&EPA
United States
Environmental Protection
Agency
Malathion Exposures During Lice
Treatment: Use of Exposure Related
Dose Estimating Model (ERDEM) and
Factors Relating to the Evaluation of
Risk
RESEARCH AND DEVELOPMENT
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EPA/600/R-07-023
March 2007
www.epa.gov
Malathion Exposures During Lice
Treatment: Use of Exposure Related
Dose Estimating Model (ERDEM) and
Factors Relating to the Evaluation of
Risk
Frederick W. Power1
Curtis C. Dary, Ph.D.1
James B Knaak, Ph.D.2
Rogelio Tornero-Velez, Ph.D.3
Jerry N Blancato,, Ph.D.4
1Human Exposure & Atmospheric Sciences Division 2Department of Pharmacology and Toxicology
and Atmospheric Modeling Division (E205-01) School of Medicine and Biomedical Sciences
National Exposure Research Laboratory SUNY at Buffalo
U. S. Environmental Protection Agency Buffalo, NY 14214
944 E. Harmon Ave
Las Vegas, NV89119
3Human Exposure & Atmospheric Sciences Division 4Deputy Director
and Atmospheric Modeling Division (E205-01) National Center for Computational Toxicology
National Exposure Research Laboratory U. S. Environmental Protection Agency
U. S. Environmental Protection Agency 109 T. W. Alexander Drive
109 T. W. Alexander Drive Research Triangle Park, NC 27711
Research Triangle Park, NC 27711
Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect
official Agency policy. Mention of trade names and commercial products does not constitute
endorsement or recommendation for use.
U.S. Environmental Protection
Agency
Office of Research and Development
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Acknowledgments
The skill and dedication of Mrs. A. Fichter in the preparation of
the manuscript is greatly appreciated. This modeling effort is a
product of the concerted abilities of Dr. Miles Okino, Dr. Marina
Evans, Dr. Peter Egeghy, and Mr. Edwin Furtaw. Without their
contributions this manuscript would not have been possible.
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PREAMBLE
The United States Environmental Protection Agency (EPA) is authorized by Congress to conduct
research related to the nation's various statutes to protect human health and the environment. To accomplish this
purpose, EPA's Office of Research and Development (ORD) often conducts intramural research in direct
support of client office needs. The Exposure and Dose Research Branch (EDRB) of the Human Exposure &
Atmospheric Sciences Division (HEASD), of the National Exposure Research Laboratory (NERL) conducted
research in collaboration with scientists of the Health Effects Division (HED) of the Office of Pesticide
Programs (OPP). This report is a product of this collaboration as it relates to the exposure assessment of
organophosphorus (OP) insecticide, malathion, (O,O-dimethyl phosphorodithioate diethyl mercaptosuccinate;
CAS 121-75-5) labeled for use as a pediculicide.
The exposure assessment was performed in response to reregistration requirements under the Food
Quality Protection Act of 1996, which significantly amended the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA) and the Federal Food, Drug, and Cosmetic Act (FFDCA). One of the major changes
is the requirement that EPA consider risk posed from aggregate exposure to the pesticide chemical residue,
including all anticipated dietary exposures and all other exposures for which there is reliable information.
Malathion is used in a variety of agricultural settings and also as a general wide-area treatment for mosquito-
borne disease control. Malathion can be purchased by the home gardener for use on vegetable gardens, home
orchards, ornamentals, and lawns. Malathion has also been approved by the FDA for the control of head lice
when prescribed by a physician.
Head lice (Pediculus humanus capitis) are human parasitic insects. The Center for Disease Control
(CDC) estimates that 6-12 million people worldwide are infected with head lice. In the U.S., pesticides
approved by Food and Drug Administration (FDA) as pediculicides for the control of head lice include
permethrin and pyrethrins, which can be purchased over-the-counter in addition to malathion, and lindane which
require prescriptions. EPA has already released its evaluation of the risks associated with the use of lindane for
controlling head lice and scabies.
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The toxicology database of laboratory animal studies for malathion is substantially complete and of
acceptable quality to assess the potential hazard to humans, including special sensitivity of infants and children.
The database includes prenatal developmental toxicity studies in rats and rabbits, a two-generation reproductive
toxicity study in rats, an acute delayed neurotoxicity study in hens, an acute neurotoxicity study in rats, a
subchronic neurotoxicity study in rats, a developmental neurotoxicity study in rats (with a supplemental range-
finding study), and a comparative cholinesterase study in adult and immature rats. In addition to these studies,
the registrant has submitted an extensive database of guideline toxicology studies, as required in 40 CFR Part
158.340 (i.e., acute, subchronic, chronic, carcinogenicity, and metabolism studies).
However, product-specific dermal disposition data are not available to provide a direct assessment of
risk in humans, particularly infants and children. Therefore, in the absence of product-specific human data,
scientists at NERL have used EPA's Exposure Related Dose Estimating Model (ERDEM,
http://epa.gov/heasd/erdem/erdem.htm) to aid in exposure and risk characterization.
ERDEM is a product of the Government Performance and Results Act (GPRA) funded under Goal 4 -
Preventing Pollution and Reducing Risk in Communities, Homes, Workplaces and Ecosystems as articulated in
the 2003-2008 EPA Strategic Plan (U.S. EPA, 2003, http://www.epa.gov/ocfo/plan/2003sp.pdf). The Program
Results Codes, 405FB2A (long-term goal HH-3) and 405FB5A (long-term goal FQ-1), assigned to the funding is
consistent with human health research as originally set forth in the Human Health Research Strategy (U.S. EPA
2003). The ORD Human Health research (U.S. EPA, 2003) has been described as "an interactive process
where the state of our knowledge is applied to test and identify research gaps and overcome shortcomings and
incorporate the emerging insights to improve methods, measurements, and models."
The ultimate goal of ORD's research (U.S. EPA, 2003) in the area of aggregate exposure and
cumulative risk: is to provide the Agency and the public with reliable methods, tools, and guidance for
quantitatively estimating distributions of total cumulative human exposure and risk for both impacted
individuals and communities and for the population at large. This research is intended to expand our
understanding of exposures and risks to single chemicals and to multiple or combined effects of exposure to
mixtures of pollutants, i.e., aggregate exposure and cumulative risk. Aggregate exposure involves human
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contact with toxic substances in media (air, water, food, dust and soil) and on surfaces where residues are
transferred along recognized pathways (inhalation, ingestion, and dermal) that may contribute to internal dose.
Cumulative risk arises from a combination of intrinsic biological susceptibility, vulnerability, and demographic
characteristics (occupation, location, personal traits, and attributes) that follow from aggregate exposure.
Susceptible subpopulations may be vulnerable to exposure because of uncontrollable circumstances or engaged
activities.
ORD is committed to improving risk assessment methodologies for susceptible sub-populations (infants
and children, the elderly, and those with genetic susceptibilities) through its Human Health Research Strategy
(U.S. EPA 2003), Strategy for Research on Environmental Risks to Children (U.S. EPA, 2000), and Asthma
Research Strategy (U.S. EPA 2002). Susceptibility depends upon certain intrinsic biological factors, such as
life stage, gender, genetics, physiological state, immunity, disease state, nutrition, stress, and personal habits
(cigarette smoking, drug, and alcohol use). Vulnerability may identify distinct highly exposed subpopulations to
specific environmental agents based on life stage, gender, occupation, and geographic location.
Model development research involves developing consistent and flexible principles and guidelines for
using and drawing inferences from scientific information. This research requires assembly of all pertinent
information on exposure, dosimetry, pharmacokinetics (PK), and toxicity such that extrapolation procedures
can be thoroughly examined to test uncertainty factors for all risk assessments, regardless of the nature of
toxicities, chemical agents, or mixtures. The long-term goal of ORD risk assessment approaches (U.S. EPA,
2003) is to:
"Derive a commonly accepted set of principles defining how mode or mechanism of action information can be
used in risk assessments, particularly as it relates to extrapolation procedures. "
The models developed, tested, and applied under Goal 4 are expected to provide information needed to
improve the precision and accuracy of dose estimations and predictions. Moreover, such models are expected to
improve the design of future exposure studies. Currently, primary clients for use of these models include the
Office of Pesticide Programs, the Office of Water, and the National Center for Environmental Analysis (NCEA).
Specifically, scientists in these Program Offices are interested in using such models to assess the relative target
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doses of environmental chemicals in the body after daily exposures that result from activities such as eating,
drinking, showering, and other activities of daily living.
The ERDEM project aims to strengthen the general scientific foundation of EPA's exposure and risk
assessment, management, and policy processes by developing state-of-the-art exposure to dose mathematical
models and solution methods. The ERDEM modeling platform is an integrated group of physiologically based
pharmacokinetic (PBPK) models. This platform can be easily modified for a variety of exposure assessment
and risk characterization problems. The models interpret uptake into the body by multiple routes of entry (e.g.,
dermal, ingestion, and inhalation). Also, the modeling framework is capable of incorporating the physiological
changes of differing activity levels as well as the anatomic and physiological differences between infants and
children during growth and development. The development of mathematical methods includes parameter
estimation methods and uncertainty analysis. These source-to-exposure-to-dose models provide the essential
linkage between experimental data and assumptions established by regulation to dose-response models designed
by toxicologists.
The ERDEM model was originally developed using the software program SIMULSOLVE, developed
by DOW Chemical. ERDEM has since evolved into a system that has a graphical user interface front end, a set
of multiple models that are available for users, and a separate research version that requires user preparation of
input command files. The current models allow input of multiple exposure chemicals for multiple routes and
scenarios.
The ERDEM framework, with its associated data base, provides an integrating function, taking
advantage of advances made under other GPRA goals inside and outside EPA. ERDEM is particularly useful
for predicting the dose from measured and modeled exposure conditions. Physiologically based
pharmacokinetic (PBPK) and pharmacodynamic (PBPK/PD) models, such as ERDEM, hold great promise for
evaluating, estimating and predicting measures of lexicologically relevant doses within the body. PBPK/PD
models produce temporal and spatially relevant risk assessments for susceptible subpopulations, e.g., children,
by making concerted use of pharmacokinetic and mode-of-action data. The ERDEM framework provides a
modeling tool for characterizing exposures of children and other groups and calculating estimated internal
doses. The ability of ERDEM to interpret children's aggregate and cumulative exposures may assist in
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identifying critical data gaps. These may include critical windows of susceptibility during development of
major organ systems. PBPK/PD models have the ability to examine animal systems as a means of predicting
human life stage susceptibility and to explore modes of action of agonistic and antagonistic groups of
chemicals/pesticides. PBPK models are often used to support risk assessment decisions concerning
extrapolation across species. In this way, PBPK/PD models such as ERDEM are able to identify relevant,
representative, complete and comparable data and test the reasonableness of default assumptions that have been
set in the absence of data. PBPK/PD models may ease the extrapolation of data obtained from experimental
animal models to humans.
This report first introduces the presumptive exposure scenario involving the use of a topical
formulation of malathion (Ovide") and the toxicology of organophosphorus insecticides. Following this
introduction, the ERDEM operating system is presented (Methods, Section 2) to explain the use of PBPK/PD
modeling in this exposure assessment. Descriptions of the unique aspects of this exposure are explored. It must
be borne in mind that the ERDEM platform is continuously being updated and, therefore, the model applications
presented in this report do not necessarily represent the most recent advances in PBPK/PD modeling.
Continuous development and refinement is a major advantage of PBPK/PD modeling; a condition that is not
easily conveyed in a written report. ERDEM describes exposure events in time and space under certain
recognizable exposure scenarios at the boundary of the body or test system. The mathematical equations that
describe exposure are needed to introduce the chemical into organ and tissue "compartments" as described
mathematically in Appendix A. ERDEM consists of the following compartments: arterial blood, brain, carcass,
closed chamber, derma, fat, Intestine, kidney, liver, rapidly perfused tissue, slowly perfused tissue, spleen, static
lung, stomach, and venous blood. The mathematical equations for these compartments are presented in
Appendix - A. Each of the compartments - brain, carcass, fat, kidney, liver, lung tissue, rapidly and slowly
perfused tissues, spleen, and the static lung - have two forms of elimination, an equilibrium binding process and
multiple metabolites.
Exposure is described in Section 2 as a scenario-based event that occurs in time and space along a
recognized pathway, in this special case, dermal uptake, where malathion gains entry into the body by way of
absorption through the skin of the scalp. Data gleaned from controlled experimental studies involving the
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metabolism chemistry of malathion were used to adjust PBPK/PD model parameters. This "validation" step
served as a means to evaluate model parameters and exposure data prior to testing exposure scenarios involving
less controllable field or epidemiological conditions where the route or routes of entry are more problematic.
Model validation is the key to acceptance of an otherwise well-tested model. Experimental data is necessary to
verify model functionality. ERDEM was designed to examine scheduled exposures from controlled
experimental and clinical studies to enhance the pharmacokinetic modeling engine. The quality assurance
process used to accomplish this model "validation" step is presented in Appendix B.
Section 3 contains the results for the presumptive exposure of a 9-year old female. A 9-year old female
was considered to be most representative of probable use of a pediculicide owing to the volume and density
(size) of the hair as explained in Appendix C. Special adaptations in cardiac output and circulation were made
to the model to account for growth and development with respect to age and sex (Appendix D). Results for
other age classes, 3-year old males and females, 9-year old males and 18-year old males and females, are
presented in Appendix E.
VI
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CONTENTS
Preamble i
1.0 Introduction 1
2.0 Methods 5
2.1 Overview of Presumptive Exposure 5
2.2 PBPK Models 7
2.2.1 Exposure Related Dose Estimating Model (ERDEM) 7
2.3 Metabolism of Malathion 10
2.4 PBPK Model Input Variables 18
2.4.1 Body Volume 18
2.4.2 Cardiac Output 20
2.4.3 Distribution 20
2.4.4 Elimination 21
2.4.5 Metabolism Parameter Determination 22
2.4.6 Urine Elimination Parameters 23
3.0 RESULTS 24
3.1 Overview of Presumptive Exposure 24
3.2 Simulation of Dermal Exposure 26
3.3 Simulation of a Single Dermal and Oral Bolus Dose 29
3.4 Estimates of Blood and Tissues Concentrations of Malathion and Malaoxon Following Oral
and Dermal Exposure 30
3.5 Estimates of Urinary Metabolite Concentrations Following Oral and Dermal Exposure. .. 34
4.0 DISCUSSION 39
5.0 REFERENCES 48
APPENDICES
Descriptions of Exposure Al
1.1 Experimental Pathways and Routes of Entry Al
1.1.1 Intraperitoneal Injection Al
1.1.2 Intramuscular Injection A2
1.1.3 Intravascular Administration A4
1.1.3.1 Infusion into the Venous Blood A4
1.1.3.1 Bolus Intravenous Injections A4
1.1.4 Inhalation Administration A5
1.2 Implementation of the Exposure Time Histories for Rate Ingestion, Inhalation, and Skin
Surface Exposures A7
1.2.1 Ingestion Into the Stomach and the Stomach Lumen A7
1.2.1.1 Bolus Dose Ingestion A7
1.2.1.2 Rate Ingestion A9
1.2.2 Inhalation Exposure A9
1.2.3 Dermal Exposure A9
1.2.3.1 Skin Surface Exposure to a Chemical in an Aqueous Vehicle A10
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1.2.3.2 Skin Surface Exposure to Transfer from a Dry Surface
A10
1.3 Variable Definitions A12
Bolus Dose Ingestions: A12
Rate Ingestions: A12
Infusions: A13
Intraperitoneal Injection: A13
Intramuscular Injection: A14
Skin Surface Exposure (Water): A15
Inhalation: A15
2.0 Chemical Disposition in silico A17
2.1 Distribution of Chemical from Blood to Tissues, Organs and in Fluids A17
2.1.1.1 Binding in the Arterial Blood A17
2.1.1.2 Calculation of Free Chemical in the Arterial Blood A17
2.1.2 The Venous Blood A18
2.1.2.1 Binding in the Venous Blood A18
2.1.2.2 Calculation of Free Chemical in the Venous Blood A18
2.1.3 Distribution in Tissues A18
2.1.3.1 Distribution in the Residual Carcass A18
2.1.3.2 Distribution in Fat Tissue A20
2.1.3.3 Distribution in Slowly Perfused Tissue A21
2.1.3.4 Distribution in Rapidly Perfused Tissue A23
2.1.4 Distribution of Chemical in Organs A24
2.1.4.1 Distribution of Chemical from Blood to the Brain A24
2.1.4.2 Distribution of Chemical to the Liver A26
2.1.4.4 The Intestine A29
2.1.4.5 The Kidney A29
2.1.4.6 The Spleen A30
2.1.4.7 The Dermal Tissue A32
2.2 Metabolism in selected Tissues and Organs A32
2.2.1 Implementation Outline A.34
2.2.2 Variable Names for Metabolism Parameters A.34
2.2.3 Calculation of Maximum Rate of Change of Metabolism A.37
2.2.4 Calculations when including Enzyme Destruction and Re-synthesis
A.37
2.2.5 The Rate of Formation of Saturable and Linear Metabolite in the Liver A.38
2.2.6 Circulating Compounds which are Metabolites A.38
2.2.7 Inhibition in the Metabolism Process A.39
2.2.8 Metabolism in the other Organs and Tissues A.40
2.2.8.1 Metabolism in the Brain A.40
2.2.8.2 Metabolism in the Kidney A.40
2.2.8.3 Metabolism in the Carcass A.41
2.2.8.4 Metabolism in the Fat A.41
2.2.8.5 Metabolism in the Slowly Perfused Tissue A.41
2.2.8.6 Metabolism in the Rapidly Perfused Tissue A.42
2.2.8.7 Metabolism in the Spleen A.42
2.3 References A.42
1.0 Quality Assurance for Data and Compartment Models Bl
2.0 Quality Assurance for the Exposure Related Dose Estimating Model (ERDEM) B5
Quality Assurance of the ERDEM Models B5
Code review B5
Mass balance checks B5
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Proper Operation with Inputs for Known Chemicals and Their Known Metabolism Paths:. . . B5
Comparisons of model runs with experimental results: B7
Proper use of an ERDEM Model: B8
References B9
Scalp Size Estimations Cl
1.0 Calculation of Cardiac Output for Children as a function of age and growth Dl
1.1 Methods and Results: m
1.2 References: D3
2.0 Estimation of human tissue :blood partition coefficients (Pt:p) formalathion and metabolites. . . . D4
2.1 Estimation of logP (log10[Kow]) for malathion and metabolites D6
2.2 Estimation of Kob for malathion and metabolites D8
2.3 Calculation of partition coefficients D8
2.4 References D15
IX
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1.0 Introduction
The purpose of this document is to assess the potential risk of insult and injury to children resulting
from the prescribed use of malathion for the control of head lice. As required by the Food Quality Protection
Act ( FQPA), the Agency has considered the pharmaceutical use of malathion in its reregistration evaluation.
Ovide® has been approved by the FDA for use to control a highly contagious parasite (Pediculus humanus
capitis L.) that infects the human scalp. This condition is of particular importance to school age children where
close human contact occurs haphazardly. The source of exposure was accordingly, the willful and prescribed
use of the topical lotion, Ovide®, containing Spercent (w/v) malathion (MEDICI, The Dermatology Company)
lotion as a pediculicide according to label directions (The Medical Letter, 1999).
Malathion (O,O-dimethyl phosphorodithioate of diethyl mercaptosuccinate; see structure below; CAS
121-75-5) is member of the phosphorodithioate class of organophosphorus (OP) insecticides (Fest and Schmidt,
1973; Eto, 1974; Gallo and Lawryk, 1991; Nigg and Knaak, 2000).
o
Jf
CHr- °v #* CHa 0-CHjCH,
ca-o' N-CH o-oys,
T
0
Since its introduction as a selective insecticide in 1950 (Bourke et al., 1968), the toxicity and toxicokinetic
disposition of malathion in non-target organisms including mammals (Pellegrini and Santi, 1972) has been
extensively investigated (Murphy, 1967 ) culminating in several monographs (Holmstedt, 1959; Dauterman and
Main; 1966, Gaines, 1969) and sections within chapters in various books devoted to pesticide toxicology Heath,
1961; O'Brien, 1960 and 1967; Corbett, 1974; Hayes, 1975; Ecobichon and Joy, 1982; Matsumura, 1985.
There is little argument among neurotoxicologists that the primary acute mechanism of toxic action involves
phosphorylation of the enzyme acetylcholinesterase (AChE) in both the central (brain) and peripheral nervous
systems Koelle, 1974 and 1975; Ecobichon, 1994; Brown and Taylor, 2001; Taylor, 2001. AChE is the enzyme
that hydrolyzes the neurotransmitter acetylcholine at cholinergic synapses and neuromuscular junctions (Taylor,
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2001). The inhibition of AChE leads to accumulation of synaptic acetylcholine resulting in the overstimulation
of postsynaptic cholinergic receptors that lead to consequent cholinergic signs of neurotoxicity such as
headache, nausea, diarrhea, and possibly death (Morgan, 1982; Brown and Taylor, 2001; Taylor, 2001 ).
