&EPA
United States
Environmental Protection
Agency
Cilice of Health and
Environmental Assessment
Washington DC 20460
EPA 600 8-88 066F
Auqust 1989
Research and Development
Interim Methods for
Development of
Inhalation Reference
Doses
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EPA/600/8-88/066F
August 1989
Interim Methods for Development of
Inhalation Reference Doses
Environmental Criteria and Assessment Offices
Office of Health and Environmental Assessment
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
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DISCLAIMER
This document 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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CONTENTS
LIST OF TABLES vii
LIST OF FIGURES viii
LIST OF ABBREVIATIONS x
AUTHORS CONTRIBUTORS, AND REVIEWERS xii
GLOSSARY xv
1. INTRODUCTION 1-1
1.1 DEVELOPING BENCHMARK VALUES IN THE U.S. EPA 1-1
1.2 GENERAL PRINCIPLES OF NONCANCER TOXICITY RISK ASSESSMENT .. 1-3
1.3 STATE-OF-THE-ART APPLICATIONS TO THE DEVELOPMENT OF THE
INHALATION RfD METHODOLOGY 1-6
2. CONCEPTUAL BASIS FOR INHALATION RISK ASSESSMENT METHODOLOGY .... 2-1
2.1 FACTORS CONTROLLING COMPARATIVE INHALED DOSE 2-1
2.1.1 Respiratory Anatomy and Physiology 2-3
2.1.1.1 Respiratory Regions and Branching
Patterns 2-3
2.1.1.1.1 Effect on aerosol deposition
mechanisms 2-4
2.1.1.1.2 Effect on gas deposition and
uptake 2-6
2.1.1.2 Clearance Mechanisms and Cell Types 2-12
2.1.1.3 Summary 2-17
2.1.2 Physicochemical Characteristics of the Inhaled
Agent 2-17
2.1.2.1 Particles 2-17
2.1.2.2 Gases and Vapors 2-19
2.1.3 Impact of Experimental Protocol 2-22
2.1.3.1 Pharmacologic Effects of Agents 2-23
2.1.3.2 Measurement Techniques 2-23
2.1.3.2.1 Anesthesia 2-23
2.1.3.2.2 Breathing pattern 2-24
2.1.3.2.3 Equipment specifications 2-25
2.1.3.3 Definitions/Underlying Assumptions 2-25
2.1.3.4 Exposure Technology 2-25
2.1.3.4.1 Inhalation modes 2-25
2.1.3.4.2 Generation and
characterization 2-27
2.1.3.4.3 Exposure regimen 2-28
2.1.4 Summary 2-30
2.2 PORTAL-OF-ENTRY CONSIDERATIONS: ASPECTS OF COMPARATIVE
PULMONARY TOXICITY 2-30
3. QUALITATIVE EVALUATION OF THE DATA BASE 3-1
3.1 GUIDELINES FOR SELECTIONS OF KEY STUDIES 3-1
3.1.1 Human Data 3-1
3.1.1.1 Epidemiologic Data 3-2
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CONTENTS (continued)
Page
3.1.1.1,1 Assessment of exposure
measures 3-2
3.1.1.1.2 Assessment of effect measures .. 3-3
3.1.1.1.3 Assessing the control of
confounding and covariables .... 3-5
3.1.1.1.4 Summary 3-5
3.1.1.2 Nonepidemiologic Data 3-5
3.1.1.2.1 Clinical studies 3-6
3.1.1.2.2 Case reports 3-6
3.1.1.3 Intraspecies Variability and Identifying
Sensitive Subgroups 3-6
3.1.2 Animal Data 3-10
3.1.2.1 Appropriateness of Species as a Model
for Humans 3-11
3.1.2.2 Study Design 3-12
3.1.2.3 Study Validity and Relevance to
Extrapolation 3-13
3.1,3 Summarizing the Evidence 3-14
3.2 TOXICOLOGICAL ISSUES IN DATA EVALUATION 3-16
3.2.1 Qualitative Evaluation of Dose Response and Dose
Effect Data 3-16
3.2.1.1 Relationship to the Uncertainty Factor
Approach 3-16
3.2.2 Selecting Effect Levels: Inhalation-Specific
Issues 3-21
3.3 DEFICIENT DATA BASES AND ALTERNATIVE SOLUTIONS 3-24
3.3.1 Guidance on Evaluating a Data Base for
Completeness 3-25
3.3.2 Historical Use and Limitations of Occupational
Exposure Limit Values 3-25
4. QUANTITATIVE METHODOLOGICAL PROCEDURES 4-1
4.1 PROCEDURES ADDRESSING LIFETIME EXPOSURE 4-1
4.1.1 Approach for RfD Estimation 4-1
4.1.1.1 Minimum Criteria 4-9
4.1.1.2 Calculation of Human Equivalent
Concentrations 4-10
4.1.1.2.1 Dose conversion: units 4-12
4.1.1.2.2 Dose adjustments for discontin-
uous exposure protocols 4-13
4.1.1.2.3 Dosimetry: particles 4-14
4.1.1.2.3.1 Respiratory tract
effects 4-17
4.1.1.2.3.2 Extrarespiratory
effects 4-19
4.1.1.2.3.3 Assumptions and
default values 4-20
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CONTENTS (continued)
Page
4.1.1.2.4 Dosimetry: Gases and vapors ... 4-22
4.1.1.2.4.1 Respiratory tract
effects 4-23
4.1.1.2.4.2 Extrarespiratory
effects 4-24
4.1.1.2.4.3 Assumptions and
default values 4-28
4.1.1.3 Route-to-Route Extrapolation 4-29
4.1.1.4 Issues for Further Investigation 4-31
4.1.2 Approach for RfD Estimation Using Human Data 4-31
4.1.2.1 Introduction 4-31
4.1.2.2 Selecting the Threshold Estimate 4-32
4.1.2.3 Defining the Exposure Level 4-33
4.1.2.4 Uncertainty Factors for Human Data ........ 4-33
4.2 PROCEDURES FOR ESTIMATING PARTIAL LIFETIME EXPOSURES 4-34
4.2.1 Acute 4-34
4.2.2 Approach for Subchronic Inhalation RfD
Estimation (RfD .) 4-34
o I
4.2.3 Issues Requiring Further Investigation 4-37
4.3 CRITERIA FOR SPECIFYING LEVEL OF CONFIDENCE 4-37
5. REFERENCES 5-1
APPENDIX A: NOVEL APPROACHES TO THE ESTIMATION OF REFERENCE DOSE
(RfD) A-l
APPENDIX B: USE OF PHARMACOKINETIC DATA IN RISK ASSESSMENT,
SELECTED EXAMPLES B-l
APPENDIX C: ADVERSE RESPIRATORY HEALTH EFFECTS (HUMAN) C-l
APPENDIX D: CRITERIA FOR ASSESSING THE QUALITY OF INDIVIDUAL
EPIDEMIOLOGICAL STUDIES D-l
APPENDIX E: CRITERIA FOR ASSESSING THE QUALITY OF INDIVIDUAL
ANIMAL TOXICITY STUDIES E-l
APPENDIX F: CRITERIA FOR CAUSAL SIGNIFICANCE F-l
APPENDIX G: CHOICE OF TOXICITY DATA G-l
APPENDIX H: CALCULATION CURVES FOR RDDR AND AN EXAMPLE APPLICATION
OF DOSIMETRIC ADJUSTMENT H-l
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CONTENTS (continued)
Page
APPENDIX I: DERIVATION OF AN APPROACH TO DETERMINE HUMAN EQUIVALENT
CONCENTRATIONS FOR EXTRARESPIRATORY EFFECTS OF GAS
EXPOSURES BASED ON A PB-PK MODEL USING SELECTED
PARAMETER VALUES 1-1
VI
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TABLES
Number
2-1
2-2
2-3
2-4
3-1
3-2
3-3
4-1
4-2
4-3
4-4
A-l
B-l
B-2
H-l
H-2
1-1
Comparative airway anatomy as revealed on casts
Normal surface airway epithelium: cell types
Some specific lung cell types and their function
Main species differences in epithelial cells and glands
Prevalence of subgroups hypersusceptible to effects of
common pol 1 utants
Proposed approach for summarizing the evidence from
di verse data
Human data for use in health risk assessment
Four types of response levels (ranked in order of
increasing severity of toxic effect) considered in
deriving inhalation reference doses (RfD.s) for systemic
toxi cants
Response levels considered in deriving inhalation RfDs
in relationship to empirical severity rating valves
(ranks are from lowest to highest severity) ,.
Guidelines for the use of uncertainty factors in deriving
reference dose (RfD)
Minimum data base for both high and low confidence
i n the RfD
Various effects levels and their definitions used in
Figure A-2 ,
Absorption of 1,3-butadiene by inhalation following
a 6-hour exposure period
14
Recovery of C-tetrachloroethylene radioactivity after
inhalation exposure for 6 hours to Sprague-Dawley rats ....
RDDR values by mass median diameter and standard
deviation for rats
Summary of systemic toxicity NOAELs for ep(a)oxide
observed in Fisher rats
Definition of symbols
Page
2-5
2-14
2-16
2-18
3-8
3-16
3-17
4-2
4-3
4-5
4-40
A-4
B-l
B-4
H-6
H-16
1-4
Vll
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FIGURES
Fijure Page
2-1 Schematic representation of selected parameters
influencing regional deposition of particles in the
respi ratory tract , 2-7
2-2 Regional deposition of monodisperse aerosols by indicated
particle diameter for mouth breathing (alveolar and
tracheobronchial) and nose breathing (alveolar) 2-8
2-3 Schematic representation of selected parameters
influencing regional deposition of gases in the
respi ratory tract 2-10
2-4 An example of the Tog-normal distribution function of an
aerosol 2-20
2-5 Plot of same aerosol as in Figure 2-4 on log-probability
paper. The curves illustrate the various size parameters
that can be computed using the Hatch-Choate equations 2-20
4-1 Flowchart for calculation of human equivalent concentra-
tions 4-11
4-2 Relationship of partition coefficients to periodicity in
F344 rat arterial blood for (90-days) subchronic and
chronic exposure regimens of 6 hours/day, 5 days/week 4-27
A-l Effect-dose-duration plot of all relevant human and
animal oral toxicity data for methoxychlor A-3
A-2 Hypothetical dose-response data for slight body weight
decrease or liver necrosis in rats and dogs A-7
H-l Schematic of the integration of aerosol distribution
and deposition efficiency curves for calculation of
Regional Deposited Dose (ROD) H-2
H-2 RDDR of the rat:human by particle diameter (MMAD) for the
tracheobronchial region H-12
H-3 RDDR of the rat:human by particle diameter (MMAD) for the
pulmonary region H-13
H-4 RRDR of the rat:human by particle diameter (MMAD) for the
total respiratory system on (A) normal augmenters and (B)
mouth breathers H-14
vm
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FIGURES
Page
H-5 RDDR of three species to the human by particle diameter
(MMAD) for the TH region. Guinea pig 1 and 2 refer to
calculations using different lung surface area data H-18
H-6 The relationship between t-, /2 and t, ,yc upon the
bioavailability of a single deposited dose of inhaled
parti culate matter over a 730-day period H-21
1-1 Schematic of the physiologically based pharmacokinetic
model assumed to describe the uptake and distribution of
i nhaled compounds 1-2
1-2 Plot of NOAEI_HEC vs. NOAELA for the rat for four possible
methods (proposed, established, similar and optimal) of
determining NOAELjcr estimates as defined in the text.
The inhaled compound is dichloromethane 1-13
1-3 Plot of NQAELjrp vs. NOAEL. for the mouse for four possible
methods (proposed, established, similar and optimal) of
determining NOAEL,EC estimates as defined in the text.
The inhaled compound is dichloromethane 1-13
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LIST OF ABBREVIATIONS
ADI Acceptable dally Intake
bw Body weight
CMS Central nervous system
D Aerodynamic equivalent diameter
etc
D__ Aerodynamic resistance diameter
3r
DMA Deoxyribonucleic acid
Dp Particle diameter
DWEL Drinking water equivalent level
PEL Frank-effect level
FEVi Forced expiratory volume at one second
FVC Forced vital capacity
GI Gastrointestinal
HA Health advisory
i.v. Intravenous
kg Ki1ogram
LEL Lowest-effect level
LOAEL Lowest-observed-adverse-effect level
LOEL Lowest-observed-effect level
MF Modifying factor
mg Milligram
ug Microgram
urn Micrometer
MMAD Mass median aerodynamic diameter
NOAEL No-observed-adverse-effect level
NOEL No-observed-effect level
PEL Permissible exposure level
ppm Parts per million
ROD Regional deposited dose
RDDR Regional deposited dose ratio
RfD. Chronic inhalation reference dose
RfD . Subchronic inhalation reference dose
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RNA Ribonucleic acid
a Geometric standard deviation
TLV Threshold limit value
UF Uncertainty factor
URT Upper respiratory tract
V, Tidal volume
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
This document was prepared under the direction of the Environmental
Criteria and Assessment Offices in Cincinnati, Ohio and in Research Triangle
Park, North Carolina.
The principal authors, listed in alphabetical order, are:
Karen Blackburn Annie M. Jarabek
U.S. EPA, ORD, OHEA, ECAO U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268 Research Triangle Park, NC 27711
Michael Dourson, Ph.D. John Overton, Jr., Ph.D.
U.S. EPA, ORD, OHEA, ECAO U.S. EPA, ORD, OHR, HERL
Cincinnati, Ohio 45268 Research Triangle Park, NC 27711
Linda Erdreich, Ph.D.*
Environmental Research Information, Inc.
New York, New York 10018-3011
* Formerly with ECAO, Cincinnati
The contributing authors, listed in alphabetical order, are:
Christopher DeRosa, Ph.D. Richard Hertzberg, Ph.D.
U.S. EPA, ORD, OHEA, ECAO U.S. EPA, ORD, OHEA, ECAO
Cincinnati, Ohio 45268 Cincinnati, Ohio 45268
Judith A. Graham, Ph.D. Bruce Peirano
U.S. EPA, ORD, OHEA, ECAO U.S. EPA, ORD, OHEA, ECAO
Research Triangle Park, NC 27711 Cincinnati, Ohio 45268
Mark Greenberg William Pepelko, Ph.D.
U.S. EPA, ORD, OHEA, ECAO U.S. EPA, ORD, OHEA, HHAG
Research Triangle Park, NC 27711 Washington, DC 20460
Elaine C. Grose, Ph.D. Greg Theiss
U.S. EPA, ORD, OHR, HERL U.S. EPA, ORD, OPPE
Research Triangle Park, NC 27711 Washington, DC 20450
The following individuals participated as peer reviewers at the workshop/
public meeting held at the U.S. EPA Environmental Research Center in Research
Triangle Park on October 5-6, 1987, and provided valuable comments and written
contributions on both the workshop and revised drafts:
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Dr. Charles Hobbs
Assistant Director, Inhalation Toxicology Research Institute
Lovelace Biomedical and Environmental Research Institute, Inc.
P.O. Box 5890
Albuquerque, New Mexico 87185
Dr. Michael D. Lebowitz
Division of Respiratory Sciences
University of Arizona
Tucson, Arizona 85724
Dr. Daniel B. Menzel
Director, Laboratory of Environmental Pharmacology and Toxicology
P.O. Box 3813
Duke University Medical Center
Durham, North Carolina 27710
Dr. Richard Schlesinger
Director, Laboratory for Pulmonary Biology and Toxicology
Institute of Environmental Medicine
New York University Medical Center
Long Meadow Road
Tuxedo, New York 10987
Dr. Vera Thomas
Department of Anesthesiology
University of Miami School of Medicine
P.O. Box 016370
Miami, Florida 33101
Dr. Theodore Torkelson
Toxicology Consultant
315 Birch Street
Roscommon, Michigan 48653
Dr. Curtis Travis
Office of Risk Analysis
P.O. Box X
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37831
The authors wish to acknowledge the scientific guidance and support
provided by Dr. Fred Miller (ORD, OHR, HERL), Ms. Margaret Menache (NSI-
Technology Services Corporation), Dr. Daniel Guth (OAR, OAQPS, PAB) and Dr.
Judith Bell in (Risk Assessment Forum).
The authors thank Bette Zwayer and Carol Haynes (ECAO-Cin) for diligently
and graciously preparing the first drafts of the manuscript and Judith Olsen
for excellent editorial support. The authors thank Ivra Bunn, Lynette Davis,
xiii
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Patricia Felix, Miriam Gattis, and Lorrie Godley of NSI-Technology Corporation
(RTF) for preparing the workshop, revised and final drafts and the final
document.
The authors would also like to express a special tribute to the late
Director of ECAO-Cin, Dr. Jerry F. Stara. Without his vision and guidance,
this effort would not have been possible.
Address changes for April 1989 Document:
Dr. Judith S. Bell in
383 0 Street, SW
Washington, DC 20024
Dr. Daniel B. Menzel
University of California at Irvine
Southern Occupational Health Center
Irvine, CA 92717
Dr. Frederick J. Miller
Department of Medicine
Duke University Medical Center
Durham, NC 27710
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GLOSSARY
Activity Median Diameter (AMD)
Refers to the median of the distribution of radioactivity, toxicological,
or biological activity with respect to particle size.
Acute Exposure
A one-time or short-term exposure with a duration of less than or equal
to 24 hours.
Aerodynamic Diameter
Term used to describe particles with common inertial properties to avoid
the complications associated with the effects of particle size, shape, and
physical density.
Aerodynamic equivalent diameter (D_a)
36
"Aerodynamic diameter" generally used. The diameter of a unit density
sphere (p = 1 g/cm3) having the same settling velocity (due to gravity)
as the particle of interest of whatever shape and density. Refer to Raabe
(1976) for equation.
Aerodynamic (viscous) resistance diameter (D__)
O.V
The "Lovelace" definition for aerodynamic diameter. Characteristic
expression based on terms describing a particle in the Stokes' regime.
Refer to Raabe (1976) for equation.
Aerosol
All-inclusive term. A suspension of liquid or solid particles in air.
Critical Effect
The first adverse effect, or its known precursor, that occurs as the dose
rate increases.
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Chronic Exposure
Multiple exposures occurring over an extended period of time, or a
significant fraction of the animal's or the individual's lifetime.
Diffusion Diameter
Diameter of a sphere having the same diffusion mobility as the particle
in question. D
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Mass Median Aerodynamic Diameter (MMAD)
Mass median of the distribution of mass with respect to aerodynamic
diameter. Graphs for these distributions are constructed by plotting
frequency against aerodynamic diameters.
Modifying Factor (MF)
An uncertainty factor that is greater than zero and less than or equal
to 10; its magnitude reflects professional judgment regarding scientific
uncertainties of the data base or study design not explicitly treated by
the uncertainty factors (e.g., the number of animals tested). The default
value for the MF is 1.
No-Observed-Adverse-Effect Level (NOAEL)
An exposure level at which there are no statistically 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 not considered as adverse,
nor precursors to specific adverse effects. In an experiment with several
NOAELs, the regulatory focus is primarily on the highest one, leading to
the common usage of the term NOAEL as the highest exposure without adverse
effect.
Portal-of-Entry Effect
A local effect produced at the tissue or organ of first contact between
the biological system and the toxicant.
Reference Dose (RfD)
An estimate (with uncertainty spanning perhaps an order of magnitude) of
a daily exposure to the human population (including sensitive subgroups)
that is likely to be without an appreciable risk of deleterious effects
during a lifetime. The inhalation reference dose is for continuous
inhalation exposures and is appropriately expressed in units of mg/m3. It
may be expressed as mg/kg/day, in order to compare with oral RfD units,
utilizing specified conversion assumptions.
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Regional Deposited Dose (ROD)
The deposited dose (mg/cm2 of lung region surface area per minute) calcu-
lated for the region of interest as related to the observed effect (i.e.,
calculated for the tracheobronchial region for an effect concerning the
conducting airways).
Regional Deposited Dose Ratio (RDDR)
The ratio of the regional deposited dose in the animal species of interest
(RDDA) to that of humans (ROD,,). This ratio is used to adjust the
exposure effect level for interspecies dosimetric differences.
Reserve Volume
Volume of air remaining in the lungs after a maximal expiration.
Respiratory Bronchiole
Noncartilagenous airway with lumen open along one side to alveoli; when
walls are completely alveolarized it is usually referred to as an alveolar
duct. Essentially absent in rats.
Stokes1 Law
The total drag force or resistance of the medium due to fluid motion
relative to the particle is the sum of form and friction drag. When
particle motion is described by this equation, it is said to be in the
Stokes regime.
Subchronic Exposure
Multiple or continous exposures occuring over about 10% of an experimental
species lifetime, usually over 3 months.
Terminal Bronchiole
Noncartilagenous airway that conducts airstream to respiratory bronchiole.
xviil
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Threshold
The dose or exposure below which a significant adverse effect is not
expected. Carcinogenicity is thought to be a nonthreshold endpoint, thus,
no exposure can be presumed to be without some risk of adverse effect.
Noncarcinogenicity is presumed to be a threshold endpoint, thus, some
exposures are presumed to be without risk of adverse effects.
Tidal Volume (VT)
Volume of air inhaled/exhaled during normal breathing
Uncertainty Factor (UF)
One of several, generally 10-fold factors, used in operationally deriving
the Reference Dose (RfD) 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 animal data to the
case of 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) the inability of any
single study to adequately address all possible adverse outcomes in
humans.
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1. INTRODUCTION
1.1 DEVELOPING BENCHMARK VALUES IN THE U.S. ENVIRONMENTAL PROTECTION AGENCY
This document focuses on toxicological issues central to the development
of an approach for the quantitative assessment of risks of health effects other
than cancer and gene mutations for inhaled agents and to the development of an
interim methodology for the estimation of inhalation reference doses (RfD.s).
An inhalation reference doses is an estimate (with uncertainty spanning perhaps
an order of magnitude) of continuous exposure to the human population
(including sensitive subgroups) that is likely to be without appreciable risk
of deleterious effects during a lifetime. The documentation discusses criteria
and information to be considered in selecting key studies for inhalation RfD
derivation, provides an overview of the respiratory system and its intra- and
interspecies variables, and discusses areas of uncertainty and data gaps in
relation to the proposed interim methodology.
The U.S. Environmental Protection Agency (U.S. EPA) has a history of
advocating the evaluation of scientific data and calculation of Acceptable
Daily Intake (ADI) values for noncarcinogens as benchmark values for deriving
regulatory levels to protect exposed populations from adverse effects. The
Office of Pesticide Programs used the concept of ADI for tolerance estimates of
pesticides in foodstuffs. The Office of Health and Environmental Assessment
used ADI values for characterizing levels of pollutants in ambient waters
(Federal Register, 1980). The National Research Council (1977, 1980) recom-
mended the ADI approach to characterize levels of pollutants in drinking water
with respect to human health; the U.S. EPA Office of Drinking Water has adopted
the National Academy of Sciences (NAS) approach.
The U.S. Environmental Protection Agency (1987a) has developed guidelines
for the evaluation of available data pertaining to xenobiotics for purposes of
developing RfDs analogous in intent to the ADI approach for oral exposures.
While similar to ADIs in intent, RfDs are based upon a more rigorously defined
methodology. In addition, guidelines for developing risk assessments have been
1-1
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promulgated for mutagenicity, carcinogen!city, mixtures, teratogenicity and
reproduction, and for estimation of exposure (Federal Register, 1986a through
e). Draft guidelines also are available for female and male reproductive
toxicity (Federal Register 1988a,b).
The U.S. EPA's effort to develop these RfDs involves several parallel
efforts: (1) development of guidelines for establishing levels of confidence
in RfDs; (2) verification of existing RfDs; and (3) identification and analysis
of toxicologic data pertinent to the development of RfDs.
In order to adapt this approach to derive inhalation benchmark values
analogous to those existing for the oral RfO, it is necessary to develop the
scientific basis for estimating inhalation values, develop guidelines, and
encourage broad scientific review.
The Agency recognizes that regional, state, and local health protection
departments need uniform and scientifically sound risk assessment procedures
for the estimation of benchmark inhalation values. The proliferation of
diverse risk assessment values for inhalation exposure and the resulting
confusion this has caused attests to the importance of a consensus approach to
uniform guidelines. It is the intention of the Agency that the interim RfD
approach described will be useful to many in their risk management programs as
one piece of the risk assessment process. The approach outlined is not intended
to discourage novel or more sophisticated risk assessment procedures when
sufficient data are available. The recognized deficiencies in this RfD approach
and other novel approaches under development are described in Appendix A, and
examples of the use of pharmacokinetic data in risk assessment are provided in
Appendix B. Current research and ongoing projects to refine inhalation dose
estimates are outlined in Appendices H and I. The interim RfD methodology
proposed is consistent with previous Agency approaches, however, and is con-
sidered suitable for implementation.
The issue paper on Occupational Exposure Limit (DEL) values, developed by
the Inhalation Technical Panel of EPA's Risk Assessment Forum, discusses the
history, use, and limitations of OELs as surrogates for ambient exposure RfD
values (U.S. Environmental Protection Agency, 1989).
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1.2 GENERAL PRINCIPLES OF NONCANCER TOXICITY RISK ASSESSMENT*
Toxic endpoints other than cancer and gene mutations are often referred
to as "noncancer toxicity" because of effects on the function of various organ
systems. Most chemicals that produce noncancer toxicity do not cause a similar
degree of toxicity in all organs, but usually demonstrate major toxicity to one
or two organs. These are referred to as the target organs of toxicity for that
chemical (Doull et al., 1980). Generally, based on our understanding of
homeostatic and adaptive mechanisms, noncancer toxicity is treated as if there
is an identifiable threshold (both for the individual and for the population);
however, the Agency is aware of the difficulties in the identification of
population thresholds (Gaylor, 1985) below which effects are not observable.
This threshold approach distinguishes noncancer endpoints from carcinogenic and
mutagenic endpoints, which are often treated operationally as nonthreshold
processes.
The individual threshold hypothesis holds that a range of exposures from
zero to some finite value can be tolerated by the organism without adverse
effects. For example, there could be a large number of cells performing the
same or similar function whose population must be significantly depleted
before an adverse effect is seen. Further, it is often prudent to focus on the
most sensitive members of the population and, therefore, regulatory efforts are
made to keep exposures below levels at which the more sensitive individuals in
the population would be expected to respond.
Empirical observation generally reveals that as the dosage of a toxicant
is increased, the toxic response (in terms of severity and/or incidence of
effect) also increases. This dose-response relationship is well-founded in
the theory and practice of toxicology and pharmacology. Such behavior is
exemplified by three types of data: (1) quantal responses, in which the number
of responding individuals in a population increases; (2) dose-graded responses,
in which the severity of the toxic response within an individual increases with
dose; and (3) continuous responses, in which changes in a biological parameter
(e.g., body or organ weight) vary with dose.
"This text is excerpted and adapted from U.S. Environmental Protection Agency
(1987a).
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The majority of previous risk assessment efforts for noncancer health
effects have been directed at oral exposures. Human data appropriate for
quantifying risk assessments for oral exposure are limited; therefore, the
majority of these assessments have relied on animal data. These animal studies
typically reflect situations in which exposure to the toxicant has been care-
fully controlled, and the problems of heterogeneity of the exposed popula-
tion and concurrent exposures to other toxicants have been minimized. In
evaluating animal data, a series of professional judgments are made involving,
among other things, consideration of the scientific quality of the studies.
Presented with data from several animal studies, the risk assessor first seeks
to identify the animal model that is most relevant to humans, based on
compatibility of biological effects using the most defensible biological
rationale; for instance, by using comparative metabolic, pharmacokinetic, and
pharmacodynamic data. In the absence of a clearly most relevant species,
however, the most sensitive species is used as a matter of science policy at
the U.S. EPA. For inhalation RfDs, the most sensitive species is the species
that shows an adverse effect at an exposure level which when dosimetrically
adjusted, results in the lowest human equivalent concentration. Guidance for
full utilization of human data has not been extensively explored because of the
limited availability of relevant human oral data. However, for the inhalation
route, a substantially greater quantity of human data useful to risk assessment
is anticipated. Subsequent sections of this document will explore the issues
associated with human data that are particularly relevant to the inhalation
route of exposure.
In the simplest terms, an experimental exposure level is selected from a
given study of a species representing the highest level tested at which no
adverse effect was demonstrated. The inhalation methodology requires
conversion of these "No-Observed-Adverse-Effect Levels" (NOAELs) observed in
animals to human equivalent concentrations (NOAEL-jcpS) before the data array
and effect levels can be evaluated and compared. A chemical may elicit more
than one toxic effect (endpoint) in tests of the same or different duration
(acute, subchronic, and chronic exposure studies), even in one test species.
In general, NOAEL,rCs for these effects will differ. The critical toxic effect
used in the dose-response assessment is the one generally characterized by the
lowest NOAELnrp. The NOAELHEC is the key datum gleaned from the study of the
dose-response relationship and, traditionally, is the first basis for the
1-4
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scientific evaluation of the benchmark level in the RfD approach. This
approach is based, in part, on the assumption that if the critical toxic effect
is prevented, then all toxic effects are prevented.
The RfD is a benchmark dose operationally derived from the NOAEL..™ of the
critical effect by consistent application of generally order of magnitude
uncertainty factors (UFs) that represent the second basis for the scientific
evaluation of the RfD. The uncertainty factors reflect potential extrapolation
uncertainty between the characteristics of the study situation and the
projection to daily exposure of humans. The RfDs and the composite uncertainty
factors vary in magnitude depending upon the particular study; for example, a
valid NOAEL for chronically exposed healthy humans is normally divided by a UF
of 10-fold to extrapolate to a more susceptible population. In addition, a
modifying factor (MF), which is based on a professional judgment of the entire
data base of the chemical, may be included. That is:
RfD (or ADI) = NOAELHEC/(UF x MF)
Inhalation RfDs pertain to continuous exposures for a lifetime. If exposure
assumptions are changed and appropriate toxicologic data utilized, benchmark
values may be calculated for exposure durations of less than a lifetime (see
Section 4.2). An evaluation of the adequacy of presently used uncertainty
factors in extrapolating from subchronic to chronic inhalation exposure is an
outstanding issue to be addressed by the Risk Assessment Forum.
The Agency is attempting to standardize its approach in determining RfDs.
This standardization will include statements on the confidence that the
evaluators have in the RfD. High confidence is an indication that the RfD is
unlikely to change as more data become available because there is consistency
among the toxic responses observed in different sexes, species, study designs
or in dose-response relationships. It is recognized, however, that increasingly
sophisticated tests may change the perspective of evaluation. Often, high
confidence is associated with RfDs that are based on human data for the
exposure route of concern. Low confidence indicates that the RfD may be
especially vulnerable to change if additional chronic toxicity data become
available.
1-5
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1.3 STATE-OF-THE-ART APPLICATIONS TO THE DEVELOPMENT OF THE INHALATION RfO
METHODOLOGY
All risk assessments involve some degree of reliance upon assumptions,
which substitute for unavailable quantitative information and thereby impart
varying degrees of uncertainty in the risk assessment methodology. However, as
state-of-the-art research and health risk science progresses, the precision of
risk assessments will be improved, insofar as these advancements are incorpo-
rated into the assessments. Risk assessments ultimately serve as the basis for
personal or governmental risk management decisions on safeguarding health and
have consequential economic impacts. This makes it imperative that scientific
advancements in risk assessment be made and that they be appropriately
incorporated into risk assessment processes, including the derivation of
inhalation RfDs. Based on this, the current inhalation RfD methodology is
termed "interim," in view of planned future updating as advancements in risk
assessment are made.
The Office of Research and Development (ORD) is conducting a rigorous
research program to improve the scientific basis of risk assessments. When key
information becomes available from this program, as well as relevant research
from other institutions, it will be incorporated into the inhalation RfD
methodology. This must be balanced against the necessity of a certain degree
of consistency in risk assessment procedures, to improve the feasibility of
broad regulatory application of the assessments. Therefore, the Office of
Health and Environmental Assessment, ORD, will regularly evaluate scientific
advancements in the field and make recommendations for significant improvements
in the inhalation RfD methodology. Every two years, these recommendations
are expected to be presented to an expert panel of EPA and extramural scientists
for peer review. Modifications in the methodology will be made as appropriate.
If research advancements having a striking impact on the methodology were to
occur prior to this two-year recurring review, then the timing of the process
would be altered appropriately.
As generic issues arise during the verification sessions of the inhalation
RfD workgroup, they will be sent to a Risk Assessment Forum made up of an
appointed technical panel of experts for review and resolution. The technical
panel of the Risk Assessment Forum then will provide recommendations and
guidance on such issues. This mechanism has provided useful input to the
oral RfD methodology to date and is anticipated to provide refinements to
the inhalation RfD methodology as well.
1-6
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This interim methodology will be buttressed by a technical support document
providing tabulated Regional Deposited Dose Ratios (RDDRs) for various species
which will be produced in the near future. These ratios are used to adjust
animal experimental exposure concentrations to human equivalent concentrations
as discussed in Chapter 4 and Appendix H. The technical support document will
provide a detailed description of their derivation and limitations of their
application. Research also is already underway to provide a second technical
support document of Regional Retained Dose Ratios (RRDRs). These ratios will
integrate clearance functions into the deposited values for estimates more
appropriate to assessing chronic exposure conditions.
At the time of the two-year review, it is expected that research advance-
ments on uptake modeling of gases (discussed in Chapter 4 and Appendix I) will
provide guidance on dosimetric adjustments for different categories of gases.
Continued work on hygroscopic particle modeling may provide chemical-specific
adjustment factors or a revised default condition for this category of aerosols.
Other ORD research projects anticipated to have significant impact on
the methodology include: (1) guidance on the limitations and application of
physiologically-based pharmacokinetic model parameters to route-to-route
extrapolation, and (2) approaches for less-than-lifetime assessment. An appro-
priate characterization of activity patterns of human ventilatory levels also
is expected to be developed so that the aerosol deposition and gas uptake
models can be utilized to provide more realistic estimates of probable human
exposure.
In summary, one objective of the Interim Inhalation RfD methodology is
that it always be scientifically based, and thus, the methodology should be
considered dynamic. Pertinent issues and their solutions will be incorporated
as identified on a continuing basis. Periodic peer review will provide quality
assurance. These actions will make the methodology sufficiently reliable to
serve as one of the key bases for decisions on protecting the public health.
1-7
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2, CONCEPTUAL BASIS FOR INHALATION RISK ASSESSMENT METHODOLOGY
As discussed in the introduction, there are some fundamental differences
to be considered in performing risk assessments of inhalation exposures to
chemicals and of oral exposures. The primary differences are the degree to
which the complex relationship between exposure dose and dose delivered to the
target site can be addressed and the more common occurrence of portal-of-entry
effects. Both of these are described below to serve as a basis for criteria
that must be added to the oral RfD methodology to facilitate development of
inhalation RfOs.
2.1 FACTORS CONTROLLING COMPARATIVE INHALED DOSE
It is anticipated that the derivation of inhalation RfDs will not be as
straightforward as that of oral RfDs, given the dynamics of the respiratory
system and its diversity across species. The various species used in inhala-
tion toxicology studies do not receive identical doses in comparable respira-
tory tract regions when exposed to the same particle or gas concentration
(Brain and Mensah, 1983). The biologic endpoint or health effect may be more
directly related to the quantitative pattern of mass deposited within the
respiratory tract than to the exposure concentration. Regional deposition
pattern determines not only the initial lung tissue dose but also the specific
pathways and rates by which the inhaled agents are cleared and redistributed
(Schlesinger, 1985).
This section presents the issues associated with the major factors con-
trolling the deposition pattern, which are: (1) respiratory anatomy and
physiology (Section 2.1.1); and (2) the physicochemical characteristics of the
inhaled agent (Section 2.1.2). Section 2.1.3 presents restrictions imposed by
experimental procedures and technology, and working assumptions that affect the
two major controlling factors.
2-1
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The factors that control inhaled dose are discussed relative to the
significant mechanisms by which particles and gases may initially be deposited
or taken up in the lung. For particles this includes inertial impaction,
sedimentation (gravitational), diffusion, interception, and electrostatic
precipitation, while mechanisms important for gases include convection, diffu-
sion, chemical reaction, and solubility. Detailed consideration of these
mechanisms is beyond the scope of this discussion. The reader is referred
elsewhere for more extensive discussions of particle deposition (U.S. Environ-
mental Protection Agency, 1982; U.S. Environmental Protection Agency, 1986b;
Hatch and Gross, 1964; Raabe, 1979; Hinds, 1982; Lippmann and Schlesinger,
1984) and gas absorption (U.S. Environmental Protection Agency, 1986c;
Fiserova-Bergerova, 1983; Overton, 1984; Overton and Miller, 1988).
It must be emphasized that dissection of the factors that control inhaled
dose into discrete discussions is deceptive and masks the dynamic nature of the
intact respiratory system. For example, although deposition in a particular
respiratory region will be discussed separately from the clearance mechanisms
for that region, retention (the actual amount of inhaled agent found in the
lungs at any time) is determined by the relative rates of deposition and clear-
ance. Retention and the toxicologic properties of the inhaled agent are
presumably related to the magnitude of the pharmacologic, physiologic, or
pathologic response. Thus, although the deposition, clearance mechanisms, and
physiochemical properties of the agent are described in distinct sections,
assessment of the overall toxicity requires integration of the various factors
into a dynamic picture.
Future improvements in this process will be accomplished in the area of
extrapolation modeling (Miller et al., 1983a; Fiserova-Bergerova, 1983). This
involves determining the effective dose delivered to the target organ of
various species and the sensitivity of the target organ to that dose. Once
such dosimetry has been established, and species sensitivity accounted for, the
effective pollutant concentration in animals can be quantitatively related to
concentration responses in humans. Extrapolation models should incorporate
parameters such as species anatomical and ventilatory differences, metabolic
processes, and the physicochemical properties of the pollutant and should be
physiologically based upon the factors that govern transport and removal of the
pollutant.
2-2
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In the interim, a qualitative knowledge and application of how regional
deposition and disposition patterns, and metabolism of an inhaled dose may
differ between humans and experimental animals commonly used in inhalation
toxicology investigations will provide more accurate cross-species dosimetric
extrapolations.
2,1.1 Respiratory Anatomy and Physiology
The respiratory systems of humans and various experimental animals differ
in anatomy and physiology in many quantitative and qualitative ways. These
variations affect air flow patterns in the respiratory system, and in turn, the
deposition of an inhaled agent, as well as the retention of that agent in the
system. The variations in anatomy and physiology will be discussed according
to respiratory regions and branching patterns, clearance mechanisms, and cell
types. Clearance mechanisms as used here include processes such as the
mucociliary escalator, solubilization in various compartments, uptake, and
metabolism.
2.1.1.1 Respiratory Regions and Branching Patterns. The respiratory system in
both humans and experimental animals can be divided into three regions on the
basis of structure, size, and function: nasopharyngeal, tracheobronchial, and
pulmonary (alveolar). The retained dose of an inhaled agent in each of these
regions is governed by the individual species anatomy (e.g., airway size and
branching pattern) and physiology (e.g., breathing rate and clearance
mechanisms).
Airway size and branching pattern affect the aerodynamics of the respira-
tory system in the following ways:
The airway diameter affects the aerodynamics of the flow and the
distance from the agent molecule or particle to the airway
surface.
The cross-sectional area of the airway determines the airflow
velocity for a given volumetric flow.
Diameter and branching pattern variations affect the mixing
between tidal and reserve air.
Differences in airway sizes and branching between species thus result in
significantly different patterns of gas transport and particle deposition.
2-3
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2.1.1.1,1 Effect on aerosol deposition mechanisms. Air flow in the extra-
thoric region is characterized by high velocity and abrupt directional changes.
Thus, the predominant deposition mechanism in the extrathoracic region is
inertial irapaction. Changes in airstream direction or magnitude of air velocity
streamlines or eddy components do not affect airborne particles due to their
inertia. Large particles (>5 pm) are more efficiently removed from the air-
stream in this region.
Impaction remains a significant deposition mechanism for particles larger
than 2.5 pm aerodynamic equivalent diameter (D „) in the larger airways of the
ae
tracheobronchial region and competes with sedimentation, with each mechanism
being influenced by mean flow rate and residence time, respectively. As the
airways successively bifurcate, the total cross-sectional area increases. This
increases airway volume in the region and the air velocity is decreased. With
decreases in velocity and more gradual changes in air flow direction as the
branching continues, there is more time for gravitational forces (sedimentation)
to deposit the particle. For particles ~4 jjm D , a transition zone between
36
the two mechanisms, from impaction to predominantly sedimentation, has been
observed (U.S. Environmental Protection Agency, 1982). This transition shifts
toward smaller particles for nose breathing.
