin and Krishnan. 1995;
Abraham and Weathersbv. 1994; Gargas et al.. 1989; Abraham et al.. 1985). The results
and comparisons of these various approaches are summarized and discussed in the Status
II Report.
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3.5.2 Quantitation using Inhalation PBPK Models for Systemic Sites
Physiologically-based pharmacokinetic (PBPK) models are biological, integrated
functioning systems of flow, volumes, and partitioning processes, with the purpose to
predict the time course distribution of a chemical in the body. The robustness of such
models is demonstrated by their ability to predict empirical observations.
When model simulations successfully predict empirical results, typically obtained
independent of the model, it is an indication that both the model and the sensitive critical
parameters within the model have predictive utility. For example, when models that are
parameterized and configured to predict interspecies dose extrapolation (e.g., between
rats and humans) are successful in their predictions, the model and its parameters are both
considered adequate. As referred to above, partition coefficients and in particular
blood:gas (air) partition coefficients (Hb/g), are among these critical determinative
parameters. It then follows that inhalation PBPK models that (1) are parameterized and
configured for interspecies extrapolation and (2) are successful in predicting empirical
results in animals and humans would be a source of representative Hb/g for both humans
and animals. It is the ratio of Hb/g between animals and humans that is the basis for RJC
Methods inhalation gas dosimetry for SYS effects (see Section 2.5.4). Consequently,
validated inhalation PBPK models were obtained and examined for these critical
parameters which were extracted and constructed as a ratio in accordance with the RJC
Methods. The results of this investigation are presented in
3-47
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Table 3-10. This table includes the PBPK model reference, chemical modeled, animal
gender, species, and strain when available, the method used to determine the Hb/g
employed in the model, and the A/H Hb/g ratio. Based on this analysis, the A/H ratios in
three instances were less than 1 (e.g., 0.7, 0.6, and 0.6). For 2-BE and 2-ME, the rat
values were assumed to be equal to human Hb/g values; and for napthalene and n-butanol,
the human values were assumed to be equal to the rat Hb/g values.
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Table 3-10 Compilation of blood:gas (air) partition coefficients used in Inhalation PBPK
models for animal to human interspecies extrapolation
Animal
Human
Chemical3
(Reference)
Hb/g
Species/
Strain
Method
Hb/g
Method
A/H
Ratio
PCE
(Dallas etal.. 1995)
18.9
(?SD rat
In vivo tissue cone -
time course
10.3
Sealed vial
1.8
TCE
14.3
5 Mouse
Not stated'
9.2
Not stated'
1.6
(Cronin et al.. 1995)
13.2
$ Mouse
Not stated'
1.4
Toluene
(Tardifet al.. 1997)
18
Rat
Sealed vial
15.6
Sealed vial
1.1
Toluene
(Benianus et al.. 1998)
18
Rat
In vivo
15.0
-
1.2
Xylene
(Tardifet al.. 1997)
46
Rat
Sealed vial
26.4
Sealed vial
1.7
EBZ
(Tardifet al.. 1997)
42.7
Rat
Sealed vial
28.0
Sealed vial
1.5
Ethanol
2,140
Rat
Sealed vialf
1,265
Sealed vial9
1.7
(Pastino et al., 1997)
1,244
Mouse
Sealed vial®
1,265
Sealed vial9
1.0
2-BE
(Lee etal.. 1998)
7,965
Rat
Not statedb
7,965
Sealed vial skin:
air
1b
7,965
Mouse
Not statedb
1b
2-ME
(Garqas et al., 2000)
32,800
Pregnant SD rat Sealed vialb
32,800
Sealed vial
1b
Naphthalene
(Willems etal.. 2001)
571
Rat
Calculated
cn
o
Calculated
1c
Ethylene glycol
(Corlevetal.. 2005)
17,901
$ SD & Wistar
rat
Sealed vial
17,542
Sealed vial
1.0
n-Butanol
(Teeauarden et al..
2005)
1,160
Rat
Sealed vial
1,160c
1c
PGME
(Corlevetal.. 2005)
4,866
Rat
Sealed vial
7,107
Sealed vial
(0.7)
PGMEA
(Corlevetal.. 2005)
1,251
Rat
Sealed vial
609
Sealed vial
2.0
n-Decane
(Hissinket al.. 2007)
21
Rat
Sealed vial
37
Sealed vial
(0.6)
1,2,4-TMB
(Hissinket al.. 2007)
148
Rat
Sealed vial
85
Sealed vial
1.7
Chloroform
20.8
Rat
Sealed vialh
7.43
Not Statedj
2.8
(Liao et al., 2007)
21.3
Mouse
Sealed vialh
7.43
Not Statedj
2.9
1,1,1 -TCE
(Lu et al.. 2008)
5.76
Rat
Sealed viald
2.53
Sealed viald
2.3
3-49
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Animal
Human
Chemical3 Species/ A/H
(Reference) Hb/g Strain Method Hb/g Method Ratio
Mel 39.3 Rat In vivo, sealed vial 18 Sealed vial 2.2
(Sweeney et al.. 2009) (male)
16 Rabbit (adult) In vivo, sealed vial 17.1 Sealed vial 1.0
(female)
12 Rabbit (fetal) In vivo, sealed vial 17.6 Sealed vial (0.6)
(fetal)
aChemical abbreviations: ethylene glycol monomethyl ether (2-ME); 2-butoxyethanol (2-BE); propylene glycol methyl ether (PGME);
propylene glycol methyl ether acetate (PGMEA); trichloroethylene (TCE); perchloroethylene (PCE); 1,2,4-trimethylbenzene
(1,2,4-TMB); ethylbenzene (EBZ); methyl iodide (Mel), 1,1,1-trichloroethane (1,1,1 -TCE).
bRat values were assumed to be equal to human Hb/g values in this model.
°Human values were assumed to be equal to the rat Hb/g values in this model.
dExperiments and values first reported by Reitz et al. (1988).
Experiments and values first reported by Pastino et al. (1996).
'Experiments and values first reported by Kaneko et al. (1994).
Experiments and values first reported for whole blood by Fiserova-Bergerova and Diaz (1986).
Experiments and values first reported by Gargas et al. (1989).
Values first reported by Fisher and Allen (1993).
Values first reported by Steward et al. (1973)
3.5.3 Results and Analysis of Systemic Interspecies Inhalation Dosimetry
Modeling
Inhalation PBPK models use air and blood flows, predicted or measured absorption rates,
various biological rate processes (e.g., metabolism) and partitioning overtime, and a
range of external exposure air concentrations to a given toxicant to predict dose metrics.
As explained above, the Hb/g, is a key, and often determinative, parameter.
A dose metric is the internal tissue concentration of a toxicant, or a form of that toxicant
such as a metabolite, associated with the external exposure to a toxicant. For a tissue that
is a focus of toxicity (i.e., a target tissue), the concentration of a toxicant in the tissue is
considered to be an essential determinant of risk. The dose metric may be a concentration
over time (e.g., area under the curve, AUC), a maximum concentration achieved (Cmax),
or a steady-state concentration. Examples of dose metrics are Cmax of parent compound
in the liver, AUC of a metabolite in the brain, or circulating blood concentration of parent
compound at steady state. The concentration in the blood is often used instead of the
concentration in a target tissue because blood concentrations are more readily measured,
allowing for model calibration and validation, and average or steady-state tissue
concentrations are expected to vary in proportion to blood levels.
PBPK models may be developed for a variety of purposes, one of which is interspecies
extrapolation, the general subject of this report. The manner in which this is performed is
to first use the animal model to estimate a dose metric (internal dose) associated with a
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given level of toxicity or response and then use the human model to estimate the external
concentration for humans that yields the same internal tissue dose metric. As stated
previously, the human estimate of the external concentration that produces that same
internal dose metric is the human equivalent concentration or HEC.
Several of the studies listed in
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Table 3-10 developed inhalation models for purposes of interspecies extrapolation.
Table 3-11 below presents specific descriptions of the dose metric and the modeling
estimates of the human equivalent concentration that corresponds to the same internal
dose metric calculated for the laboratory animal based on the animal exposure scenario.
Table 3-11 Estimations from inhalation PBPK models of human equivalent
concentrations (HECs) from effect levels and internal dose measures in
laboratory animals
Chemical
(Reference)
Level and
Effect
Dose Metric
Comments
PBPK
Derived
HEC
Isopropanol
(Gentry et al.
2002)
NOAEL
2,500 ppm
renal tissue of
female rats
LOAEL
3,500 ppm
developmental
Arterial blood
concentrations,
AUC
HEC derived from Table 4 (in (Gentry et al.. 2002) 4,767 ppm
by applying uncertainty factor of 30:
(159.8 x 30 = 4,767 ppm);
189.8 ppm x 30 = 5,700 ppm.
Animals exposed for 6 hr/day, 5 days/week.
Contiguous exposure modeled in humans.
5,700 ppm
n-Butanol
(Teeauarden
et al.. 2005)
NOAEL
500 ppm
weight gain
Arterial blood
concentrations,
AUC
NOAEL
3,000 ppm
neurotoxicity
Arterial blood
concentrations,
AUC
Weekly average blood conc. estimated for rats at 169 ppm
6 hr/d, 5 d/wk and continuous for humans. Model
estimates compared against human blood levels
.from 30 min inhalation exposure. Tables and
equations are provided for HEC calculation over 1,066 ppm
wide range of butanol concentrations.
PGME
(Kirman et al.
2005b)
NOAEL
3,000 ppm
presence of
sedation
Cmax, richly
perfused tissues
Model simulations estimated NOAEL internal dose 560 ppm
metric values in rodents ranging from 2,300-5,000
mg/L for exposures from 3,000 ppm for 1-78 wks
of exposure (6 hr/d, 5 d/wk). The arithmetic mean
of the NOAEL was 4,036 mg/L. This value was
used to estimate an HEC for a continuous 24 hr
exposure.
White spirits
(Hissink et al.
2007)
NOEL
600 mg/m3
neurotoxicity
LOEL
2,400 mg/m3
neurotoxicity
Brain concentration
of 1,2,4-TMB or
decane determined
. in rats exposed for
6 hr/day
Model and 4-hr HEC based on main components
ofWS, 1,2,4-TMB and decane. Estimates are for
acute exposure CNS effects. Human model
validated with blood and alveolar air kinetics.
344-721
mg/m3
1,669 -
4,431c
mg/m3
2-ME
(Garaas et al..
2000)
NOEL
10 ppm
developmental
LOEL
50 ppm
developmental
Blood Cmax or
average daily
AUC for 2-MAA
(acetic acid;
metabolite of 2-ME)
in rats exposed for
6 hr/d, 5 d/wk
The model was used to calculate an HEC for 12 ppm
pregnant women exposed for 8 hr/day, 5
days/week for 270 days at various 2-ME. Human
validation information from urinary excretion rates
of 2-MAA from volunteers exposed to 5 ppm 2-ME qq ppm
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a PBPK
Chemical Level and Derived
(Reference) Effect Dose Metric Comments HEC
Ethylene
glycol
(Corlev et al.
2005)
1,1,1-TCE
(Lu et al..
2008)
11 ppm
(28 mg/m3)b
developmental
NOAEL
1,500 ppm
liver effects
Cmax for
glycolic acid (GA) in
blood
Model was used to generate a dose-response -79 ppm
comparison of internal dose surrogates (Cmax for (~200
GA in blood) in female Sprague-Dawley rats and in mq/m3)
humans (Figure 10B in (Corlev et al.. 2005)).
Several controlled rat and human metabolism
studies were used to validate the PBPK model.
Average daily
venous blood, AUC.
Calculated in rats
exposed for 6 hr/d,
5 d/wk
Table 5 (in (Lu et al.. 2008)) shows HEC
calculations over a wide range of exposures
concentrations for continuous human exposure.
Four human data sets were used in evaluating
model selection.