In the case of malathion and other phosphorothio-ester containing structures, e.g., dialkyl
ary/amide/ester phosphorodithioates and phosophorothionates (Eto,1974; Gallo and Lawryk, 1991; Nigg and
Knaak, 2000), metabolic activation (oxidation of the P=S moiety to P=O) is required to gain potency in insects
and mammals. Indeed, activation of malathion to the oxygen analog, malaoxon, increases insecticidal potency
10-fold, Krueger and O'Brien, 1959. This property, combined with rapid hydrolysis by carboxylesterase
(Bourke et al., 1968). contributes to malathion selective insecticidal potency and low mammalian toxicity
(Knaak and O'Brien, 1960; Roberts and Hutson, 1999). Detoxified of malathion by carboxyesterases and other
metabolic processes (see structure below) are expected to compete with activation (oxidative desulfuration) to
the AchE inhibitor, malaoxon (Roberts and Hutson, 1999).
Malathion
S 0
(MeOUP-SCHCOOEt — ± (MeO)-P-SCHCOOEt
CH2COOEt CH2COOEt
PBPK modeling techniques (Krishnan et al., 1994) offer the opportunity to characterize exposure and
internal dose from the prescribed use of Ovide® in children in ways that are difficult by a weight-of-evidence
approach (Clewell and Anderson, 1985). The reliability of the output from such a model is dependent on the
necessary input information (Knaak et al., 2004). Physiological, pharmacokinetic (PK) and pharmacodynamic
(PD) data are gleaned from in vivo and in vitro studies reported in the literature to establish key values for PK
and PD parameters and the time course for disposition of the compounds in the body (Anderson et al., 1987;
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Anderson and Krishnan, 1994). In general, as more and more pharmacokinetic data become available, PBPK
models will improve and their reliability will increase (Rabovsky and Brown, 1993; Bouchard et al., 2003). The
results reported herein reflect the current state of reliability of the Exposure Related Dose Estimating Model
(ERDEM) as they apply to this risk assessment (ERDEM, http://epa.gov/heasd/erdem/erdem.htm).
ERDEM is a PC-based modeling framework that allows for using existing models and for building new
PBPK and PBPK/PD models. For the user, ERDEM requires no special software other than the basic Microsoft
Windows® Environment commonly used on PCs. The ERDEM framework provides a modeling tool for
characterizing exposures of presumably susceptible sub-populations, e.g., infants and children and calculating
estimated internal doses. This framework consists of two parts: the model engine, built in Advanced Continuous
Simulation Language (ACSL), and a user-friendly front-end for creating and executing models (Appendix A).
ERDEM is comprised of the ERDEM Front-End, the ERDEM Model and the ACSL Viewer. The
ACSL Viewer is part of the ACSLTOX modeling engine environment that allows the user to start and view
model-run results. The ERDEM Front-End is a Windows-based application which allows the user to enter
exposure parameters and store them in a database for later use and export into ERDEM. The ERDEM
pharmacokinetic modeling engine contains differential equations that use the physiological, biological, and
pharmacodynamic modeling data that are entered via the ERDEM Front-End.
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2.0 Methods
2.1 Overview of Presumptive Exposure
ERDEM has been used in the current assessment as a tool for aiding in risk characterization of
malathion when used to treat head lice.
At the present time, appropriate pharmacokinetic studies with blood or urine levels of malathion or its
metabolites in humans exposed to malathion from the treatment of head lice are not available. PBPK models
are well suited to help assess risk from the deliberate application of a topical formulation of malathion (Ovide®)
to the scalp of children to control head lice. For this simulation, three ages of children, 3, 9, and 18 years were
considered representative of likely prescribed use of Ovide" lotion as a pediculicide (Table 1).
Table 1 Topical application of Ovide® lotion containing 0.5 percent (w/v) malathion (5.0 mg/mL) at the maximum
allowable volume (59 mL) to the hair and scalp of children, ages 3, 9, and 18 years (295 mg malathion per individual
treatment with 6 and 50 percent delivered to the scalp for dermal absorption)
Age
(yrs)
3
9
18
3
9
18
Sex
Males
Females
Body
Weight
(kg)
11.1
20.4
50.7
10.8
21.4
44.5
Applied
Dose
(mg/kg)
26.6
14.5
5.8
27.3
13.8
6.6
Scalp
surface
area*
(cm2)
412
528
644
374
489
524
Initial concentration
on the scalp (mg/cm2)
6%*
0.0430
0.0335
0.0275
0.0473
0.0362
0.0338
50%**
0.358
0.279
0.229
0.394
0.302
0.281
Treatment dosage
(fig/cm2)/kg
6%*
3.87
1.64
0.542
4.38
1.69
0.760
50%**
32.3
13.7
4.5
36.5
14.1
6
'As taken from Burmaster and Crouch (1997) to represent the 5* percentile of the body weight distribution for
males.
* As determined from the Exposure Factors Handbook, August 1996 .
'Assumes that 94 percent of dose (277.3 mg) is tied up in hair and fipercent (17.7 mg) is delivered to the scalp for
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possible dermal absorption.
Assumes that 50 percent of dose (147.5 mg) is tied up in hair and 50 percent (147.5 mg) is delivered to the scalp for
possible dermal absorption.
The source of exposure involved the topical application of malathion (O.Spercent w/v) to the scalp and
hair as an alcohol-based lotion (Ovide®). Ovide® lotion contains 0.005 grams of malathion per milliliter (mL) of
vehicle (isopropyl alcohol,78 percent). Ovide® is supplied in bottles containing 2 fluid ounces (59 mL) of liquid
formulation. The Ovide® label (NDC 99207-650-02) directs that the lotion be applied to dry hair in an amount
that is sufficient to thoroughly wet the hair and scalp. Special attention should be paid to application around the
back of the head and neck A maximum of two fluid ounces (59 mL) is indicated for use. This maximum
volume was used in this simulation to test the upper limit of exposure. In this regard, the 5th percentile of the
body weight distribution for males and females were used to establish the treatment dose (Table 2). This is a
health protective assumption since, for the same exposure, a smaller body weight child will achieve a larger
milligram per kilogram (mg/kg) absorbed dose.
In this simulation, it is assumed that from 6 to 50 percent of the applied malathion may be bound to the
hair away from contact with the scalp depending on the volume and density of the hair. Therefore, not all of the
applied malathion is available for dermal absorption. Thus six or 50 percent of the original treatment dose of
malathion is available for absorption through the scalp. The 50 percent assumption is considered health
protective but unlikely given the nature of infestation that involves greater hair volume and density. With the
above-noted assumptions, the modeled fraction of malathion absorbed was approximately 10 percent of the
treatment dose on the scalp. As described in the Hazard Identification Assessment Review Committee (HIARC)
report (6/13/2002), a 10 percent dermal absorption factor was selected for route-to-route extrapolation based on
recovery of radio-labeled malathion in urine following application to unprotected skin on the forearms of seven
human subjects. This value of 10 percent is being used in the occupation and residential exposure assessment
for the agricultural and home garden uses of malathion. This fraction is absorbed over the entire exposure period
(12 hours). However, the simulation was run for up to 72 hours to follow the disposition and elimination of the
absorbed dose.
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The malathion applied to the scalp is assumed to be distributed to three different surface types: hair
shafts, hair follicles, and stratum corneum (SC). The entire surface area of the scalp is expected to be treated.
Scalp size is age and sex dependent (Table 2). The concentration on the scalp is assumed to be uniform over the
entire area of the scalp. Absorption through follicles is known to be more rapid than through SC (Scheuplein,
1967). As directed on the Ovide® label, for the in silico simulation, the lotion is allowed to remain on the scalp
for 12 hours where malathion may be absorbed into the viable epidermis. After 12 hours, the scalp and hair are
washed. It is assumed that all of the malathion on the scalp is washed off at 12 hours.
2.2 PBPK Models
2.2.1 Exposure Related Dose Estimating Model (ERDEM)
The National Exposure Research Laboratory (NERL) of the Office of Research and Development
(ORD) has developed a PBPK model that has been used to simultaneously model the disposition of three OP
insecticides in humans following dermal, oral, and inhalation exposure (Knaak et al., 2004; ERDEM,
http://epa.gov/heasd/erdem/erdem.htm). Physiologically based pharmacokinetic (PBPK) models describe the
time course disposition of chemicals and their metabolites andprovide a representation of the distribution,
metabolism, and excretion of xenobiotics. PBPK models, which may be used to predict the transfer of
chemicals to various tissue compartments, typically are a system of first order differential equations describing
the mass balances, disposition, and rate of change of the chemicals and their metabolites in the body.
ERDEM is an exposure and dose-modeling system developed by ORD scientists. The heart of ERDEM
is a PBPK model that simulates the absorption, distribution, metabolism, and elimination of chemicals in
mammals. Simulated chemicals are introduced into the physiological system by any of several routes including
injection, ingestion, inhalation, and/or dermal absorption. The ERDEM system is very complex, with over 30
physiological compartments such as arterial and venous blood, brain, derma, fat, intestine, kidney, liver, rapidly
and slowly perfused tissue, and stomach (Figure 1).
7
-------
-------
Inputs
Bolus Dose -
Ingestions j i
Rate ;
Ingestions J OQO
Intraperitoneal
Injection j
Intramuscular
injection I
J
Skin Surface
Water
J
Bolus Dose \...
^ Injections J \
Infusions • • • -:
IXST.IN rVIN.FEC
.....^. ST Stomach
^- SP Spleen 0.00 Kg
.. ^
•^
^
J ^^
OB so ^ '
^. Portal
^ IN Intestine *^~{ Intestinal
, V Elimination
KlMBB ! " "
,PS 3
1 V
•-Spleen Metabolites There are N <*emicals modeled.
liver, kidney, fat, carcass,
brain slowlv nerfused ranidlv
Blood perfused tissue and spleen.
^
LV Liver "."
QB LV
CR Carcass
QB CR
KD Kidney
QB KD i
^
FT Fat
^
J
QBFT
SL Slowly
Perfused
QBsL
RP Rapidly
Perfused
QBRP
QBDR
BR Brain
QBBR
VB Venous
f
_^-
QA .f y i *•
PL) Static Lung
-^
QB V
- AB Arterial
i ne siaiic lung, ana lung ussue
>Liver Metabolites are modeled with bindin9'
elimination, and metabolism
' •>- —Carcass Metabolites There are M enzymes that may
be inhibited by a chemical in
any of the brain, liver, or
^ - ^Kidney Metabolites arteria| and venous b|ood
•^^ Kidney "A
'^\^_ Elimination J
-. >Fat Metabolites
There are up to K metabolites
of each of the N chemicals. Each
Slowly metabolite is one of the
- -^.Perfused N cnemicals- There is binding in
"" ^Tjssue the arterial blood and venous blood.
Metabolites
^ ^ Rapidly Perfused
Tissue Metabolites
. Brain
""^ Metabolites
j Open Chamber |
^ Ooen ~^ ^ Inhalation J
J uPen ]
\ Chamber J
Exhalation ^ cc ctosBd
^ Lung
Metabolites
Figure 1 ERDEM system flow with static lung and stomach intestine Gl.
-------
Metabolism can occur in many of these compartments; multiple metabolites are tracked as they
and the parent compound(s) circulate through the system. It is important to note that adjustments are
made for differences in metabolism and physiology between children and adults. ERDEM is
programmed in the Advanced Continuous Simulation Language (ACSL). A model is implemented in
ERDEM when the user enters physiological, biological, and pharmacodynamic modeling data specific
to their chemical and/or scenario of interest (ERDEM, http://epa.gov/heasd/erdem/erdem.htm)
Any PBPK model, including ERDEM, is made up of a series of the differential equations that
describe the rates of inflow, distribution, metabolism, or outflow of a chemical and various metabolites
in each separate biological compartment. Appendix A describes in detail the equations for each of the
biological compartments contained in ERDEM.
2.3 Metabolism of Malathion
Metabolic pathway data are required to construct a PBPK model describing the metabolic fate of
a pesticide. The metabolism of organophosphorus insecticides has been the subject of a large number of
reviews (Hodgson, 1968; Casida and Lykken, 1969; Menzie, 1966 and!969; Menzer and Dauterman,
1970; Dauterman, 1971 and 1982; Hutson, 1981; Sultatos, 1994; Nigg and Knaak, 2000) and in three
books on metabolic pathways (Knaak and O'Brien, 1960; Roberts and Hutson, 1999; Hawkins, 2000).
The metabolism of malathion in rats has been studied by Knaak and O'Brien (1960), Bradway and Shafik
(1977) and MRID (no. 41367701). Metabolic pathways for malathion (Figure 2) were constructed from
analysis of the products eliminated in urine and feces after the oral administration of the OP insecticide
(Knaak and O' Brien, 1960; Bradway and Shafik, 1977; Krieger and Dinoff, 2000) as adapted from
Roberts and Hutson (1999).
10
-------
(MeO)2P-SH
DMDTP
S
(MeO),P-OH
DMTP
0
ii
(MeQ)2P-SCHCOOEt
CH2COOEt
Malaexoii
0
(MeO)2-P-OH
DMP
Malathion
(MeO)2P-SCHCOOEt
CH2COOEt
(MeO)2P-SCHCOOEt
CH2COOH
MCA
l
I
T
HCCOOEt
HOOCCH
Ethyl Fumarate
I
i
Y
HCCOOH
HOOCCH
Fmiiaric Acid
HS-CHCOOEt
- CH2COOEt
2-MercaptoD
di ethyl succinate
S
(MeO)2P-SCHCOOH
CH2COOH
DCA
(MeO)2P-SCHCOOH
CH2COOH
DCA
^ HS-CHCOOH
CH2COOEt
G e m -[in e rca p to, ethyl a ceto] -D
acetic acid
iS-CHCOOH
CH2COOH
Bis-siiccinic AcidD
distilficle
Figure 2 Metabolic pathway for malathion as adapted from Roberts and Hutson (1999).
11
-------
In this assessment, malathion was modeled as being predominately metabolized in the liver with
secondary sites in the kidney or brain compartments. Malaoxon was modeled as being metabolized in the
liver, brain, and the arterial and venous blood. The metabolite monocarboxylic acid (MCA) is modeled
as metabolizing to dicarboxylic acid (DCA) in the liver (Table 2).
The major metabolites of malathion are MCA and DCA. The rapid decarboxylation of malathion
to MCA and DCA by carboxylesterase is expected to reduce the titer of substrate (malathion) for P-450
activation to the toxic metabolite malaoxon. This metabolism may be seen as a rapid rise in the
elimination of [14]C-MCA and [14]C-DCA in urine of the Sprague-Dawley (SD) rat (Figure 3).
Table 2. Metabolism structure for the Sprague-Dawley rat and the human
Metabolic Reaction
Malathion to Malaoxon
Malathion to DMTP
Malathion to DMDTP
Malathion to MCA
Malathion to DCA
Malaoxon to DMP
MCA to DCA
Malaoxon to DMP
Malaoxon to DMP
Malaoxon to DMP
Enzyme
P-450 mix
MFO, hydrolases
MFO, hydrolases
Carboxylesterase
Carboxylesterase
MFO, hydrolases
Carboxylesterase
MFO, hydrolases
MFO, hydrolases
MFO, hydrolases
Compartment
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Brain
Arterial Blood
Venous Blood
12
-------
MCA+DCA
Experimental Data
20 30 40 SO
Time (Hours)
Figure 3 Female Sprague-Dawley rats exposed to
40 mg/kg malathion oral dose MRID(no. 41367701)
The model fit to these data may be seen as the curve under the data points for the combined [14]C-
MCA and [14]C-DCA. MRID(no. 41367701) is a 14-C study with the radio-label directed to the carboxyl
moiety. Therefore the analysis would provide for identity of MCA and DCA and perhaps other [14]C-
labeled carboxylic products. It would not account for elimination of the dialkylphosphate moiety as
represented by dimethyl phosphorodithioate (DMDTP), dimethyl phosphorothionate (DMTP), and
dimethyl phosphate (DMP).
The metabolism is therefore incomplete using the [14]C-MCA and [14]C-DCA data from MRID(no.
41367701) as indicated by the model fit. The material balance for the model does not account for the
presumptive peaks 'A' and 'B' identified by MRID(no. 41367701) as 2-mercaptosuccinic acid disulfide
and fumaric acid. These metabolites were considered by the EPA (as part of an external review) as
specific to malaoxon metabolism to account for the conversion of malaoxon to 2-mercaptosuccinic acid
and fumaric to total 4-6 percent of the 40 mg/kg dose of malathion. This conclusion did not agree with
the proposed metabolic pathway of MRID(no. 41367701). The parent compound, malathion, has been
13
-------
shown by Roberts and Hutson (1999) to be metabolized to 2-2-mercaptosuccinic acid disulfide and
fumaric acid (Figure 2). This observation nullifies the use of the [14]C-MCA and [14]C-DCA data as
indicators of malaoxon disposition and redirects model development to the dialkylphosphate moiety,
most especially DMP.
Malaoxon metabolism is clearly identified by hydrolysis to DMP (Bradway and Shafik, 1977;
Krieger and Dinof, 2000). Knaak and O'Brien (1960) observed a substantial reduction in [14]C-DCA
content and a corresponding increase in phosphothiolate cleavage to DMTP and DMP as a result of the
metabolic synergistic inhibition of carboxylesterase activity by EPN. These findings were indicative of a
stoichiometric relationship between DMP and malaoxon. To account for phosphothiolate metabolism,
the model was fit to the di alkylphosphate data of (Bradway and Shafik, 1977).
The model was "calibrated" between two potential exposure extremes, repeated oral (gavage)
doses of 0.69 mg/day (1.62 mg/kg) and 69 mg/day (162.4 mg/kg) for three dose episodes. These doses
represented 0.001 and 0.1 of the rat oral LD50, respectively. These doses were expected to bracket
anticipated dermal exposures. The model fit for DMP was consistent with experimental data from
Bradway and Shafik (1977), for both the 0.69 mg/day (Figure 4A) and 69 mg/day (Figure 4B) doses.
This process was repeated for DMDTP (Figures 5 A, B), DMTP (Figure 6 A, B), MCA (Figures 7 A, B),
and DCA (Figures 8 A, B).
40 60
Tin t (Ho I K)
100 120
5
.c
1
I
60
Ttn
-------
Figure 4A Cumulative amount of DMP in the
urine after a Sprague-Dawley rat is exposed to
0.69 mg malathion oral dose.
Figure 4B Cumulative amount of DMP in the
urine after a Sprague-Dawley rat is exposed to 69
mg malathion oral dose.
1
40 60
Time (HOIK)
40 so m
Tin e (Hoi ft
Figure 5A Cumulative amount of DMDTP in the
urine after a Sprague-Dawley rat is exposed to
0.69 mg malathion oral dose.
Figure 5B Cumulative amount of DMDTP in the
urine after a Sprague-Dawley rat is exposed to 69
mg malathion oral dose.
15
-------
The aim was to fine-tune metabolic parameters used in the model to conform with expected
experimental outcomes. These modulations were necessary and essential to establish model fidelity with
established biomarkers and to satisfy overall material balance for phosphothiolate cleavage (Bradway and
Shafik, 1977) and [14]C-labeled carboxylic acid hydrolysis (MRID,no. 41367701). With the successful
modulation of the dialkylphosphate data, adjustments in metabolism were made to satisfy carboxylic acid
hydrolysis. These adjustments may be seen as the reasonable fit of the model to data for MCA at doses
of 0.69 mg/day (Figure 6A) and 69 mg/day (Figure 6B) and DCA at the same dosing levels (Figures 7 A,
B).
16
-------
«o m ao iso
Time (Heirf*
30 40 a>
Time»Ho
Iffi 131
Figure 6A: Cumulative amount of DMTP in the
urine after a Sprague-Dawley rat is exposed to
0.69 mg malathion oral dose.
Figure 6B: Cumulative amount of DMTP in the
urine after a Sprague-Dawley rat is exposed to 69
mg malathion oral dose.
I
-MCA
93
Till 61 (HO I If)
106 133
2 ID-
I
•fi
IQQ 136
Till t i'HO I If 1
Figure 7A: Cumulative amount of MCA in the
urine after a Sprague-Dawley rat is exposed to
0.69 mg malathion oral dose.
Figure 7B: Cumulative amount of MCA in the
urine after a Sprague-Dawley rat is exposed to 69
mg malathion oral dose.
17
-------
c
i
s
i
Tins 4101 is)
Figure 8A: Cumulative amount of dca in the urine
after a Sprague-Dawley rat is exposed to 0.69 mg
malathion oral dose.
Figure 8B: Cumulative amount of dca in the urine
after a Sprague-Dawley rat is exposed to 69 mg
malathion oral dose.
2.4 PBPK Model Input Variables
Parameters for PBPK models include three distinct types of data: physiological, physiochemical,
and biochemical. The physiological data are independent of the chemical being modeled and refer to
such things as organ volumes and blood flows. Distribution within, between and among organs, tissues,
and fluid is modeled according to compartmental volumes, blood flow rates, and blood tissue
partitioning. Compartments are modeled in ERDEM based on the information available for the exposure
to a particular chemical or chemicals and the metabolites. The compartments used for a metabolite may
be a subset of those used for the parent chemical, based on differences in the rate of metabolism (Knaak
et al, 2004).
2.4.1 Body Volume
The body volume is determined for each demographic group based on sex and age. The
compartment volumes are then calculated as a percentage of the body volume (Table 3).