Differences in airway size and branching pattern are a major source of
interspecies variability in inhaled dose for the tracheobronchial region.
Larger airway diameter results in greater turbulence for the same relative
flow velocity (e.g., between a particle and air). Therefore, flow may be
turbulent in the large airways of humans, while for an identical flow velocity,
it would be laminar in the smaller experimental animal. Relative to humans,
experimental animals also tend to have tracheas that are much longer in relation
to their diameter. This could result in increased deposition in humans because
of the increased likelihood of laryngeal jet flow extending into the bronchi.
Humans are characterized by a more symmetrical dichotomous branching than that
found in most laboratory mammals, which have highly asymmetrical branching
(monopodial). The more symmetrical dichotomous pattern in humans is susceptible
to deposition at the carina because of its exposure to high air flow velocities
toward the center of the air flow profile. These comparative airway anatomy
differences are summarized in Table 2-1.
Sedimentation becomes insignificant relative to diffusion as the particles
become smaller. Deposition by diffusion results from the random (Brownian)
2-4
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ro
i
en
Mammal/
Body Mass
Hunan/70 kg
Rhesus
monkey/2 kg
Beagle dog/
10 kg
Ferret/
0.61 kg
Guinea pig/
1 kg
Rabbit/
4.5 kg
Rat/0.3 kg
Golden
hamster/
0.14 kg
Left Lung
Lobes
upper and
lower
superior,
middle, and
inferior
apical,
intermediate,
and basal
NRb
superior
and
inferior
superior
and
inferior
one lobe
superior
and
inferior
Gross
Right Lung
Lobes
upper, middle
and lower
superior,
middle, and
inferior.
azygous
apical,
intermediate,
and basal
NR
superior,
middle and
inferior
cranial ,
middle, caudal
and postcaval
cranial ,
middle, caudal.
and postcaval
cranial , middle
caudal , and
postcaval
TABLE 2-1.
Structure
Airway
Branching
relatively
symmetric
monopodial
strongly
monopodial
strongly
monopodial
monopodial
strongly
monopodial
strongly
monopodial
strongly
monopodial
COMPARATIVE AIRWAY ANATOMY AS
Trachea
L/D3
(cm)
12/2
3/0.3
17/1.6
10/0.5
5.7/0.4
6/0.5
2.3/0.26
2.4/0.26
Major
Airway
Bifurcations
Sharp for about
the first 10
generations.
relatively
blunt thereafter
Mixed blunt
and sharp
Blunt trachea!
bifurcation.
others sharp
Sharp
Very sharp
and high
Sharp
Very sharp and
very high
throughout lung
Very sharp
REVEALED ON CASTS
Typical Structure
(Generation 6)
Branch Angles
Airway (Major Daughter/
L/D Minor Daughter)
(ratio) (degrees)
2.2 11/33
2.6 20/62
1.3 8/62
2.0 16/57
1.7 7/76
1.9 15/75
1.5 13/60
1.2 15/63
Typical Number
of Branches
to Terminal
Bronchiole
14-17
10-18
15-22
12-20
12-20
12-20
12-20
10-18
Respiratory
Bronchioles
About 3-5 orders
About 4 orders
About 3-5 orders
About 3-4 orders
About 1 order
About 1-2 orders
Rudimentary
About 1 order
L/D = Length/diameter ratio
bNR = Not reported
Source: Phalen and Oldham, 1983; Patra, 1986; Crapo, 1987
-------
motion of very small particles caused by the collision of gas molecules in air.
The terminal settling velocity of a particle approaches 0.001 cm/s for a unit
density sphere with a physical diameter of 0.5 urn, so that gravitational forces
become negligible. The main deposition mechanism is diffusion for a particle
whose physical (geometric) size is <0.5 urn. Impaction and sedimentation are
the main deposition mechanisms for a particle whose size is greater than
0.5 urn. Hence, D_Q = 0.5 urn is convenient for use as the boundary. Although
3,6
this convention may lead to confusion in the case of very dense particles, most
environmental aerosols have densities below 3 g/cm (U.S. Environmental
Protection Agency, 1982). Diffusional deposition is important in the small
airways and in the pulmonary region where distances between the particles and
ai rway epitheli urn are smal1.
These mechanisms for particle deposition in the respiratory tract are
schematically represented in Figure 2-1. Experimental deposition data and
extrapolated estimates on humans that illustrate these same concepts are shown
by the curves for pulmonary (alveolar) and tracheobronchial deposition in
Figure 2-2. Deposition fraction is shown plotted against particle diameter.
It is important to note that over half of the total mass of a typical ambient
mass distribution would be deposited in the extrathoracic region during normal
nasal breathing, with most of this being coarse particles (U.S. Environmental
Protection Agency, 1986B). With mouth-only breathing, the regional deposition
pattern changes dramatically, with extrathoracic deposition being reduced and
both tracheobronchial and pulmonary deposition enhanced. Oronasal breathing
(partly via the mouth and partly nasally), however, typically occurs in healthy
adults while undergoing moderate to heavy exercise. Thus, the appropriate
activity pattern of subjects for risk assessment estimation remains an
important issue. Miller et al. (1988) recently examined extrathoracic and
thoracic deposition as a function of particle size for ventilation rates
ranging from normal respiration to heavy exercise. A family of deposition
estimate curves were generated as a function of breathing pattern. Anatomic
and functional differences between adults and children are likely to yield
complex interactions with the major mechanisms affecting respiratory tract
deposition, again with implications for risk assessment. Age-dependent
dosimetric adjustments may be possible, pending data availability for children,
2.1.1.1.2 Effect on gasdeposition and uptake. The major processes affecting
gas transport involve convection, diffusion, absorption, solubility, and
chemical reactions. These mechanisms are schematically represented in
2-6
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DIRECTIONAL
CHANGE
VERY
ABRUPT
AIR
VELOCITY
I
IMPACTION
1
SEDIMENTATION
LESS
ABRUPT
^•fr fflNTERCEPTION
IMPACTIONl
MILD
ELECTROSTATIC
PRECIPITATION
Figure 2-1. Schematic representation of selected parameters influencing
regional deposition of particles in the respiratory tract.
Source: Adapted from Casarett, 1975; Raabe, 1979; Lippmann and Schlesinger,
1984.
2-7
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u
EG
1/3
s.
1,0
0,9
0,8
0.7
0.6
0.5
0.4
0.3
0.2
0,1
Oi
0.1
I I I 1111 1 1 I
RANGE OF ALVEOLAR DEPOSITION,
MOUTH BREATHING
-- — ESTIMATE OF ALVEOLAR DEPOSITION, NOSE BREATHING
RANGE OF TRACHEOBRONCH1AL DEPOSITION.
MOUTH BREATHING
I 1 I \ I
— --- — EXTRAPOLATION OF ABOVE TO POINT I
BY MILLER Mai., (1979)
PREDICTED
•O« EMMETTetal. (19821, 337 em3!'1,
dm HEYDER (19861; 750 cm;js,,4j B(
&A HEYDER 119851; 2SQ cm3 i'1. 4» Bl
Bj BREATHING CYCLE
BREATH ING CYCLE
HEYDER 119851; 2SQ ctnj i-l. 4» BREATHING CYCLE
-O+SVAHTENGREN (19861
OPEN SYMBOLS TRACHEOBRONCHIAL DEPOSITION
SOLID SYMBOLS; ALVEOLAR DEPOSITION
0.2
0.3 0,4 0.5
1.0
2,0 3,0 4,0 5.0
10 121416 20
PHYSICAL DIAMETER, j
AERODYNAMIC DIAMETER,
Figure 2-2. Regional deposition of monodisperse particles by indicated particle
diameter for mouth breathing (alveolar and tracheobronchial) and nose breathing
(alveolar). Deposition is expressed as fraction of particles entering the
mouth or nose. The alveolar band indicates the range of results found by
different investigators using different subjects and flow parameters for pul-
monary (alveolar) deposition following mouth breathing. The tracheobronchial
(TB) band indicates intersubject variability in deposition over the size range
measured by Chan and Lippmann (1980). The extrapolation of the upper bound of
the TB curve in the larger particle size range also is shown and appears to be
substantiated by data listed in the legend.
Source: U.S. Environmental Protection Agency, 1986b.
2-8
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Figure 2-3, The bulk movement of inspired gas in the respiratory tract is
induced by a pressure gradient and is termed convection (U.S. Environmental
Protection Agency, 1982). Convection can be broken down into components of
advection (horizontal movement of a mass of air relative to the airway wall)
and eddy dispersion (air mixing by turbulence so that individual fluid elements
transport the gas and generate flux). Molecular diffusion is superimposed at
all times on convection (bulk flow) due to local concentration gradients.
Absorption removes gases from the lumen and affects concentration gradients.
The average concentration of a gas in a tube (i.e., an "idealized" airway)
can be described by one-dimensional convection and dispersion. A pulse of sub-
stance moves down a tube with an average air velocity equal to the medium's
(air's) average velocity, and its spread in the axial direction is governed by an
effective dispersion coefficient that can be described by Pick's law of diffu-
sion (Overton, 1984). This effective dispersion coefficient is larger than the
molecular diffusion coefficient except in the pulmonary region. As illustrated
in Figure 2-3, perpendicular transport in this region can carry a gas molecule
into the alveoli, but because of the alveolar walls, there is no net axial
transport as is present in the central channel. The average axial transport is
slowed because only a fraction of the molecules in the cross-sectional average
can move axially, resulting in a dispersion process with a dispersion
coefficient less than the molecule coefficient. The coefficient is a function
of the molecular diffusion coefficient, the total air volume, and the genera-
tion's alveolar airspace volume (Overton, 1984).
Molecules are transferred from the flowing gas into the liquid layer
lining the airway wall by molecular diffusion. A simple description for this
process postulates a thin, stagnant layer based on the assumption that the air
velocity becomes very small as the air-liquid interface is approached.
Transfer through this layer depends on the gas-phase diffusion coefficient,
layer thickness, and the gas concentrations at the boundaries of the layer. If
the molecules are absorbed, then the concentration of the gas in the diffusion
layer is decreased at the liquid boundary. As the ability of the liquid to
remove the gas increases, the relative concentration at the gas-liquid boundary
decreases, and the mass transfer from the gas phase to the liquid phase
increases. For poorly soluble, hydrophobia, and nonreactive gases, little gas
is removed by the airways. The transport and chemistry into the adjacent
liquid and tissue layers will be described in Section 2.1,2.2, which describes
2-9
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INSPIRATION
DIFFUSION LAYERi
Figure 2-3. Schematic representation of selected parameters influencing
regional deposition of gases in the respiratory tract.
Source: Overton, 1984.
2-10
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the physicochemical characteristics of gases and vapors. These next layers can
serve as a "sink" to help "drive" the delivery of gas across this layer.
Capillary blood flow (i.e., perfusion) is important to the gas uptake in that
it removes the gas or its chemical reaction products on the other side of these
liquid and tissue layers. Thus, addressing species differences in alveolar
ventilation and cardiac output is critical to estimate initial absorbed dose.
The importance of regional differences (e.g., the distance from the air to the
capillaries in the tracheobronchial region is 7-20 times that in the pulmonary
region [Overton and Miller, 1988]) and interspecies differences in the anatomic
relationship of the airspace to capillary blood should be considered. Transfer
also is enhanced by a reduction in diffusion layer thickness that is dependent
on the nearby rate of airflow; the higher the flow velocity, the thinner the
layer, again emphasizing the significance of airway morphology.
To attempt to model the effects that the intricate morphological structure
of the respiratory tract has on the nature of gas mixing and flows, representa-
tions of the mechanical mixing imparted by tube bifurcations, turbulence, and
secondary flows due to molecular diffusion must be formulated. Location
identity, diameter, and length are considered to be the relevant measurements
for gas transport (Overton, 1984). Because of the morphology of the respiratory
tract and air flow patterns, the relative contribution of these gas transport
processes is a function of location and point in the breathing cycle (i.e.,
depth and rate) (U.S. Environmental Protection Agency, 1982; Overton, 1984).
The interspecies differences in the nature and structure of the respiratory
tract, as summarized in Table 2-1, critically influence the differences in
transport and deposition of gases across species. The airways also show a
considerable degree of intraspecific size variability and are most likely the
primary factor responsible for the deposition variability seen within single
species (Schlesinger, 1985). Additionally, gender influences airway anatomy,
and age has dramatic influences on respiratory dynamics.
The differences in airway anatomy summarized in this Section (2.1.1) form
the structural basis for the species differences in gas and aerosol deposition.
Extensive investigations that resulted in the quantisation of the effects that
these differences have on the deposition of insoluble particles have resulted in
the dosimetry adjustments for inhaled dose that are outlined in Section 4.1.1.3.
Current research on interspecies differences for gas distribution and deposi-
tion should result in similar adjustments for gaseous inhaled agents. In
2-11
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addition to the structure of the lung, the regional thickness and composition
of the airway epithelium (a function of cell types and distributions) is an
important factor in gas absorption, and contributes to the solubility and
extent of reaction of the gas. Other anatomic and physiologic factors that
influence gas uptake include: (1) ventilation, which affects the tidal volume
and ventilation to perfusion ratios; (2) body build, which affects the volume
of distribution (including cardiac output and tissue volume); and (3) metabolic
capacities. These are all factors to evaluate when estimating inhaled dose,
interpreting injury response, and extrapolating effects between species.
2.1.1.2 Clearance Mechanisms and Cell Types. Inhaled material is removed from
the respiratory tract by clearance mechanisms, which vary depending on the site
of deposition and the properties of the inhaled agent. For gases, the sequence
in which anatomic sites are affected appears to be more dependent on concentra-
tion than on exposure duration. However, at a given local anatomic site and at
a specific concentration, the stages in the pathogenesis of the lesion relate
to the duration of exposure (U.S. Environmental Protection Agency, 1986c). The
speed and efficiency by which the agents are cleared can be critical determi-
nants of their toxic potential. Rapid removal lessens the time available to
cause critical damage to the pulmonary tissue and to permit systemic absorption
of agents that have target organs other than the lung (Menzel and Amdur, 1986).
The mechanisms involved include (1) exhalation of volatiles; (2) mucociliary
transport; (3) macrophage phagocytosis; (4) chemical reactions; (5) metabolism
by various cell types; and (6) dissolution and absorption into the blood,
lymphatic, or lung fluids.
The transport and chemical uptake mechanisms for gases described in
Section 2.1.2.2 are a function of respiratory tract region. Conceptually, a
gas can move from the airway lumen, through the liquid lining layer, through
the tissue layer, through the capillary endothelium, to reach the blood. This
passage is influenced by the physiochemical properties of the gas as well as
the biochemistry and thickness of the layers between the lumen and blood. For
example, a very highly reactive gas may not reach the blood if it reacts
biochemically with mucus and the mucus has sufficient volume (thickness) to
serve as a sink. This same gas may not react with the saturated lipid of
surfactant, and if deposited significantly in the pulmonary region, could reach
alveolar tissue. The thickness and efficiency of the epithelial barrier also
influences absorption. Both of these main factors (liquid lining and epithelial
2-12
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barrier) are present in all species but have species-specific differences, only
a few of which have been quantified. Mucous is a complex secretion with
contributions from various epithelial cells. The numbers and distribution of
these cells may affect the composition and properties of the mucous, which in
turn interacts with the physicochemical properties of the agent. The species
differences in the thickness of the alveolar epithelial cells could account for
variations observed in the diffusion of gases into the bloodstream (Crapo
et al., 1983). The lung also is a very efficient excretory organ for volatile
organic chemicals after the exposure ceases or is lowered. The efficacy of
pulmonary excretion correlates indirectly with the saturated vapor pressure of
the chemical.
Clearance of particles involves different mechanisms. Particles deposited
on the anterior nares are cleared by mechanical processes such as nose wiping,
blowing (humans), or sneezing (animals/humans). Particles in this area can
have long biological half-lives. Those deposited in the nasopharynx or
oropharynx, however, are swallowed within minutes and passed through the
esophagus down to the gastrointestinal tract.
Particles deposited in the tracheobronchial region are transported out of
the respiratory tract by the mucociliary system, an interaction between the
mucous secretions and the cilia that provide the mechanisms of movement. Such
transport occurs along the area from the larynx to the terminal bronchioles.
Insoluble particles are transported up to the esophagus where they are swal-
lowed. The rate of this transport also affects the gas transport mechanisms in
the diffusion layer. The rate varies with the depth of the airways (greater
velocities in the proximal airways) and across species. Generally, the bio-
logical half-lives of particles deposited in the tracheobronchial region are on
the order of hours.
Clearance from the pulmonary region of the lung takes the longest, usually
a rapid phase of hours, and slower phases with biological half-lives of days,
months, or years, depending on particle size and solubility. Processes contri-
buting to the removal of deposited materials in this area include phagocytosis
by macrophages and removal by the blood or lymph, and dissolution into the
blood, lymph, or lung fluids (Johanson and Gould, 1977).
The numerous cell types found in different species also contribute to the
varying clearance patterns from the respiratory regions and differences in the
nature of the response. Table 2-2 presents the distributions of various cell
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TABLE 2-2. NORMAL SURFACE AIRWAY EPITHELIUM: CELL TYPES
Migratory
Lymphocyte
Globule leukocyte
Mast cell
Macrophage
Neural
Neuroepithelial body
Nerve terminals
Guinea
Humans Monkey Dog Ferret Pig Rabbit Rat Hamster
Mouse
Epithelial
Ciliated
Mucous
Serous
Clara
Endocrine
Type I
Type II
Transitional
Special type
Brush
Intermediate
Basal
+ + + + + + + + +
+ + + + + + + + +
a -__ - -be
+ + + + + + + + +
+ +-- + + + + +
+ +++ + + + + +
+ +++ + + + + +
d - _ _ - -eg f
h -+- - ...
--+ + + + - +
+ -++ - - + + +
+ +++ + + + + +
+ = reported present;
(+) = not specifically reported in sources cited;
- = unidentified; a = fetal tissue;
b = in specific pathogen-free rats;
c = only young animals;
d = ciliomucous, mucoserous, endocrine-mucous;
e = seromucous;
f = ciliomucous, seromucous;
g = ciliomucous;
h = not in "normal" biopsy material;
i = "migratory cell";
j = bronchiolus only
Source: Jeffery, 1983; Crapo et al., 1983
-------
types across species commonly used in inhalation toxicologic investigations.
Recent investigation have also shown species differences in cellular
organization at the terminal respiratory bronchioles/alveolar duct junctions
and in the ultrastructure of the same cell type across species (St. George
et al., 1988). The possible functions of these cell types are provided in
Table 2-3, while the differences seen in the cell types across species are
summarized in Table 2-4. Such species differences are important to consider
when determining if the animal is an appropriate model for the chemical's
mechanism of action. For example, the rat may be an inappropriate species for
the evaluation of hypersensitivity because of its lack of mast cells.
Due to the major influence of respiratory tract structure on the dosimetry
of inhaled agents, extrapolation from animal models to humans requires analysis
of toxicological studies complicated by the complexity and diversity of the
respiratory tract across species. Because of this, it is imperative that both
similarities and differences across species in respiratory tract structure be
incorporated into modeling efforts. More recent data on cellular morphometrics
and interspecies differences in cell populations (Mercer and Crapo, 1987;
St. George et al., 1988) will aid in dosimetry adjustments for clearance,
metabolism, and uptake. As an example, modeling for the metabolic capacity of
the human lung instead of considering it only as a physical barrier can result
in disparate estimates of extrapulmonary dose. Epithelial secretions in
response to injury may recruit scavenger cells such as polymorphonuclear
leukocytes, which can biotransform inhaled agents. Different species have
different amounts, distribution, and levels of cytochrome P-450 of their Clara
cells, which could account for differences in metabolism of some agents.
Interspecies differences in clearance rates have the potential to alter
the estimated dose to a given species and thus could significantly alter the
derived RfD.. Differences in clearance rates now are being calculated into the
interspecies ratios used for dosimetric adjustment of the exposure concen-
trations used in RfD. derivation for estimation of a retained dose (see
Chapter 4 and Appendices H and I). Similar adjustments for differences in gas
uptake due to differences in ventilation, perfusion, metabolism, and excretion
are also warranted.
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TABLE 2-3. SOME SPECIFIC LUNG CELL TYPES AND THEIR FUNCTION
Cell Types
Location and Function
Epithelium
Clara cells
Ciliated cells
Type II alveolar
cells
Type I alveolar
Mucous
Serous
Brush cells
Globule leukocyte
Endocrine
high metabolic activity; secretory; nonciliated;
function not well-defined; may serve as precursor of
goblet and ciliated cells
most common epithelial cells in airways; may secrete
mucous-like substances; controls perciliary fluid
covers 3 percent of alveolar surface; secrete
surfactant; replace injured Type I cells; high
metabolic activity
large and covers considerable surface area per cell;
covers >95 percent of alveolar surface; forms the
alveolar epithelium and facilitates gas exchange;
low metabolic activity; incapable of self-reproduction
mucous-secreting
mucous-secreting; perciliary fluid; stem cell
chemoreceptor cells; preciliated
immunoglobulin transportation; releases inflammatory
mediators
secreto-and vaso-regulatory
Subtnucosal
Goblet (mucous)
cells
Serous cells
Endocrine cells
Lymphocytes
Myoepithelial
Bronchoalveolar mast
cells
epithelial linings; common in trachea and
bronchioles; contribute to mucous production
mucous-secreting; perciliary fluid; stem cell/
proliferative
secretes amines and neuropeptides
immunoresponsive
expulsion of mucous
migratory cells located throughout respiratory
tract; release mediators of bronchoconstriction
when antigens bind to IgE antibodies on surface
(continued on the following page)
2-16
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TABLE 2-3. (continued)
Cell Types Location and Function
Macrophage phagocytic; secrete mediators of inflammatory
reactions; modulate lymphocytes and otherwise
participate in immune response
Endothelial cells 40 percent of lung parenchyma cells; metabolize
blood-borne substances; proliferative
Fibroblasts predominant in alveolar wall and constitutes the
(interstitial) basement membrane; become activated during disease
states and produce elastin and collagen; proliferation
leads to fibrosis, modulation of growth, bronchial
tone, and mucosal secretion
Source: Jeffery, 1983; Bowden, 1983; Marin, 1986; Nadel et al., 1985;
Plopper et al., 1983; Burn", 1985; Brain, 1986.
2.1.1.3 Summary. This comparative overview of the complexity and diversity of
the respiratory system in different species of mammals that are used in risk
assessment, although difficult to use in a quantitative manner at this point,
strongly suggests the potential for wide variation in deposited dose, cellular
function, metabolism, and response to injury. Until the comparative morpho-
metric and physiologic studies quantitate the functional implications of these
differences, the risk assessor who is extrapolating across different species
must choose results judiciously, based on a qualitative knowledge of comparative
airway structure and function.
2.1.2 Physicocnemical Characteristics of the Inhaled Agent
The physicochemical characteristics of the inhaled agent will influence
the deposition and retention within the respiratory tract, translocation within
the respiratory system, distribution to other tissues, and ultimately, the
toxic effect. It is therefore important to consider characteristics of the
inhaled agent as well when attempting to evaluate and extrapolate the effects
of a particular exposure.
2.1.2.1 Particles. For a given particle exposure, the two most important
parameters determining deposition are the mean diameter and the distribution of
the particle diameters. The size and shape of the particles influence their
aerodynamic behavior and, thus, their deposition. The definition of diameter
for a spherical particle is unambiguous, but for irregular particles, a variety
2-17
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TABLE 2-4. MAIN SPECIES DIFFERENCES IN EPITHELIAL CELLS AND GLANDS
Epithelial Morphology
Thickness and pseudostratification
Thickness and structure of "basement membrane"
Mucous-secreting cells
number
histochemistry
predominant ultrastructure type
Clara cells
morphology (smooth endoplasmic reticulum)
distribution
Endocrine cell frequency
Cilia
extent of coverage
structure of rootlet
lamellar bodies
glycogen stores
Presence of brush cell
Basal cells
number
shape
tonofilaments
Presence of Globule Leukocyte
Innervation
extent
distribution
type
Gland Morphology
Amount
Distribution
Main histochemical cell type
Presence of collecting duct
Innervation
*Source: Jeffery, 1983.
2-18
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of definitions exist. Nonspherical particle size often is described by its
aerodynamic properties. Fibrous material may be described by actual length,
actual diameter, coil length, coil diameter, aspect ratio, or coil to aspect
ratio.
Information about particle size distribution aids in the evaluation of the
effective inhaled dose (Hofmann, 1982). Recommendations defining the particle
size ranges for inspirability to the various regions have been published by an
ad hoc working group of the International Standards Organization (1981).
Particle size distribution should be provided to the risk assessor in addition
to the particle diameter to more completely characterize the aerosol. For
studies where total mass of inhaled particles is used in assessing health
effects, it is appropriate to evaluate the particle size distribution in terms
of mass, such as the mass median diameter. Figures 2-4 and 2-5 illustrate the
distribution of various parameters used to characterize aerosol size.
It is useful to consider the particle's physical parameters that are
responsible for the health effect of concern. The activity diameter of a par-
ticle may be the most appropriate expression of size for this purpose. This
expression takes into account the "activity" of the physical property of the
particle. For example, if the toxin is distributed only on the surface, then
the activity median diameter is equal to the surface median diameter; calcula-
tions based on total mass would be inappropriate in such situations. If the
toxicant is soluble, the surface area of the particle will influence the rate
of dissolution since solubilization occurs at the surface. Such a situation
needs to be understood better, especially for complex particles.
2.1.2.2 Gases and Vapors. The deposition site and rate of uptake of a
volatile chemical are determined by its reactivity and solubility characteris-
tics. Thus, the pharmacokinetics of gases and vapors are governed by;
Rate of transfer from the environment to the tissue,
Capacity of the body to retain the material, and
Elimination of the parent compound and metabolites by chemical
reaction, metabolism, exhalation or excretion.
As mentioned in Section 2.1.1.1.2, the transport processes in the liquid
and tissue layers adjacent to the airway lumen influence the relationship of
2-19
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Count mode (0.619 urn) d*
Count median (1.0 urn) d«
Count mean (1.272 pm) d
Diameter of average
area (1.614 urn) d,
Diameter of average
mass 12.056 urn} dm
Area median (2.614
-Area mean (3.324 jim) ds
- Mas median
(4.226 urn) d'm
Mass mean
(5.374 Mm) dm
2 4
PARTICLE DIAMETER,
Figure 2-4. An example of the log-normal distribution function of an aerosol.
Source: Orr and Keng, 1976.
LO 10
PARTICLE DIAMETER ,D,fjjn
Figure 2-5. Plot of same aerosol as in Figure 2-4 on log-probability paper.
The curves illustrate the various size parameters that can be computed using
the Hatch-Choate equations.
Source: Marple and Rubow, 1980.
2-20
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the gas with the air-liquid boundary. Physicochemical characteristics of the
gas that contribute to the relative importance of these processes include its
chemical reactivity and solubility.
The chemical reactions of the gas with both the liquid and tissue layers
may be important. For example, reactions with the liquid layer could result in
an increased flux from the airway but reduce (relative to no reactions) the
delivery of the gas to the tissue. If the gas is the only toxic molecule, then
this reaction would protect the tissue. Conversely, if the reaction products
are toxic, then reactions with the tissue layer would increase the delivery of
toxic molecules to the tissue (Overton, 1984). Chemical reactivity with the
biological constituents of the tissue is similarly important to the gas1 toxic
potential to the lung tissue and to the amount of gas and reaction products
that enter the blood for potential extrapulironary toxicity. Theoretically,
knowledge of all the chemical species involved and the reaction rates of the
reactants and products is necessary to characterize a system for dosimetry.
Sometimes the complexities may be reduced into relative classifications (e.g.,
slow, fast, instantaneous) using approximation techniques for time and spatial
dependence (Overton and Miller, 1988). Gases that are not soluble or reactive
are relatively inert to the airways and penetrate to the alveoli. Examples are
nitrogen and volatile hydrophobic chemicals. The major factor driving the
uptake of these gases is the removal of the gas from alveolar air by capillary
blood. The concentration in alveolar air and capillary blood is generally
considered to reach equilibrium. Thus, uptake of alveolar gases depends on air
to blood partitioning, ventilation/perfusion, and air and blood concentrations.
For gases that are soluble, uptake is linearly related to solubility
(Overton and Miller, 1988), There are many different expressions for the
solubility of gases, differing in terms of units as well as in terms of what
chemical form of the gaseous species in the liquid phase is related to the
gas-phase quantities. As long as the concentration of dissolved gas is small,
and the pressure and temperature is not close to the critical temperature and
pressure, then Henry's Law is obeyed (Overton and Miller, 1988). It should be
noted that the Henry's Law constant is independent of chemical reactions so
that it relates the molecular form of the gas in water and air, and not the
total quantity absorbed in water to air quantities. Considering the importance
of chemical reactions as described above, solubilities as indicated by Henry's
Law constants may not be appropriate to fully describe uptake. Further,
2-21
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extrapolation of Henry's Law constants from water data to biological fluids and
tissues is not always appropriate, particularly for organic compounds.
Because uptake and disposition of inhaled vapors and gases are driven by
the equilibration of their partial pressures in tissues with their partial
pressures in ambient air, solubility may be aptly described by Ostwald
solubility coefficients at body temperature. Ostwald solubility coefficients
and partition coefficients (concentration ratios of the volatile chemical in
two phases with equilibrated partial pressures) have the same values
(Fiserova-Bergovera et al,, 1984). The tissue-gas partition coefficient of a
chemical has been shown to correlate with its fat-gas and blood-gas partition
coefficients so that linear correlation equations may provide a useful means of
estimating tissue-gas and blood-gas partition coefficients (Fiserova-Bergovera
and Diaz, 1986).
Thus, a thorough consideration of both reactivity and solubility is needed
when evaluating a gas for its absorption potential. Absorption generalizations
based on molecular weight are not recommended. As an example, the difference
in solubility between methanol and ethane, which have similar molecular
weights, is a result of the presence of the hydroxyl group on methanol.
Interspecies comparisons necessitate consideration of the effects of the
differences in anatomy and physiology described previously, but it can generally
be stated that the more soluble and less reactive the gas, the more similar the
deposition will be between humans and animals. Interspecies differences in
body fat induce interspecies differences in uptake and distribution of lipo-
philic chemicals.
The physicochemical gas characteristics of reactivity and solubility will
interact with physiologic parameters such as pulmonary ventilation, cardiac
output (perfusion), metabolic pathways, tissue volumes, and excretory
capacities. The relative contribution or interaction of these is, in turn,
affected by the exposure conditions (concentration and duration), so that as
emphasized previously, integration of these various factors is necessary to
estimate the deposited (on airway surfaces) and absorbed doses in order to
assess toxicity.
2.1.3 Impact ofExperimental Protocol
The techniques and measurements used in inhalation toxicology investiga-
tions may affect the exposure conditions or the interpretation of toxic
2-22
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effects, thereby altering the results used for risk assessment. Areas that
introduce uncertainty into interspecies extrapolations of inhaled dose include
measurement techniques, the definitions and underlying assumptions used in the
procedures, and the exposure technology. Careful consideration should be given
to each when estimating the effective inhaled dose.
2.1.3,1 Pharmacologic Effects of Agents. The test agents may affect lung
ventilation. Administration of a chemical with narcotic properties will lower
physical activity, while an irritant might increase movement. The test agent
could also alter clearance mechanisms. All these states would affect deposi-
tion, uptake and relention of the dose. In addition, the agent could disrupt
the immune system and render the animal more susceptible to disease during
long-term testing, thereby altering the study results.
There are several examples of irritating or potentially anesthetic chemi-
cals that can depress ventilation. Chang et al. (1983) reported a 40 percent
decrease in minute volume in mice exposed to 15 ppm formaldehyde. This inhibi-
tion was maintained during the entire course of the daily exposure period.
Ventilation was decreased to as little as 1/15 of resting values during exposure
of mice to 10 ppm ozone, and to as little as 1/3 of resting values during
exposure of mice to acrylate esters (Bruce et al., 1979).
2.1.3.2 Measurement Techniques. Since measurements of ventilation and breath-
ing mechanics often are used to evaluate respiratory functional alterations or
to estimate inhaled/retained dose, performance parameters of such measurements
are critical to their interpretation. The patterns of respiration (breathing
route, depth, and rate) affect the air flow characteristics which, in turn,
influence the relationship between competing particle deposition mechanisms and
the relative contribution of gas transport processes. The penetration depth of
the exposure air is determined by the tidal volume (V,.), the airway caliber,
and the ratio of functional residual capacity to total lung capacity (FRC/TLC).
As the FRC/TLC increases, deposition would be expected to increase (Schlesinger,
1985). For example, rapid breathing often is associated with increased deposi-
tion of larger particles in the upper respiratory tract, as compared to slow,
deep breathing. Thus, performance parameters include both the factors that
influence the test species (including human) respiration characteristics and
the performance limitations of the techniques.
2.1.3.2.1 Anesthesia. Anesthesia greatly influences the respiration char-
acteristics of the test animal. This is a consideration when evaluating
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pulmonary function parameters for adverse effects. Prolonged anesthesia can
compromise the respiratory system, altering normal function and response.
Anesthesia also can alter the metabolism of the study compound. Anesthesia has
been reported to interfere with autonomic control, produce atelectasis,
decrease lung compliance, block reflex responses, and introduce an undesirable
risk to animals committed to long-term toxicology studies (Dorato et al.,
1983). These alterations in ventilation and breathing mechanics produced by
anesthesia could have severe effects on the results of respiratory function
measurements. This possibility provided the impetus to the development of
procedures for measuring respiration in unsedated laboratory animals (Amdur and
Mead, 1958; Mauderly et al., 1979). Data now are available on respiratory
characteristics in sedated and unsedated animals; consideration of anesthesia
should be included in data analysis to ensure appropriate comparisons.
2.1.3.2.2 Breathing pattern. Consideration should be given to the possible
alteration of breathing pattern due to the exposure concentration which would,
in turn, alter the delivered dose. Exposure of certain agents such as
irritants may lead to concentration-dependent changes in pulmonary mechanics
measurements (Costa and Tepper, 1988; Alarie, 1981). Correct quantification of
inhaled dose may therefore require measurement of respiratory rate and tidal
volume during the course of the exposure. Such differences in delivered "dose"
correlated with the species-dependent differences have been reported for
formaldehyde toxicity (Chang et al., 1983).
Although clinical exposures and respiratory measurements (at least the
noninvasive ones for functional mechanics) will be done on nonsedated humans,
the breathing pattern remains an important consideration. Experimental
protocol often dictates the breathing pattern (i.e., nonspontaneous breathing)
where a subject patterns his or her breathing to a metronome or is instructed
to take a deep breath on every fifth inhalation. Since the efficiency of
time-dependent deposition mechanisms is greater during inspiration than
expiration, an ideal "academic" breathing pattern would keep the inspiration
time/expiration time ratio (t./t ) constant (Heyder et al., 1975). Relevance
of this academic pattern to risk assessment, however, remains equivocal and
most investigations do not attempt to maintain a constant ratio. Documentation
of breathing patterns should be included in the experimental protocol and
considered in the extrapolation of dose.
2-24
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2.1.3.2.3 E q u ipm ent specifications. The equipment used will impart restric-
tions on any interpretation (i.e., limitations of sensitivity for exposure
analysis) of investigative results. Any underlying assumption or limitation of
the equipment used should be considered when evaluating test results. The
reader is referred to Costa and Tepper (1988) for a discussion of pulmonary
function testing principles, methods, and equipment limitations.
2.1.3.3 Definitions/Underlying Assumptions. Additional variability and
uncertainty in evaluating available inhalation studies occur because investi-
gators have used different definitions of various respiratory regions and have
employed different methods to estimate total or regional deposition. For
example, total deposition often is estimated by calculating the difference
between the amount of compound in the inhaled air and that in the exhaled air.
By making assumptions about mixing and dead space, estimates of regional
deposition may be obtained using measurements of the compound concentration in
different volume fractions of the expired air. As another example, the
definition of upper respiratory tract in various studies has included any or
all of the following anatomic regions: nasopharynx, oropharynx, larynx or
upper trachea. In other studies, deposition values based on chemical or
radiologic assays of tissues after exposure assume no particle translocation
before or during dissection. Some investigators include measurement of
material in the gastrointestinal tract (GI) in their reported value for upper
respiratory tract deposition, while others ignore this translocation. The
underlying assumptions and working definitions for different experimental
conditions can contribute a large degree of variability in reported results.
Conversion to some common basis will be necessary in order to calculate and
accurately compare inhaled doses.
2.1.3.4 Exposure Technology. Generation of the compound under study and
subsequent exposure also will affect the derived inhaled dose. Exact deter-
mination of the dose achieved in inhalation studies is a complex process.
Proper generation, appropriate characterization, and accurate delivery of the
test atmosphere are integral to this determination. Varieties and limitations
of the available technology must be considered when evaluating the selection of
methods and interpreting experimental results.
2.1.3.4.1 Inhalation modes. The various exposure techniques can be divided
according to the extent to which the test species are exposed. The techniques
range from whole body exposure at the one extreme to exposures limited only to
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the lower respiratory tract (Lippmann, 1980). These techniques include whole
body, head-only, nose-only, nasal, oral, and tracheal cannula exposures, and
tracheal instillations. Practical considerations such as economic feasibility,
special precautions for safe and efficient performance, amount of material,
test compound stability, exposure duration, and the measurements desired
dictate the selection of an exposure technique for a given study design. For
example, whole body exposure of laboratory animals in cages is the most common
method to conduct chronic inhalation exposures for more than 1-2 hours per day,
while nose-only exposures are most often used for short durations. A syste-
matic investigation of the effects of these different delivery techniques
on the regional deposition in various species is needed.
Wolff et al. (1982) studied the deposition and retention of 0.1 urn 67Ga90,
£. <3
aggregate aerosols in Fischer 344 rats following whole body and nose-only
exposures. In this investigation, lung deposition for whole body exposures was
similar to that for nose-only exposures (~15 percent of the inhaled particles).
Due to preening, passage of material into the GI tract, however, was 1.6-fold
greater for whole body exposures than with nose-only exposures. This could be
important in cases where there is either a specific GI response (i.e., stomach
lesions) or substantial GI absorption which may result in a systemic effect.
Rotation of animals in whole body chambers is recommended and should be included
in the experimental design (Griffis et al., 1981) to minimize dosimetric
differences that would result if the aerosol was not uniformly distributed in
the chamber. The effects of factors such as thermal and/or other stress upon
animals in confinement tubes used for nose- or head-only exposures need to be
considered, particularly since these factors may be species-dependent. For
example, rats in confinement tubes for short exposures have been shown to
have respiratory values and body temperatures that remain constant, while
Syrian golden hamsters exhibit increasing ventilation and temperature (Raabe
et al., 1973). Adaptation to exposure or measurements may be a function of
behavior, such as ability to be trained (Mauderly and Kritchevsky, 1979),
but in general, animals in confinement tubes or animals forced to breathe
through mouthpieces will experience abnormal stress (Raabe et al., 1973).
This should be accounted for in the experimental protocol. The tubes can be
modified into plethysmographs to monitor respiratory function changes, or
cooled to a constant temperature. The inhalation mode affects human exposures
as well. Since the nasal passages are more efficient at removing particles
2-26
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(particularly for large particles) than the oral cavity, increased lung deposi-
tion of larger particles could occur through mouth breathing. This would
affect both the amount and the size distribution of an inhaled aerosol. Even
the specific configuration of the mouthpieces used in oral exposures can affect
the extent of deposition (Schlesinger, 1985). Miller et al. (1988) showed
that regional respiratory tract deposition of insoluble particles in humans is
a complex function of breathing route, ventilatory level, and the particulate
physicochemical and aerodynamic properties,
2.1.3.4.2 Generation and characterization. Just as the working definitions
and underlying assumptions alter the interpretation of measurement techniques,
the operative exposure level (e.g., for use in risk assessment, prediction
models, etc.) of a test agent is a function of how its particulate composition
(mean particle diameter and distribution) and gas concentration are expressed.
Other specific characteristics (e.g., adequate test substance mixing in chamber,
hygroscopicity, charge density) should be accounted for as part of this
description. The soundness and interpretation of the animal data are dependent
on the methods employed to generate and analyze the test atmosphere data since
the methods influence deposition calculations.
