DRAFT EPA/100/J-11/001

DO NOT CITE OR QUOTE External Review Draft

May 2011

Guidance for Applying Quantitative Data 

to Develop Data-Derived Extrapolation Factors for

Interspecies and Intraspecies Extrapolation

NOTICE

THIS DOCUMENT IS AN EXTERNAL REVIEW DRAFT. It has not been formally released 

by the U.S. Environmental Protection Agency and should not at this stage be construed to 

represent Agency policy. It is being circulated for comment on its technical accuracy and policy 

implications.

Office of the Science Advisor

Risk Assessment Forum

U.S. Environmental Protection Agency

Washington, DC 20460
This document is a draft for review purposes only and does not constitute Agency policy.

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DISCLAIMER

This document is distributed solely for the purpose of predissemination peer review under 

applicable information quality guidelines. It has not been formally disseminated by EPA. It 

does not represent and should not be construed to represent any Agency determination or policy.

Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use.

ABSTRACT

This document describes the U.S. EPA’s approach for developing quantitative factors for 

extrapolating effect levels from animals to humans and to address human variability. These 

extrapolations have been covered by the uncertainty factors UFA and UFH, respectively. In the 

absence of quantitatively-valuable data, default values for these uncertainty factor values may be 

applied. However, informative data that describe variability in chemical distribution 

(toxicokinetics, TK) and dose-response (toxicodynamics, TD) should be first considered. This 

document describes the separation of UFA and UFH into TK and TD components and describes 

the process for identifying pertinent data useful for quantifying inter- and intraspecies differences 

to serve as the basis for nondefault, data-derived extrapolation factors (DDEFs). Key 

considerations include identifying a tissue concentration associated with a given response level, 

and identifying and measuring a biological response associated with the corresponding toxicity. 

Interspecies TK variability is quantified on the basis of doses or concentrations that produce the 

same tissue concentration in animals and humans; intraspecies TK variability is defined as 

differences in tissue concentration attained from the same human exposure; TD variability is 

defined on the basis of doses or concentrations that produce the same response. This approach is 

consistent with the approach for deriving reference concentration (RfC) values; it represents a 

point in the continuum of approaches that includes default approaches, categorical default values 

(e.g., body-weight scaling), and integrated biologically based dose-response models. 
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CONTENTS

LIST OF TABLES.......................................................................................................................... v

LIST OF FIGURES ........................................................................................................................ v

LIST OF ABBREVIATIONS........................................................................................................ vi

PREFACE.................................................................................................................................... viii

AUTHORS, CONTRIBUTORS AND REVIEWERS .................................................................. ix

1. INTRODUCTION ..................................................................................................................... 1

1.1. BACKGROUND ............................................................................................................... 1

1.2. PURPOSE AND SCOPE................................................................................................... 2

2. TECHNICAL CONCEPTS AND PRINCIPLES FOR DDEF .................................................. 5

2.1. BACKGROUND ............................................................................................................... 5

2.2. EXTRAPOLATION WITH DDEFs.................................................................................. 7

2.2.1. Approaches to Deriving DDEFs............................................................................. 7

2.2.1.1. TK and TD Models.................................................................................. 8

2.2.1.2. Use of Ratios to Calculate DDEF.......................................................... 10

2.2.2. Qualitative Considerations.................................................................................... 13

2.2.3. Information Quality .............................................................................................. 13

2.2.4. Additional Considerations .................................................................................... 14

2.2.4.1. Mode of Action...................................................................................... 14

2.2.4.2. Use of In Vitro Data .............................................................................. 15

2.2.4.3. Uncertainty and Variability ................................................................... 16

2.2.4.4. Multiple Critical Effects........................................................................ 17

2.2.4.5. Screening Level vs. Refined Risk Assessments.................................... 17

3. DDEFs BASED ON TOXICOKINETICS (TK) ..................................................................... 18

3.1. GENERAL CONSIDERATIONS................................................................................... 18

3.1.1. Dose Metric........................................................................................................... 19

3.1.2. Dose Selection ...................................................................................................... 22

3.1.3. In Vitro Data ......................................................................................................... 22

3.2. INTERSPECIES TOXICOKINETIC EXTRAPOLATION (EFAK) ............................... 23

3.2.1. Considerations for EFAK ....................................................................................... 23

3.2.2. Computation.......................................................................................................... 25

3.2.2.1. Use of TK Models................................................................................. 25

3.2.2.2. Use of Ratios ......................................................................................... 25

3.2.3. Relationship to Other EPA Guidance ................................................................... 27

3.2.4. Conclusions for EFAK ........................................................................................... 28

3.3. INTRASPECIES TOXICOKINETIC EXTRAPOLATION (EFHK)............................... 29

3.3.1. Considerations for EFHK ....................................................................................... 29

3.3.1.1. Susceptible Groups or Individuals......................................................... 29

3.3.1.2. Target Tissues........................................................................................ 31

3.3.1.3. Dose Response....................................................................................... 31
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CONTENTS (continued)

3.3.2. Computation.......................................................................................................... 32

3.3.2.1. Use of TK Models................................................................................. 32

3.3.2.2. Use of Ratios ......................................................................................... 32

3.3.3. Conclusions for EFHK ........................................................................................... 33

4. DDEFs BASED ON TOXICODYNAMICS (TD) .................................................................. 34

4.1. GENERAL CONSIDERATIONS................................................................................... 34

4.1.1. Mode of Action..................................................................................................... 35

4.1.2. Relating Response to Dose or Concentration ....................................................... 35

4.1.3. Range of Doses or Concentrations........................................................................ 36

4.2. INTERSPECIES TOXICODYNAMIC EXTRAPOLATION (EFAD) ............................ 36

4.2.1. Considerations for EFAD ....................................................................................... 37

4.2.1.1. Mode of Action...................................................................................... 37

4.2.1.2. Target Tissues........................................................................................ 37

4.2.1.3. Dose Response....................................................................................... 38

4.2.1.4. In Vitro Data.......................................................................................... 38

4.2.2. Computation.......................................................................................................... 39

4.2.2.1. Use of TD Models................................................................................. 39

4.2.2.2. Use of Ratios ......................................................................................... 39

4.2.3. Conclusions for EFAD ........................................................................................... 40

4.3. INTRASPECIES TOXICODYNAMIC EXTRAPOLATION (EFHD)............................ 40

4.3.1. Considerations for EFHD ....................................................................................... 41

4.3.1.1. Susceptible Groups or Individuals......................................................... 41

4.3.1.2. Target Tissues........................................................................................ 41

4.3.1.3. In Vitro Data.......................................................................................... 41

4.3.2. Computation.......................................................................................................... 42

4.3.2.1. Use of TD Models................................................................................. 42

4.3.2.2. Use of Ratios ......................................................................................... 42

4.3.3. Conclusions for EFHD ........................................................................................... 44

5. FINAL STEPS ......................................................................................................................... 46

6. REFERENCES ........................................................................................................................ 47

APPENDIX: CASE STUDIES TO ACCOMPANY DATA-DERIVED 

EXTRAPOLATION FACTOR DOCUMENT .................................................... A-1
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LIST OF TABLES

Table 1. Example equations used to derive DDEFs .................................................................... 11

LIST OF FIGURES

Figure 1. Derivation of RfDs/RfCs using uncertainty factors. ...................................................... 6

Figure 2. Decision process for DDEFs.......................................................................................... 9

Figure 3. Interspecies toxicokinetics............................................................................................ 24

Figure 4. Interspecies extrapolation based on AUC .................................................................... 27

Figure 5. Intraspecies toxicokinetics............................................................................................ 30

Figure 6. Interspecies toxicodynamics......................................................................................... 37

Figure 7. Intraspecies toxicodynamics......................................................................................... 43
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LIST OF ABBREVIATIONS

ADME absorption, distribution, metabolism, elimination; toxicokinetics

AUC area under the curve

AUCsens area under the curve at a percentile of interest in the sensitive human population

AUCA area under the curve in the animal

AUCgen area under the curve at a measure of central tendency in the general human 

population

AWQCD Ambient Water Quality Criterion document

BBDR biologically based dose response

BMD benchmark dose

BMD10 benchmark dose corresponding to a 10% increase in response

BMDL lower 95% confidence limit of the benchmark dose

BMDL05 benchmark dose corresponding to a 5% increase in response

Cl clearance

Clint intrinsic clearance

Cmax maximum concentration

CSAFs chemical-specific adjustment factors

CYP cytochrome P450

DDEFs data-derived extrapolation factors

ED10 effective dose producing a 10% response rate

EGBE ethylene glycol monobutyl ether

HED human equivalent dose

ILSI International Life Sciences Institute

IPCS International Programme on Chemical Safety

IRIS Integrated Risk Information System

Km Michaelis constant, substrate concentration at rate of 1/2 Vmax

LOAEL lowest-observed-adverse-effect level

MOA mode of action

NOAEL no-observed-adverse-effect level

NRC National Research Council

PBPK physiologically-based pharmacokinetic

POD point of departure

RfC reference concentration

RfD reference dose

TD toxicodynamics

TK toxicokinetics

U.S. EPA U.S. Environmental Protection Agency

UF uncertainty factor

UFA interspecies uncertainty factor

EFAD toxicodynamic component of interspecies extrapolation factor

EFAK toxicokinetic component of interspecies extrapolation factor

UFH intraspecies uncertainty factor 
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LIST OF ABBREVIATIONS (continued)

EFHD toxicodynamic component of intraspecies extrapolation factor

EFHK toxicokinetic component of intraspecies extrapolation factor

Vmax theoretical maximal initial velocity

WHO World Health Organization
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PREFACE

This guidance document is intended to help U.S. Environmental Protection Agency 

(EPA) risk assessors take a data-based approach to nonlinear low-dose extrapolation. It 

describes data recommendations and methods or procedures to calculate data-derived 

extrapolation factors (DDEFs) for chemicals for inter- and intraspecies extrapolation. While 

serving the same purpose as uncertainty factors, these extrapolation factors are based on data 

directly pertinient to the chemical of interst, rather than having their basis on default assumptions 

about inter- and intra-species variability. EPA’s 2005 Guidelines for Carcinogen Risk 

Assessment (or ―Cancer Guidelines‖) (U.S. EPA, 2005) describes the EPA’s current practice of 

examining all relevant and available data first when performing a risk assessment. When 

chemical—and/or—site-specific data are unavailable or insufficient, the EPA uses default 

assumptions or processes in order to continue with the risk assessment. Under this practice, EPA 

guidance suggests invoking defaults only after the data are determined not to be usable at a 

particular point in the assessment. Both the Cancer Guidelines and the EPA document, An 

Examination of EPA Risk Assessment Principles and Practices: a Staff Paper (U.S. EPA, 2004), 

note that this is a shift in paradigm from the older practice of assuming that default values or 

processes will be used unless there are sufficient data to warrant a departure from the default. 

Neither document describes specific approaches to using data rather than default uncertainty 

factors in low-dose extrapolation. This document, Guidance for Applying Quantitative Data to 

Develop Data-Derived Extrapolation Factors for Interspecies and Intraspecies Extrapolation,

describes those approaches and processes.

The members of the Risk Assessment Forum Technical Panel on Data-Derived 

Extrapolation Factors emphasize that the information offered here is neither a checklist nor a set 

of requirements that must be met in order to perform inter- and intraspecies dose extrapolation. 