18
-------
Table 3. Volumes of compartments for humans by percentage for PBPK modeling with ERDEM
Ages
Volume of the Body (Kg)a
Compartments (% of
Volume of the Body)
Arterial Blood (2)
Brainf(2.1)
Dermisb(5.1)
Fatc
Kidney" (0.4)
Liver" (2.6)
Rapidly Perfused Tissue"
Slowly Perfused Tissue6
Venous Blood (4)
Female
3
10.8
9
21.4
18
44.5
Male
3
11.1
9
20.4
18
50.7
Compartment Volume (kg)
0.216
0.227
0.551
1.480
(13.7%)
0.0432
0.281
0.767
(7.1%)
7.236
(67.0%)
0.432
0.428
0.449
1.091
3.574
(16.7%)
0.0856
0.556
1.519
(7,1%)
12.840
(60.0%)
0.856
0.890
0.935
2.270
10.992
(24.7%)
0.178
1.157
2.270
(5.1%)
24.052
(54.0%)
1.780
0.222
0.233
0.566
1.277
(11.5%)
0.0444
0.289
0.566
(5.1%)
7.459
(67.2%)
0.444
0.408
0.428
1.040
2.346
(11.5%)
0.0816
0.530
1.040
(5.1%)
13.709
(67.2%
0.816
1.014
1.065
2.586
5.831
(11.5%)
0.203
1.318
2.586
(5.1%)
34.070
(67.2%
2.028
a. Body volumes estimated from the Exposure Factors Handbook, Tables 7.2, 5th Percentile (1996).
b. Value from Corley et al. (1990).
c. Boot et al. (1997), the age range is 4-21 years in both males and females.
d. Fisher et al. (1998) Adjusted based on fat volume percentage.
e. Value estimated from the Fat content using Fisher et al. (1998).
f. Estimated from many sources, Milner (1990).
19
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2.4.2 Cardiac Output
Cardiac output is determined for each demographic group. The compartment blood flows are
chosen as a percentage of the cardiac output. The compartments requiring blood flow input are the brain,
liver, kidney, fat, dermis, slowly perfused tissue (muscle), and rapidly perfused tissue. The same blood
flow percentages are used for each demographic group (Table 4).
Table 4. Blood flows (percentage of cardiac output)
Cardiac Output (L/hr)a
Compartment
Brain (10.0%)b
Dermis (7.4%)b
Fat (5.0%)b
Kidney (13.5%)"
Liver (20.8%)b
Rapidly Perfused Tissue (28.3%)b
Slowly Perfused Tissue (15.0%)b
Male and Female Children
3 Years
242
9 Years
337
18 Years
414
Blood Flow by Compartment-Percentage of
Cardiac Output
24.20
17.91
12.10
32.67
50.34
68.49
36.30
33.7
24.98
16.85
45.50
70.10
95.37
50.55
41.40
30.64
20.70
55.89
86.11
117.16
62.10
a. Agata et al. (1994), Schmitz et al. (1998); see Appendix C.
b. The blood flow percentages Fisher et al. (1998). No age adjustment was made for blood flow percentages.
2.4.3 Distribution
The distribution of OP pesticides and their metabolites between body tissues and sub-cellular
organelles is largely depended upon the manner in which they partition between water and lipids (Knaak
et al., 2004). The derivation of malathion specific tissues to blood partition coefficients examined in
Appendix B. A summary of partition coefficients for each physiological compartment in relation to the
blood flow are presented in Table 5 for malathion and its metabolites.
Table 5. Partition coefficients for malathion and metabolites in the human"
Compartment to
Venous Blood
Malathion
Malaoxon
DMTP
DMDTP
DMP
MCA
DCA
20
-------
Brain/Blood
Dermis/Blood
Fat/Blood
Kidney/Blood
Liver/Blood
Rapidly Perfused
Tissue/Blood
Slowly Perfused
Tissue/Blood
12.8
6.06
10.0
4.9
8.0
5.2
5.0
1.45
NA
0.63
1.04
1.17
1.0
1.02
1.05
NA
0.19
0.91
0.93
0.86
0.88
1.05
NA
0.19
0.91
0.93
0.88
0.88
0.93
NA
0.19
0.86
0.85
0.84
0.83
6.52
NA
0.19
2.78
4.24
2.98
2.8
3.49
NA
0.19
1.74
2.41
1.70
1.73
"Calculated using techniques of Paulin and Thiel (2000).
NA= the dermal compartment is not active for this chemical.
2.4.4 Elimination
Elimination is modeled as a linear function using rate constants. The elimination parameters for
five metabolites modeled by ERDEM are presented in Table 6. These elimination rate constants were
initially set for parathion-ethyl (Knaak et al.,2004) and adjusted through several iterations based on the
data of Bradway and Shafik (1977) and MRID(no. 41367701) for the SD rat.
Table 6. Parameters for urine elimination
Metabolite
DMTP
DMDTP
DMP
MCA
DCA
Parent Chemical
Malathion
Malathion
Malaoxon
Malathion
Malathion/MCA
Rate Constant
1/hr
10.5
12.0
20.0
7.5
8.0
2.4.5 Metabolism Parameter Determination
The metabolism processes were defined by saturable Michaelis-Menten kinetics. The parameters
are the maximum velocity of metabolism (Vmax) and the Michaelis-Menten constant, Km. The reported
values in Table 7 are the result of fine-tuning the parameters to obtain a reasonable comparisons with the
21
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rat data of Bradway and Shafik (1977) and MRID(no. 41367701)
Table 7. Metabolism data for the human: maximum velocity of metabolism and Michaelis Menten constant
Metabolism Reaction
Malathion to malaoxon
Malathion to DMTP
Malathion to DMDTP
Malathion to MCA
Malathion to DCA
Malaoxon to DMP
MCA to DCA
Malaoxon to DMP
Malaoxon to DMP
Malaoxon to DMP
Enzyme
P-450 mix
MFO, hydrolases
MFO, hydrolases
Carboxylesterase
Carboxylesterase
MFO, hydrolases
Carboxylesterase
MFO, hydrolases
MFO, hydrolases
MFO, hydrolases
Compartment
Liver
Liver
Liver
Liver
Liver
Liver
Liver
Brain
Arterial Blood
Venous Blood
Vmax
(mMoles/hr/kg)
0.000008
0.0015
0.0008
0.2
0.004
0.03
0.0991
0.015
0.015
0.015
Km
(mMoles/Liter)
0.008
0.7
0.55
11.0
0.08
0.04
0.3
0.04
0.04
0.04
A saturable Michaelis-Menten form of metabolism was assumed
dA,
dt
where the metabolism rate of the jth metabolite of the ith chemical in the liver is determined from
the Vmax and Km in the liver and the concentration of the ith chemical in the liver.
The parameters, maximum velocity of metabolism (Vmax) and the Michaelis-Menten
constant (Km), were determined using ERDEM by adjusting these values to fit to the data of
Bradway and Shafik (1977) and MRID(no. 41367701) as discussed above. Initial values were
assumed based on the work of Knaak et al. (2004) for the metabolism of parathion-ethyl and
modified to conform to a malathion specific metabolism (Figure 4). The Vmax was scaled as to body
weight at the 0.7 power for the SD rat at different body weights. Vmax was further scaled from the
rat to the human according to age and sex (male and female child, 3, 9, and 18 years of age).
22
-------
The Vmax and Km values, obtained after a number of iterations of comparing model runs with both
sets of experimental data for the SD rat (Bradway and Shafik, 1977 and MRID(no. 41367701)), were
then used in the modeling of the human. This is an important quality assurance step for establishing
model parameters based on experimental data. The Vmax for the metabolism of malathion to
malaoxon was reduced by a factor of 1 0 from the rat to the human based on differences in the rate of
metabolism (Knaak et al., 2004).
2.4.6 Urine Elimination Parmaters
The urine elimination rate for the ith chemical was determined from the amount of the ith
chemical in the kidney and the urine elimination rate constant for the ith chemical.
_ ~
jx Urine ,i
Elimination rate constants (units of I/hour) were determined for the SD rat by fitting the data of
Bradway and Shafik (1977) and MRID(no. 41367701) to the model as described above. The starting
values were determined from the metabolism rate for a given variable. The scaling for the SD rat was
set by body weight to the -0.25 power.
-0.25
where the subscript US represents the unsealed quantity.
The human values for the urine elimination rate constant were scaled from the SD rat to the
human. These urine elimination rates for the human may not be representative of actual human excretion
because of the substantial and distinctive differences in elimination between the SD rat and man. The
model would benefit from malathion specific elimination data in humans from which to determine
23
-------
appropriate values for rate constants in man.
3.0 RESULTS
3.1 Overview of Presumptive Exposure
Percutaneous absorption was expected to apply to any age group and sex where some portion of
the mass of malathion applied to the hair and scalp actually reaches the skin of the scalp. The amount of
malathion reaching the scalp would depend on the rate of application, and the volume and density of the
hair. For this assessment, the maximum allowable rate of 2.0 fluid oz (295 mg malathion) per individual
treatment was assumed. Therefore, the maximum permissible dose available for percutaneous absorption
may range from 27.3 mg/kg for 3 year old female children to 5.8 mg/kg for 18-year old males.
These estimates do not take into consideration the influences of scalp size and hair density and
volume. These factors were expected to impact the available dermal dose and the amount of residue
available for evaporation and inhalation and hand-to-mouth transfer. Estimates of scalp surface area
were obtained from distributions of head size relative to height and body weight (Table 2 in the Methods
section). These values were found to be age and sex dependent. However, scalp size does not account
for hair density and volume which in a greater sense are factors more likely to influence malathion
availability by trapping some portion of the treatment dose by adsorption onto or absorption into hair
shafts. Moreover, the volume of the treatment dose is dependent on the "size" of the hair.
It is therefore reasonable to assume that treatment dose and ultimately the exposure dose is
strongly influenced by hair volume and density. This assessment focused on male and female children at
two extremes, 3 and 18 years of age, and those children (9 year old females) considered to be most
representative of product use. In this regard, it is assumed that treatment of 3 year old males and females
would represent the most highly exposed individuals based on low body weight, small scalp surface area,
and minimal hair growth. Because of these conditions, the actual volume of product used would likely be
much less than the assumed maximum of 2.0 fluid ounces. However, this assessment assumes a single
maximum treatment dose of 2.0 fluid ounces for each age group regardless of hair "size." It was assumed
that under a worst case scenario 50 percent of treatment dose might become available for dermal
24
-------
absorption with the remaining 50 percent bound in the hair and unavailable for dermal absorption (Table
2, Methods). It was further assumed that a small portion of the adsorbed/absorbed treatment dose might
be lost to evaporation or transferred to the hands as a result of contact with the hair and scalp. This
presumptive hand-to-mouth activity may be restricted to 3 year old children having a propensity for this
kind of behavior.
In the case of older children, exposure would likely be confined to primarily dermal absorption
and limited inhalation. The amount of malathion reaching the skin of the scalp would be dependent on
the thoroughness of application onto the scalp and the volume and density of the hair. The 50 percent
worse case scenario might apply to individuals with "small hair," 9 and 18-year old boys. The
representative prescribed use scenario would likely involve 9 and 18-year old girls with long and perhaps
dense and voluminous hair. In these cases, a greater amount (estimated at approximate 94 percent), of
the treatment dose is likely to be tied up (adsorbed/absorbed) in the hair away from direct contact with
the scalp (Table 2, Methods). This supposition arises from the fact that hair shafts comprise
approximately 94 percent of the effective area of the scalp, while the SC comprises about 5 percent of the
effective area and the follicles represent about 1 percent of the effective area. The implication of this
observation is that the majority of applied malathion adsorbs to hair shafts and hence is not available for
percutaneous absorption. It implies that only about 6 percent of the total applied malathion is available
for absorption into skin and blood resulting from deposition on the skin of the scalp and in and around
the hair follicles. This portion would be available for dermal absorption, while some portion of the
residual mass in the hair might evaporate and become available for inhalation.
3.2 Simulation of Dermal Exposure
The presumptive representative case for prescribed use of Ovide® involving a 9 year old girl was
simulated to compare results with the other sex and age groups and the 5 mg/kg single oral bolus dose.
This example case is reported with figures and comparative tables. In many cases, the figures illustrating
tissue disposition are quite similar for each age and sex. These figures are contained in Appendix D.
Absorption of malathion from the treatment vehicle into the skin was simulated over the
25
-------
presumptive maximum exposure period of 12 hours and beyond to simulate residual absorption out to 72
hours. The rapid decline of mass from the scalp to near zero simulates complete wash-off of excess
malathion. Figure 9A illustrates the decline of malathion for the case where 50 percent of the treatment
dose is available for dermal absorption. This rate of decline is consistent with what may be expected
when a maximum of 6 percent of the treatment dose becomes available for dermal absorption (Figure
9B).
_
ey>
c 100-
60-
40-
33-
0
Ho i r I off
5 12-
-Mabtlbi
12 Heur Semplele Wartioff
32 4*3
Tins- .HOII,->
0 S IS 2* 3Z 40 48 3S 5*
Tim* m our**
Figure 9A: Amount of malathion on skin for 50 Figure 9B: Amount of malathion on skin for 6
percent of applied dose available for dermal percent of applied dose available for dermal
absorption for the 9 year old female. absorption for the 9 year old female.
Movement of malathion into the skin (Figures 10 A, B) accounts for absorbed dose that enters
the vascular compartment followed by a sharp loss of mass in the skin at wash-off. Residual absorption
continues following wash-off as indicated by the tail of the profile. The rate of appearance of mass into
the vascular compartment was expected to impact the rate of distribution and metabolism and ultimately
target tissue dose.
26
-------
•I 2 H sir D cm p it fc ytfashetf
12 Hmir Comple fe IfsJ
Figure 10A: Amount of malathion in skin for 50
percent of applied dose available for dermal
absorption for the 9 year old female .
Figure 10B: Amount of malathion in skin for 6
percent of applied dose available for dermal
absorption for the 9 year old female.
The concentrations of malathion in the venous blood (milligrams per Liter) increased gradually
to reach a peak at 12 hours (Figure 11 A, B). There was an order of magnitude difference in the peak
concentrations between the 50 percent treatment case (Figure 11 A) and the 6 percent treatment case
(Figure 1 IB). Following wash-off, malathion concentrations declined rapidly in both cases. There was
nearly a 3 orders of magnitude difference between the concentrations of malathion and malaoxon.
12 Hsif Catipteit
Figure 11 A: Concentration of malathion and
malaoxon in venous blood for 50 percent of
applied dose available for dermal absorption for
the 9 year old female.
Figure 11 B: Concentration of malathion and
malaoxon in venous blood for 6 percent of applied
dose available for dermal absorption
for the 9 year old female.
Malathion and malaoxon concentrations in brain (Figure 12 A, B) tended to follow concentrations in
venous blood. However, peak concentrations of malaoxon in brain were reduced by several orders of
magnitude from the 50 percent treatment case (Figure 12B) to the 6 percent case (Figure 12A).
27
-------
a
& \e-3-.
o ;
o
U IE-+-
12 Hour Com pie Ir Waihoff
16 2* 32 A 4®
"flint *Hour*,i
Figure 12A: concentration of malathion and
malaoxon in brain for 50 percent of applied dose
available for dermal absorption for the 9 year old
female.
'12 Hoi rCompteft Hatlofl
is 2* 2
-------
"
f Ofll,
e
a
K 2t 312
Time
-------
Smg/kg oral dose (gavage) and presumptive topical treatment of ovide lotion to 9-year old females where 6
and 50 percent of the malathion reaches the scalp and is available for dermal absorption
Dose
Time
(hrs)
4
8
12
24
48
72
Chem
Malathion
Malathion
Malathion
Malathion
Malathion
Malathion
6 % of Applied Dose*
(ng/g)
Venous
Blood
0.0049
0.0081
0.0109
0.0071
0.0031
0.0013
Brain
0.0611
0.103
0.138
0.0902
0.0388
0.0167
50% of Applied Dose*
(ng/g)
Venous
Blood
0.0410
0.0683
0.0909
0.0589
0.0255
0.0110
Brain
0.510
0.861
1.15
0.758
0.328
0.141
5 mg/kg (jig/g)
V enous
Blood
0.485
0.457
0.429
0.341
0.190
0.0967
Brain
6.22
5.86
5.50
4.38
2.45
1.24
* Permeation rate coefficient (Kp) = 0.0082 cm/hr.
3.4 Estimates of Blood and Tissues Concentrations of Malathion and Malaoxon Following Oral
and Dermal Exposure
Concentrations of malaoxon in venous blood and brain were reduced by at least four orders of
magnitude as compared to malathion over the entire time course of the simulation (Table 10). Moreover,
substantial differences in tissue concentrations of malaoxon were observed among treatments. There was
at least one order of magnitude difference in the malaoxon concentrations in venous blood and brain
between the 50 percent dermal treatment and the 5 mg/kg oral gavage. More substantially, there was a
three orders of magnitude difference in tissue concentrations between the 6 percent dermal treatment and
the 5 mg/kg oral gavage.
Table 10. Time course of tissue concentrations of malaoxon in the venous blood and the brain following
5mg/kg oral dose (gavage) and presumptive topical treatment of Ovide® lotion to 9 year old females where 6
and 50 percent of the malathion reaches the scalp and is available for dermal absorption
Tissue Concentrations of Malaoxon (jig/g
Exposure
Dermal Exposure
Oral Exposure
30
-------
Time (hrs)
4
8
12
24
48
72
6% of Applied Dose*
Venous Blood
0.0440
0.0824
0.114
0..0820
0.0357
0.0155
Brain
0.0556
0.104
0.144
0.104
0.0454
0.0196
5 0% of Applied Dose*
Venous Blood
0.337
0.597
0.786
0.599
0.283
0.127
Brain
0.426
0.757
0.997
0.761
0.360
0.162
5 mg/kg
Venous Blood
2.36
2.34
2.28
2.04
1.46
0.902
Brain
3.00
2.97
2.89
2.59
1.86
1.15
* Permeation rate coefficient (Kp) = 0.0082 cm/hr.
The tissue half-life for malathion was slightly less than the half-life for malaoxon for both dermal
treatments (Table 11). The dermal half-life for malathion was more protracted than for the 5 mg/kg oral
gavage case. A substantive difference was observed in tissue half-life between malathion and malaoxon
for the 5 mg/kg oral gavage case. The half-life for malaoxon for the 5 mg/kg oral gavage case was
substantially greater than both dermal cases.
31
-------
Table 11. Comparison of tissue half-life for a 5 mg/kg oral gavage with presumptive exposure of 9 year old
females to a prescribed topical scalp treatment of Ovide® lotion where dermal absorption was assumed to
result from 6 and 50 percent of available malathion reaching the skin of the scalp
^^^Chemical
Tissue
Brain
Venous Blood
Dermal Absorption (Half Life, hrs)
6% of Applied Dose
Malathion
17.42
17.20
Malaoxon
19.94
19.94
50% of Applied Dose
Malathion
17.60
17.38
Malaoxon
23.14
23.17
Oral Gavage (Half Life, hrs)
Malathion
1.32
0.74
Malaoxon
54.49
54.48
For a 3 year old male subject, this might be interpreted as a peak malathion concentration of 1.23
(O-g/mL in venous blood as compared to 0.24 ng/mL for malaoxon for the case involving the presumptive
dosing of 5 mg/kg malathion by oral gavage (Table 12). These differences were consistent across age
groups. Gender differences were not remarkable. However, age differences were substantial. These age-
related increases were attributed to increases in compartment volumes.
Table 12. Comparison of peak concentrations of malathion and malaoxon in venous blood and brain of
selected age groups of male and female humans following presumptive oral absorption of 5 mg/kg of
malathion
Chemical
Gender
Male
Female
Age
3
9
18
3
9
18
Malathion
B rain Cone
(ng/g)
13.55
14.38
16.11
12.82
13.84
16.11
Blood Cone
(ng/g)
1.23
1.37
1.74
1.16
1.32
1.74
Malaoxon
Brain Cone
(Hg/g*10-4)
3.11
3.07
2.75
3.06
3.01
2.75
Blood Cone (jig/g*10'4)
2.43
2.41
2.29
2.39
2.37
2.29
These findings are useful for making gender- and age-related tissue comparisons with the dermal
treatments where 6 percent (Table 13) or 50 percent (Table 14) of the treatment dose of Ovide® lotion is
available for dermal absorption. Malathion and malaoxon concentrations in brain declined substantially
with age and expected body volumes (Table 13). Gender differences were not remarkable. Substantial
32
-------
age-related differences in tissue concentrations for malathion and malaoxon were evident for the 6
percent dermal case (Table 13) as compared with the 50 percent dermal treatment (Table 14).
Table 13. Comparison of peak concentrations of malathion and malaoxon in venous blood and brain of
selected age groups of male and female humans following treatment with Ovide® lotion where 6 percent of
applied dose of malathion is available for dermal absorption
Chemical
Gender
Male
Female
Age
3
9
18
3
9
18
Malathion
Brain Cone
(ng/g)
0.271
0.156
0.0739
0.267
0.145
0.0739
Blood Cone
(ng/g)
0.0213
0.0122
0.00583
0.0210
0.0114
0.00583
Malaoxon
Brain Cone
(Hg/g*10-4)
0.290
0.165
0.070
0.287
0.152
0.070
Blood Cone
(Hg/g*10-4)
0.226
0.129
0.058
0.236
0.120
0.058
Table 14. Comparison of peak concentrations of malathion and malaoxon in venous blood and brain of
selected age groups of male and female humans following treatment with Ovide® lotion where 50 percent of
applied dose of malathion is available for dermal absorption
Chemical
Gender
Male
Female
Age
3
9
18
3
9
18
Malathion
Brain Cone
(ng/g)
2.27
1.30
0.576
2.24
1.21
0.617
Blood Cone
(ng/g)
0.178
0.102
0.0456
0.176
0.0949
0.0487
Malaoxon
Brain Cone
(Hg/g*10-4)
1.73
1.12
0.475
1.72
1.05
0.526
Blood Cone
(Hg/g*10-4)
1.35
0.876
0.402
1.34
0.824
0.438
Tables 12, 13, and 14 were restricted to tissues of toxicological interest (venous blood and brain),
although tissues concentrations can be generated using ERDEM (Figure 8, Methods) for many other
tissues as a means of addressing material balance (Table 3, Methods). Material balance was further
addressed by monitoring the elimination of urinary metabolites. The elimination profiles were used to
33
-------
evaluate model performance.