The two most common ways in which particle size is expressed are the
count median diameter (CMD) and mass median diameter (HMD). The toxicity of
a material is most consistently related to its mass distribution. Measurement
of mass has the further advantage of a minor quantitative error at the small
end of the size spectrum. To assess risk, however, the activity diameter
may be a more appropriate expression of particle size as discussed in
Section 2.1.2.1. Methods of particle measurement include settling, filtration,
wet arid dry impingement, multiple impaction, electrical precipitation, thermal
precipitation, centrifugation, and observation of optical effects. Each of
these has its own principle of operation and limits of sensitivity which, in
turn, affect the expression or characterization of the test aerosol. Fiber
exposures are further complicated by the need to describe the aspect criteria
and distributions. As discussed in the section on anatomy and physiology,
certain mechanisms contribute to the deposition fraction in each respiratory
region. Failure to account for characteristics such as hygroscopicity or
charge density when generating an aerosol could change its deposition in
certain regions. This variability in the aerosol characterization would be
expressed as uncertainty in the risk assessment.
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Gaseous contaminant atmospheres are usually somewhat easier to charac-
terize. Delivered concentrations must be consistent across exposure location
and duration and may be less than the generated concentration. If the gas is
extremely reactive, loss due to reactions with the walls of the transport
system (e.g., tubing) will occur. Losses due to decomposition or alteration of
the test substance during some generation procedures also may be a factor. Gas
flow rate (delivery) must be known, steady, and calibrated for the given gas
since it is density-dependent. Analysis of the air is limited by the detection
device specifications. If on-line analysis is not feasible, consideration
should be given to the frequency of samples taken. The period between samples
for intermittent analysis should be less than one-tenth of the total exposure
time for any given day (McKenna, 1982).
For all generation and characterization of pollutants, periodic calibra-
tion of all measurement systems is a critical quality control/quality assurance
step. This also needs to be considered when evaluating the study.
2.1.3.4.3 Exposure regimen. Extrapolation from one exposure regimen to
another has uncertainties, most of which are not quantified. For most chemi-
cals, either particles or gases, the quantitative relationship between concen-
tration and duration of exposure is not studied. Some studies have indicated
that the relationship is dependent on many factors, including (1) number of
exposure hours per day; (2) the exposure scenario, that is, continuous vs.
interrupted (e.g., 1 week of exposure, 1 week of air, 1 week of exposure,
etc.), vs. intermittent (X hours per day, Y days per week) regimens; (3) time
of endpoint assessment (i.e., acute vs. subchronic vs. chronic studies);
(4) endpoint(s); and (5) the mechanisms of toxicity. Examples for particles
and gases follow which illustrate some of the complexities involved in extra-
polating across exposure scenarios.
The actual amount of particles or gas found in the lungs at any time is
determined by the relative rates of deposition and clearance. The efficiencies
of the deposition mechanisms are different in each region of the lung. The
defense mechanisms and clearance rates for each of these regions also are
different. Thus, it is expected that the kinetics of the toxic effect of
an exposure will be influenced by the duration of exposure. There is experi-
mental evidence for such a differential dependence of effect on exposure
duration. Albert et al. (1971) showed that low single doses or early effects
of repeated exposure to cigarette smoke were associated with acceleration of
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clearance rates in the tracheobronchial trees of both donkeys and humans.
Heavier doses and long-term repeated exposures were associated with sporadic
clearance, stasis intervals, and some retrograde movement. Unfortunately, there
has not been a systematic comparison and quantification of differential clear-
ance rates across species. This will be necessary before the effects of
duration can be assessed in the same models or default values can be developed.
Ozone can be used as an illustration for gases since it has a large health
effects data base. Kenoyer et al. (1981) showed that rats exposed to ozone for
4 hours showed delays in the early clearance and an acceleration in the late
clearance rate of tracer particles. These investigators postulated that the
delays in early clearance could be caused by effects that decrease mucous
transport (e.g., decreased ciliary beat rate or change in mucous properties),
while acceleration of the late clearance rate was most likely due to an increase
in numbers or activities of alveolar macrophages. Rats exposed intermittently
(7-8 hours/day to 0, for approximately one week) had similar changes in lung
antioxidant enzymes to animals exposed continuously (24 hours/day), even though
the dose, as expressed as the product of concentration (C) and time (T) of
exposure, was different (Mustafa and Lee, 1976). Monkeys exposed to 03 for
18 months continuously, or for 9 months bimonthly for 18 months had some similar
alterations in lung morphology; additional alterations were observed in the
intermittent exposure group having a lower (C x T) (Tyler et al., 1985).
Huang et al. (1988) has shown, using morphometric measurements of the proximal
alveolar region of lungs of rats receiving prolonged low level exposures to
of 03, that the increase in the relative volume of Type I epithelial cells was
related to the (C x T), whereas other morphometric indices were more dependent
on concentration than on time.
For NOp, the data base is equally complex on the exposure scenario issue.
Using the mouse infectivity model (an index of antibacterial lung defenses),
concentration was found to be more important than duration of exposure in
causing the effect (Gardner et al., 1979). When a typical urban pattern of
NCL was used (i.e., a baseline of continuous exposure to a low level of NO- on
which were superimposed two 1-hour peaks of N02 each weekday), the study
indicated that on a (C x T) basis, this regimen was not more toxic than a
continuous exposure to the baseline level after a short period of exposure
(Graham et al., 1987). After a chronic exposure, the spikes to the baseline
increased the effects relative to the baseline exposure only (Miller et al.,
1987a).
2-29
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The topic of extrapolating across different exposure scenarios is beyond
the scope of this document. However, the few examples provided illustrate the
complexity of the issue. Risk assessors will have to consider the effects of
exposure on a case-by-case basis and utilize default assumptions until the
needed research data are available.
2.1.4 Summary
This Section (2.1) has provided an overview of critical anatomic and
physiologic interspecies differences, significant physicochemical
characteristics of an agent that should be considered when evaluating an
exposure, and the experimental procedures which may influence exposure
conditions and interpretations of toxic responses. It was intended to
emphasize areas that should be given careful consideration and integration into
an overall risk estimate when analyzing the data base used for the derivation
of an inhalation reference dose. The next Section (2.2) discusses the
significance of the lung as the portal-of-entry for inhalation exposure.
2.2 PORTAL-OF-ENTRY CONSIDERATIONS: ASPECTS OF COMPARATIVE PULMONARY TOXICITY
Inhalation represents a route of exposure in which a variety of inter-
related factors influence not only the nature of the effects (portal versus
systemic) but also the manner by which they occur. The influence of target
cell populations in the respiratory tract on the nature of the response is a
factor unique to the inhalation route of exposure. Unlike the liver, a
first-pass organ in oral exposures that has a more homogenous population of
limited types of cells, the respiratory tract has more than 40 cell types
(Sorokin, 1970). Xenobiotics which exert their action by direct effects of the
parent compound or by metabolites can manifest profound differences in the
nature and degree of response, depending on the route of exposure.
The likelihood of adverse effects in the respiratory tract can be affected
by (1) production, distribution, and reactivity of metabolites by and among
specific cell types; (2) the degree to which detoxification systems are over-
whelmed (e.g., glutathione depletion); (3) efficiency and sensitivity of repair
processes (e.g., type II cell proliferation); (4) efficiency of clearance pro-
cesses; (5) airway mechanics; and (6) mechanism of action (e.g., pharmacologic
or immunologic) (Boyd, 1980; Calabrese, 1983; Gram et al., 1986; Thrush et al.,
1982; Nadel et al., 1985; Marin, 1986).
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There are numerous pulmonary defense systems that protect the respiratory
tract. While some pulmonary defense systems are truly protective, it must be
kept in mind that many "activate" inhaled agents and may be responsible for
adverse effects. Pulmonary defense systems can be physical in nature (e.g.,
filtration of particles by nasal hair), mechanical (e.g., expiration),
enzymatic, or cellular (e.g. phagocytosis).
Nasal hair can be envisioned as a first line of defense. However,
trapping of agents in the nose can serve as a source of irritation and/or more
serious adverse effects. Some agents (e.g., formaldehyde, acrolein) have been
shown to cause severe lesions in nasal epithelial cells (Morgan et al., 1986).
The mouth also can be envisioned as another first-line defense system. Mouth-
breathing in humans can result in solubilization of vapors in saliva and
deposition of particles. Swallowing can reduce pulmonary exposure but
increase presentation of the agent systemically via gastrointestinal tract
absorption.
Once an agent penetrates to the tracheobronchial region, agent deposition
and/or solubilization occurs in the mucous blanket covering the surface epithe-
lium. Clearance is discussed in Section 2.1,1.2.
Metabolism of potentially toxic inhaled compounds is achieved by a variety
of enzyme reactions involving oxidation, reduction, hydrolysis, and conjugation.
The enzymes may work individually, concurrently, or consecutively to detoxify
or, in some cases, toxify inhaled foreign compounds (Ohmiya and Mehendale,
1984; Minchin and Boyd, 1983; Dahl et al., 1987). These enzymes may vary in
activity across species and organs (Ohmiya and Mehendale, 1984; Ziegler,
1980; Tynes and Hodgson, 1985; Plopper et al., 1983; Litterst et al., 1975).
The oxidation, reduction, and hydrolysis reactions are catalyzed primarily
by the cytochrome P-450 and FAD containing monooxygenases. The cytochrome
P-450 isoenzymes are ubiquitous hemoproteins located in the endoplasmic reti-
culum of a variety of cells and are responsible for the oxidation of foreign
compounds. Recent studies have elucidated isoenzyme specificity, inducibility,
catalytic activity, and localization in the rabbit and rat lung (Domin and
Philpot, 1986; Vanderslice et al., 1987). Until recently, it was thought that
the cytochrome P-450 isoenzymes were the only primary monooxygenases in the
lung. However, recent studies have shown that the FAD-containing monooxygenases
play an important role in detoxification of foreign compounds.
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The Clara cells lining the respiratory and terminal bronchioles are
thought to be the primary site of cytochrome P-450 because of the presence of
endoplasmic reticulum. However, the ultrastructure of the Clara cell varies
across species (Plopper et al., 1980). In the ox, cat, and dog, more than 60%
of the cytoplasmic volume is glycogen with a relatively small proportion of
the cell volume containing endoplasmic reticulum or mitochondria. Thus,
species differences in Clara cell ultrastructure can be reflected in signifi-
cant differences in xenobiotic metabolism potential (Plopper et al., 1983;
St. George et al., 1988). Differences in localization of cytochrome P-450
activity have been suggested as a likely basis for some differences in respira-
tory tract toxicity (O'Brien et al., 1985).
Individually or in concert with the cytochrome P-450 isoenzymes,
conjugation reactions are catalyzed by the GSH-S-transferases which transform
potentially toxic parent compounds or activated metabolites into nontoxic
water soluble compounds. The cofactor required for these reactions is the
tripeptide, glutathione (GSH). GSH is synthesized in the lung, as well as
in other major organs, and also is reduced from the oxidized state (GSSG) to the
reduced state (GSH) by GSH reductase. Under extreme conditions of GSH
depletion in the lung, it has been hypothesized that extrapulmonary GSH is
mobilized and transported to the lung from the liver (Berggren et al., 1984).
GSH has been identified in isolated Type II epithelial cells, Clara cells, and
ciliated cells of the lung, but it is not known if it is present in all
pulmonary cells. GSH also is the cofactor utilized by the enzyme GSH
peroxidase. GSH peroxidase catalyzes the metabolism of hydrogen peroxide and
organic peroxides formed by the ozonization of unsaturated fatty acids. Other
key antioxidant components in the lung include ascorbic acid, alpha-tocopherol,
superoxide dismutase, and catalase (Massaro et al., 1988).
A variety of other cellular defense mechanisms can be marshaled which can
diminish or enhance toxic insult. Certain cell types can be stimulated to
release mediators, such as mast cell release of histamine. Histamine can cause
bronchoconstriction, which can be protective, by limiting the amount of pollut-
ant inhaled, or can be toxic, in terms of limiting oxygen uptake. Synthesis or
metabolism of prostaglandins also can affect airway and vascular caliber. The
chemotactic factors released can recruit phagocytic cells involved in clearance.
It should be recognized that the respiratory tract contains a variety of
different cell types that possess different metabolizing potential and are
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distributed in a manner which varies among species. A list of common cell types
and their function is provided in Table 2-3 in Section 2.1.2,1. Macrophages,
for example, constitute a cellular protection system and not only protect
inner surfaces of the respiratory tract from damage caused by particles and
microorganisms, but also have the potential to cause damage themselves (Rossi,
1986). Macrophages contain a variety of proteases and mediators that are
useful in destroying xenobiotics but are destructive to healthy tissue (Brain,
1986). Although recruitment of macrophages to the lung is related to the dose,
the adaptive increase in macrophages can be exceeded (Bowden, 1986). This
threshold may vary among species. The alteration of macrophage functioning
has the potential to shift the balance between protective and adverse effects.
Concurrent with the action of inhaled agents upon critical cell types in
the respiratory tract, a portion of the dose in the pulmonary region is likely
to be transported across the alveolar epithelium and enter systemic circula-
tion. Changes in permeability can result from the action of some of the
mediators and proteases mentioned. The greater the amount reaching systemic
circulation, the greater the likelihood for adverse effects in other systems
(e.g., liver, kidney, central nervous system). The rapidity and extent to
which systemic absorption occurs and the time-to-steady-state blood levels are
influenced by (1) ventilation rates and airway mechanics, (2) blood transit
time in capillary beds (i.e., perfusion limited), (3) metabolic conversion in
the respiratory tract and other organs, (4) alveolar surface area, (5) thickness
of the air-blood barrier, and (6) the blood:air and blood:tissue partition
coefficients. Many of these factors vary among species and, thus, should be
considered in key study identification.
After the inhaled agent enters systemic circulation, the liver may produce
additional metabolites that, if the half-life is sufficiently long, may
re-enter the lungs and exacerbate the portal-of-entry effects or produce
additional adverse effects (Boyd and Statham, 1983). Other agents, that do
not require bioactivation, have been shown to damage the lung when applied
systemically (Kehrer and Kacew, 1985).
Exhalation of volatile agents (including from administration routes other
than inhalation) is an important excretory pathway that is dependent on tissue
levels and exposure regimen. For inhalation exposures, the exposure duration
influences the amount of chemical entering the systemic circulation, the amount
metabolized, and the concentration of the chemical in tissues. Using a
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simulation model, Fiserova-Bergovera et al. (1984) demonstrated that for
chemicals that are not metabolized, tissue concentrations of "poorly soluble"
^oil/aas <10^ Cnemi'ca1s change very minimally after two hours of exposure.
The pulmonary uptake rate approaches zero at the end of a 2-hour exposure and
apparent equilibrium is established. "Easily soluble" chemicals (10 > ^04i/aas
<10,000) require more than one day of exposure to reach apparent equilibrium
and "highly soluble" chemicals (\j4-i/aas >10,QQO) require more than 1 year of
exposure. If the chemical is metabolized, pulmonary uptake and the amount
metabolized increase with exposure duration, but the effect of metabolism may
be more complex if exposure concentrations are so high that metabolic pathways
approach saturation kinetics and cause metabolism to deviate from first order
kinetics.
Conversely, pulmonary clearance decreases with increasing biosolubility
(refers to solubility of gases and vapors in biologic materials) and thereby
affects the cumulation of chemicals during intermittent exposure regimens.
Simulation of an 8 hour/day, 5 days/week schedule for a three-week exposure
duration to a 70 kg man showed that poorly soluble chemicals (as defined
previously) have no tendency to accumulate in the body, while easily and highly
soluble chemicals do have a tendency to accumulate because the intermissions
between exposures are not long enough to allow the chemical to be removed from
adipose tissue (Fiserova-Bergovera et al., 1984). Excursions in exposure
concentrations had a great effect on tissue concentrations of poorly soluble
chemicals, but had little effect on tissue concentrations of highly soluble
chemicals. Concentrations in well-perfused tissues were more affected by
excursions in exposure concentrations than concentrations in muscle or adipose
tissues.
The results of these simulation efforts emphasize the uncertainty that the
dual function (uptake and exhalation) of the respiratory system adds to any
attempt to estimate either respiratory tract or extrarespiratory "dose" of
volatile agents. These simulations also emphasize the need for careful con-
sideration of the uptake, metabolism, and excretion parameters for these agents
when attempting the exposure duration and concentration conversions discussed
in Chapter 4, and when ruling out the possibility of a pulmonary endpoint when
using oral data as part of the data base.
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3. QUALITATIVE EVALUATION OF THE DATA BASE
The aim of the inhalation RfD methodology is to establish a relationship
between a particular agent in the air and a specific health effect. Evidence
must be collected from diverse sources and synthesized into an overall judgment
of health hazard (Hackney and Linn, 1979). Qualitative evaluation of a diverse
data base necessitates a systematic approach for obtaining agreement on the
validity and selection of studies to be used in the quantitative methodological
procedures of the risk assessment.
3,1 GUIDELINES FOR SELECTIONS OF KEY STUDIES
Key studies are those that contribute most significantly to the weight of
evidence as to whether or not a particular chemical is potentially hazardous in
humans (U.S. Environmental Protection Agency, 1987a). The studies also may be
used in the quantitative dose-response analysis of risk assessment. These
studies are of two types: (1) epidemiologic, clinical or case reports on
humans; and (2) experimental studies on animals. Each has unique considerations
that will be addressed separately. Once the key studies demonstrating the
critical toxic effect have been identified, the selection of effect level and
the inhalation RfD derivation arises from an objective scientific evaluation of
the data available on the chemical. The limitations and the uncertainty
factors involved in this derivation are a function of the quality of the key
study and will be addressed in Section 3.2. Data base deficiencies and alter-
native approaches for risk assessment will be presented in Section 3.3.
3.1.1 HumanData
Utilization of human data avoids the necessity of extrapolating from
animals to humans, thereby decreasing uncertainty in the risk assessment.
Such data have often been useful to the oral RfD work group in qualitatively
establishing the presence of an adverse effect in exposed human populations
3-1
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(U.S. Environmental Protection Agency, 1987a), There are significantly more
human data on inhalation than on ingestion exposures, however, so that criteria
for evaluating studies and their results need to be stated explicitly. Since
1977, when the Clean Air Act identified goals related to air quality and
health, the task of clarifying how population studies can be used for deter-
mining scientifically reasonable standards and how to define an adverse
respiratory health effect has been rigorously debated (Lebowitz, 1983; American
Thoracic Society, 1985; National Research Council, 1985). Many of the results
from these efforts can be applied as guidelines for the inhalation RfD
methodology.
Three types of human studies are most often utilized to obtain data
pertinent to understanding the risk of chemicals to humans: (1) epidemiologic
studies, (2) clinical studies or controlled exposure experiments, and (3) case
reports (Erdreich and Burnett, 1985). Each of these three study types can
provide important information needed to protect public health. When using
these studies for risk assessment, several factors are important in evaluating
their quality and in determining the level of certainty associated with their
use. The factors that are considered when evaluating an epidemiologic study
are relevant in evaluating the other types of human studies, but the discussion
on epidemiologic studies is the most extensive.
3.1.1.1 EpidemiologicData. There are essentially three areas of concern in
assessing the quality of an epidemiologic study. These involve the design and
methodological approaches used for: (1) exposure measures, (2) effect measures,
and (3) the control of covariables and confounding variables (Lebowitz, 1983).
The study population and study design must adequately address the health
effect in question in order to support a risk assessment (Lebowitz, 1983).
In order to accomplish this goal, the exposure measures must be appropriate
and of sufficient quality; the statistical analysis methods must be suitable to
the study design and goals; the health effect measures must be reliable and
valid; and the cpvariables and confounding variables need to be controlled or
eliminated.
3.1.1.1.1 Assessment of exposure measures. The problem of the accuracy and
relevance of exposure measurements is not unique to epidemiologic investiga-
tions, but it can be exacerbated due to the longterro nature of these studies.
For example, the nature of aerometric data changes over time because of
different industrial hygiene practices and because individuals change jobs and
3-2
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residences, and thus their exposures change over time. Accurate documenta-
tion of air toxicant levels is, therefore, critical in determining the
usefulness of an investigation as well as documentation that the analysis of
the air toxicant is appropriate and of sufficient sensitivity. It also is
advisable to have the concentrations of other pollutants reported to help rule
out confounding or interactive effects. The number, location, and timing of
monitors must be suitable to allow an appropriate determination of exposure of
the subjects to the pollutant being studied and to the pollutants that could
confound the results. When appropriate, the exposure measure/estimate should
take into account indoor/outdoor exposures and activity and subject location
data. The exposure measure/estimate needs to represent the actual exposure in
a sufficiently satisfactory way so as to represent the "true" exposure.
3.1.1.1.2 Assessment of effect measures. Effect measures refer to the methods
used to ascertain disease indices. For epidemiologic studies these include
incidence, standardized mortality ratios, and relative risk ratios.
Criteria for assessment require the proper selection and characterization
of both the exposed and control groups. For example, criteria for inclusion in
the control category of a case-control study must ensure that this group has
no exposure to the agent of concern. Another selection issue is that of
needing reference populations or control groups for studies without internal
control groups, particularly when evaluating spirometric data (Ferris, 1978;
American Thoracic Society, 1979; Crapo et al., 1981; Knudson et al., 1976).
Each population used to predict "normal" pulmonary function tests has its own
characteristics, which should be considered when used for comparisons. Other
considerations include the adequacy of study duration and quality of the
follow-up. A disease with a long latency before clinical presentation requires
a longer study duration than one with an acute onset. Valid ascertainment
(such as verification according to the International Classification of Diseases
IX) of the causes of morbidity and death also is necessary.
Evaluation of epidemiologic studies may require interpretation of a variety
of subjective health effects data. Questionnaire responses may be biased by
the way in which questions are worded, the training of an interviewer, or the
setting. A committee of the American Thoracic Society (ATS) charged with
defining an adverse respiratory health effect, however, has come to a consensus
that "in general, increased prevalence of chronic respiratory symptoms as deter-
mined from questionnaire surveys should be considered to be an adverse health
3-3
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effect" (American Thoracic Society, 1985). Questionnaires should be validated
as part of the investigation protocol unless a standard questionnaire that has
previously been validated is used (Medical Research Council, 1960; Ferris,
1978; National Institute for Occupational Safety and Health, 1986).
In order to assess quantitative results, it is very important to consider
differences between statistical significance and medical or biological signifi-
cance. Both the variability of an outcome measure and the magnitude of an
exposure's effect determine the level of statistical significance. For
example, data from a large study population analyzed with sophisticated
techniques may yield statistically significant effects of small magnitude that
cannot readily be interpreted biologically. Conversely, large effects of
clinical importance may not be statistically significant if the study popula-
tion is too small; that is, if the studies presented negative or no-effect
results due to the lack of power or the small number of subjects in the study.
Judgments concerning medical or biological significance should be based on the
magnitude of effect. For example, cough and/or phlegm production can be
considered less important than effects resulting in hospital admissions.
Underlying assumptions and nuances of the statistical procedures applied to the
data also need to be considered. This will probably best be accomplished on a
case-by-case basis, as has been done by the oral RfD work group.
Because the inhalation RfD considers both portal-of-entry and systemic
effects, it would be helpful to define an "adverse respiratory health effect."
An ATS committee published guidelines that defined such an effect as medically
significant physiologic or pathologic changes generally evidenced by one or
more of the following (American Thoracic Society, 1985):
Interference with the normal activity of the affected person or
persons
Episodic respiratory illness
Incapacitating illness
Permanent respiratory injury or
Progressive respiratory dysfunction
Appendix C provides detailed descriptions of adverse respiratory effects in
humans.
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3.1.1.1,3 Assessing the control of confounding and covariabTes. Epidemiologic
investigations have to relate an exposure to a given health effect, but this
includes accounting for the "background" health effect (pathologic condition)
that exists in individuals due to predisposing factors and pre-existing health
conditions, or from other variables, such as occupational exposures.
Various host factors contribute as risk factors for disease and can
influence the health indices assessed. For example, asthmatics may be particu-
larly susceptible to effects from exposure to irritant gases. Epidemiologic
evaluation of these factors often not only accounts for such interactions but
also can help to characterize susceptible or sensitive groups. Covariables can
be as important as the major aerometric variables themselves in affecting human
health. Other exposures, such as concomitant occupational exposures and
smoking, in particular, can affect the disease outcome. Meteorologic variables
such as air velocity, temperature, and humidity also are very important factors
when considering respiratory health effects. These covariables should be
controlled by both the study design and analysis as appropriate.
Assessment of individual epidemiologic studies should bear in mind that
the final step in the inferential process from an epidemiologic investigation
requires the extension of its results to persons, populations, or settings not
specifically included in the study. The confidence with which this is done for
positive results is usually based implicitly on how successful the investiga-
tors have been in identifying and handling the potential risk factors and
covariables that produce or influence the pollution-effect association they
have observed. Uncertainties also arise because the general population includes
some people, such as children, who may be more susceptible than people in the
sample from which the epidemiologic data were derived. Factors such as the
"healthy worker" effect and the bias of a predominantly male worker sample must
be considered when using occupational studies (National Research Council, 1985).
Intraindividual variability concerns are addressed in Section 3.1.1.3.
3.1.1.1.4 Summary. Specific recommendations for the evaluation of epidemio-
logic investigations have been adapted from Lebowitz (1983), American Thoracic
Society (1985), and Interagency Regulatory Liaison Group (1981). Appendix D
provides guidelines for evaluating individual epidemiologic studies and the
considerations involved in evaluating the statistical analyses.
3.1.1.2 Nonepidemiologic Data. Human data also include clinical studies and
case reports. The case reports and acute exposures provide support for the
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weight-of-evidence decision, but are often of limited utility in establishing a
quantitative relationship between environmental exposures and anticipated
effects (U.S. Environmental Protection Agency, 1987a). They are often valuable
in determining the nature of the effect in humans.
3.1.1.2.1 Clinical studies. Clinical studies may contain exposure-response
information that can be used in estimating effects. Most clinical studies
combine the strong point of animal toxicology, rigorous control of the experi-
mental exposure and subject, with the strong point of epidemiology, the
unquestioned relevance to human health (Hackney and Linn, 1983). In addition,
clinical studies can be independently replicated somewhat more easily
(requiring a reasonably short time and resource commitment) than the other
types. There are limitations, however, that include short exposure duration,
"noninvasive" techniques that might not ascertain the full array of effects,
and small groups of test subjects. The test atmospheres are usually within
that expected to produce only mild and temporary health effects. Certainly,
clinical studies should be recognized and given credence to the extent that
they are scientifically rigorous, relevant to human health concerns, and can be
independently replicated. They may be particularly useful for less-than-
lifetime risk assessment. The prediction of long-term effects from short-term
observations remains questionable, but confidence in clinical findings can be
bolstered by supporting evidence from epidemiology and animal toxicology, and
vice versa.
3.1.1.2.2 Case reports. Individual case reports of adverse effects due to a
specific agent also can provide some help in evaluating the potential risk from
exposure to a toxic air pollutant. These reports are especially valuable
qualitatively for indicating that the quantitative effect observed in animals
occurs in exposed humans. These reports must be examined carefully and used
with discretion since they represent a very small sample and are usually
related to heavy exposures (Goldstein, 1983). Nevertheless, these observations
should not be overlooked, especially when a large number of case histories
exist with the same endpoint. Research needs to address the interrelationships
of findings from short-term observations, epidemiology, and animal toxicology,
and to establish appropriate links among them in order to support regulatory
decisions.
3.1.1.3 Intraspecies Variability and Identifying Sensitive Subgroups. In
order to control factors other than the chemical being tested, animals used
in toxicity studies (e.g., rodents) are often bred for homogeneity. In
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contrast, the human population is heterogeneous. The broad genetic variation
of the human population in metabolism and in tissue response to chemicals
causes individual differences in susceptibility to toxic chemicals. A sensi-
tive or hypersusceptible individual is one who will experience an adverse
health effect to one or more pollutants significantly earlier in the course of
exposure, or at lower doses than the average individual, because of host
factors that predispose the individual to the harmful effects. Sensitive
individuals may be those whose genetic makeup puts them at the extreme end of a
continuous distribution of a biological function, such as the amount of enzyme
production, or those who possess a unique genetic difference, such as an
altered enzyme, that makes them markedly different from the general population.
In addition to genetic factors, personal characteristics such as age, sex,
health status, or habits make some people more susceptible (Calabrese, 1978).
The activity pattern of people is a major host factor influencing the dose-
response by its effect on delivered dose. Generally, exercise increases the
delivered dose and alters the regional deposition of the dose. The principles
involved have not been quantified sufficiently to date, but should be considered
qualitatively when comparing studies or population subgroups.
Environmental risk assessment should consider host factors that both
increase susceptibility and that occur relatively frequently in the population.
Erdreich and Sonich Mull in (1984) estimated the prevalence of population
subgroups of individuals who are potentially hypersusceptible to some common
pollutants. Table 3-1 shows five categories of individuals who, based on
empirical observations or compromised physiological functions, are assumed
hypersusceptible to the listed chemicals.
As a result of epidemiologic investigations, it is well recognized
that a population of adult workers experiences less morbidity and mortality
than the general population (Fox and Collier, 1976; Wen et al., 1983; Monson,
1986). However, sufficient qualitative and quantitative information on
interindividual variability and hypersusceptibility for specific chemicals
rarely exists.
If the decisions on the RfD are to be made on data derived from subgroups
of the general population such as workers who are generally a selected group of
healthy adults, the extrapolation procedure must contain appropriate adjust-
ments to account for the anticipated broader variability in the general popula-
tion. Worker populations are nonrepresentative in terms of age distribution
3-7
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TABLE 3-1. PREVALENCE OF SUBGROUPS HYPERSUSCEPTIBLE TO EFFECTS OF
COMMON POLLUTANTS3
Hyper- .
susceptible Prevalence Chemicals Reference
Embryo, fetus,
neonate
pregnant women;
21/1000°
Young children ages 1-4:
70/1000
Lung disease
Coronary heart
disease
Liver disease
emphysema,
asthma:
37/10006
coronary heart
disease:
16-27/1000e
liver abnor-
malities:
20/10001
carcinogens, solvents,
CO, mercury, lead,
PCBs, pesticides
hepatotoxins, PCBs,
metals
ozone, Cd, partic-
ulates, S02, N02
chlorinated solvents,
fluorocarbons, CO
Rice, 1981; Kurzel
and Cetrulo, 1981;
Saxena et al. ,
1981
Calabrese, 1981;
Friberg et al. ,
1979
Holland et al.,
1979; Redmond,
1981
McCauley and Bull,
1980; Aviado, 1978
U.S. Environmental
Protection Agency,
1984a,b
carbon tetrachloride, Calabrese, 1978
PCBs, insecticides,
carcinogens
Source: Adapted from Erdreich and Sonich Mull in, 1984.
All estimates based on 1970 census.
Representative samples of chemicals to which these individuals may be hyper-
susceptible. Some evidence from animal studies only.
Authors' estimate from 1970 census statistics data.
eHealth Interview Survey (National Center for Health Statistics, 1970).
fHealth Interview Survey (National Center for Health Statistics, 1975).
and general health status. Hypersusceptible people may not be represented
because they may not seek or sustain employment, particularly in situations
such as those represented in workplace exposure studies. Occasionally, data
are available on more sensitive subgroups such as children or asthmatics. In
these cases, risk assessments can be made for the general population with
greater confidence. In the absence of data on the more susceptible individuals
in the population or lack of identification of such individuals, uncertainty
factors are used to protect unidentified individuals at greater risk.
There are two steps necessary to obtain information addressing the problem
of sensitive individuals: (1) examine chemical-specific data for empirical
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evidence of sensitivity and hypersusceptibility, and (2) ascertain whether
the mechanism of toxicity for a given chemical suggests that any population
group would be extremely sensitive.
In addition to this chemical-specific evaluation, guidance should be
developed concerning the prevalence of sensitive subgroups and the range of
sensitivities in the general population exposed to inhaled toxicants. The U.S.
Environmental Protection Agency (1986a) has initiated research to assess the
magnitude of interindividual variability in pharmacokinetic parameters related
to the delivery of the biologically effective dose, in order to develop
guidance for appropriate uncertainty factors. Differences among normal healthy
adults may be as much as 10-fold (U.S. Environmental Protection Agency, 1986a).
Therefore, the potential that exists for broad differences when children, the
elderly, the ill, and those previously exposed are included must be considered.
The issues discussed in this section are summarized as follows:
Evaluation of the Epidemiclogic Data Base
Examine epidemic!ogic and clinical data for dose-response infor-
mation in potential or previously identified sensitive groups
(e.g., studies in asthmatics, children).
Examine animal data for studies in models of sensitive individ-
uals.
Evaluate epidemiologic studies to ascertain genetic and personal
factors that increase the risk of adverse response. Evaluate
implications of these risk factors for identifying sensitive
groups.
Examine data for reports of ranges of responses or response
variables, and for data containing individual responses.
This is particularly important in evaluating human data for
assessing the range of variability in response because epidemi-
ologic studies may not include exposure levels associated with
a NOEL, but with an effect.
Evaluate available biological monitoring data and clinical and
experimental data for indications of characteristics of increased
susceptibility. For example, respiratory irritants may induce
responses earlier in individuals with orl-antitrypsin deficiency.
Evaluate data on mechanisms of toxicity, pharmacokinetics, and
critical target organs to identify characteristics that may
imply broad interindividual variability or hypersusceptible
individuals. For example, the elderly may be more sensitive to
certain chemicals in relation to age-related changes in
oxidative metabolism potential.
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Evaluation of Individual Studies
Assess the makeup of the study population and control groups
to identify presence or absence of sensitive individuals. Data
on healthy workers, for example, are not representative of the
general population and will require reduction of NOAELS or LELs
by uncertainty factors.
Consider the activity pattern of the subjects. Whether the
subjects received exposure while at rest or at level(s) of
exercise will influence the inhaled dose as well as the pattern
of deposition.
In longitudinal (cohort) studies, evaluate information in rela-
tion to the natural history of the disease, i.e., the progres-
sion of lesions. Normal changes over time, such as increased
FEVi as children get older, and decline of FEV]. with aging in
older adults, should not be adversely affected. Cross-sectional
studies may suggest such associations but will not support
causality as strongly as will cohort studies.
For parameters that have known variability with age, such as
FEVlt evaluate results within age groups and ascertain whether
appropriate reference populations were used.
Areas for further investigation and development of specific guidance
include:
To what extent can we develop guidance on which conditions and
diseases predispose individuals to hypersusceptibility? It is
important to emphasize conditions that are more common in the
population (3-5%). Susceptibility factors can be linked with
characteristics of chemicals or to specific chemical classes to
facilitate generic risk assessment procedures.
How do known differences in components of respiratory function,
such as age-related differences in FEVt, affect susceptibility
to systemic toxicity from airborne chemicals?
3.1.2 Animal Data
When the data base lacks appropriate information on effects in humans, as
is frequently the case, the principal studies are drawn from experiments
conducted on nonhuman mammals. Animals most often used include the rat, mouse,
guinea pig, hamster, rabbit, monkey, and dog. Such animal studies have often
been conducted with controlled exposure conditions on relatively homogenous
populations, but nevertheless, present the risk assessor with concerns about
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evaluating dose and exposure regimen. Unlike the human, the laboratory rodent
strains, because of inbreeding, have homogeneous constitutions. Genetic back-
ground differences and numerous other interspecies differences are confounding
factors during key study selection.
Evaluation of the quality of individual animal toxicity studies requires
consideration of factors associated with the study's hypothesis, design,
execution, analysis, and interpretation (U.S. Environmental Protection Agency,
1987a). Guidelines for assessing individual animal studies are provided in
Appendix E and are adopted from a number of recommendations (National Research
Council, 1984; Society of Toxicology, 1982; James, 1985; Muller et al., 1984;
Lu, 1985a). The reader is referred to this appendix for a more detailed
description of those issues discussed here.
3.1.2.1 Appropriateness of Species as a Modelfor Humans. Identification of
the most appropriate animal species is the end result of an interpretative
process that examines all facets of a data base from study design to data
relevance to the extrapolation methodology.
The most sensitive species is selected from evaluation of key studies.
While this approach (i.e., NOAEL identification) may have the advantage of
affording a greater degree of protection, the species most sensitive to an
agent may not be as toxicologically relevant as other species for extrapolation
to man because of a variety of interspecies variables.
Selection of an appropriate animal model and key study depends on the
depth of understanding of the human disease syndrome, adverse effect, or
indicator of toxicity selected as the criterion for evaluation. While a
particular animal species may share a number of similarities with humans in
respiratory tract physiology, it may be dissimilar in crucial parameters and
thus, make it a less than adequate source as a model. This subject area has
been reviewed recently (Hakkinen and Witschi, 1985) and various mammalian
species (rat, hamster, rabbit) were identified as appropriate species for
extrapolation from several perspectives. Other reviews that discuss the
current limitations and need for the development of animal models as surrogates
for humans include those of Reid (1980), Slauson and Hahn (1980), and Calabrese
(1983).
For agents whose toxicological outcome is dependent on the degree to which
it is metabolized, the most appropriate animal species is contingent upon
proper evaluation of the numerous interspecies differences with respect to
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metabolism (see also Section 2.2). The studies of Plopper et al. (1983)
suggest that animal species differ widely in metabolizing potential of the
respiratory tract. Hamsters and rabbits have much greater metabolizing
potentials than do monkeys and rats. Interspecies differences in the metabolic
pathway, as shown for xylene (National Toxicology Program, 1986), may serve as
a basis for selecting one study for RfD derivation and rejecting another.
Appropriate animal model selection may be contingent upon pathological
identification of early changes consistent with the human syndrome; for example,
a clear choice of an appropriate animal species has not been established for
emphysema (Snider et al., 1986). The hamster may be considered as most similar
to man, with respect to emphysema, as measured by serum orantitrypsin levels.
Hamsters have the lowest antiprotease levels of 10 species tested (Snider
et al., 1986). Individuals with deficient blood levels because of a genetic
defect are characterized as a high-risk subgroup for emphysema. However,
primates have comparable antitrypsin profiles (Ihrig et al., 1971).
Species-dependent variables in mucous production and secretion are factors
in selecting an appropriate animal model (see also Section 2.2). Ozone
exposure, for example, increases mucous secretion in rats but not in monkeys
(Gardner, 1984).
3.1.2.2 StudyDesign. An ideal study addresses a clearly defined hypothesis,
follows a carefully prescribed protocol, is conducted in adherence to good
laboratory practice, and includes appropriate and sufficient subsequent
analysis to support its conclusions. The U.S. EPA Good Laboratory Practice
Standards (Code of Federal Regulations, 1983a,b) are designed to ensure the
quality and integrity of data used in hazard evaluation. These regulations
contain detailed guidance on provisions for personnel, facilities for animal
care, animal supply, handling of test and control substances, equipment,
operation of testing facilities, characterization of test and control
chemicals, protocol and conduct of a laboratory study, report records, record
storage, and record retrieval. Studies that do not precisely follow these
guidelines may still be judged adequate if the committee to develop inhalation
RfDs determines that, in the context of results, the deviations are not
important. The type of deviation (variation) and its magnitude, as well as the
potential for its interaction among all the variables, must be assessed by the
committee (National Research Council, 1984). For example, a study may still be
judged adequate, despite an insufficient number of test animals specified by
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the appropriate reference protocol guidelines, if the results are so definitive
that the addition of more test animals would almost certainly not have affected
the conclusion. Risk assessments that use studies with deficiencies may
include a modifying factor to account for the added uncertainty in its use (see
Section 4.1.).
The appropriate application of statistics in both the design and inter-
pretation of studies is an area in animal toxicity testing that is often
neglected or distorted (Muller et al., 1984). Consideration of statistical
applications restricted to confirmatory analysis (i.e., outcome is dependent
on the mathematically randomized test condition and is independent of other
observations) vs. exploratory analysis (i.e, many tests on a variable) should
be emphasized.
3.1.2.3 Study Validity and Relevance to Extrapolation. The validity of the
study and its relevance to human extrapolation is another major area to
consider when assessing individual animal studies. It involves the evaluation
of a number of factors, including all elements of exposure definition (dose,
duration, administration route, and physicochemical characterization of the
chemical used), reliability of and limits to the procedures used for both
exposure and effects measurements, relevance of the dose level tested to the
anticipated human exposure level, nature of the effect (consistency with the
area of toxicology assessed and the suspected mechanism of action), and the
similarities and differences between the test species and humans (e.g., in
absorption and metabolism).
Animal studies are conducted using a variety of exposure scenarios in
which the magnitude, frequency, and duration of exposure may vary considerably.
Studies may use different durations (acute, subchronic, and chronic) as well as
schedules (single, intermittent, and continuous). All of these studies contri-
bute to the hazard identification of the risk assessment. Special considera-
tion should be addressed to those studies of appropriate duration for the
reference level to be determined (i.e., chronic investigations for the RfD).