It presents guidance and codifies many of the best practices available when evaluating data for 

the purpose of inter- and intraspecies extrapolation factor development. This will contribute to 

consistency in the EPA processes and decisions.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS

AUTHORS

John Lipscomb, Co-Lead, U.S. Environmental Protection Agency, Office of Research and 

Development, Cincinnati, OH 45268

Anna Lowit, Co-Lead, U.S. Environmental Protection Agency, Office of Pesticide Programs, 

Arlington, VA 22202

Brenda Foos, U.S. Environmental Protection Agency, Office of Children’s Health Protection,

Washington, DC 20460

Audrey Galizia, U.S. Environmental Protection Agency, Office of Research and Development, 

Edison, NJ 08837

Elaina Kenyon, U.S. Environmental Protection Agency, Office of Research and Development, 

RTP, NC 27709

Ginger Moser, U.S. Environmental Protection Agency, Office of Research and Development, 

RTP, NC 27709

Rita Schoeny, U.S. Environmental Protection Agency, Office of Water, Washington, DC 20460

CONTRIBUTORS

Rory Conolly, U.S. Environmental Protection Agency, Office of Research and Development, 

RTP, NC 27709

Deirdre Murphy, U.S. Environmental Protection Agency, Office of Air and Radiation, RTP, NC 

27709

Bruce Rodan, U.S. Environmental Protection Agency, Office of Research and Development, 

Washington, DC 20460

Risk Assessment Forum Staff

Michael Broder, Science Coordinator/DDEF Project Staff Lead, U.S. Environmental Protection 

Agency, Office of the Science Advisor, Washington, DC 20460

Gary Bangs, Acting Executive Director, 2009, U.S. Environmental Protection Agency, Office of 

the Science Advisor, Washington, DC 20460

Kathryn Gallagher, Executive Director, 2010, U.S. Environmental Protection Agency, 

Washington, DC 20460

Elizabeth Lee Hofmann, Executive Director, 2007-2008, U.S. Environmental Protection Agency, 

Washington, DC 20460

Resha Putzrath, former Health Science Coordinator, U.S. Environmental Protection Agency,

Office of the Science Advisor, Washington, DC 20460

ACKNOWLEDGMENTS

The technical assistance of Ms. Bette Zwayer is gratefully acknowledged.
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1 1. INTRODUCTION

2 1.1. BACKGROUND

3 Risk assessment is the U.S. Environmental Protection Agency (EPA or Agency)’s key 

4 process for deriving scientific information for making sound decisions on managing risks to 

5 human health and the environment (U.S. EPA, 2004). No risk assessment can reflect true risk 

6 with absolute certainty, so it is important that uncertainties be handled in a predictable, 

7 scientifically defensible way that is both consistent with the EPA’s mission and responsive to the 

8 needs of decision makers (U.S. EPA, 2004). This involves decreasing uncertainty in estimates 

9 wherever possible, defining uncertainty and variability in estimates, and quantifying the 

10 uncertainty when feasible. Historically, the Agency has used default uncertainty factors to 

11 compensate for a lack of information. As science has advanced, however, there has been a 

12 growing effort to increase reliance on the available data to modify the values for these 

13 uncertainty factors (IPCS, 2005); this guidance describes an approach for using such information

14 for developing Data-Derived Extrapolation Factors (DDEFs).

15 An Examination of EPA Risk Assessment Principles and Practices: a Staff Paper

16 (U.S. EPA, 2004) notes that the EPA has published a number of documents that provide 

17 direction in describing uncertainty in risk estimates including the following:

18

19

20 Risk Characterization Handbook (U.S. EPA, 2000)

21 Guiding Principles for Monte Carlo Analysis (U.S. EPA, 1997)

22 May 1997 Policy for Probabilistic Analysis (U.S. EPA, 1997a, b)

23 Risk Assessment Guidance for Superfund: Volume III—Part A, Process for Conducting 

24 Probabilistic Risk Assessment (U.S. EPA, 2001a)

25

26

27 Given that risk assessors never have a complete data set, it is accepted practice to use 

28 default values and processes in order to allow a risk assessment to proceed in the absence of data. 

29 The EPA uses the definition of default assumption articulated by the National Research Council 

30 (NRC): ―the option chosen on the basis of risk assessment policy that appears to be the best 

31 choice in the absence of data to the contrary‖ (NRC, 1983). The NRC, in its report Science and 
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1 Judgment in Risk Assessment (NRC, 1994), supported EPA’s use of defaults as a reasonable way 

2 to deal with uncertainty. That report stated that the EPA should have principles for choosing 

3 default options and for judging when and how to depart from them.

4 Since then, the EPA now initiates the process of choosing a method for developing 

5 uncertainty factors by evaluating the available data—guidance suggests invoking default values 

6 only when data are unavailable or insufficient. This contrasts with the previous position of using 

7 the strength of the data as the basis for moving away from default values for uncertainty factors. 

8 Specifically, the 2005 Guidelines for Carcinogen Risk Assessment (or ―Cancer Guidelines‖) 

9 (U.S. EPA, 2005) state “these cancer guidelines view a critical analysis of all of the available 

10 information...as the starting point from which a default option may be invoked if needed to 

11 address uncertainty or the absence of critical information.” Thus, while risk assessors have 

12 generally tried to make maximum use of available data, the shift away from standard default 

13 assumptions was codified as EPA science policy with the publication of the 2005 Cancer 

14 Guidelines. Evaluating the available data will improve the scientific basis of risk assessments 

15 when data are sufficient for refining uncertainty factors (UFs). In cases where data are not 

16 sufficient, hazard and risk characterizations will be improved, and data needs can be noted and 

17 potentially filled in the future (Murray and Andersen, 2001; Meek, 2001; Meek et al., 2001; 

18 Bogdanffy et al., 2001).

19

20 1.2. PURPOSE AND SCOPE

21 U.S. and international efforts have improved the scientific basis for human health risk 

22 assessments by increasing the use of mechanistic and kinetic data. For example, the EPA’s 2005 

23 Cancer Guidelines (U.S. EPA, 2005) emphasize the use of mode-of-action (MOA) information 

24 in characterizing potential health effects of exposure to environmental agents. International 

25 efforts, including those by the International Life Science Institute (ILSI) and the World Health 

26 Organization (WHO)’s International Programme on Chemical Safety (IPCS), have developed 

27 frameworks for evaluating animal data to determine the human relevance of described MOAs

28 (Boobis et al., 2008; Seed et al., 2005; Sonnich-Mullin et al., 2001). These documents guide the 

29 qualitative and quantitative evaluation of the relevance of a particular animal model of action in 

30 humans and discuss the use of in vivo and in vitro data when considering animal-to-human 
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1 extrapolation. The 2005 Cancer Guidelines (U.S. EPA, 2005), and other documents like IPCS’s 

2 chemical-specific adjustment factors (CSAFs) guidance (IPCS, 2005), the Methods for 

3 Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry

4 (U.S. EPA, 1994), and An Examination of EPA Risk Assessment Principles and Practices:Staff 

5 Paper Prepared for the U.S. EPA by Members of the Risk Assessment Task Force (U.S. EPA, 

6 2004) also encourage the use of sophisticated models like physiologically based pharmacokinetic 

7 (PBPK) and biologically based dose-response (BBDR) models in interspecies extrapolation.

8 This document deals specifically with the development and use of data-derived factors in 

9 the calculation of nonlinear low-dose estimates, or safety assessments. The goal of DDEFs is to 

10 maximize the use of available data and improve the scientific support for a risk assessment. The 

11 processes described herein have benefited from the continuing discussion in the scientific 

12 community on ways to replace the 10-fold uncertainty factors (10× UFs) that have historically 

13 been used in deriving safety assessments such as reference doses (RfDs), minimal risk levels,

14 and acceptable daily intakes. WHO’s IPCS guidance for deriving CSAFs was finalized in 2005. 

15 This CSAF guidance describes approaches for use of kinetic and mechanistic data to refine inter16 and intraspecies extrapolation factors. The IPCS guidance is largely based on analyses by 

17 Renwick (1993) and Renwick and Lazarus (1998), which describe the use of toxicokinetic and 

18 toxicodynamic data as a means of replacing the traditional 10× UFs for human sensitivity and 

19 experimental animal-to-human extrapolation. This data-derived approach assigns values for 

20 toxicokinetic and toxicodynamic differences as replacements for each traditional 10× UF. 

21 Important distinctions between IPCS (2005) and the present EPA guidance are that IPCS restricts 

22 toxicokinetic evaluations to the central compartment, disallowing local tissue metabolism to be 

23 quantified as part of the toxicokinetic processes; division of the animal to human extrapolation 

24 unevenly, attributing a greater fraction of default uncertainty to TK than to TD; and a general 

25 level of depth.

26 The current document describes the EPA’s approach to calculating extrapolation values 

27 based on data; these are called data-derived extrapolation factors (DDEFs). DDEFs are similar 

28 in concept to IPCS/WHO’s CSAFs in that the standard extrapolation factors are separated into 

29 toxicokinetic (TK) and toxicodynamic (TD) components, and kinetic and mechanistic data are 

30 used to derive refined interspecies or intraspecies extrapolation factor(s). Conceptually, DDEFs 
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1 (and CSAFs) may not be limited to a specific chemical but may also apply to chemicals with 

2 common structural characteristics, common MOA, or common toxicokinetic characteristics or 

3 determinants. An appendix to this document contains case study examples taken from the 

4 Integrated Risk Information System (IRIS) and from program office records. These case studies 

5 present the application of principles contained in this document to data and modeling studies for 

6 actual chemicals and should serve as instructional aides.

7 Topics most relevant to the derivation and use of DDEFs are the focus of this document. 

8 Thus, there are concepts beyond the scope of this guidance that are not discussed in detail here: 

approaches for selecting critical effects; establishing key events in an MOA analysis;19 deriving 

10 points of departure; performing benchmark dose analysis; and developing and evaluating PBPK 

11 and BBDR models. In addition, this document deals only with DDEF for the areas of inter- and 

12 intraspecies extrapolation; there is no discussion of factors that have been used for other areas of 

13 uncertainty or variability (e.g., duration, database deficiencies, or lack of a 

no-observed-adverse-effect-level [NOAEL]).214

15

 

1 Mode of action (MOA) refers to a series of key, determinant, and necessary interactions between the toxicant and 

its molecular target(s) that lead to the toxic response. Refer to Section 2.2.4.1 for further information.

2 Note:The Food Quality Protection Act (FQPA) mandates the use of a presumptive 10-fold factor for the protection 

of infants and children in addition to inter- and intraspecies factors. This factor can only be modified based upon 

reliable data. The FQPA factor is not discussed in this document.
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1 2. TECHNICAL CONCEPTS AND PRINCIPLES FOR DDEF

2 2.1. BACKGROUND

3 The methodologies of the EPA derivation of reference concentrations (RfCs) and RfDs 

4 (the predominant EPA nonlinear approaches) recognize steps for inter- and intraspecies

5 extrapolation, both of which may include the application of uncertainty factors to an 

6 experimental result to account for recognized uncertainties in, and variability inherent in, the 

7 extrapolations from the experimental data conditions to estimates appropriate to the assumed 

8 human scenario (U.S. EPA, 2011, 2002b, 1994, 1993). This document describes an approach to 

9 performing inter- and intraspecies extrapolations based on the use of the best available science 

10 and data. DDEFs are factors estimated from quantitative data on interspecies differences or 

11 human variability (illustrated in Figure 1). DDEFs may consider both toxicokinetic and 

12 toxicodynamic properties. These factors can be derived for a single agent or chemical, for a 

13 class of chemicals with shared chemical or toxicological properties, and for a group of chemicals 

14 that share a mode or mechanism of action or toxicokinetic characteristics. As described below, 

15 DDEFs can be calculated using sophisticated toxicodynamic or toxicokinetic models or can be 

16 calculated as ratios using key kinetic or dynamic data. With regard to interspecies extrapolation, 

17 the EPA currently recognizes a hierarchy of approaches ranging from the preferred approach 

18 using PBPK modeling (U.S. EPA, 2006, 1994) down to default approaches for situations for 

19 which data do not support an alternate approach, with DDEFs falling intermediate in this 

20 hierarchy.