3.5 Estimates of Urinary Metabolite Concentrations Following Oral and Dermal Exposure
Urinary profiles of dialkylphosphate elimination indicated rapid and complete metabolism. The
biomarker of malaoxon metabolism, di-methyl phosphate (DMP), reached a peak of elimination at
approximately 24-40 hours, depending on the metabolite, after dermal treatment of a 9 year old female
with Ovide® lotion where 50 percent of the treatment dose reached the skin of the scalp and became
available for dermal absorption (Figure 15).
Similar profiles were obtained for the case where 6 percent of the treatment dose reached the
skin of the scalp (Figure 16). However, the amount of each dialkylphosphate eliminated was an order of
magnitude less than for the 50 percent treatment case. The predominant dialkylphosphate metabolite was
DMTP followed by DMDTP and lastly DMP.
OOX-,
K
5
.c cms..
1
S
P" OJOQ2-
n QjOOE-
D
W
cc
B 2* 32 *
Tlllf (MOIE)
K, 2t s; tfj 43 m,
Tin* 'H.M B.
Figure 15 Urinary rate of dmtp, dmdtp, and dmp Figure 16: Urinary rate of dmtp, dmdtp, and dmp
for 50 percent of applied dose available for dermal for 6 percent of applied dose available for dermal
absorption for the 9 year old female. absorption for the 9 year old female.
The oral bolus dose of 5 mg/kg showed a factor of 60 greater urinary rates than the dermal
treatments profiles for the case with 6 percent of the applied dose available for absorption into the scalp
(Figure 17). The production and elimination of DMP was extremely shallow indicating rapid and
complete hydrolysis of malathion to DMTP and DMDTP and decarboxylation to free MCA and DCA
metabolites.
34
-------
ODD
Figure 17: Urinary rate of DMTP, DMDTP, and DMP for oral bolus dose of 5 mg/kg body
weight for the 9-year-old female.
The total dialkylphosphate accounted for approximately 5.0 percent of the absorbed dermal dose
(14.7 mg) of malathion for the case where 50 percent of the treatment dose (295 mg) applied to the hair
and scalp reaches the skin of the scalp and becomes available for dermal absorption (Table 15). The
MCA and DCA metabolites of malathion represented approximately 50 percent of the absorbed dermal
dose.
35
-------
Table 15. Cumulative mass (mg) and percent of the absorbed dose (14.7 mg) of metabolites of malathion
eliminated in urine following dermal treatment of the hair and scalp with Ovide® lotion where 50 percent of
the treatment dose (295 mg) is available for dermal absorption
Sex
Male
Age
(yrs)
3
9
18
Carboxylic Acids
mg (% Absorbed
Dose)
DCA
6.56
(44.6)
5.56
(37.8)
4.11
(28.0)
MCA
1.3 (8.8)
1.13 (7.7)
0.90 (6.1)
Dialkylphosphates
mg (% Absorbed Dose)
DMTP
0.29
(2.0)
0.25
(1.7)
0.19
(1.3)
DMDTP
0.21 (1.4)
0.18 (1.2)
0.14
(0.95)
DMP
0.045 (0.31)
0.044 (0.30)
0.037 (0.25)
Total mg
(% Absorbed
Dose)
8.4 (57.1)
7.2(48.7)
5.4 (36.6)
Female
3
9
18
6.39
(43.5)
5.58
(37.9)
4.55
(31.0)
1.2 (8.4)
1.12(7.6)
0.95(6.5)
0.28
(1.9)
0.25
(1.7)
0.20
(1.4)
0.20 (1.4)
0.18 (1.2)
0.15 (1.0)
0.045 (0.31)
0.044 (0.30)
0.038 (0.26)
8.1 (55.5)
7.2(48.7)
5.9 (40.1)
mg = milligrams per Kilogram.
The elimination of DCA was more complete than MCA as indicated by the relative amounts
eliminated (Figure 18). Peak elimination for DCA was achieved after 40 hours where as peak
elimination of MCA occurred in approximately 24 hours. These differences were a consequence of
basing the model inputs on rodent data rather than human data.
The elimination rate profile for the 6 percent dermal treatment case (Figure 19) was consistent
with the 50 percent dermal treatment case (Figure 18) with the exception of the eliminated amount. The
amounts of MCA and DCA eliminated were 2 orders of magnitude less for the 6 percent treatment case
(Table 16).
36
-------
Figure 18 Urinary rate of dca and mca for 50
percent of applied dose available for dermal
absorption for the 9-year-old female.
Figure 19 Urinary Rate of dca and mca for 6
percent of applied dose available for dermal
absorption for the 9-year-old female.
Table 16. Cumulative Mass (nig) and percent of the absorbed dose (1.76 mg) of metabolites of malathion
eliminated in the urine following dermal treatment of the hair and scalp with Ovide® lotion where 6 percent
of the treatment dose (295 mg) is available for dermal absorption
Sex
Male
Age
(yrs)
3
9
18
Acids, mg
(% Absorbed Dose)
DCA
0.79
(45.2)
0.67
(38.1)
0.49
(28.1)
MCA
0.15
(8.4)
0.13
(7.6)
0.11
(6.1)
Dialkylphosphates, mg
(% Absorbed Dose)
DMTP
0.035
(2.0)
0.030
(1.7)
0.023
(1.3)
DMDT
P
0.025
(1.4)
0.022
(1.2)
0.017
(0.96)
DMP
0.0067
(0.38)
0.0060
(0.34)
0.0047
(0.27)
Total mg
(% Absorbed Dose)
1.01
(57.4)
0.86
(48.9)
0.64
(36.7)
Female
3
9
18
0.77
(44.0)
0.67
(38.3)
0.55
(31.1)
0.15
(8.3)
0.13
(7.6)
0.11
(6.5)
0.034
(1.9)
0.030
(1.7)
0.025
(1.4)
0.024
(1.4)
0.022
(1.2)
0.018
(1.0)
0.0066
(0.37)
0.0059
(0.34)
0.0049
(0.28)
0.98
(56.0)
0.86
(49.1)
0.71
(40.3)
mg = milligrams per Kilogram.
37
-------
The elimination rate profiles for the dermal treatment cases were compared with the presumptive
oral gavage case (5 mg/kg). Elimination of DCA occurred more rapidly although peaks elimination for
both DCA and MCA were consistent with the dermal treatments (Figure 20).
+ 32 <0
Tim e » H e ur f :i
Figure 20. Urinary Rate of dca and mca for oral bolus dose of
5 mg/kg body weight for the 9 year old female.
The amount of DCA and MCA eliminated was greater for the gavage case, although the total
percent elimination was marginally less than for the dermal treatments (Table 17). The rate of elimination
of the DCA and MCA for the oral gavage case was about 60 times the rates for the case where 6 percent
of the applied dose is available for dermal absorption.
38
-------
Table 17. Cumulative mass (mg) and percent of the absorbed dose of metabolites of
malathion eliminated in the urine following presumptive oral gavage with malathion at a
dose of 5 mg/kg body weight
Sex
Male
Age
(yrs)
3
9
18
Absorbed
Dose(mg)
55.5
102.0
253.5
Acids, mg
(% Absorbed
Dose)
DCA
23.6
(42.6)
36.6
(35.9)
67.9
(26.8)
MCA
4.84
(8.7)
8.16
(8.0)
17.1
(6.7)
Dialkylphosphates, mg
(% Absorbed Dose)
DMTP
1.12
(2.0)
1.80
(1.8)
3.58
(1.4)
DMDT
P
0.80
(1.4)
1.3
(1.3)
2.60
(1.0)
DMP
0.11
(0.20)
0.17
(0.17)
0.31
(0.12)
Total, mg
(% Absorbed Dose)
30.5
(54.9)
48.0
(47.2)
91.5
(36.0)
Female
3
9
18
54.0
107.0
222.5
22.4
(41.6)
38.7
(36.1)
66.2
(29.8)
4.61
(8.5)
8.51
(8.0)
15.7
(7.0)
1.06
(2.0)
1.88
(1.8)
3.33
(1.5)
0.76
(1.4)
1.35
(1.3)
2.40
(1.1)
0.11
(0.20)
0.18
(0.17)
0.30
(0.13)
28.9
(53.7)
50.6
(47.4)
87.93
(39.5)
mg = milligrams per Kilogram.
4.0 DISCUSSION
PBPK modeling techniques offer the promise of interpreting this type of exposure. However, the
reliability of the output from such a model is dependent on the necessary input information. Continued
development and testing of the model with quality data is necessary to refine the input parameters and
values. In this regard, physiological and pharmacokinetic (PK) data are gleaned from in vivo and in vitro
studies as reported in the literature to establish key values for PK parameters and the time course for
disposition of the compounds in the body. This is an iterative process. It is anticipated that further
refinements will continue to improve and evolve as more reliable data becomes available. Therefore, the
results reported herein reflect the current state of reliability with the understanding that further anticipated
39
-------
refinements may influence the conclusions.
The ERDEM is a source-to-exposure-to-dose modeling platform (U.S. EPA, 2003) that can be used
to test standard procedural exposure assumptions involving single or multiple chemicals (Blancato et
al.,2000). ERDEM has been successfully used to examine occupational exposure of farm workers (Knaak
et al., 2000 and 2001) and nonoccupational residential exposure to infants and children (Blancato et al.,
2000; Knaak et al., 2001; Dary et al., 2000) resulting from aggregate and sequential contact with treated
surfaces, indoor air, food and water via oral, inhalation, and dermal pathways. Exposure assumptions
along the risk paradigm, from a source, to an exposure pathway (oral, inhalation, or dermal) to dose, can be
tested within ERDEM by using reliable and reasonable input parameters and values.
For this malathion exposure simulation, the organophosphorus (OP) module within ERDEM
(Blancato et al., 2000) was adapted to conform with the prescribed clinical use of a topical lotion, Ovide®,
containing 5 percent (w/v) malathion (MEDICI, The Dermatology Company). The source of exposure was
accordingly, the willful and prescribed use of the lotion as a pediculicide according to label directions (The
Medical Letter, 1999). The test subjects, males (boys) and females (girls) ages 3, 9 and 18 years, were
selected based on probable use (Gomez et al., 1986; Chossidow et al., 1994). The prescribed use of
Ovide" on neonates and infants is contraindicated (MEDICI, The Dermatology Company).
The maximum exposure period of 12 hours was in accordance with the label (MEDICI, The
Dermatology Company). The label directs that the lotion be applied to dry hair in an amount that
thoroughly wets the hair and scalp. The entire volume of the bottle (59 mL) was used in the simulation as a
means to approach maximum exposure. However, the amount of malathion reaching the scalp is uncertain.
Under extreme conditions, it may be argued that 100 percent of the malathion (295 mg) reaches the scalp
and becomes available for absorption. It is reasonable to assume, however, that the majority of the
malathion is absorbed into or adsorbed on the hair where it becomes unavailable for dermal absorption.
The mass of malathion that reaches the scalp is likely dependent on the volume and density of the hair
(Chossidow et al., 1994).
For this simulation, it was assumed that 50 percent of the malathion was absorbed/adsorbed on the
hair and the remaining 50 percent (147.5 mg) reached the scalp and became available for dermal
40
-------
absorption. This situation was expected to apply to individuals having limited hair growth or closely
cropped hair. It was reasonable to assume that pediculcidal treatments would involve individuals with
advanced hair growth with greater follicle density and volume, particularly for 9 to 18-year-old females. In
these cases, it was assumed that a greater portion (94 percent) of the applied lotion would be adsorbed onto
or absorbed into the hair and far less malathion (6 percent) would reach the scalp to become available for
dermal absorption.
The skin of the human scalp is remarkable because of the density of hair follicles, which are
implanted more deeply into the skin than elsewhere on the body (Scott et al., 1991; Giacometti, 1965). The
scalp has been characterized (Giacometti, 1965) as the hairy skin that overlies the cranial vault (Morris,
1953) consisting of five layers: the skin, subcutaneous adipose layer, epicranius (and its aponeurosis),
subaponeurotic areolar layer, and the pericranium. The thickness of the adult scalp ranges from 4.7 to 5.2
mm (Giacometti, 1965). The skin is composed of a thick epidermis and dermis. The maximum mean skin
thickness for adults (30-40 years) was estimated to be 2.3 mm (Giacometti, 1965).
The average thickness of the skin (2.4 mm) of the newborn is thinner (Giacometti, 1965). Skin
thickness appears to increase with age as indicated by the average thickness for 3 month old to 1-year-old
infants (3.2 mm) as compared with 20-30-year-old adults (5.2 mm). The hair of the newborn are of a finer
"vellus" type that are gradually replaced by coarser pigmented "terminal" hair. However, the density of the
hair follicles decreases with age from an average of 1135 follicles per cm2 in the newborn to 795
follicles/cm2 for infants to 615 follicles/cm2 in 20-30-year-old adults (Table 18).
Table 18. Anatomy of the scalp.
Age (years)
Newborn
Infants
0.25-1
Adults
20-30
Average
Scalp
Thickness
(mm)t
2.4
3.2
5.2
Average Skin
Thickness
(mm)t
0.80
0.80
2.2
Average
Epidermis
Thickness
oot
33
38
40
Scalp
surface
area*
(cm2)
ND
465
500
Capillary
Density
(mm2)t
ND
ND
130
Sweat
Gland
Density
(cm2)f
830
478
254
Hair
Follicle
Density
(cm2)f
1135
795
615
41
-------
Table 18. Anatomy of the scalp.
Age (years)
Adults
30-50
Adults
50-70
Adults
70-80
Average
Scalp
Thickness
(mm)t
5.4
4.8
4.7
Average Skin
Thickness
(mm)t
2.3
1.8
1.4
Average
Epidermis
Thickness
oot
47
37
35
Scalp
surface
area*
(cm2)
ND
ND
ND
Capillary
Density
(mm2)t
130
109
100
Sweat
Gland
Density
(cm2)f
243
249
248
Hair
Follicle
Density
(cm2)f
485
465
465
ND = not determined.
f As adapted from Giacometti (1965).
} As determined from the Exposure Factors Handbook (1996).
It was further recognized that absorption through the scalp is complicated by the density and size
(area) of the hair follicles in the scalp (Scott et al., 1991). The rate and extent of absorption is dependent on
body location and the abundance of "appendages." hair follicles, sebaceous glands, and sweat glands
(Scheuplein, 1967; Scott et al., 1991; U.S. EPA, 1992). The opinion that diffusional shunts have a
negligible impact on absorption into the capillary bed because they represent less than 1 percent of the skin
surface area (U.S. EPA, 1992) probably does not apply to the scalp. Regional variation in absorption
(Feldman and Maibach, 1974; Maibach et al., 1971) is likely a combination of chemical lipophilicity, SC
thickness, appendage density, and follicle opening area (Scott et al., 1991).
Blank and Scheuplein (1969) observed that transappendageal absorption may be a dominant
pathway of dermal permeation for slowly diffusing chemicals during the period immediately following
application (U.S. EPA, 1992). Indeed, diffusion of water and ethanol appears to be controlled by the SC,
while absorption of slowly diffused manitol and paraquat may be facilitated by follicular or shunt diffusion
(Scott et al., 1991). Maibach and coworkers (1971) observed a five-fold difference in absorption between
skin of the forearm and scalp of human volunteers exposed to slowly diffused chemicals such as
hydrocortisone, parathion, and malathion (Scott et al., 1991). Malathion has been found to localize around
42
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hair follicles in the rat as determined by autoradiography and FTIR microscopy (Dary et al., 2001).
The rate of absorption is a key parameter for determining disposition of malathion in fluids and
tissues. The rate by which malathion enters the vascular compartment effectively controls the rate of
metabolism to inactive species (MCA, DCA and dialkylphosphates) and active and toxic malaoxon. Even
with the assumed absorption of 10 percent (Maibach et al., 1971; HIARC, 2002) of the dose on the scalp, it
is the rate of absorption, as determined by the permeation coefficient (Kp), that ultimately controls
distribution, metabolism, and elimination. The influence of Kp on the rate of elimination is illustrated in
Figure 21.
Pfl
M en -
w
1 § 4n
SB —
-------
and permeation through the viable epidermis without the influence of appendages (hair follicles) and
shunts. The model was allowed to reiteratively adjust to a value of Kp until the total amount of [14C]-
malathion equivalents eliminated matched the empirical data. This Kp value (3.55 X 10"4) was considered
to be representative "non-follicular" dermal absorption.
It must be stressed that under these experimental conditions involving a finite dose, derivation of
Kp is not strictly representative of the in vitro infinite dose protocols upon which Pick's first and second
laws of diffusion are based (Bounds and Hawkins, 1999). Indeed, it may be more appropriate to refer to
Kp derived from in vivo protocols using finite doses as "apparent" Kp with the understanding that certain
parameters required to satisfy the mathematical solution of Pick's first and second laws of diffusion, e.g.,
donor solution volume, are undeterminable. For this model simulation, the volume of the donor solution,
Ovide® lotion (vehicle), was considered to form a thin film (< 1.0 cm) over the skin of the scalp.
Thus, the volume Vsk (cm3) is an expression of the surface area treated Ask (cm2) and the thickness
of the thin film Dflm (cm) as given by:
The rate that malathion moves from the skin surface into the dermis is given by:
dA
sxs,ar.
L_ = £• AC
dt sks,dr,prm. sk sks.
! I
where Ask is the scalp surface area treated (cm2), A., , is the amount of malathion (mg) moving from the
skin surface into the skin, C is the concentration of malathion on the skin surface (mg/cm3) as expressed
by
and Kss ssf^M represents Kp for malathion involving a follicular pathway.
44
-------
According to the dermal model, movement of malathion from the vehicle into the skin (Figures 1 A
B) is determined by the mass (mg) of malathion in the vehicle, the thickness of the vehicle (cm), and Kp
(cm/hr). The mass of malathion absorbed into the skin over the 12-hour treatment period (Figure 2) slowly
enters the vascular compartment (Figure 3). This amounted to 10 percent of the mass delivered to the scalp
(1.77 mg or 14.7 mg for the cases where 6 percent or 50 percent of the applied dose is delivered to the
scalp, respectively). The excess mass on the scalp and hair was removed by washing. Only the mass
absorbed into the skin contributed to the internal dose.
The peak concentrations of malathion and malaoxon in venous blood and brain resulting from
scenario-based dermal exposure may be contrasted with what might result from hypothetical ingestion of
malathion (5 mg/kg) as a single bolus dose by gavage (Table 8, Results). The distribution of malathion and
malaoxon in these selected tissues was based on compartment volumes (Table 3, Methods), blood flows
(Table 4, Methods) and tissue blood partition coefficients (Table 5, Methods). Within the blood, malathion
was assumed to be stoichiometrically converted to metabolites thath are eliminated in urine.
Urinary elimination in the Sprague-Dawley rat served as the means to augment model parameters.
The Vmax for metabolism in the rat was scaled by body weight to the 0.7 power in the rat and from the rat
to the human. The urine elimination rate constant is scaled by body weight to the minus 0.25 power within
the rat and from the rat to the human. The cardiac output for the rat was scaled by body weight to the 0.75
power, while for the human, cardiac output was taken from literature values by age and weight.
These differences between the rat and human were effectively normalized to determine the dose
metrics for malathion and malaoxon concentrations in the brain and blood. It was found that the dose
metrics were most sensitive to metabolism parameter values (Table 7, Methods) and the urine elimination
rates. (Table 5, Methods). The elimination rate profiles for the di-alkylphosphates were adjusted to agree
with the results of Bradway and Shafik (1977). Elimination was further augmented to conform with the
disposition of MCA and DCA as gleaned from MRID(no. 41367701). Therefore, the dose metrics for
malathion and malaoxon concentrations in the brain and blood were in agreement with data gleaned from
the available sources (Knaak and O'Brien, 1960; Bradway and Shafik, 1977; MRID(no. 41367701); Dary
et al., 1994; and Krieger and Dinof, 2000). The model results may be subject to further refinement as new
45
-------
data becomes available.
46
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Appendix A
Description of Chemical Exposures
1.0 Descriptions of Exposure
Exposure occurs at the boundary of the body or test system. It is of considerable interest to EPA
to limit, reduce, and in specific instances, eliminate exposure. Humans become exposed to
chemical and biological substances, physical energy and radiation through the activities they
perform routinely in everyday life, or occupationally as part of certain policies, practices, or
procedures. Exposure can occur accidentally as a random event, or during an occupationally
related task or as a result of a purposeful action such as a (terrorist) attack.
Humans may become incidentally and unknowingly exposed. Exposure to particles and gasses in
the air we breath may be unavoidable. Dermal contact with surface residues may be unforseen
and unrecognizable. Ingestion of particles and residues in food may be unintended and
unsuspected. Under certain conditions, exposure can be limited or reduced through education,
managerial oversight, regulatory responsiveness, and use of proper personal protection devices
(Ness, 1994).
When exposure is perceived as unavoidable, we may wish to describe exposure events in time
and space under certain recognizable exposure scenarios. This may be accomplished more easily
for occupationally related exposures where policies, practices, and procedures have been
established than for those that occur randomly or incidentally. However, regardless of the nature
of the exposure, exposure follows along recognized pathways, e.g., inhalation, ingestion, and
dermal, and routes: respiratory, oral, percutaneous.