640 ppm
aChemical abbreviations: ethylene glycol monomethyl ether (2-ME); propylene glycol methyl ether (PGME); 1,1,1-trichloroethane
(1,1,1-TCE), 1,2,4-trimethylbenzene (1,2,4-TMB), 2-methoxyacetic acid (2-MAA), glycolic acid (GA), white spirit (WS).
bThe threshold blood concentration for developmental effects of 2 mM is not attainable in humans based on the modeling and
maximum tolerated inhalation exposures reported in this paper (Corlev et al.. 2005). The maximum vapor concentration for EG is
only 79 ppm (~200 mg/m3) due to low volatility (0.06 mm Hg at 20°C) (Corlev et al.. 2005). Therefore, for this comparison, the
human Cmax at the maximum vapor concentration (200 mg/m3) was estimated by the model to be ~6.5 |jM. The exposure
concentration predicted by the model that would yield the same Cmax in the rat is ~28 mg/m3.
°Range of values is presented because exposure concentrations were estimated that yielded brain concentrations equivalent to
observed values for 1,2,4-TMB or decane. Values at the lower end of the range correspond to WS estimates based on 1,2,4-TMB
brain concentrations, while the higher values are based on decane brain concentrations.
Table 3-12 combines data from
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Table 3-10 and Table 3-11 to present examples comparing approaches in estimating HEC
from laboratory animal data for systemic effects. With n-butanol, for example, an
systemic effect level of 500 ppm in the laboratory animal study is duration and
dosimetrically adjusted to an HEC using the RfCMethods default approach (a DAF of 1;
see Section 2) to yield 90 ppm. The neighboring column to the right shows the HEC
derived using the PBPK model at 169 ppm. The ratio of these HECs are then compared to
indicate the extent and direction of difference, such that the n-butanol default HEC is
two-times less than estimated by the PBPK model. For further comparison, the actual
A/H Hb/g ratio is also given, here shown for n-butanol which in this case is the same as the
RfC Methods default.
As can be seen, the extent of difference encountered between the default and PBPK HEC
values is quite wide, spanning over 10-fold (e.g., isopropanol default method gives an
HEC of 446 ppm and the PBPK method gives 4,767 ppm) even for this small set of
example chemicals. In all cases, the default RfC Method provides a lower HEC than
those derived using PBPK modeling, except for PGME which is nearly equal. Additional
modeling results for propylene oxide and VOCs have shown DAFs to be approximately
1. The propylene oxide PBPK model of Csanady et al. (2007) predicted similar blood
concentrations of propylene oxide in humans and rats up to 50 ppm exposure. The
simplified, steady-state PBPK model solution for inhaled VOCs shows that on the basis
of internal dose (blood concentration), humans develop similar liver venous blood
concentrations and lower rates of metabolism per volume of liver and tend to develop
target tissue doses that are similar to or lower than those in the experimental animals for
the same external air concentration (Avlward et al.. 2011). No general trend can be
discerned to explain this range of differences, either between the default and PBPK HEC
or between the actual Hb/g and the PBPK HEC. It may be that other covariates, such as
concentration-dependent metabolism may need to be further explored and evaluated. In
application of PBPK models, it may also be necessary to thoroughly evaluate the
origination of model parameters, including the Hb/g. Taken together, these results support
the use of a default DAF of 1 for gases producing systemic effects.
Table 3-12 Comparison of approaches for calculating human equivalent concentrations
(HECs) for several gases with systemic (SYS) effects
Chemical
(Reference)
Rat POD
(Table 3-7)
PODadj
RfC Method
( H b/g )a / ( H b/g )H
(Table 3-6) DAF
HECa
-HEC-PBPK
Method
(Table 3-7)
PBPK/RfC
HEC Ratio
n- Butanol
500 ppm
90 ppm
1.0 1
90 ppm
169 ppm
1.88
(Teeguarden et
al. (2005)
1,1,1-TCE (Lu
et al. (2008)
1,500 ppm
270 ppm
2.3 1
270 ppm
640 ppm
2.4
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PGME (Kirman
et al. (2005a)
3,000 ppm
540 ppm
0.7
1 540 ppm
560 ppm
1.04
2-ME (Gargas
et al. (2000)
10 ppm
1.8 ppm
1
1 1.8 ppm
2.9 ppmb
1.61
Isopropanol
2,500 ppmc
446 ppm
1.5
1 446 ppm
4,767 ppm
10.7
(Gentry et al.
(2002)
3,500 ppmd
907 ppm
907 ppm
5,700 ppm
6.28
Ethylene glycol
(Corley et al.
(2005)
11 ppm
—
1
1 11 ppm
79 ppm
7.18
aHEC derived by default RfC Methods'. PODadj * DAF = HEC where the PODadj is the POD adjusted for duration of exposure in the
animal study and a default DAF of 1 is applied for (Hb/g)A / (Hb/g)H-
(e.g., for n-butanol, the PODadj = 500 ppm x 6 hr/24 hr x 5 days/7days = 90 ppm.)
bln the PBPK model for 2-ME, the HEC was calculated for a discontinuous exposure and was therefore adjusted for duration
(8hr/24 hr x 5 days/7 days).
°Based on renal effects.
dBased on developmental effects.
3.6 Current Science Related to Children's Inhalation Dosimetry
3.6.1 Introduction and Focus
This section is focused on identification and preliminary evaluation of data, evidence, and
information relating directly to gas dosimetry in children.
The 1996 Food Quality Protection Act (FQPA), refocused interest in matters of child
risk. Title III of this act specifically tasked the Agency in their assessments under the
FQPA to . ensure that there is a reasonable certainty that no harm will result to infants
and children
Although this Act was directed at oral ingestion of pesticides, specifically those used on
foodstuffs, the Agency considered its implications both with regard to pesticide risk
assessments and more broadly to EPA methodology. For example, EPA developed
approaches for interpretation and implementation of the requirements specifically to
FQPA-required pesticide assessments (e.g., see
http://www.epa.gov/oppfeadl/trac/science/'). and additionally implemented a full review
of the Agency's RfC/RfD processes to insure they appropriately considered the potential
for increased childhood susceptibility (U.S. EPA. 2002V
This Act eventually affected many organizations and resulted in a spectrum of
implementation actions and strategies. One of the most prominent is that undertaken by
the state of California in implementing their Children's Environmental Health Protection
Act (Senate Bill 25) of 1999. The state's Technical Support Document for the Derivation
of Noncancer Reference Exposure Levels (OEHHA. 2008) provides extensive
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information on children as a population of concern and on pharmacodynamic and
pharmacokinetic differences between children and adults. Appendix E of that same
document includes an extensive analysis of children related data and models, including
PBPK models, that provide insight into the range of inter-individual variability in
general, but focus extensively on the differences among infants, children and adults. This
report does not intend to provide a comprehensive review of these reports, but notes them
as examples of the risk assessment community's movement toward additional
consideration of children in dosimetry and dose-response toxicity assessments.
In 1993, the NAS published its findings regarding chemical toxicity in children compared
to adults (NRC. 1993). The report addressed both specific findings and recommendations.
Conclusions of the committee included that infants and children may be more, or less,
susceptible than adults depending upon the chemical and the age of the subject. It was
acknowledged that substantial changes occur in organ size, structure, and function from
infancy through puberty; such changes could substantially affect the pharmacokinetics
and pharmacodynamics of chemicals. Accordingly, there may be periods, or lifestages
ofincreased susceptibility, when developing tissues are much more sensitive to toxicants
than later in life. The NAS report (1993) also stresses the importance of recognizing that
the younger the individual, the more pronounced his/her structural and functional
anomalies and thus the greatest differences from adults in susceptibility to chemical
toxicity can be anticipated, with continuous diminishment of those differences thereafter.
The report also stated the need for scientifically defensible means to deal with toxic
agents that cannot be directly studied in children. A specific recommendation in the
report following from this realized the need to use PBPK models. PBPK models can be
used both to simulate the time course of parent compounds and bioactive metabolites in
blood and tissues of adult animals and humans and to predict target organ doses of toxic
chemicals/metabolites for different exposure scenarios in children of different ages.
A recommendation following from the potential use of PBPK models was that they be
reliably developed by obtaining accurate measurements of respiratory parameters,
circulation, metabolism, tissue and fat volumes, and partition coefficients. These
parameters can be measured in primates or in children of different ages by noninvasive
procedures. The parameters would be used in PBPK models, which could then be utilized
to better estimate the concentration time course of chemicals/metabolites in potential
target organs. It is the recommendations and statements from the NAS report (NRC.
1993) that guides the structure and content of this section of this report. This report
focuses on those reports and studies that directly inform the state of the science related to
gas dosimetry in children. How this information informs the default RfC Methods is also
considered.
The 1994 RfC Methods considers all lifestages, including children, in the intraspecies
uncertainty factor that is designed to incorporate the range of response variability in
human populations. This uncertainty factor is typically considered to have two
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components, pharmacodynamics and pharmacokinetics, with the latter component being
the basis of dosimetry. It is within the kinetic portion of this uncertainty factor that
potential dosimetry differences of susceptible lifestages, including children, are
considered.
Recognizing that young children have a greater ventilation rate per body weight or per
surface area in the respiratory tract compared with adults, Ginsberg et al. (2005) analyzed
the outcomes of gas dosimetry approaches of RJC Methods utilizing infant child (3 mo)
and adult male values available from various sources for the principal determinants of VE,
SAET Pu, and BW. The TB region was characterized differently from RJC Methods as
comprising two separate regions termed tracheobronchial (BB) and bronchioles (bb) by
the authors. Dosimetry was estimated for 3-month-old infants and adults for reactive and
nonreactive gases. Estimations of comparative dosimetry were made using a reasonable
range of assumed Kg values. The authors used the same Kg values for both children and
adults indicating that no basis exists for assuming a difference. The modeling results
suggested similar dosimetry of gases for infants and adults for the ET and BB regions.
Dosimetry for the bb region generally showed a higher dose of gases in adults than in 3-
month-old infants. It was also noted that, based on the Kg value, dosimetry for adults
versus 3-month-old infants in the PU region could be slightly different, either higher or
lower but not greater than 2-fold different. There were no cases in which gas dose was
substantially greater in the respiratory regions of 3-month-old children compared to
adults. Estimates of systemic doses of nonreactive gases were greater in 3-month-old
children than in adults, especially for liver doses (up to 2-fold) of metabolites for rapidly
metabolized gases. Overall, these results suggest the potential for a 2-fold greater
inhalation dose in 3-month-old infants (based on data from 3-month-old children) than in
adults, although there are cases in which this differential could be greater or less.
As PBPK models configured for elucidating dose to children and infants were
recommended in the NAS report and are prominent in the current literature, they will be
featured in this section. Studies that provided insight and data for parameters needed for
these models and/or for general knowledge about development in early lifestages related
to aspects of dosimetry are also presented. These include studies on air flow and CFD
modeling as well as respiratory tract growth. Information on inhalation rates in children
have been presented earlier (see Section 3.4.1). In addition to the TB and PU regions,
information on the ET regions is also included in this section.
3.6.2 Results and Analysis of Inhalation Dosimetry Modeling Considering
Children
Firestone et al. (2008) reported results based on analyses conducted by the California
EPA's Office of Environmental Health Hazard Assessment (OEHHA) that investigated
the potential differences between adult and child (0-18 yr) internal doses resulting from
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inhalation exposure to a toxicant. Modified PBPK models for 24 compounds were used to
assess child/adult ratios for at least three dose metrics. Detailed methods, equations, and
model parameters were not included in the manuscript; however, the chemicals were
classified into one of three categories pertaining to the intrahuman uncertainty factor for
toxicokinetic variability (UFh.tk, default value = 3.16): UFh.tk < 3.16; UFH-tk > 3.16 to
9.9; and UFh_tk > 10.0. Twelve of the compounds examined had child/adult ratios < 3.16,
eight had ratios between 3.16 and 9.9, while four had ratios greater than 10. The authors
found that majority of the higher ratios were in infants (< 1 yr) and child vs. adult
metabolic differences likely account for this observation.
In addition, as reported in Firestone et al. (2008). OEHHA applied modeling to evaluate
alternative methods for interspecies extrapolation of gas dosimetry in a limited number of
test chemicals. Limited information on the model structures and parameters employed
were provided; however, detailed methods, equations, and model parameters were not
described. Blood Cmax and AUC for parent and metabolite and amount metabolized
were the dose metrics modeled for a 24 hr simulation. Chemical-specific principal effects
(i.e. POE vs. systemic) and thus potential target-tissue doses were not modeled. In
general, the DAFs calculated using model output for this set of chemicals were lower in
adults (Gmean = 1.85) and higher in children (Gmean = 1.94) compared to the current
default methods. With the exception of one case (amount of ethylbenzene metabolized),
the child/adult DAF ratios were within a 2-fold range. Without more detailed information
regarding the methods and parameters used in either analysis, however, broader
conclusions cannot be drawn.