21 The default approach for the inhalation exposure route involves a combination of 

22 application of a categorical dosimetric adjustment factor and a residual uncertainty factor 

23 (U.S. EPA, 1994). The dosimetric adjustments are based on the following:

24

25 Anatomic and physiologic differences between species

26 Physical differences between particles and gases

27 Whether the toxic effect(s) are portal of entry or systemic in nature

28
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review purposes only and does 

not constitute Agency policy

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Figure 1. Derivation of RfDs/RfCs using uncertainty factors.
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1 For the oral exposure route, the default approach for interspecies extrapolation involves 

2 scaling the applied dosing, according to body weight, to the 3⁄4 power, and a residual uncertainty 

3 factor (U.S. EPA, 2011). Apportioning the default values for both inter- and intraspecies 

4 extrapolation is based on data for various chemicals. It is generally recognized that toxicokinetic 

5 data are more widely available than toxicodynamic data. The magnitude of variation in the 

6 available TK data suggests that the interspecies uncertainty factor might be evenly divided 

7 between TK and TD components. These values are 1⁄2 order of magnitude in value and can be 

8 seen in various documents as values of 3, 3.0, 3.16, or 3.2. Regardless of their values, the 

9 mathematical combination of two factors of 1⁄2 order of magnitude each results in a value of 

10 10 (i.e., 3 × 3 = 10). After quantifying TK differences between species, the residual uncertainty 

11 factor associated with either route (oral or inhalation) has a default value of 3, which may be 

12 modified based on available data (U.S. EPA, 2011, 1994). In accordance with the hierarchy of 

13 approaches, when available agent-specific data are supportive of DDEF derivation, a 

14 data-derived approach is preferred over using the RfC approach or 3⁄4 body-weight scaling.

15

16 2.2. EXTRAPOLATION WITH DDEFs

17 The foundation of DDEFs is the concept that the toxicity of a particular agent is due to a 

18 combination of both toxicokinetic and toxicodynamic factors and that those factors can be 

19 quantified in animals and humans. For purposes of this guidance, toxicokinetics (TK) is defined 

20 as the determination and quantification of the time course and dose dependency of absorption, 

21 distribution, metabolism, and excretion of chemicals (sometimes referred to as pharmacokinetics 

22 or ADME). Toxicodynamics (TD) is defined as the determination and quantification of the 

23 sequence of events at the cellular and molecular levels leading to a toxic response. There is no 

24 clear separation between TK and TD because the processes leading to biological responses 

25 include aspects of both—including interactions between TK and TD processes.

26

27 2.2.1. Approaches to Deriving DDEFs

28 The focus of this guidance is on extrapolation from animals to humans, and within the 

29 human population. Extrapolation can be accomplished by one of several approaches ranging 

30 from the use of highly sophisticated BBDR models to the calculation of relatively simple ratios 

31 using TK or TD data describing critical factors in inter- or intraspecies extrapolation. The 
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1 following text describes these approaches. In the absence of data for performing sophisticated 

2 modeling or for deriving DDEF values, default approaches are used. Figure 2 is a flowchart 

3 depicting the decision process used in deriving and applying extrapolation factors.

4

5 2.2.1.1. TK and TD Models

6 TK and TD models represent the preferred approach to intra- and/or interspecies 

7 extrapolation. They vary in level of complexity from classical compartmental and simple 

8 statistical response models to physiologically realistic models of TK and TD processes, up to and 

9 including BBDR models. These models provide a quantitative description of the biological 

10 processes involved in the toxicokinetics and/or MOA of chemical(s). In these TK and TD 

11 models, some measure of the internal dose is related to the external dose and mode of action, 

12 respectively. 

13 TK modeling is the process of developing a mathematical description of ADME in a 

14 living organism. Two common types of models are (1) data-based classical noncompartmental 

15 or compartmental models and (2) PBPK models. Data-based models, also known as classical 

16 models, mathematically describe the temporal change in chemical concentration in blood, tissue,

17 or excreta of the species in which the data were generated. The classical models treat the body 

18 as a single homogenous or multicompartment system with elimination occurring in a specific 

19 compartment; the characteristics of the compartments (number, volume, etc.) are hypothetical in 

20 that they are chosen for the purpose of describing the data rather than a priori based on the 

21 physiological characteristics of the organism. Due to these characteristics, classical models are 

22 used for interpolation, i.e., within the range of doses, dose route, and species in which the data 

23 were generated (Renwick, 1994).

24 PBPK models differ from classical compartmental models in that they are composed of 

25 compartments with realistic tissue volumes that are linked by blood flow. Other parameters used 

26 in these models account for chemical-specific characteristics that can be independently measured 

27 in both humans and laboratory animals (usually using in vitro techniques); these 

28 chemical-specific parameters include tissue solubility (i.e., partition coefficients), binding, and 

29 metabolism. These models are used to simulate the relationship between applied dose and 

30 internal dose. They are more data intensive to develop compared to classical compartmental 
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1

Figure 2. Decision process for DDEFs. The availability of an adequate 

pharmacokinetic (PK) or pharmacodynamic (PD) model is first considered followed by 

analysis of the availability of adequate data to describe the toxicokinetics (TK) and/or the 

toxicodynamics (TD) of the chemical. With the availability of an adequate model or 

data, data-derived extrapolation factors for intraspecies (EFAK, EFAD) and interspecies 

extrapolation (EFHK, EFHD) are developed. Such data-derived factors are preferred over 

default factors. In the absence of an adequate model or data, default factors are used.

2

3 *For interspecies extrapolation, the default procedure is 3⁄4 body-weight scaling for oral 

4 (U.S. EPA, 2006) and the RfC method (U.S. EPA, 1994) for inhalation to account for 

5 potential TK differences with a 3× factor for potential TD differences. The composite 

6 factor (CF) accounts for inter- and intraspecies extrapolation and can comprise default or 

7 DDEF values for the four extrapolation factor components.

8
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1 models, but they are advantageous because they can be used for extrapolation (i.e., across dose 

2 range, among animal species, between routes of exposure, and across exposure scenarios) 

3 (Krishnan and Andersen, 1994; U.S. EPA, 2006).

4 TD models can be developed when there are sufficient data to both ascertain the MOA 

5 and to quantitatively support model parameters that represent rates and other quantities 

6 associated with key precursor events in the MOA. A BBDR model describes biological 

7 processes at the cellular and molecular levels in such a way as to link target tissue dose with 

8 adverse effect; in practice, BBDR models are often described as a combined TK/TD model. 

9 These models may be used for extrapolation. However, with adequate understanding of the 

10 nature of the response, empirical data describing the dose-response function in relevant species 

11 or population groups are sufficient to serve as the basis for DDEF derivation; in these cases, a 

12 fully developed TD model may not be required.

13 DDEF values are extrapolation factors, as opposed to uncertainty factors, per se. DDEF 

14 values are quantitatively derived based on TK and/or TD data for the chemical under evaluation. 

15 DDEF values are not the same as the default uncertainty factor values, but the values for the 

16 DDEF components may sometimes be similar to default values for uncertainty factors. 

17 Developing a DDEF value reduces uncertainty and carries with it a change in nomenclature.

18

19 2.2.1.2. Use of Ratios to Calculate DDEF

20 In the absence of sufficient data to develop a robust TK or TD model, the risk assessor 

21 need not necessarily use default 10× UFs. DDEFs can be calculated as ratios using data from 

22 key studies evaluating TK or TD profiles or properties of a particular chemical. Example 

23 equations for calculating DDEFs are provided in Table 1 and are described in more detail in 

24 Sections 3 (TK) and 4 (TD).

25 In general, interspecies extrapolation involves calculating a ratio of human data for a 

26 kinetic or dynamic parameter to animal data for a kinetic or dynamic parameter. Similarly, for 

27 intraspecies extrapolation, a ratio is calculated using data from the sensitive population and that 

28 for the general, or average, population. Data to derive the TK factors may come from in vivo or 

29 in vitro studies. For TD, in general, interspecies extrapolation may come from in vivo studies

30 but will often be accomplished with in vitro data in a relevant tissue. When data on toxic effects 
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1

Table 1. Example equations used to derive DDEFs

Extrapolation

Toxicokinetics

(see Section 3)

Toxicodynamics

(see Section 4)

Animal to human 

(interspecies)

A

AK

H

Dose

EF =

Dose

A

AD

H

Concentration

EF =

Concentration

Within human 

(intraspecies)

gen

HK

%tile

AUC

EF =

AUC

gen

HD

sens

Concentration

EF =

Concentration

2

3 EFAK = extrapolation factor for interspecies extrapolation covering toxicokinetics.

4 DoseA = administered or external dose to the animal.

5 DoseH = administered or external dose to the human.

6 EFAD = extrapolation factor for interspecies extrapolation covering toxicodynamics.

7 ConcentrationA = concentration of the agent at the tissue in the animal.

8 ConcentrationH = concentration of the agent at the tissue in the human.

9 EFHK = extrapolation factor for intraspecies extrapolation covering toxicokinetics.

10 AUCgen = area under the curve at a measure of central tendency in the general human population.

11 AUCsens = area under the curve at a percentile of interest in the human population.

12 EFHD = extrapolation factor for intraspecies extrapolation covering toxicodynamics.

13 Concentrationgen = concentration at a measure of central tendency in the general human population.

14 Concentrationsens = concentration at a percentile of interest in the human population.

15

16

17 are available in humans, these data may be used directly for the point of departure (POD) 

18 development, eliminating the need for the interspecies extrapolation. Likewise, they can be used 

19 to inform an interspecies factor when the POD is derived from animals. 

20 For DDEFs involving interspecies extrapolation, it is preferred that the ratio be based on 

21 data at or near the POD. When sufficient data are available, DDEF values should be calculated 

22 for a range of doses near the POD because the shape of the dose-response curve can vary among 

23 species. Metabolism and kinetic properties can vary across doses, particularly in the higher dose 

24 ranges; thus, using estimates at or near the POD helps avoid introducing significant uncertainty 

25 in the DDEF estimate caused by nonlinearity in kinetic properties. Evaluating a range of PODs 

26 takes into account the variability of the DDEFs based on the POD selected. The interspecies 

27 DDEF should be derived using an estimate of central tendency, such as the mean, median, or 

28 mode, depending on the characteristics of the data. It is, however, important to evaluate 
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1 variability in the DDEF. Thus, it is recommended that, to the extent possible, the hazard and risk 

2 characterizations reflect the upper and lower confidence bounds on the DDEF. 

3 By contrast with interspecies extrapolation, when calculating intraspecies DDEFs, the 

4 ratio involves consideration of a measure of central tendency of the general population and lower 

percentiles of interest (e.g., 1st, 2.5th, or 5th 5 ) to represent the sensitive populations. As the needs 

6 of risk managers and decision makers vary, it is recommended that a range of percentiles be 

7 evaluated and reported in the hazard and risk characterizations.

8 Toxicokinetic ratios (for either interspecies or intraspecies extrapolation) are based upon 

9 the relevant dose metric, such as area under the curve (AUC) and the maximum concentration 

(Cmax).310 Other metrics (e.g., AUC above a threshold) may be used if supported by the data or if 

11 relevant for a particular chemical or MOA. For toxicants that bind covalently or cause 

12 irreversible damage, especially as a consequence of subchronic or chronic exposure, an 

13 integrated measure of dose over time such as AUC is generally used (O’Flaherty, 1989). In the 

14 case of effects occurring as a consequence of acute exposure, Cmax may be more appropriate 

15 (Boyes et al., 2005; Barton, 2005). When data on chemical-specific AUC, Cmax, or clearance 

16 (Cl) are not available, a chemical-related physiological parameter (e.g., renal glomerular 

17 filtration rate) that is critical to the onset of toxicity or to the MOA may be used.

18 As Table 1 indicates, there are generally four DDEFs that can be calculated, given 

19 sufficient information. Two are for extrapolation from animal data to humans: EFAK is 

20 calculated to account for TK variability, while EFAD deals with TD variability. Likewise, there 

21 are two factors dealing with variability within the human population: EFHK for TK and EFHD for 

22 TD. Table 1 provides example equations for calculating these DDEFs. Section 3 describes 

23 specifics for TK factors for interspecies (see Section 3.2) and intraspecies (see Section 3.3)

24 extrapolation. Section 4 describes TD factors for both animal to human (see Section 4.2) and 

25 within human (see Section 4.3) extrapolation. Section 5 describes how to combine the EFAK, 

26 EFAD, EFHK, and EFHD into the composite UF.

27 The overall goal of DDEFs is to maximize the use of available data and improve the 

28 overall scientific support for a risk assessment. Figure 2 provides a flowchart of the decision 

29 process for extrapolation used in deriving DDEFs. As shown in the figure, inter- and 

 

3 Clearance can be used to calculate this ratio when it can be assumed or demonstrated that the relevant dose metric 

is AUC or concentration at steady state.
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1 intraspecies extrapolation can be accomplished using a combination of TK or TD models, 

2 DDEFs derived from ratios, and/or use of defaults. As described in more detail in Sections 3 and 

3 4, it is important for the hazard and/or risk characterizations to include thorough and transparent 

4 discussions of methods and data used to support extrapolation approaches.