These exposure concerns (dose and duration) are compounded when the risk
assessor is presented with data from several animal studies. An attempt to
identify the animal model most relevant to humans should be made on the most
defensible biological rationale (e.g., comparable metabolism and pharmacokine-
tic profiles). In the absence of such a model, the most sensitive species
(i.e., the species showing a toxic effect at the lowest administered dose) is
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adopted for use as a matter of science policy at the U.S. Environmental
Protection Agency (1987a). This selection process is more difficult if the
animal data are for various exposure routes, especially if the routes are
different from that in the human situation of concern.
Because the data base may be deficient for the route of exposure of
interest, it is the Agency's view that the toxicity potential manifested by
one route is relevant to any other exposure route unless convincing contrary
evidence exists (U.S. Environmental Protection Agency, 1987a). Consideration
must be given to the differences in the pharmacokinetics for the chemical
resulting from the different exposure routes. Bioavailability of the chemical
administered is another important factor for consideration/uncertainty in the
evaluation of dose. Detailed consideration is given to this topic in
Section 4.1.1.2.
3.1.3 Summarizing the Evidence
The culmination of the hazard identification phase of any risk assessment
involves integrating a diverse data collection into a cohesive, biologically
plausible toxicity "picture"; that is, to develop the weight-of-evidence that
the chemical poses a hazard to humans. The salient points from each of the
animal and human studies in the entire data base should be summarized as should
the analysis devoted to examining the variation or consistency among factors
(usually related to the mechanism of action), in order to establish the likely
outcome for exposure to this chemical. From this analysis, an appropriate
animal model or additional factors pertinent to human extrapolation may be
identified.
The utility of a given study is often related to the nature and quality
of the other available data (Erdreich and Burnett, 1985). For example,
clinical descriptions can provide insight on pharmacokinetics and may validate
that the target organ or disease in animals is likely to be the same effect
observed in the exposed human population. However, if a cohort study describing
the nature of the dose-response relationship were available, the clinical
description would rarely give additional information. An apparent conflict may
arise in the analysis when an association is observed in toxicologic but not
epidemiologic data, or vice versa. The analysis then should focus on reasons
for the apparent difference in order to resolve the assessment. For example,
the epidemiologic data may have contained other exposures not accounted for, or
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the animal species tested may have been inappropriate for the mechanism of
action. A framework for approaching data summary is provided in Table 3-2.
Table 3-3 provides the specific uses of various types of epidemiologic data in
such an approach. These guidelines have evolved from criteria used to establish
causal significance, such as those developed by the American Thoracic Society
(1985) to assess the causal significance of an air toxicant and a health
effect. The criteria for establishing causal significance can be found in
Appendix F. In general, the following factors enhance the weight-of-evidence
on a chemical (U.S. Environmental Protection Agency, 1987a):
Clear evidence of a dose-response relationship
Similar effects across sex, strain, species, exposure routes, or
in multiple experiments
Biologically plausible relationship between metabolism data,
the postulated mechanism of action, and the effect of concern
Similar toxicity exhibited by structurally related compounds,
Some correlation between the observed chemical toxicity and
human evidence
Developing improved weight-of-evidence schemes for various noncancer health
effect categories is the focus of ongoing efforts by the Agency to improve
health risk assessment methodologies (Perlin and McCormack, 1988).
The greater the weight of evidence, the greater the confidence in the
conclusion derived. Another difficulty encountered in this process is when
certain studies produce clearly positive or negative results, yet may have to
be considered as flawed. The flaws may have arisen from inappropriate design
or execution in performance (i.e., lack of statistical power or adjustment of
dosage during the course of the study to avoid undesirable toxic effects). The
treatment of flawed results is critical; although there is something to be
learned from every study, the extent that a study should be used is dependent
on the nature of the flaw (Society of Toxicology, 1982). A seriously flawed
negative study could only provide a false sense of security, whereas a flawed
positive study may be entitled to some weight. Although there is no substitute
for good science, grey areas such as this are ultimately a matter of scientific
judgment. The risk assessor will have to decide what is and is not useful
within the framework outlined earlier.
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TABLE 3-2. PROPOSED APPROACH FOR SUMMARIZING THE EVIDENCE FROM DIVERSE DATA
CONCEPT 1: STRENGTH OF THE ASSOCIATION
The stronger the association, the greater the confidence that the agent
causes the effect.
Presence of low LD50, low NOEL, high potency index
Dose-response gradient evident
High incidence rate, large excess risk
High level of statistical significance in relevant studies
CONCEPT 2: CONSISTENCY
The association is observed in various circumstances.
Observed in a number of experimental species
Various routes
Different dose regimens
Descriptive epidemiologic data
Analytical epidemiologic studies
CONCEPT 3: BIOLOGICAL PLAUSIBILITY
The association is plausible in terms of other scientific information
related to the causal mechanism.
A gradient of responses observed
Short-term or i_n vitro tests
Pharmacokinetics
Molecular action and pathology
Structure-activity relationship
Preclinical indicators
Biological monitoring of exposure
Source: Erdreich, 1988,
Studies meeting the criteria detailed in Sections 1.1 and 1.2 (epidemi-
ologic, nonepidemiologic and/or experimental studies on animals that "fit" into
this framework) are used in the risk assessment phase.
3.2 TOXICOLOGICAL ISSUES IN DATA EVALUATION
3.2.1 Qualitative Evaluation of Dose Response and Dose EffectData
3.2.1.1 Relationship to the Uncertainty Factor Appjroach. Evaluation of dose-
consequence relationships involves two distinct steps. The first relates to
the evaluation of an individual study with emphasis on the following:
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TABLE 3-3. HUMAN DATA FOR USE IN HEALTH RISK ASSESSMENT
Study (Alternative Terms)
Comment on Potential Use
Cohort (longitudinal,
prospective, incidence)
Case-control (retrospective,
dose or case-referent)
Cross-sectional (prevalence)
Geographic correlation
Clinical trials
EPIDEMIOLOGIC DATA
Rates as percent response useful in risk
characterization. Measure of excess risk
can be obtained. If dose or exposure data
are available, dose-response curves can be
constructed. Studies with ordinal exposure
data support strength of evidence and hazard
identification.
No direct measure of disease rates. If
exposure daia are available, a NOEL may be
identified. Studies with ordinal or nominal
exposure data may support strength of
evidence and hazard identification.
Similar to case-control for short-term
effects. Prevalence data less reliable for
effects from chronic exposures.
An inexpensive screening procedure. Crude
indicator of potential hazard. Rates are
usually only indirectly related to exposure.
Generates hypotheses for analytical studies.
Generally not applicable to environmental
issues, because exposures are treatments or
preventive measures. Intervention trials in
which an exposure is removed or changed
(e.g., medication, smoking, diet) are useful
in strength of the evidence for evaluating
causality.
Experimental studies
"Exposed-control" comparisons
(noncohort; see text for
discussion)
NONEPIDEMIOLOGIC DATA
The only human data with controlled exposure
levels. Usually interval level exposure
data but low dose, limited exposure time.
Use for hazard identification, dose-response,
risk characterization.
Rates may be biased because of self-
selection or incomplete ascertainment of
exposed population. Cannot be used to
support absence of hazard. Clinical
descriptions useful for hazard identifi-
cation.
(continued on the following page)
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TABLE 3-3. (continued)
Study (Alternative Terms) Comment on Potential Use
Case series Can be used to demonstrate hazard if syndrome
is unusual. Usually high level, short-term
exposure. May yield data point for adverse-
effect levels. Cannot be used to show
absence of hazard.
Case reports Suggests nature of acute endpoints in humans.
Cannot be used to support absence of hazard.
Source: Adapated from Erdreich and Burnett, 1985.
'Exposure history is d
occupational setting.
aExposure history is difficult to reconstruct, particularly outside of the
May be available pertinent to air pollution exposure.
GSeveral cases seen by or reported by a single investigator. Cases may be
attributed to unique exposure incident, but total exposed population is not
defined.
Identifying the critical effect. The critical effect has been
defined as the effect that occurs first on the increasing dose
scale. The critical effect is either an adverse effect or a
known precursor to an adverse effect (U.S. Environmental
Protection Agency, 1987a). The American Thoracic Society has
proposed a classification scheme for severity of respiratory
effects in humans which is presented in Appendix C.
Evaluating the dose-response curve for the critical effect with
the goal of identifying doses that bracket the experimental
threshold region.
These issues are selected based on the assumption that the study has already
been evaluated for adequacy in terms of design and conduct. Issues pertaining
to the evaluation of inhalation studies are discussed in Chapters 2 and 4.
The second step involves comparison of dose-response and dose-effect
curves across studies (within and across species). The first comparison is
a qualitative evaluation of effects. When disparity in dose-effect patterns
is apparent, studies need to be evaluated to ascertain, if possible, whether
the differences are due to differences in the monitored endpoints or procedure
across studies, or whether they suggest that species differences exist in
dose-effect curves (see Section 4.1).
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If species differences are apparent, the question arises as to which
species is the most appropriate model for humans. Differences in dose-effect
curves could be due to inherent differences in target receptor sensitivity
(pharmacodynamics) or to differences in concentration of the compound or
metabolite reaching the receptor (pharmacokinetics). This distinction is
important when trying to identify the most appropriate species for modeling
the human response.
The dose delivered to the target tissue is important when evaluating
dose-effect and dose-response curves across species. The target tissue dose
is determined by absorption, distribution, metabolism, and excretion. For the
inhalation route, the absorption component is particularly problematic.
Although absorbed doses per se have not been estimated as part of the RfD
process, the assumption has been made implicitly that absorption is either
equivalent across species, or that the divergences are minimal and can be
subsumed within the interspecies uncertainty factor along with other
pharmacokinetic and pharmacodynamic considerations. For inhalation, not only
is there a question of absorption estimates, but there also is uncertainty in
estimating the amount of material inhaled and/or deposited and, thus, available
for absorption, as well as potential differences in uptake of material from
the pulmonary tract due to the wide differences across species in airway
anatomy and physiology and body fat compartments (see Section 2.1). These
differences suggest that until more sophisticated methods of estimating
"equivalent" inhalation doses across species are developed, estimation of
equivalent dose, as one subpart of the interspecies extrapolation question, may
be more uncertain than for oral exposures. Procedures applicable to relatively
insoluble particles for adjusting doses based upon described differences in
deposition across species are discussed in Chapter 4. Where appropriate,
adjustments in doses based upon known interspecies differences in pulmonary
deposition must be applied before arraying the dose effect data to compare
species sensitivity. Approaches for estimating interspecies dose differences
for gases and vapors of organic solvents which are metabolized have been
developed (Fiserova-Bergerova, 1983) using physiologically-based pharmaco-
kinetic models. This type of model has been applied by EPA for quantitative
cancer risk assessment for perchloroethylene and methylene chloride
(U.S. Environmental Protection Agency, 1986e, 1987b), but general applicability
is not yet possible due to the need for chemical- and species-specific
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information on metabolism which is not available for most chemicals. Further
validation of these models and development of the necessary data base should
result in a routinely applicable approach to interspecies dose adjustments.
Equivalent approaches for dose adjustment for soluble gases and hygroscopic
particles are not yet as fully developed. Error in estimation of equivalent
dose also may complicate selection of the most appropriate animal model for
extrapolation. In particular, difficulties may be encountered when human
studies with inadequate exposure information suggest effects that differ from
the animal models, or when human data are absent and the critical effect in
animals has no known human counterpart.
The final stage in the data evaluation process is the accurate estimation
of a subthreshold exposure level for the heterogeneous human population.
Although it would be easy to project "safe" doses for many compounds which
are orders of magnitude below actual threshold doses with a great deal of
confidence, achieving these minimal exposure levels could be very costly and/or
technologically infeasible. Therefore, the goal is to accurately project a
subthreshold dose that is close to the threshold. If we could precisely
characterize the human dose-response curve for the known human critical effect
while completely characterizing human variability, then there would be little
uncertainty in these RfD estimates. The current RfO process is geared to
develop subthreshold estimates in the presence of uncertainty. For example, if
a range of species sensitivities is apparent (following dose correction as
described in Chapter 4) and human data are unavailable, it is assumed that the
most sensitive species should be used to develop an RfD. When chronic data are
unavailable, subchronic data are adjusted by an empirical factor when, in some
instances, there may not be a progressive dose-time interaction. As a result,
with the elimination of uncertainty many of the determined subthreshold doses
could potentially be higher or lower than those presently proposed.
The uncertainty factor approach addresses major areas of uncertainty
relating to the inability to know the collective human dose-response curves
for the critical effect. These factors are empirically based. Their initial
proposal and implementation have been restricted to oral exposures. Validation
of these factors based upon experimental data has been attempted, but is
difficult primarily due to deficiencies in the available data base. If this
empirical factor approach is applied to the inhalation RfD process, a critical
question becomes whether or not any component(s) of the extrapolation process
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leading to the RfD estimate appears to be inherently more uncertain or variable
for the inhalation route. Particular aspects of this question will be dis-
cussed in later sections of this document. Specific information relevant to
uncertainty factors per se is presented in Chapter 4.
3.2.2 Selecting Effect Levels: Inhalation-Specific Issues
Traditionally, ADI levels have been calculated by dividing the appropriate
effect or no-effect level of the critical toxic effect from human or animal
toxicity studies by one or more uncertainty factors. The critical effect is
defined as either the adverse effect that first appears in the dose scale as
dose is increased, or as the known precursor to the first adverse effect. It
is assumed that if the critical effect is prevented, then all subsequent
adverse effects are prevented. The derivation of the RfD follows these same
principles. Henceforth, the term RfD will be used.
As is often the case, NOELS, NOAELs, and LOAELs exist in a given data base
for several animal species. When comparing effect levels across species, it is
assumed that the doses will be adjusted to reflect currently characterized
interspecies differences in pulmonary deposition (see Chapter 4). What is the
appropriate choice of no-effect or effect level given this diversity? In the
course of verification discussions on various RfDs during the last year, the
oral RfD work group has provided some common ground on this issue. The work
group suggested the following conditions in choosing the appropriate animal
effect or no-effect level as a basis of an RfD:
When all scientific issues and effect or no-effect levels are
generally equal, choose the most appropriate effect level of a
species that is known to resemble the human in response to this
particular chemical, for example, by similar toxicokinetics.
When the previous condition is not met, choose the most sensitive
species as judged by an interspecies comparison of the highest
individual species NOAEL (or NOAEL) and its LOAEL (or LEL).
*Here adverse effects are considered to be functional impairments or pathologi-
cal lesions that may affect the performance of the whole organism, or that
reduce an organism's ability to respond to an additional challenge (Federal
Register, 1980). One of the major problems encountered with this concept is
the reporting of "observed effect levels" as contrasted to "observed adverse
effect levels." The terms "adverse" and "not adverse" are at times satisfac-
torily defined, but more subtle responses are being identified because of
increasingly sophisticated testing protocols, resulting in a need for judgment
regarding the exact definition of adversity.
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If scientific issues or effect or no-effect levels are judged
to be generally equal, choose the effect or no-effect level that
yields the RfD with greatest confidence reflecting quality of
the study and data base.
An expanded discussion and an example exercise of choosing the effect
level is provided in Appendix G.
In order to implement the guidance as described, adverse and nonadverse
effects must be distinguished. Historically, the distinction between adverse
effects and nonadverse effects has been problematic. Although numerous groups
have addressed this issue, most often conclusions contain an element of scien-
tific judgment in addition to objective criteria. Considerable experience and
precedent for such decisions have accrued over the last several years in the
process of developing oral RfDs and other health-related guidelines. Although
inhalation data have in some instances been utilized for the development of
oral estimates, the information content of the studies in terms of respiratory
system effects has not been rigorously evaluated, because it was appropriately
not considered relevant to the oral guideline efforts. As a result, the
question of adversity for pulmonary endpoints has not been extensively explored
in the context of oral RfD development. However, other groups have addressed
this and consensus guidelines have been developed. The American Thoracic
Society committee report has been discussed previously and is reproduced in
Appendix C.
There still appears to be considerable uncertainty concerning how to
differentiate in the early stages of respiratory disease between acute reversi-
ble effects, which are the immediate consequence of an exposure episode, and
potential progression to chronic, nonreversible pulmonary pathology. This is
an important issue both in terms of evaluation of pulmonary effects per se, as
well as for decisions concerning the critical effect in inhalation studies.
For inhalation studies in particular, there is a dichotomy in terms of
the types of endpoints monitored in human versus animal studies. Human data
concerning the consequences of inhalation exposure generally consist of infor-
mation on subjective symptoms along with clinical data concerning pulmonary
function. The relationship between the clinical picture and lung pathology is
poorly defined. On the other hand, animal standard toxicological protocols
generally incorporate pulmonary tissue evaluation as part of the routine
necropsy, but do not evaluate pulmonary function. Of course, once the lung has
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been identified as a target tissue, more detailed studies of it as a target
organ may be conducted. When these more detailed data are available, two
additional questions are raised: (1) how do we evaluate the significance of
alterations in test species' pulmonary performance in terms of potential human
effects and, (2) if tests showing differences in pulmonary biochemistry are
available, what is the utility of the biochemical changes as predictors of
disease? Correlations between functional decrements and immunologic, bioche-
mical, and pathologic changes need to be quantitated. Work in progress on
animal models (see Section 3.1.2.1), biological exposure indices (Lowry, 1986),
anc' ID. yi^.ro alterations of lung biochemistry as predictive of lung disease
(Last, 1983) will contribute to this end.
For present purposes, each inhalation study should be evaluated for
possible indications that the respiratory system is the critical target organ.
Animal data that provide only cursory evaluation of pulmonary endpoints make
careful evaluation of human studies essential. Human data should be carefully
evaluated with special emphasis on the significance of respiratory system
endpoints. In instances where extrarespiratory effects are the critical
effect, effect levels would be evaluated in a manner consistent with decisions
made in the oral RfD process. This approach was initially described in Federal
Register (1980). Existing, verified RfD cover sheets provide insight into
current judgments concerning adversity of particular endpoints. Extrapolation
from oral to inhalation exposures may be utilized only after careful considera-
tion of factors presented in Sections 4.1.1.3 and 4.3.
For compounds that appear to produce their critical effect within the
respiratory system itself, decisions concerning adversity need to be made on
a case-by-case basis. Appendix C provides specific information concerning
evaluation of the severity of pulmonary endpoints in humans. Costa and
Tepper (1988) provide an excellent summary of lung function assessment in
animals.
Although most pollutants would be expected to elicit a dose-response upon
exposure, some pollutants cause tolerance/adaptation and some are atypical,
such as those that act by allergic or asthmatic mechanisms. These allergic
sensitizers may be considered a subgroup under agents that produce their
critical effect in the respiratory system. Toluene diisocyanate is a well-known
example of a sensitizing agent that affects immunological and pharmacological
mechanisms and induces asthma. Sensitizing responses appear to be triggered by
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high initial doses. Subsequently, any level of exposure may be sufficient to
induce the asthmatic syndrome in sensitized individuals. There is evidence
that IgE antibody levels and inflammatory pulmonary reactions play a role in
such syndromes. If these are indeed nonthreshold phenomena upon challenge
exposure, then methods other than the traditional uncertainty factor approach
will be required to address this subclass of compounds for quantitative risk
assessment.
Areas for further investigation and development of specific guidance
include the following:
Specific guidance for evaluation of pulmonary endpoints in terms
of adversity/severity for both human data and animal investiga-
tions.
Specific guidance for interpreting effects when both human and
animal data are available.
Specific guidance for interpreting the impact of short-term
exposures to human subjects and subsequent pulmonary effects
to chronic exposure situations, if any.
Specific guidance concerning the comparability of effect levels
following intermittent exposures to continuous exposure
scenarios.
Specific guidance on how to deal with sensitizing agents
in the RfD process.
3.3 DEFICIENT DATA BASES AND ALTERNATIVE SOLUTIONS
The assessment of the total toxicological data base available for the
chemical at that time must be evaluated to derive an RfD (Clegg, 1979). In
addition to the uncertainties discussed in Section 3.2, determination of an
RfD also involves a judgment about the study used in the RfD calculation.
These judgments relate to quality and completeness of the entire data base,
including uncertainty in the dose-response information and the estimated NOEL.
Although there is no readily definable way to measure the magnitude of uncer-
tainty in any given RfD (Environ Corporation, 1985) at present, research to
address this issue is underway. The minimum data needs for establishing an RfD
predicated on addressing this uncertainty are discussed in Section 4.1.1.1.
Section 3.3.2 discusses the role of occupational exposure limit values in RfD
development.
3-24
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3.3.1 Guidance on Evaluating a Data Base for Completeness
Current toxicity testing strategies are hierarchical sequences of tests
designed to develop a profile of a chemical's toxicity (Environ Corporation,
1985). Initial testing tiers consist of relatively rapid, inexpensive tests
designed to identify acute toxicity. This information is not directly useful
in predicting chronic adverse effects in humans, but can be used to guide
decisions as to type and extent of continued testing, such as subchronic,
chronic or reproductive bioassays.
The toxicity "profiles" or information required as a minimum data base
also are somewhat structured according to this hierarchy. The magnitude of
insufficiency varies on a case-by-case basis and is reflected in the rating
of uncertainty in the data base. This also would be tempered by the existing
data base. Section 4.3. discusses the data base from the perspective of
confidence in the RfD.
The information available in an incomplete data base also may indicate
that the RfD should be provisional pending further investigations. For example,
the U.S. Food and Drug Administration (1982) suggests that if a chemical tested
in a subchronic study is found to cause focal hyperplasia, metaplasia, pro-
liferative lesions or necrosis, then a carcinogen!city study in two rodent
species is warranted. Likewise, if reproductive effects are found, then
teratology testing also should be conducted.
3.3.2 Historical Use and Limitations of Occupational Exposure Limit Values
OEL values, particularly the Threshold Limit Value (TLV) recommended by
the American Conference of Governmental and Industrial Hygienists (ACGIH), have
had widespread use in risk assessment/management programs because of a lack of
uniform benchmark values relevant to ambient air exposures. The use and
limitations of OELs have been discussed in an issue paper, prepared by the
Inhalation Technical Panel of the Risk Assessment Forum, that is supplementary
to this document (U.S. Environmental Protection Agency, 1989).
OELs have historically been considered as surrogates for benchmark values
for ambient exposures because they comprise the largest documented summary of
toxicological, epidemic logical, and clinical information pertaining to human
exposure to airborne contaminants. They include the Occupational Safety and
Health Administration Permissible Exposure Limits (PELs) or full text standards,
the National Institute of Occupational Safety and Health Recommended Standards,
3-25
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and the ACGIH TLVs. OELs differ among themselves in regard to the philosophy
of the sponsoring organization, legal mandate, objectives, assumptions, and
evaluation of scientific data. They share the common elements of inhalation
exposure and goal of protection of human health.
Although OELs represent a large body of readily available information
(e.g., there are >600 OELs), there are several factors which limit their
usefulness in the derivation of RfDs. First, DELs may not be established based
on chronic effects and may differ from RfDs in severity of effect. Second,
OELs assume intermittent exposure periods, whereas RfOs are set to protect
against continuous exposure. Third, OELs may not incorporate the most current
toxicological information because toxicological review is not on a regular
basis. Fourth, the unavailability of unpublished corporate documentation
precludes scientific scrutiny of the primary basis for a number of TLVs
(Castleman and Ziem, 1988). Fifth, the evaluation of toxicity data by agencies
deriving OELs may differ from that of EPA with respect to weight-of-evidence
classification, application of uncertainty factors, and other issues. Finally,
the use of OELs is established to protect the average healthy worker (ages 18
to 65 years) against the adverse effects of inhaled pollutants; inhalation
RfDs, on the other hand, are relevant to those of any age and/or health status.
The Agency does not endorse the general use of OELs in deriving RfDs.
The DEL data base should be evaluated on a case-by-case basis according to the
methodology for inhalation RfD derivation. The biological endpoint, quality
and nature of the underlying data sets, the exposure scenarios, and applica-
bility to highly-sensitive subpopulations are among those factors that must be
considered for relevance to nonoccupational exposures.
3-26
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4. QUANTITATIVE METHODOLOGICAL PROCEDURES
4.1 PROCEDURES ADDRESSING LIFETIME EXPOSURE*
An inhalation RfD (RfD.) has a numerical value, and hence, a quantitative
nature. As will be discussed, numerous theories, assumptions, and empirical
data provide the quantitative framework for the RfD. calculations. At present,
the methodology is more advanced for addressing lifetime exposure (Section 4.1),
but approaches for estimating partial lifetime exposures (Section 4.2) are
under development. To account for inherent uncertainties in the chemical-
specific data base and essential qualitative judgements, levels of confidence
(Section 4.3) are assigned, enhancing the interpretation of a numerical RfD..
4.1.1 Approach for RfD Estimation
RfDs are typically calculated using a single exposure level and uncer-
tainty factors that account for specific deficiencies in the toxicity data
base. Both the exposure level and the uncertainty factors are selected and
evaluated in the context of all available chemical-specific literature. After
all toxicological, epidemiologic, and supporting data have been reviewed and
evaluated, a key study is selected that reflects optimal data on the critical
effect. Dose-response data points for all reported effects are examined as a
component of this review. Issues of particular significance in this endeavor
include:
*Parts of this text are excerpted from U.S. Environmental Protection Agency
(1987a).
4-1
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TABLE 4-1. FOUR TYPES OF RESPONSE LEVELS (RANKED IN ORDER OF INCREASING
SEVERITY OF TOXIC EFFECT) CONSIDERED IN DERIVING
RfD.s FOR SYSTEMIC TOXICANTS
NOEL: No-Observed-Effect-Level. That exposure level at which there are
no statistically or biologically significant increases in frequency
or severity of effects between the exposed population and its
appropriate control.
NOAEL: No-Observed-Adverse-Effect-Level. That exposure level at which
there are no statistically or biologically significant increases in
frequency or severity of adverse effects between the exposed
population and its appropriate control. Effects are produced at
this level, but they are not considered to be adverse.
LOAEL: Lowest-Observed-Adverse-Effect-Level. The lowest exposure level
in a study or group of studies that produces statistically or bio-
logically significant increases in frequency or severity of adverse
effects between the exposed population and its appropriate control.
FEL: Frank Effect Level . That exposure level which produces frankly
apparent and unmistakable adverse effects, such as irreversible
functional impairment or mortality, at a statistically or biologic-
ally significant increase in frequency or severity between an
exposed population and its appropriate control.
aAdverse effects are defined as any effects resulting in functional impair-
ment and/or pathological lesions that may affect the performance of the whole
organism, or that reduce an organism's ability to respond to an additional
challenge.
Frank effects are defined as overt or gross adverse effects (e.g., severe
convulsions, lethality, etc.).
A delineation of all toxic effects and associated exposure
levels.
Determination, to the extent possible, of effect-specific
experimental threshold regions (i.e.,the NOAEL-LOAEL interface
or bracket) (see Tables 4-1 and 4-2).
Determination of the critical effect. Of the multiple toxic
endpoints potentially observed, the critical effect selected is
defined as the one associated with the lowest NOAEL-LOAEL
bracket.
Special consideration of species, portal-of-entry effects, and/or
route-specific differences in pharmacokinetic parameters and the
slope of the dose-response curve.
4-2
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TABLE 4-2. RESPONSE LEVELS CONSIDERED IN DERIVING INHALATION RfDs IN
RELATIONSHIP TO EMPIRICAL SEVERITY RATING VALUES. (RANKS ARE FROM
LOWEST TO HIGHEST SEVERITY.)*
Effect or
No-Effect Level Rank General Effect
NOEL 0 No observed effects.
NOAEL 1 Enzyme induction or other biochemical
change, consistent with possible
mechanism of action, with no patho-
logic changes and no change in organ
weights.
NOAEL 2 Enzyme induction and subcellular
proliferation or other changes in
organelles, consistent with possible
mechanism of action, but no other
apparent effects.
NOAEL 3 Hyperplasia, hypertrophy or atrophy,
but no change in organ weights.
NOAEL/LOAEL 4 Hyperplasia, hypertrophy or atrophy,
with changes in organ weights.
LOAEL 5 Reversible cellular changes including
cloudy swelling, hydropic change, or
fatty changes.
**
(LQ)AEL 6 Degenerative or necrotic tissue
changes with no apparent decrement
in organ function.
(LO)AEL/FEL 7 Reversible slight changes in organ
function.
PEL 8 Pathological changes with definite
organ dysfunction that are unlikely
to be fully reversible.
PEL 9 Pronounced pathologic changes with
severe organ dysfunction with long-
term sequelae.
PEL 10 Death or pronounced life shortening.
*
Adapted from DeRosa et al. (1985) and Hartung (1986).
The parentheses around the "LO" in the acronym "LOAEL" refer to the fact
that any study may have a series of doses that evoke toxic effects of rank
5 through 7, All such doses are referred to as adverse effect levels (AELS).
The lowest AEL is the (LO)AEL.
4-3
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The threshold concept is the basis for the derivation of the RfD. Essen-
tially, an experimental exposure level is selected from the available studies
which represents the highest level tested in which the critical effect was not
demonstrated. Conversion of experimental exposure levels to human equivalent
concentration ^OAELrHrC-i) estimates, by adjustment for dosimetric differences
between the experimental species and humans, should be made before these
choices are performed (see Section 4.1.1.2 and Appendices G, H, I). This
chosen human equivalent concentration (N^ELr,,™-,) represents the first
quantitative basis for the scientific evaluation of the risk posed to humans by
noncancer toxicants. The inhalation RfD is operationally derived from this
NOAELuE- by consistent application of generally order of magnitude uncertainty
factors (UFs) that reflect the second quantitative basis of this scientific
evaluation of risk. Uncertainty factors are associated with various specific
recognized uncertainties in extrapolating from the type of study serving as the
basis for the RfD to the scenario of interest for the risk assessment. An
additional modifying factor (MF) reflects professional judgment of the entire
data available on the specific agent (see Table 4-3).
The RfD.. is derived from the NOAEL as:
RfDi = NOAEL, HEC-,/(UF x MF) (4-1)
where:
NOAELpurQi = NOAEL, adjusted for dosimetric differences between animal
L J species and humans, expressed as human equivalent concentration,
UF = an uncertainty factor suited to the characteristics of the data
(Table 4-3), and
MF = a modifying factor based on professional judgment of the entire
data base (e.g., sample size).
In general, the choice of these factors reflects the uncertainty
associated with estimation of an RfD from different human or animal toxicity
data bases. For example, if sufficient data from chronic duration exposure
studies are available on the threshold region of a chemical's critical toxic
effect in a known sensitive human population, then the UF used to estimate the
RfD may be 1. That is, these data are judged to be sufficiently predictive of
a population subthreshold dose, so that additional UFs are not needed.
4-4
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TABLE 4-3. GUIDELINES FOR THE USE OF UNCERTAINTY FACTORS IN DERIVING REFERENCE DOSE (RfD)*
Standard Uncertainty Factors (UFs)
H Human to sensitive human
Animal to human
Subchronic to chronic
LOAEL to NOAEL (refer also
to Table 4-1)
Incomplete to complete
data base
Modifying Factor (MF)
Use a 10-fold factor when extrapolating from valid
experimental results from studies using prolonged
exposure to average healthy humans. This factor is
intended to account for the variation in sensitivity
among the members of the human population.
Use an additional 10-fold factor when extrapolating from
valid results of long-term studies on experimental
animals when results of studies of human exposure are not
available or are inadequate. This factor is intended to
account for the uncertainty in extrapolating animal data
to the case of average healthy humans.
Use up to an additional 10-fold factor when extrapolating
from less than chronic results on experimental animals or
humans when there are no useful long-term human data. This
factor is intended to account for the uncertainty in
extrapolating from less than chronic NOAELs to chronic
NOAELs.
Use up to an additional 10-fold factor when deriving an
RfD from a LOAEL, instead of a NOAEL. This factor is
intended to account for the uncertainty in extrapolating
from LOAELS to NOAELs.
Use up to a 10-fold factor when extrapolating from valid
results in experimental animals when the data are "incom-
plete." This factor is intended to account for the
inability of any single animal study to adequately
address all possible adverse outcomes in humans.
Use professional judgment to determine another uncer-
tainty factor (MF) that is <10. The magnitude of the
MF depends upon the professional assessment of scientific
uncertainties of the study and data base not explicitly
treated above; e.g., the number of animals tested. The
default value for the MF is 1.
*Adapted from: U.S. Environmental Protection Agency, 1987a.
**Use of this UF is now undergoing discussion in Risk Assessment Forum (see also discussion in Section 4-3).
-------
A UF of 10 is generally used to estimated RfDs with appropriate chronic
human data, and reflects intraspecies human variability to the adverse effects
of a chemical (i.e., H in Table 4-3). A UF of 100 is generally used to
estimate RfDs with chronic animal data, thereby accounting for both interhuman
and interspecies variability (i.e., H x A), It is generally acknowledged that
these estimates are uncertain. If specific information exists to indicate a
different but more exact interhuman or interspecies extrapolation procedure for
that chemical, it should be used and the rationale underlying its use clearly
explained.
An RfD based on a NOAEL with satisfactory subchronic animal data would
require a factor to address the uncertainty in extrapolating data from sub-
chronic to chronic exposures (i.e., S), as well as the two former uncertainty
factors (i.e., H x A).
A UF of 10 generally is applied to estimated RfDs using LOAELs if NOAELs
are unavailable (i.e., L). This UF is employed to define an exposure level
below the LOAEL expected to be in the range of a NOAEL.
Under some circumstances, the U.S. Environmental Protection Agency applies
a UF up to 10 when the data base is deficient in some major aspect; for example,
if it lacks a two-generation reproductive study (i.e., D). The U.S. Food and
Drug Administration has addressed this issue with the use of a twofold safety
factor. Thus, in situations where a subchronic animal bioassay was available,
but information in a second experimental species was lacking, a 2,000-fold
safety factor (i.e., 2.. x 10,, x 10. x 10r) was used to estimate an acceptable
daily intake (Shibko, 1981).
It is important to note that when sufficient human data are available on
a chemical's critical effect and pharmacokinetics, the UFs may be smaller than
those described in Table 4-3, or unnecessary. Likewise, in cases where data do
not completely fulfill the conditions for a category or UF, or appear to be
intermediate between two categories, an intermediate UF is suggested to
estimate the RfD (Federal Register, 1980).* When a single subchronic study
*0ther authors have discussed these areas of uncertainty or UFs in general.
The interested reader is referred to Zielhuis and van der Kreek (1979) for a
discussion of these factors in setting health-based permissible levels for
occupational exposure, and Dourson and Stara (1983) for a summary of these
factors regarding oral exposures. Other publications include Gaylor (1983),
who discusses the use of safety factors for controlling risk; Crump (1984),
who discusses problems with the current methods that includes UFs; Krewski
et al. (1984), who contrast safety factors and mathematical models as methods
for determining "safe" levels of exposure; Calabrese (1985), who discusses UFs
and interindividual variation; and Lu (1983, 1985b), who discusses safety
factors from the perspective of the World Health Organization.
4-6
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that does not define a NOAEL is the only available information, the U.S. EPA
recognizes that all five areas of uncertainty are present. In this case, the
overall UF used is generally 10,000. This coalescing of several areas of
uncertainty is based on the knowledge that each individual factor is generally
conservative from the standpoint of the behavior of the average chemical
(Dourson and Stara, 1983), and that the multiplication of four or five values
of 10 is likely to yield unrealistically conservative RfDs.
The areas of scientific uncertainty discussed in the preceding section do
not represent all the uncertainties in a dose-response assessment; for example,
the number of animals that determines the NOAEL is not normally considered in
the previous factors. The fewer the number of animals used at a dose, the more
likely the dose is to be a NOAEL (other factors being equivalent). The effect
of small sample size has long been recognized in toxicology (Bliss, 1938) and
recent research has focused on adjusting for this by taking the power of
individual studies into account (Brown and Erdreich, 1989). Although never
explicitly stated, when faced with such an uncertainty scientists have modified
the usual 10-fold factors either up or down. For example, a 100-fold UF may be
raised to 125 if the number of animals in a chronic study was fewer than
thought reasonable by the risk assessor. While this evaluation is scien-
tifically in the correct direction, it introduces two difficulties in the
resulting assessment. The first is that the adjustment of the standard 10-fold
values is perceived as arbitrary, and the second is that the precision of some
of the resulting UFs is not at all appropriate in relationship to the under-
lying biology (in this example a UF of 125 has a precision of three digits).
The U.S. EPA's use of the MF is an attempt to separate the "traditional"
areas of scientific uncertainty that have been quantified to some extent, from
these latter areas of scientific uncertainty that have not been quantified.
The intent is to arrive at the best choice of an RfD, which in many cases will
include an analysis of the same overall uncertainties as addressed histori-
cally, while avoiding the perception of arbitrariness and, moreover, be
consistent with the overall precision of the value.
There are certain circumstances specific to inhalation that may require
changes in UFs. For example, the UF used when extrapolating from a subchronic
to a chronic study is assumed to be adequate for oral studies in the great
majority of cases. A UF of extrapolation of subchronic to chronic exposures
for inhalation studies also should be adequate with certain exceptions.
4-7
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Possible exceptions include the following:
Exposure to chemicals that are considered likely to induce
hypersensitivity (e.g., beryllium)
Exposure to chemicals that are considered likely to induce very
slowly developing ("smoldering") effects
Exposure to inhaled relatively insoluble particulate matter
where the clearance rate may slow or stop when a threshold for
clearance is reached. Thus, after long-term exposure lung loads
can reach much higher levels than could reasonably be expected
from lower level, chronic exposure conditions
The appropriate UF for these situations should be decided on a case-by-case
basis until more definitive guidelines are available.
If multiple NOAELs are available in one animal species, the highest NOAEL
for that individual species is used in comparison to other species NOAELs. If
multiple NOAELs for the critical effect are available in different species, the
lowest of these NDAELs generally is selected as the exposure level that most
closely defines the threshold for adverse effects of the dose-response curve.
It is consistent with U.S. EPA policy to use data on the most sensitive animal
species as a surrogate to humans unless data exists to the contrary. Often an
appropriate NOAEL will not be available. In that event, other estimates of
effect-specific thresholds may be used. Based on the dose-response classifi-
cation system presented in Table 4-1, the following guidelines may be employed
(adapted from Federal Register, EPA, 1980):
An PEL from a study with no other dose-response levels is
unsuitable for the derivation of an RfD.
A NOEL from a study with no other dose-response levels is
unsuitable for the derivation of an RfD. If multiple NOELs are
available without additional data, NOAELs, or LOAELs, the highest
NOEL should be used.
A NOAEL or LOAEL may be suitable for RfD derivation. A well-
defined NOAEL from at least a 90-day study may be used directly,
applying the appropriate UF. In the case of a LOAEL, an
additional UF (10.) is applied.
Note: caution must be exercised not to substitute FELs for LOAELs.
If, for reasonably closely spaced doses, only a NOEL and a LOAEL
of equal quality are available, then the appropriate uncertainty
factor is applied to the NOEL.
4-8
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Please refer to Section 3.2 and Appendix G for a complete discussion of
these issues.
4.1.1.1 Minimum Criteria. Data bases vary considerably in their completeness.
With a more complete data base, the magnitude of the required UF is reduced.
Well-defined and conducted subchronic toxicity studies are generally considered
to be reliable predictors of many forms of toxicity with the notable exceptions
of carcinogenic, teratogenic, or reproductive effects. Consequently, the
minimum data base acceptable for development of an RfD is a subchronic toxicity
study that clearly identifies the "threshold region" of the dose-response
curve. Section 4.3 also discusses this minimum data base from the viewpoint of
distinguishing between high and low confidence in the RfD.
It should be recognized, however, that for some substances, results of
other studies may suggest the possibility of effects not detected in the sub-
chronic studies that constitute this minimum data base. When such findings
are reported, it is desirable to consider the results of the risk assessment
as tentative, indicate that the confidence in the estimate is low, and pursue
additional toxicity testing. For example, if a compound tested in a subchronic
study is found to cause focal hyperplasia, metaplasia, proliferative lesions,
or necrosis, then a cancer bioassay is clearly indicated. Alternatively, if a
subchronic study demonstrates reproductive organ toxicity or neurotoxic effects,
reproductive/teratologic or neuropathology studies may be appropriate.
Extrapolation from subchronic to chronic exposure conditions (S in
Table 4-3) necessitates the utilization of an additional UF of 10 in most
cases. Empirical evidence supports the proposition that subchronic toxicity
data can be used in this way for risk assessment purposes. McNamara (1976) has
demonstrated that a 10-fold factor applied to a subchrom'c NOEL would predict a
chronic NOEL for 95 percent of the 122 compounds for which both chronic and
subchronic data for the oral route of exposure were available. To the degree
that route-specific and duration-specific data are not available, increased
reliance on additional extrapolation assumptions and larger UF is necessary.