Ginsberg et al. (2008) analyzed ozone gas dosimetry in the TB region using a
mathematical model for uptake. The TB model consisted of 15 generations of
symmetrically-branched airway bifurcations. Air was modeled starting at the entrance of
the trachea and thus did not simulate reactions possible in the ET region. The numerical
simulations of reactive gas uptake utilized airway and ventilatory parameters specific to
children of different ages (0, 4, 8, 12, 16, and 18 yr). The model was exercised to
examine the uptake distribution of ozone along the gas-mucus and mucus tissue
interfaces of these children at a constant inhalation concentration of 0.1 ppm. The results
demonstrated that for all ages and all airway generations, the controlling resistance to
uptake was the mucus layer and the overall Kg was not significantly different across ages.
In addition, there were no significant differences in the predicted flux of ozone to the
mucus and tissue for children of different ages (0-18 yr) modeled in this study. These
results are similar to those obtained by Overton and Graham (1989). In their study, an
ozone dosimetry model was used to estimate regional and total uptake of ozone in adults
(20 yr) and children (0-14 yr), and no appreciable differences in regional or total uptake
were predicted.
More recently, Valcke and Krishnan (201 la) examined the impact of exposure route on
the kinetic portion of the intrahuman uncertainty factor, UFh.tk- A multiroute,
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steady-state, PBPK model was modified from the literature and used to compute the
internal dose metrics of the area under the parent compound's arterial blood
concentration vs. time curve (AUCpc) and amount metabolized per 24 hours (AMET).
Dose metrics were computed for adults (18-64 yr), neonates (10-30 d), children (1-3 yr),
elderly (65-90 yr) and pregnant women (15-44 yr) for a 24 hour inhalation exposure
scenario to chloroform, bromoform, tri- or per-chloroethylene (TCE or PERC). The
inhalation exposure scenarios were performed at a concentration of 5 (ig/m3
representative of a low, environmental level. Monte Carlo simulations were performed
and the UFh.tk was calculated as the ratio of the 95th percentile value of internal dose
metrics in the various population groups to 50th percentile value in adults. On the basis of
AUCpc, the highest UFh.tk values were demonstrated in neonates for each scenario
compound. The highest UFh_tk computed was 3.6 for bromoform, but in all other cases
the UFh-tk values ranged from 1.2 to 2.2. A synthesis of the results from this study are
presented in Table 3-13. These results are in agreement with those presented by Firestone
et al. (2008) for PERC and chloroform; however, TCE was categorized as having a
UF,[_•[ ,,> 10 by Firestone et al. (2008) and < 3.16 by Valcke and Krishnan (2011a). The
reason for this difference cannot be determined from the limited information provided in
the Firestone et al. (2008) report.
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Table 3-13 Human kinetic adjustment factors (UFH-tk) obtained for inhalation exposure in
each population group using a dose surrogate of 24 hour AUCpc
Substance: Chloroform
Bromoform
Trichloroethylene
Perch loroethylene
Adults (41,18-64 yr)b
median
15.8
25.7
21.8
37.3
95th percentile
20.2
37.5
28.8
47.2
UFh-tk
1.3
1.5
1.3
1.3
Neonates (14, 0-30 days)
95th percentile
33.4
93.1
48.4
66.6
UFh-tk
2.1
3.6
2.2
1.8
Children (2,1-3 yr)
95th percentile
25.2
51.7
35.1
58.8
UFh-tk
1.6
2.1
1.6
1.6
Elderly (78, 65-90 yr)
95th percentile
20.4
37.6
28.8
45.8
UFh-tk
1.3
1.5
1.3
1.2
Pregnant women (29,15-44 yr)
95th percentile
22.9
44.4
30.6
46.4
UFh-tk
1.5
1.7
1.4
1.3
Note: AUCpc, area under the arterial blood concentration vs. time curve (|jg 24 hr/L)
bShown in parentheses are the median age, range for each population group.
°Bolded values indicate the population group with the greater UFH-tk for corresponding internal dose surrogate for each compound.
Source: Reprinted with permission of Elsevier; Valcke and Krishnan (2011a)
In a related study, Valcke and Krishnan (2011b) assessed the impact of exposure duration
and concentration on the human kinetic adjustment factor (UFH-tk)- A minimally
validated, generic inhalation PBPK model was used to compare the dose metrics (blood
concentration and hepatic metabolism) in adults, neonates (0-30 days), toddlers (1-3 yrs)
and pregnant women following inhalation to benzene, styrene, 1,1,1-TCA, and 1,4-
dioxane. The parameters varied across the life stages were BW, height, and hepatic
CYP2E1 and the UFH _TK was calculated based on these Monte Carlo simulations. In the
low exposure concentration (associated with steady-state chronic inhalation) scenario
ranges of blood concentration-based UFh_tk were 1-6.8 depending on the chemical and
lifestage, while it ranged from 0.8-2.0 for rate of hepatic metabolism. Neonates were
always the most sensitive based on blood concentration, and pregnant women were
generally most sensitive based on metabolism (Valcke and Krishnan. 201 lb). The
greatest difference in internal dose metrics was observed in neonate vs. adult for 1,4-
dioxane (628/199=3.2) blood concentration.
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Additional research was done to compare the variability in the whole-population vs.
distinct sub-population when determining the UFh.tk (Valcke et al.. 2012). In the whole
population approach the entire population's upper percentile value (99th) was compared
to the median value in the entire population. For the distinct sub-population approach,
the 99th percentile values in each sub-population was compared with the median adult
value or the median individual in the whole population as a referent. For UFh_tk values,
associated with the steady-state blood concentration dose metric, for the whole
population approach varied between 1.2 and 2.8, while the distinct sub-population values
ranged from 1.6 to 8.5. Similar to what was found in Valcke and Krishnan (2011^. the
neonate appeared most susceptible based on blood concentration, while the pregnant
woman was more susceptible based on rate of metabolism.
Another study by Valcke and Krishnan (201 la) evaluated the impact of physico- and bio-
chemical characteristics on UFh_tk as they impacted systemic clearance of hypothetical
chemicals. Here they utilized a physiologically based steady-state algorithm (not a PBPK
model) to look at concentration in the blood (Cbi00d) and rate of hepatic metabolism
(RAMl) for some hypothetical chemicals (Hb/g between 1-10,000 and hepatic extraction
[Ehr] ratios between 0.01-0.99) in neonates (0-30d), adult, elderly (65-90yr), and
pregnant women (29yr, GW0-40). UFh_tk in neonates was the only one exceeded the
typically applied factor of 3.16 for intrahuman variability, when EHr = 0.3-0.7 and Hb/g >
100 for inhalation exposures to CYP2E1, CYP3A4, and ADH substrates, while it was
higher for CYP1A2 metabolized compounds. This study showed the impact of chemical
characteristics, metabolic pathways, and lifestages on intrahuman variability.
Liao et al. (2007) developed a hybrid PBPK/pharmacodynamic (PD) model to investigate
chloroform toxicity and carcinogenicity. The PBPK model was configured for rats, mice,
and humans with the human configuration expanded to consider different age groups (1
month, 3 month, 6 month, 1 year, 5 year, and 25 year old) with the age-specific
physiological values being obtained from documented literature sources (see the Status II
Report for details). The PD model was used to quantitatively estimate rates for
mode-of-action processes known to be prominently involved in the toxicity of chloroform
(metabolism, reparable cell damage, cell death, and regenerative cellular proliferation).
The human model was used to estimate internal doses at steady state over a range of
inhalation exposures (and oral, drinking water) concentrations for different age groups to
identify the threshold for labeling indices (LI) below which no cytolethality would be
expected in each age group. The simulations presented in Table 3-14 indicated that for
liver effects, a young child (< 5 years) was more sensitive than adults by a factor of about
2. For renal effects, however, the results indicated age-related increases in sensitivity to
the toxicity of chloroform with 1-month-old infants nearly 7- to 8-fold less sensitive than
adults, 1-year-olds about 3-fold less sensitive than adults, and no difference in
concentration corresponding to kidney effects between adults and 5-year-old children.
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Table 3-14 Air concentration of chloroform at various ages and genders corresponding
to threshold of damage in human liver and kidney
Age
Gender
Air Concentration (ppm)
Liver
Kidney
1 Month
Male
5.16a
7.56
Female
4.86
8.08
3 Month
Male
4.80
2.60
Female
4.79
2.85
6 Month
Male
5.13
2.19
Female
4.90
2.29
1 Year
Male
6.07
3.17
Female
5.66
3.00
5 Year
Male
6.61
1.18
Female
6.81
1.35
Adult
Male
9.24
0.887
Female
12.7
1.06
Note: Values generated from model simulations of a PBPK-PD model.
aResults given as point values only, as estimates of variability were problematic in the absence of data on cell proliferation in human
liver and kidneys.
Source: Reprinted with permission of John Wiley and Sons; Liao et al. (2007'
Nong et al. (2006) used a PBPK model to explore the interindividual variability in the
internal dose of toluene in various age groups of children (
-------
metabolite concentrations in blood, liver, and lung. Results for the dose metrics were
expressed relative to the young adult (25-year-old) model which were all set at unity. The
results from the model indicated that tissue dose metrics at any age generally fell within a
factor of 2 of the young adult values for parent ozone, vinyl chloride, styrene,
isopropanol, and perchloroethylene. Little variability due to gender was apparent at any
age for any of the gases or metrics examined. The only exceptions were those observed in
early childhood (either gender), where dose metrics (especially for metabolites) were as
much as 12 times higher for a 1-month-old child than young adult values, declining to 2
times by age 5-10 years, for these same compounds. This is shown for the parent
isopropanol and its water soluble metabolites (Table 3-15).
Table 3-15 Age-dependent and gender-specific dose metric comparison of inhaled
isopropanol
Parent Chemical Concentration
Metabolite Concentration
Age
Male
Female
Male
Female
1 Month
1.75
1.74
8.02
11.44
3 Month
1.77
1.78
6.68
9.14
6 Month
1.77
1.75
5.70
8.01
1 Year
1.54
1.54
4.12
5.96
5 Year
1.25
1.18
1.98
2.55
10 Year
1.05
1.03
1.53
2.04
15 Year
1.09
1.14
1.46
1.70
25 Year
1
1
1
1
50 Year
0.94
1.00
0.80
0.82
75 Year
1.04
1.03
0.89
0.93
Note: Comparisons presented as % ratio of metric at a specific age to the 25-yr-old adult set at 100%.
Source: Reprinted with permission of Informa Healthcare; Sarangapani et al. (2003)
An inhalation PBPK model for furan predicted steady-state blood concentrations in
children, modeled ages of 6, 10, and 14 years old, to be 1.5 times greater than the blood
concentration of adults exposed under the same conditions (1 ppb for 30 hrs), while the
difference in liver concentration of furan metabolite was less (a factor of about 1.25)
(Price et al.. 2003). This PBPK prediction could be similar for other highly metabolized
inhaled chemicals when comparing adults and children 6-14 yrs of age. Other age groups
were not considered in this analysis due primarily to lack of data on liver blood flow
information, which was found to be a critical model parameter for these differences.
Pelekis et al. (2001) developed a PBPK model for adults of low (50 kg) and high (90 kg)
body weights and for a 10 kg child (1 to 2 years old). The model was applied to
inhalation exposures of dichloromethane, tetrachloroethylene, toluene, m-xylene, styrene,
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carbon tetrachloride, chloroform, and trichloroethylene. The parent compound
concentrations in arterial blood (CA) and venous blood (CV), and tissues (Ctlssue) (but no
metabolites) were evaluated. The ratios of the metrics from these different runs
characterize the pharmacokinetic behavior of the child relative to the adult (e.g.,
adulthigh/childaverage) • The exposure scenario simulated was 1 ppm continuous for 720 hrs
(30 days).These ratios indicated that the estimation of concentrations in children's blood
were about the same as for the adult. With other tissues metrics, however, values were
considerably higher in a few instances. For example, the adulthlgh /childaverage ratio for the
concentration in the liver (which was dependent on metabolism) was predicted as 0.033
for styrene, 0.037 form-xylene, 0.061 for trichloroethylene, 0.092 for dichloromethane,
and 0.11 for chloroform. These predictions indicate concentrations of the VOC chemicals
in livers of 1 -2 year-old children that ranged from similar to the adult liver concentrations
up to 10- to 30-fold higher for three of the eight chemicals. The average
adulthlgh/childaVerage ratios for the various dose metrics estimated for the composite runs by
Pelekis et al. (2001) are shown in Table 3-16.