ERDEM was designed to examine three pathways of exposure, inhalation, ingestion and dermal,
and eight routes of entry into the in silico test system. Experimental pathways and routes of entry
were included (Section 2.1) along with what might be perceived as naturally occurring
unscheduled or not experimentally controlled pathways and routes (Section 2.2). This approach
greatly enhanced the database to include laboratory animal and clinical studies in addition to
environmental field studies. For example, enteral administration is represented by intraperitoneal
(IP) injection of chemical into the gastrointestinal (GI) tract via the portal blood (liver for the
stomach/intestine GI model).
1.1 Experimental Pathways and Routes of Entry
1.1.1 Intraperitoneal Injection
Intraperitoneal Injections into the portal blood may be given for multiple chemicals for up to nine
scenarios starting at time Tmp , and repeated at the inter IMP, IT . The amount of chemical
to be injected is calculated from the concentration of the chemical times the body volume. The
amount injected decreases at an exponential rate. All injections start before the simulation start
time (T0). When the scheduled event occurs to start the injection, the amount is calculated as:
ERDEM Exposures Al September 25,2006
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Appendix A
Description of Chemical Exposures
A A 4- C V
•rL " •n.' T -' r >
where Aj^p CUR,J *s me amount remaining to be absorbed from the previous interval. The
amount of the ith chemical from the jth exposure remaining to be absorbed is:
-MWK
where AT = T - Ts^p $n j Ts,ffi STL j , is the start time for the last IP in
exposure, and MIN is the mean minimum of the two terms. The amount of the ith chemical
remaining to be absorbed for all exposures is:
AT
_• A3W>,CO!S,J (1.1,1-3)
J'-l
and the rate of change of the amount of chemical injected into the portal blood is given by:
dA N
A^-1M> r- 1
V
At the start of the next injection interval the IP amount remaining to be absorbed is accumulated
for each chemical; the elapsed IP simulation time is reset to zero, and the next injection
occurrence is scheduled.
1.1.2 Intramuscular Injection
Parenteral administration is represented by intramuscular injection (IM) in the muscle (slowly
perfused tissue). Intramuscular injections may be given for multiple chemicals and up to nine
ERDEM Exposures A2 September 25,2006
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Appendix A
Description of Chemical Exposures
scenarios starting at time T3^M , and repeat at the inte Tmtf TT . The amount of chemical
to be injected is calculated from the concentration of the chemical times the body volume. The
amount injected decreases at an exponential rate. All injections that start before the simulation
start time (T0) and before the simulation end time are scheduled. When the scheduled event
occurs, to start the injection the amount is calculated as:
AS\M,/, , = AQW,CUJ?,/. , + ^-*jw,/ , £• (1.1.2-1)
where ^-j^f ore/ ^s me amount remaining to be absorbed from the previous interval. The
amount of the ith chemical from the jth exposure remaining to be absorbed is:
where A T = T - T- STL f T^^ S7l f , is the start time for the last I1V
jth exposure, and MIN means find the minimum of the two terms. The amount of the ith
chemical remaining to be absorbed for all exposures is:
j\r
., (J.l.Z-J)
J-l
and the rate of change of the amount of chemical injected into the slowly perfused tissue
(muscle) is given by:
dA,
(1.1.2-4)
•*JW ^n
dt
At the start of the next injection interval, the IM amount remaining to be absorbed for each
chemical is reset to zero and scheduled for the next injection occurrence.
1.1.3 Intravascular Administration
ERDEM Exposures A3 September 25,2006
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Appendix A
Description of Chemical Exposures
There are two forms of intravascular administration into the venous blood, bolus intravenous
injection or infusion.
1.1.3.1 Infusion into the Venous Blood
Infusion is the direct insertion of chemical into the venous blood at time TJWF for a period of
time, Tgjp, D , which can be repeated at the l^. rr . There can be many chemicals ii
each infusion, each with its own concentration. The rate of change of the amount of the ith
chemical infused into the venous blood versus time is given by:
**** - ^ C O 0131-1)
j, - _ ^JKF iiJW • ^i. 1. J. 1 1^
A _
The flow rate, Q^p , is independent of the chemical. There is one flow rate for each
exposure. However, the concentration of the ith chemical in the jth exposure, (-• jj^a - can be
different for each chemical. The total amount of the ith chemical passed to the venous blood by
infusion is:
JA
ft """•j
(1.1.3.1-2)
1.1.3.1 Bolus Intravenous Injections
Bolus dose intravenous (IV) injections start at a given time, TBW: , and may be repeated at an
input interval, TRK TT , Bolus dose intravenous injections (IVs) injected before simulation start
time are not modeled. Those that occur at simulation start time (TO) are modeled as true bolus
doses into the venous blood. The equation for the initial values for the amount of chemical in the
bolus IV dose and in the venous blood are given by:
ERDEM Exposures A4 September 25,2006
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Appendix A
Description of Chemical Exposures
(1.1.3.2-1)
(1.1.3.2-2)
for the exposures that start at simulation start time. A bolus dose IV that occurs after the
simulation start time is simulated with a rate input normally having a time duration of
one-quarter of a communication interval or one-quarter of a maximum integration step,
whichever is less. The equation for the jth exposure for the ith chemical then takes the form:
(1.1.3.2-3)
T - T
i B&. ^ SSf.;
and for all exposures to the ith chemical at time t:
M
-5-1
v
"Ti dt
(1.1.3.2-4)
(1.1.3.2-5)
1.1.4 Inhalation Administration
There are two types of inhalation, open or closed chamber inhalation. Open chamber inhalation
is assumed.
The subjects in each simulation are in a closed chamber or an open chamber. They cannot be
mixed. If the simulation uses an open chamber, then:
• If no exposure is defined, then the simulation starts with an open chamber with no
ERDEM Exposures A5 September 25,2006
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Appendix A
Description of Chemical Exposures
concentration of chemical.
• There is no change in the concentration of chemical in an open chamber due to exhaled
air.
• If one or more open chamber exposures are defined and none are designated the starting
exposure, then exposure number one is the exposure starting the simulation.
• Any number of chemicals can have a concentration in an open chamber exposure.
• The simulation cannot switch in the middle from open to closed chamber inhalation.
If the simulation uses a closed chamber then:
• There must be a closed chamber exposure defined to start the simulation.
• Only one closed chamber exposure can be active at once.
• Any number of chemicals can be assigned a concentration for a closed chamber exposure.
• The chemicals in the exhaled air change the concentration of each chemical in the closed
chamber.
• If closed chamber inhalation is chosen, then the whole simulation will be with a closed
chamber.
• Open chamber inhalation can be approximated with an extremely large closed chamber.
The input concentration for an open chamber is in units of parts per million. The input for closed
chamber inhalation can be the amount (mass units), or the concentration (units of parts per
million). The open chamber concentration for the ith chemical in mass per unit volume is
calculated from:
V jf-f fi /o I A
—• wj*ffi/ ^ j£R,lPPM \£..l.1-
3-1
For closed chamber inhalation, the volume of the chamber is required. The volume of the air in
the chamber is calculated by subtracting the volume of the number of subjects:
If the input into the closed chamber is a concentration, then it is given in parts per million and
converted to mass per unit volume units. But, if the input is an amount, then the amount is
converted to concentration by:
ERDEM Exposures A6 September 25,2006
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Appendix A
Description of Chemical Exposures
A,
(1.1.4-3)
V CCJiAS
There are two types of lung included in ERDEM, static lung and breathing lung. These
compartments are described in Sections 3.1 and 3.2. The inhalation pathway involves entry
through the open or closed chamber static lung or the breathing lung.
1.2 Implementation of the Exposure Time Histories for Rate Ingestion, Inhalation, and
Skin Surface Exposures
Exposure time histories have been implemented in ERDEM for rate ingestion, open chamber
inhalation, and skin surface exposure time histories. There can be up to nine time histories for
each exposure type (except for skin surface exposure which can have up to five exposures), but
only one for each chemical. The time histories may be repeated periodically. Each time history
will have a start time and duration interval. Any exposure can be expressed as an exposure time
history.
Each exposure route has most of these variables:
• Concentration of chemical in a volume of food, water, or air;
• Volume of the food, water or air;
• Flow rate as volume per unit time;
• Start time of exposure, duration of exposure, and interval between exposures.
If an exposure starts on or before the simulation start time, then the simulation starts with the
exposure in effect. Otherwise, there is an event to start and one to terminate the exposure. There
can be overlapping exposures of the same type in most cases (not for closed chamber inhalation
exposures). If the exposure is an exposure time history, then only one chemical can be modeled
and there can be only one exposure time history of a particular type in any one simulation.
1.2.1 Ingestion Into the Stomach and the Stomach Lumen
Rate ingestion input is a time history of the time and the amount per unit time (concentration
times flow rate) of the chemical. Linear interpolation is used to obtain intermediate values.
1.2.1.1 Bolus Dose Ingestion
Bolus dose ingestion occurs when chemical is taken into the gastrointestinal tract very rapidly;
for instance in one big bite or drink. Bolus dose inputs that occur at simulation start time (T0) are
ERDEM Exposures A7 September 25,2006
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Appendix A
Description of Chemical Exposures
modeled as true bolus doses. The initial value for the integration in the stomach or stomach
lumen is the sum of all exposures that start at simulation start time. The equation is:
AT
-"•$33 ~ ^SIS + __• '"'Mi V£fa (1,2.1-1)
/-I
for all exposures to the ith chemical at time T0.
Bolus dose inputs with start time TBIGj that are greater than the simulation start time and before
the simulation stop time are simulated by rate inputs that start at the scheduled bolus dose start
time with a duration of 1/4 of a communication interval, or 1/4 of a maximum integration
interval, whichever is less. The bolus dose for the jth exposure can be repeated at the input
interval, TBIG r^ . The approximation of a bolus dose input via a rate input of a relatively short
duration produces results very similar to those achieved with an actual bolus dose while allowing
a more accurate evaluation of amounts and concentrations via numerical integration. The
equation for the jth exposure for the ith chemical then takes the form:
(1.2.1-2)
T - T
SIS, SIS,.
and for all exposures to the ith chemical at time t:
.
or j_i at
(1.2.1-3)
The variable A.T>rr is the initial value in the numerical integrations for the total amount of the
HUjj-Qj °
ith chemical in the bolus dose ingestion, and the amount in the stomach or stomach lumen:
(1.2.1-4)
Jr.- dt
ERDEM Exposures A8 September 25,2006
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Appendix A
Description of Chemical Exposures
1.2.1.2 Rate Ingestion
The rate ingestion for each exposure starts at a given time, TRIG.- , occurs over a duration of time,
TgjQ D , and may be repeated at an input T^g, rr , The concentration of the chemica
in the food or drink and the flow rate are required inputs. The product results in the rate of
change of chemical in the stomach or stomach lumen versus time. Overlapping exposures are
allowed. The rate of change of the ith chemical in rate ingestion versus time is given by:
*
_ v
-
Thus, there is one flow rate for each exposure. But the concentration of each chemical in the jth
exposure may be different. The numerical integration to obtain the total amount of the ith
chemical passed to the stomach by rate ingestion is:
lt + AW (1.2.1.2-2)
T>
1.2.2 Inhalation Exposure
Inhalation exposure input follows a time history and concentration in parts per million (ppm) of
chemical. Linear interpolation is used to obtain intermediate values. The inhalation pathway
involves entry through the open or closed chamber of the static lung or the breathing lung as
outlined in Section 1.4.
1.2.3 Dermal Exposure
There are two types of dermal exposure modeled in ERDEM, one for chemicals in an aqueous
vehicle, most often a water based diluent, and chemicals as a dried residue or adsorbed onto
particles as a dry source.
ERDEM Exposures A9 September 25,2006
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Appendix A
Description of Chemical Exposures
1.2.3.1 Skin Surface Exposure to a Chemical in an Aqueous Vehicle
Skin surface exposure input follows a time history where a surface area of the skin (square
centimeters) becomes exposed to a chemical in an aqueous vehicle as a concentration (mass per
centimeter squared). This concentration and area of the skin are used to compute the rate of
change of the amount of chemical absorbed. Linear interpolation is used to obtain intermediate
values. The skin surface is exposed to chemical in an aqueous vehicle (water) at time Tsmf for
a period of time, Tgy D , which can be repeated at the Tsm TT . Skin surface (w
exposures progress from a simulation start time and end at a scheduled termination time point.
The concentration of the ith chemical at the skin surface is found from summing the
concentrations from each of the up to five exposure scenarios:
AT.
" (1.2.3.1-1)
The rate of change of chemical in the epidermis due to the concentration OSKS on the skin
surface is given by:
_ (
~ ^ -^ ^ • \.L.^,J.L
U
1.2.3.2 Skin Surface Exposure to Transfer from a Dry Surface
A chemical exists on a surface represented as a mass per unit area. It is transferred to the skin of
a subject represented by a transfer coefficient. A short exposure period would represent a bolus.
The rate of change of chemical on the dermis due to a dry exposure is:
dA ,
sks,ex.
; L= A ~K, , (1.2.3.2-1)
dt surf. slcs,r£. * '
j, J,
ERDEM Exposures A10 September 25,2006
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Appendix A
Description of Chemical Exposures
Integrating this equation gives the total applied dose.
The rate of loss of chemical from the skin surface due to evaporation is given by:
ev.
dt wof sks. evl sks,evli. ev2 sks,ev2
where 6 , =1 if a wash-off is in progress and zero otherwise,
wo/
<5 i=l if the first evaporation rate constant is active and zero otherwise,
$ _ = 1 if the second evaporation rate constant is active and zero otherwise.
The rate that the ith chemical moves from the skin surface into the dermis is given by:
sks, dr.
= K , , A , C , (12.3.2- 3)
SK SKS. ^ '
•"•ija
where Cife = — (12 32 - 4)
' sk
If no wash-off is in progress, then the rate of change of the amount of the ith chemical on the skin
is given by the rate of application minus the rate of chemical moving into the dermis minus the
rate of loss due to evaporation:
dA , dA , dA , , dA ,
SKS. SKS, ex. sks, or. SKS,SV.
—T^= ;—L- ;—-~ ;—L. (1.2.312-5)
dt dt dt dt
If a wash-off is in progress, then:
ERDEM Exposures Al 1 September 25, 2006
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Appendix A
Description of Chemical Exposures
sk. sks, waf.
L= L (1.2.3.2-
dt dt
where the wash-off is scheduled at timef f for one time step, At, to remove all chemical on
WQJ
the dermis:
dA t, f A i
SKS, wo/. SKS.
- l-(t ) = L(t (2.2.3.2-7)
dt woj A/ wo/)
1.3 Variable Definitions
Bolus Dose Ingestions:
BIG = ^e amount of the ith chemical in all of the bolus dose ingestions at time t,
A DT/V = The total amount of the ith chemical in the bolus dose at simulation start time,
s^rai
€ prf-f = The concentration of the ith chemical in the jth bolus dose,
•^ BIG = ^Q number of bolus dose ingestion exposures,
TgjG = The time that the bolus dose ingestion starts,
jfgjG = The time that the bolus dose ingestion ends, and
]$
V j>Tf-> = The volume of the jth bolus dose.
Rate Ingestions:
^4p,GO = The initial value for the ith chemical in the rate ingestions,
3
A 9jQ ?r = The total amount of the ith chemical passing from rate ingestions to the
ERDEM Exposures A12 September 25,2006
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Appendix A
Description of Chemical Exposures
Stomach at time t,
C gjQ = The concentration of the ith chemical in the jth rate ingestion exposure,
= The rate of change of the ith chemical in the rate ingestions at time t,
at
RIG = ^e number of rate ingestion exposures, and
= The flow rate for the jth rate ingestion.
Infusions:
The total amount of the ith chemical in infusions to Venous Blood at time t,
The rate of change of the ith chemical in infusions versus time at time t,
eft
C £jyp . = The concentration of the ith chemical in the jth infusion,
2-J
~ The infusion flow rate for the jth exposure.
Bolus Dose Intravenous Injection (Bolus IV):
A^ = The amount of the ith chemical in the jth bolus dose IV,
ABIy = The total amount of the ith chemical in the bolus dose IV at simulation start time,
N Biy = The number of bolus dose IV exposures,
Tm, = The time that the bolus dose IV starts,
TBS/f = The time that the bolus dose IV ends.
Intraperitoneal Injection:
A^ /yJR j = The amount of the ith chemical remaining to be absorbed from the previous
interval for the jth IP scenario,
Agjp j = The amount of the ith chemical currently in the IP injection for the jth scenario,
ERDEM Exposures A13 September 25, 2006
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Appendix A
Description of Chemical Exposures
AMP = The amount of the ith chemical currently in the IP injection,
Cs-s> j = The concentration of the ith chemical in the IP injection for the jth scenario,
= The rate of change of the amount of the ith chemical in the IP injection,
dt
KIHP mff j = The first order absorption rate constant for the jth set of IP injections of the ith
chemical,
•^-IKP IJM = The factor to limit the minimum amount of the ith chemical from IP injections
remaining to be absorbed.
Ys = Volume of the body of each subject.
Intramuscular Injection:
era / = The amount of the ith chemical remaining to be absorbed from the previous
interval for the jth IM injection scenario,
j = The amount of the ith chemical currently in the IM injection for the jth scenario,
= The amount of the ith chemical currently in the IM injection,
j = The concentration of the ith chemical in the IM injection for the jth scenario,
•AM
= The rate of change of the amount of the ith chemical in the IM injection,
dt
= The first order absorption rate constant for the jth set of IM injections and the
ith chemical,
ERDEM Exposures A14 September 25, 2006
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Appendix A
Description of Chemical Exposures
LM = The factor to limit the minimum amount of the ith chemical from IM
injections remaining to be absorbed.
Skin Surface Exposure (Water):
Agg = Area of the skin covered by the solution containing the chemical,
DR = The amount of the ith chemical that has moved from the skin surface to the
dermis,
= The rate of change in the amount of the ith chemical moving from the skin
dt
surface to the dermis,
CSKS = The concentration of the ith chemical on the skin surface due to all overlapping
exposures,
= The concentration of the ith chemical for the jth exposure on the skin surface,
DR ARM" = The permeation coefficient for the ith chemical from skin surface to dermis,
Inhalation:
,c j = The amount of the ith chemical in the jth closed chamber,
PPM = The concentration of ith chemical in air for 1 ppm at one atmosphere and 25 °C. This
is used to convert concentration in ppm to mass per unit volume.
f = The concentration of the ith chemical in the jth exposure in ppm.
= The concentration of the ith chemical in inhaled air, units of mas per unit volume.
jy = The number of subjects in the jth closed chamber,
ERDEM Exposures A15 September 25, 2006
C,
-------
Appendix A
Description of Chemical Exposures
= The volume of the closed chamber for the jth inhalation exposure,
GM = The volume of the gas in the chamber adjusted for the volume of the subjects,
ERDEM Exposures A16 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
2.0 Chemical Disposition in silica
Absorption involves entry of a drug or chemical into the body. We have observed that a chemical
may enter directly into the GI tract from intraperitoneal injection (Section 1.1.1) or more naturally
from ingestion of food or from purposeful (pica or geophagia) or accidental "non-dietary ingestion of
filth and extraneous matter.
Intravascular parenteral administration directly into the blood stream, intravenously or intra-
arterially, was considered as an exposure route although this route of administration is important for
laboratory or clinical testing (Section 1.2.1). This approach was also developed for intramuscular
injection (Section 1.2.2) as an avenue of comparison with other parenteral routes of exposure,
especially dermal.
Once the drug or chemical enters the blood stream, its disposition in blood and other fluids, e.g.
cerebrospinal fluid (CSF), organs and tissues determines its access to the site or sites of action. Drug
and chemical disposition involves distribution from blood and fluids to tissues and organs,
metabolism in liver and other organs of metabolism, and elimination in exhaled breath, fluids, e.g.,
milk, and excreta.
2.1 Distribution of Chemical from Blood to Tissues, Organs, and in Fluids
2.1.1.1 Binding in the Arterial Blood
The binding in the arterial blood is of the Michaelis-Menten form but is an equilibrium relationship
so that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is
'-'.43 '
(2.1.1-1)
2.1.1.2 Calculation of Free Chemical in the Arterial Blood
The free chemical in the arterial blood is calculated by subtracting the amount bound from the total
amount as follows:
"jiff,? ~ -^-AS -"-jIBJ (4.1.1 - 2)
ERDEM Chemical Disposition A17 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
2.1.2 The Venous Blood
The venous blood contains chemical output from the compartments and input to the static lung or the
breathing lung. The chemical output to blood from the new GI walls is passed to the portal blood.
2.1.2.1 Binding in the Venous Blood
The binding in the venous blood is of the Michaelis-Menten form but an equilibrium relationship so
that the amount of the ith chemical that is bound is calculated rather than the rate. The equation is:
^ vs
2.1.2.2 Calculation of Free Chemical in the Venous Blood
The free chemical in the venous blood is calculated by subtracting the amount bound from the total
amount as follows:
•"• ~ •" -" V*1- 1 -^
2.1.3 Distribution in Tissues
2.1.3.1 Distribution in the Residual Carcass
The rate of change of the ith chemical in the carcass is given by the rate that the chemical enters from
the arterial blood and the chylomicrons from the lymph pool (when the four walled GI model is
used), and then exits via the venous blood. Elimination is modeled and the rate of elimination of the
ith chemical is subtracted. Chemical may be metabolized and the rate of metabolism further reduces
the rate of increase of the chemical in the carcass. Other metabolites may metabolize to the ith
chemical and their rate of formation is added. The equation is:
ERDEM Chemical Disposition A18 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
(.& L_^ ™jrj i_," j-f j*, yp t_i^ri «f«p »
" (2.1.3.1-1)
ju. s,c&cli£ xp^ £p s,cs.>
Ar-- dA dA
U^- U^
where the variable Iclm is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in Section 2.2. Binding and elimination
equations are presented below.