5

6 2.2.2. Qualitative Considerations

7 Although in some cases there may not be sufficient data for a quantitative estimate of a 

8 DDEF, there may still be information to support a UF different from the default. For example, 

9 there may be qualitative evidence that a MOA identified in animals is not relevant to humans. A 

10 framework developed by ILSI for evaluating the relevance of an animal MOA can be found in 

11 Seed et al. (2005), Meek et al. (2003), and Boobis et al. (2008). The human relevance 

12 framework provides a transparent and logical thought process by which animal and human MOA

13 data can be evaluated on both a qualitative and quantitative basis. In these cases, where only 

14 qualitative data are available, a thorough weight-of-evidence analysis should be considered with 

15 the hazard and/or risk characterization to discuss the derivation of the DDEF along with 

16 associated uncertainties in the available database.

17

18 2.2.3. Information Quality

19 Critical evaluation of all data used to support the development of DDEFs is necessary. 

20 This includes data used to provide qualitative support for the MOA and choice of dose metric, as 

21 well as data used in the quantitative derivation of the DDEF itself. Supporting studies can be 

22 evaluated using criteria set forth in various EPA guidance documents, including the recently 

23 published 2005 Cancer Guidelines, as well as earlier guidelines specific to neurotoxic, 

24 reproductive, and developmental endpoints (U.S. EPA, 1998, 1996, 1991). In addition, the 

25 general principles outlined in the EPA information quality guidelines are applicable in the 

26 critical evaluation of data used to support DDEF development (U.S. EPA, 2002a). The 

27 remainder of this section highlights some areas of special emphasis that are particularly relevant 

28 to the DDEF derivation process including MOA, uncertainty and variability, and dealing with 

29 multiple responding organs or tissues.

30 Use of secondary data sources is one particular area of concern. Examples of secondary 

31 data sources include compilations of pharmacokinetic parameters (e.g., Brown et al., 1997) and 
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1 studies cited and summarized in toxicity profiles and review articles. In general, for principal

2 and supporting studies used directly in DDEF derivation, review of the original literature is 

3 necessary. In the case of critical assumptions and data, contradictory results from different 

4 studies are best resolved by review of the original publications.

5 Quantitative TK and TD data used in the DDEF-derivation process requires particular 

6 attention to the appropriateness of the study design, the analytical methodology used, and the 

7 statistical analysis of the data. Consideration of appropriate study design extends beyond simply 

8 verifying that the methods used were adequate for the goals of the study; it also encompasses 

9 consideration of the relevancy of the animal species or in vitro test system to evaluate MOA. 

10 Relevance can be assessed in both qualitative and quantitative terms. For example, if there is a 

11 lack of species concordance (i.e., a particular TK or TD process does not occur in humans) or 

12 effects occur only under physiologically unrealistic conditions or not in the tissue evaluated, then 

13 its relevancy is questionable and uncertain. Criteria used in arriving at such a determination have 

14 been published for both the more general case (Seed et al., 2005) and particular endpoints 

15 including various forms of rodent cancer (Proctor et al., 2007; Maronpot et al., 2004). Particular 

16 considerations relevant to the use of in vitro data are discussed below. Another important factor 

17 in terms of relevancy is consideration of whether the TK or TD response represents a uniquely 

18 susceptible tissue, process, or population. This is a critical determinant in evaluating the use of 

19 data to describe intraspecies variability.

20

21 2.2.4. Additional Considerations

22 2.2.4.1. Mode of Action

23 Information on MOA can greatly enhance DDEF derivation, even when a complete 

24 explication of mechanism is not available. In the 2005 Cancer Guidelines, the EPA describes 

25 MOA evaluation as the critical information that defines the conditions under which a toxicant 

26 causes its effect, the relevance of animal data for hazard identification, and the most appropriate 

27 approach to low-dose extrapolation. The 2005 Cancer Guidelines also present a framework for 

28 evaluating data in support of MOA determination. Major components of this framework include 

29 a description of the hypothesized MOA and a discussion of the experimental support for the 

30 hypothesized MOA based on modified Hill criteria (U.S. EPA, 2005) for demonstrating 

31 associations in human studies.
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1 MOA is defined as a sequence of key events and processes, starting with the interaction 

2 of an agent with a cell, proceeding through operational and anatomical changes, and resulting in 

3 toxicity. A key event is an empirically observable precursor step that is itself a necessary 

4 element of the MOA or is a biologically based marker for such an element. MOA is contrasted 

5 with ―mechanism of action,‖ which implies a more detailed understanding and description of 

6 events, often at the molecular level, than is meant by MOA (U.S. EPA, 2005).

7 DDEFs for both TK and TD are endpoint driven—that is, considered in the context of the 

8 toxic endpoints most relevant for purposes of the risk assessment. Understanding MOA for the 

9 agent(s) of interest helps to ensure that the TK or TD parameter used to derive the DDEF will be 

10 robust scientifically. The key events in MOA are likely to identify important metabolite(s) and 

11 potential species differences. Moreover, data on key events may be used directly to estimate the 

12 EFAK or the EFAD.

13

14 2.2.4.2. Use of In Vitro Data

15 In vitro assays play an important role in defining DDEFs; however, care must be taken to 

16 avoid taking isolated findings out of context. Consideration of interspecies differences in ADME 

17 is essential because the dose to target tissue in any given exposure scenario is a balance among 

18 multiple and competing ADME processes. Thus, in vitro data should not be used for quantitative 

19 purposes unless interpreted in the context of the intact system. Among the questions to be 

20 considered when applying in vitro data to DDEFs are the following:

21

22

23 Was the toxicologically active form of the agent studied?

24 How directly was the measured response linked to the toxic effect?

25 Are the biological samples used in the assays derived from equivalent organs, tissues, cell 

26 types, age, stage of development, and sex of the animals/humans in which the target 

27 organ toxicity was identified?

28 What is the range of variability (e.g., diverse human populations and lifestages) that the 

biological materials cover?429

 

4 Quality (purity, viability, source) of the samples is of particular concern, with biological materials derived from 

human organ donors.
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1 If the effect occurs or can be measured in several tissues, is the studied tissue or tissue 

2 preparation an appropriate surrogate? Or, in situations where the effect is not localized, 

3 is the effect consistent across tissues?

4 Does the design of the study allow for statistically valid comparisons based on such 

5 factors as replication and sample size?

6 Was chemical uptake considered when the chemical was applied to the samples so as to 

7 give comparable intracellular concentrations across tissues, and similar tissues across 

8 species?

9 Do the concentrations in the in vitro studies allow for comparison with in vivo

10 conditions?

11

12

13 All of these issues affect the utility of applying in vitro data for risk assessment: a clear 

14 discussion of these points helps to clarify the appropriateness of the information used for 

15 deriving DDEFs.

16

17 2.2.4.3. Uncertainty and Variability

18 The application of the inter- and 

19 intraspecies UFs attempts to account for both 

20 the variability (true heterogeneity) and 

21 uncertainty (lack of knowledge) the in the data 

22 available (see Textbox 1, U.S. EPA, 2002b). 

23 The DDEFs described in this document 

24 evaluate variability within the data. Evaluation 

25 of the sources and magnitude of uncertainty is 

26 appropriate (U.S. EPA, 2005, 2001, 1997a, b). 

27 Quantitative uncertainty analyses may be 

28 undertaken but are not presented in this document. When quantitative approaches are not 

29 feasible, qualitative uncertainty analyses may be developed. As is consistent with the 2005 

30 Cancer Guidelines: ―a default option may be invoked if needed to address uncertainty or the 

31 absence of critical information.”

32

Textbox 1

Variability refers to true heterogeneity or diversity. 

This may be due to differences in exposure as well as 

differences in response. Those inherent differences are 

referred to as variability. Differences among 

individuals in a population are referred to as 

interindividual variability, while differences for 

one individual over time are referred to as 

intraindividual variability.

Uncertainty occurs because of lack of knowledge. It is 

not the same as variability. Uncertainty can often be 

reduced by collecting more and better data, while 

variability is an inherent property of the population 

being evaluated. Variability can be better characterized 

with more data but cannot be eliminated. Efforts to 

clearly distinguish between variability and uncertainty 

are important for both risk assessment and risk 

characterization.

Source: U.S. EPA (2002b).
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1 2.2.4.4. Multiple Critical Effects

2 For some toxicants, multiple critical effects may be identified during hazard 

3 identification. In some cases, these effects may be the result of a single MOA. However, for 

4 others, the critical effects may have different or unknown MOAs. It is possible that the 

5 uncertainty and/or variability associated with the TK and/or TD of each effect may differ, 

6 resulting in different DDEFs. The results generated for the multiple responding tissues/organs, 

7 particularly if multiple MOAs are operational or MOA is unknown, should be presented for 

8 comparison (for example, in a table that is accompanied by a discussion of the methods used). 

9 Unless there is scientific support for doing so, it is important not to mix DDEFs derived for one 

10 tissue or one MOA with DDEFs derived from a different tissue. For example, DDEF values for 

11 kidney effects may not apply to liver effects.

12

13 2.2.4.5. Screening-Level vs. Refined Risk Assessments

14 Extrapolation is most scientifically robust when data are first evaluated prior to the use of 

15 defaults. However, with a multitude of types of data, analyses, and risk assessments, as well as 

16 the diversity of needs of decision makers, it is neither possible nor desirable to specify 

17 step-by-step criteria for decisions to invoke a default option. Some risk assessments may be 

18 limited by time or resource constraints. Other risk assessments may provide only screening level 

19 evaluations. In these cases, the risk assessment may be more likely to resort to one or more 

20 default assumptions. On the other hand, risk assessments used to support significant risk 

21 management decisions will often benefit from a more comprehensive assessment.

22
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1 3. DDEFs BASED ON TOXICOKINETICS (TK)

2 3.1. GENERAL CONSIDERATIONS

3 Toxicokinetics is concerned with delivery of the biologically active chemical species to 

4 the target tissue of interest. Data on tissue concentrations of toxicants or clearance rates of 

5 toxicant removal serve as the basis for deriving extrapolation factors foor toxicokinetic 

6 components. This section provides a discussion of factors common to derivation of both inter7 and intraspecies uncertainty factors to account for TK variability. Data on the quantitative 

8 differences in the TK between animals and humans are used for interspecies extrapolation 

9 (EFAK); differences in susceptibility within the human population are used for the intraspecies

10 extrapolation (EFHK). Thus, the factor EFAK accounts for extrapolation from laboratory animals 

11 to the general human population. The EFHK factor accounts for the variation in the 

12 dose/exposure-response relationship between the general human and potentially susceptible 

13 human individuals or groups. Note, the term susceptible is also used to describe sensitive

14 individuals or groups, as these two terms are often used interchangeably, and no convention for 

15 their use is widely accepted (U.S. EPA, 2004). Developing a DDEF for TK requires knowledge 

16 about the relationship between external dose and internal (target tissue) concentrations. This 

17 information can come from studies in which tissue concentrations are observed, both types of 

18 data are recorded, or can come from adequate TK models, which expand the range of confidence 

19 from that of the empirical observations. TK models, especially PBPK models, represent an 

20 important tool through which in vitro observations can be interpreted in the context of the intact 

21 system. As such, they represent an advantageous means to evaluate the impact of studies 

22 (especially those using human tissues) conducted in vitro. 

23 The TK portion of each factor (EFAK, EFHK) is combined with the corresponding TD 

24 factors to assemble the composite extrapolation factor (see Section 5). Where the data are not 

25 sufficient to derive a DDEF for TK, other approaches can be considered for EFAK or EFHK. For 

26 example, the RfC approach (U.S. EPA, 1994) when evaluating inhalation data or 3⁄4 body-weight 

27 scaling, or a default as described in Figure 2.