These adult/child ratios calculated by Pelekis et al. (2001) differ from other laboratories
(Valcke and Krishnan, (2011a). in that the 95th percentile child values are more often
compared to median or average adult values. The approach used by Pelekis et al. (2001)
is more likely to underestimate the potential differences between children and adults.
Table 3-16 Tissue concentrations in various compartments expressed as adult/child (1 to
2 years old) ratios for 8 different gases
Adulthigh/Childaverage Ratios of Concentrations3
Gas
Venous Blood
Arterial Blood
Fat
Liver
Dichloromethane
0.70
0.91
0.25
0.092
Tetrachloroethylene
1.61
1.74
0.47
0.75
Toluene
0.86
0.98
0.27
0.34
m-Xylene
0.50
0.63
0.17
0.037
Styrene
0.34
0.45
0.12
0.033
Carbon tetrachloride
1.81
2.20
0.60
0.57
Chloroform
0.78
1.02
0.28
0.11
Trichloroethylene
0.77
0.97
0.27
0.061
Average ± SD
0.92 ± 0.52
1.11± 0.58
0.30 ± 0.16
0.25 ± 0.28
aSteady-state concentration ratios for 1 ppm continuous exposures.
Note: Initial values are all from PBPK simulations.
Source: Reprinted with permission of Elsevier; Pelekis et al. (2001).
In an effort to evaluate the potential effects in the nasal cavity of inhaled methyl iodide
(Mel) exposure, a PBPK model was developed by Mileson et al.(2009) and Sweeney et
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al. (2009). complete with parameters for sensitive populations and lifestages including
children (for model details see the Status IIReport). These models relied on recent data
gathered using a novel method to provide measures of Mel nasal absorption and
clearance in intact animals (Thrall et al.. 2009). The modeled point-of-departure for the
effect of Mel in the nasal tract was a decrease (either 25% or 50% decrement from
untreated levels) in glutathione (GSH) concentrations in the olfactory epithelium. The
adult human model indicated that depletion of GSH in the dorsal olfactory epithelium to
50% of control would be achieved after 24 hours of exposure to 72 ppm Mel. For
workers exposed for 8 hrs, 50% GSH depletion would be achieved by the end of the shift
at an exposure concentration of 110 ppm. At a target POD of 25% GSH depletion at
24 hrs, the 24-hr adult value was 36 ppm and the 8-hr (worker) value was 50 ppm. When
configured for the 3-month-old child the corresponding 24-hr concentration for 25%
depletion of olfactory GSH was 8.2 ppm under these conditions. No other age-related
results were given in the study. This concentration differential for the POD, 36 ppm for
the adult and 8.2 ppm for the 3-month-old child, indicates differential sensitivity of 3-4
fold resulting from a combination of biochemical (e.g., GSH turnover) and physiological
(e.g., respiration rate) factors. The equivalent rat exposure concentration (associated with
a 50% depletion of GSH) upon which the adult and child modeled HECs were based was
3.8 ppm (21 ppm for 6 hr/day, 5 d/wk for 4 or 13 wks).
Clewell et al. (2004) constructed a PBPK lifestage model specifically to evaluate age-
and gender-specific differences in tissue dosimetry for oral, dermal, and inhalation
exposures to a range of chemicals with various physical and toxic properties. The model
was mostly parameterized using equations that described various age-dependent
alterations derived from U.S. EPA (1997). which was also the source for ventilation rates
(m3/day); pulmonary ventilation for various ages were converted to alveolar ventilation
based on the assumption that alveolar ventilation is approximately two-thirds of
pulmonary ventilation. The results for the isopropanol inhalation model are the only ones
discussed here; however, the predictions of this age-dependent model were only able to
be validated against human kinetic data for the adult. The arterial blood concentrations of
isopropanol and acetone (the principal metabolite of isopropanol), were estimated for a
1 ppb continuous inhalation exposure and summarized in age-group ranges of birth to 6
months, 6 months to 5 years, 5 to 25 years, and 25 to 75 years. In general, the model
estimations for the average internal concentration of inhaled isopropanol and its
metabolite acetone varied 2 to 4-fold across the range of lifestages. The highest dose ratio
(constructed from the lifestage/average daily inhalation dose for a 25-year-old adult)
among the lifestages was 2.0 for isopropanol (birth-6 months) and 3.9 (birth-6 months)
for acetone.
Ginsberg et al. (2002) investigated child/adult pharmacokinetic differences through
analysis of pharmacokinetic (PK) data from 45 different chemicals, nearly all therapeutic
drugs and all administered by routes other than inhalation. In an initial metabolic
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evaluation, the drugs were classified as to their excretion: unchanged in urine, CYP
(various) metabolism, glucuronidation, sulfation, GSH conjugation or unclassified. The
infants/children were classified in age as premature neonates (< 1 week), full-term
neonates (< 1 week), newborns (1 week-2 months), early infants (2-6 months), toddlers (6
months-2 years), preadolescents (2-12 years), adolescents (12-18 years) and adults. There
were data from 118 adults and 248 infants/children. The kinetic parameters evaluated
included AUC, clearance, Cmax, half-life (ti/2), and volume of distribution (Vd).
Relationships between age groups and the kinetic parameters were evaluated by
regression analysis.
The combined results showed that, for those chemicals with clearance data (27
substrates), premature to 2-6 months of age infants showed significantly lower clearance
(P<0.01) whereas 6-month-old to 12-year-old children had significantly higher clearance
(P<0.0001) than adults. The combined results (40 substrates) indicated also that the drug
half lives in the youngest age groups (premature neonates, full-term neonates, and
newborn infants up to 2 months) tended to be longer (average 2-to-4-fold) than adults,
although the infant half lives then declined such that half lives for infants 2-6 months of
age reflected those of adults. Other results included those for the chemicals identified as
CYP1A2 substrates (caffeine and theophylline) for which neonates to infants 2 months of
age showed about 4 to 9-fold longer half-lives than adults while older age groups
(6-months to 12 years) had significantly shorter half-lives than adults. A similar pattern
was observed with those chemicals thought to be metabolized primarily through CYP3A.
These data are for drugs orally administered, rather than from toxics being inhaled, but
nonetheless are relevant to situations involving dosimetry at systemic sites of children
versus adults, and thus indicate a potential for internal dose of some chemicals to be
greater during the short period of early infancy (prior to 2 months of age) than later
lifestages. These data also demonstrate empirically the prominent feature and likely
mechanism for susceptibility during early infancy, decreased clearance functions.
3.6.3 Respiratory Tract Air Flow Models Considering Children
Garcia et al. (2009) obtained the MRI or CT head scans of seven individuals including
those of two children, a male (7 years) and a female (8 years) and five adults in a
vanguard study to examine inter-individual variability of nasal air flows in human
subjects using CFD. Several prior studies had shown actual airflow patterns in the nasal
tract of both animals and (adult) humans are highly non-uniform with highly localized
areas of flow that have been correlated with (at least in laboratory animals) areas of focal
pathology in air exposures to reactive gases. Breathing rates for the flow simulations
were set at 5.5 L/min for the 7-year-old boy and 5.8 L/min for the 8-year-old girl with
flows for the adults each allometrically adjusted with a final range of between 6.8 and 9.0
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L/min. Simulations of nasal uptake of inhaled gas (concentration in ambient air defined to
be 1 ppm by volume) were conducted under one of two boundary conditions - one to
simulate a maximum gas uptake and a second boundary condition to simulate moderate
uptake (approximately 80% of maximum) at the nasal tract walls. The simulations
predicted that, under both boundary conditions, gas was rapidly absorbed by the nasal
mucosa once it entered the nostrils. At the end of the nasal septum, gas concentration in
the inspired air had dropped to -13% and -29% of the inlet concentration for the
maximum and moderate uptake scenarios, respectively. The spatial distribution of wall
fluxes, especially under the maximum uptake boundary condition, were shown to be
highly non-uniform for all scans including those of the two children. Further analysis of
the subjects showed that the extent of the non-uniform flows (where areas of
non-uniformity were divided into categories of increasing mass flux) were not
appreciably different among the subjects, including between adults and the two children
(the minimal number of subjects precluded any statistical analysis). Additional analysis
also showed that the overall rate of uptake in the nasal region, although highly
non-uniform under localized internal conditions as shown by this study, was very similar
from one individual to the next with no apparent differences between adults and the two
children. Importantly, delivered dose estimated in terms of maximum (99th percentile) or
average flux was not different between adults and children. These principal results from
the maximum uptake condition, including some of the first available ET surface areas for
children, are shown in Table 3-17.
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Table 3-17 Summary listing of findings on morphometry and gas flow/uptake simulations
for human nasal cavities
Subjects
Parameter (units)
Adults
Children
Gender
Male3
Male
Female
Female
Female3
Male
Female
Age (years)
53
NA
NA
NA
37
00
ET area (cm2)
20,085
23,219
16,683
20,688
17,752
12,093
13,027
ET volume (mL)
18.0
26.5
15.4
23.8
18.7
10.7
13.7
Total gas uptake,
maximum conditions (%)
93.5
93.1
92.4
89.2
91.5
92.0
88.2
Average flux, left cavity
(10"8 kg /sm2)b
1.8
1.6
1.5
1.2
1.4
1.9
1.6
Maximum fluxc, left cavity
(10"8 kg/sm2)b
10.8
11.0
10.8
10.6
10.8
11.8
12.3
aData obtained from repaired casts.
bGas absorption rate
°The 99th percentile flux (i.e., the flux value below which 99% of flux values fall)
NA = data not available
Source: Reprinted with permission of Informa Healthcare; Garcia et al. (2009).
In a follow-on study to Garcia et al. (2009). Schroeter et al. (2010) utilized the reactive
gas hydrogen sulfide (H2S) to characterize the interhuman variability of H2S dosimetry to
the olfactory region arising from inter-individual differences in nasal anatomy, airflow,
and inspiratory uptake patterns using CFD. This study used essentially the same
conditions of CFD modeling as employed by Garcia et al. (2009). Olfactory regions were
mapped into the nasal models of all subjects as consistently as possible based on the prior
descriptions of the extent of olfactory epithelial in humans. The H2S specific kinetic
parameters used were previously estimated by the authors by fitting in vivo uptake data in
rats, then allometrically scaled to humans based on nasal surface areas. Flows were
simulated at three different concentrations, 1, 5 and 10 ppm. Comparisons among
individuals were made for the 99th percentile flux (i.e., the flux value below which 99%
of flux values fall) and average flux in the olfactory regions at an exposure concentration
of 1 ppm.
Results included morphological measurements in human adults and children of nasal
cavity surface areas and estimates of olfactory epithelia and airflow apportionment. The
modeling results in terms of average flux, maximum flux, and distribution of flux ranges
within the target area of olfactory epithelium showed uniform responses despite the
morphological ranges characterized. Differences in nasal anatomy and ventilation among
adults and children were not predicted to have a significant effect on H2S dosimetry in
the olfactory region (Table 3-18). The 99th percentile flux ranged from 153.1 to 170.1 in
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adults compared to 149.2 and 159 in children, while the average flux ranged from 12.2 to
13.6 in adults compared to 11.8 and 12.1 in children.
Table 3-18 Selected morphologic and simulated modeling results of hydrogen sulfide
dosimetry in casts of human nasal cavities
Subject
Parameter (units)
Adults
Children
Gender
Male3
Male
Female
Female
Female3
Male
Female
Age (years)
53
NA
NA
NA
37
00
Surface area of main nasal cavity (cm2)
198.7
231.5
167.3
207.9
177.0
118.9
135.1
Surface area of olfactory region (cm2)
14.4
11.5
10.5
9.9
11.2
9.1
9.6
Olfactory airflow allocation (%)
4.8
5.5
7.9
2.6
4.9
16.2
1.6
99th percentile flux (pg/cm2-s) @ 1ppm
167.7
170.1
158.9
161.3
153.1
149.2
159.0
Average flux (pg/cm2-s) @ 1 ppm H2S
13.6
13.5
12.7
12.8
12.2
12.1
11.8
aData obtained from repaired casts.