In summary, with more extensive data the threshold region of the dose-
response curve is more reliably approximated and the magnitude of the associ-
ated uncertainty in the risk assessment is reduced. For this reason it is
desirable to state qualitatively the confidence level attached to the RfD, and
the study from which the NQAEL was selected, and to rate the overall data base
as high, medium, or low, as described in Section 4.3.
4-9
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4,1.1.2 Calculation of Human Equivalent Concentrations . Extrapolation of
animal inhalation data to humans requires estimation of the "dose" (i.e.,
agent mass deposited per unit tissue volume considered along with physiological
and biological factors) delivered to specific target sites in the respiratory
tract or made available to uptake and metabolic processes for systemic distri-
bution (Martonen and Miller, 1986). To this end, physiologically based
pharmacokinetic (PB-PK) and mathematical dosimetry models have evolved into
particularly useful tools for predicting disposition differences for risk
assessment (Miller et al., 1987b). Their use is predicated on the assumption
that an effective (target-tissue) dose in a particular species is expected to
be equally effective when achieved in some other species. However, it is
likely that species differences in sensitivity occur due to such species-
specific factors as host defense, repair processes, and genetics, so that
the use of a ten-fold UF to account for interspecies variability, despite
application of dosimetric adjustments, requires additional research. This
section outlines the methods for calculating HECs estimates by using adjustment
factors that have resulted from similar modeling efforts of species dosimetric
differences. The factors are used to adjust the observed exposure effect
levels (i.e., NOAELs, LOAELs, etc.) in animals to estimate a concentration that
would be an equivalent exposure to humans. These human equivalent concentra-
tions then can be the basis for comparison and choice of the critical effect
and study as discussed in Appendix G.
Figure 4-1 is a flowchart for the calculation of HECs and provides an
outline for the contents of this section. Conversion of units from ppm to
3
mg/m is required before dosimetric adjustments can be applied and this
calculation is discussed in Section 4.1.1.2.1. The next step in calculating a
HEC is to convert the exposure regimen of the experiment in question to that of
the human exposure scenario; that is, a continuous (24-hour) lifetime (70-year)
exposure, as described in Section 4.1.1.2.2. The third phase of the approach
is to apply the dosimetric adjustments appropriate for the type of agent to be
assessed (particle or gas/vapor), and the effect to be assessed (respiratory
tract or extrarespiratory toxicity beyond the respiratory tract [systemic]
resulting from an inhalation exposure). The dosimetric adjustments to derive
HECs for respiratory tract effects and extrarespiratory effects of particles
are provided in Sections 4.1.1.2.3.1 and 4.1.1.2.3.2, respectively. The
dosimetric adjustments to derive HECs for respiratory tract effects of gases
4-10
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CONVERT
ppm TO mg/m3
(EQN. 4-2a, b)
ADJUST FOR
EXPOSURE REGIMEN
(EQN. 4-3)
1. EVALUATE
GENERATION
SYSTEM
2. CHARACTERIZE
BY MMAD, 0g, OR
3. DEFAULT VALUES
IDENTIFY
PARTICLE TYPE OF
AGENT
GAS
1. EVALUATE
^ GENERATION
SYSTEM
2. CHARACTERIZE BY
CONCENTRATION,
TEMPERATURE,
PRESSURE, OR
3. DEFAULT VALUES
IDENTIFY
THE TARGET
EFFECT(S)
RESPIRATORY
(EQN. 4-4, 4-5)
EXTRARESPIRATORY MORE REACTIVE
(EQN. 4-6, 4-7) VS. SOLUBLE?
IDENTIFY
THE TARGET
EFFECT(S)
X
RESPIRATORY
1 EXTRARESPIRATORY
f t
(EQN
S I
YES I
. 4-8, 4-9) f
YES
9) f (EQN. 4-10) NO
NO (EQN. 4-11)
(EQN 4-10)
Figure 4-1, Flowchart for calculation of Human Equivalent Concentrations.
4-11
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are discussed in Section 4.1.1.2.4.1 and for extrarespiratory effects of gases
in Section 4.1.1.2.4.2.
Although the presentation in this section divides the dosimetry calcula-
tions into those applied to extrapolate respiratory tract effects vs.
extrarespiratory effects, it should be recognized that there is no strict
compartmentalization of effects of a given chemical. A given inhaled chemical
could cause both respiratory tract effects and extrarespiratory effects. Thus,
the decision on which of the equations to use in this chapter is governed by
the endpoint of interest in concert with the properties of the chemical to be
assessed.
4.1.1.2.1 Dose convers ion: U nits . In the rare event that investigations
using particulate exposures would report the concentration in ppm, a mass-
density relationship would be used to convert the exposure concentration to
mg/m . Inhalation toxicity studies on gases typically employ exposure levels
expressed as mg/m and/or ppm. Exposure levels for gases, including the NOAEL
3
selected for RfD. derivation, should be expressed in standard units of mg/m .
For exposure levels expressed as ppm, the Ideal Gas Law can be used to derive
the corresponding mg/m level:
where:
ppm - concentration expressed on a volumetric basisji
MW = molecular weight in grams,
22.4 £ = the volume occupied by 1 g-mol of any compound in the
gaseous state at 0°C and 760 mm Hg,
T = actual temperature in degrees Kelvin, and
P = actual pressure in mm Hg.
At 25°C and 760 mm Hg, 1 g-mole of a perfect gas or vapor occupies 24.452.
Therefore, under these conditions, the conversion becomes:
(4-2b)
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4.1.1.2.2 Doseadjustments for discontinuousexposure protocols. Many inhala-
tion toxicity studies entail exposure regimens that are discontinuous. Often
exposures are for 6-8 hours/day and 5 days/week. RfD-s are constructed to
reflect a benchmark level for continuous exposure. By extension, the RfD.
also is assumed to be protective for discontinuous exposures at the same air
concentration. A normalization to some given exposure (e.g., 24 hours/day for
a lifetime of 70 years) is needed to adjust for the wide variety of experimental
exposures to permit comparisons between studies. As discussed earlier, the
RfD. proposed herein is to reflect lifetime continuous exposure, and this
scenario is the objective of normalization. Attention should be paid to the
degree the applied situation deviates from the experimental, and to the
physicochemical (solubility and reactivity) parameters of the inhaled agent and
species-dependent factors (e.g., distribution volumes and metabolic pathways)
that might temper this conversion. To calculate duration-adjusted exposure
levels in mg/m for experimental animals, the appropriate equation is:
NOAELrflnn(mg/m3) = E(mg/m3) x D(hours/day/24 hours) x W(days/7days)
LAUJJ (4.3)
where:
E = experimental exposure level,
D = number of (hours exposed/day)/24 hours, and
W = number of (days of exposure/week)/? days.
Use of extreme caution is emphasized with this conversion equation,
especially as the effect in question increases in its severity. The toxicity
of an exposure is fundamentally dependent upon the character of the
"concentration-time" (C x T) curve, which is a hyperbola whose arms converge
asymptotically toward the axes of the coordinates (Bliss, 1940). Bliss and
James (1966) have shown that such curves can be extrapolated with minimal error
when the time points in the experiment are located on the segment of the curve
asymptotically approaching the axes of the coordinates. The exposure duration
should ideally embrace the time span in which the rate of onset of specific
toxic effects sharply change, reflecting the degree of arc in the curve of the
(C x T) relationship. Fiserova-Bergovera et al. (1980), using a compart-
mentalized model based on first-order kinetics, demonstrated that duration of
exposure to a gas can have profound effects on the fractions of uptake exhaled
4-13
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or metabolized. Concentrations in tissues reflected the concentration varia-
tions in exposure, but the variation in tissues was greater during exposure to
low solubility gases than to lipid soluble vapors (blood to air partition
coefficients of 0.5 and 10.0, respectively), due to the faster equilibration of
partial pressures of the low solubility gases. Variations between tissue and
exposure concentrations were diminished if the substances were metabolized.
Since the toxic effect is related to tissue concentration, consideration should
be given to these duration and solubility effects. Extrapolation should be
attempted only if a steady-state was attained. Likewise, linear extrapolation
from one concentration .exposure to another is possible only if all processes
involved in the uptake and elimination of the inhaled agent are first order.
Differences are caused primarily by concentration-dependent metabolic clearance
(Fiserova-Bergerova et al., 1987). Limitations of this type of conversion also
are discussed in Section 2.2.
4.1.1.2.3 Dosimetry: Particles. Inhalation toxicologists have advanced their
ability to measure the deposition values for particles in the various regions of
the lungs across species. Initially the data were primarily total deposition
values for polydisperse and sometimes unstable aerosols, but data now exist for
insoluble monodisperse aerosols of different sizes under different breathing
conditions (U.S. Environmental Protection Agency, 1982). Data are available
across most experimental species of interest on the regional deposition of
applicable particle size ranges and on the necessary physiologic parameters
(e.g., tidal volumes and regional surface areas) incorporated in dose adjust-
ments (Overton et al., 1987; Miller et al., 1987b; Miller et al., 1988; Raabe
et al., 1988; Patra et al., 1986; Patra, 1986). Deposition data are usually
presented or modeled as the deposition fraction for each respiratory tract
region of the species of interest. Deposition fraction is the ratio of the
number or mass of particles deposited in the respiratory tract to the number or
mass of particles inhaled, as illustrated in Figures 2-2 and H-l [B]. Deposi-
tion data also may be normalized for the percent entering a region, particularly
for the tracheobronchial region. Although not presented in the approach
outlined below, iterative calculations are available to make normalized data
amenable to the deposition fraction application (Miller et al., 1988). Refer
to Appendix H for an explanation of these calculations.
A vast amount of knowledge also has been gained in the technology and
methods for generating and characterizing aerosols. State-of-the-art inhala-
tion toxicology studies will have characterized the particulate exposure by a
4-14
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given particle diameter (e.g., D._, D.,,., MMAD) and the geometric standard
3c 3i
deviation (a ). The distribution of particle sizes for the aerosol then can
y
be conveniently described (and/or graphically plotted as in Figures 2-5 and
H-1[A]) as a probability density function.
Because of these advances in quantisation of species-specific regional
respiratory tract deposition and physiologic parameters, the following
describes how interspecies dosimetric comparisons can be made using data
typical for particles. This application is an adaptation (Miller et al.,
1983b; Graham et al., 1985) and is limited at this time to relatively insoluble
and nonhygroscopic particles. The calculations to derive HECs lung effects and
extrarespiratory effects of particles will then be discussed in
Sections 4.1.1.2.3.1 and 4.1.1.2.3.2, respectively.
The product of deposition efficiency and particle distribution curves can
be integrated to compute the deposited dose of exposure particles in a given
region of the respiratory tract for the experimental species in question. That
is, for each particle size range, the product of the particle distribution and
deposition fraction in that range can be computed for a given respiratory tract
region. Summation of these products across all the particle size ranges yields
an estimate of the mass deposited in the region. These estimates then can be
adjusted for ventilation parameters and lung surface area to calculate the
2
regional deposited dose (RRD) in mg/cm of respiratory tract per minute.
Determining the ROD in this manner for each species allows regional deposited
dose ratios (RDDR) to be calculated in order to adjust the exposure effect
level for dosimetric differences between the experimental species and humans.
Notationally, for the i size range of an exposure aerosol with a given
particle diameter and a , let
P. = the particulate mass fraction in that size range, and
E. = the deposition efficiency for the species and respiratory tract
region (i.e., extrathoracic, tracheobronchial and/or pulmonary, or
total) of interest;
4-15
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2
then the ROD expressed as mg/cm of respiratory tract region per minute can be
computed as:
10"6 YVTf n
ROD = «-! I P. E. (4-4)*
b i=l 1 n
where:
n = number of size ranges,
Y = exposure level (mg/m3),
Vj = tidal volume (m£),
f = breathing frequency (breaths/minute), and
S = regional surface area (cm2) of toxic effect observed.
This ROD can be calculated for each region of interest; that is the extra-
thoracic (RDDr-p), the tracheobronchial (RDOTB), the pulmonary (RDDp.,) region
the thoracic (RDD-r.,) or the total respiratory tract (ROD,.,,,.), It should be
calculated according to the effect of interest. For example, the ROD summed
across the TB and PU regions, the thoracic ROD (ROD,-,,), would be used to
compute the ROD for assessment of a "lung effect" (RDDTH = RDDTB + RDDpu);
whereas the RDDET alone would be calculated for an effect concerning the nasal
turbinates.
The ROD in each species then can be used to adjust the exposure effect
level for dosimetric differences between species by calculating the
*This is an adaptation (Miller et al., 1983b and Graham et al., 1985) limited
to insoluble and nonhygroscopic particles only.
4-16
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RDDR, defined as the ratio of ROD in the animal species of interest (subscript
A) to that of humans (subscript H) as:
NOAEL[HEC] (mg/m3) = NOAEL[ADJ] (mg/m3) x RDDR (4-5)
where:
NOAELr,,rC-| = the NOAEL human equivalent concentration,
NOAELr.r,,-. = the NOAEL adjusted for duration according to Equation 4-3,
and
RDDR = (RDD)a/(RDD)HJ the ratio of regional deposited
n n
in animal species to that of humans for region
of interest for the toxic effect.
Appendix H describes the derivation of the ROD values for humans and
discusses the surface area values used for both animals and humans. The
surface area values used are the best available estimates for the various
species at this time. Research as described in Appendix H under Research and
Development may provide estimates of greater accuracy as the methodology
develops. Appendix H also provides a table for the calculation of RDDR for
rats and an example application of its use in dosimetric adjustment.
4.1.1.2.3.1 Respiratorytract effects. The general dosimetric approach for
insoluble aerosols outlined above provides the basis for calculations for
estimating HECs when the toxic effect of interest is in the respiratory tract.
The equivalent dose across species is assumed to be the aerosol mass (mg) per
2
surface area (cm ) of the respiratory tract region of concern.
The initial step of the calculation is to characterize the particulate
exposure by its MMAD and a . This information will be used in conjunction with
deposition efficiency to calculate a regional deposited dose. The respiratory
tract region of the observed toxic effect dictates the ROD calculated. For
4-17
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example, If the toxic effect of interest was an effect on the nasal epithelium,
Equation 4-4 would be modified to calculate the ROD for that region only as:
10" 6 YVTf n
RDD = _ - " - z p E (4-4)
U 5ET i=l n !
where:
P. = the parti cul ate mass fraction in the exposure size distribution
1 (MMAD, o-g)
E- = the deposition efficiency of that size distribution
(MMAD, CT ) in the extrathoracic region for the species of
interest?
n = number of size ranges,
Y = exposure level (mg/m3),
Vj = tidal volume (ml),
f = breathing frequency (breaths/minute), and
Srj = surface area of the extrathoracic region (cm2).
The ROD in the species that exhibited the ET effect then is related to
the human ROD, also calculated for the ET region and the same MMAD and a ,
as a ratio. This ratio then is used as in Equation 4-5, to calculate a human
equivalent concentration for the exposure NOAEL as follows:
NOAEL[HEC] (mg/m3) = NOAEL[ADJ] (mg/m3) x RDOR(ET) (4-5)
where:
- the NOAEL human equivalent concentration,
= the NOAEL adjusted for duration according to Equation 4-3, and
RDDR = (RDDET),/(RDDET)|,) the ratio of regional deposited
dose in the extrathoracic region in the animal species
to that of humans.
4-18
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4.1.1.2.3.2 Extrarespi ratgry effects. When the toxic effect of interest
for RfD. evaluation is observed outside the respiratory tract, the following
equation is used to calculate the ROD expressed as mg/kg per minute:
10~6 YVTf n
RDD£R = g^— I P. E. (4-6)
C.K DW * I I
where:
P. = the particulate mass fraction in the exposure size distribution
1 (MMAD, o-g),
E. = the deposition efficiency of that size distribution
(MMAD, a ) in the entire respiratory tract for the species
of interest,
n = number of size ranges,
Y = exposure level (mg/m3),
V-,- = tidal volume (mi),
f = breathing frequency (breaths/min), and
BW = body weight (kg).
In the case of extrarespiratory effects of particles, the equivalent dose
across species is assumed to be the mass of particles (mg) deposited per body
weight (kg). Until clearance and distribution parameters can be incorporated,
it is assumed that 100 percent of the deposited dose to the entire respiratory
system is available for uptake to the systemic circulation. This assumption
may result in slightly less conservative HEC estimates than using retained
dose and accounting for differential uptake from various respiratory regions,
but is more accurate than using the exposure concentration.
The ratio of the extrarespiratory RDDs calculated for the experimental
species and the human then is used to calculate the HEC for a systemic effect
as follows:
4-19
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NOAEL[HEC] (mg/ffla) = NOAEL[ADj;] (mg/m3) x RDDR£R (4-7)
where:
= the NOAEL human equivalent concentration,
NOAEL,ADJv = the NOAEL adjusted for duration according to Equation 4-3, and
RDDRER = (RDDER)A/(RDDER)H> the ratio of the dose available for
uptake from the entire respiratory system of the
experimental animal species to that of humans.
4.1.1.2.3.3 Assumptions and default values. The initial step in the calcula-
tion of HECs, after evaluation of the generation system for its adequacy,
involves characterization of the aerosol exposure by its MMAD and a . Studies
that do not provide this information should be suspect for deficient quality.
Some of the older toxicology literature may not provide this information,
however, and a default value may need to be invoked. The first approach in
this situation is to attempt an estimate of particle size and distribution
based on the generation apparatus used. In conjunction with this information,
the knowledge that prior to the late 1970s, the generation technology was not
sufficiently sophisticated to deliver consistent exposures of particle sizes
above 3 pro (MMAD) can be used to construct a default approach. The recommended
default approach is to use the particle diameter (MMAD) and distribution (a )
y
characteristic for the given generation system that is <3 urn and that yields
the smallest (i.e., most conservative) RDDR values for the lung region of
interest. The Hatch-Choate equations can be used to convert lognormal distri-
butions of one type of diameter (e.g., count median diameter) to another (e.g.,
MMAD) (Hinds, 1982).
The MMAD for liquid and hygroscopic particles may vary with location in
the respiratory tract since its size, shape, and density may change due to water
uptake in the humid respiratory tract. Consequently, the deposited dose may
be different from that of nonhygroscopic particles of like size distribution
upon inhalation (Martonen et al., 1985). Theoretical models have been
developed to analyze the influences of hygroscopic growth on inhaled aerosol
behavior (Martonen et al., 1985; Martonen, 1982; Martonen and Patel , 1981), but
application in risk assessment awaits definition of the primary factors
influencing hygroscopic growth on species- and agent-specific bases. The
factors include initial particle geometry and density, material hygroscopic
4-20
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growth characteristics, respiratory parameters, and temperature and relative
humidity profiles. Observations on the data from modeling efforts to date
indicate that hygroscopic particles in the diffusion-dominated regime have
reduced deposition relative to nonhygroscopic particles of identical pre-
inspired size, whereas those hygroscopic particles affected by inertial and
gravitational forces have an increase in deposition relative to nonhygroscopic
particles (Martonen et al., 1985). These observations may be explained by
changes in the relative effectiveness of the particle deposition efficiency
mechanisms. Thus, dosimetric adjustment of an inhaled dose by the deposition
efficiency for nonhygroscopic particles would underestimate (i.e., be more
conservative than) the deposited dose for the larger (affected by inertial and
gravitational forces) hygroscopic particles, and overestimate the deposited
dose for the smaller diffusion-dependent hygroscopic particles. The total
deposited dose of inhaled nonhygroscopic particles, however, is always less
than the initial total dose (exposure dose). Also, the relative changes in
deposition will be in a similar direction in experimental animal species and
humans. Dosimetric adjustment by the insoluble (nonhygroscopic) deposition
efficiencies is recommended as a conservative default for the hydroscopic
particles, pending modification by the elucidation of the hygroscopic models.
It is recognized that this approach is based on deposition efficiency
data obtained or derived under a particular set of ventilatory parameters;
that is, the experimental parameters for the animal and a derived human
breathing pattern (13.8 JJ/min or 20 m /day). The assumption in this applica-
tion is that it is valid to linearly extrapolate from these values to other
sets of breathing parameters. The parameters of this assumption, such as the
effect of activity pattern and allometric relationships between lung weight,
lung surface area and body weight (Adolph, 1949; Weibel, 1972; U.S. Environ-
mental Protection Agency, 1988c) will be investigated as part of this
methodology development. A discussion of the impact that breathing pattern has
on the human deposition estimates can be found in Appendix H. Also, the human
ambient exposure scenario, when known, may be characterized by a different MMAD
and a than that used to derive the health risk assessment. Comparisons
between ratios calculated with a MMAD and o the same as the animal exposure
y
and calculated with the human estimate using the anticipated ambient MMAD and
a may provide some insight on the uncertainty of this extrapolation.
4-21
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In addition to inspired air concentration, minute volume respiration
rate, surface area, and deposition efficiency, the effective dose of inhaled
particulate matter will vary with bioavailability. The fraction of particulate
matter dissolved and assumed to be bioavailable can be expected to increase
with greater particle solubility, as well as with longer residence time in the
lungs. The U.S. EPA has recognized the importance of incorporating clearance
components to its RDDR exposure concentration adjustments, particularly for
estimates of long-term lung burdens. In addition, consideration will also be
given to the issues concerning bioavailability as discussed in Appendix H.
4.1.1.2.4 Dosimetry: Gases and vapors. The approach outlined in the insolu-
ble particle application illustrates the feasibility of interspecies dosimetry
calculations for extrapolating the toxicological results of inhaled agents to
human exposure conditions for risk evaluation. Dosimetry data facilitates
evaluation of concentration-response data with respect to dose-response
relationships, Dosimetry models also should be developed to account for the
physical, biological, and chemical factors that affect gas uptake and the
clearance mechanisms for various inhaled agents. Predictive physiologically
based modeling for reactive gases has been demonstrated (Overton and Miller,
1988). Predictive physiologically based modeling has also been demonstrated
for gases and vapors of organic solvents that may be metabolically activated
(Fiserova-Bergerova, 1983; Andersen etal., 1987; Overton, 1989). For these
agents, the uptake and distribution of the parent compound depends on the
physicochemical properties of the agent (i.e., solubility in blood and tissue)
and physiological properties (i.e., ventilation, perfusion, tissue mass). The
toxicological effects can be a function of the parent compound or are a
function of metabolism of the parent compound to a toxic metabolite, which
depends on the rate of toxification and detoxification reactions. Considera-
tion should be given to the discussion by the National Research Council (1986)
on interspecies extrapolation based on mechanism of action. Three classes were
distinguished based on whether the parent compound, stable metabolite, or
reactive metabolite produces the toxic effect and suggests measures of dose for
each of these classes. These factors are often species-specific and dose-
dependent, as well as being chemical-specific and, therefore, require a
substantial data base (beyond that which exists in most circumstances) in order
to model comparative species dosimetry of gases based on mechanism of action.
A project is underway by ECAO-RTP and HERL to identify the key determinants of
4-22
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uptake and tissue dose for a variety of gases with different properties (see
"Research and Development", Appendix I). Identification of the limiting
anatomic and physiologic parameters, physicochemical characteristics, and
exposure concentration and duration conditions will facilitate the application
of these models routinely to interspecies dose adjustments.
4.1.1.2.4.1 Respiratory tract effects. For gases and vapors that are very
reactive and that have their toxic effect in the lung, an analogous approach to
that of the insoluble particles approach for repiratory tract effects is used.
The equivalent dose across species again is assumed to be the mass (mg) of
2
toxic agent per surface area (cm ) of the lung region of concern. Ventilatory
parameters and regional lung surface areas are used to dosimetrically adjust
for the species differences, as in Equations 4-4, but the particle distribution
and deposition efficiency integration term is dropped. Thus, the regional gas
dose, (RGD), is calculated as:
10"6 YV,f
RGD = g-J (4-8)
where:
Y = exposure level (mg/m3),
Vt = tidal volume (mje),
f = breathing frequency (breaths/minute), and
S = regional surface area (cm2) of toxic effect observed.
It should be noted that this approach assumes that the entire inspired concen-
tration goes to the region of concern, whereas not all inspired gas is
necessarily deposited. For example, an alveolar ventilation rate would be
appropriate to use with a strictly pulmonary effect. As in the case of the
ROD for aerosols, the toxic effect observed will dictate the RGD to calculate.
That is, the appropriate surface area (i.e., ET, TB, PU, TH, or TOT) must be
used in Equation 4-8 to correspond with the region of observed toxicity. The
ratio of the appropriate RGDs, calculated for the experimental species and
humans, is then derived. This regional gas dose ratio (RGDR) then is used
to dosimetrically adjust the experimental NOAEL to a human equivalent concen-
tration:
4-23
-------
NOAEL (mg/m3)[HEC] = NOAEL[ADJ] (mg/m3) x RGDR (4-9)
where:
NOAELrHEC, = the NOAEL HEC
NOAEL,ann = the NOAEL adjusted for duration according to Equation 4-3,
tftuj; and
RGDR = (RGD)./(RGD)H, the ratio of regional gas dose
in anrmal species to that of humans for region
of interest for the toxic effect.
For gases with respiratory tract effects that have significant solubility
in the blood relative to their reactivity with lung tissue (e.g., methyl
bromide), the approach outlined below for gases which reach periodic concen-
trations and cause extrarespiratory effects is recommended (Equation 4-10).
This default is used to account for uptake into the systemic circulation which
may have decreased the amount of gas causing a direct effect in the lung and to
account for the concentration available to the lung via blood circulation.
4.1.1.2.4.2 Extrarespiratory effects. For gases and vapors that exhibit
their toxic effects outside of the respiratory tract, an approach for the
scenario when the arterial concentration (leaving the lung) of the gas in the
animal was periodic (or could be expected to be) with respect to time
(Equation 4-10) is recommended. A default approach for the case when such
periodicity is suspected not to have occurred also is provided (Equation 4-11),
Derivation of the procedure and Equation 4-10 for estimating NOAELr,,rC-|S
for gases with extrarespiratory effects was based on a PB-PK model described in
Appendix I. The procedure will give equivalent or more conservative values
for the NOAELrHEC-,s than those obtained by using the PB-PK model, and can be
used with compounds for which modeling would be applicable, but for which some
or all values of the important parameters (\, V , K ) are not available.
ITicLX ID
The approach assumes that physiologic and kinetic processes can be described
by a PB-PK model, assumes allometric scaling of physiologic and kinetic para-
meters, and assumes that all concentrations of the inhaled compound within
the animal are periodic with respect to time. Based on the PB-PK model of
Ramsey and Andersen (1984), algebraic equations that relate organ and tissue
compartment concentrations to exposure concentrations under equilibrium con-
ditions were derived. Since toxic effects observed in chronic bioassays are
4-24
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the basis for the determination of NOAELs from which RfD values for human
exposures are derived, the procedure assumes that chronic animal exposure
scenarios are equivalent to human lifetime exposures. The procedure also
assumes that the toxic effects observed are related to the arterial blood
concentration of the inhaled compound and that NOAELrnpC-iS should be such that
the human time-integrated arterial blood concentration is less than or equal to
that of the exposed laboratory animal. This latter assumption is equivalent to
assuming that time-average concentrations are equal to the equilibrium concen-
tration adjusted for exposure duration (i.e., Equation 4-3). A mathematical
derivation was used to obtain the proposed method of simple algebraic equations
to compute NOAELr,.c£-|S. A more detailed description of the development of the
procedure is given in Appendix I.
Assuming the animal alveolar blood concentrations were periodic with
respect to time for the majority of the experiment duration, the NOAELrucrn
extrarespiratory effects of gases or vapors is calculated as;
NOAELtHEC] (mg/m3) = NOAEL[ADJ] (mg/m3) x ^ (4-10)
where:
NOAELrHEC-. = the NOAEL human equivalent concentration,
NOAEL,ann = the NOAEL adjusted for duration according to Equation 4-3,
tHUJJ and
A.AM = the ratio of the blood to air partition coefficient of
the chemical for the animal species to the human value,
used only jf A. < A,,.
For the cases where \, > A,,, model results have shown that the generalized
Equation 4-10 may not provide conservative estimates. The detailed derivation
of boundary limits on \ is given in Appendix I. For the situation in which
A, > Xu anc' in tne case wnere ^ values are unknown, the default value of
A^/A^ = 1 is recommended. An analysis of the available data on rats for
blood to air partition coefficients shows that the A. is greater than AM in
most cases. Practically, the conditions of periodicity should be met during
"most" of exposure duration. For example, if this condition is met for
nine tenths of the time (e.g., periodic during the last 90 weeks of a 100 week
4-25
-------
experiment), then estimates of average concentrations will be in error by less
than 10%.
Figure 4-2 provides guidance on the relationship of the blood to air and
fat to blood partition coefficients with respect to achieving periodicity of
an inhaled agent in the arterial blood of a 380-gram F344 rat. (It should be
noted that often tissue to air partition coefficients are reported, e.g., fat
to air. The fat to blood partition coefficient can be calculated by multiplying
the fat to air partition coefficient by the blood to air partition coefficient.)
The PB-PK model as described in Appendix I was run to simulate a 6 hours/day,
5 days/week exposure regimen of 10 ppm. Physiologic parameters, such as
ventilation rate, were scaled as described in Appendix I. No metabolic
parameters were incorporated in the model for the simulations, since the
arterial blood concentration takes longer to reach periodicity without
metabolism. This figure thus represents the most conservative values for the
partition coefficients for that exposure regimen. The blood to air and fat to
blood partition coefficients were chosen based on sensitivity analyses that
indicated these two parameters were important to describing the time course of
the concentration of an agent in the arterial blood, and upon data availability.
The importance of the relationship between the partition coefficients and
the attainment of periodicity is particularly significant when extrapolating
from studies of different durations. For example, for an agent with a blood
to air partition coefficient of 1,000 and a fat to blood partition coefficient
of 100, it would be inappropriate to extrapolate from a subchronic exposure
regimen since the criterion of attaining periodicity for 90% of the exposure
duration is not met. Periodicity is attained with these same parameters when
the study is carried out for a longer duration, however, so that the approach
based on the ratio of animal to human partition coefficients can be used on a
chronic study without violation of critical assumptions.
Similar matrices to Figure 4-2 can be developed for the relationship
between partition coefficients and the attainment of periodicity of the agent
in the arterial blood of each experimental species of interest. Use of
physiologic parameters for other species and/or different exposure regimens at
various concentrations will influence this relationship and should be
considered when determining the extrapolation approach to use for derivation of
a human equivalent concentration.
4-26
-------
Y - FAT.-BLOOD PARTITION COEFFICIENT
Y
X
10
100
1,000
10,000
UJ
o
10,000
LJ
O
O
o
1,000
on
<
CL
100
Q
O
O
10
DD
I!
X
0.1
0.01
= CHRONIC
= CHRONIC 4- SUBCHRONIC
Figure 4—2. Relationship of partition coefficients to periodicity
in F344 rat arterial blood for subchronic (90-days) and chronic
exposure regimens of 6 hours/day, 5 days/week.
4-27
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The default calculation for the situation in which periodicity during
10% of exposure duration is suspected not to have been achieved is given by:
(VBW)A
NOAELrHrri(mg/m3) = NOAELr.nn(mg/m3) x —2 - *- (4-11)
[HECJ [ADJJ (V./BW)H
M n
where:
= the NOAEL Human equivalent concentration,
r-n-n = the NOAEL adjusted for duration according to Equation 4-3,
LADJJ and
(V./BW)
— • - = the ratio of the alveolar ventilation rate (nut/mi n)
(V./BW)., divided by BW (kg) of the animal species to the same
parameters for humans.
Since this default approach engenders more uncertainty and is less
conservative with respect to the above approach, use of a modifying factor
should be considered.
Use of the alveolar ventilation rate is recommended to account for the
volume of the respiratory tract in which no gas exchange occurs; often termed
the "physiologic dead space". The alveolar ventilation rate is the volume of
inspired air per minute available for gas exchange with blood that enters the
alveoli. Alveolar ventilation rates are approximately 67 percent of minute
volumes for mice, rats, and humans (U.S. Environmental Protection Agency,
1988c).
4.1.1.2.4.3 Assumptions and default values. As with aerosols, after evalua-
tion of the adequacy of the generation system, the initial step in the calcula-
tion of HECs is characterization of the exposure.
Gas exposures are characterized by concentration (mg/m ), temperature, and
pressure. If the concentration is expressed in ppm, the actual temperature
and pressure should be used to convert the units to (mg/m ) (see
Section 4.1.1.2.1). When the actual temperature and pressure values are not
provided in a study, it should be suspect for deficient quality. Some studies,
however, express values already corrected for these parameters, usually
corrected at 25°C and 760 mm Hg. These values are the recommended default
values for temperature and pressure, respectively.
4-28
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Other assumptions and default values for gas and vapor extrapolations
have been discussed in Section 4.1.1.2.4.1 and 4.1.1.2,4.2 and details are
provided in Appendix I.
4.1.1.3 Route-to^Route Extrapolation. Estimating equivalencies of dose-
response relationships from one route of exposure to another introduces an
additional uncertainty in the derivation of an inhalation RfD. Consequently,
whenever possible, the inhalation RfD should be based on data involving inhala-
tion exposures. If inhalation data are insufficient, data from other routes
of exposure may be useful in the inhalation RfD derivation process, provided
that portal of entry effects in the lung can be ruled out (see Section 4.3).
Oral data are the most common alternatives to inhalation data. Dose-
response data from other routes of exposure, such as intravenous, intraperi-
toneal, subcutaneous, dermal, and intramuscular routes also may be available.
Intravenous data provide reliable information on blood levels. The other
routes generally have a much more limited usefulness in route-to-route extrap-
olation because the pharmacokinetics are, in general, poorly characterized.
When portal-of-entry effects have been ruled out, estimates of equivalent
doses can be based upon the following:
Available pharmacokinetic data for the routes of interest
Measurements of absorption efficiency by each route of interest
Comparative excretion data when the associated metabolic path-
ways are equivalent by each route of interest
Comparative systemic toxicity data when such data indicate
equivalent effects by each route of interest.
If sufficient pharmacokinetic data are available, physiologically based
pharmacokinetic (PB-PK) models are particularly useful tools for predicting
disposition differences due to exposure route differences. Their use is
predicated on the assumption that an effective (target-tissue) dose achieved by
one route in a particular species is expected to be equally effective when
achieved by another exposure route or in some other species. For example, the
proper measure of target-tissue dose for a chemical with pharmacologic activity
would be the tissue concentration divided by some measure of the receptor-
binding constant for that chemical. Such models account for fundamental
physiologic and biochemical parameters such as blood flows, ventilatory
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parameters, metabolic capacities, and renal clearance, tailored by the physico-
chemical and biochemical properties of the agent in question. The behavior of
a substance administered by a different exposure route can be determined by
adding equations that describe the nature of the new input function. Similarly,
since known physiologic parameters are used, different species (e.g., humans
vs. test species) can be modeled by replacing the appropriate constants. It
should be emphasized that PB-PK models must be used in conjunction with
toxicity and mechanistic studies in order to relate the effective dose
associated with a certain level of risk for the test species and conditions to
other scenarios.
This concept can break down when considering chemicals that exhibit
first-pass effects (a pharmacologic phenomenon) and/or portal-of-entry effects
(a toxic response). It is imperative to rule out pulmonary portal-of-entry
endpoints before attempting route-to-route extrapolation from other data.
Where a chemical is known or suspected of having a first-pass effect by the
tested route, or where a portal-of-entry effect is known or suspected, then
route-to-route extrapolation for derivation of an RfD is not appropriate.
Agents for which this approach must be used with particular caution include
metals, irritants, and sensitizers. Before route-to-route extrapolations are
attempted, it is strongly suggested that articles by Pepelko and Withey (1985),
the National Research Council (1986), and the publication on Pharmacokinetics
in Risk Assessment (National Research Council, 1987) be reviewed for a better
understanding of the complexities and limitations of some of the available
extrapolation methods. Limitations also are outlined in Section 4.3.
Outstanding issues in route-to-route extrapolation include the following.
When are the available data too sparse for estimating the
different route absorption parameters?
What default positions, if any, will be used when one or both of
the route-specific absorptions cannot be estimated?
How should the different exposure regimens by the different
routes (e.g., continuous vs. intermittent exposures) be dealt
with?
How should vehicle effects on the pharmacokinetics of the oral
studies (e.g., ppm in diet vs. ppm in water) be dealt with?
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4.1.1.4 Issuesfor Further Investlgation. Consistent application of the
procedures in this chapter will require consensus on the most appropriate data
sets (e.g., species deposition data) and reconciliation of data values for use
in the dosimetry calculations. Default values used among the U.S. EPA offices
should be reviewed, including a discerning reevaluation of the data source,
selection rationale, and application limitations. Recent documents on recom-
mended values for use in risk assessment (U.S. Environmental Protection Agency,
1988c) and for use in physiologically based models (U.S. Environmental Protec-
tion Agency, 1988b) are useful sources of default values for parameters such as
ventilation rates and body weights for use in these equations when these values
are not supplied in individual investigations. Available allometric equations
(Adolph, 1949; Weibel, 1972; U.S. Environmental Protection Agency, 1988b,c),
relating body size to the parameters of interest such as ventilatory rates
and lung surface areas also may be appropriate. Currently, a task group of the
Agency's inhalation RfD verification workgroup is addressing the issue of the
use of default parameters. It must be emphasized at this time that the use of
default or derived values must be consistent with the dosimetric modeling
parameters and approaches used in adjusting concentrations to human equivalent
values, such as the parameters used to derive the regional RDDR (see discussions
in Sections 4.1.1.2.3 and 4.1.1.2.4 and Appendices H and I).
4.1.2 Approach for RfDEstimation Using HumanData
4.1.2.1 Introduction. Whenever possible, a human study is selected as the
critical study for derivation of an RfD to avoid the myriad problems of
extrapolating from animals to humans.
When using epidemiologic data to assess risk in the context of a method
designed for data on experimental animals, the dependence of epidemiologic
studies on existing exposure conditions and the necessity of using noninvasive
diagnostic methods present two complicating factors. One is that existing
exposure levels may not include a NOAEL. Toxicologic studies are generally
designed to identify the NOAEL. For ethical reasons, many clinical studies in
humans often focus on exposure scenarios that are associated with minimal
effects and short exposure durations, although they also may identify a NOEL.
In contrast, epidemiologic studies cannot be so designed because exposure
levels are dependent on existing exposures. In both controlled human and
animal studies, the estimates are biased by the dose or exposure level selected
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or available far study. These estimates are subject to random error, the
magnitude of which depends on various design aspects, such as the size of the
study population or test groups, and the underlying variability of the test
animals or study subjects.
The second factor to consider for epidemiological studies is that the
entire spectrum of potential adverse effects cannot be evaluated, thus, it is
difficult to determine the critical effect. Prospective epidemic logic studies
that assess biological markers or preclinical endpoints are better sources of
NOAELs to estimate the threshold region. Clinical studies may be based on low
exposure levels selected by the investigator and investigate sensitive end-
points, but these studies are generally of short duration and are more useful
for estimating short-term effects (see Section 4.2). The following discussion
describes approaches to address these obstacles.
4.1.2.2 Selecting the Threshold Estimate. In some epidemiologic studies only
severe effects such as mortality are examined. In such studies a NOAEL has
inherent limitations. A study in which sensitive endpoints are evaluated may
contain a LOAEL but no NOAEL. If the effect is sensitive (i.e., it occurs
early in the natural history of the disease), a LOAEL may be judged suitable
for use in calculating an RfO in lieu of a NOAEL, because the uncertainty of
extrapolating human data for a well-defined critical effect from a LOAEL to a
NOAEL is judged to be less than the uncertainty involved in extrapolating from
animal data to humans. The circumstances governing this selection include
deficiency in toxicologic and physiologic data bases, small sample size in the
experimental studies, or physiologic or pharmacokinetic data suggesting that
animal data are unlikely to be good predictors for humans. The use of the
UF for extrapolating from a LOAEL to a NOAEL has been explained previously in
Section 4.1.1.
The data base supporting an occupational exposure level (DEL) may be
examined for data to be incorporated in the data array for analysis supporting
RfD. derivation. Caution is recommended: While the OELs are based on the
concept of a biological threshold, there are no standardized criteria for the
data base and safety factors used. Furthermore, the OELs are designed to
protect "nearly all workers" and not the entire population. These and other
limitations are discussed in the issue paper (U.S. Environmental Protection
Agency, 1989).