2.1.3.1.1 Binding in the Carcass
The binding in the carcass is of the Michaelis-Menten form but is an equilibrium relationship so that
the amount of the ith chemical that is bound is calculated rather than the rate. The equation is
f \
^os1
1.1.3.1-2)
2.1.3.1.3 Calculation of Free Chemical in the Carcass
The free chemical in the carcass is calculated by subtracting the amount bound from the total amount
as follows:
*"•€%,£ (2.1.3.1-3)
2.1.3.1.4 Elimination in the Carcass
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
static lung and a saturable Michaelis-Menten form. The linear form is
ERDEM Chemical Disposition A19 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
(2,1,3,1 - 4)
and the saturable form for elimination is:
r*
=r
di
2.1.3.2 Distribution in Fat Tissue
The rate of change of the ith chemical in the fat tissue is given by the rate that the chemical enters
from the arterial blood and the chylomicrons from the lymph pool and then exits via the venous
blood. Elimination is modeled and the rate of elimination of the ith chemical is subtracted.
Chemical may be metabolized and the rate of metabolism further reduces the rate of increase of the
chemical in the fat tissue. Other metabolites may metabolize to the ith chemical and their rate of
formation is added. The equation is:
f1
FT -^
(2.1,3.2-1)
£ dt ,"., dt
where the variable /c/m is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in Section 2.0. Binding and elimination
equations are presented below.
2.1.3.2.1 Binding in Fat Tissue
The binding in the fat is of the Michaelis-Menten form but is an equilibrium relationship so that the
amount of the ith chemical that is bound is calculated rather than the rate. The equation is:
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Appendix A
Description of Chemical Disposition in Silico
(2.1.3.2-2)
2.1.3.2.2 Calculation of Free Chemical in Fat Tissue
The free chemical in the fat tissue is calculated by subtracting the amount bound from the total
amount as follows:
(2.1.3.2-3)
2.1.3.2.3 Elimination in Fat Tissue
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
static lung and a saturable Michaelis-Menten form. The linear form is
—— = w A
j FT,S FT F.
and the saturable form for elimination is
dt
0.1.32 - 5)
\ /
2.1.3.3 Distribution in Slowly Perfused Tissue
The rate of change of the ith chemical in the slowly perfused tissue is given by the rate that the
chemical is input from intramuscular injections, from the lymph pool as chylomicrons and as input
from the arterial blood that exits via the venous blood. Elimination is modeled and the rate of
ERDEM Chemical Disposition A21 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
elimination of the ith chemical is subtracted. Chemical may be metabolized and the rate of
metabolism further reduces the rate of increase of the chemical in the slowly perfused tissue. Other
metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:
"-"
zsfL-^SL/' Lf^L.LP.
al al
dt ~\ dt /~"_j dt
where the variable Iclm is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in Section 2.2. Binding and elimination
equations are presented below.
2.1.3.3.1 Binding in the Slowly Perfused Tissue
The binding in the slowly perfused tissue is of the Michaelis-Menten form but is an equilibrium
relationship so that the amount of the ith chemical that is bound is calculated rather than the rate.
The equation is:
si si
Sl,DS
(2.1.3.3-2)
2.1.3.3.2 Calculation of Free Chemical in the Slowly Perfused Tissue
The free chemical in the slowly perfused tissue is calculated by subtracting the amount bound from
the total amount as follows:
*x.r = AX - AX.B, (2.1.3.3-3)
2.1.3.3.3 Elimination in the Slowly Perfused Tissue
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Description of Chemical Disposition in Silico
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
static lung and a saturable Michaelis-Menten form. The linear form is:
Q~"-SL,2
dt
and the saturable form for elimination is:
(2.1.3 J-4)
v^c;? F1
f ? 1 1 1
{"
2.1.3.4 Distribution in Rapidly Perfused Tissue
The rate of change of the ith chemical in the rapidly perfused tissue is given by the rate that the
chemical enters from the lymph pool as chylomicrons and from the arterial blood and exits via the
venous blood. Elimination is modeled and the rate of elimination of the ith chemical is subtracted.
The chemical may be metabolized and the rate of metabolism further reduces the rate of increase of
the chemical in the rapidly perfused tissue. Other metabolites may metabolize to the ith chemical
and their rate of formation is added. The equation is:
v Kf Jf ~ vtSJiP^'SPJ' T ^LfJ&^-lP K.$,$P p Jf V/i. l.J.t 1,1
AT..
v
where the variable Iclm is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in Section 2.2. Binding and elimination
equations are presented below.
ERDEM Chemical Disposition A23 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
2.1.3.4.1 Binding in the Rapidly Perfused Tissue
The binding in the rapidly perfused tissue is of the Michaelis-Menten form but is an equilibrium
relationship so that the amount of the ith chemical that is bound is calculated rather than the rate.
The equation is:
(Z. 1 . 3.4 - Z J
2.1.3.4.2 Calculation of Free Chemical in the Rapidly Perfused Tissue
The free chemical in the rapidly perfused tissue is calculated by subtracting the amount bound from
the total amount as follows:
(2,13,4-3)
2.1.3.4.3 Elimination in the Rapidly Perfused Tissue
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
static lung and a saturable Michaelis-Menten form. The linear form is:
%P E
&* (2.1.3,4-4)
and the saturable form for elimination is:
A A
(2134 5}
" '
ERDEM Chemical Disposition A24 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
2.1.4 Distribution of Chemical in Organs
2.1.4.1 Distribution of Chemical from Blood to the Brain
The rate of change of the ith chemical in the brain is given by the rate that the chemical enters from
the arterial blood and in the chylomicrons from the lymph pool (when the four walled
gastrointestinal model is used), and then exits via the venous blood. The blood/brain barrier is
modeled by properly choosing the partition coefficients. Elimination of chemical from brain is
modeled and the rate of elimination of the ith chemical is subtracted. The chemical may be
metabolized and the rate of metabolism further reduces the rate of increase of the chemical in the
brain. Other metabolites may metabolize to the ith chemical and their rate of formation is added.
The equation is:
(Z. 1.4. 1-1)
'
-'. dt
where the variable Iclm is the index to the circulating compound that is the mth metabolite of the /th
circulating compound. The equations for metabolism are presented in Section 2.0. Binding and
elimination equations are presented below.
2.1.4.1.1 Binding in the Brain
The binding in the brain is of the Michaelis-Menten form but is an equilibrium relationship so that
the amount of the ith chemical that is bound is calculated rather than the rate. The equation is:
2.1.4.1.2 Calculation of Free Chemical in the Brain
The free chemical in the brain is calculated by subtracting the amount bound from the total amount
as follows:
ERDEM Chemical Disposition A25 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
(2.1.4.1 -3)
2.1.4.1.3 Elimination from the Brain
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
Static Lung and a saturable Michaelis-Menten form. The linear form is:
£lf S J8W F t '
dt
and the saturable form for elimination is
2.1.4.2 Distribution of Chemical to the Liver
2.1.4.2.1 Stomach/Intestine Model of Distribution to the Liver
The liver compartment has the ith chemical input from the stomach and intestine following
intraperitoneal injections. Input from the arterial blood is also included. The ith chemical is moved
from the liver to venous blood where it may be lost due to elimination. An additional chemical is
bound in the liver using an equilibrium process. The chemical may be metabolized and the rate of
metabolism further reduces the rate of increase of the chemical in the liver. Other metabolites may
metabolize to the ith chemical and their rate of formation is added. The equation is:
ERDEM Chemical Disposition A26 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
dt dt
!A AT
/„,,„, HX
(2.1.4.2.1-1)
where the equations for the input to portal blood from the stomach and the intestine are, respectively:
(2.1,4.2.1-2)
and
jr . (2.1.4.2.1-3)
where the variable /c/m is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in the Section 2.0. Binding and elimination
equations are presented below.
2.1.4.2.2 Gastro-Intestinal Model of Distribution to the Liver
The liver compartment for the complete GI tract (Section 3.3.0) has the ith chemical input from the
portal blood (from intraperitoneal injections) and lymph pool as chylomicrons in addition to the
input from the Arterial Blood. The ith chemical is moved from the liver to the venous blood to the
bile which is passed to the Duodenum Lumen, and may be lost due to elimination. Additional
chemical is bound in the Liver using an equilibrium process. Chemical may be metabolized and the
rate of metabolism further reduces the rate of increase of the chemical in the Liver. Other
metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:
IF I If' F
- fit ^ \C*, ~ ^^-J + e«^r CH - -=-- - (2. 1,4.2.2 -
t ^ *, «^r H
IF/J1
ALP ~ QSL ~D ^
/Ti
ERDEM Chemical Disposition A27 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
The intraperitoneal injection in this case is passed to the portal blood.
2.1.4.2.3 Binding in the Liver
The binding in the liver is of the Michaelis-Menten form but is an equilibrium relationship so that
the amount of the ith chemical that is bound is calculated rather than the rate. The equation is
(2,1.4,2.3-1)
IV,D£
2.1.4.2.4 Calculation of Free Chemical in the Liver
The free chemical in the liver is calculated by subtracting the amount bound from the total amount as
follows:
~ •"•sf AZT.S (2.1.4.2.-1)
2.1.4.2.5 Elimination in the Liver
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
liver and a saturable Michaelis-Menten form. The linear form is:
(2.1.4.2.5-1)
di
and the saturable form for elimination is:
JA C*
afllV,S u IV.F
f
dt
(2.1.4.2.5-2)
ERDEM Chemical Disposition A28 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
2.1.4.3 Absorption and Distribution in the Stomach
The stomach has the ith chemical input by bolus ingestion (a plug of food or drink) and rate
ingestion (food or drink input over time), with chemical output to portal blood via the liver to the
intestine. The equation for the rate of change of ith chemical in the stomach is:
dAm
dt dt di
(2.1.4.3-1)
where the bolus ingestion and the rate ingestion exposures are discussed in Section 2.0.
2.1.4.4 The Intestine
The rate of change of the ith chemical in the intestine is given by the rate of input from the stomach
and the rate of output to the portal blood via the liver to feces. The equation is:
•"•S" ^-ABSM.fS •"•Sf ^•W.FSC Aw (2.1.4.4-1)
di
2.1.4.5 The Kidney
The rate of change of the ith chemical in the kidney is given by the rate that the chemical enters from
the arterial blood and in the chylomicrons from the lymph pool (when the four walled GI model is
used), and then exits via the venous blood and the urine. The chemical may be metabolized and the
rate of metabolism further reduces the rate of increase of the chemical in the kidney. Other
metabolites may metabolize to the ith chemical and their rate of formation is added. The equation is:
(2.1.4.3-1)
-j dt ,"-'„• dt '
ERDEM Chemical Disposition A29 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
where the variable Iclm is the index to the circulating compound that is the mth metabolite of the /th
circulating compound. The equations for metabolism are presented in Section 2.0. Binding and
elimination equations are presented below.
2.1.4.5.1 Binding in the Kidney
The binding in the kidney is of the Michaelis-Menten form but is an equilibrium relationship so that
the amount of the ith chemical that is bound is calculated rather than the rate. The equation is:
* KB
(2,1.4,5-2)
2.1.4.5.2 Calculation of Free Chemical in the Kidney
The free chemical in the kidney is calculated by subtracting the amount bound from the total amount
as follows:
(2.1.4,5-3)
2.1.4.5.31 Elimination in the Kidney
There are two types of urine elimination currently implemented in ERDEM. A linear form in which
the rate of elimination is proportional to the rate of change of the amount of the free ith chemical in
the static lung and a saturable Michaelis-Menten form. The linear form is
1 , r A\
. 1 .4, j - 4J
and the saturable form for elimination is:
ERDEM Chemical Disposition A30 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
(.2.1.4.- -4)
JCD/"
2.1.4.6 The Spleen
The rate of change of the ith chemical in the spleen is given by the rate that chemical enters from the
arterial blood, and the chylomicrons from the lymph pool (when the
four walled GI model is used), and then exits via the portal blood (or into the liver if the
stomach/intestine GI is used). Elimination is modeled and the rate of elimination of the ith chemical
is subtracted. The chemical may be metabolized and the rate of metabolism further reduces the rate
of increase of the chemical in the spleen. Other metabolites may metabolize to the ith chemical and
their rate of formation is added. The equation is:
—-T- = Q*JS?CSPJ- + KLPJP^LP ~ Qsjy *** ~ *l~ ~ (2.1.4.6-n
j*sj» ^^£»_Ar ^r ,F ijT-ftJ^ ^tjr ^~f*t ~**F r~) ^/#>
SAL &-V-K SB C*t
•^*, xJ /I >J /I
di
where the variable /c/m is the circulating compound that is the mth metabolite of the /th circulating
compound. The equations for metabolism are presented in Section 2.2. Binding and elimination
equations are presented below.
2.1.4.6.1 Binding in the Spleen
The binding in the spleen is of the Michaelis-Menten form but is an equilibrium relationship so that
the amount of the ith chemical that is bound is calculated rather than the rate. The equation is:
(2.1.4.6-2;
2.1.4.6.2 Calculation of Free Chemical in the Spleen
The free chemical in the spleen is calculated by subtracting the amount bound from the total amount
as follows:
ERDEM Chemical Disposition A31 September 25, 2006
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Appendix A
Description of Chemical Disposition in Silico
ASP, = An - A-, (2.1.4.6-3)
2.1.4.6.3 Elimination in the Spleen
There are two types of elimination currently implemented in ERDEM. A linear form in which the
rate of elimination is proportional to the rate of change of the amount of the free ith chemical in the
static lung and a saturable Michaelis-Menten form. The linear form is:
(2.1.4.6-4)
and the saturable form for elimination is:
dt
(2.1.4.6-5)
2.1.4.7 The Dermal Tissue
The dermal dissue receives the ith chemical by permeation through the skin and from the arterial
blood and is released to the venous blood according to the equation:
d A f
*SKS ZXS DJi
' '
&$&
(2.1.4.7-1)
where
(2.1.4.7-2)
A32
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Appendix A
Description of Metabolism
2.2 Metabolism in Selected Tissues and Organs
The term metabolism refers to any reaction that produces a new compound. The term metabolite
could be replaced with a term such as reactant. ERDEM has been designed to handle multiple
circulating compounds. It is assumed that all metabolites are circulating and the metabolism
structure is the same in all compartments. The metabolism parameters, however, can be different in
each compartment. The equations implemented in ERDEM are presented for the following areas:
• Enzyme Destruction and Resynthesis:
Maximum rate of change of metabolite formation, taking enzyme destruction and resynthesis into
consideration, is calculated.
• Maximum Rate of Metabolite Formation:
The maximum rate of formation of the metabolite is found for the liver by scaling for species and
body volume. The maximum rate for other compartments is scaled from the liver value.
• Saturable and Linear Metabolites:
Equations and parameters for calculating the rate of metabolite formation.
• Inhibition:
A metabolite or circulating compound may work in such a manner as to inhibit the formation of
another metabolite. There are four types of inhibition modeled here, competitive inhibition, mixed
inhibition, strictly noncompetitive inhibition, and uncompetitive inhibition.
Equations are presented for the liver metabolism with circulating metabolites. The other
compartments use similar equations. This is a general form which can be applied to the test case for
trichloroethylene (TCE). Chart 1 shows one rendering of TCE that has five (six if DC A is included)
circulating compounds, including the parent chemical. The chloral and DCA may be treated as if
they are circulating compounds in the metabolism structure, but metabolism parameters are set so
that they do not circulate (the DCA is excreted completely in feces and urine so there must be some
circulation). The chemical CH is a metabolite of chloral and of TCOH. There is no inhibition
depicted in this chart. All seven compounds are handled as circulating compounds in all
compartments. The equation for each metabolism process would be the same in each compartment.
The metabolism parameters, maximum velocity (V-Max) and the Michaelis-Menten constant (Km),
could be different in each compartment. Each circulating compound may or may not be
metabolizedJJ in any compartment. Chart 2 shows the separate metabolism for each compound from
ERDEM Metabolism A33 September 25, 2006
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Appendix A
Description of Metabolism
Chart 1 with the numbering that would be applied. Each circulating compound is shown with its
metabolites. The separate numbering of the metabolites of a circulating compound is required since
separate metabolism parameters are required for each metabolite.
We are only concerned with the V-Max for metabolism in the liver. The V-Max for metabolism in
other compartments is calculated from that used in the liver. If the units of volume are changed, the
units of the input V-Max cannot be changed. Also, the units of the volume of the body used for the
scaling conversion cannot be changed. In other words, there can be no volume units conversion
before the calculation of the scaled version of the V-Max.
An input reference body volume is assumed (currently one unit) and the V-Max input is assumed to
be in units of amount per unit time. The calculation of V-Max will then always work. A volume
units change is applied both to the reference body volume as well as the current body volume. This
then would be consistent for the scaling of the V-Max for elimination as will the maximum binding
value in the calculation of the amount bound. This ratio of body volumes will be used throughout
the scaling processes in ERDEM.
2.2.1 Implementation Outline
If a circulating compound is metabolized, then one or more metabolites are defined. These may be
linear, saturable, or be effected by one of four types on inhibition. Each of these metabolites are
themselves considered to be circulating.
The user will input the circulating compound number for each metabolite. The number of
metabolites for each circulating compound is used as the input. These metabolites are also assumed
to be circulating. The user will input metabolism parameters using i,j with "i" being the index to the
circulating compound and "j" being the metabolite counter for the metabolites of circulating
compound i. The user will need to input set, print, display, and plot statements using the index i.
The individual metabolite amounts are calculated in the compartmental calculations for the
individual chemical. The metabolism section for each compartment calculates two sums. The first
is the sum of all rates of metabolite formation of the ith circulating compound. The second is the
sum of the rate of formation of all metabolites that are the same as the ith circulating compound.
These rate sums are integrated in the circulating compound section for each compartment.
2.2.2 Variable Names for Metabolism Parameters
Table 2.1 presents variable names, with a short description, that are used globally in all
compartments. The variables used in the metabolism calculations are shown in Table 2.2 (the liver
compartment, for example). Those variables that now have one or two indices but have unchanged
names are not listed.
ERDEM Metabolism A34 September 25, 2006
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Appendix A
Description of Metabolism
Table 2.1. Metabolism Variables used in all compartments
Variable Name
CH_NM_SH(I)
CH_NM_LG(I)
N_M(I)
I_CMPD(I,J)
TYPE_M(I,J)
Variable Description
Chemical short name for ith circulating
compound. In SET statements use
CH_NM_SH(1,I)
Chemical long name for ith circulating
compound.
Number of metabolites of the ith circulating
compound.
Number of the circulating compound that is the
jth metabolite of the ith circulating compound.
Type of the jth metabolite (equation(s) to use)
of the ith circulating compound. In SET
statements use TYPE_M(1,I,J).
Notes
Eight characters, used in
error statements.
Thirty characters for use
in descriptive text.
A two-digit integer.
Maximum value is six.
NMt
Forj=l toN_M(i)
In eqns: IC;1J
Up to three characters to
specify equation(s) to use.
ERDEM Metabolism
A35
September 25, 2006
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Appendix A
Description of Metabolism
Table 2.2. Variables used in metabolism calculations (liver example)
Variable Name
(Used in Program)
A_LV_F(I)
C_LV_F(I)
A_LV_M_SUM(I)
A_LV_MC_SUM(I)
DA LV M(I,J)
DA_LV_M_SUM(I)
DA_LV_MC_SUM(I)
DCM_M_LV(I,J)
DRM LV _MEDR(I,J)
K LV ML(I,J)
K MD1 LV(I,J)
K MD2 LV(I,J)
K_MM_LV(I,J)
K1_MER(I,J)
K2_MED(I,J)
VM_M_LV(I,J)
Variable Description
Amount of the ith chemical that is free.
Concentration of the ith circulating compound that is free.
Sum of the amounts of liver metabolite of the ith chemical, (mg)
Sum of amounts of liver metabolite that are the same as the ith
chemical, (mg)
Rate of formation for the kth liver metabolite for the ith chemical.
(mg/H)
Sum of rates of formation for all liver metabolites of the ith
chemical. (mg/H)
Sum of rates of formation of all metabolites that are the same
chemical as the ith circulating compound. (mg/H)
Maximum rate of change of kth liver metabolite concentration for
the ith chemical. (mg/L/H)
Rate of change of the maximum jth liver metabolite metabolic
rate including enzyme destruction and resynthesis for the ith
chemical.
The rate constant for the linear form of the metabolism
calculation.
First dissociation constant for the inhibitor to formation of the jth
liver metabolite of the ith chemical.
Second dissociation constant for the inhibitor to formation of the
jth liver metabolite of the ith chemical.