NA = data not available
Source: Reprinted with permission of Informa Healthcare; Schroeter et al. (2010)
3.6.4 Respiratory Tract Growth
It has been well established that the human respiratory system passes through several
distinct stages of maturation and growth that involve branching morphogenesis and
cellular differentiation during the first several years of life and into adolescence
(Pinkerton and Joad. 2000). The proportion of surface area to ventilation volume may be
markedly different during these developmental stages. The significance of these
disproportions with regard to toxicant exposure overall or to the sites of active cellular
differentiation have yet to be elucidated. The major proposed processes in human lung
growth and development are:
• an increase in numbers of alveoli via septation of elementary saccules, followed
by
• increases in dimensions of all of the lung structures including alveolar size and,
most prominently, the diameter of airways, followed by
• distension of lung due to changes in the mechanical properties of the chest wall.
These changes are postulated to result in a relative under-distension of the lung followed
by a relative ovcr-distcnsion(Zcltncr et al.. 1987V De Jong et al. (2003) postulated that
indications of these processes could be determined through in situ scanning and
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visualization techniques. Therefore an institutionally sanctioned study was conducted
where the CT scans of 35 children (age range from 15 days to 17.6 years of age; 17
males, 18 females) were obtained and examined for these indications of growth and
development. The data on lung expansion expressed as gas volume per g of tissue (mL/g)
showed a decline from birth to 2 years of age and an increase thereafter. This finding
would be anticipated as alveolar tissue is rapidly added, the alveoli are of uniform size
and divisions of existing airspace into smaller units via septation would cause the gas
volume to fall. The subsequent increase in gas volume of tissue from age 2-8 years
would be consistent with expansion in the size of alveoli in combination with a gradual
increase in functional residual capacity (FRC) due to changes in the mechanical
properties of the lung and chest wall.
In a companion study de Jong et al. (2006) used CT scans from a group of 50 young
individuals (age range 0 - 17.2 years) to obtain estimates of various lung dimensions also
through the period of growth. Clinical CT scans were performed and analyzed as above
for lung weight, gas volume, lung expansion, lung surface/volume ratio, airway wall area,
airway lumen area, airway lumen perimeter, arterial area and airway surface length/area
ratio. The authors discussed the nature of these ratios in relation to length and growth of
the individual but did not give specifically determined estimates of measures such as
surface areas. For example, lung alveolar surface area to total lung volume ratio (S/V)
was calculated using the lung expansion values at total lung capacity (TLC) per the
following equation:
S/V lung=e6,84(0,32xkmg exPans'on at tlc)
Equation 3-3
The regression of these ratios against other growth parameters, such as body length,
suggest that the relationship between these various measures is closely linked.
Collectively these results provide functional indications of lung growth processes using
noninvasive methods and demonstrate that CT scans can be used to provide valuable
information about normal lung growth in addition to the more typical application of
diagnosis of lung disease.
Rao and coworkers (2010) evaluated lung growth and development in vivo in infants and
toddlers using multi-slice CT. The developmental process is thought to be sequential in
terms of the alveoli, with new alveoli being added until about 24 months of age followed
by alveolar expansion with no new alveoli added after 24 months. The high resolution
capability of CT was applied to a group of 38 subjects (14 male, 24 female) of ages in
this range (17 to 142 weeks; 4 to ~ 36 months). This in vivo assessment suggests that the
growth of the lung parenchyma in infants and toddlers occurs with a constant relationship
between air volume and lung tissue, which is consistent with lung growth occurring
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primarily by the addition of alveoli rather than the expansion of alveoli. In addition, the
central conducting airways grow proportionately in infants and toddlers.
The pulmonary growth sequence in early life of alveolar septation followed by alveolar
expansion was examined by Balinotti et al. (2009) with pulmonary function testing. The
basis of the hypothesis relates to the ratio between pulmonary diffusion capacity of
carbon monoxide (DLCO) and alveolar volume (VA). During the process of
alveolarization, usually considered to be in the first two years of life, this ratio would
remain constant whereas during alveolar expansion, i.e., in children older than 2 years, it
would decrease. The authors measured DLCO and VA using single breath-hold
maneuvers at elevated lung volumes in 50 sleeping infants and toddlers between the ages
of 3 and 23 months. Both alveolar volume and pulmonary diffusing capacity increased
with increasing age in both male and female children. Significantly, ratio of pulmonary
diffusing capacity to alveolar volume remained constant in this age group. The constant
ratio for DLCO/VA in infants and toddlers is consistent with lung growth in this age
occurring primarily by the addition of alveoli rather than the expansion of volume.
Zeman and Bennett (2006) employed in vivo methodology, aerosol-derived airway
morphometry (ADAM), to measure the age-related changes in air space caliber of the
small airways and alveolar dimensions. The subjects recruited from the general local
population included 53 children (6-22 years) and 59 adults (23-80 years). The principal
of ADAM related to predictable gravitational settling of small inhaled particles to infer
the vertical distance or effective air space dimension, (EAD), that the particles must have
settled to become lost to the airway wall. ADAM involves individuals inhaling to TLC a
particle aerosol of known size characteristics followed by breath-holds for 0-10 seconds
and (non-deposited) particle recovery upon exhalation. Data were collected, then
regressed according to age. Alveolar diameters were found to increase with age, from 184
(.im at age 6 to 231 |_im at age 22 based on the regression equations derived. This
observation would account for the increase in TLC observed over this age range. The
caliber of transitional bronchioles (average 572 |_im) did not increase with TLC, but did
increase with subject age and height when the entire age range of 6-80 years was
included (Zeman and Bennett. 2006). The anatomical dead space scaled linearly with
lung volume, but relative to TLC did not change with age, averaging 7.04 ± 1.55% of
TLC. The authors concluded that from childhood (6 years) to adulthood a constant
number of respiratory units is maintained; however, both the smallest bronchioles and
alveoli expand in size to produce the increased lung volume with increased age and
height.
It has been hypothesized by Thurlbeck (1982) that humans grow new alveoli from a few
weeks before term birth until approximately 8 years of age, after which the alveoli are
thought to enlarge as the lungs increase in volume or size with no new alveoli formed. To
this end, Altes and coworkers (2004b) examined the apparent diffusion coefficient (ADC)
with a gaseous contrast agent for MRI, hyperpolarized helium-3 (3He), in a cohort of
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twelve individuals. An increase in ADC is a measure of volume maturation. It was
expected that in the pediatric age group, the increase in alveolar size with increasing age
will be reflected in an increase in 3He ADC with age. The age range of the 12-member
cohort was 7 to 29 years (mean 15.6, standard deviation 6.9 years). All 12 of the subjects
had homogenous appearing ADC maps. Comparing the mean ADC with other measures
of maturation or lung volume gave correlation coefficients of 0.74 with height, 0.64 with
weight, 0.76 with forced vital capacity (FVC) in liters, 0.81 with the predicted FVC based
on the subject's age and height, and 0.34 with the percent predicted FVC. In summary, it
was found that the mean ADC increased with age in the pediatric population and that the
mean ADC was lower in the pediatric age group than in young adults. These observations
suggest that the pediatric subjects had smaller airspaces than the young adults. Further,
the variability of the airspace structure, as measured by the standard deviation of the
ADC values, did not change with age, as expected. Thus 3He diffusion MRI of lung
appears to be able to detect this normal maturation process of increased lung volume via
increases in the size of the functioning alveoli.
Altes et al. (2004a) used advanced imaging techniques to detect age-related development
in lung microstructure that relate to both lung volume and surface area. 3He diffusion
magnetic resonance scanning produces in vivo images of tissues weighted as to water
diffusion through local microstructure. MRIs were acquired for each of 29 individuals (2
separate trials for each), aged four to 30 years, and used to determine the mean ADC and
lung volume for each subject. The mean ADC was reported to increase with increasing
subject age (r = 0.8; P < 0.001), with a 55% increase in mean ADC from the youngest (4
years) to oldest (30 years) subject. The lung volumes measured on MRI were highly
repeatable for the two acquisitions (r = 0.980) and also reflected increased volumes
concordant with the ADC. These advanced imaging results gave functional indications
that alveoli increase in size rather than number during childhood.
Menache et al. (2008) generated quantitative whole-lung models from silica casts of the
lungs from 11 subjects between 3 months and 21 years of age. The models were based on
a combination of cast data and published information on distal airway dimensions and
were inclusive of the conducting airways (trachea through terminal bronchioles), the
respiratory bronchioles, and the alveolar airways, which include alveolar ducts and sacs.
Parameters evaluated from the data included airway generation number count, length and
diameter of terminal bronchioles and alveolar ducts, acinar length and alveolar
dimensions (assumed spherical), and total alveolar number. Further estimates from these
parameters and reasonable assumptions were made for alveolar volumes and the
physiological volumes of TLC and FRC. Model dimensions for the conducting airways,
as well as the estimated dead space, for all children fell within the range of the limited
published information. The assumptions and estimates used produced results that were
reasonably consistent with available physiological data for children 8 years and older.
The predicted TLC for the older individuals (aged 8 to 21 yr) fell within or near the range
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arising from published scaling equations. However, the models for children 3 years of
age and younger resulted in predicted TLCs well below those predicted using these same
equations by as much as an order of magnitude (data not shown). Another unexpected
result was the total number of model calculated alveoli compared to the published
number of alveoli as a function of age. As shown in Figure 3-10, the calculated number
of alveoli increased linearly as a function of age in contrast to the data of Dunnill (1962)
and Thurlbeck (Thurlbeck. 1982). This suggested that the fixed relationship between
respiratory airway volumes and alveolar volumes assumed for all ages was incorrect and
that the relationship must be different in the younger children. These differences might be
explained by growth in early childhood when the alveolar region is growing more than
the airways. The airways show symmetric growth since they are complete, while the
alveoli are increasing in both number and size. These results suggest that the geometry
model airway dimensions for all ages are appropriate for use with dosimetry models;
however, they also point out a need for a greater understanding of lung development for
children 3 years of age and under.
500
400
1
^ 300
§ 200
100
0
Dunnill (1962)
Thurlbeck (1982) (male)
Thurlbeck (1982) (female)
Model Values
0
10 15
Age, years
20
25
Source: Reprinted with permission of Informa Healthcare; Menache et al. (2008): using data from Dunnill (1962) and Thurlbeck
(Thurlbeck. 1982)
Figure 3-10 Alveoli count per lung as a function of age.
Ogiu et al. (1997) presented detailed physical mass measurements of various organs in
4,667 Japanese subjects, aged 0-95 years, including 3,023 males and 1,644 females.
Analyses of age-dependent changes in weights of the brain, heart, lung, kidney, spleen,
pancreas, thymus, thyroid gland, and adrenal gland and also of correlations between
3-73
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organ weights and body height, weight, or surface area were carried out. It was concluded
that organ weights, including lung, in the growing generation (under 19 years) generally
increased with a coefficient expressed as (body height) x body weight0 5. Specific
coefficients were derived for both right and left lungs and for both males and females. It
was also noted that adult males had heavier lungs than adult females, and that the
male:female lung weight ratios were nearly the same, 1.27 for the right lung and 1.28 for
the left lung. The age-specific weights presented in this study for lungs only, 0-15 years
of age, are shown in Table 3-19.
In a translated Japanese study, Inagi (1992) described the collection and measurement of
the heights of the mucous membrane in the human nasal septum from 74 cadavers,
including 5 males and 4 females classified as "fetal/infant," and 5 males and 3 females
aged 1 to 19 years referred to as the "infant/adolescent" group, as well as older aged
groups. The purpose of the study was to examine histological changes in mucosal tissues
although measurements were made in relation to age including heights of the mucous
membrane, including both the epithelium and the underlying lamina propria. The average
height for the epithelium of the "fetal/infant" group was estimated to be -0.4 |_im with a
range of -0.35 - 0.5 (.un. For the remainder of the groups, the average and range of height
was estimated to be -0.7 |_im with a range of - 0.4 - 0.9 |_im. Estimation of the lamina
propria heights (described and given as being from the convex and concave sides of the
nasal septum) yielded: average height for fetal/infant group -500 |_im with a range of -
300 -700 (.im; for the remainder of the groups the average and range of height was
estimated to be -900 |_im with a range of -400 - 1500 |_im. Such data and results may
have utility in gas dosimetry as they give a basis for diffusion distance in mass transport
processes, in this case across age groups including the very young.