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4.1.2.3 Defining the Exposure Level. Epidemiologists cannot control the expo-
sure levels for a study in a systematic fashion, but instead attempt to measure
the levels to which the study population is exposed insofar as the measurement
technology permits. In actual exposure situations, the levels vary in time and
location. Epidemiologic studies can utilize a variety of parameters to
characterize exposure, although in retrospective studies they are usually quite
limited by the available data.
The ideal exposure measure for humans who move about in their environment
is individual data, such as might be obtained with the use of a personal
monitor. However, in addition to the expense and practical difficulties, this
technology is available for measuring only a few chemicals. Individual expo-
sure can be constructed by mapping the individual's time in various exposure
zones, rooms, or areas. If information on levels in the environment is not
available, duration of employment often is used as a surrogate for exposure.
Parameters commonly used to measure environmental levels are cumulative
exposure, peak exposure level, time-weighted average, and ratio of average to
peak exposure. Currently it is unclear which of these is best related to
disease and under what circumstances or chemical characteristics of the agent
is one parameter better than another. For example, cumulative exposure is more
appropriate as half-life of a substance is increased, therefore, to derive
RfD-s that identify levels of environmental exposures that are free of adverse
effects, cumulative exposure or time-weighted averages are appropriate for
substances with long half-lives. The circumstances can be evaluated on a
case-by-case basis and different exposure parameters may be used if the ration-
ale is presented. For conversion of units, the approach is the same as that
for animal data (Equations 4-2a and 4-2b). Conversions are the same for
exposure duration (Equation 4-3), again, with the same precautions as discussed.
Considerations for route extrapolation would be the same as for animal data;
however, it is highly unlikely that human ingestion data would be available in
a form useful for quantitative risk assessment.
4.1.2.4 Uncertainty Factors for Human Data. The best data to use for calcu-
lating an RfD. would be a population study of humans that includes sensitive
individuals exposed for lifetime or chronic duration, and evaluates the
critical endpoint or an appropriate early marker for the disease. A NOAEL
derived from a well-done epidemiologic study of this description may require no
l)F. A similar study in humans that contains only a LOAEL would require the use
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of a factor of up to ten-fold to reduce the exposure to the range of a NOAEL
(see Table 4-3, 10L). Chronic studies on populations that do not include
sensitive individuals may require a 10-fold UF. For example, studies of
workers are considered to contain only relatively healthy adults. A NOAEL from
a study that entails subchronic exposure would require a reduction by a 10-fold
UF (see Table 4-3, 10S). However, the amount of exposure in a human study that
constitutes subchronic is not defined, and could depend on the nature of the
effect and the likelihood of increased severity or greater percent response
with duration. In the absence of data on the relationship of animal to human
lifespan for predicting health effects, a linear correlation of percent life-
span is assumed. Therefore, if a chronic study in animals is 12% of lifespan,
then 9 years of human exposure must be studied. Information on the natural
history and progression for the diseases should be considered and explained;
information on follow-up after exposure, often available in epidemiologic
studies, is important.
In some cases, short-term studies of effects in humans can give important
information on irritation, sensory effects, or sensitivity and reversibility,
yet give no information on the effect of chronic exposure. If the data base
suggests that the effective level of a short-term human study is below that
which would cause chronic health effects, this can be used to derive the RfD,
designated as a subchronic inhalation RfD (RfD .). This is described further
in Section 4.2.2.
4.2 PROCEDURES FOR ESTIMATING PARTIAL LIFETIME EXPOSURES
4.2.1 Acute
Application of the RfD. approach to acute exposures is contingent upon
determination of relevant exposure durations for humans. Documentation on
this area of interest is under development in the U.S. EPA.
4.2.2 Approach for Subchronic Inhalation RfD Estimation (RfD .)
The RfD . strictly parallels the inhalation RfD in concept. The distinc-
tion is one of exposure duration. While the RfD is specifically developed to
be protective for daily exposure to a compound over the course of a lifetime,
the RfD . applies to specified durations that are less than lifetime. Multiple
duration-specific RfDs may be developed for a compound depending upon the
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medium and possible exposure scenarios, as well as the needs of a particular
program office. For example, the Office of Drinking Water develops oral
drinking water health advisories for 1-day and for 10-day exposures.
Once the duration of a particular exposure is defined, all of the labora-
tory and epidemiclogic data need to be evaluated in this exposure-time context.
When adequate data on humans or on laboratory animals are available for the
required exposure-time interval, RfD . development proceeds in the same manner
as described for the RfD. (see Section 4.1). Data on humans may be available
for short-term exposures even when the chronic value (RfD.) has been based on
animal data. It is important therefore to examine the available human data to
ascertain whether less-than-lifetime exposures are included.
Determining exposure-time equivalencies among species is an issue
requiring further investigation. Research on the boundary limits of the blood
to air and blood to fat partition coefficients for establishing periodicity of
arterial concentrations during intermittent exposures as described in
Section 4.1.1.2.4.2, may provide some insight. These limits will be different
for 90-day versus chronic bioassays. Previous discussions have utilized the
concept of percent of the lifespan. For example, chronic studies often are
defined as having a duration of >90 days. Whether short-term exposures should
also be evaluated in terms of percent of the lifespan, physiological time, or
by some other method, requires further investigation. Essentially, an index
of the damage process relative to the repair process for a number of different
lesion types is necessary. In addition to exposure duration, postexposure
observation time is also an important issue. For example, brief exposure to
certain pulmonary irritants may result in no immediately observable adverse
effects, but may be linked with pulmonary pathology at a later evaluation time.
No guidance is currently available concerning adequate periods of postexposure
observation for acute, short-term and subchronic exposure regimens. The
duration of an adequate postexposure time period may be compound-specific.
When experimental data are available only for shorter "equivalent" expo-
sure durations than the desired duration-specific RfD ., or when postexposure
observation is deemed inadequate, application of a UF may be appropriate.
This is similar to the application of a UF for duration when estimating a RfD.
from subchronic animal data. Criteria are needed to determine the degree of
divergence between the experimental exposure duration and time to elicit
effects, which would necessitate application of an additional UF. In addition,
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it needs to be determined if a standard factor, such as 10, would be applied
whenever the criteria for duration are not met, or whether UFs of graded
magnitude might be employed, depending upon the degree of divergence between
the experimental exposure duration and the duration interval modeled by the
RfD5l.
It is important to evaluate any proposed RfD . in the context of all
available toxicity data. Although free-standing NOELs/NOAELs* are not
recommended for either RfD. or RfD . estimation, on occasion they represent the
only data available. Use of a dose level well below an actual threshold value
can result in an anomalous RfD ., when compared to longer exposure-duration
RfD . or RfD.s that are based on a more complete data set. In other words, it
would be inappropriate to estimate a RfD . that is of smaller magnitude than an
RfD.j for the same compound.
The RfD . can be calculated for any required exposure interval when
adequate toxicological data are available, utilizing the approaches described
in Section 4.1 as shown below:
RfD5J * NOAEL[HEC]/(UF x MF) (4-12)
The UFs are the same as described in Section 4.1.1. except that the NOAEL from
Table 4-3 would be more generally interpreted to reflect discrepancies between
the available duration-specific data and the duration of the proposed RfD ..
This may necessitate correction for added uncertainty.
For human data, the exposure concentration associated with a human NOAEL
may be utilized directly to develop a subchronic RfD . in units of air concen-
tration. This concentration needs first to be adjusted for exposure duration
(i.e., converted to represent an equivalent continuous exposure level) as shown
in Equation 4-3, with the noted caution pertaining to this type of extrapo-
lation. Following this adjustment, the RfD$1- may be calculated as:
RfDsi (mg/m3) = NOAEL[ADJ] (mg/m3)/(UF x MF) (4-13)
*"Free-standing" NOELs or NOAELs are those without corresponding LOAELs. In
such cases the experimental threshold region has not been determined.
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Some agents may not be suitable for either chronic or subchronic RfD esti-
mation because they act in a manner distinct from those agents whose action is
concentration and/or time-dependent. An example of such compounds are those
that cause occupational asthma (Chan-Yeung and Lam, 1986) or induce hypersensi-
tivity reactions. Others include agents in which adverse effects continue to
progress over a period of years.
4.2.3 Issues Requiring Further Investigation
Development of guidance on how to compare exposure duration for
subchronic animal exposures with duration for subchronic human
exposures for the purpose of determining whether the criterion
of "equivalent duration" is met by a particular data set
Development of specific guidance concerning application of
duration-related UFs for partial lifetime exposure development
4.3 CRITERIA FOR SPECIFYING LEVEL OF CONFIDENCE
The selection of a NOAEL or other appropriate measure of threshold
response involves a process that incorporates scientific subjective judgment
and statistical measures of significance. The qualitative and quantitative
nature of this process results in estimated benchmark values such as the RfD
associated with varying degrees of confidence that can be described as high,
medium, and low. The confidence ascribed to the result is a function of both
the quality of the individual study and the completeness of the supporting data
base. For example, the RfD verification work group assigns confidence levels
to the individual study, the data base, and the RfD. Thus, if the individual
study is of excellent quality, it most likely will receive a high confidence
rating, even though it may be subchronic in duration. Duration of the chosen
study, as well as supporting studies and the spectrum of investigated endpoints
(e.g., reproductive effects), are considered in the rating of confidence in the
data base. Low confidence in the data base might be given to an excellent
chosen subchronic study with few supporting studies and few endpoints examined.
The confidence in the RfD then would reflect these two ratings by a rating of
medium to low, indicating uncertainty (lack of confidence) and suggesting that
further investigations may refine this number.
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The degree of confidence in a particular laboratory animal study involves
a number of parameters. These parameters include, but are not limited to, the
following.
Adequacy of study design
Is the route of exposure relevant to humans?
Were an appropriate number of animals and/or sexes used for
determination of statistical significance?
Was the duration of exposure sufficient to allow results to be
extrapolated to man under different exposure conditions?
Were appropriate statistical techniques applied?
Were the analytical techniques sufficient to adequately measure
the level of the test substance in the exposure protocol,
including biological media?
Is the animal species and strain appropriate as a surrogate for
man?
Are the techniques for measurement of the biological endpoints
scientifically sound and of sufficient sensitivity?
To what degree are the biological endpoints qualitatively
and/or quantitatively extrapolatable to humans?
Demonstration of dose-response relationships
Were sufficient exposure levels used to demonstrate the highest
NOAEL for the endpoint of concern?
Is the shape of the dose-response curve consistent with the
known pharmacokinetics of the test substance?
Has the dose-response curve been replicated by or is it consis-
tent with data from other laboratories and other laboratory
animal species?
Species differences
Are the metabolism and pharmacokinetics in the animal species
similar to those for man?
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Is the species response consistent with that in other species?
Is the species from which the threshold value derived the most
sensitive species?
Other factors
The number of biological endpoints evaluated and associated with
dose-response relationships
Sufficient description of exposure protocol, statistical tests,
and results to make an evaluation
Condition of animals used in the study
The degree of confidence in a particular data base also involves a number
of parameters. These parameters include, but are not limited to, the following.
Minimum data base for high confidence in an inhalation RfD:
Pulmonary, two well-performed chronic inhalation studies.
Nonpulmonary, same as oral RfD (Table 4-4) (oral studies may be
appropriate for addressing questions of potential developmental
and reproductive toxicity); chronic pulmonary studies may
substitute for chronic oral bioassays if they are comprehensive
(i.e., examined all critical endpoints)
Minimum data base for low confidence in RfD:
One inhalation subchronic bioassay (that examined lung para-
meters in addition to others)
A subchronic oral study can be used, if information on inhala-
tion is not available, with sound professional judgment.
Oral data should not be used in the following instances:
(1) When groups of chemicals that are expected to have different
toxicity by the two routes; for example, metals, irritants
and sensitizers;
(2) when a first-pass effect is expected by the liver, or when
the pulmonary system was not adequately studied in the oral
studies;
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TABLE 4-4. MINIMUM DATA BASE FOR BOTH HIGH AND LOW CONFIDENCE IN THE RfD
Mammalian Data Base3 Confidence Comments
1.
A.
B.
C.
Two toxicity studies High
in different species
One reproductive study
Two developmental
Minimum data base for
high confidence
toxicity studies in
different species
2. 1A and IB, as above
3. Two of three studies,
as above in 1A and IB;
one or two developmental
toxicity studies
4. Two of three studies,
as above in 1A and IB
5. One of three studies,
as above in 1A and IB;
one or two developmental
toxicity studies
6. One of three studies,
as above in 1A and IB
Medium to high
Medium to high
Medi urn
Medium to low
Low
Minimum data base for
estimation of an RfD
Composed of core minimum Office of Pesticide Programs-rated studies, or
studies published in refereed journals. It is understood that adequate
toxicity data in humans can form the basis of a RfD and yield high confi-
dence in the RfD without this data base.
(3) when a pulmonary effect is established but dosimetry
comparison cannot be clearly established between the two
routes; and
(4) when short-term inhalation studies or in vitro studies
indicate potential portal-of-entry effects at the lung, but
studies themselves are not adequate for an RfD development.
Other considerations are encouraged.
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The interested reader is also referred to Pepelko and Withey (1985) and
National Research Council, 1986, 1987).
The level of confidence in a particular threshold value will be higher
if it is derived from human data and supported by animal data. The parameters
and factors involved in the evaluation of human data are described in
Section 3.1.1.
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APPENDIX A
NOVEL APPROACHES TO THE ESTIMATION OF REFERENCE DOSE (RfD)
I. INTRODUCTION*
Current methods for estimating human health risks from exposure to
threshold-acting toxicants in water or food, such as those established by the
U.S. Environmental Protection Agency (Federal Register, 1980; U.S. Environmental
Protection Agency 1987a; Stara et al., 1981), the Food and Drug Administration
(Kokoski, 1976), the National Research Council (1977, 1980) or the World Health
Organization, and the Food and Agricultural Organization (Bigwood, 1973;
Vettorazzi, 1977, 1980; Lu, 1983), consider only chronic or lifetime exposure
to individual chemicals. These methods generally estimate a single, constant
daily intake rate which is low enough to be considered safe or acceptable,
referred to as an acceptable daily intake (ADI).
Two general scientific problems with this approach have been long recog-
nized (Krewski et al., 1984), in addition to its limited usefulness (i.e.,
lifetime health risk assessment only). The first problem is that this method
does not readily account for the number of animals used to determine the
appropriate NOEL. For example, if a chemical has a NOEL based on 10 animals
and a similar NOEL based on 100 animals, the risk assessor often will choose
the NOEL based on the larger study because it yields greater confidence in the
resulting ADI**. However, if these NOELs were for different chemicals, similar
RfDs might be derived even though one would be associated with much less confi-
dence. It might be useful if the number of animals used to determine the
appropriate NOEL would in some way affect the value of the resulting RfD, in
addition to the level of confidence. The second problem with the current
approach is that the slope of the dose-response curve of the critical toxic
effect is generally ignored in the estimation of the RfD. Many scientists have
argued that this slope should in some way directly affect the resulting RfD,
with steep curves presumably yielding higher values because threshold is more
quickly obtained.
*Note: Although material presented in this appendix is based upon oral data,
the approaches may be applicable to the inhalation RfD methodology as well.
Applications would necessarily give consideration as well to the inhalation-
specific issues (e.g., dose adjustment) discussed in this document.
**Now referred to by the U.S. EPA as a Reference Dose (RfD) (U.S. Environmental
Protection Agency, 1987a).
A-l
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Furthermore, the current approach to noncancer risk assessment yields an
RfD that is presented as a single number. As such, it reflects neither the
statistical variability in the NOAEL resulting from design factors of critical
studies nor the known variability in uncertainty factors used to account for
deficiencies in the data base. The results of this variability is the unknown
range of uncertainty in the estimate. Risk management decisions for regulation
or enforcement need more quantitative information on the inherent and recog-
nized uncertainties in this assessment.
The purpose of this text is to illustrate several revised approaches to
estimate RfDs that include methods for partial lifetime assessment, methods for
RfD estimation with quantal or continuous toxicity data, and methods for
estimating the statistical variability of NOELs and uncertainty factors. These
methods address to a degree the known scientific problems with the current
approach. The development of these methods can be found in Stara and Erdreich
(1984a,b); these methods also are described in Stara et al. (1985) and Stara
et al. (1987), and more fully in Crump (1984), Dourson (1986), and Dourson
et al. (1985, 1986, and 1987).
II. AN APPROACH TO USE ALL TOXICITY DATA AND SUPPORT PARTIAL LIFETIME RISK
ASSESSMENTS
a. Proposed Ap_proach. Health risk assessments generally require evalua-
tion of several types of toxicity data derived from several different species,
different doses, different exposure durations, varied endpoints, and varied
quality. This variety often makes the health risk assessment extremely diffi-
cult. Therefore, it is valuable to have all such toxicity data displayed
simultaneously, if possible.
A graphic method is presented for this purpose (see Figure A-l). After
thorough evaluation of the literature, toxicity data on a particular chemical
might be summarized by several variables: (1) dose rate (mg/kg/day), (2) expo-
sure duration, and (3) ranking of effects. The basis of the proposed method is
empirical observation. The toxicity data from all studies (including human)
are assigned to categories of severity based on observed effects in the case of
graded data, or on the statistical or biological significance in the case of
quantal or continuous data. Each of the effect severity levels described above
is represented by a unique symbol (Table A-l).
A-2
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10O.OOO -
1O.OOO -
FEL
l.OOO -
EQUIVALENT
HUMAN
DOSE
tmg/d)
0.07
O.7
7.0
UFESPAN 4y**"l
Figure A-l. Effect-dose-duration plot of all relevant human and animal oral
toxicity data for methoxychlor. Effect levels indicated by symbols are defined
in Table A-l. Animal doses have been converted by a body surface area factor
to approximate the equivalent human dose. Dose durations are divided by the
appropriate species lifespan to yield a fraction, which, when multiplied by
70 years (the assumed average human lifespan), gives the corresponding position
on the x-axis. Study usefulness is denoted by symbol size. Target organs are
LV (liver), RP (reproductive organ), GR (growth reduction), and SP (spleen).
The dose axis is divided into areas expected to cause either (A) gross toxicity
and death, (B) adverse effects, (C) nonadverse effects, or (D) no effects.
Source: Dourson (1986).
After graphic representation of all available toxicity data, a boundary
line is estimated (in Figure A-l the line has been fitted by eye) that repre-
sents for any given time the highest NOAEL for which no lower AEL is observed.
Recent work by the U.S. EPA discusses statistical approaches to this boundary
estimation (Hertzberg, 1989). Interpolation along this NOAEL curve can be
performed to estimate the NOAEL for any desired partial-lifetime exposure. In
order to obtain a corresponding acceptable intake, the estimated NOAEL could be
divided by an uncertainty factor. In Figure A-l an uncertainty factor of 100
is used and accounts for the expected intrahuman and interspecies variability
A-3
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TABLE A-l. VARIOUS EFFECT LEVELS AND THEIR DEFINITIONS USED IN FIGURE A-2
Effect
Level Symbol Definition
PEL A Frank-Effect Level. That exposure level which produces
unmistakable adverse effects, such as irreversible
functional impairment or mortality, at a statistically
or biologically significant increase in frequency or
severity between an exposed population and its appro-
priate control.
AEL • Adverse-Effect Level. That exposure level at which
there are statistically or biologically significant
increases in frequency or severity of adverse effects
between the exposed population and its appropriate
control.
NOAEL O No-Observed-Adverse-Effect Level. That exposure level
at which there are no statistically or biologically
significant increases in frequency or severity of
adverse effects between the exposed population and
its appropriate control. Effects are produced at this
level, but they are not considered to be adverse.
NOEL 0 No-Observed-Effect Level. That exposure level at which
there are no statistically or biologically significant
increases in frequency or severity of effects between
exposed population and its appropriate control.
Listed in order of decreasing severity.
Adverse effects are considered as functional impairment or pathological
lesions which may affect the performance of the whole organism, or which
reduce an organism's ability to respond to an additional challenge (Federal
Register, 1980).
to the toxicity of a chemical (in lieu of chemical-specific data). Both the
choice of the highest NOAEL line (without lower AELs) and the suggested uncer-
tainty factor of 100 are consistent with and a logical extension of previously
established scientific principles of the U.S. EPA (Federal Register, 1980;
U.S. Environmental Protection Agency, 1987a), the Food and Drug Administration
(Kokoski, 1976), and the National Research Council (1977, 1980) in the use of
effect levels and uncertainty factors in order to estimate ADIs or RfDs.
b. Assumptions and Limitations. The primary advantage of the graphic
method is that it provides a mechanism for viewing all of the data simultane-
ously, resulting in an integrated profile of a compound's toxicity. In addi-
tion, exposure duration-response trends, if present, are clearly delineated,
A-4
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providing a possible strategy for estimating acceptable intakes for partial-
lifetime exposures.
The graphical method relies on a simple severity ranking system for data
presentation (for example, NOEL, NOAEL, AEL, and PEL). Obviously with such a
simple system, effects within a given category (that is, all AELs) may not be
identical, nor is it assumed that they are. Indeed, the critical effect is
often a function of exposure duration. In these cases the effects within a
given category will not be the same across time. However, the change in
critical effect over duration (and, therefore, the change in effects within a
category) is perhaps only of secondary regulatory importance. Since the NOAEL
line is based on NOAELs of critical effects from all durations, the approach is
consistent with the regulatory objective of guarding against any adverse
effect. Moreover, while assumptions are needed in the process of extrapolation
of dose and duration from animal studies to their human equivalent
counterparts, this graphical method should enable regulatory scientists, at a
glance, to judge the overall strength of evidence of toxicity and to determine
data gaps wherever they appear.
One limitation of this proposed procedure is that the development of the
dose rate scale does not make provisions for incorporating interspecies
differences in the metabolic patterns of dealing with different chemicals; that
is, the method does not take into account differences in activation and detoxi-
fication, and such. It also is assumed that the log-log plot does not overly
compress the data. The problems are particularly great for very short durations
of exposure. In general, the dose rate to duration ratio plots that the U.S.
EPA has done so far on other chemicals have been characterized by a paucity of
data for short-term exposures. Another limitation is that the time interval to
develop pathologic signs after acute toxic insult may be more related to body
size and pharmacokinetic parameters than a given measure of exposure duration
such as days. In addition, most chemicals have scant data, and, thus, plots of
these data may not yield useful generalizations.
The experiments used to develop the data base which was used to derive
acceptable limiting concentrations for short durations were rarely, if ever,
designed with that purpose in mind. Short-term experiments have been done in
animals of many ages representing most phases of the total life span. Long-
term experiments (of necessity) start with young animals and follow them
A-5
-------
through their life span. If there are age-dependent differences in the sensi-
tivity of the experimental species, these would confound the data sets we are
using.
c. Status. In summary, this novel method for estimating RfDs utilizes
more of the available toxicity data than the current methodologies, and offers
a consistent approach for possibly estimating health risks for less-than-
lifetime toxicant exposure. A computer program facilitates use of this
approach and produces the graphical display (Hertzberg, 1989). Moreover,
statistical methods are being developed in order to estimate boundaries.
III. APPROACH* WITH QUANTAL OR CONTINUOUS TOXICITY DATA
a. Proposed Approach. Traditionally, NOAELs have been defined for
quantal endpoints that have nonzero background incidences by choosing an
experimental dose level which does not contribute to a statistically signifi-
cant increase in incidence of adverse effects when compared to a control group.
In parallel, NOAELs have been defined for continuous data by choosing an
experimental dose level which does not constitute a significantly different
mean value for a parameter, indicating an adverse effect when compared to a
mean value for a control group.
As previously discussed in Section II, two limitations are inherent in
this approach. The first problem is related to the insensitivity of the
current method to NOELs that use different numbers of animals, 0/10 vs.
0/1,000. The second limitation is related to the general lack of use of the
slope of the dose-response curve in the current approach.
The approach suggested here is not as subject to these limitations because
it uses more of the dose-response or dose-effect curve. For example, an RfD
might be calculated from a dose-response curve by defining an adverse effect as
a risk level of more than a certain percentage above background, such as 10%.
In this presentation, 10% is chosen because many of the mathematical models in
current use agree well at estimated risks in this range and because the better
studies have sufficient numbers of doses and animals per dose to measure this
''This method is described in more detail by Crump (1984).
A-6
-------
70
60
a, 50
in
Z
a
a.
LLJ 40
cc
30
u
cc
LU
O.
20
10 —
• SILGHT BODY WEIGHT DECREASE
A LIVER NECROSIS
DOSE ADJUSTMENT FACTOR=5.6 /
ADIR AD1D
- ,
10 kg DOGS
DOSE ADJUSTMENT FACTOR-1.9
I I
0.1
1.0
10
DOSE RATE, mg/kg bw/day
100
1000
Figure A-2. Hypothetical dose-response data for slight body weight decrease ( • } or liver necrosis
( A ) in rats and dogs, respectively. Solid lines indicate hypothetical data; dashed lines represent
lower 95% confidence limits (CLs). See text for additional explanation.
Source: Dourson (1986).
level directly. The lower 95% confidence limit (CL) on the dose associated
with this risk then is calculated. In order to obtain an RfD, the dose associ-
ated with this lower 95% CL might be reduced by a chemical-specific, species
adjustment factor, a tenfold uncertainty factor (this reflects the common
practice), remove hyphen or as in the case of Figure A-2, the cube root of the
animal body weight to human body weight ratio. Uncertainty factors might then
be used to divide this adjusted value to yield the RfD.
In this presentation, uncertainty factors range between 10 and 100. The
first uncertainty factor of 10 is interpreted as accounting for the expected
variability in the general human population to the toxicity of the chemical.
The second uncertainty factor, between 1 and 10, is thought to be necessary
because the adjusted 95% CL corresponding to 10% response is considered to
represent a LOAEL rather than a NOAEL. In this example, the choice for the
value of this variable factor depends on both the severity of the adverse
A-7
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effect (i.e., more severe effects yield a larger factor) and the slope of the
dose-response, or dose-effect curve (i.e., shallower slopes also yield a larger
factor). For example, a choice for this variable uncertainty factor of 1
should be associated with both a minimal adverse effect and a steep dose-
response or dose-effect curve.
An example of this procedure is given in Figure A-2, which is a hypotheti-
cal plot of the percentage of rats responding with a slight body weight
decrease of 5% vs. dose rate or the percentage of dogs with liver necrosis vs.
dose rate. Hypothetical responses are indicated by solid lines; lower 95%
CLs on the dose rate are shown as dashed lines. The lower 95% CLs of the dose
rates at a 10% response are adjusted by division by the cube root of the ratio
of body weight between humans and rats or dogs. For rats of 400 g weight, this
value is 5.6; for dogs of 10 kg weight, it is 1.9; both calculations assume a
70-kg body weight. In order to estimate RfD from the rat data (shown in
Figure A-2 as ADIR) the adjusted lower 95% CL is divided by a tenfold uncer-
tainty factor to account for the expected variability in the general human
population to the toxicity of a chemical in lieu of specific data, and an
additional 1.0-fold factor because the effect is both minimally severe and has
a steep dose-response slope. Thus, the total uncertainty factor is 10. In
order to estimate an RfD from the dog data (shown in Figure A-2 as ADIQ) the
adjusted lower 95% CL is divided by a 10-fold uncertainty factor to account for
the expected human variability, as before, and an additional 10-fold uncertainty
factor because the effect is more severe than a slight body weight decrease
and the slope of the dose-response is shallower. Thus, the total uncertainty
factor is 100.
b. Assumptions andLimitations. The proposed methods for estimating the
10% dose-effect or dose-response levels for continuous and quantal data,
respectively, offer several advantages when compared with traditional methodol-
ogies (Crump, 1984). These advantages, as well as difficulties with this
approach, have been discussed (Dourson et al., 1985; Crump, 1986). For
example, with this new approach, both the slope of the dose-response curve and
the number of animals used in an experiment can affect to some degree the
estimation of the RfD when quantal or continuous toxicity data are available.
Difficulties include finding appropriate data sets to model, choosing among
equally good data sets that may yield different RfDs, and, for cost-benefit
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analysis, assuming that a certain percentage response in an animal study is
equivalent to a similar percentage response in humans.
c- Status. This novel method utilizes more of the available toxicity
data than the current methodology, and perhaps offers a consistent approach for
possibly estimating health risks above the RfDs. It also addresses to some
degree several of the criticisms of the current approach, such as use of
dose-response slopes and the number of animals tested in defining NOELs. This
method will be tested on a large set of toxicity data.
IV. RESEARCH ON REFINEMENTS TO THE RfD APPROACH
1. Improved Estimates of Uncertainty Factors
a. Proposed Approach. The objective of this research is to improve
quantitative estimates of uncertainty factors and modifying factors used in the
U.S. EPA's current approach. By evaluating the effect of deviations from the
ideal desirable data base, uncertainty factors can be expressed as a range
rather than as a single number. Models are being developed of the likely
distribution of probability in the standard uncertainty factors.
The first step in this approach is to assemble an appropriate data base
for the issue in question (i.e., which uncertainty factor is being addressed,
such as the use of 10 to extrapolate subchronic to chronic data). To evaluate
the standard uncertainty factors (UFs) for the RfD and to develop better
estimates, it is necessary to have a relatively complete data base for a group
of chemicals; for example, one that contains subchronic and chronic data and
NOELs and LOAELs. Since UFs have been designed to reduce, for example, the
LOAEL to a NOAEL or to reduce a subchronic NOAEL to a chronic NOAEL, the
variable of interest is a ratio. This approach is to plot a frequency histo-
gram of the ratio of the surrogate NOAEL, the LOAEL, or the subchronic NOAEL,
to the best data point and fit a probability distribution to the data.
Sufficient toxicity data on sensitive populations are generally not
available to test the UF for interindividual variability. However, the U.S.
EPA has identified components of variability that contribute to sensitivity,
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and has evaluated the distribution of these pharmacokinetic parameters which
determine variation in delivered dose, such as areas under the curve of blood
concentration over time. Pharmacokinetic variables that affect target organ
dose fit a log normal distribution. The analysis shows that values vary as
much as 10-fold among normal healthy individuals (U.S. Environmental Protection
Agency, 1986a); (Haitis et al., 1987).
The next step would be to model the likely overall variability in a risk
estimated by means of a Monte Carlo simulation using these distributions for
input. Currently, not all results fit a known probability distribution.
b. Assumptions and Limitations. This approach is designed to obtain
better quantitative estimates for some assumptions currently used, such as the
10-fold UF for adjusting subchronic data to chronic. It assumes data similar
to that currently used to derive RfOs.
c- Status. More data are needed to model these UFs. The data base for
interindividual variability could be expanded from a pilot study. When the
probability distributions for each component of uncertainty in an RfD can be
approximated, it will be possible to perform a Monte Carlo simulation to
indicate the overall variability in the data and to estimate the probability
for the RfD given the standard UFs. Further analyses of data on the sources of
variability are needed before distribution assumptions can be made.
The estimate of the range of uncertainty for the UFs is not chemical
specific. This approach will convey the scientific uncertainty to risk manag-
ers more completely than does the current approach. Uncertainty/sensitivity
analysis presents data in a different form from that which risk managers are
accustomed to and, therefore, will require explanation of these modifications.
2. - A Statistical Procedure for Improved Estimates of the NOAEL
a. Proposed Approach. A statistical procedure has been developed that
is applicable to dichotomous data (i.e., presence/absence of a response of
interest), for which comparison of unadjusted response rates is valid,
(Unadjusted for differences in intercurrent mortality, or other factors that
could be confounded with a treatment effect.) In samples at a control, low,
and high dose, the responses are assumed to be independently distributed from
A-10
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binomial distributions with parameters Pg, PI, Pg, respectively. It is further
assumed that PQ < PI < P,,, and that a treatment effect, if present, increases
the response rate. An important aspect of the statistical method employed here
is that observed response rates are replaced by the maximum likelihood estimates
of P0, P1> and P2.
The procedure estimates the maximum likelihood for all doses and estimates
the standard deviation of the NOAEL estimate. It also estimates, for each
experimental dose, the probability of getting the observed result under the
hypothesis of "no treatment effect." Thus, the NOAEL can be expressed as a
range. The power of the test is a function of background rate, with lower
backgrounds yielding higher power. The test characteristics are discussed in
U.S. Environmental Protection Agency (1988a).
The following example demonstrates the type of results obtained from this
procedure. In a study using a control and doses of 30 and 100 mg/m , the
procedure rejects the hypothesis of no treatment effect at the high dose
(p £0,04). The expected value of the NOAEL is 47 mg/m , and the bounds at one
standard deviation are 17 and 77 mg/m . The probability of obtaining the
observed response under the null hypothesis is 76% at 30 mg/m and 24% at
100 ppm. In comparison, under the existing risk assessment procedure, the
study would provide only a NOAEL of 30 mg/m .
The response probabilities express the level of certainty of confidence in
the data. The range of one standard deviation could easily be expressed in the
RfD simply by applying UFs to upper and lower limits of the estimate.
b. Assumptionsand Limitations. This procedure is designed for dichoto-
mous (incidence) data and is a sequential test appropriate for three dose
groups. While initially designed for three doses and sample sizes up to 20, it
has the capacity to be extended for more dose groups and larger sample sizes.
It assumed that a treatment effect, if present, increases the response rate,
and that responses are to be independently distributed from binomial distribu-
tions.
c. Status. The document describing the method developed (U.S. Environ-
mental Protection Agency 1988a) has been reviewed by U.S. EPA statisticians and
revised according to these comments. The procedure has been presented at two
scientific meetings. A computer program is available for easy implementation
of the procedure on PCs.
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APPENDIX B
USE OF PHARMACOKINETIC DATA IN RISK ASSESSMENT, SELECTED EXAMPLES
While the U.S. Environmental Protection Agency has had little experience
in the development of inhalation reference doses, potency estimates for
inhalation exposure to carcinogens have been developed for quite some time.
Examples of the way that the Agency has utilized pharmacokinetic data to adjust
dose estimates for carcinogens illustrate both the necessity for utilizing all
available pharmacokinetic data, as well as the kind of empirical adjustments
which can be made to dose estimates, even in situations where complex physio-
logically based pharmacokinetic modeling is not feasible.
Example 1: Nonlinear absorption with increasing air concentration.
This example is taken from a U.S. Environmental Protection Agency publica-
tion (1985) which discusses the carcinogenicity of butadiene. The retained
dose vs. exposure concentration data that were developed separately from the
carcinogenicity evaluation are shown in Table B-l.
TABLE B-l. ABSORPTION OF 1,3-BUTADIENE BY INHALATION FOLLOWING
A 6-HOUR EXPOSURE PERIOD
Species
Rats
Mice
Exposure
(ppm)
70
930
7,100
7
80
1,040
(Mg/£)
125
1,700
12,800
13
145
1,900
1,3-
Butadiene
inhaled
(umol)
235
3,100
17,000
1.7
34.7
435.0
1,3-
Butadiene
retained
(umol)
16.3
64.7
243.0
0.9
3.2
19.1
(umol /kg)
40
160
660
33
120
660
Percent
Retained
7.1
3.1
1.5
54.0
9.6
4.7
The actual exposure concentrations in the cancer bioassays were 625 ppm
and 1,250 ppm for mice, and 1,000 ppm and 8,000 ppm for rats. By graphing log
ppm exposure vs. log-retained dose from the pharmacokinetic study, the U.S.
Environmental Protection Agency (1985) estimated the retained doses for each
of the experimental exposure concentrations used in the cancer bioassay; that
is 25.7 and 38.9 mg/kg retained dose for mice, and 10.5 and 37.1 mg/kg for
rats. After developing a unit risk estimate based on the relationship between
retained dose and tumor incidence, the unit risk was converted back into units
B-l
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of air concentration by making an assumption concerning percent retention by
humans at low exposure concentrations. If a model that assumed that retained
dose was proportional to exposure concentration were assumed, the data would
have suggested a greater than 100-fold difference 1n retained dose from low
dose to high dose in the rat study, when in fact only a 5-fold difference was
apparent, based upon retained dose estimates. Similarly, a dose proportional
to concentration assumption for mice would have suggested a 150-fold difference
between low and high dose while the retained dose fraction suggests only an
11-fold difference.
The significance of this for inhalation RfD estimation is considerable,
especially in situations where an RfD might be derived based upon a LOAEL. For
example, if we theoretically had a single exposure concentration inhalation
study of butadiene which provided data indicating that 1,040 ppm was a LOAEL,
the following situation could be envisioned. If a dose proportional to concen-
tration model is assumed, either based upon computing dose utilizing ventila-
tory volume or using a metabolic rate estimate, the following scenario could be
envisioned:
1,040 ppm = 1,900 mg/m3
1,900 mg/m3 x 0.01 mVday (mouse ventilatory volume for 6 hours) -r
0.03 kg (mouse body weight) r UF of 1,000 (10 LOAEL to NOAEL, 10 for
interspecies, 10 for sensitive subgroups) - 6.3 mg/kg/day x 70 kg -r
20 m3 = 2.22 mg/m3 as the reference air concentration for 24-hour
human exposure.
In contrast, using the retention data, the mouse exposure concentration
corresponding to a 10-fold lower retained dose (estimated from data in Table B-l)
is 45.9 mg/m3. This would be equivalent to estimating a NOAEL exposure level
for the mouse based upon retained dose:
45.9 mg/m3 x 0.01 m3/6 hours r 0.03 kg * 100 UF (10 for interspecies,
10 for intraspecies) = .11 mg/kg/day x 70 kg -r 0.5 (estimate of human
retained dose at low concentrations) -r 20 m3 = 1.07 mg/m3 as the
reference air concentration for 24-hour human exposure.
B-2
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This represents a twofold difference which would be essentially equivalent
to reducing the UF for extrapolating from a LOAEL from 10 to 5. This example
assumes that a steady state is reached within the 6-hour exposure period. If
this is not the case, linear extrapolation to a 24-hour exposure period would
be inappropriate.
Example 2: Metabolic Saturation at High Exposure Concentrations
Since animal bioassays are traditionally conducted at high exposure
concentrations and the results extrapolated to lower exposure concentrations,
the issue of saturable metabolic capacity is relevant. This consideration is
equally appropriate to both the oral and inhalation exposure routes. While the
impact of capacity-limited metabolism may be of greater concern for carcinogen
exposures where a linear, nonthreshold dose-response curve is assumed and risks
resulting from human exposures to very small quantities of the chemical of
concern are extrapolated from high dose or concentration animal exposures, a
potential for impact in the assessment of noncancer endpoints still exists.
Typically, an RfD is developed by applying a composite uncertainty factor of
from 100 to up to 10,000, to an exposure level or dose which has been experi-
mentally evaluated in an animal test system. If the critical effect is the
result of the interaction of a metabolite with the target tissue, and if
nonlinearity in the metabolized fraction of the dose exists within the range of
doses or exposure concentrations encompassed by the difference between the
experimentally evaluated dose and the projected RfO exposure level, the actual
difference between the experimental and extrapolated dose will be less than
that predicted, based upon a linear relationship between exposure and effective
dose to the target tissue. The result of this could be interpreted as an
effective erosion of the magnitude of the composite uncertainty factor. On the
other hand, if good pharmacokinetic data are available for both the experimen-
tal animal system and the human, it may be feasible to reduce the magnitude of
the uncertainty factor.
An impediment to the use of pharmacokinetic data for the adjustment of
animal dose response data in evaluations of noncancer endpoints is that the
chemical species resulting in the critical effect is less frequently identified
than for carcinogenic responses. However, it is still appropriate to evaluate
all of the available pharmacokinetic data for potential relevance to the RfD
B-3
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derivation exercise. This will become increasingly important as the Agency
moves from single medium, single route assessments towards methods for
effectively partitioning RfDs across media/routes.
The following example is taken from U.S. EPA (1986e). In this assessment,
unit risk estimates were developed for human exposure to low levels of tetra-
chloroethylene by first developing animal dose-response relationships based
upon the extrapolated animal metabolized dose at each inhalation exposure
concentration.
Table B-2 illustrates that while the total radioactivity recovered in the
72 hours following exposure of rats for a 6-hour time interval to two concen-
trations of C-tetrachloroethylene showed linearity between total recovered
radioactivity and exposure concentration, there was nonlinearity in the frac-
tion of the radioactivity attributed to metabolism.