Michaelis-Menten constant for jth liver metabolite of ith
chemical. (mg/L)
First order rate of jth liver metabolite enzyme resynthesis for ith
chemical (for CHCL3, zero for human and rat). (1/H)
Second order rate of jth liver metabolite enzyme destruction for
the ith chemical, (for CHCL3, zero for human and rat). (L/MG)
Maximum rate of jth liver metabolite metabolism for the ith
chemical. (mg/H)
Variable Name
(in Documents)
A
•"•17,F
(-"XF,F
4
•aM,lV£UM
^ .
dA
dt
dA
dt
M
dt
*M*..jy
^^Mx^Vftlr
dt
^ML,LV
&M3i,lV
^-M)2,iy
^•mmj.?
^-LfcT/r
^•IMfd
¥
ERDEM Metabolism
A36
September 25, 2006
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Appendix A
Description of Metabolism
Variable Name
(Used in Program)
VM MEDR LV(I,J)
Variable Description
Maximum rate of jth liver metabolite metabolism
enzyme change into account for the ith chemical.
after taking
(mg/H)
Variable Name
(in Documents)
VM. ,Uffdf
2.2.3 Calculation of Maximum Rate of Change of Metabolism
The equation for the maximum rate of change of metabolism in the liver for the jth metabolite of the
ith chemical is given by
V ]'
ref
(2.2.3-1)
where
V
= volume of the body,
= reference volume VM -ZF
= power of the volume of the body for interspecies scaling.
2.2.4 Calculations When Including Enzyme Destruction and Resynthesis
The equation for the rate of change of maximum metabolic rate in the liver including enzyme
destruction and resynthesis for the jth metabolite of the ith circulating compound is given by:
dt
(2.2.4-1)
*
,
' '
where the variable definitions are given in Table 2.2 and VLV = the volume of the liver. The value of
the maximum metabolic rate taking enzyme destruction and resynthesis into consideration is
obtained by integration as:
ERDEM Metabolism
A37
September 25, 2006
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Appendix A
Description of Metabolism
»"^
= J
(2.2.4-2)
2.2.5 The Rate of Formation of Saturable and Linear Metabolite in the Liver
The rate of formation of the jth metabolite, when saturable, in the liver from the ith circulating
compound is given by:
lv
(2-2.5-1)
where the indices i, andy are defined above and parameters are defined in Table 2.2. For those
metabolites which the user wants to be strictly linear, then the linear form of the equation would
apply. The rate of formation of a linear metabolite in the liver is:
di
(2.2,5-2)
The sum of the rates of formation of the metabolites for the ith circulating compound can be
calculated according to where the rate of formation of metabolites determines the loss in the rate of
increase of amount in the liver for the ith circulating compound:
dt "- dt
2.2.6 Circulating Compounds Which Are Metabolites
The rates of formation of metabolites, in this case in the liver, which are the same as one of the
circulating compounds, are summed and then added to the rate of increase of the amount of the
circulating compound. This is accomplished by assuming that every metabolite could be any of the
ERDEM Metabolism A38 September 25, 2006
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Appendix A
Description of Metabolism
circulating compounds. An index Ic tj is saved for each metabolite. If they'th metabolite of the ith
circulating compound is the same compound as the kih circulating compound, then the index k for
the circulating compound is saved in Ic tj. otherwise the index Ic tj is set to zero. If the index is non-
zero, then the rate of formation of that metabolite is added to a sum for that circulating compound.
The rate of formation of a circulating metabolite may be linear or saturated (with inhibition if
applicable) where equations (2.2.5-1) and (2.2.5-2) apply. Then,
(2.2.6-1)
s-.
• - ;
- dt
,
where = the contribution to the rate of change of the Ath chemical in the liver from the
(_i'L~
rate of formation of the jth metabolite of the ith chemical.
2.2.7 Inhibition in the Metabolism Process
Compounds elsewhere in the metabolism chains for any of the circulating compounds may inhibit
the formation of a given metabolite. There are four kinds of inhibition addressed here. They are
defined by the formulas for an apparent Vmax, and an apparent Michaelis-Menten constant K^ (see
Table 2.3).
r
= ¥ — (21-1}
Kms<• > ^'' LJ
where VmaXtApp and Kmm_App are taken from Table 2.3 for the inhibition case that applies.
ERDEM Metabolism A39 September 25, 2006
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Appendix A
Description of Metabolism
Table 2.3. Parameter formulas for four types of inhibition of metabolism
Type of Inhibition
Competitive Inhibition
Mixed Inhibition
Pure non-competitive
Inhibition
Uncompetitive
Inhibition
17
' max,APP
"AS,^/*
Mt,Uffd- ,
(1 + Cflfp i -STjaa^iy )
Mt,UTf& .
v + ^Ef/1. ' &-Mm,&f )
M,,£ffd-
(1 + Cgfj.. ' ^Mm,iy )
Tf
mm,App
-^-iHffi^F 0 + ^ Dff > ^MDl,IF )
M + ^£y/- ' K-M>\jy )
zh™/? . fi . f-> / ir \
\L T -'ii'/" ' ^Mm,sf /
K-xmtjy ,
V~
sm,jr
(1 + ^xf/": ' &-Mm,H? )
where /z = zero or the index to the chemical that is the inhibitor to the jth metabolite of the ith
circulating compound,
2.2.8 Metabolism in the Other Organs and Tissues
2.2.8.1 Metabolism in the Brain
The brain metabolism equations are the same as those for the liver except that the equation for the V-
Max is given as a function of the liver value from the equation:
V
where
""-
y*
LW
(2.2.8,1-1)
is a scaling factor for V-Max in the brain for the jth metabolite of the ith chemical.
2.2.8.2 Metabolism in the Kidney
The kidney metabolism equations are the same as those for the liver except that the equation for the
V-Max is given as a function of the liver value from the equation:
ERDEM Metabolism
A40
September 25, 2006
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Appendix A
Description of Metabolism
IV
(4.2.8.2-1)
where RM KD LV is a scaling factor for V-Max in the kidney for the jth metabolite of the ith
chemical.
2.2.8.3 Metabolism in the Carcass
The carcass metabolism equations are the same as those for the liver except that the equation for the
V-Max is given as a function of the liver value from the equation:
IV
where RM CR LY is a scaling factor for V-Max in the carcass for the jth metabolite of the ith
chemical.
2.2.8.4 Metabolism in the Fat
The fat metabolism equations are the same as those for the liver except that the equation for the V-
Max is given as a function of the liver value from the equation:
VFT
7?
v
(i.Z. 0.4-1)
where RM FT LV is a scaling factor for V-Max in the fat for the jth metabolite of the ith chemical.
2.2.8.5 Metabolism in the Slowly Perfused Tissue
The slowly perfused tissue metabolism equations are the same as those for the liver except that the
equation for the V-Max is given as a function of the liver value from the equation:
ERDEM Metabolism A41 September 25, 2006
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Appendix A
Description of Metabolism
v iv
(2.2.8.5-1)
where RM SL ry is a scaling factor for V-Max in the slowly perfused tissue for the jth metabolite of
the ith chemical.
2.2.8.6 Metabolism in the Rapidly Perfused Tissue
The rapidly perfused tissue metabolism equations are the same as those for the liver except that the
equation for the V-Max is given as a function of the liver value from the equation:
(2.2.8.6-1)
iv
where RM & LV_ is a scaling factor for V-Max in the rapidly perfused tissue for the jth metabolite
of the ith chemical.
2.2.8.7 Metabolism in the Spleen
The spleen metabolism equations are the same as those for the liver except that the equation for the
V-Max is given as a function of the liver value from the equation:
(2.2.8.6 -
IV
where RM sp LV is a scaling factor for V-Max in the spleen for the jth metabolite of the ith
chemical.
2.3 References:
Abbas R and Fisher WF. 1997. A physiologically based pharmacokinetic model for
trichloroethylene and its metabolites, chloral hydrate, trichloroacetate, dichloroacetate,
trichloroethanol, and trichloroethanol glucuronide in B6C3F1 Mice. Toxicol. Appl. Pharmacol. 147,
15-30 (page 18).
ERDEM Metabolism A42 September 25, 2006
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Appendix B
Quality Assurance of Input Data and Models
1.0 Quality Assurance for Data and Compartment Models
The purpose of this section is to identify the sources and quality of input data. The data fall into
seven general categories, as described in Table Bl. Data deemed of the highest quality were gleaned
from publications in peer reviewed journals available in the open literature or from reports conducted
under Good Laboratory Practices (GLP) protocols (Category I). These data were used in accordance
with the purpose intended by the measurement such as urinary metabolite data from studies designed
to quantify and identify urinary metabolites according to specific protocols involving methods for
which quality assurance requirements were set a priori. Secondary data (Category II) may be
defined as environmental, exposure, or health data developed for another purpose such as dermal
absorption parameters involving structurally related chemicals. Secondary data may be viewed as
inputs to the ERDEM model for the purpose of estimating absorbed dose, tissue concentrations, and
urine eliminations.
Many diverse types of data, including physical data, chemical data, and physiological data, may also
be used for PBPK modeling (Categories III and IV). These data are taken from a variety of sources,
including databases, peer reviewed publications, and estimation techniques. For example, these data
might include organ volumes modeled as compartments designed to reasonably represent the flow of
chemicals within the blood as well as clearance from these compartments. Data from non peer
reviewed sources, such as government documents or internal reports (Category V), are evaluated
against peer reviewed data. These data may be chemical-specific for a single purpose such as the
clinical use of a congeneric compound having similar physicochemical properties as the target
chemical. Estimates may also be gleaned or inferred from a method or statistical process (Categories
VI or VII). The method or process may be standardized (ASTM) but the resultant data presented to
support the method may not be intended for any other purpose other than to explain the accuracy and
precision of the method. Estimates gleaned from statistical processes, such as Quantitative Structure
Activity Relationships (QSAR), may also represent a means to test a mechanism rather than predict
biological activity. In all of these cases, however, estimates must be accompanied by supporting
statistics that express the level of uncertainty surrounding the method or process.
The sources of all data contained within this report have been documented by reference or footnote
describing the source of the data. Chemical reactions are modeled as metabolism. A general
summary of the models and data utilized in PBPK modeling are presented in the following tables.
The data fall into seven general categories, as described in Table Bl. The sources of the major data
utilized are categorized and described in Table B2. The compartment models utilized are categorized
and described in Tables B3 and B4.
ERDEM Quality Assurance Bl September 25, 2006
-------
Appendix B
Quality Assurance of Input Data and Models
Table Bl. Categories of data sources and models
Category
I
II
III
IV
V
VI
VII
Description
Taken from peer reviewed literature or GLP report, used for the purpose
by the measurement.
Taken from peer reviewed literature or GLP report, used for the purpose
intended by the measurement.
intended
other than
Taken from peer reviewed database compiled for the purposes in which it is being
used.
Taken from non peer reviewed database compiled for the purposes other
for which it is being used.
than those
Taken from other non peer-reviewed source
Estimated based on peer reviewed method or data.
Estimated based on non peer reviewed method.
Table B2. Quality and Sources of Data Used in the ERDEM Model
Variables
Ovide® Concentration
Cardiac Output
Body Weight
Scalp Size
Body Compartment
Blood Flow percentages
Body
Compartme
nt Volumes
Dermis
Fat
Category
I
VI
II
II
I
V
II
Description
Concentration of malathion in lotion applied to
the scalp.
The data from Agata et al. (1994) and Schmitz
et al. (1998) and a curve was fitted to it
(Appendix D).
Studies to determine the distribution of body
weights by age for humans, 5% value chosen
because the effects are inversely proportional
to the body weight.
See Appendix C for various data sources and
methods.
Values of percentages are modified with the
addition of the skin model.
Value used for PBPK modeling of chloroform.
Values for children aged 4-21 years.
Citation
Medici
Agata et al. (1994),
Rosenthal and Bush (1998),
Schmitz et al. (1998)
Burmaster and Crouch
(1997) and Exposure Factors
Handbook, Tables 7.2, 5th
Percentile
Exposure Factors Handbook,
August 1996 (Appendix A).
Fisher et al. (1998)
Corleyetal. (1990).
Boot et al. (1997)
ERDEM Quality Assurance
B2
September 25, 2006
-------
Appendix B
Quality Assurance of Input Data and Models
Variables
Liver,
Kidney
Rapidly
Perfused
Slowly
Perfused
Brain
Skin Permeation
Coefficients
Gastrointestinal
Absorption Rates
Tissue to Blood Partition
Coefficients
Metabolism Constants for
Saturable Metabolism
Urine Elimination Rate
Constants
Category
I
VI
III
II
VI
VI
VI
VI
VI
Description
Values taken from Reference Man
Modified as necessary for additional of dermal
model
Estimated from the fat content
Measurements
Value taken from literature and adjusted to
give 10% absorption.
Values used for trichloroethylene for the mouse
by Abbas and Fisher, (1997), exposure by corn
oil gavage
Methods of Paulin and Thiel (2000) used to
calculate values for rat and human for
malathion and the modeled metabolites.
(Appendix D)
Utilized experimental results for rats; modified
values to improve match of simulation and
experimental data; scaled by body weight to the
0.7 power for rat and human.
Utilized experimental results; modified values
to improve match of simulation and
experimental data; scaled by bodyweight to the
-.25 power
Citation
ICRP,No. 23, Task Group
on Reference Man
Fisher et al (1998
Fisher, etal (1998)
Milner(1990)
Dary et al. (1994)
Abbas and Fisher, (1997)
Paulin and Thiel (2000),
personal communication; R
Tornero-Velez (2003)
Match to data from Bradway
and Shafik (1977), personal
communication; James
Knaak (2003)
Match to data from Bradway
and Shafik, (1977), Personal
communication, James
Knaak (2003)
ERDEM Quality Assurance
B3
September 25, 2006
-------
Appendix B
Quality Assurance of Input Data and Models
Table B3. Categories of compartment model approaches
Category
A
B
C
Description
Widely accepted modeling approach
Approach similar to commonly used and accepted approaches, but adapted to
satisfy project specific requirements
Novel approach addressing specific requirements of estimating absorption and dose.
Table B4. Quality of compartment models
Model
Brain
Dermis
Kidney
Fat, Liver,
Rapidly Perfused,
Slowly Perfused
Blood
Stomach/Intestine
Category
A
B
A
A
A
A
Description
The blood-brain barrier is not modeled. A permeation coefficient determines
the amount of chemical remaining in the brain and that passed to the venous
blood. Metabolism is modeled as a saturable Michaelis-Menten process.
Malathion is placed on the scalp over a short application period and then a
permeation coefficient is used to determine absorption into the skin
A permeation coefficient determines the amount of chemical remaining in the
kidney and that passed to the venous blood. Urine elimination is modeled
with a urine rate constant or saturable Michaelis-Menten constants.
A permeation coefficient determines the amount of chemical remaining in the
compartment and that passed to the venous blood. Metabolism in the liver is
modeled as a saturable Michaelis-Menten process
The arterial blood enters a compartment; a permeation coefficient determines
the amount of chemical remaining in the compartment and that passed to the
venous blood. Metabolism in the blood is modeled as a saturable Michaelis-
Menten process
Modeled with rate constants from stomach to intestine, stomach to portal
blood, intestine to portal blood, and intestine to feces.
ERDEM Quality Assurance
B4
September 25, 2006
-------
Appendix B
Quality Assurance of the Exposure Related Dose Estimating Model (ERDEM)
2.0 Quality Assurance for the Exposure Related Dose Estimating Model (ERDEM)
Quality Assurance of the ERDEM Models
There are many different methods of checking the quality of the ERDEM model. The key is to never
assume that everything is working properly. Continuous checking and testing are required. The
inputs to the model must be checked and rechecked. The outputs from model runs must be carefully
checked.
Code review
The model code is written in the Advanced Continuous Simulation Language (ACSL). It is reviewed
periodically, when any problems occur, or when changes are to be made. The following types of
data are checked: Input variable initializations, those variables that are sent from one compartment
to the next, the warnings of improper data input, the initialization and setting of exposure events, and
the equations in the derivative section.
Mass balance checks
During any model run, mass balance checks are automatically performed by compartment, for each
chemical, and an overall mass balance ratio check is performed. Failure of these checks can be due
to improper coding of an equation, or it could be the result of lack of input exposures or faulty
inputs. The following is an example of the mass balance ratios by compartment for trichloroacetic
acid. Values on the order of 10~14 are expected because the model is run in double precision:
MASS BALANCE
R_AL_DIF=
R_CN_DIF=
R_CR_DIF=
R_DUL_DIF=
R_KD_DIF=-
R_LP_DIF=
R_PB_DIF=
R_SI_DIF=
R_SP_DIF=
R_SW_DIF=
R VB DIF=
DIFFERENCE
0.000000
0.000000
0.000000
0.000000
1 . 1164446E-
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
RATIOS (ADIF/AINPUT):
R_BN_DIF=-
R_CNL_DIF=
R_DR_DIF=-
R_FT_DIF=
15 R_LD_DIF=
R_LV_DIF=-
R_PU_DIF=
R_SIL_DIF=
R_ST_DIF=
R TS DIF=
11
0
0
0
0
0
0
0
3UT) :
1746669E-
.000000
1091668E-
.000000
.000000
1064165E-
.000000
.000000
.000000
.000000
R
15
15
17
AB
R
R
R
R
R
R
R
R
R
R
DIF i
CC
CP
DU
IN
LG
0V
RP
SL
STL
UD
! 2)
DIF
DIF
DIF
DIF
DIF
DIF
DIF
DIF
DIF
DIF
0 . 2371175E-15
0.000000
0.9002710E-17
0.000000
0.000000
0.1035248E-15
0.000000
0.000000
0.000000
0.000000
0.000000
Proper Operation with Inputs for Known Chemicals and Their Known Metabolism Paths
An example of proper operation with inputs for methyl tertiary-butyl ether (MTBE) is shown in
ERDEM Quality Assurance B5 September 25, 2006
-------
Appendix B
Quality Assurance of the Exposure Related Dose Estimating Model (ERDEM)
Figure 1. Plots of the concentration of MTBE in the liver, kidney, and venous blood are shown for a
continuous inhalation in an environment with 400 ppb of MTBE. A basic set of such model runs are
performed that are repeatable so that if the model is changed, then these sets of data can be rerun and
output results checked against earlier results. Compartments are checked for proper circulation of
chemicals and proper metabolism and elimination.
o
o
o
CO"
O
D
O
a
03"
ct:
O
o
MTBE 400 PPM RflT INHfiL CONTINUOUS
Concentrations in Kidney, Liver
Venous Blood
an
o
CO"
CO
D
m
CD
O
r\i
o
o
ID
(XI
en
d
CL
m
8 12
TIME Hours
16
20
Figure Bl, Concentrations of MTBE in kidney, liver, and venous blood for continuous
exposure at 400 ppm.
ERDEM Quality Assurance
B6
September 25, 2006
-------
Appendix B
Quality Assurance of the Exposure Related Dose Estimating Model (ERDEM)
Comparisons of model runs with experimental results
The results of a PBPK/PD model mean very little unless some measure of confidence in the
results may be obtained. One way to do this is to test the model against experimental data. The
ERDEM model has been compared with results of at least three different sets of chemicals and
experimental data sets:
• Fisher et al. 1998 with trichloroethylene on human volunteers.
• Comparison of ERDEM runs with MTBE experimental results for humans, rats, and mice.
• Comparison with radio-labeled isofenphos and parathion.
These model runs with ERDEM validate the model for each special case. Each new chemical and
demographic group require separate comparison with experimental data.
Proper use of an ERDEM Model
In order to gain confidence in the results of model runs, the user of an ERDEM model must
consider the following:
• The source for the values of input parameters to the ERDEM model must be specified,
even if an approximation. If the model is functioning as designed but the input values are
improper, then it is the same as if the model itself did not work properly.
• Mistakes in the setting of input values, even though they are well chosen, will cause
improper model operation.
• Part of quality assurance for given sets of model runs is the review of the outputs for
various organs and determining whether they are reasonable.
• The user must check for error messages output by the model engine when processing
improper inputs.
• The ERDEM model checks input values and outputs faulty data warnings, and may even
abort the model run when illegal values are input. The user should check all warnings.
• The user must check output results for reasonableness.
ERDEM Quality Assurance B7 September 25, 2006
-------
Appendix B
Quality Assurance of the Exposure Related Dose Estimating Model (ERDEM)
The absorbed dose from the various exposures, the amounts eliminated, the amounts
metabolized, and the amount of chemical eliminated due to enzyme inhibition should all
be checked. These are supplied as part of the output printed at the completion of a model
run.
The relative and absolute error limits must be set according to guidelines available in the
Front-End.
The mass balance ratios for each compartment, each chemical, and total mass balance
must be on the order of 10~14 or less.
References
Agata Y, Hiraishi S, Misawa H, Hirota H, Nowatari M, Hiura K, Fujino N, Oguchi K, and
Horiguchi Y. 1994. "Regional blood flow distribution and left ventricular output during early
neonatal life: A quantitative ultrasonographic assessment." Pediatric Research. Vol. 36, pp. 805-
810.
Abbas R and Fisher JW. 1997. "A physiologically based pharmacokinetic model for
trichloroethylene and its metabolites, chloral hydrate, trichloroacetate, dichloroacetate,
trichloroethanol, and trichloroethanol glucuronide in B6C3F1 mice." Toxicol. Appl. Pharmacol.
127, 15-30.
Boot AM, Bouquet J, de Ridder MAJ, Krenning EP, and de Muicnk Keizer-Schrama S. 1997.
"Determinants of body composition measured by dual-energy X-ray absorptiometry in Dutch
children and adolescents." Am. J. Clin. Nutr. 66: 232-8.