28 Important questions to address for TK are given below:

29

30 What is/are the critical effect(s) and POD being used for this assessment?
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1 What is the MOA or mechanism for that toxicity? Have the key events been identified 

2 and quantified? Do these key events identify important metabolic steps? 

3 Are the process of absorption, distribution, metabolism, and elimination of the chemical 

4 well characterized? Do animals and humans metabolize the chemical(s) in a similar way 

5 (qualitatively and quantitatively)?

6 Are there data in human populations describing variation in important kinetic 

7 parameter(s) for this chemical(s)? Do these data identify a susceptible population(s) or 

8 lifestage(s)? Can the degree of this susceptibility be estimated?

9

10

11 TK data may be developed empirically or through compartmental or physiologically 

12 based TK models. Section 2.2.2 describes how data, models, and approaches are evaluated for 

13 their appropriateness. For each critical effect identified for a particular agent, separate DDEF

14 analyses are conducted for EFAK and EFHK. As such, data for multiple susceptible 

15 tissues/endpoints can be evaluated, concentrating on those tissues that demonstrate adverse 

16 responses near the POD for the critical effect.

17

18 3.1.1. Dose Metric

19 Dose metric is a term used to identify a measure of the internal dose that is associated 

20 with the health outcome of interest. It describes target tissue exposure in terms of the toxic 

21 chemical moiety (parent or metabolite) and is expressed in appropriate time-normalized terms. 

22 For example, acute effects are often most related to peak concentrations, whereas effects 

23 occurring following a prolonged exposure are often best correlated with time-normalized (e.g., 

24 area under the concentration-time curve) measures of exposure. The choice of the dose metric is 

25 an important component in TK extrapolations. This choice depends on whether toxicity is best 

26 ascribed to a momentary or transient tissue exposure or a cumulative dose to target tissue. For a 

27 given chemical, the appropriate dose metric will also be determined by, and can vary with, both 

28 the duration of exposure and the adverse effect of concern (U.S. EPA, 2006). Selection of an 

29 appropriate dose metric based upon specific endpoints involves several elements including those 

30 described in more detail below:

31

32 Duration of exposure and effect;

33 Identification of the active chemical moiety;
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1 Selection of the organ or tissue group in which some measure of internal dose is desired; 

2 and

3 Selection of the measure of exposure that best correlates with toxicity. 

4

5

6 Whether an adverse effect is a consequence of acute- or chronic-duration exposure 

7 impacts the choice of dose metric. For acute, reversible effects (e.g., sensory irritation, narcosis), 

8 a measure of instantaneous or peak tissue exposure such as Cmax may be the most appropriate 

9 dose metric (Alarie, 1973; Boyes et al., 2005). For chronic effects, in the absence of MOA

10 information to the contrary, it is generally assumed that some integrated cumulative measure of 

11 tissue exposure to active toxicant is the most appropriate dose metric, e.g., area under the curve 

12 (AUC). Alternative choices such as amount of chemical or rate of metabolite production can be 

13 used as appropriate for a particular agent or MOA (U.S. EPA, 2006). For example, there may be 

14 a case where a temporally large influx of active chemical to a target site in a relatively short 

15 period of time (peak exposure) is observed, in which case, a less commonly used metric such as 

16 time above a critical concentration (TACC) may be most appropriate. In such an instance, the

17 data and rationale in support of a particular dose metric need to be presented.

18 Clearance, while not typically considered a dose metric, can be useful in DDEF

19 derivation. Clearance is mathematically inversely related to AUC (e.g., AUC = dose/clearance);

20 thus, differences in clearance values can be used in calculation of ratios. When metabolism 

21 represents the primary or sole clearance mechanism, either of two clearance models may be 

22 applicable. Intrinsic clearance (Clint) has been used for interspecies scaling of administered 

23 doses in drug development (Houston and Carlile, 1997). Clint is calculated as Vmax/Km, and is in 

24 units of volume cleared of the substrate per unit time. Vmax is the theoretical maximal initial 

25 velocity of the reaction, and Km is the substrate concentration driving the reaction rate at one-half 

26 Vmax. Clint can be extrapolated to the whole body with knowledge of protein binding and the 

27 recovery of the protein or cellular or subcellular fraction used in the in vitro investigations 

28 (Carlile et al., 1997). Hepatic clearance (Clhep) is also based on Vmax/Km measurements but 

29 includes a substrate delivery term, whose value is governed by hepatic blood flow. These 

30 measures of clearance differ in that Clint is not bounded by hepatic blood flow, but Clhep cannot 

31 exceed hepatic blood flow. While metabolic rate constants (Vmax and Km) derived from in vitro
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1 data can also be scaled up and incorporated into PBPK models, the use of these clearance models 

2 is a simpler approach, useful when an appropriate PBPK model is unavailable. Classical, 

3 compartmental TK analyses and measures of clearance are best suited for conditions where 

4 metabolism represents a detoxication process, when substrate concentration is less than the Km

5 value, and when metabolism represents the major clearance mechanism.

6 Whether toxicity is attributable to a parent chemical, a metabolite, or some combination 

7 of metabolites is a critical consideration. The active chemical moiety can be identified through 

8 studies in which the toxicities induced by the parent chemical and metabolite(s) are compared or 

9 from the results of studies using enzyme inhibitors and/or inducers. In vitro studies can also be 

10 quite useful in this regard under appropriate conditions (see Sections 2.2.3.2 and 3.1.3). 

11 Quantifying differences in dosimetry can be difficult when metabolic pathways become complex 

12 (e.g., where competition among pathways may be concentration dependent). If the metabolic 

13 pathway bifurcates and the identity of the bioactive metabolite(s) are unknown or unquantifiable, 

14 determination of the appropriate dose metric can be highly uncertain.

15 The organ or tissue group where the toxic effects occur is ideally the site from which 

16 estimates of internal dose (tissue concentration) are generated. In practice, this information may 

17 be unavailable in the absence of an appropriate PBPK model. It may be necessary to use 

18 absorbed dose of the parent chemical as a surrogate measure of internal dose. Another surrogate 

19 dose metric is measurement of parent chemical or active metabolite in circulating blood if the 

20 relationship between target tissue dose and blood is known or can be reliably inferred from 

21 experimental data. Some data have demonstrated that blood:air partition-coefficient values may 

22 vary appreciably between species but that tissue:air (e.g., liver:air) partition coefficients are 

23 similar among mammalian species (Thomas, 1975). It seems reasonable to use the cross-species 

24 similarity in the primary determinant of diffusion from blood into tissues as a justification to rely 

25 on concentrations of the toxicant in blood as a surrogate for tissue concentrations. However, 

26 when local tissue bioactivation may be a determinant of the toxic response, this should be given 

27 careful consideration. Those issues notwithstanding, measures of internal dose in circulating 

28 blood (see IPCS, 2005) may be used as the basis for DDEF derivation under either of these 

29 conditions:

30

31
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1 When evaluating interspecies differences, the distribution from blood to sensitive 

2 (critical) tissues is shown to be or can be assumed to be the same between animals and 

3 humans.

4 When evaluating intraspecies differences, the distribution from blood to sensitive 

5 (critical) tissues is shown to be or can be assumed to be the same between members of 

6 the general human and potentially sensitive human groups.

7

8

9 Because few data are available for concentrations of toxicants in human solid 

10 tissues―such as liver, kidney, etc.—compared with data describing toxicant concentrations in 

11 human blood, model predictions for solid tissue compartments are less certain than predictions of 

12 toxicant concentrations in blood. Partitioning of the active chemical from blood into systemic 

13 target tissues may be governed more by physicochemical than by biological processes. This may 

14 be considered another basis for relying on data describing the concentration and variability of the 

biologically active metabolite in the central compartment.

5

15 For example, the ratio of blood lipid 

16 to tissue lipid concentrations may be a key determinant in the diffusion of lipophilic compounds 

17 out of blood; however, differences in tissue lipid composition between species may be fairly 

18 small compared to differences in blood flow and metabolic activity.

19

20 3.1.2. Dose Selection

21 Because variability in internal dosimetry may be a function of dose, the selection of the 

22 external exposure (inhaled concentration or orally ingested dose) is important. In cases where 

23 toxicokinetics is nonlinear, the dose selected for the DDEF derivation will impact the magnitude 

24 of EFAK or EFHK. Using a dose at or near the POD alleviates some concerns regarding 

25 nonlinearities in metabolism. Alternatively, data that show a linear relationship between external 

26 dose and internal dose metrics can indicate generalizability of the EFAK or EFHK to doses that 

27 may be higher or lower than those used in its calculation.

28

29 3.1.3. In Vitro Data

30 In vitro techniques are important tools in evaluation of toxicokinetics as information can 

31 be gathered that are impractical or unethical to collect in the intact animal or humans. However, 

 

5 The central compartment is defined as blood, plasma, or serum in the systemic circulation. All tissues except those 

representing the portal of entry are defined as peripheral compartments.
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1 it is important when deriving EFAK and EFHK to consider interspecies differences in ADME. In

2 vitro data should used for quantitative purposes only when interpreted in the context of the intact 

3 system, as discussed in Section 2.2.4.2. Care must be taken to avoid taking isolated findings out 

4 of context. 

5

6 3.2. INTERSPECIES TOXICOKINETIC EXTRAPOLATION (EFAK)

7 This section provides a discussion of the quantitative differences in the TK between 

8 animals and humans that are used for interspecies extrapolation (EFAK). In this process,

9 toxicokinetic differences between species are characterized as the ratio of applied doses in the 

10 test species of interest and humans that result in the same level of the internal dose metric (see 

11 Figure 3). These values for the dose metric may be calculated from the external doses actually 

12 used in the dose-response evaluations, or by normalizing the dose metric to account for 

13 administered dose (e.g., correcting AUC for each species by dividing the AUC by the external 

14 dose) when the relationship between the values for the dose metric value and the applied dose are 

15 linear in the range of extrapolation. Illustrative case studies are included in the appendix.

16

17 3.2.1. Considerations for EFAK

18 Extrapolation of TK data obtained in animal models to humans (EFAK) is evaluated for 

19 each potential critical effect of interest. Data for multiple susceptible tissues/endpoints are 

20 evaluated, concentrating on those tissues that demonstrate adverse responses near the POD for 

21 the critical effect. This analysis includes consideration of MOA, identification of the active 

22 chemical agent for this particular effect, and determination of the appropriate dose metric. This 

23 is evaluated as a weight-of-evidence approach emphasizing both qualitative and quantitative 

24 evidence. An important part of this process is evaluating concordance of metabolic processes 

25 between the animal model and humans. An additional consideration is whether the kinetic data 

26 are from a ―typical‖ or average adult animal as opposed to an animal model system that may be 

27 unusually sensitive for a particular effect (e.g., metabolic knockout).

28
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1

Figure 3. Interspecies toxicokinetics. In keeping with guidance set for inhaled 

substances (U.S. EPA, 1994), interspecies differences in toxicokinetics are defined as 

differences in the external dose producing the same level of the dose metric in the target 

tissue of interest in test animals and in humans representative of the general population. 

Because it is the default assumption of the Agency that humans are considered more 

sensitive than animals, EFAK is the ratio of animal dose:human dose suitable for 

developing a value consistent with its placement in the denominator (i.e., reference value 

= POD/UF). For clarity in presentation, EFAK is applied to the external dose in animals.

2

3

4 Furthermore, it is important to assess the relationship of externally applied dose to 

5 internal dose metric over the entire range of dose levels used in the critical study. There should 

6 be careful attention paid both to measures of central tendency and to variability, particularly in 
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1 the range of concentrations or doses close to the point of inflection (where the shape or slope of 

2 the dose response curve changes) because of potential nonlinearities in metabolism.