TABLE B-2. RECOVERY OF 14C-TETRACHLOROETHYLENE RADIOACTIVITY
AFTER INHALATION EXPOSURE FOR 6 HOURS TO SPRAGUE-DAWLEY RATS
10 ppm 600 ppm
mg-equivalent tetrachloroethylene
Expired Unchanged 1.008 (68%) 68.39 (88%)
Metabolized 0.467 (32%) 9.11 (12%)
Total 1.475 77.5
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APPENDIX C
ADVERSE HUMAN RESPIRATORY HEALTH EFFECTS*
These criteria were developed to assist in the interpretations of the
epidemiologic literature on what constitutes an adverse respiratory health
effect of air pollution. Adverse human health effects caused by air pollution
are listed in hierarchical order, with the most severe at the top and the
least severe at the bottom. The reader is referred to the American Thoracic
Society (1985) guidelines for more detailed discussion.
1. Increased mortality, (Increased as used here and subsequently means
significantly (p <0.05) increased above that recorded in some standard,
comparable population. In selected situations, p <0.1 may be appro-
priate.)
2. Increased incidence of cancer.
3. Increased frequency of symptomatic asthmatic attacks.
4. Increased incidence of lower respiratory tract infections.
5. Increased exacerbations of disease in humans with chronic cardiopulmonary
or other disease which could be reflected in a variety of ways, including
the following:
Less able to cope with daily activities (i.e., shortness of
breath or increased anginal episodes).
Increased hospitalizations, both frequency and duration.
Increased emergency ward or physician visits.
Increased pulmonary medication.
Decreased pulmonary function.
6. Reduction in forced expiratory volume at one second (FEVi) or forced vital
capacity (FVC) or other tests of pulmonary function such as the following:
Chronic reduction in FEVj or FVC associated with clinical
symptoms.
*Source: American Thoracic Society, 1985.
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A significant increase in number of persons with FEVj below
normal limits; chronically reduced FEVt is a predictor of
increased risk of mortality. Transient or reversible reductions
that are not associated with an asthmatic attack appear to be
less important. It should be emphasized that a small but
statistically significant reduction in a population mean FEVj or
FEVo-75 is probably medically significant to them, but when
diluted with the rest of the population, the change appears to be
small.
An increased rate of decline in pulmonary function (FEVj),
relative to predicted value in adults with increasing age or
failure of children to maintain their predicted FEVj growth-
curve. Such data must be standardized for sex, race, height,
and other demographic and anthropometric factors.
7. Increased prevalence of wheezing in the chest, apart from colds, or of
wheezing most days or nights. (The significance of wheezing with colds
needs more study and evaluation.)
8. Increased prevalence or incidence of chest tightness.
9. Increased prevalence or incidence of cough/phlegm production requiring
medical attention.
10. Increased incidence of acute upper respiratory tract infections that
interfere with normal activity.
11. Acute upper respiratory tract infections that do not interfere with normal
activity.
12. Eye, nose, and throat irritation that may interfere with normal activity
(i.e., driving a car) if severe.
13. Detection of odors.
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APPENDIX D
CRITERIA FOR ASSESSING THE QUALITY OF INDIVIDUAL EPIDEMIOLOGICAL STUDIES*
A minimally acceptable study should meet the following criteria, which
fundamentally represent good scientific practice. The study should have been
reported or should be in press in the peer-reviewed literature.
I. The pertinent scientific background, such as reviews and supporting
rationale upon which the study was based, should be given. Sponsorship
and funding sources should be acknowledged.
2. The objectives of the study should be clearly stated and the study design
described in relation to them. Underlying assumptions and limitations of
the design also should be given.
3. The study population and comparison group description should include the
specific population from which they were drawn and the method of selec-
tion. The rationale and criteria for inclusion/exclusion in the study
should be given, particularly for exposure classifications. The appro-
priateness and limitations of the comparison group should be discussed.
The extent to which the choice of subjects depended on existing or
specially developed record systems, and implications of this upon the
analysis, should be considered. The steps taken to ensure confidential-
ity of the subjects should be accounted for.
4. Methods of data collection should be described in detail, since these
procedures will influence the derived interpretation and inferences. The
validity (accuracy) and reliability (reproducibility) of the methods used
to determine exposure should be stated. Response rates, including reasons
for implications of differing rates, should be given. The direction and
possible magnitude of any bias introduced into the study as a result of
these rates should be described. The procedures used for following the
study, methods to ensure completeness, and length of follow-up for each
group or subgroup must be included. Other validity checks (e.g., avoiding
bias by the independent ascertainment and classification of study
variables, such as blind reading of histologic slides or clerical
processing of data) also should be included.
^Adapted from: Interagency Regulatory Liason Group, 1981; Lebowitz, 1983;
American Thoracic Society, 1985.
D-l
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5. Major demographic and anthropometric confounding factors should have
been accounted for, such as age, sex, ethnic group, socioeconomic status,
smoking status, and occupational exposure. Temperature, season, and day
of the week are particularly important for acute studies of respiratory
effects and also should be accounted for.
6. The procedures and statistical methods used to describe the data, estimate
parameters, or test specific hypotheses should be presented. References
and/or specific formulae also should be given for the statistical tests
and for any programming procedures or packages that were applied.
The underlying assumptions and potential bias of the statistical methods
should be stated. Explicit description of any method used to account for
confounding factors (e.g., adjustment or matching) should be described
explicitly. This includes methods to account for missing data, such as from
nonresponse, attrition, or loss-to-follow-up. When reporting hypothesis tests,
the measure of effect, statistical significance, power, and other criteria
(e.g., one- vs. two-tailed test rationale) should be given. The point estimates
and their standard errors and/or confidence intervals should be given when
using estimation.
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APPENDIX E
CRITERIA FOR ASSESSING THE QUALITY OF INDIVIDUAL ANIMAL TOXICITY STUDIES*
A minimally acceptable study should meet the following criteria, which
fundamentally represent good scientific practice.
1. All elements of exposure should be clearly defined.
The exposure amount, administration route, exposure schedule,
and exposure duration must be described. Consideration should
also be given to the concentration and time of exposure used vs.
the expected level of human exposure.
If animal body weights, ages, or gender are not provided, con-
sideration should be given to the uncertainty in appropriate
default values.
Exposure information should include physicochemical character-
istics of the substance used, such as purity, stability, pH,
partition coefficient, particle size distribution, and vehicle.
These properties can influence the local effects and the rate
and extent of absorption, which can subsequently modify the
toxic manifestations.
Exposure information should include description of generation
and characterization technology used. The number of air changes,
temperature, and relative humidity are exposure chamber charac-
teristics which should be monitored. Cage (or other animal
holder) rotation schedule should be described.
Animal care and holding procedures should be described.
2. Controls should be comparable with test animals in all respects except
the treatment variable ("negative").
Concurrent controls must minimally include an "air-only" exposure
group; if a vehicle is used, it is desirable that there be a
"vehicle-only" group.
Historical control data can be useful in the evaluation of
results, particularly where the differences between control and
treated animals are small and are within anticipated incidences
based on examination of historical control data.
*Adapted from: Society of Toxicology, 1982; Muller et al., 1984; National
Research Council, 1984; James, 1985; and Lu, 1985a.
E-l
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3. Endpoints should answer the specific hypothesis addressed in the study,
and the observed effects should be sufficient in number or degree
(severity) to establish a dose-response relationship that can be used in
estimating the hazard to the target species.
The outcome of the reported experiment should be dependent on
the test conditions and not influenced by competing toxicities,
4. The test performed must be valid and relevant to human extrapolation.
The validity of using the test to mimic human responses must always be
assessed. Issues to consider include the following;
Does the test measure a toxicity directly or does it measure a
response purported to indicate an eventual change (i.e., severity
of the lesion)?
Does the test indicate causality or merely suggest a
chance correlation?
Was an unproven or unvalidated procedure used?
Is the test considered more or less reliable than other tests
for that endpoint?
Is the species a relevant or reliable human surrogate? If this
test conflicts with data in other species, can a reason for the
discrepancy be discerned?
How reliable is high exposure (animal) data for extrapolation to
low exposure (human scenario)?
5. Conclusions from the experiment should be justified by the data included
in the report and consistent with the current scientific understanding
of the test, the area of toxicology being tested, and the suspected
mechanism of toxic action.
6. Due consideration in both the design and the interpretation of studies
must be given for appropriate statistical analysis of the data.
Statistical tests for significance can be performed only on
those experimental units that have been randomized (some excep-
tions include weight-matching) among the dosed and concurrent
control groups.
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Some frequent violations of statistical assumptions in toxicity
testing include:
Lack of independence of observations.
Assuming a higher level of measurement than available
(e.g., interval rather than ordinal).
Inappropriate type of distribution assumed.
Faulty specification of model (i.e. linear rather
than nonlinear).
Heterogeneity of variance or covariance.
Large Type II error.
7. Subjective elements in scoring should be minimized. Quantitative grading
of an effect should be used whenever possible.
8. Evidence of adherence to good laboratory practices is required unless
exceptions have been negotiated (current testing) or considered (data
obtained from studies carried out many years ago). See also
Section 3.1.2.3.
9. Peer review of scientific papers and of reports is extremely desirable
and increases confidence in the adequacy of the work.
10. Reported results have increased credibility if they are reproduced by
other researchers and supported by findings in other investigations.
11. Similarity of results to those of tests conducted on structurally related
compounds should be considered.
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APPENDIX F
CRITERIA FOR CAUSAL SIGNIFICANCE
Statistical methods cannot establish proof of a causal relationship but
can define an association with a certain probability. The causal significance
of an association is a matter of judgment that goes beyond any statement of
statistical probability. To assess the causal significance of an air toxicant
and a health effect, a number of criteria must be used, no one of which is
pathognomonic by itself. These criteria include the following:
Consistency (reproducibility) of the association. Causal infer-
ences are strengthened when a variety of investigators have
reproduced the findings under a variety of circumstances.
Strength of the association. The larger the calculated relative
risk, the greater the likelihood that the observed association
is causal.
Specificity of the association. Causality is more likely if a
particular exposure is associated with only one illness and
vice versa. This guideline rarely applies to air pollution
research, in which all the diseases of major concern are multi-
f acton al.
Temporal relationship of the association.
Coherence of the association. An epidemiologic inference of
causality is greatly strengthed when it conforms to knowledge
concerning the biologic behavior of a toxin and its mechanism
of action. This evidence may be obtained from clinical research
or toxicologic studies.
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APPENDIX G
CHOICE OF TOXICITY DATA
Empirical observation generally reveals that as the dosage of a toxicant
is increased, the toxic response (in terms of severity and/or incidence of
effect) also increases. This dose-response relationship is well-founded in
the theory and practice of toxicology and pharmacology. Such behavior is
observed in: (1) quantal responses, in which the proportion of responding
individuals in a population increases with dose; (2) graded responses, in which
the severity of the toxic response within an individual increases with dose;
and (3) continuous responses, in which changes in a biological parameter (e.g.,
body or organ weight) vary with dose.
However, in evaluating a dose-response relationship, certain difficulties
arise. For example, one must decide on the critical endpoint to measure as
the response. One also must decide on the correct measure of dose. In
addition to the interspecies extrapolation aspects of the question of the
appropriate units for dose, the more fundamental question of administered dose
vs. absorbed dose vs. target organ dose should be considered. These questions
are the subject of much current research.
1. Critical Study and Species. Often animal data are selected as the govern-
ing information for quantitative risk assessments, since human data are
generally either unavailable or insufficient for this purpose. These
animal studies typically reflect situations in which exposure to the
toxicant has been carefully controlled, and the problems of heterogeneity
of the exposed population and concurrent exposures to other toxicants have
been minimized. In evaluating animal data, a series of professional
judgments are made that involve, among others, consideration of the
scientific quality of the studies. Presented with data from several
animal studies, the risk assessor first seeks to identify the animal model
that is most relevant to humans, based on the most defensible biological
rationale; for instance, using comparative pharmacokinetic data. In the
absence of a clearly most relevant species, however, the most sensitive
Mdapted from U.S. Environmental Protection Agency, 1987a.
G-l
-------
species (i.e, the species showing a toxic effect at the lowest administered
dose) is adopted as a matter of science policy at EPA, since no assurance
exists that humans are not innately more sensitive than any species
tested. This selection process is made more difficult if animal tests
have been conducted using different routes of exposure, particularly if
the routes are different from those involved in the human situation under
investigation.
In any event, the use of data from carefully controlled studies of
genetically homogeneous animals inescapably confronts the risk assessor with
the problems of extrapolating between species, and the need to account for
human heterogeneity and concurrent human exposures to other chemicals, which
may modify the human risk.
While there has generally been a lack of well-controlled cohort studies
that investigate noncancer endpoints and human exposure to chemicals of
interest by the oral exposure route (a useful exception being the cases of
cholinesterase inhibition), it is anticipated that there will be considerably
more human data which may be selected as the critical data for inhalation
exposure assessments. Risk assessments based on human data have the advantage
of avoiding the problems inherent in interspecies extrapolation. In many
instances, as is the case with the animal investigations, use of such studies
involves extrapolation from relatively high doses and relatively healthy
populations (such as those found in occupational settings) to the low doses
found in the environmental situations to which the general population is more
likely to be exposed. In some cases, a well-designed and well-conducted
epidemiologic study that shows no association between known exposures and
toxicity can be used to directly project an RfD, as has been done in the case
of oral exposure to fluoride (U.S. Environmental Protection Agency, 1986d).
2. Critical Data. In the simplest terms, an experimental exposure level is
selected from the critical study that represents the highest level tested
in which the critical toxic effect was not demonstrated. Where appro-
priate, adjustments in doses based upon known interspecies differences in
respiratory tract deposition must be applied before arraying the dose-
effect data to compare species sensitivity. This NOAEL is the key datum
gleaned from the study of the dose-response relationship and, tradition-
ally, is the primary basis for the scientific evaluation of the risk
G-2
-------
posed to humans by systemic toxicants. This approach is based on the
assumption that if the critical toxic effect is prevented, then all toxic
effects are prevented.
3. Dosimetric Adjustments. Exposure effect levels observed in animal studies
of any given data array on a chemical must be converted to human equivalent
concentrations before comparisons of species sensitivity and the choice
of the appropriate animal effect and critical study can be made. Conver-
sions to human equivalent concentrations are made by applying adjustment
factors to account for dosimetric differences of agents (particles or
gases) between individual animal species and humans, as discussed in
Chapter 4 and Appendices H and I.
4. Examples of "Appropriate" Choice. In the course of many risk assessment
discussions during the last several years, the Agency has decided on the
following conditions in choosing the appropriate animal effect or no-
effect level as a basis of an RfD. If an appropriate human study with a
well-defined NOAEL is available as to a chemical's critical effect, it is
used in preference to animal toxicity data in estimating RfDs. When such
human data are not available, the following sequence is used to choose
the appropriate study, species and NOAEL as a basis of RfO estimation.
It should be noted that this choice should be based on human equivalent
concentrations, that is, concentrations adjusted for dosimetric differ
ences between animals and humans as described in Chapter 4.
The Agency chooses the most appropriate NOAEL of the critical
effect from a well-conducted study on a species that is known
to resemble the human in response to this particular chemical
(e.g., by comparative pharmacokinetics).
When the above condition is not met, the Agency generally
chooses the most sensitive study, species, and NOAEL, as judged
by an interspecies comparison of the NOAEL and LOAEL. Table G-l
outlines examples of this condition.
G-3
-------
TABLE G-l. COMPARISON OF THE HIGHEST INDIVIDUAL SPECIES HUMAN
EQUIVALENT* NOAEL AND ITS LOAEL (OR LEL)
Effect Level
(mg/m3)
Species
Comments
Dog
Rat
Mouse
(GTven The Same Critical Effect)
Example 1:
LOAEL (LEL) 100 120
NOAEL 50 60
Example 2:
LOAEL (LEL) 120 100
NOAEL
90
75
Example 3:
LOAEL (LEL)
NOAEL
75
80
The proper choice is generally the
highest dog NOAEL of 50 mg/m3,
80 since the potential experimental
threshold in dogs (i.e., the
potential LOAEL) may be below the
highest NOAELs in both rats and
mice.
90 The proper choice is generally the
mouse LOAEL (or LEL) of 90 mg/m3,
since the potential experimental
threshold in mice may be less than
the highest NOAELs for both dogs
and rats. Judgment is needed in
this example to ensure that the
adverse effects seen in all three
species are truly minimal. For
example, if any of the LOAELs
(or LELs) in the species represented
an increase in mortality, no firm
basis for the development of an
RfD exists. This is based on the
general observation that mortality
data are far removed quantitatively
from chronic LOAELs and NOAELs,
and thus, the data base has failed
to establish the likely experi-
mental threshold for the most
sensitive endpoint.
90 The proper choice is generally the
dog LOAEL of 75 mg/m3, since by
definition this represents the
most sensitive species (see,
however, the caution in
Example 2).
(continued on the following page)
G-4
-------
TABLE G-l. COMPARISON OF THE HIGHEST INDIVIDUAL SPECIES HUMAN
EQUIVALENT* NOAEL AND ITS LOAEL (OR LEL) (continued)
Effect
(mg/m
Level
3)
Dog
Species
Rat
Mouse
(Given
The
Comments
Same Critical
Effect)
Example 4;
LOAEL (LEL) - The proper choice is generally the
highest rat NOAEL of 90 mg/m3,
NOAEL 100 90 120 since no assurance exists that
the experimental threshold in rats
is not below the highest NOAELs of
both dogs and mice. This situa-
tion is unusual and should be
judged carefully; since a LOAEL
(or LEL) has not been determined,
the RfD may be unduly conservative.
Strict interpretation of this
example might lead to strikingly
lower RfDs if other species are
tested at much lower doses. Such
RfDs may not be appropriate.
*Human equivalent NOAEL or LOAEL refers to concentrations adjusted for
dosimetric differences between animals and humans.
G-5
-------
APPENDIX H
CALCULATION OF RDDR AND AN EXAMPLE APPLICATION
OF DOSIMETRIC ADJUSTMENT FOR PARTICLE EXPOSURES
INTRODUCTION
The purpose of this appendix is to illustrate how the Regional Deposited
Dose Ratio (RDDR) is calculated for use in the adjustment of exposure effect
levels for dosimetric differences between species as in Section 4.1.1.2.3.
Further refinement of this adjustment, as recommended by the external workshop
review committee, is described in the research and development section at the
end of this Appendix. The adjustment of exposure effect levels in rats for the
theoretical compound ep(a)oxide will be used to illustrate this application.
The health effects data shown for the compound ep(a)oxide are motivated by
actual data on the toxicological effects of various aerosols.
METHODS
The initial regional respiratory tract deposition of a given aerosol
exposure to an experimental species can be calculated using typical aerosol
distribution data (i.e., an aerosol characterized by a given mass median
aerodynamic diameter (MMAD) and a geometric standard deviation [0 ]). The
i9
Regional Deposited Dose (ROD), or mass of aerosol initially deposited, for a
given species is generated by integrating the product of an aerosol distribution
and the deposition efficiency curves in regions (extrathoracic, tracheobron-
chial, and pulmonary) of the lung. A schematic of this integration is shown in
Figure H-l for the rat. The area under the particle distribution curve of each
particle size diameter interval, for example, the interval of 2-3 pm (grey
shading), is integrated with the deposition efficiency curve of a particular
lung region for that same interval. Summation of these products across all the
particle size ranges yields the RDD to that region (computed notationally in
Equation 4-4). The RDD is calculated for each region of the lung; that is the
extrathoracic (ET), region the tracheobronchial (TB) region, the pulmonary
(PU), region the thoracic (TH), region and the total respiratory (TOT) system.
These estimates are then adjusted for ventilatory parameters and lung surface
areas.
H-l
-------
A, Aerosol Distribution
O
s
I
0
o
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
MM AD = 1.0
-------
The rat data used in this presentation for ROD and RDDR calculations
(Jarabek et al,, 1989a) are those of Raabe et al. (1988), The ET deposition
deposition was calculated as the sum of the laryngeal, nasopharyngeal, and
gastrointestinal fractions reported. These data were reported as means so
that it was not possible to fit nonlinear regression models as was done for
the humans. RDOs were estimated by linear interpolation instead.
The human ROD values were calculated similarly to calculations for the
rat. Extrathoracic deposition was estimated as a function of (pd2Q) where p is
3
particle mass density (g/m ), d is the geometric particle diameter (urn), and Q
3
is the airflow rate (cm /sec). Equations were estimated separately for experi-
ments in which nasal breathing or oral breathing was used (Miller et al. 1988).
Extrathoracic deposition then was calculated for normal augmenters (people who
habitually breathe through the nose except in exercise conditions) and for
mouth breathers using a proportionality factor for the split in airflow between
nose and mouth as given in Niinimaa et al., (1981). Logistic regression models
were used to estimate the human TB region deposition as a function of aero-
dynamic diameter. The models used were those developed by Miller et al.
(1988), based on percentage of particles entering the trachea and were fit to
TB deposition from several laboratories. The PU region deposition estimates
for humans were calculated based on a theoretical model presented in Martonen
and Miller (1986).
The surface area value of the ET region for the rat was calculated from
the length and perimeter data in Schreider and Raabe (1981). For humans, the
ET region surface area value was estimated by representing the region as
sequential cylinders, using empirical data for volume and length values from
solid silicone casts (Patra et al. 1986). The "whole" lung model of Yeh
et al. (1979) was used to estimate the surface-area values for the TB and PU
regions of the rat. The human data of Weibel (1963) on the number of dimensions
of airways (represented as cylinders) in each generation were modified in a
manner similar to that of Paiva (1973) to estimate the human surface-area
values for the TB and PU regions (Miller et al. 1985). The procedure used to
adjust the airway dimensions of the TB and PU from total lung capacity to
function residual capacity (FRC = 50% TLC) is described in Overton et al.
(1987). The minute volume reported by Raabe et al. (1988) was used for the
rat. The default vz
for the human value.
rat. The default value used by the U.S. EPA, 20 m3/day (13.8 £/nrin), was used
H-3
-------
It is recognized that this approach is based on deposition efficiency data
obtained or derived under a particular set of ventilatory parameters; that is,
the experimental parameters for the animal and a derived human breathing
pattern (13.8 £/min or 20 m /day). The assumption in this application is that
it is valid to linearly extrapolate from these values to other sets of breathing
parameters. The parameters of this assumption, such as the effect of activity
pattern and allometric relationships between lung weight, lung surface area,
minute volume, and body weight (Adolph, 1949; Weibel, 1972; U.S. Environmental
Protection Agency, 1988c) remain to be investigated as part of this methodology
development.
The ROD for the species in question then can be divided by the corre-
sponding ROD for humans to calculate the relative ratio of deposition in that
species to the deposition in humans. That is, the Regional Deposited Dose
Ratio (RDDR) then is calculated by:
RDDR = (RDD)./(RDD)U
rt n
where: (ROD), = regional deposited dose in species of interest,
adjusted for surface area and ventilatory volumes, and
(RDD)u = regional deposited dose in humans, adjusted for surface
area and ventilatory volumes.
The appropriate RDDR to calculate is dictated by the observed toxicologic
effect. For example, the RDDR for extrarespiratory (ER) effects (RDDRpR) would
be computed (Equation 4-6, 4-7) to determine the dose to the respiratory system
in order to assess an ER toxic effect (i.e., the assumed default until
clearance, uptake, metabolism, and distribution functions are incorporated).
However, the RDDR for the TB region alone (RDDTB) would be calculated for an
effect involving conducting airways, and the RDDRpu for an effect involving the
PU region. An effect involving the entire respiratory system would be correct
by RDDRTOT.
It should be noted that for "lung" (TH) effects, the appropriate RDDR to
use for adjustment is the RDDR for the TB and PU regions together. The RDDR
values for the TB and PU regions cannot be added together as they appear in
Table H-l, however, due to the surface area and ventilatory parameter correc-
tions to the respective deposited dose of each. Therefore, a TH column has
been provided which includes the appropriate calculations.
H-4
-------
The RDDR then can be used to scale the exposure concentration associated
with the observed effect to an equivalent concentration which reflects dosi-
metric differences between humans and the experimental species in question.
That is, the RDDR provides a factor for adjusting the no observed adverse
effects level (NOAEL), according to Equation 4-5 for respiratory tract effects:
NOAEL[HEC] (na/«») = NOAEL[ADJ] (mg/m") x RDDR(£T>
where; NOAELrflrm = the NOAEL adjusted for duration according to
-[ADJ]
Equation 4-3, and
RDDR = (RDD)A/(RDD)H, the ratio of regional dose in animal
species to that of humans across regions of inter-
est for the toxicologic effect.
This is the NOAEL level that then would be arrayed with other NOAELS to deter-
mine the most sensitive species and the key study as described in Appendix D.
RDDR values for the rat to the human deposition are provided in Table H-l.
As mentioned, the RDDR^rn^ is computed to adjust for ER effects. Equation 4-6
is used to calculate the ROD expressed as mg/kg per minute:
10"6 YVTf n
= ! i P E.
ER 1i
where:
P. = the particulate mass fraction in the exposure size distribution
(MMAD, ag),
E. = the deposition efficiency of that size distribution (MMAD, a ) in
the entire respiratory tract for the species of interest, *
n = number of size ranges,
Y - exposure level (mg/m3),
Vt = tidal volume (m£),
f = breathing frequency (breaths/min), and
BW = body weight (kg).
H-5
-------
TABLE H-l. RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1,200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.200
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
MMAD
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10. 000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
ET
1.5195
0.4432
0.2263
0.1437
0.1023
0.0782
0.0663
0.0634
0.0704
0.0829
0.1383
0.1643
0.1796
0.1835
0.1794
0.1747
0.1728
0.1731
0.1740
0.1738
0.1730
0.1713
0.1691
0.1670
0.1650
0.1632
0.1615
0.1604
1.3296
0.3940
0.2046
0.1313
0.0988
0.0837
0.0807
0.0842
0.0912
0.1008
0.1407
0.1630
0.1734
0.1766
0.1770
0.1760
0.1746
0.1735
0.1723
TB
H
61.8242
20.6081
12. 3648
8.8320
6.3086
4.5963
3.8552
3.3463
3.0191
1.5052
0.9147
0.6871
0.7164
0.8607
0.9277
0.8472
0.6849
0.5029
0.3458
0.2408
0.1802
0.1460
0.1305
0.1262
0.1322
0.1388
0.1461
H
61.8242
20.6081
12.6172
7.8857
5.4674
4.3372
3.4847
2.9602
2.5641
1.4140
0.9813
0.8327
0.7969
0.7796
0.7433
0.6787
0.6022
0.5297
PU
0.6385
1.1253
1.5359
1.7485
1.4387
1.1253
1.0277
1.0760
1.2105
1.3301
1.2869
1. 0862
0.9317
0.8296
0.7494
0.6628
0.5933
0.4945
0.4720
0.3544
0.3102
0.2690
0.2846
0.2914
0.3618
0.3643
0.4601
0. 5464
0.6245
1.0824
1.4387
1.4760
1.3577
1.2386
1.1990
1.1972
1.2316
1.2554
1.2310
1.0812
0.9466
0.8474
0.7615
0.6842
0.6162
0.5766
0.5204
TH
1.1165
1.9483
2.5809
2.8390
2.3108
1.8061
1.6071
1.6400
1.7682
1.8755
1.5325
1.1512
0.9376
0.9024
0.9648
0.9856
0.9131
0.7586
0.5915
0.4337
0.3252
0.2546
0.2232
0.2064
0.2132
0.2162
0.2344
0,2513
1.0919
1.8748
2.4236
2.4448
2.1744
1.9370
1.8297
1.7697
1.7556
1.7315
1.4575
1. 1841
1.0325
0.9645
0.9175
0.8645
0.7944
0.7251
0.6487
TOT
1.7661
2.5931
2.6349
2.2689
1.7196
1.3298
1.1469
1.1125
1.1877
1.3024
1.6286
1.7450
1.8156
1.8413
1.8293
1.8051
1.7844
1.7680
1.7608
1.7502
1. 7484
1.7466
1. 7449
1.7431
1.7466
1.7466
1.7484
1.7518
1. 7196
2.4566
2.4383
2.0151
1.6362
1.4026
1.3164
1.3022
1.3369
1.3936
1.6255
1.7403
1.7912
1.8075
1.8083
1.7989
1.7850
1.7773
1.7681
ER
0.0096
0.0141
0.0143
0.0123
0.0093
0.0072
0.0062
0.0060
0.0064
0.0071
0.0088
0.0095
0.0098
0.0100
0.0099
0.0098
0.0097
0.0096
0.0095
0.0095
0.0095
0,0095
0.0095
0.0094
0.0095
0.0095
0.0095
0.0095
0.0093
0.0133
0.0132
0.0109
0.0089
0.0076
0.0071
0.0071
0.0072
0.0076
0.0088
0.0094
0.0097
0.0098
0.0098
0.0098
0.0097
0.0096
0.0096
(continued on the following page)
H-6
-------
TABLE H-l. (continued)
Sigma g
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.400
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.600
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
MMAD
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
ET
0.1713
0.1700
0.1686
0.1671
0.1659
0.1644
0.1630
0.1618
0.1605
1.0637
0. 3431
0.1859
0. 1262
0.1040
0.0964
0.0973
0.1011
0.1081
0.1149
0. 1441
0. 1607
0.1690
0.1726
0.1740
0.1736
0.1729
0.1720
0.1709
0.1697
0. 1684
0.1671
0.1660
0.1649
0.1635
0.1623
0.1615
0.1605
0.9670
0.2995
0.1699
0.1265
0.1118
0.1089
0.1099
0.1145
0.1201
0. 1249
TB
0.4534
0.3836
0.3337
0.2804
0.2456
0.2220
0.2062
0.2019
0.1971
H
30.9121
15.7715
9.0123
6.5609
4.8666
3.7187
3.0492
2.6105
2.1990
1.3458
1.0281
0.8755
0.7987
0.7333
0.6778
0.6207
0.5698
0.5232
0.4797
0.4347
0.4024
0.3685
0.3441
0.3184
0.2912
0.2748
0.2704
61.8242
31.5429
12.6172
8. 0434
5.5726
4.0820
3.2116
2.7104
2.3263
1.9922
PU
0.4726
0.4371
0.4371
0.4007
0.4163
0.4371
0.4512
0.4708
0.5322
0.6144
1.0332
1. 2915
1.3577
1.3211
1.2932
1.2562
1.2466
1.2333
1.2468
1.1704
1.0519
0.9500
0.8529
0.7838
0.7286
0.6712
0.6411
0.5971
0.5749
0.5564
0.5335
0.5380
0.5208
0.4996
0.5198
0.5299
0. 5246
0.6014
0.9714
1. 2148
1.2924
1.3021
1.2846
1.2562
1.2420
1.2399
1.2224
TH
0.5696
0.4981
0.4530
0.3909
0.3599
0.3390
0. 3228
0.3182
0.3223
1. 0742
1. 7744
2.1791
2.1980
2.0919
1.9810
1.8576
1.7829
1.7185
1.6619
1.3927
1.1928
1.0614
0.9693
0.8978
0.8374
0.7739
0.7250
0.6718
0.6290
0.5842
0.5479
0.5177
0.4886
0.4563
0.4327
0,4163
0.4086
1.0460
1.6847
2.0222
2.0745
2.0228
1.9252
1.8130
1.7433
1.6819
1.6047
TOT
1.7626
1.7572
1.7554
1.7501
1.7501
1.7501
1.7483
1.7501
1. 7483
1.6755
2. 2848
2.2014
1.8621
1.6288
1.4980
1.4390
1.4272
1.4504
1.4806
1.6340
1.7234
1.7662
1.7826
1.7885
1.7850
1.7785
1.7761
1.7702
1. 7682
1.7627
1.7590
1.7590
1.7554
1.7501
1.7466
1.7466
1. 7448
1.6255
2.1303
2.0311
1.7987
1. 6442
1.5605
1. 5108
1.5125
1.5297
1.5401
ER
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0091
0.0124
0.0119
0.0101
0.0088
0.0081
0.0078
0. 0077
0.0079
0.0080
0.0089
0.0093
0.0096
0.0097
0.0097
0.0097
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0088
0.0115
0.0110
0.0098
0.0089
0.0085
0.0082
0.0082
0.0083
0.0083
(continued on the following page)
H-7
-------
TABLE H-l. (continued)
Sigma g
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
1.800
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2,000
MMAD
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
ET
0.1468
0.1594
0.1560
0.1694
0. 1707
0.1709
0.1707
0.1700
0.1692
0. 1682
0.1670
0.1660
0.1652
0.1640
0.1632
0.1624
0.1615
0. 1607
0. 7598
0.2664
0.1632
0.1307
0.1201
0.1198
0.1209
0.1250
0.1286
0.1324
0.1489
0. 1590
0.1639
0.1668
0.1681
0.1686
0.1685
0.1681
0.1675
0.1668
0.1660
0.1652
0.1644
0.1637
0.1630
0.1623
0.1616
0.1611
TB
1.3024
1.0281
0.8766
0.7886
0.7161
0.6641
0.6200
0.5715
0.5330
0.4937
0.4648
0.4393
0.4129
0.3891
0.3725
0.3552
0.3373
0.3186
61.8242
21.0286
10.7246
6.5609
4.4581
3.4697
2.8262
2.4393
2.1269
1.8926
1.2821
1.0222
0.8874
0.7823
0.7210
0.6659
0.6194
0.5797
0.5456
0. 5162
0.4817
0.4673
0.4363
0.4166
0.4004
0.3920
0.3748
0.3659
PU
1.1323
1. 0435
0.9538
0.8893
0.8107
0.7714
0.7178
0.7034
0.6557
0. 6467
0.6263
0.6020
0.5933
0.5828
0.5952
0.5693
0.5828
0. 5828
0.5920
0.9240
1. 1486
1. 2258
1.2668
1. 2764
1. 2442
1.2256
1. 2240
1.2021
1. 1167
1.0286
0.9647
0.8979
0.8493
0.8029
0.7686
0.7406
0.7083
0.6946
0. 6943
0.6661
0.6520
0.6358
0.6514
0.6325
0.6358
0.6245
TH
1.3548
1.1905
1.0653
0.9826
0.8992
0.8474
0.7931
0.7520
0.7026
0.6676
0.6357
0.6046
0.5776
0.5517
0.5390
0.5134
0.4981
0.4778
1.0297
1.5911
1.9047
1.9551
1.9130
1.8595
1.7568
1.6924
1.6351
1.5712
1.3387
1.1798
1.0780
0.9832
0.9214
0.8632
0.8153
0.7738
0.7340
0.7049
0.6758
0.6524
0.6196
0.5955
0.5848
0.5695
0.5530
0.5402
TOT
1.6454
1. 7134
1. 7468
1.7670
1.7696
1.7744
1.7736
1.7730
1.7689
1.7668
1.7611
1.7592
1.7573
1.7537
1.7537
1.7501
1. 7484
1. 7449
1. 5784
1. 9877
1. 9289
1.7619
1. 6507
1.6105
1. 5707
1.5741
1.5807
1.5890
1.6595
1.7105
1.7379
1.7505
1.7601
1.7653
1.7666
1.7660
1.7638
1. 7635
1.7596
1.7595
1.7540
1.7521
1.7520
1.7502
1.7484
1. 7466
ER
0.0089
0.0093
0.0095
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0086
0.0108
0.0105
0.0096
0.0089
0.0087
0.0085
0.0085
0.0086
0.0086
0.0090
0.0093
0.0094
0.0095
0.0095
0.0096
0.0096
0.0096
0.0096
0.0096
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
(continued on the following page)
H-8
-------
TABLE H-l. (continued)
Sigma g
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.200
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
MMAO
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
5.500
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
4.500
5.500
ET
0.6648
0.2416
0.1593
0.1354
0.1281
0.1279
0.1289
0.1319
0.1351
0.1382
0.1510
0.1582
0.1625
0.1649
0.1661
0.1666
0.1667
0.1664
0.1660
0.1655
0.1649
0.1644
0.1637
0.1633
0.1627
0.1622
0.1615
0.1610
0.5674
0.2278
0.1595
0.1404
0.1350
0.1344
0.1357
0.1377
0.1398
0.1424
0.1523
0.1579
0.1614
0.1637
0.1645
0.1651
0.1651
0.1651
0.1648
TB
61.8242
15.7715
9.1925
5.4674
4.0078
3.1543
2.6636
2.2869
2.0119
1.8025
1.2417
1.0029
0.8746
0.7886
0.7121
0.6673
0.6249
0.5951
0.5542
0.5306
0.5118
0.4927
0.4686
0.4489
0.4287
0.4206
0.4122
0.3911
63.0859
15.7715
7.1497
4.7765
3.5859
2.9440
2.4799
2.1523
1.8764
1.6918
1.2217
1.0127
0.8803
0.7954
0.7284
0.6687
0.6309
0.6002
0.5757
PU
0.5859
0.8946
1.0748
1.1714
1.2171
1.2223
1.2157
1.2093
1.2031
1.1814
1.1004
1.0339
0.9767
0.9238
0.8656
0.8283
0.8055
0.7702
0.7622
0.7325
0.7349
0.7263
0.7166
0.6934
0.6800
0.6857
0.6780
0.6842
0.5769
0.8548
1.0339
1.1276
1.1823
1.1990
1.2042
1.1819
1.1710
1.1604
1.0832
1.0239
0.9714
0.9337
0.8921
0.8649
0.8345
0.8216
0.8070
TH
1.0191
1.5204
1.7733
1.8342
1.8312
1.7711
1.7168
1.6555
1.6000
1.5382
1.3147
1. 1742
1.0775
1.0011
0.9234
0.8760
0.8357
0.7982
0.7633
0.7320
0.7172
0.6973
0,6721
0.6457
0.6218
0.6153
0.6043
0.5851
1.0125
1.4548
1.6730
1.7577
1.7573
1.7297
1.6787
1.6082
1.5430
1.4942
1.2975
1.1746
1.0783
1.0102
0.9476
0.8942
0.8533
0.8248
0.7994
TOT
1.5485
1.8827
1.8340
1.7305
1.6684
1.6331
1.6155
1.6141
1.6202
1.6251
1.6710
1.7052
1.7310
1.7444
1.7482
1.7538
1.7593
1.7589
1.7585
1.7564
1.7581
1.7579
1.7541
1.7522
1.7504
1.7503
1.7467
1.7450
1.5217
1.8103
1.7783
1.7420
1.6838
1.6637
1.6524
1.6430
1.6385
1.6451
1.6795
1.7074
1.7240
1. 7401
1. 7442
1. 7480
1.7497
1.7534
1.7550
ER
0.0084
0.0102
0.0099
0.0094
0.0090
0.0089
0.0088
0.0087
0.0088
0.0088
0.0091
0.0092
0.0094
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0082
0.0098
0.0096
0.0093
0.0091
0.0090
0.0090
0.0089
0.0089
0.0089
0.0091
0.0093
0.0093
0.0094
0.0095
0.0095
0.0095
0.0095
0.0095
(continued on the following page)
H-9
-------
TABLE H-l. (continued)
Sigma g
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
2.400
MMAD
6.000
6.500
7.000
7.500
8.000
8.500
9.000
9.500
10.000
ET
0.1645
0.1641
0.1636
0.1633
0.1629
0.1625
0.1619
0.1616
0,1611
TB
0. 5512
0.5267
0.5022
0.4900
0.4701
0.4622
0.4497
0.4290
0.4206
PU
0.7798
0.7744
0.7568
0.7494
0.7286
0.7362
0.7312
0.7061
0.6994
TH
0.7682
0.7457
0.7178
0.7036
0.6779
0.6730
0.6593
0.6308
0.6201
TOT
1.7548
1.7546
1.7508
1.7525
1.7506
1.7524
1.7487
1. 7468
1.7450
ER
0,0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
0.0095
*H = Humans receive some deposition, but rats do not.
R = Rats receive some deposition, but humans do not.
Source: adapted from Jarabek et al., 1989a.
The ratio is the extrarespiratory RDDs calculated for the experimental species
and human then is used to calculate the HEC Equation 4-7:
NOAEL[HEC](mg/m3) = NOAEL[ADJ](mg/m3) x RDDR£R
where:
NOAELrupp-i = the NOAEL human equivalent concentration,
NOAELr.ni1 = the NOAEL adjusted for duration according to
L J Equation 4-3, and
RDDRrR = (RDDrR)fl/(RDOER)H' the ratl° of tne dose available
for the entire respiratory system of the experimental
animal species to that of humans.
It should be noted that body weight and not surface area is in the denominator
of the calculation for ROD for ER effects. THE RDDR VALUES IN TABLE H-l FOR ER
EFFECTS DO NOT HAVE BODY WEIGHT FACTORED IN AT THIS TIME, PENDING RESOLUTION ON
RECOMMENDED VALUES FOR BODY WEIGHTS, (SEE SECTION 4.1.1.4). THUS, THESE RATIOS
WILL NEED TO BE MULTIPLIED BY (BW)H/(BW)ft WHEN USED. THOSE VALUES FOR WHICH AN
"H" APPEARS INDICATE NUMBERS FOR WHICH HUMANS RECEIVE SOME DEPOSITION BUT RATS
DO NOT. THE "R"s INDICATE VALUES FOR WHICH RATS RECEIVE SOME DEPOSITION AND
HUMANS DO NOT. IN THESE CASES, ROD VALUES MAY PROVIDE SOME INSIGHT ON THE
ASSESSMENT, BUT SHOULD BE DISCUSSED WITH AN EPA SCIENTIST FIRST.