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Table 3-19 Lung weights (right and left) of males and females from birth to adulthood
Males Females
Left Lung Right Lung Left Lung Right Lung
Age
N
Average wt
(g ± SD)
N
Average wt
(g ± SD)
N
Average wt
(g ± SD)
N
Average wt
(g± SD)
0
39
22.3 ± 5.7
40
28.4 ± 8.0
54
23.1 ± 7.1
52
29.1 ± 8.3
1 mo
5
42.1 ± 12.7
6
49.3 ± 16.1
4
38.7 ± 7.7
4
43.8 ± 8.1
2
11
48.4 ±6.6
12
56.6 ± 11.0
7
45.7 ± 9.9
7
52.2 ± 8.7
3
3
46.3 ±6.4
3
62.7 ± 11.7
6
50.2 ± 8.1
6
66.3 ± 15.6
4
11
51.1 ± 9.7
11
62.7 ± 11.5
5
51.5 ± 12.8
4
61.6 ± 14.5
5
4
51.8 ± 16.1
4
58.0 ± 18.6
7
48.3 ± 10.1
8
58.9 ± 9.6
6
8
55.5 ± 12.1
8
68.3 ± 12.4
6
62.1 ± 6.9
6
70.2 ±6.8
7
6
72.2 ± 8.5
6
86.7 ± 12.1
1
55.0
1
68.0
8
7
66.5± 8.9
8
82.2 ± 19.0
5
62.0 ±10.2
5
74.8 ± 16.8
9
1
66.0
2
108.0 ± 31.1
8
67.6 ± 12.9
8
81.3 ± 16.1
10
4
71.9 ± 24.9
4
77.7 ±27.6
3
53.3 ± 10.4
3
67.3 ± 9.3
11
1
50.0
1
62.0
-
-
-
-
1 yr
15
83.9 ± 20.2
15
93.9 ±21.9
22
76.8 ± 23.7
23
87.4 ± 30.4
2
7
100.5 ±28.4
7
101.4 ± 21.2
14
94.8 ± 26.7
13
107.7 ± 32.0
3
17
108.4 ±28.3
16
129.4 ± 36.0
11
112.5 ± 21.0
11
117.9 ± 25.8
4
11
118.5 ± 38.5
10
122.8 ± 32.2
4
117.3 ± 23.6
5
158.4 ±43.2
5
8
138.1 ± 38.2
9
159.7 ± 34.3
4
113.0 ± 50.6
5
128.6 ±43.2
6
4
194.8 ±16.0
4
230.3 ± 18.4
3
143.3 ± 12.6
4
197.5 ±62.9
7
13
170.8 ±61.6
14
186.9 ±63.7
8
163.8 ±44.2
8
200.0 ±47.8
8
10
169.8 ± 54.7
10
204.4 ±63.7
8
215.0 ± 50.1
8
242.5 ± 70.3
9
10
232.5 ± 75.5
9
243.3 ±60.0
9
208.3 ±62.8
9
247.9 ± 81.2
10
12
245.6 ± 71.9
13
255.2 ± 96.9
5
314.2 ±42.0
4
368.0 ± 85.4
11
10
254.8 ± 77.1
10
298.5 ± 78.1
10
287.5 ± 77.4
9
300.6 ± 90.1
12
12
370.8 ± 130.1
12
398.8 ± 129.6
6
289.2 ± 89.4
6
280.0 ± 107.9
13
8
248.5 ± 131.9
8
383.4 ± 131.8
4
269.5 ± 93.2
4
303.5 ±62.9
14
10
402.5 ± 146.1
10
467.0 ± 203.0
6
339.3 ± 54.4
6
389.2 ± 72.3
15
14
442.0 ± 155.6
13
500.3 ± 127.7
8
297.6 ± 191.9
7
344.0 ±224.8
20-24
68
363.8 ± 129.1
61
444.9 ± 164.6
37
343.9 ± 118.1
41
363.6 ± 122.8
Source: Reprinted with permission of Lippincott Williams & Wilkins; Ogiu et al. (1997'
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4 FINDINGS AND CONCLUSIONS
The overall goal of this report is to summarize and put into context the scientific
developments that have occurred in the fundamental areas of target tissue dosimetry of
gases both between species and between children and adults related to the RfCMethods.
An alternative method to the use of the strict gas categorization is also presented. Much
of the information discussed in this report was presented in more detail in the prior
reports (U.S. EPA. 2011b. 2009b). This report examines the state of the science in
specific areas of research related to inhalation gas dosimetry and the RfC Methods.
Consequently it has limitations in its scope. Other active and future areas of research not
addressed in this report include: (1) the effects of common diseases on gas distribution
within the respiratory tract and on mucus composition and thickness; (2) the effects of
exercise on ventilation, oral breathing, and gas dosimetry; (3) the potential effects of race
and other genetic factors on upper airway anatomy, and on metabolism; (4) the effects of
defensive or toxic responses, e.g., changes in ventilation that exposure to some gases can
produce; and (5) the potential effects of particles on gas dosimetry. Each of these
components should be carefully considered in inhalation risk assessment as the science
becomes available.
One of the most basic aspects in describing target tissue dosimetry presented in these
reports is a scheme for characterizing gases that differs fundamentally from the RfC
Methods. The RfC Methods gas scheme related physicochemical properties of gases to a
numerical category; this category was then related to the observed toxicity, including that
of the target tissue. The alternative scheme proposed by Medinsky and Bond (2001).
and featured in these reports, provides a direct and simplified descriptor approach
for characterizing gases that relates the properties of the gas to the site of the
observed toxicity without the need for categorization. Rather than assigning specific
numerical categories to gases, these descriptors are placed on a chart that represents
reactivity and water solubility as continuous variables (see Figure 3-1). It is important to
note that this scheme provides examples of gases that fit these discrete descriptors, but
that the majority of gases may not fit one particular descriptor. In addition, the potential
role of metabolism and its influence on uptake and toxicity is not directly accounted for
in this scheme. Thus, this approach also has limitations.
The scientific developments presented in these reports should inform users of the utility
and inherent limitations in the existing default RfC Methods which employ dose estimates
from VE and SA, especially in the ET region. The general aspects to consider are air
phase transport (related to VE in the default approach) and disposition of gases into the
liquid/tissue phase (related to SA).
The capacity of CFD techniques to solve and describe air phase behavior in complex
geometries represented by different species has been clearly demonstrated. For the ET
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region, this technique has been repeatedly shown to estimate potential exposure (through
flux) of target tissues with greater accuracy than the use of VE and SA ratios as a basic
default procedure.
The development of PBPK models to describe the toxicokinetic behavior of gas flow and
disposition into the tissue phase relating to SAET has been illustrated and demonstrated to
be a valid approach. Models comprised of tissue stacks with underlying blood flow that
simulate gas flow into and through the tissues have demonstrated a correlation of CFD-
estimated flux and target tissue dose with observed effects.
The capabilities of CFD and PBPK models to describe these individual phases have been
integrated in a quantitative manner with CFD- PBPK hybrid modeling approaches. The
development of this approach through use of a "permeability" coefficient (i.e. overall
mass transport coefficient, Kg) to combine elements of flux from CFD modeling and of
permeability from PBPK modeling in defining the diffusion of the gas through the air and
tissue phases has been demonstrated. For target tissue dose, CFD- PBPK hybrid models
represent a best available model for air and tissue phase elements of target tissue
dosimetry that the VE /SAET surrogate attempts to approximate. Finally, this section
demonstrates the application of the best available model of tissue dosimetry, the CFD-
PBPK hybrid models. These published applications consistently demonstrate that
interspecies target tissue doses (human:animal) in the ET region relative to external
exposure are close to or greater than 1:1.
The results of the analysis for the ET region are graphically summarized in Figure 4-1.
This figure may be viewed as an update to Figures 2-3 and 3-6 reflecting the state of the
science presented in these reports related to the underlying assumptions and outcome of
applying the current default method. Studies have shown that air flow and gas deposition
to surface areas in the ET region are nonuniform. Also, the results from modeling
approaches such as CFD-PBPK hybrid models which have the capability to integrate and
apply conditions of nonuniform gas behavior in predicted target-tissue dose have been
entered into this schematic as well. As shown in Table 3-7, the interspecies dosimetry
modeling results indicate that for the ET region, the dose (i.e., HEC) to animals is either
greater (up to sevenfold) compared to humans or close to unity. Comparison of these
approaches demonstrates this point whether the dosimeter is quantitative (e.g., based on
target tissue flux, target tissue concentration of parent or metabolite) or qualitative (e.g.
based on overall or regional mass transfer coefficients - Kg and kg) over a range of gases
having differing solubility, reactivity, uptake, and partition coefficients.
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23
ET Geometry
& Anatomy
ET Lesions
&
Flux-lesion correlation
nonuniform gas flow in ET region
nonuniform ET SA
nonuniform gas deposition over ET SA
Dye-flow
CFD
ET Models
ET Models
Rat
CFD PBPK
Hybrid Dosimetry
Models
lx dose
Human
or > lx dose
Figure 4-1 A revised schematic representation of the outcomes for interspecies
inhalation dosimetry of gases for the ET region following from the advances
presented.
The new studies dealing with overall gas dosimetry in the TB and PU airways support
many of the principles and approaches of dosimetry in RfC Methods. Although the use of
a simplified geometric model of the airways limited the breadth of their conclusions, the
tissue metric for the alveolar area (g/cm2/min) arrived at by Tsujino et al. (2005) is
similar to that used in the RfC Methods. Morris and Hubbs (2009) showed a similar result
in TB region for diacetyl using CFD-PBPK hybrid modeling. However, in the PU region
Gloede et al (2011) observed target tissue concentrations to be 3-7 times greater in the
human than the rat for diacetyl. The methods for extension of CFD evaluation to the
lower airways of Zhang et al.(2011; 2006). Madasu (2007). and Harding and Robinsion
(2010) should provide refinement and further resolution to flow and dose in the lower
airways as has been done extensively for the upper airways. Additional studies , such as
the novel work by Corley et al. (2012). need to encompass CFD simulations in the rat and
human lower respiratory tracts to be able to compare gas uptake rates between species,
similar to what has been done for the URT.
The studies and information relating directly to dosimetry of the tracheobronchial
(TB) and pulmonary (PU) regions generally support the dosimetric approaches and
assumptions of RfC Methods. Methodological advances and increased resolution of
several in vivo imaging techniques indicate highly homogenous and uniform flows in the
alveolar regions. On the other hand, examination of the tracheobronchial (TB) region
with human models and advanced dynamic fluid flow programs reveal a degree of
non-uniformity of flow for this region although apparently not to the extent that has been
documented for the upper airway. As discussed in the Status I Report (U.S. EPA. 2009b).
these assumptions and thus, the default dosimetric procedure for the ET region were not
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supported as studies consistently demonstrated highly non-uniform airflow and
deposition to airway surfaces, and advance kinetic models clearly demonstrated the
animal/human dose to be > 1.
Marked advances in morphometry of these regions are being achieved with the
development and application of stereology. These techniques, described as the estimation
of higher dimensional information from lower dimensional samples, have and continue to
provide more accurate estimates of measures and vital parameters such as alveoli number
and size characteristics, volumes and surface areas in both humans (e.g.. Ochs et al..
2004) and laboratory animals (Knust et al.. 2009). all of which may influence and refine
inhalation dosimetry of gases.
The significance of the blood:gas (air) partition coefficient (Hb/g) to the advanced PBPK
models have apparently been responsible for the generation of a number of direct and
surrogate approaches for providing these values, both animal and human. The critical and
comprehensive analyses of Payne and Kenny (2002) and Abraham et al. (2005) of human
and animal (rat) Hb/g for a large number of volatile organics from several sources and
approaches made several conclusions. A major indirect conclusion affecting interspecies
dosimetry is that there is no significant difference for VOCs between rat and human Hb/g.