3

4 3.2.2. Computation

5 For TK extrapolation, the goal is to determine differences in dosimetry between animals 

6 and humans. For interspecies extrapolation, toxicokinetically equivalent exposures (doses) are 

7 determined by fixing the internal dose (level of the dose metric at or near the POD) and 

8 determining the ratio of external (applied) dose that results in the same level of the dose metric in 

9 animals and humans. This approach is consistent with that in the RfC guidance for inhalation 

10 toxicants (U.S. EPA, 1994).

11

12 3.2.2.1. Use of TK Models

13 A PBPK or other TK model provides the most biologically appropriate approach for 

14 evaluating interspecies TK extrapolation. The model is subject to evaluation as previously 

15 described (U.S. EPA, 2006). The model can be used in different ways, depending on the model 

16 and the circumstances. In some cases, the TK model may be used directly to perform 

17 interspecies extrapolation (i.e., to derive a Human Equivalent Concentration or Dose that 

18 includes TK considerations), thus alleviating the need for EFAK. In other cases, the TK model 

19 may be used to derive EFAK.

20

21 3.2.2.2. Use of Ratios

22 When AUC or concentration at steady state is the relevant dose metric, and if advanced 

23 TK models are not available, EFAK can be derived using a ratio of doses producing the same 

24 AUC value. This is accomplished by identifying external doses associated with the AUC value 

25 produced in animals at the point of departure (AUCA), demonstrated below (see Equation 1 and 

26 Figure 4). The human dose that produces the same AUC value as observed in animals (AUCA) is 

27 the TK equivalent dose. In these cases, differences between the animal and the human dose 

28 producing the same AUC value in each species are captured as the DDEF. See below:

29

30

31

32
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1 AUC = Dose/Clearance (1)

2

3 AUCA = DA / ClA = DH / ClH

4

5

6 Using these data, EFAK is calculated according to Equation 2.

7

A

H

or Cl

=

or Cl

A

AK

H

D

EF

D

(2)

8

9

10

11 Using AUC as an example, the value for the dose metric would be AUCA, that is the 

12 AUC value determined in animals at the POD. Thus, in this example, Equation 2 can be 

13 conceptualized as 

14

15 EFAK = DA producing AUCA / DH producing AUCA (3)

16

17 where

18 AUCA = area under the curve in the animal

19 DA = the animal external dose

20 DH = the human external dose.

21

22

23 This is graphically presented in Figure 4.

24
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1

2

Figure 4. Interspecies extrapolation based on AUC. In this example, AUC is the 

appropriate dose metric, and the relationship between dose and AUC is determined in 

animals and in humans. This method can be used to develop a Human Equivalent Dose

or Concentration, or in the calculation of EFAK. Calculation of EFAK requires knowledge 

of applied doses in animals (DA) and in humans (DH) that produce the AUC value 

determined in animals at the point of departure (AUCA).

3

4

5 Because clearance values are the mathematical reciprocal of internal dose (AUC), they 

6 may also be used to calculate a DDEF value. As humans are assumed to be more sensitive than 

7 experimental animals (in this instance, they clear toxicants more slowly), the human clearance 

8 value remains in the denominator. Calculations using Cmax are developed in a manner similar to 

9 that for AUC.

10

11 3.2.3. Relationship to Other EPA Guidance

12 The development and use of advanced information on tissue dosimetry to serve as the 

13 basis for quantitative, nondefault uncertainty factors is consistent with existing EPA guidance 

14 and philosophy (U.S. EPA, 2006, 2002). The EPA’s Inhalation Reference Concentration 

15 Methodology presents a continuum of approaches from complete lack of knowledge to 
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1 biologically based dose-response models (U.S. EPA, 1994). There are two approaches that each 

2 cover the interval between lack of knowledge and knowledge of target tissue and target tissue 

3 concentrations in test species and humans. The first is for reactive (Category 1) gases and 

4 inhaled particles that damage portal of entry (respiratory tract) tissues, and the second is for 

5 gases that are absorbed and produce their toxicity in tissues bathed by circulating blood 

6 (Category 3 gases). For Category 1 gases, toxicity information identifies the affected region of 

7 the respiratory tract and species differences in regional respiratory tract surface area and airflow

8 (respiratory rate) serve as the basis to quantify species differences in dosimetry. For Category 3 

9 gases, species differences in the solubility of the compound in blood (the blood:air partition 

10 coefficient) serve as the basis upon which to quantify species differences in dosimetry. More 

11 recent guidance (U.S. EPA, 2011) on orally encountered toxicants instructs the application of 

12 dose scaling by the ratio of species body weights raised to the 3⁄4 power (human BW:animal

BW)3/4 13 . Each of these guidance documents indicates that their approaches are intermediates, to 

14 be superceded when more detailed information on tissue dosimetry can be developed. The 

15 subject of the present guidance is the development and interpretation of quantitative 

16 toxicokinetic data for the purpose of developing nondefault values for inter- and intraspecies 

17 uncertainty/extrapolation.

18

19 3.2.4. Conclusions for EFAK

20 Mathematically, EFAK may be the ratio of the external doses in animals at or near the 

21 POD to the value at a central tendency measure of external doses in the general human 

22 population that each result in the same level of the dose metric (this represents developing the 

23 Human Equivalent Concentration or the Human Equivalent Dose). EFAK values may be 

24 calculated for multiple organs/effects. The same PODs should be used for test animals and 

25 humans for a given organ/effect. Quantitatively, DDEF values for UFA components might be 

26 less than 1 if humans are less sensitive.

27 Confidence in EFAK is increased when decisions and calculations are well documented 

28 and made transparent. This includes description of toxicity data identifying the target tissue, 

29 chemical species, MOA, and species concordance of effects. Data describing the TK, the 

30 metabolism of the compound, and the relationship between administered dose and internal dose 
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1 are also summarized. Data that show a linear relationship between external dose and internal 

2 dose metrics can be specifically reiterated in this description; this will indicate generalizability of 

3 the EFAK to doses that may be higher or lower than those used in its calculation. Because 

4 animal-to-human differences in target tissue concentrations may not be consistent for all 

5 responding tissues or organs, a comparison of POD and DDEF values from multiple affected

6 organs will increase confidence in health-protectiveness of the developed DDEF value for the 

7 critical organ. Results can be presented in tabular form for ease of comparison across endpoints.

8

9 3.3. INTRASPECIES TOXICOKINETIC EXTRAPOLATION (EFHK)

10 This section provides a discussion of the quantitative differences in the TK among 

11 humans for intraspecies extrapolation (EFHK). From a toxicokinetic standpoint, among humans 

12 experiencing the same external dose, susceptibility is due to higher target tissue concentrations of 

13 the toxicant in some individuals or groups relative to the majority of the human population. TK 

14 differences for intraspecies extrapolation are characterized as the ratio of an internal dose metric 

15 attained in the general human population to the dose metric from susceptible humans exposed to 

16 the same external dose or concentration. Illustrative examples are included in the appendix.

17

18 3.3.1. Considerations for EFHK

19 3.3.1.1. Susceptible Groups or Individuals

20 Potentially susceptible groups/lifestages (e.g., children, elderly, or age-related 

21 susceptibility, or those with a disease making them susceptible to a toxicant) can be identified. 

22 In some instances, individuals may have a condition or difference in some physiologic or 

23 biochemical process that may be a deterministic factor in dosimetry that serves as the basis for 

24 assumed sensitivity (e.g., polymorphism of a given metabolizing enzyme). In these cases, the 

25 distribution of the dose metric among the population will not have a unimodal distribution (see 

26 Figure 5, left panel). In other cases, sensitivity may be distributed throughout the general 

27 population, and sensitive individuals may be those in the tail of the distribution (i.e., unimodal; 

28 see Figure 5, right panel). Documenting this information and/or these assumptions serves as the 

29 basis for selecting a unimodal or a bimodal distribution of sensitivity. The selection of a bimodal 

30 or unimodal-based analysis will be a function of the available data and must be made on a 
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Figure 5. Intraspecies toxicokinetics. From a toxicokinetic standpoint, susceptibility is 

based on attaining higher target tissue concentrations of the toxicant. For this evaluation, 

a fixed exposure is studied, and human interindividual variability is measured as 

differences in the value of the dose metric between sensitive and generally representative 

population groups. When a group of the population can be identified as potentially 

susceptible, toxicokinetic data from that group can be compared to the general population 

(left panel). EFHK should be determined as the ratio of the level of the dose metric at 

some point in the upper 50% of the distribution (i.e., 95th, 97.5th, 99th percentile) for those 

deemed sensitive to the level of the dose metric at a central tendency measure of the 

general population. However, when a separate distribution of values for the susceptible 

population is not available, then a slightly different analysis is conducted (right panel). In 

this case, EFHK should be determined as the ratio of the level of the dose metric at a 

percentile of the population considered sensitive (i.e., 95th, 97.5th, 99th percentile) to the 

level of the dose metric at a central tendency measure of the general population.

1

2

3 chemical-by-chemical basis. The decision to quantify differences via a unimodal or bimodal 

4 analysis should be undertaken on a chemical-by-chemical basis and be based on the strength of 

5 the available data for the chemical and for the susceptible population group. One of the 

6 challenges in conducting a bimodal analysis will be the availability of data describing chemical 

7 disposition and/or physiological and biochemical parameters for the susceptible population 

8 group.

9
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1 3.3.1.2. Target Tissues

2 When responses are observed in several organs at or near the same dose level, a 

3 comparison of EFHK values developed for those tissues will be informative. However, the 

4 selection of a target organ for calculating human variability other than the one serving as the 

5 basis for animal-to-human extrapolation needs to be accompanied with a justification. This also 

6 extends to instances in which human studies identify effects in an organ or tissue and variability 

7 is assessed in another organ or tissue. The extrapolation approach will be perceived as most 

8 consistent when the same tissue or organ is used for each phase of the extrapolation procedure.

9

10 3.3.1.3. Dose Response

11 Because variability in internal dosimetry may be a function of dose, the selection of the 

12 external exposure (e.g., inhaled concentration or orally ingested dose) is important. 

13 Conceptually, the animal POD is first extrapolated to produce a toxicokinetically equivalent 

14 human dose or concentration. The DDEF for interspecies differences in TD should be applied to 

15 the tissue concentration defining the Human Equivalent Exposure to complete interspecies 

16 adjustment. It is this species-adjusted concentration that is most appropriate for application in 

17 the evaluation of human interindividual variability.

18 The basis for comparison of human variability is at the level of the internal, rather than 

19 the external dose. Thus, it is important that the relationship between internal and external doses 

20 be well characterized. Comparisons of the external doses that produce the same level of the 

21 internal dose (between susceptible and nonsusceptible groups) can be substantially complicated 

22 when there are nonlinearities in the relationship between external dose and internal 

23 concentration. This is particularly difficult when nonlinearity of dose is for one group and not 

24 for the other. This complication can be avoided when the comparison is made because

25 differences in internal doses developed from the same external exposure.

26 Specific differences among humans, particularly those demonstrated in vitro (i.e., 

27 intrinsic clearance), are most reliable when they are translated into differences in dosimetry. 

28 This may include evaluations of multiple different doses. 

29
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1 3.3.2. Computation

2 For intraspecies TK extrapolation, differences in dosimetry are characterized for human 

3 groups or lifestages by comparison to the general population. Thus, the comparisons are among 

4 differences in internal dosimetry (or target site dose) resulting from the same external exposure. 

5 To address human variability, some attention must be devoted to identifying a susceptible 

6 population group or a fraction of the general population considered to be sensitive. TK data are 

7 then analyzed to determine values for the dose metric in the general and sensitive groups.

8

9 3.3.2.1. Use of TK Models

10 A PBPK or other TK model provides the most robust approach for evaluating 

11 intraspecies TK extrapolation. The model is subject to evaluation as previously described 

12 (U.S. EPA, 2006). When an appropriate model is available, it can be used in different ways 

13 depending on the model. In some cases, the TK model may directly account for within human 

14 variation and/or include data from the sensitive group, thus eliminating the need for EFHK. In 

15 other cases, the TK model may be used to derive dose metric values for calculating the value of 

16 EFHK. 

17

18 3.3.2.2. Use of Ratios

19 Where TK models are not available, EFHK can be derived using a ratio (see Equation 4). 

20 The value for the dose metric employed for the general population (e.g., AUCgen) would be that 

21 level of the dose metric identified from initial studies with animals or humans and further 

22 extrapolated to account for interspecies differences in toxicodynamics. The value for the dose 

23 metric in sensitive individuals (e.g., AUCsens) should be determined from empirical data or 

24 pharmacokinetic modeling.