H-10
-------
A plot of the RDDR for rats vs. humans for the TB region is shown in
Figure H-2 and for the PL) region in Figure H-3, The plots show two different
standard deviations of aerosol distributions, a a of 1,4 and 2.4 (essentially
y
monodisperse and polydisperse distributions), to illustrate the sensitivity of
the burden ratios to that parameter. The line is drawn across the plot from
the RDDR value of 1.0 as a demarcation. Values of RDDR greater than 1.0
indicate where the rat receives more of an inhaled dose relative to humans,
and thus adjustment by the RDDR would result in a larger NOAELur/. than the
animal NOAEL..,, estimate. Below the demarcation line, the animals receive less
of that characteristic dose relative to humans, and adjustment by the RDDR
would result in a decreased NOAEL,rp relative to the animal NOAEL.DJ estimate.
Note that the rat receives a much higher burden in the TB region (Figure H-2)
relative to humans for particles less than 2 jjm, while humans receive higher
relative doses in the TB region for particles greater than 2 pm. With the
exception of the particle size range of 0.2 to 2 urn, where the rat receives
more, humans receive a greater dose relative to rats across the entire particle
size range in the PU region (Figure H-3), and the equivalent exposure concen-
trations would be scaled downward. These plots help to illustrate the effect
of dosimetric adjustment on the apparent (observed) effect concentration.
The influence of breathing route (i.e., nose-breathing with normal augmen-
tation through the mouth vs. mouth breathing alone) on DDRs is significant
as illustrated in Figure H-4, plots A vs. B. The total RDDR for mouth
breathers (B) is higher for the entire particle size range in comparison to
normal augmenters (A). This difference emphasizes the need for an activity
pattern scenario for humans (e.g., x hours rest, y hours light activity,
z hours heavy exercise) to account for changes in deposition pattern due to
breathing patterns, rather than calculating RDDRs for humans using an assumed
2
default ventilatory parameter (i.e., 20 m /day or 13.8 £/min). A range in
minute ventilation from 12 to 132 JH/min has been associated with representative
types of exercise from light to severe (U.S. Environmental Protection Agency,
1986c). Humans normally augment respiratory airflow with oronasal breathing
when minute ventilation exceeds approximately 35 Jfc/min (U.S. Environmental
Protection Agency, 1986c), and this breathing mode significantly alters the
regional deposition of inhaled particles (Miller et al., 1988). This altera-
tion in regional deposition then significantly alters the RDDR used to adjust
the experimental exposure concentration to a human equivalent concentration,
H-ll
-------
Minute Volume, ml
Surface Area, cm2
Rat
130.0
37.6
Human
13800.0
5036.0
0123456789 10
Particle Diameter (MMAD, pm)
Figure H-2. RDDR of the rat to the human by particle diameter (MMAD) for the
TB region.
Source: Jarabek et al., 1989a.
H-12
-------
1.6
1.4
1.2
§0.6
a
0.4
0.2
0.0
Minute Volume, mt
Surface Area, cm2
Rat
130.0
3424.0
Human
13800.0
635545.0
01 23456789 10
Particle Diameter (MMAD, |jm)
Figure H-3. RODR of the rat to the human by particle diameter (MMAD) for the
PU region.
Source: Jarabek et al. 1989a.
H-13
-------
3.5
(A)
3.0
.2
OB
0) -"w
Q 1.5
1.0
0.5
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3473.2
Human
13800.0
640758.0
(B)
01 23456789 10
Particle Diameter (MMAD, |jm)
3.5
3.0
CO
1.0
0.5
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3473.2
Human
13800.0
640758.0
01 23456789 10
Particle Diameter (MMAD,
Figure H-4. RDDR of the rat to the human by particle diameter (MMAD) for the
TOT system in (A) normal augmenters and (B) mouth breathers. A proportionality
factor for the split in air flow between nose and mouth (Niinimaa et al., 1981)
was used in human deposition calculation for plot (A).
Source: Jarabek et al. 1989a.
H-14
-------
and thus, significantly alters the derived RfD.. Computation of a representa-
tive activity pattern for humans as proposed will make better use of models
that estimate deposition burdens as a function of the complex interaction
between breathing route, ventilation level, and particle aerodynamic properties.
This will provide a more realistic estimate of probable human exposure.
EXAMPLE CALCULATIONS
Ep(a)oxide is a hypothetical noxious agent found as a insoluble particulate
emission from municipal waste combustion sources, and there is a need to
calculate a proposed RfD.. Associated health effects of ep(a)oxide include
both central nervous system (CMS) and respiratory functional and structural
abnormalities. Recently, two well-conducted, chronic inhalation toxicology
investigations have been performed by two different laboratories that evaluate
these effects in rats. The NOAELS of the critical effect data evaluated in
these investigations are summarized in Table H-2, but since dosimetric adjust-
ments have not been made for the exposure conditions or the observed toxic
effects, comparison is not possible. The following outlines the steps which
would need to be executed to perform this adjustment. It should be noted that
in this example both investigations were performed on the rat, while other
studies may require that an RDDR be tabulated for other species in question.
Equation 4-3 would first be applied to the results in order to adjust for
the discontinuous exposure protocol.
NOAEL,ADJ-, (mg/m3) = E (mg/m3) x D (hours/day/24/hours) x W (days/7 days)
where: E = experimental exposure level,
D = number of hours exposed/day/24 hours, and
W = number of days of exposure/7 days.
The calculation for duration adjustment of the Laboratory 1 exposure is:
NOAELr.nn(mg/m3) = 120.0 x 8/24 x 5/7
LADJJ = 29 mg/m3.
The calculation for ep(a)oxide results from Laboratory 2 is given by:
NOAELr.nn (rag/in3) = 12 x 8/24 x 5/7
LADJJ =2.9 mg/m3.
H-15
-------
TABLE H-2. SUMMARY OF SYSTEMIC TOXICITY NOAELS* FOR EP(a)OXIDE
OBSERVED IN FISCHER 344 RATS
Exposure
120 mg/m3
MMAD = 2. 0 un»
G = 1.6
g
12 mg/m3
MMAD = 0.2 pro
c = 1.8
y
Duration
8 h/day
5 days/week
for 9 months
8 h/day
5 days /week
for 12 months
System
Exami ned
CNS
Respiratory
Effects
No exposure-related
effects on EMG or
limb tremor
No exposure- related
decrease in
mucociliary clear-
ance or alterations
in epithelial
archi tecture/gobl et
cell hypertrophy
Reference
Lab 1
Lab 2
*It Should be noted that only the NOAEL data (adverse effects occurred at
higher exposure concentrations in each investigation) is provided for this
ep(a)oxide and not a full data array. Choice of toxicity data is discussed
in Appendix G and entails an analysis of all data, NOAEL/LOAEL interfaces,
and such. This table is provided only to illustrate the dosimetnc
adjustments.
The RDDR for each exposure condition and toxicologic effect then is
calculated by using Table H-2.
The effect of interest is an ER effect for the exposure conditions (o =
y
1.6, MMAD = 2.0 urn) investigated by Laboratory 1 so that an RDDR corresponding
to a o of 1.6 and MMAD of 2.0 should be read from the ER column (see page H-7).
y
The resulting RDDR is 0.0093. However, as previously discussed, these values
in Table H-l for RDDRrn do not have the ratio of body weights factored in, so
this value will need to be adjusted by (BW)u/(BW)ft. The default value for body
n H
weight for male Fischer 344 rats is .38 kg (U.S. Environmental Protection
Agency, 1988c), and the default body weight for humans is 70 kg, thus,
.0093 multiplied by 70/.38 results in a RDDR£R of 1.7. This ratio then is used
in Equation 4-7 to calculate the NOAELHcC for ER effects as:
NOAEL[-HEC-|(mg/ni3) =
= 29 x 1.7
=49.3 mg/m3
x RDDR
ER
H-16
-------
For the results of Laboratory 2, an RDDR is calculated for only the TB
region since measurements of mucociliary clearance and histopathology were
used to assess effects in the tracheobronchial region. Therefore, dose
adjustment by the TB region RDDR value is appropriate. The RDDR for the TB
region corresponding to an exposure condition of a = 1.8 and an MMAD = 0.2 urn
is 31.54 (see page H-7).
Equation 4-5 then is used to adjust the exposure effect levels for dosi-
metric differences as follows:
NOAEL[HEc;] (mg/m3) = NOAEL[ADJ] (uig/m3) x RDDRpu<
The NOAEL observed in the investigations of Laboratory 2 adjusted for dosi-
metric differences is:
rHEC-, (mg/m3) = 2.9 rag/m3rADJ-,
= 91.5 mg/it^
Thus, after dosimetric adjustment, the NOAELH£C for ER effects (CNS) of
49.3 mg/m from the investigations of Laboratory 1 is lower than that observed
3
for the TB effects (91.5 mg/m ) observed by Laboratory 2.
This emphasizes the need for dosi metric adjustments prior to data array
analysis and key study selection, since, as in this example, an observed NOAEL
in the same animal species that appears to be 10-fold greater than another
NOAEL may actually result in a smaller NOAEL,,pp once such adjustments are
made. Dosimetric adjustments also will affect comparisons across species. As
illustrated in Figure H-5, exposure to rats, mice, and guinea pigs to the same
exposure concentration with an MMAD of 2.0 urn and a a of 1.4 would result in
different NOAEL..™ estimates (1.1, 1.7 and .74 times the exposure concentration,
respectively). Again, this illustration emphasizes the need to correct
exposure concentrations to human equivalents before choosing the critical
effect and key study.
RESEARCH AND DEVELOPMENT
The EPA recognizes that the establishment of RfD.s critically depends on
the quantitative extrapolation of regional respiratory tract doses from animals
to humans. The RDDR as described in this Appendix must address both the
H-17
-------
8.0
7.0
06.0
'•55.0
§3.0
D2.0
1.0
0.0
M
tl
I
I
f
I
I
I
I
I
I
I
I
I
t
\
*
Minute Volume, ml
Surface Area, cm2
Rat
130.0
3461.6
Mouse
27.0
294.8
Guinea
Pigl
175.0
9112.8
Guinea
Pig 2
175.0
9656.0
Human
13800.0
640581.0
Rat
Mouse
Guinea Pig 1
Guinea Pig 2
0123456789 10
Particle Diameter (MMAD, pm)
Figure H-5. RDDR of three species to the human by particle diameter (MMAD)
for the TH region. Guinea pig 1 and 2 refer to calculations using different
lung surface area data.
Source: Jarabek et al. 1989b,
H-18
-------
deposition and fate of deposited particles to adequately accomplish this. That
is, factors must be incorporated into the RDDR derivation which account for the
continuous redistribution and clearance of inhaled particles within the lungs
of the species of interest to risk assessment (including humans) during chronic
exposures.
A work group has been formed with members of ECAO-RTP and HERL to expand
the current RDDR methodology, using empirical data and existing theoretical
models to incorporate clearance and to derive a similar dose adjustment factor
for gas and vapor exposures. The mission of this group is to incorporate into
the methodology for particles as many of the following factors as is feasible.
Regional Deposition
particle size
particle distribution (a )
particle volatility or hygroscopicity
detailed regional respiratory tract morphology for multiple
species
extrathoracic and intrathoracic deposition
alternative modes of breathing (nasal, oronasal, and oral)
and activity patterns
Fate of Inhaled Particles
mucociliary transport and clearance
alveolar clearance
phagocytosis and translocation by macrophages
dissolution
free particle translocation
particle solubility
chemical activity: local vs. systemic
Pepelko (1987) investigated the feasibility of dose adjustments based on
reported pulmonary clearance rates. The bioavailabilities of single inhaled
doses of particulate matter having dissolution half-times ranging from one day
to over five years were estimated by calculating the amount dissolved each day
and summing over a two-year period. Two years was selected because it
approximated the remaining lifetime of an exposed rat.
H-19
-------
The equation used to carry out this calculation is:
100k -fk + k H
Total bioavailable percentage = -r — 3717- 1-e ^ p s'
371
where:
k = the rate constant for elimination via physical transport of particles
P from the lungs
k = the rate constant for particle dissolution
and
t = time in days.
Values of 60 and 240 days were selected as representative of physical
clearance rates in rats and humans, respectively. It should be cautioned that
these values were selected only as examples, since actual clearance rates are
somewhat uncertain and vary with conditions.
The results are shown in Figure H-6. As can be seen, for very short
dissolution half-time (t-./2S) values, physical clearance rates had little
effect upon total bioavailability. In fact, for a t,/2 of one day, the
calculated bioavailable percentages were 98.4 and 99.6 for particle removal
half-time (t, ,„) values of 60 and 240 days, respectively. On the other hand,
when t,/_ is increased to 120 days, the estimated bioavailability equals
only 32% for a t., ,„ of 60 days, compared to 67% when the t.. ,„ is equal to
240 days. For particles with very long dissolution half-times, the total
bioavailability is predicted to be small in both cases, although the relative
amount will continue to be up to three times as great when the t. ,„ equals
240 days.
Other uncertainties in the estimation of bioavailability result from
regional and interspecies differences in physiology. Particles deposited in
alveolar regions, for example, are almost invariably taken up by phagocytic
cells, which have been shown to alter the rate of dissolution (Andre et al.,
1987). Considerable quantities of particles are transported to and stored in
the lung-associated lymph nodes of dogs (Snipes et al., 1983). Since this
material is still in the body and subject to dissolution and absorption, use of
reported clearance half-times will result in an underestimate of bioavaila-
bility, unless the rates of translocation to the lymph nodes are known,
allowing an appropriate adjustment to be made. Certain metals, such as
H-20
-------
Cal Removal Equals 60 Days
160 320 460 640 800 860 112012801440180017601920
Solublllzatlon 11/2 (days)
Figure H-6. The relationship between particle removal half-time (t-i/pn^ anc'
dissolution half-time (t-,/2s^ uPon the bioavailability of a single deposited
dose of inhaled participate matter over a 730-day period.
Source: Pepelko (1987).
beryllium, cadmium, lead, and arsenic have very long-term clearance components
(Rhoads and Sanders, 1985; Reeves et al., 1967). While the slow clearance may
be partially ascribed to toxicity, at least a portion was considered by the
authors to be due to uptake by lung cells and formation of a stable complex
with metallothionein-like proteins. Although there also is some evidence that
alveolar clearance is better described by two exponential rate constants than
one, in both small animals (Snipes et al., 1983) and in humans (Bohning et al.,
1982), only a single value has been reported in most studies.
H-21
-------
The use of reported clearance rates also may result in an underestimate of
bioavailability in animals when extrapolating from a chronic toxicology study,
because continuous exposure at high concentrations may result in lung
overloading with concomitant decreases, or even cessation of clearance (Chan
et al., 1984; Griffis et al., 1983). Further, there are few comparisons across
species using the same type of particles. This investigation helps to
illustrate the interaction of clearance with bioavailability for chronic dose
adjustments and serves to emphasize that these and other considerations must be
addressed in the model development.
The initial output of the research effort to expand the scope of the
methodology will be an analytic model from which RRDRs for particles are
derived. The most difficult task of the research work group will be the
development of a model that satisfies all of the criteria listed on page H-19.
The achievement of this goal will involve compromises between scientific
accuracy and general applicability in risk assessment procedures. The project
has already identified some data gaps that has initiated an investigation to
obtain regional surface area and clearance rates using consistent methodologies
across species in order to ensure compatible and precise estimates for model
input. The output is anticipated to be a support document of RRDR tables to be
used in the RfD. risk assessment methodology for dose adjustment and reduction
of uncertainty in interspecies extrapolation for aerosol exposures. Specifi-
cations on how to apply these ratios as scaling factors and limitations (e.g.,
duration of exposures) will be explicitly stated. Compilation of regional
surface area data, using consistent inflation, fixation, and morphometry
techniques across species, will facilitate investigation of the limitations on
linear extrapolation of minute volumes and surface areas as well as the
allometric relationships between lung weight, lung surface area and body
weight. Further, it is expected that the characterization of anatomic and
physiologic parameters across species, involved in the development of the
aerosol model, will provide the basis for mass transport estimates needed
to expand and refine existing gas deposition and uptake models (e.g., ozone and
volatile organics). A gas and vapor model which accounts simultaneously for
characteristics such as solubility, reactivity, and metabolic transformation
then may be developed (see Appendix I). A similar support document of
adjustment factors for these agents is envisioned.
H-22
-------
OLD HOLD
TABLE H-l. RDDR VALUES BY MASS MEDIAN DIAMETER AND
STANDARD DEVIATION FOR RATS*
Sigma g
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
1.60
MMAD
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
ET
0.6
0.2
0.2
0.4
0.5
0.7
0.9
1.2
1.5
1.9
2.4
2.9
3.5
4.2
4.9
5.8
6.3
7.0
7.8
8.8
9.4
0.5
0.2
0.3
0.4
0.6
0.7
0.9
1.1
1.4
1.7
2.1
2.5
2.9
3.4
4.0
4.5
5.1
5.8
6.3
7.0
7.7
TB
109.8
10.4
2.7
1.4
1.0
0.8
0.8
0.8
0.7
0.7
0.6
0.5
0.5
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.2
54.9
8.5
2.3
1.4
1.0
0.9
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.4
0.3
0.3
0.3
0.3
PU
0.9
0.9
1.0
0.9
0.8
0.8
0.7
0.6
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.8
0.9
0.9
0.9
0.8
0.8
0.7
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Total
1.6
1.6
1.9
2.6
3.1
3.6
4.0
4.4
4.9
5.3
5.9
6.4
7.0
7.7
8.3
9.1
9,9
10.6
11.5
12.4
13.4
1.6
1.6
2.1
2.7
3.2
3.6
4.0
4.5
4.9
5.4
5.9
6.4
6.9
7.5
8.1
8.8
9.4
10.2
10.8
11.6
12.4
(continued on the following page)
H-23
-------
Sigma g
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
1.80
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
2.00
MMAD
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
TABLE H-l.
ET
0.4
0.2
0.3
0.4
0.6
0.8
0.9
1.1
1.4
1.6
1.9
2.2
2.6
2.9
3.3
3.7
4.2
4.7
5.3
5.7
6.3
0.4
0.2
0.4
0.5
0.6
0.8
1.0
1.1
1.4
1.6
1.8
2.1
2.4
2.6
3.0
3.3
3.7
4.1
4.5
4.9
5.3
(continued)
TB
54.9
7.3
2.2
1.3
1.0
0.9
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.4
36.6
5.9
2.1
1.3
1.0
0.9
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.4
0.4
0.4
0.4
PU
0.8
0.9
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.8
0.9
0.9
0.9
0.8
0.8
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.5
0.6
Total
1.6
1.7
2.2
2.8
3.2
3.7
4.1
4.6
5.0
5.4
5.9
6.4
6.9
7.4
8.0
8.5
9.1
9.8
10.4
11.0
11.8
1.5
1.7
2.3
2.9
3.3
3.8
4.2
4.6
5.0
5.5
5,9
6.4
6.9
7.3
7.8
8.3
9.0
9.5
10.1
10.6
11.3
(continued on the following page)
H-24
-------
Sigma g
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
2.40
MMAD
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
0.20
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
6.50
7.00
7.50
8.00
8.50
9.00
9.50
10.00
TABLE H-l.
ET
0.3
0.3
0.4
0.5
0.7
0.8
1.0
1.2
1.3
1.5
1.7
2.0
2.2
2.5
2.7
3.0
3.3
3.6
3.9
4.3
4.7
0.3
0.3
0.4
0.6
0.7
0.9
1.0
1.2
1.3
1.5
1.7
1.9
2.1
2.3
2,5
2.8
3.0
3.3
3.6
3.9
4.2
(continued)
TB
27.4
5.3
2.0
1.3
1.0
0.9
0.8
0.7
0.7
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
27.8
4.8
1.9
1.3
1.0
0.9
0.8
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.5
0.5
0.5
0.5
PU
0.8
0.9
0.9
0.9
0.8
0.8
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.8
0.9
0.9
0.9
0.8
0.8
0.8
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.6
0.6
0.6
0.6
Total
1.5
1.8
2.4
2.9
3.4
3.9
4.3
4.7
5.1
5.5
5.9
6.4
6.8
7.3
7.8
8.3
8.7
9.2
9.8
10.3
10.9
1.5
1.9
2.5
3.0
3.5
3.9
4.4
4.7
5.2
5.6
6.0
6.4
6.8
7.2
7.7
8.2
8.6
9.0
9.5
10.1
10.5
*Source: Jarabek et al., 1988.
H-25
-------
APPENDIX I
DERIVATION OF AN APPROACH TO DETERMINE HUMAN EQUIVALENT
CONCENTRATIONS FOR EXTRARESPIRATORY EFFECTS OF GAS EXPOSURES
BASED ON A PB-PK MODEL USING SELECTED PARAMETER VALUES
INTRODUCTION
This appendix describes in detail the derivation of the procedure used in
Chapter 4 to estimate No-Observed-Adverse-Effect level human equivalent con-
centrations (NOAELuppS) for extrarespiratory effects of gases or vapors. The
derivation is mathematical in nature in that the equations of state that
describe the disposition of inhaled compounds in a generalized physiologically
based pharmacokinetic (PB-PK) model are manipulated so as to obtain a conser-
vative estimate of NOAEL..™5 as a function of the average animal exposure
concentrations (NOAEL.pj). A PB-PK model is used because of the success of
this type of model. For example, PB-PK models that describe the body as five
compartments (gas exchange and the fat, poorly-perfused, richly-perfused, and
liver/metabolizing tissue groups) have been applied successfully to estimating
the internal concentrations of chemicals (e.g., styrene, methanol, and ethylene
uichloride) for the purpose of risk assessment. Although, PB-PK modeling is
the choice procedure in risk assessment for dose extrapolation, this approach
is not possible without the values of physiological and biochemical parameters,
which are used in the modeling process, and without a better understanding of
the agent's mechanism of action. These data generally are not available for
most compounds.
The proposed method is based on a PB-PK model in which all of any number
of compartments are in parallel and in which for any compartment there can be
any number of paths of removal by linear and saturable processes. Selected
relevant parameter values are replaced by qualitative assumptions about species
similarity and the response of internal concentrations to exposure scenarios.
In order to obtain a NOAELupri the assumption is made that the effective dose
for dose-response purposes is the arterial blood concentration of the gas or
it's concentration multiplied by time (C x T). (These assumptions are specified
in detail in the METHODS section.) This latter assumption is consistent with
our current understanding of systemic toxicity for a majority of chemicals,
since the toxicity of most environmental chemicals is related to the concen-
tration of the parent compound at the target site over a period of time.
In addition to deriving conservative NOAELnrf. estimates based on arterial
blood concentrations, the method also predicts that the blood concentration of
an inhaled compound in any human tissue compartment does not exceed the blood
concentration in the corresponding animal compartment. Although the present
approach does not directly address the issue of metabolites being the toxic
1-1
-------
agent, the procedure predicts (based on the similarity assumption) that the
rate of metabolite production per cardiac output rate or per target tissue
perfusion rate in humans does not exceed that in animals.
METHODS
Assumption imposed bythe RfD. methodology:
Assumption I. Noncancer toxic effects observed in chronic animal bioassays
are the basis for the determination of NOAELs and the operational derivation
of RfD.s for human exposures, as described in Chapter 4, The animal exposure
scenario is experiment-dependent and usually intermittent (e.g., 6 h/day,
5 days/week for many weeks). Human exposure concentration is continuous and
constant for 70 years. The "lifetime" chronic animal exposure scenario is
equivalent to the human chronic exposure scenario for the purpose of extra-
polating the NOAEL.
Additional assumptions for the proposed method:
Assumption II. Relatively soon after the beginning of the experiment, and for
most of the experiment, all the concentrations of the inhaled gas within the
animal's body are periodic with respect to time. Practically, these conditions
should be met during "most" of the experiment duration. For example, if the
condition is met for nine-tenths of the time (e.g., periodic during the last
90 weeks of a 100-week experiment), then estimates of average concentrations
will be in error by less than 10%. During most of the time humans are exposed,
given Assumption I of continuous exposure, their internal concentrations are
constant and in dynamic equilibrium with their exposure concentration.
Assumption III. A PB-PK model describes the uptake and disposition of inhaled
compounds in animals and humans. The model is diagramed in Figure 1-1 and the
equations of state are given by Equations (1-1) to (1-6). Table 1-1 defines
the variables and constants in the equations.
1-2
-------
CE
QP
CV
QC
Cp
f
CA
.££.
QP
QC
*
CA
r.
LJ
!
°i
I
CN
1
ON
GAS-EXCHANGE
COMPARTMENT
ANY NUMBER OF
METABOLIZING AND
NON-METABOLIZING
COMPARTMENTS
rN
Figure 1-1. Schematic of the physiologically based pharmacokinetic model
assumed to describe the uptake and distribution of inhaled compounds.
1-3
-------
TABLE 1-1. DEFINITION OF SYMBOLS
General
V
N
HP
M
Subscripts
i
P
j
A
H
NEC
Compartment volume
The number of non-gas exchange compartments
Mass of inhaled compound in gas exchange compartment
Mass in compartment other than gas exchange
Multiplication symbol
Overbar indicates average
Blood to air partition coefficient
Period of exposure time
i-th path of loss of primary compound
Gas exchange compartment
j-th non-gas exchange compartment
Animal
Human
Human equivalent concentration
Flow Rates (ml/h)
QP
QC
Q
Alveolar ventilation
Cardiac output
Into and out of non-gas exchange compartment
Concentrations (mg/1)
C
CE
S
CA
CV
Biochemical
r
VMAX
KM
KF
VKF
In venous blood within and leaving a non-gas exchange compartment
Exposure
In air of pulmonary region
In arterial blood
In venous blood entering gas exchange region
Removal rate due to metabolism, reactions, excretion, etc. (mg/h)
Maximum velocity of saturable path (mg/h)
Michael is constant (mg/1)
First-order rate constant (1/h)
Equals to V x KF (1/h)
1-4
-------
dM /dt = QP*(CE - C ) + QC*(CV - CA) - r (CA) (1-1)
dM./dt = Q *(CA - C.) - r.(C.); j = 1, 2, 3, ... N (1-2)
J J J J J
r (CA) = IVKF .*CA + ZVMAX .*CA/(KM . + CA) (I~3a)
P i " i " "
r.(C.) = ZVKF..*C. + ZVMAX,,*C./(KM.. + C.); j = 1 to N (I-3b)
J *J 4 J J 4 J J J J
QC*CV = ZQ,*C. (1-4)
j J J
QC = ZQ, (1-5)
CA = X*C (1-6)
Equations (1-1), (1-2), (1-4), and (1-5) are the dynamical equations of
state or mass balance equations for the model. Equations (I-3a,b) define the
possible loss rates in each compartment in terms of linear rates (e.g.,
VKF..*C.) and rates of the Michaelis-Menton type (e.g., VMAX .*CA/[KM . + CA]).
In each compartment, the model allows for more than one path of elimination or
metabolism or for no losses (i.e., set all of a compartment's kinetic
parameters, VKF and VMAX, to zero). Equation (1-6) gives the assumed relation-
ship between the arterial blood concentration and the concentration in the air
of the pulmonary region.
According to Assumption I, the exposure concentration is periodic with
period of exposure time (T) for animals and constant for humans; in both cases,
concentration of exposure (CE) can be written as:
CE = f(t)*CE (1-7)
where:
CE = the average exposure concentration, and
f = a periodic function of time (t) such that:
f(t)*dt = 1. (1-8)
1-5
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Assumption IV. Because the biologically effective toxic dose to a given target
tissue depends on the animal species and chemical compound, its specification
is typically not available so that definition of a surrogate dose must be
somewhat arbitrary. However, the toxic effects of some compounds are expected
to be directly related to the inhaled parent compound in the blood. Further-
more, the choice of the average blood concentration is conservative and is an
internal dose "closer" to the target than a dose based on exposure concentra-
tion. Basing the effective dose extrapolation on another surrogate (e.g.,
metabolite) would require knowledge of the mechanisms of action and additional
information about human and animal physiological parameters. Thus, for animal
to human exposure extrapolation, the human equivalent exposure concentration
(CEncp) is defined in terms of the time-integrated arterial blood concentration
(CA x T) of the inhaled parent compound by requiring that (CA x T)H < (CA x TA).
This assumption (combined with Assumption I) is equivalent to requiring that
the human equilibrium concentration of arterial blood (leaving the lung) be
less than or equal to the time-averaged arterial blood concentration of the
animal; that is, CA,. < CTT.. The equality condition defines the upper limit on
an acceptable human arterial blood concentration; thus, for mathematical
simplicity this assumption is formulated as:
CAH = CAA. (1-9)
Because of this requirement, CAu is a function of C?A, since CA^ depends on CC^.
Assumption V. Similarity of species is assumed in that KM and the ratios Q/QP,
VKF/QP, and VMAX/QP are defined as species independent for each removal
process (see Table 1-1 for definitions). The invariance of the first ratio is
based on the assumption that the percent of blood flow to any compartment is
independent of species and that cardiac output (QC = sum of all Q.) scales,
J
with respect to body weight, in the same way as the ventilation rate (QP);
i.e., the ratio of QC to QP is species-independent. The metabolic constants
VMAX and VKF are assumed to scale in the same way as QP. Justification for
this assumption about rates is based on the observation that for many species,
rates scale in the same way with respect to body weight; e.g., in proportion to
basal metabolism, body surface area, or body weight to some some power
(Travis and White, 1988). The invariance of the ratios VKF/QP and VMAX/QP
follows.
1-6
-------
Subject to the Assumptions, Equations (1-1) to (1-9) must be manipulated
to determine CE,,rC as a function of the average animal exposure concentration,
C¥fl. Because the concentrations and masses of a parent compound within a
compartment are assumed to be periodic, the integral of the left-hand side
(LHS) of Equations (1-1) and (1-2) over a time length of the period is zero;
for example;
(dM/dt')*dt' = M(t + T) - M(t) = 0. (1-10)
Also note that for equilibrium or steady state, as in the human case, the LHS
of each of these equations is zero by definition. Performing the period
average of both sides of Equations (1-1) to (I~6), the following are obtained.
0 = QP*(tE - C~) + QC*(£V - CA") - r (1-11)
0 = Q..*(CA - C7) - ?..; j - 1, 2, 3, ... N (1-12)
r = IVKF .*CA + IVMAX * [CA/(KM . + CA)] (I-13a)
IVKF .*CA + IVMAX * [CA/(KM . + CA)]
rj = *VKFji*Cj + *VMAXji*l"c/(KMji + Cj)]; j = l t0 N (
QC*CV = !Q,*C. (1-14)
j J J
QC = IQ. (1-15)
j J
CA = \*C~ (1-16)
The steady state equations for humans are obtained from Equations (1-1) and
(1-2) by setting the LHS of these equations to zero (the equilibrium or
steady-state condition). The complete set of equations of state for humans can
be obtained from Equations (1-11) through (1-16) by redefining the average
concentrations or terms as equilibrium values (i.e., remove the overbars).
1-7
-------
The above equations are simplified by combining Equations (1-11) and
(1-16) to give:
(QP/X + QC)*CA = QP*CI + QC*CV - 7 (1-17)
and Equation (1-12) is expressed as:
q j*™ = V^J + V J = l t0 N- (I~185
Both sides of Equations (1-17) and (1-18) are divided by QP and Q.,
respectively, to give:
u*£fi = CE" + w * CV - r /QP, and (I-19a)
(!A = U . + r./Q,; j = 1 to N (I-19b)
where:
w = QC/QP, and (I-19c)
u = (A"1 + QC/QP). (I-19d)
Generally, the constants w and u are species-dependent, and will be identified
as such with subscripts A and H for laboratory animal and human, respectively.
However, for simplicity and unless otherwise noted, averaged concentrations
(indicated by overbar) will be those of animals and nonaveraged concentra-
tions will be those of humans.
Applied to humans, Equations (I-19a) and (I-19b) are written as:
UH* CA = CE + WH * CV - rpH(CA)/QPH, and (I-20a)
CA = C. + r^CCjO/Q^; j = 1 to N. (I-20b)
1-8
-------
For animals, Equations (I-19a) and (1-1%) are written as:
UA* CA" = Cl + WA * CV - rpA/QPA, and (I-20c)
CA = £T + rjA/QjA; j = 1 to N. (I-20d)
The loss terms in Equations (1-3), rD(CA) and the r.(C.)'s, are concave
functions with the property that their second derivatives with respect to CA
and C-, respectively, are less than or equal to zero. As a consequence, the
J
average of each of these functions is less than or equal to the function
evaluated at the average concentration. Suppressing the subscripts, this
property is expressed as:
r < r(D. (1-21)
Considering Equations (1-21), (I-20c), and (I-20d), the following is noted:
U* CA > £E + W* CV - r(CE)/QP, and (I-22a)
rjA (CjO/Q^; j = 1 to N. (I-22b)
Using Equation (1-9), Assumption IV (that is, CA,, = CA~, Equations (I-20a)
and (I-20b) for human are written in terms of the animal arterial blood con-
centration by replacing CA with CA as follows:
UH* CA = CE + w * CV - rpH(CA)/QPH; (I-23a)
CA = C + r(C)/(J; J = 1 to N- d*23b)
1-9
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Subtract the LHS and the right hand side (RHS) of Equation (I-23a) from
the LHS and RHS of Equation (I-22a), respectively, to obtain:
(UA - uH)*CA > CT - CE + (WA * CV - WH * CV) - (rpA(Cl)/QPA - rpH(CA)/QPH).
(1-24)
Because of Assumption V, for any concentration value, C:
rpA(C)/QPA = rpH(C)/QPH' and d-25a)
r,-fl(C)/Q,A = T-.H(C)/Q.U; (I-25b)
JA JM JH Jn
also,
WA = WH, and (I-25c)
u. - uu = \ , - X u. (]
n n H n
Thus, Equation (1-24) can be written as:
(\"J - \"J) * CA > CE - CE + w*(CV - CV), or (I-26a)
CE > CE" + w^tCV - CV) + (\"1 - X"J) * CA. (I-26b)
Comparing Equations (I-22b) and (I-23b), one sees that the blood concen-
tration of the inhaled compound in any human compartment is less than or equal
to the average blood concentration in the corresponding animal compartment;
that is:
Because of Assumption V (Q,-A/QCA = Qju/QC..). it follows from
Equation (1-14) applied to both humans and animals, and from Equation (1-27),
that:
CV < CV. d-28)
1-10
-------
Thus, the term w * (CV - CV) > 0 can be dropped from Equation (I-26b)
without affecting the inequality as follows:
CE > CE + (\"j - \"J) * CA. (1-29)
Note that CE is the constant inhaled human concentration that would give rise
to a human constant blood level that is no greater than CA. If we choose the
actual human exposure concentration to be less than or equal to the CE, defined
by CA = CA, then the actual CA will be less than or equal to C~A.
The following two cases are now considered with respect to the partition
coefficient.
Case I: A,. >^ A.,,.
The second term on the RHS of Equation (1-29) is greater than or equal to
zero; thus, the term can be dropped from the RHS without affecting the
inequality. Obviously, a conservative human exposure concentration is~C~E.
Therefore, in terms of the variables in Chapter 4, a conservative NOAEL.™ is
given by;
NOAELHEC = CE = NOAEI_ADJ (1-30)
where:
NOAEL.ni = the observed NOAEL concentration adjusted for exposure
ftuj duration (Equation 4-3).
Case II: A.. < XM.
The second term on the RHS of Equation (1-29) is negative in this instance.
The quantity of chemical inhaled must be greater than or equal to the quantity
exhaled; this requires that CE > C~ or CA < \A* CE. In Equation (1-29), CA
can be replaced by the larger value, A.* CE, and still preserve the inequality,
1-11
-------
hence:
CE > CE" + (\"J - \~A) * XA * CE, or (I-31a)
CE > CE *(VV'
In this case, a conservative NOAEL,EC is given by:
NOAELHEC = (XA/\H) * CT = (XAAH) * NOAEL ADJ (1-32)
where:
NOAELAOj = the observed NOAEL concentration adjusted for exposure
duration (Equation 4-3).
RESULTS
A perspective on the proposed method can be attained by examination of
Figures 1-2 and 1-3, plots of NOAELHEC vs. NOAELA for the rat and mouse,
respectively. These plots were created by choosing the equivalent exposure
concentration that resulted in the human arterial blood concentration being
equal to the average arterial blood concentration of the animal, using several
methods, for the representative volatile organic compound dicholoromethane
(DCM).
The "established" method refers to using ratio of the ventilation rate
divided by body weight in the animal to the ventilation rate divided by body
weight in the human ratio for calculating NOAELHEC estimates (Federal Register,
1980), with the modification that alveolar ventilation rates are used (U.S.
Environmental Protection Agency, 1988b). The NOAELAn, of the animal
(Equation 4-3) is multiplied by the ratio to calculate the NOAEL,,EC estimate
using this method. The "optimal" method refers to the use of the model with an
extensive set of experimentally determined physiological parameters for the
three species (Andersen et al., 1987). The same model and human parameters
were used for the "similar" method, but the animal parameters were determined
by scaling from the human values, as defined in Assumption V.
1-12
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1,000:
P3
O 100:
i
LU
§
10
DICHLOROWETHANE
RAT
10
OPTIMAL
-• SIMILAR
- PROPOSED
" ESTABLISHED
100 1,000
NOAELA (mg/m3)
10,000
Figure 1-2, Plot of NOAELur vs- NOAEL, for the rat for four possible methods
upr
(proposed, established, similar and optimal) of determining
as defined in the text. The inhaled compound is diehloromethane.
Source: Qverton and Jarabek, 1989.
1,000 4
estimates
DICHLOROMETHANE
MOUSE
LU
10
OPTIMAL
SIMILAR
-— PROPOSED
ESTABLISHED
10 100 1,000 10,000
NOAELA (mg/m3)
Figure 1-3. Plot of NOAEL,,rC vs. NOAELft for the mouse for four possible
methods (proposed, established, similar and optimal) of determining NOA
estimates as defined in the text. The inhaled compound is dichloromethane.
Source: Overton and Jarabek, 1989.
1-13
-------
In keeping with the results of the derivation that is the subject of this
Appendix, the "proposed" NOAEL..™ estimates are less than the "similar" method
estimates. With respect to the relationship of the proposed predictions to the
other methods of calculation, the following observations are noted.
The "proposed" method lines are parallel to the "established" lines and
result in 3.4 and 6.9 times smaller, or more conservative, NQAEL,rC estimates
for the rat and mouse, respectively. The "proposed" rat NOAEL,rC estimates
also fall below (i.e., are more conservative than) those of the "optimal"
method by a range of 1.4 to 2.4. Except at high exposure concentrations (above
approximately 1,600 mg/m ), where the estimates are smaller by about 1.3, the
"proposed" mouse NOAEL..™ estimates are up to 1.5 times greater than the
"optimal" NOAEL,£r estimates. This supports current evidence that the mouse is
not "similar" to humans in some cases (Reitz et al., 1988), The "proposed"
method estimates, however, more closely approximate the "optimal" method
estimates than do the "established" estimates. It also should be noted that
the "optimal", "similar", and "proposed" methods result in more conservative
estimates for the mouse vs. rat, whereas the established methodology results in
the opposite relationship of estimates between the two species.
DISCUSSION
Considering the "optimal" method estimates to represent the best possible
dose extrapolation based on internal blood concentrations, then the "proposed"
method is more realistic than the "established" method. Since the blood to
air partition coefficients are more readily available than are complete
physiological parameter data, the proposed method represents a simple default
approach when extensive PB-PK modeling is not feasible.
RESEARCH AND DEVELOPMENT
The approach presented in this Appendix has resulted from modeling
research focused on determining the key parameters of gas uptake, distribution
and target tissue accumulation. The future effort will incorporate the
anatomic and some aspects of the clearance data being compiled for research to
support the particle modeling as described in Appendix H. Model evaluation
plans include comparing the efficiency of various dose surrogates and an
approach to address the apparent non-similarity of the mouse. Application of
the model to address mixtures of gases and of dose partitioning between gas and
particles is also envisioned.
1-14
•ft U.S. GOVERNMENT PWNT1NG OFFlCf: 19W- 6 <« 8 - 163' 00345
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