The other strategy to evaluate the Hb/g for purposes of interspecies dosimetry involved
inspection of published inhalation PBPK models that were configured for interspecies
extrapolation, and therefore had Hb/gs that were validated with simulations compared to
relevant human empirical data. The modeling results indicate the current dosimetry
approach in the RfCMethods that uses ratios of animal to human Hb/g as a basis of
dosimetry for systemic (SYS) sites may result in human equivalent concentrations
that are less than those estimated by PBPK models.
An overview of the literature available on children's dosimetry closely follows the
recommendations and guidance of the NAS on children's risk (NRC. 1993). These
recommendations include the proposal to use PBPK models to explore and evaluate
potential child susceptibility. A recommendation linked to the development and
utilization of models is the need to generate accurate measurements and parameters to be
used in these models. Accordingly there exist a number of studies examining various
parameters essential to inhalation modeling including physiological daily inhalation rates,
lung tissue and lower airway measures and function. A compelling dataset (orally
administered therapeutics) documents the generally slower clearance rate in children
(Ginsberg et al.. 2002). Flow models are available that examine uptake differences of
gases in the upper airways of both adults and children. Also, several PBPK models that
are configured to specifically consider child versus adult dosimetry have been developed.
Although the actual number of datasets and models relating to gas dosimetry in children
is not yet plentiful, a number of methods and approaches are available. The available
methods and modeling approaches are fairly uniform in their indications of
potential higher inhaled doses in young children (3 mo), which may be 2- to 3-fold
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more than in adults, but can be more or less. The RJCMethods uses the human
interindividual uncertainty factor (UFH) of 10 to accommodate pharmacokinetic and
pharmacodynamic variability and for consideration of potential sensitive population and
lifestages including children. In some cases, chemical-specific information may
warrant consideration of alternative modeling approaches or adjustments to
account for this lifestage. For example, in the development of shorter-term reference
values specific to a very early lifestage (e.g., infancy), when available chemical-specific
information - such as demonstrated differences in metabolism - may indicate
consideration of a data-informed approach differing from the default dosimetric
adjustments established for the RfC. It is anticipated that information will continue to
become available to further inform this issue.
An overview of the major findings related to the current default procedure for
interspecies dosimetric extrapolation of gases and for children's inhalation dosimetry is
presented below in Table 4-1 and Table 4-2.
4-5
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Table 4-1 Overview of major findings related to the state of the science for inhalation
dosimetry of gases
Extrathoracic
(ET)
Tracheobronchial (TB)
Pulmonary
(PU)
Systemic
(SYS)
Basis for
Default DAF
(addresses PK
only)
Ve / SA ratio in animals
and humans
Ve / SA ratio in animals
and humans
Ve / SA ratio in
animals and
humans
Hb/g (blood:gas (air)
partition coefficient)
animal to human ratio
Assumptions
for default
Uniform flow to SA,
uniform deposition to SA
Uniform flow to SA,
uniform deposition to SA
Uniform flow to SA,
uniform deposition
to SA
Human and animal
exposure scenarios
are equivalent. Human
blood concentration
integrated overtime is
< animal, animal blood
concentration =
human equilibrium
blood concentration
Default DAF
0.2-0.3
> 2
>2
1
Models vs.
DAF
Robust PK and CFD
modeling database for a
variety of chemicals
shows dose metric in
animals > humans (i.e.
DAF is > 1 not 0.2)
Limited to 2. Shows
equivalent animal and
human dose. Other
modeling information is
descriptive and does not
provide information for
extrapolation purposes.
Limited to 1. Shows
potential for greater
dose in humans for
specific chemical.
Fairly robust PK
database shows
modeled derived DAFs
to be > 1.
Current
Evidence and
Conclusions
Strong evidence
indicating that in the
absence of modeling the
default DAF = 1.
Uniformity of flow and
deposition to SA
assumptions not
supported in studies
examining airflow
patterns, airflow and
lesion correlation, nor by
CFD modeling.
Limited evidence. The
available information from
airflow modeling
suggests assumptions
may hold or that there is
not any compelling
evidence that they do not.
Limited evidence.
The available
information from
airflow modeling
suggests
assumptions may
hold or that there is
not any compelling
evidence that they
do not.
Modeling and partition
coefficient information
suggests that the
default DAF may be
conservative.
However, there is no
apparent pattern of the
relationship between
modeled derived
DAFs/HECs, and PCs.
Source
Status I Report (2009b)
Status II Report (2011b)
Status II Report
(2011b)
Status II Report (2011b)
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Table 4-2 Summary of major finding related to state of the science of children's
inhalation dosimetry
Extrathoracic
(ET)
T racheobronchial
(TB)
Pulmonary
(PU)
Systemic
(SYS)
Children's
Dosimetry
Current information
based on CFD model
simulations and other
analyses suggest no
significant differences
between children
(e.g., 3 mo, and 7-8
year old) and adults
are apparent.
Current but limited
evidence suggests no
significant differences
between children and
adults are apparent.
Evidence suggests the
potential for a 2 to 3-fold
greater inhalation dose in
children (3 mo) vs. adults.
Select cases may be
more or less. Limited
modeling information. No
models are available that
extrapolate from
laboratory animals or
adult humans to human
models for specific early
lifestages.
A few well parameterized
PK models available.
Evidence suggests the
potential for a 2-fold
greater systemic dose in
children vs. adults from
inhalation exposure.
Select cases may be
more or less depending
upon the chemical and/or
parent vs. metabolite.
Source
Status II Report (U.S. EPA. 2011b)
4-7
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APPENDIX A. SUMMARY AND DISPOSITION OF
INDEPENDENT EXTERNAL PEER REVIEW
COMMENTS
The report "Advances in Inhalation Gas Dosimetry for Derivation of a Reference
Concentration (RfC) and Use in Risk Assessment" has undergone a formal external peer
letter review performed by scientists in accordance with EPA guidance on peer review
(U.S. EPA. 2006b). The reviewers were tasked with providing written answers to charge
questions on both general and specific scientific aspects of the report. A summary of
significant comments made by the external reviewers to these charge questions and
EPA's responses to these comments arranged by charge question follow. Several
reviewers suggested additional references for consideration and incorporation into the
document. Those references incorporated into the document are indicated in the
responses to specific charge questions where appropriate. Editorial comments were
considered and incorporated directly into the document as appropriate.
A.1 External Peer Reviewer Comments - Comments and Response
to Charge:
A. 1.1 Charge Question 1
This report provides new information on the pharmacokinetic component of interspecies
gas dosimetry for effects in the ET, TB, PU regions, and SYS sites as it relates to the
current default procedures. Issues related to pharmacodynamics, including variability in
response, are specifically excluded from this report. Is the scope and primary focus of this
report clear?
Comments:
All of the reviewers were in agreement that the scope and primary focus of the report is
clear. One suggested breaking up Chapter 3 into two new chapters (adults and children).
A second reviewer understood the scope of the document, but asked why a detailed
discussion of pharmacodynamics was excluded from the report. A third reviewer thought
that the title is more general than the actual scope and focus of the document and
suggested the addition of a subtitle to clarify that this report focuses on interspecies
extrapolation. This reviewer also provided several suggestions for consideration for the
future evolution of this work. Another reviewer commented that it is stated several times
that the report does not include new data on pharmacodynamics or on variability of
response. This reviewer thought that the scope and primary focus of this report are clear
and includes an extensive summary of the recent (since 1994) advances in the
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pharmacokinetic component of interspecies dosimetry. The fifth reviewer noted that the
scope and focus are very clear and the report is both educational and valuable.
Response:
Consideration was given to dividing Chapter 3 into separate chapters for children and
adults; however, the subsections for these chapters would not be compatible for children
and adults since there is a paucity of data for children compared to the available data for
adults.
The scope of this report was limited to evaluating the scientific advances related to the
default procedure for interspecies extrapolation as presented in the 1994 RfCMethods.
As described in this document the focus of this current work was to evaluate the new
science as it relates to the kinetic portion of this extrapolation. To further clarify the
scope of this report, additional text was added to the Executive Summary. Subtitles were
considered, but a subtitle was not added to this report. The method to derive an RfC relies
on interspecies extrapolation, thus the current title does implicitly pertain to interspecies
extrapolation.
A. 1.2 Charge Question 2
Have the principal studies examining interspecies gas dosimetry for effects in the ET,
TB, PU regions, and SYS sites that have been reported since the issuance of the 1994 RfC
Methods been identified in this report? Please identify and provide a rationale for any
other key studies that could contribute to support or refinement of the current default
procedures for derivation of an RfC.
Comments:
Four reviewers commented that it is clear a comprehensive and focused review of the
literature pertaining to the focus of this report. These reviewers also thought that the
major developments in this area were presented in a clear and concise manner, providing
sufficient information to support understanding of these developments. Three of these
four reviewers suggested additional references for consideration in this work. One
reviewer suggested including research conducted by the radiological community on
development of dosimetry lung models, the emerging field of molecular dosimetry, while
another reviewer suggested including references that are not directly related to
interspecies gas dosimetry but may provide insight into respiratory uptake. A reviewer
suggested the effects of particles on gas dosimetry should be covered.
Lastly, one reviewer commented that there was an incomplete analysis of uptake
modeling in the lower respiratory tract, as several studies dealing with whole-lung
dosimetry modeling are missing. This reviewer suggested a few relevant references for
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consideration that predict the uptake of various gases in specific locations and the entire
lung, thus allowing interspecies extrapolation based on gas flux to the lung surfaces or
lung tissue concentration.
Response:
All references suggested by the reviewers were evaluated for consideration in this report
and many of them are included in this final report. The references pertaining to
dosimetry of radioactive gases were reviewed in depth, but were found not to include
novel information related to interspecies inhalation gas dosimetry. The effect of particles
on gas dosimetry was not within the scope of this report; however, additional text was
added in Section 4 stating that the effect of particles should be considered in inhalation
risk assessment.
Molecular markers are emerging as useful dosimeters. The references suggested by a
reviewer were included in Section 3.2.5.3; however, a detailed discussion of molecular
dosimetry was not provided.
Osterman-Golkar, S.; Czene, K.; Lee, M. S.; Faller, T. H.; Csanady, G. A.; Kessler,
W., . . . Segerback, D. (2003) Dosimetry by means of DNA and hemoglobin adducts
in propylene oxide-exposed rats. Toxicol Appl Pharmacol 191: 245-254.
Rios-Blanco, M. N.; Ranasinghe, A.; Lee, M. S.; Faller, T.; Filser, J. G.; Swenberg, J.
A. (2003) Molecular dosimetry of N7-(2-hydroxypropyl)guanine in tissues of F344
rats after inhalation exposure to propylene oxide. Carcinogenesis 24: 1233-1238.
The references provided by a reviewer regarding whole-lung dosimetry modeling were
useful and added to this report in Section 3.3.4 and 3.6.2, respectively.
Asgharian, B.; Price, O. T.; Schroeter, J. D.; Kimbell, J. S.; Singal, M. (2012) A lung
dosimetry model of vapor uptake and tissue disposition. Inhal Toxicol 24: 182-193.
Overton, J. H. and Graham, R. C. (1989) Predictions of ozone absorption in human
lungs from newborn to adult. Health Phys 1: 29-36.
One reviewer suggested other studies that are related to the interspecies extrapolation for
gas dosimetry. These additional studies were included in Section 3.2.2.2, 3.2.2.2, 3.6.2,
and 3.3.1, respectively.
Finck, M.; Hanel, D.; Wlokas, I. (2007) Simulation of nasal flow by lattice
Boltzmann methods. Comput Biol Med 37: 739-749.
Mosges, R.; Buchner, B.; Kleiner, M.; Freitas, R.; Horschler, I.; Schroder, W. (2010)
Computational fluid dynamics analysis of nasal flow. B-ENT 6: 161-165.
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Thrall, K. D.; Woodstock, A. D.; Soelberg, J. J.; Gargas, M. L.; Kinzell, J. H.;
Corley, R. A. (2009) A real-time methodology to evaluate the nasal absorption of
volatile compounds in anesthetized animals. Inhal Toxicol 21: 531-536.
Zhang, Z. and Kleinstreuer, C. (2011) Deposition of naphthalene and tetradecane
vapors in models of the human respiratory system. Inhal Toxicol 23: 44-57.