25

26

sens gen

gen sens

AUC or Cl

AUC or Cl

EFHK

27 (4)

28
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1 where

2 EFHK = factor for intraspecies extrapolation covering toxicokinetics

3 AUCgen = area under the curve at a measure of central tendency in the general human 

4 population

5 AUCsens = area under the curve at a percentile of interest in the sensitive human population 

6 Clgen = clearance at a measure of central tendency in the general human population

7 Clsens = clearance at a percentile of interest in the sensitive human population.

8

9

10 Because clearance values are the mathematical reciprocal of internal dose (AUC), they 

11 may also be used to calculate a DDEF value. Because susceptible humans may be assumed to be 

12 more sensitive than general humans (e.g., they clear toxicants more slowly), the sensitive human 

13 clearance value remains in the denominator.

14

15 3.3.3. Conclusions for EFHK

16 The EFHK is essentially a comparison of internal doses resulting from the same external 

17 exposure to both the general population and sensitive individuals. When using an empirical 

18 ratio, the EFHK is the ratio of the dose metric value at a percentile of the distribution intended to 

19 represent sensitive individuals to the dose metric value at a central tendency measure of the 

20 general population. Quantitatively, DDEF values for human interindividual variability cannot be 

21 less than one.

22 The dose selected for quantifying human interindividual variance may have an impact on 

23 the magnitude of variability. The dose adjusted from the animal POD is the preferred dose for 

24 quantitation of human variability. While DDEF values may be calculated for multiple 

25 organs/effects (or even doses), the same external dose needs to be used as the starting point when 

26 calculating dose metric values for the sensitive and the general populations.

27 Documentation should describe the mathematical method to be employed, the type of 

28 distribution and percentile(s) of interest; the dose metrics (e.g., AUC of parent compound in 

29 kidney), and the target tissues for which EFHK are developed. Confidence in the extrapolation is 

30 improved when distribution types are justified or explained.

31
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1 4. DDEFs BASED ON TOXICODYNAMICS (TD)

2 4.1. GENERAL CONSIDERATIONS

3 Toxicodynamics describes the critical interaction of the active chemical moiety with the 

4 target site and the ensuing sequence of events leading to toxicity. Data that describe the dose5 response relationship serve as the basis for deriving extrapolation factors for toxicodynamic 

6 components. This section provides a discussion of factors common to inter- and intraspecies

7 extrapolation for TD. In contrast to TK, which focuses on differences in internal dosimetry, TD

differences are quantified as differences in concentration68 producing the same level of response

9 between animals and humans, as well as among humans. TD evaluations may include multiple 

10 response levels, critical effects, key events, or analytical methods. Developing a DDEF for TD 

11 requires knowledge about the relationship between an event measured in vitro or in vivo (e.g., 

12 receptor binding) and the end result (the adverse health condition). Doses or concentrations 

13 producing the measured event can be obtained in vitro or in vivo, but they must be obtained in 

14 the tissue of interest or a suitable surrogate. TD models, the most complex of which may be 

15 represented by BBDR models, represent an important tool through which predictions may be 

16 confidently made to extend the range of empirical observations. Comparisons between animals 

17 and humans or to quantify human interindividual differences are made on the basis of doses or 

18 concentrations that produce the same level of the same measured response.

19 Quantitative differences between animals and humans are used for interspecies 

20 extrapolation (EFAD), whereas differences in susceptibility within the human population are used 

21 for the intraspecies extrapolation (EFHD). Thus, the EFAD is used to extrapolate findings in 

22 laboratory animals to the general human population, and the EFHD is used to extrapolate to 

23 sensitive human groups and life stages. The TD portion of each (EFAD, EFHD) is combined with 

24 the corresponding TK factor to form the composite extrapolation factor.

25 Although a complete MOA understanding is not required, derivation of a DDEF for TD 

26 relies heavily on understanding a MOA for the critical effect(s) identified for risk assessment. 

27 Important questions to address include the following:

 

6 When using a biologically based dose-response model, differences may be quantified as the ratio of dose metrics in 

respective species or population groups producing the same level of the response. However, DDEF values for TD 

may also be quantified as the ratio of in vitro concentrations producing the same level of the response.
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1 What is/are the critical effect(s) and POD(s) being used for this assessment? (Each of 

2 these should be considered in this process.)

3 What is the MOA or mechanism(s) for that toxicity? Have the key events been 

4 identified? Can they be measured?

5 Is the MOA or mechanism(s) in the animal model relevant to humans (qualitatively 

6 and/or quantitatively)?

7 Are the data on the key events amenable to modeling such that a uniform measure in 

8 animals and humans can be derived? If not, do the available data points include a 

9 response level that is sufficiently similar in animals and humans?

10 Are there data in human populations that describe population variation? Do these data 

11 identify a sensitive group(s) or lifestages? Can the degree of this sensitivity be 

12 estimated?

13

14

15 4.1.1. Mode of Action

16 TD extrapolation should be endpoint driven (considered in the context of the toxic 

17 endpoints most relevant for purposes of risk assessment). The choice of critical effect should be 

18 justified, when possible, based on findings of response in exposed humans. Understanding MOA 

19 for the agent(s) of interest helps to ensure that the TD responses used to derive the DDEF will be 

20 robust scientifically. These responses could include receptor affinity, enzyme inhibition, and 

21 molecular changes, among others. Repair of DNA or tissue damage, biological thresholds, 

22 residual function, and other processes that could contribute to nonlinearity in effect are 

23 considered and discussed. Experimental systems and measured responses should be the same or 

24 comparable.

25

26 4.1.2. Relating Response to Dose or Concentration

27 TD data may be developed from the results of in vivo or in vitro studies. In some cases, 

28 in vivo data may be used, but care should be taken to control for the influence of toxicokinetics. 

29 If there are existing human data measuring the response, then these data can be used to derive the 

30 POD, thereby removing the need for the UFA. Likewise, if there are data from the most sensitive 

31 human group, those data could be used for the risk assessment, and an UFH may not be needed.

32 Care should be taken to ensure that kinetic factors do not confound the interpretation of 

33 response data. Comparisons based on internal dose metric are preferred over applied dose; that 
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1 is, the concentration producing the level of response (rather than applied dose or concentration) 

2 is preferred so that TD response may be distinguished from TK differences. Tissue-specific 

3 metabolism could also influence the actual target concentrations and must be considered. Blood 

4 levels are an acceptable measure of internal dose when it can be shown that they are proportional 

5 to concentrations of toxicant in target tissue or biological preparation.

6 When using in vitro systems, the response measured should be representative of the 

7 toxicity; that is, the measured endpoint should be the same as—or highly related to—the critical 

8 effect or key event. In cases where the measured response differs, it is important to describe the 

9 potential impact of these differences on the final DDEF. When using in vitro data, the 

10 comparability of chemical uptake between animal and human tissues should be demonstrated. In 

11 vitro results should include data describing the test chemical metabolism by the system used to 

12 generate response data.

13

14 4.1.3. Range of Doses or Concentrations

15 The relationship between the doses, tissue concentrations, and/or in vitro concentrations

16 used to derive the DDEF, and those attained in the toxicity studies characterizing the POD,

17 should be characterized. Optimally, the concentrations used in studies characterizing response 

18 include the concentration at the POD. This is important because the variability in the response 

19 may change with increasing or decreasing dose or concentration. Doses and/or tissue 

20 concentrations may also be compared to those expected from environmental exposure; this is

21 part of the consideration of overall relevance of the test system.

22

23 4.2. INTERSPECIES TOXICODYNAMIC EXTRAPOLATION (EFAD)

24 This section provides information on calculating TD differences between species. These 

25 are characterized as the ratio of the concentrations (concentrations used in vitro or the level of 

26 the dose metric) in animals and humans producing the same level of response. Note that the 

27 level of comparison is the response, and it is not a comparison of responses produced by the 

28 same concentration (see Figure 6). Illustrative examples are included in the appendix.
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Figure 6. Interspecies toxicodynamics. The TD difference between test animals and 

humans is calculated from dose or concentration-response relationships most often 

determined in test systems or animals generally representative of the respective species. 

The comparison is made for doses or concentrations producing the same level of 

response, and comparisons are made using values representing the central tendency.

1

2

3 4.2.1. Considerations for EFAD

4 4.2.1.1. Mode of Action

5 Endpoint(s) must be chosen from which to derive the EFAD, and it is preferred for the 

6 endpoint(s) to be the actual critical effect or a key event in the TD pathway. For each endpoint 

7 evaluated, it must be determined whether the data are from an animal typical of the responding 

8 species/strain/sex/lifestage opposed to an animal model system that may be unusually sensitive 

9 for a particular effect. This might be important in transgenic animals and in animal models used 

10 to study specific human diseases (e.g., spontaneously hypertensive rats). Animal models and 

11 MOA(s) based on them are evaluated for human relevance.

12

13 4.2.1.2. Target Tissues

14 Where there are data from the molecular targets in both species, but the data are not from 

15 the critical target organ, the data can be used only if there is sufficient information that one tissue 

16 is an appropriate surrogate for another. For example, a target enzyme may be present in several 
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1 tissues, including blood; however, blood is easily obtained from humans, but tissue from the 

2 target organ is not. Studies may show that the chemical effect on this target enzyme in blood 

3 correlates very well and is entirely predictive of the effect in the target organ, and indeed the 

4 enzyme structure, function, and chemical affinity is the same regardless of tissue. Such 

5 information would be necessary to allow using comparisons of chemical effects in the blood 

6 enzyme in both humans and laboratory animals.

7

8 4.2.1.3. Dose Response

9 The choice of response level to use for comparison depends on many factors: 

10

11 Completeness of dose response

12 Linearity of the dose response

13 Understanding of the effect along the toxic pathway at that response level

14

15

16 The magnitude of EFAD may be a function of the response level chosen for extrapolation. 

17 Confidence is increased when the response level employed for EFAD calculation approximates 

18 the response level at the POD and when the ratio of doses producing the same response level in 

19 animals and humans is similar over a range of doses. When data are available to describe the full 

20 dose-response curve, evaluating the shape of the dose curves for animals and humans can 

21 provide important information. If the shapes of the curves are different, then the magnitude of 

22 EFAD will depend on the response level selected. Note that concentrations causing greater 

23 magnitudes of effect may also produce nonspecific cellular changes that could confound the 

24 comparisons. 

25

26 4.2.1.4. In Vitro Data

27 When using data from in vitro systems, the activity of the parent chemical and/or 

28 metabolites, as well as the extent of metabolism of the compound by the in vitro system, should 

29 be known. The in vitro assay should measure a response that can be linked to the toxic outcome, 

30 and the assays should employ the same (responding) tissues from the in vivo test animal 

31 species/strain/sex/lifestage and from humans. Experimental systems should be as closely 
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1 matched between species as possible, and the concentrations of toxicant in vitro should be 

2 compared to tissue concentrations (the dose metric) at the POD. See Section 2.2.3.2 for other 

3 general considerations.

4

5 4.2.2. Computation

6 4.2.2.1. Use of TD Models

7 A biologically based dose response or other TD model provides the most robust approach 

8 for evaluating interspecies TD extrapolation. The model is subject to evaluation as described 

9 previously (U.S. EPA, 2006). When available, the model can be used to inform several 

10 decisions, depending on the model. In some cases, the TD model may be used directly to 

11 perform interspecies extrapolation and to derive a Human Equivalent Concentration or Dose that 

12 includes TD considerations, thus alleviating the need for EFAD. In other cases, the TD may be 

13 used to derive the EFAD used.

14

15 4.2.2.2. Use of Ratios

16 When PD models are not available, EFAD would be calculated as a ratio describing the 

17 relationship between the tissue concentrations producing a set response in human compared to 

18 animal tissues, preferably at, or near, the response level at the POD. For example, ―x response 

19 level‖ may be a 10% response if ED10 or BMD10 is used. The EFAD would be calculated as 

20 follows:

21

A- response level

H- response level

Concentration

Concentration

x

AD

x

22

EF

(5)

23

24 where

25 EFAD = factor for interspecies extrapolation covering 

26 toxicodynamics

27 ConcentrationA--x response level = concentration of the agent at the tissue in the animal 

28 resulting in a x% response

29 ConcentrationH--x response level = concentration of the agent at the tissue in the human 

30 resulting in a x% response, where x is the same response

31 value.
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1 To evaluate the extent to which the shape of the dose-response curve varies between 

2 animals and humans and, thus, impacts the magnitude of the EFAD, a range of response levels

3 should be evaluated. The rationale and implications for choosing the point for extrapolation 

4 should also be presented.

5

6 4.2.3. Conclusions for EFAD

7 Mathematically, the EFAD will be the relationship between the concentrations or dose 

8 metric values resulting in the same level of response in both the test species and the generally

9 representative human (often the 70-kg adult male). DDEF values may be calculated for multiple 

10 PODs and organs/effects, but the same response levels in animals and humans should be used for 

11 quantitation when possible. Confidence in the value (knowing whether the DDEF value may 

12 vary depending on the response level) is improved when EFAD values developed from multiple 

13 points on the concentration-response curve are compared; the shape/slope of the curves may also 

14 influence these values. Quantitatively, EFAD can be less than one if the data show humans are 

15 inherently less sensitive than animals.

16 A summary of all conclusions and their scientific support should be provided. Data 

17 describing the dose response of the compound in animals and humans can be specifically 

18 reiterated in this description; this will indicate the extent to which the EFAD value can be 

19 generalized to doses that may be higher or lower than those used in its calculation.

20

21 4.3. INTRASPECIES TOXICODYNAMIC EXTRAPOLATION (EFHD)

22 This section describes derivation of the EFHD. TD variability within the human

23 population is calculated as the relationship between concentrations or dose metric values 

24 producing the same level of the response in the general population and in susceptible groups or 

25 individuals. From a toxicodynamic standpoint, susceptibility is based on attaining a given level 

26 of response at a lower concentration of toxicant. For this evaluation, multiple response levels, 

27 critical effects (or key events), analytical methods, or susceptible groups or individuals may be 

28 considered. No data sets were identified upon which a conclusive case study example could be 

29 developed for intraspecies toxicodynamic extrapolation.

30
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1 4.3.1. Considerations for EFHD

2 4.3.1.1. Susceptible Groups or Individuals

3 Susceptibility in the human population may be due to lifestage, health status or disease 

4 state, genetic disposition, or other factors. Considering susceptibility to more than one critical 

5 effect may require consideration of more than one life-stage; critical windows of development, 

6 and, therefore, windows of susceptibility, occur at different times for various tissues, organs, and 

7 systems. Currently, sufficient data to address susceptibility are rarely available; however, 

8 research in this area is rapidly expanding. For example, population variation, such as genetic 

9 polymorphisms, is an expanding area of study. It is anticipated that the increased availability 

10 and experience applying ―omics‖ technologies will benefit the derivation of DDEFs, in general,

11 and EFHD, in particular. A data-derived EFHD is feasible, given human data are of sufficient 

12 quality; the data address aspects of the critical effect consistent with that identified from 

13 applicable human or animal studies; and the studies have been conducted in the segment(s) of 

14 individuals or the population deemed sensitive.

15 Ideally, data will be robust enough to enable more than point estimates in the general and 

16 susceptible groups. As discussed in more detail below, distributional analysis of response data 

17 should be conducted to identify points for use in quantitation. The relationship between the 

18 measured response and the toxicity endpoint of concern (e.g., critical effect or key event) should 

19 be described, whether determined in vivo or in vitro.

20

21 4.3.1.2. Target Tissues

22 For calculation of EFHD, data for multiple responding tissues can be evaluated, and 

23 multiple DDEFs can be derived. It is particularly important to evaluate those tissues that 

24 demonstrate response at doses or concentrations near those for the critical effect. 

25

26 4.3.1.3. In Vitro Data

27 Given the constraints on generation of human response data in vivo, in vitro studies offer 

28 an appealing alternative. Samples selected for in vitro investigation should represent the general 

29 human population as well as those groups or individuals thought or demonstrated to be 

30 susceptible. See Section 2.2.3.2 for other general considerations.

31
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1 4.3.2. Computation

2 For TD extrapolation, the goal is to determine the difference between humans on the 

3 basis of concentration producing the same response level. For quantitation, data on the critical 

4 response(s) are derived from a population that includes susceptible groups or individuals. 

5 Because the data available to define potentially susceptible groups or individuals could be 

6 viewed in different ways, a statistical analysis may be helpful to determine distribution type (see 

7 Figure 7):

8

9 A unimodal distribution where the potentially susceptible group(s) represent the tail of 

10 the distribution because they cannot be separated from the general population.

11 A bimodal (or multimodal) distribution where the group(s) can be readily identified.

12

13

14 Documenting critical response data, assumptions made, and the distribution selected will 

15 serve as the basis for quantitation.

16

17 4.3.2.1. Use of TD Models

18 A biologically based dose response or other TD model provides the most robust approach 

19 for evaluating intraspecies TD extrapolation. When sufficient data are available, these TD 

20 models can be structured and exercised to include differences in mode-of-action components that 

21 may be lifestage-dependent or influenced by other potentially susceptibility-inducing conditions 

22 such as genetic polymorphisms. Specific to EFHD, it is critical that the model parameter 

23 reflecting the underlying cause of susceptibility in a group be well documented. When an 

24 appropriate model is available, it can be used in different ways depending on the model. In some 

25 cases, the TD model may directly account for within human variation and/or include data from 

26 the sensitive group, thus eliminating the need for EFHD. In other cases, the TD may be used to 

27 derive EFHD.

28

29 4.3.2.2. Use of Ratios

30 When TD models are not available and there are groups or individuals that can be 

31 identified as sensitive, then the EFHD may be defined as the ratio between the concentrations 

32
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Figure 7. Intraspecies toxicodynamics. DDEF values for TD are defined by ratios of 

concentrations producing the same level of response in the general population and some 

defined percentile (e.g., 1st, 2.5th, 5th, etc.) for the distribution representing sensitive 

individuals. When a specific group of the population can be identified as potentially 

susceptible, TD data from that group can be compared to the general population (Panels A1 

and A2). Panel A1 presents a dose-response curve (cumulative distribution plot) for both 

populations that demonstrates the central tendency (solid line) and confidence bounds, or 

bounds of variability (dashed lines), for data obtained from the general population and from 

an identifiable sensitive group. In this example, the level of response (Y-axis) has been 

selected (e.g., 10%-response level), and the concentrations producing this level of response in 

the general and sensitive populations/groups are obtained from the X-axis. Panel A2 is 

derived from the same data used for Panel A1, but it presents the distribution of 

concentrations producing the defined level of response only; no other dose-response data are 

carried over into Panel A2. Alternately, when potentially susceptible individuals represent a 

small percentage of the general population (Panels B1 and B2), a slightly different analysis is 

conducted. In this case, EFHD should be determined as the ratio of the concentrations 

producing the same level of response (1) at a measure of the central tendency in the 

population to (2) the concentration producing the response level at a percentile of the general 

population considered sensitive (e.g., 1st, 2.5th, 5th percentile). Panel B1 demonstrates this 

comparison using the concept of confidence bounds on the dose-response relationship, and 

Panel B2 demonstrates the distribution of concentrations producing the response, only at the 

response level chosen for comparison (e.g., the 10%-response level).
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1 producing the same level of response in the general population and a lower percentile in the 

2 sensitive group (see Figure 7) using Equation 6:

3

gen

sens

Concentration

Concentration

EFHD

4 (6)

5

6 where

7 EFHD = factor for intraspecies extrapolation covering toxicodynamics

8 Concentrationgen = concentration producing the in response the general human 

9 population 

10 Concentrationsens = concentration producing the response at a percentile of interest for 

11 the sensitive group.

12

13

14 When sensitivity among the population exhibits a unimodal distribution, the EFHD is the 

15 ratio of the concentration that elicits a level of response at the central tendency of the distribution 

to the concentration that elicits the same level of response in sensitive individuals (e.g., 5th, 2.5th 16 ,

and 1st 17 percentiles of the distribution; sensitive individuals will respond at lower concentrations). 

18 It is important to define and justify the point(s) in the distribution representing sensitive groups 

19 or individuals.

20 When sensitivity among the population exhibits a bimodal (or multimodal) distribution, 

the DDEF is determined in a similar manner, using the concentrations (e.g., 5th, 2.5th, and 1st 21

22 percentiles of the concentration distribution) that elicit the specific level of response in the 

23 sensitive individuals for the most susceptible group(s). The values selected to describe the 

24 potentially sensitive group(s) or individuals are defined and presented at varying levels. The 

25 selection of the response level and the percentile of the distribution used to describe the 

26 potentially sensitive group(s) or individual(s) is an important issues. This is a situation where the 

27 communication between risk assessment and risk management is essential.

28

29 4.3.3. Conclusions for EFHD

30 A biologically based dose response or other TD model provides the most robust approach 

31 for evaluating intraspecies TD extrapolation. When using empirical ratios, the EFHD will be the 
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1 ratio of the concentration producing the specified level of response in the general human 

2 population to the concentration producing the same level of response in susceptible groups or 

3 individuals. Increased confidence in the EFHD is developed when the concentration used for the 

4 comparison of responses is compared to doses or concentrations at the POD. Quantitatively, 

5 EFHD cannot be less than one.

6 The risk assessor describes all choices and their rationales, including the use of multiple 

7 response levels, critical effects (or key events), analytical methods, or data from susceptible 

8 groups or individuals. The conclusions include a clearly worded description of the mathematical 

9 method(s) employed and a presentation of the relationship between the measured response and 

10 toxicity (i.e., critical effects or key events). This description should clearly identify and provide 

11 the justification for available data and points in the distribution(s) representing sensitive 

12 individuals. Attention should be paid to characterizing the distribution type employed for 

13 analysis; uncertainty in the choice of distribution type can be reduced by presenting DDEF

14 values resulting from multiple distribution types.

15
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1 5. FINAL STEPS

2 The composite DDEF is calculated after the appropriate DDEF values for inter- and 

3 intraspecies differences in TK and TD have been derived. The composite factor is calculated by 

4 multiplying the specific factors (default and/or DDEFs), as shown in Equation 7. This is entirely 

5 analogous to calculating composite UFs when using the 10× defaults for UFA and UFH. The 

6 composite DDEF may be less or greater than 100.

7

8

9 CF = EFAK × EFAD × EFHK × EFHD (7)

10

11 where

12 CF = composite uncertainty factor

13 EFAK = factor for interspecies extrapolation covering toxicokinetics

14 EFAD = factor for interspecies extrapolation covering toxicodynamics

15 EFHK = factor for intraspecies extrapolation covering toxicokinetics

16 EFHD = factor for intraspecies extrapolation covering toxicodynamics

17

18

19 In practice, data may only be available to develop a DDEF for one component of 

20 extrapolation or another (e.g., data for EFAK but not EFAD). In these cases, the remaining 

21 extrapolation is done by an appropriate default procedure. As such, DDEFs and defaults (i.e., 

22 UFs) are used in combination. Often this default will be a 3× UF—as described in the existing 

23 RfC methodology and the 3⁄4 body-weight procedure (U.S. EPA, 2011, 1994). When data are not 

24 available to develop DDEFs for either component of interspecies or intraspecies extrapolation, 

25 the 10× default value for the uncertainty factor is applied.

26 Finally, the composite factor provides the total magnitude of the factor. The values 

27 derived for each of the components and the resulting extrapolations should be clearly reported 

28 and characterized. The relationship of each of these doses or concentrations to both the POD and 

29 to doses or concentrations likely attained from environmental exposures should be presented.

30
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1 6. REFERENCES

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