A. 1.3 Charge Question 3
The state of the science that serves as the basis for this report is presented in detail in the
Status I andII Reports. Are the summaries and critical information included in this report
related to gas dosimetry clearly and accurately presented? Are the analyses and evaluations of
the scientific evidence supported by the studies cited?
Comments:
Four reviewers indicated that the document clearly and accurately presented an overview
of the science pertaining to inhalation gas dosimetry and that it adequately covers the
available material. Two of these reviewers each suggested a few places in the document
that could be improved: Section 2.4.3 regarding VE/SA substance and clarity; while an
improvement upon the numerical scheme, the descriptor scheme limitations should be
expanded upon; and lack of gender specific information.
A fifth reviewer commented on a few issues with the description and interpretation of
some studies on gas dosimetry procedures related to the 1994 RfCMethods. This
reviewer thought that there appears to be a misunderstanding regarding the basis of
Ve/SA as DAF for category I gases, its interpretation from a dose-metric perspective, and
that the description in the report is a bit confusing. This reviewer commented that a 5-
fold higher VE/SA in humans than in animals means a 5 fold lower HEC than CA and not
a 5 fold higher dose. This reviewer also suggested more thought be placed into clarifying
the descriptor scheme, noting limitations of the CFD modeling, and made several
comments to verifying Equation 3-1 and a related equation.
Response:
The assumptions regarding the application of VE/SA in Section 2.4.3 were modified by
removing confusing text and clarifying language on the assumptions. Clarification that
the Medinsky and Bond scheme is not a perfect scheme either, was added to the report in
Section 3.1 and the lack of consideration of metabolism in these categorization schemes
was acknowledged. Figure 3-1 was also updated to include a systemic acting gas. There
is a lack of gender specific data available for the majority of the models, and the values
presented are typically considered to be population averages.
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The description of VE/SA in Section 2 of this report is depictly correctly based upon the
1994 RfC Methods. The reviewer is correct that, for the ET region, a 5-fold higher
Ve/SA in humans means a 5-fold lower HEC. Therefore, for a given animal POD (CA)
the corresponding human dose per unit surface area is up to 5-fold greater.
The limitations of CFD modeling and other models are described throughout the report,
specifically for CFD modeling in Sections 3.2 and 3.3, and additional text was also added
at the beginning of Section 3.3. However, these models represent a vast improvement
over the default RfC Methods.
Equations 3-1 and 3-2 were verified with the source material (Frederick et al.. 1998) as
being correctly depicted in this report.
A. 1.4 Charge Question 4
Please comment on the effectiveness of the report in describing advances in the state of the
science since publication of the 1994 RfC Methods document. Please identify any additions,
deletions or changes that would improve the effectiveness of this document in presenting the
state of the science as it relates to the default procedures for interspecies gas dosimetry in RfC
Methods.
Comments:
One reviewer commented that the report is effective, but mentioned additional limitations
that should be listed to benefit readers and future users. A second reviewer offered that
the effectiveness of the report would be enhanced if there was a discussion of future
research needs, including the potential for increased use of molecular dosimetry
measurements.
Another reviewer stated that this document identifies a number of existing issues
surrounding the 1994 RfC Methods; however, many of the difficulties have been resolved
for the purpose of describing interspecies inhalation dosimetry while certain assumptions
and uncertainties are likely to always be present in human health risk assessments. The
reviewer also thought that this document adequately summarized the new information
and appropriately weighted the strengths and limitations of the literature when drawing
the conclusions.
A fourth reviewer commented that report is very effective in describing advances in the
state of the science specifically related to procedures for interspecies gas dosimetry in
RfC Methods. This reviewer also commended the selection of the collection of figures
cited in the document that help to communicate the insights offered by the more
advanced modeling methods. This reviewer suggested improvements to several figures
and the accompanying text to improve clarity and effectiveness.
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Lastly, one reviewer commented that for the upper airways (oral and nasal passages), the
report nicely presents recent advances and highlights the short comings of RfCMethods.
In regards to the gas categorization scheme, this reviewer said the report correctly
concludes that dose extrapolation should be based on the effect in the target tissue (i.e.,
dose metric) and not the physico-chemical properties of gases. In addition, the reviewer
said the report provides adequate information on the dose-based extrapolation approach.
Importantly, this reviewer provided information and references describing the the state of
the science regarding whole-lung uptake modeling and suggested more clarity be given
on how various lower airway modeling approaches should be considered.
Response:
Text was added to the end of Section 3.2.5.3 regarding the use of molecular markers as
dose metrics. In response to reviewer comments, Figure 3-1 was improved with the
addtion of a systemic acting gas, and the accompanying text was updated and clarified.
In addition, Figure 3-6 was corrected such the graphic better reflected the science
indicating nonuniform flow, surface area, and deposition. Whole-lung uptake modeling
papers suggested in response to this charge question were also suggested under Charge
Question 2. These references were evaluated and were included as appropriate (see
Response to Charge Question 2). Additional text was added to the beginning of Section
3.3 that provides more information regarding the consideration of the use of various
lower airway modeling approaches.
A. 1.5 Charge Question 5
Section 3 of this report summarizes the advanced state of the science dosimetry models
including CFD and the CFD-PBPK hybrid models. The capabilities of these models to
estimate target-tissue dose are highlighted in Section 3. However, as with any state of the
science approach, limitations exist with respect to application and outcome of these models -
some of which have been recognized and discussed (see in particular Section 3.2.5.3). Have
the limitations of these advanced models been sufficiently characterized? Similarly, how well
does this report differentiate the relative scope of the limitations that exist with these models
compared with the existing RfC Methods default approach in estimating actual target-tissue
dose? Is there any information that would further support as well as limit the overall
conclusions drawn from the results of these models?
Comments:
One reviewer commented that the pharmacokinetic literature appears to be reviewed
thoroughly and the limitations and applicability discussed when appropriate, but
limitations regarding CFD models should be clarified.
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Another reviewer commented that a number of advances have become available for
improving dosimetry models, including CFD and CFD-PBPK hybrid models, these
models do reflect the state-of-the-art of the science, and certain uncertainties and
limitations do exist. This reviewer notes that the report discusses the value and limitations
of these models and how they could aid in advancing interspecies dosimetry as well as
that the report has weighted the strengths and limitations of these modeling approaches,
and these factors have been sufficiently characterized. This reviewer also referred to
additional references that should be considered in response to Charge Question 2.
A third reviewer commented that the report in general succeeds to differentiate clearly
the relative scope of the limitations that exist within the state-of-the-science (CFD, hybrid
CFD-PBPK) models vis a vis those incorporated in the default approach described in the
1994 RfCMethods for estimating target-tissue dose. This reviewer noted, however, this
differentiation takes place very specifically in the (rather limited) context of direct
dosimetric interspecies extrapolation for the different regions of the respiratory tract.
Thus, the reviewer suggested that for current or future work it would be very useful to - at
least briefly - discuss both the default and the more advanced approaches for RfC
derivation in a wider but highly relevant context such as their implications in the use for
public health problems. This reviewer provided some references related to these broader
issues.
A fourth reviewer thought that the CFD-PBPK hybrid models were well-presented in
Section 3 and the discussion on page 3-20 was especially helpful. However, this reviewer
commented that the metabolic capacity of the target tissue will affect the uptake of the
gas into the tissue. This reviewer further stated that although metabolism may not fit
neatly into a classification system based on the physicochemical properties of the
compound, metabolism in the target tissue will definitely affect uptake of the compound
into the target tissue. This reviewer suggested that modifying the classification system to
include another property: "Potential for metabolism at target site." This reviewer was aslo
pleased to see that molecular dosimetry data are being considered in the CFD-PBPK
model shown in Table 3-4.
A final reviewer referred to their commentary provided in response to Question 4.
Response:
Limitations and challenges related to the use of CFD modeling were expanded upon in
Section 3.3, as noted in response to Charge Questions 3 and 4. Also, the gas descriptor
scheme was updated to reflect systemic acting gases and the accompanying text was
modified to include the importance of metabolism. These changes were also described
earlier in response to Charge Question 3.
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A. 1.6 Charge Question 6
The state of the science pertaining to children's inhalation dosimetry is presented in Section
3.6. Is the description of the studies in this report, as they pertain to inhalation gas dosimetry,
clearly and accurately presented? Are the analyses and evaluations of the scientific evidence
supported by the studies cited? Are there additional evidence-based studies and information
specific to children's inhalation dosimetry that should be considered for inclusion that
contribute to the science and understanding of inhalation gas dosimetry in children?
Comments:
Three reviewers commented that the report does a good job describing studies pertaining
to children's inhalation gas dosimetry, the analyses and evaluations of the scientific
evidence are supported by the studies cited, and the report covers the theoretical (i.e.,
breathing pure gases) children's gas dosimetry adequately for normal healthy children.
Two of these reviewers provided examples of additional recent studies that may have
potential value to characterizing children's inhalation dosimetry for consideration.
One reviewer was not clear how the recent findings on lung growth and breathing
parameters helps with the improvement of the RfC Methods, and how geometry and
airflow information can help with improved assessment of DAF for children to replace
the uncertainty factor of 10 in the RfC Methods.
Another reviewer commented that the document is a very useful and comprehensive
review of the methodologies presently available for evaluating reduced-risk models
appropriate for understanding inhalation gas dosimetry in children. This reviewer stated
that while this report meets the challenge of summarizing this rapidly advancing field, it
also clearly identifies that much research still remains to be done. As such, in the
reviewers opinion this report is a welcome contribution and should be of benefit to chart
the activity in this field so that better scientific evidence and interpretation may be made
available to assess whether and to what extent age may influence risk assessment. This
reviewer suggested an additional reference for consideration.
Response:
Information presented relating lung growth and breathing parameters might be useful in
informing model parameter selection and future model development. None of this
information or the children's inhalation dosimetry data were presented as a means to
replace the UF.
Additional references added to the document related to estimates of daily inhalation rates
for children were included in Section 3.4:
A-8
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Brochu, P.; Brodeur, J.; Krishnan, K. (2011) Derivation of physiological inhalation
rates in children, adults, and elderly based on nighttime and daytime respiratory
parameters. Inhal Toxicol 23: 74-94.
Kawahara, J.; Tanaka, S.; Tanaka, C.; Aoki, Y.; Yonemoto, J. (In Press) Daily
Inhalation Rate and Time-Activity/Location Pattern in Japanese Preschool Children.
Kawahara, J.; Tanaka, S.; Tanaka, C.; Aoki, Y.; Yonemoto, J. (2011) Estimation of
daily inhalation rate in preschool children using a tri-axial accelerometer: a pilot
study. Sci Total Environ 409: 3073-3077.
The new Valcke et al. (2011) reference pertaining to intrahuman variability as suggested
by a reviewer was added to Section 3.6.2:
Valcke, M. and Krishnan, K. (2011) Assessing the impact of the duration and
intensity of inhalation exposure on the magnitude of the variability of internal dose
metrics in children and adults. Inhal Toxicol 23: 863-877.
Other Comments
One reviewer commented that, in general, this document meets the challenge of
insightfully summarizing and critically reviewing a wide range of evidence and
interpretation of the pertinent scientific developments and advancements in inhalation gas
dosimetry. The reviewer commented that the authors have successfully interpreted a wide
range of evidence focusing on risk assessment and this report is a contribution to the on-
going search for proper traditional methodologies focusing on improving our
understanding of the problems in today's attempts to assess human health risk using
available toxicological data from a number of animal species.
A second reviewer commented that the report is well-written, logical, accurate, and
thorough within the intended scope. The reviewer stated that risk assessment related
applications for extrapolations and for children are clearly described and the report has an
scope and level detail that supports its validity and its superiority over previous gas
dosimetry modeling approaches. However, this reviewer also suggested that although the
assumptions are clearly stated throughout the report where they are relevant, more could
be done to discuss the limitations. This reviewer provided some additional important
limitations to add to Section 4 to guide further research and educate users of the report on
the use of this approach in risk assessments. The reviewer commented that these
limitations do not detract from the value of the science in the report, but they are worth
listing for educational and cautionary purposes. In addition, this reviewer suggested a few
formatting changes to Table 4-1.
A-9
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1 Response:
2 Additional text regarding the limitations of the scope of this report are provided in
3 Section 4.
A-10
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