Corley RA, Mendrala AL, Smith FA, Staats DA, Gargas ML, Conolly RB, Andersen ME, and
Reitz RH.1990. "Development of a physiologically based pharmacokinetic model for
chloroform." Toxicol. Appl. Pharmacol. 103: 512-527
Dary CC, Blancato JN, Castles M, Reddy V, Cannon M, Saleh MA, and Cash GG. 1994. Dermal
absorption and disposition of formulations of malathion in Sprague-Dawley rats and humans.
Chapter 15 in: Biomarkers of human exposure to pesticides. Saleh MA, Blancato JN, andNauman
ERDEM Quality Assurance B8 September 25, 2006
-------
Appendix B
Quality Assurance of the Exposure Related Dose Estimating Model (ERDEM)
CH, eds. Am. Chemical Soc. Symposium Series No. 542.
Fisher JW, Mahle D, and Abbas R. 1998. "A human physiologically based pharmacokinetic
model for trichloroethylene and its metabolites, trichloroacetic acid and free trichloroethanol."
Toxicol. Appl. Pharmacol 152: 339-359.
International Commission on Radiological Protection. 1975. Report of the Task Group on
Reference Man. ICRP no. 23.
Milner R. 1990. "Cranial capacity." The Encyclopedia of Evolution: Humanity's Search For Its
Origins. New York, NY: Holt, 98.
Poulin P and Theil FP. 2000. ""A priori prediction of tissue:plasma partition coefficients of drugs
to facilitate the use of physiologically based pharmacokinetic models in drug discovery." J.
Pharm. Set. 89: 16-35.
Rosenthal M and Bush A. 1998 . "Haemodynamics in children during rest and exercise: methods
and normal values." Eur. Respir. J. Vol. 11: pp. 854-865.
Schmitz L, Koch H, Bein G, and Brockmeier K. 1998. "Left ventricular diastolic function in
infants, children, and adolescents. Reference values and analysis of morphologic and physiologic
determinants of echocardiographic doppler flow signals during growth and maturation." Journal
of the American College of Cardiology. Vol. 32, pp. 1441-1448.
ERDEM Quality Assurance B9 September 25, 2006
-------
Appendix C
Scalp Size
Scalp Size Estimations
A. Scalp size estimations were based on the evaluation of three sources of data:
• Summary statistics of head length and breadth measurements of
approximately 4,000 children by sex and age in Anthropometry of U.S.
Infants and Children by Snyder et al. 1975
• Head surface area as percent of total body surface area from approximately
20 children by sex and age in the Exposure Factors Handbook (U.S. EPA,
1996)
• Percentiles of total body surface area by sex and age (American Industrial
Health Council, 1994)
Estimates were drawn from age- and sex-stratified medians and upper percentiles of scalp
sizes for children using head length and breath data obtained from measurements of almost
4,000 children (Snyder et al., 1975). Although the data were only presented as summary
statistics by sex and age, the fact that the mean is almost exactly the same as the median
for each stratification level indicates the data for each strata follow a normal distribution.
Also, since the sample sizes are quite large, the estimated percentiles should be reasonably
accurate. Therefore, it is reasonable to assume that the percentiles can easily be estimated
from the means and the standard deviations.
The crown of the head may be represented by a hemi-ellipsoid. The scalp may be assumed
to cover 90% of a hemi-ellipsoid with a mean scalp area estimated from mean head length
and breadth values (Table Cl).
Scalp Size Cl September 25,2006
-------
Appendix C
Scalp Size
Table Cl. Mean scalp surface area by sex and age. Uses Snyder et al. (1975) length and
breadth data together with elipsoid surface area equation, assuming scalp is 45% of
ellipsoid
age (mo.)
21.5
27.5
33.5
39.5
45.5
51.5
57.5
63.5
69.5
75.5
81.5
90.5
102.5
114.5
126.5
138.5
150.5
age (yr)
2
2.5
2.5
3.5
3.5
4.5
4.5
5.5
5.5
6.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
Female scalp (cm2)
343
361
360
370
372
376
379
379
390
386
390
395
403
405
408
413
424
Male scalp (cm2)
366
375
381
386
395
400
401
408
408
416
411
414
422
421
434
432
429
Alternatively, estimates of scalp area may be calculated from the "Percentage of Total
Body Surface Area by Body Part for Children" based on data from the Exposure Factors
Handbook (1996). However, the dataset is quite sparse in that it does not represent all age
strata. Moreover, the represented strata have very small sample sizes. Another drawback
is that the data are stratified by age but not grouped by sex. For example, for the age 4-5
stratum, there are four subjects, consisting of one male and three females. It must be
assumed that the maximum value belongs to the male and the minimum to one of the
females. Only a rough estimate can be obtained for the females and a single value must be
assumed for the male.
One strategy for dealing with ages that are not represented is to interpolate between age
strata bearing in mind that the small sample size might lead to large errors. A more
appropriate strategy would be to regress head size percentage based on age for males and
for females. Prediction equations are not valid beyond the range of the original data and
the oldest female subject is less than 10 years old, preventing estimates from being made
for females ages 11 and beyond (Figure Cl).
Scalp Size
C2
September 25, 2006
-------
Appendix C
Scalp Size
M a fei
13 .
- -O.ioSl + 16.008
10 15 20
a g s (V * a r s j
Ma lei
- -0.0*3 II + 1.8282
10
age
15 _
13 .
F e m a \e s
- -O.t603l + 15.408
10 I;
age
-------
Appendix C
Scalp Size
Table C2a. Multiple linear regression of Exposure Factors head percent data
Males vs Females, age > 2
The REG Procedure
Dependent Variable: headpc
Analysis of Variance
Source
Model
Error
Corrected Total
Sum of Mean
DF Squares Square F Value
Pr>F
3
13
16
79.65307
6.95623
86.60929
26.55102
0.53509
49.62 <.0001
Root MSB 0.73150 R-Square 0.9197
Dependent Mean 12.27941 Adj R-Sq 0.9011
CoeffVar 5.95714
Parameter Estimates
Parameter Standard
Variable DF Estimate Error t Value Pr > |t|
Intercept 1 16.35565 0.59339 27.56 <.0001
sex 1 -1.89422 0.81267 -2.33 0.0365
age 1 -0.51210 0.05000 -10.24 <.0001
agesex 1 0.25782 0.11266 2.29 0.0395
Scalp Size
C4
September 25, 2006
-------
Appendix C
Scalp Size
Table C2b. Multiple linear regression of log-transformed Exposure Factors head percent
data.
Log-transfored Head as percent of body surface area
Males vs Females, age > 2
Dependent Variable: Inheadpc
Analysis of Variance
Pr>F
<.0001
Source
Model
Error
Corrected Total
DF
3
13
16
Sum of
Squares
0.68505
0.04804
0.73309
Mean
Square
0.22835
0.00370
F Value
61.79
Root MSB
Dependent Mean
CoeffVar
0.06079
2.48807
2.44327
R-Square
Adj R-Sq
0.9345
0.9193
Parameter Estimates
Parameter Standard
Variable DF Estimate Error t Value Pr > |t|
Intercept 1 2.85198 0.04931 57.83 <.0001
sex 1 -0.17471 0.06754 -2.59 0.0226
age 1 -0.04684 0.00416 -11.27 <.0001
agesex 1 0.02690 0.00936 2.87 0.0131
The regression equations for estimating head percent from age and sex are summarized in Table
C3 and head as percent of total body area for each age group is estimated by the four equations in
Table C4. All four equations produce similar values, but the values from Method 2 (multiple
linear regression) for older females are dissimilar from the value of "7.1" that might be expected.
Estimated scalp surface areas are presented in Table C5.
Scalp Size
C5
September 25, 2006
-------
Appendix C
Scalp Size
Table C3. Regression estimates of head percent
Method #1
Uses Head % data from Exposure Factors Handbook
Only children age 2 and above, and equation forced through final point.
males Head% = -0.4580*age + 16.008
females Head% = -0.4603*age + 15.408
Method #la
Same as Method 1 but with log-transformation of Head%
males Head% = exp(-0.0431*age + 2.8282)
females Head% = exp(-0.0464*age + 2.7791)
Method #2
Multiple regression of data from Exposure Factors Handbook
Only children age 2 and above.
formula Head% = -0.5121*age + (-1.89422)*sex + 0.25782*age*sex + 16.35565
males Head% = -0.5121*age + 16.3557
females Head% = -0.2543*age + 14.4614
Method #2a
Same as Method 2 but with log-transformation of Head%
formula Head% = -0.04684*age + (-0.17471)*sex + 0.0269*age*sex + 2.85198
males Head% = exp(-0.04684*age + 2.85198)
females Head% = exp(-0.01994*age + 2.67727)
Scalp Size C6 September 25,2006
-------
Appendix C
Scalp Size
Table C4. Head as % of total body surface area. Comparison of results from four different
regression equations.
Age
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
Method 1
14.6
14.4
14.2
13.9
13.7
13.5
13.3
13.0
12.8
12.6
12.3
12.1
11.9
11.7
11.4
11.2
11.0
10.7
10.5
10.3
10.1
9.8
9.6
9.4
9.1
8.9
8.7
8.5
8.2
8.0
7.8
Males
Meth la Method 2
14.9
14.5
14.2
13.9
13.6
13.3
13.1
12.8
12.5
12.2
12.0
11.7
11.5
11.2
11.0
10.8
10.5
10.3
10.1
9.9
9.7
9.5
9.3
9.1
8.9
8.7
8.5
8.3
8.1
8.0
7.8
14.8
14.6
14.3
14.1
13.8
13.5
13.3
13.0
12.8
12.5
12.3
12.0
11.7
11.5
11.2
11.0
10.7
10.5
10.2
10.0
9.7
9.4
9.2
8.9
8.7
8.4
8.2
7.9
7.7
7.4
7.1
Meth 2a
15.1
14.7
14.4
14.0
13.7
13.4
13.1
12.8
12.5
12.2
11.9
11.6
11.4
11.1
10.8
10.6
10.3
10.1
9.9
9.6
9.4
9.2
9.0
8.8
8.6
8.4
8.2
8.0
7.8
7.6
7.5
Method 1
14.0
13.8
13.6
13.3
13.1
12.9
12.6
12.4
12.2
12.0
11.7
11.5
11.3
11.0
10.8
10.6
10.3
10.1
9.9
9.7
9.4
9.2
9.0
8.7
8.5
8.3
8.0
7.8
7.6
7.4
7.1
Females
Meth la Method 2
14.0
13.7
13.4
13.1
12.8
12.5
12.2
11.9
11.6
11.4
11.1
10.9
10.6
10.4
10.1
9.9
9.7
9.4
9.2
9.0
8.8
8.6
8.4
8.2
8.0
7.8
7.7
7.5
7.3
7.1
7.0
13.7
13.6
13.4
13.3
13.2
13.1
12.9
12.8
12.7
12.6
12.4
12.3
12.2
12.0
11.9
11.8
11.7
11.5
11.4
11.3
11.2
11.0
10.9
10.8
10.6
10.5
10.4
10.3
10.1
10.0
9.9
Meth 2a
13.7
13.6
13.4
13.3
13.2
13.0
12.9
12.8
12.7
12.5
12.4
12.3
12.2
12.0
11.9
11.8
11.7
11.6
11.5
11.3
11.2
11.1
11.0
10.9
10.8
10.7
10.6
10.5
10.4
10.3
10.2
Scalp Size
C7
September 25, 2006
-------
Appendix C
Scalp Size
Table C5. Scalp surface area percentiles, based on Exposure Factors data.
Sex |Age Age
M 2<3 2.5
M 3<4 3.5
M 4<5 4.5
M 5<6 5.5
M 6<7 6.5
M 7<8 7.5
M 8<9 8.5
M 9<10 9.5
M 10<11 10.5
M 1K12 11.5
M 12<13 12.5
M 13<14 13.5
M 14<15 14.5
M 15<16 15.5
M 16<17 16.5
M 17<18 17.5
F 2<3 2.5
F 3<4 3.5
F 4<5 4.5
F 5<6 5.5
F 6<7 6.5
F 7<8 7.5
F 8<9 8.5
F 9<10 9.5
F 10<11 10.5
F 1K12 11.5
F 12<13 12.5
F 13<14 13.5
F 14<15 14.5
F 15<16 15.5
F 16<17 16.5
F 17<18 17.5
Body Surface Area
(m22)
50% 90% 95%
0.603 0.661 0.682
0.664 0.729 0.764
0.731 0.809 0.845
0.793 0.895 0.918
0.866 1.01 1.06
0.936 1.06 1.11
1 1.17 1.24
1.07 1.25 1.29
1.18 1.4 1.48
1.23 1.53 1.6
1.34 1.62 1.76
1.47 1.75 1.81
1.61 1.84 1.91
1.7 1.9 2.02
1.76 2.03 2.16
1.8 2.03 2.09
0.579 0.637 0.653
0.649 0.721 0.737
0.706 0.794 0.82
0.779 0.902 0.952
0.843 0.989 1.03
0.917 1.06 1.13
1 1.11 1.18
1.06 1.31 1.41
1.17 1.37 1.43
1.3 1.56 1.62
1.4 1.64 1.7
1.48 1.75 1.86
1.55 1.76 1.88
1.57 1.76 1.83
1.6 1.84 1.91
1.63 1.84 1.94
Head%
Meth la
15.19
14.55
13.93
13.35
12.78
12.24
11.73
11.23
10.76
10.30
9.87
9.45
9.05
8.67
8.31
7.96
14.34
13.69
13.07
12.48
11.91
11.37
10.86
10.36
9.89
9.45
9.02
8.61
8.22
7.85
7.49
7.15
Scalp
est
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Scalp Surface Area
(m2)
50% 90% 95%
0.041 0.045 0.047
0.043 0.048 0.050
0.046 0.051 0.053
0.048 0.054 0.055
0.050 0.058 0.061
0.052 0.058 0.061
0.053 0.062 0.065
0.054 0.063 0.065
0.057 0.068 0.072
0.057 0.071 0.074
0.060 0.072 0.078
0.063 0.074 0.077
0.066 0.075 0.078
0.066 0.074 0.079
0.066 0.076 0.081
0.064 0.073 0.075
0.037 0.041 0.042
0.040 0.044 0.045
0.042 0.047 0.048
0.044 0.051 0.053
0.045 0.053 0.055
0.047 0.054 0.058
0.049 0.054 0.058
0.049 0.061 0.066
0.052 0.061 0.064
0.055 0.066 0.069
0.057 0.067 0.069
0.057 0.068 0.072
0.057 0.065 0.070
0.055 0.062 0.065
0.054 0.062 0.064
0.052 0.059 0.062
Scalp Surface Area (cm2)
50% 90% 95%
412 452 466
435 477 500
458 507 530
476 537 551
498 581 610
516 584 612
528 617 654
541 632 652
571 678 716
570 709 742
595 719 782
625 744 770
656 750 778
663 741 788
658 759 807
644 727 748
374 411 421
400 444 454
415 467 482
437 506 535
452 530 552
469 542 578
489 542 576
494 611 658
521 610 637
553 663 689
568 665 690
573 678 720
573 651 695
554 621 646
539 620 644
524 592 624
An obvious question is how well do the median scalp surface area values based on Exposure
Factors data compare with the mean area values calculated by applying the ellipsoid equation to
the data of Snyder et al. (1975). As is readily apparent in Table C6, the two estimates do not
compare well. In fact, when plotted against each other in Figure C2, it is evident that the
difference increases as scalp surface (or age) increases. The estimates are similar at age 3, but as
age increases, either the ellipsoid/Snyder length and breadth data approach underestimates scalp
Scalp Size
C8
September 25, 2006
-------
Appendix C
Scalp Size
area, or the head as percent of body surface area approach overestimates scalp area.
Table C6. Comparison of estimates of scalp area between 1) estimates using
the ellipsoid equation with the Snyder data and 2) estimates using head percent
data from the Exposure Factors Handbook with total body surface area data
sex
M
M
M
M
M
M
M
M
M
M
M
F
F
F
F
F
F
F
F
F
F
F
age (mo.)
30
42
54
66
78
90
102
114
126
138
150
30
42
54
66
78
90
102
114
126
138
150
age (yr)
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
9.5
10.5
11.5
12.5
Ellipsoid/Snyder
scalp (cm2)
378
390
400
408
413
414
422
421
434
432
429
360
371
378
385
388
395
403
405
408
413
424
Exposure Factors
scalp (cm2)
412
435
458
476
498
516
528
541
571
570
595
374
400
415
437
452
469
489
494
521
553
568
Scalp Size
C9
September 25, 2006
-------
Appendix C
Scalp Size
(/> o
O *m*
"Q
-------
Appendix D
Cardiac Output and Partition Coefficients
1.0 Calculation of Cardiac Output for Children as a Function of Age and Growth.
Cardiac output measurements used in the simulations were obtained from Rosenthal and Bush
(1998). Pulmonary blood flow was determined in a small number of boys and girls of different
ages. The neonatal values were obtained from Agata et al. (1994). Values for children 0.5 to 19
years of age were obtained from Schmitz et al. (1998). These final two studies were selected
because they combined information for both males and females and the total number of subjects
was larger than in other studies surveyed. The values used for the simulations represent averages
for each age group and gender.
1.1 Methods and Results
The average value of 96 hours after birth was selected as the starting value for neonates. This
value was selected because there was a steady decline in cardiac output after birth, reaching a
steady state value at 96 hours. This data was plotted with the data from Schmitz et al. (1998) and
a regression equation was obtained (Figure Dl).
Cardiac output (liters/hour) = 174.64 x02989, where x is age in years.
This equation was used to calculate the average cardiac output for children ages 3, 9, and 18 years.
The calculation is the same for both boys and girls. After the calculation, cardiac outputs were
rounded to three significant digits (Table Dl). These values were used in the simulations for boys
and girls.
Table Dl. Cardiac ouput for children by age
Age
(Years)
3
9
18
Cardiac Output
(Liters/Hour)
242
357
414
Cardiac Output
Dl
September 25, 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Cardiac Output in children y = l74.64x02989
R2 = 0.9945
cnn
OUU
yjcn
4DU
.C Ann
«^ "4-UU
*- ^n
^ oJU
Q- ^nn
^_ OUU
5 fjz\r\
O ^^u
o inn
« zuu
re
ss 1 r,n
T3 I OU
TO 1 nn
+
^ — *•
^ *^^
^*~^"^
/ -
/
f
o 10U |
c;n J
DU ^
n
U ill
0 5 10 15 20
Age in years
Figure Dl. Cardiac Output data and the fitted equation for children.
Cardiac Output
D2
September 25, 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
1.2 References
Agata Y, Hiraishi S, Misawa H, Hirota H, Nowatari M, Hiura K, Fujino N, Oguchi K, and
Horiguchi Y. 1994. "Regional blood flow distribution and left ventricular output during early
neonatal life: A quantitative ultrasonographic assessment." Pediatric Research. Vol. 36: 805-810.
Rosenthal M and Bush A. 1998. "Haemodynamics in children during rest and exercise: Methods
and normal values." Eur. Respir. J. Vol. 11: 854-865.
Schmitz L, Koch H, Bein G, and Brockmeier K. 1998. "Left ventricular diastolic function in
infants, children, and adolescents. Reference values and analysis of morphologic and physiologic
determinants of echocardiographic doppler flow signals during growth and maturation." Journal
of the American College of Cardiology. Vol. 32: 1441-1448.
Cardiac Output D3 September 25,2006
-------
Appendix D
Cardiac Output and Partition Coefficients
2.0 Estimation of Human Tissue:Blood Partition Coefficients (Pt:p) for Malathion and
Metabolites
Tissue:blood partition coefficients (Pt:p) are physiochemical input parameters. The Pt:p represent
the relative distribution of a chemical between tissues and plasma at equilibrium. In vitro and In
vivo methods are available for direct estimation of the Pt:ps; however, these methods are often
resource and time intensive, requiring equilibrium conditions and the use of appropriate analytical
methods. In recent years, algorithms have been developed for predicting Pt:p based on the
solubility of a chemical in n-octanol and water and the relative distribution of lipids in tissue and
plasma (Poulin and Theil, 2002; Poulin and Krishnan, 1995; Haddad et al, 2000).
The basis of this approach is the solubility of a chemical in tissue (or plasma) as governed by
relative lipid and water solubility (Poulin and Krishnan, 1995). Solubility is approximated by the
n-octanol and water partition coefficient (Kow). Structure activity relationships (SAR) have been
used in estimating Kow (Meylan et al., 2001; http://www.logp.com.) The quantitative relation
between Kow and Pt:p may be used to predict tissue distribution based on the following equation:
Pt:p, nonadipose* = Kow (Vnlt + 0.3Vpht) + [Vwt + OJVphtl (1)
Kow (Vnlp + O.SVphp) + [Vwt + O.VVphp]
where V is the fractional tissue volume content of neutral lipids (nl), phospholipids (ph), and
water (w), t is tissue and p is plasma (Table D2).
Partition Coefficients
September 25,
D4 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D2. Human physiological parameters for volumes used in estimating Pt:p (Poulin and
Theil, 2002)
Tissue
Adipose
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Tissue (Vt)a
0.120
0.086
0.020
0.017
0.005
0.004
0.026
0.008
0.400
0.037
0.003
0.042
Water (Vw)
0.180
0.439
0.770
0.718
0758
0.783
0.751
0.811
0.760
0.718
0.788
0.945
Vnl
0.790
0.074
0.051
0.049
0.012
0.021
0.035
0.003
0.024
0.028
0.020
0.004
Vpl
0.002
0.001
0.057
0.016
0.017
0.016
0.025
0.009
0.007
0.011
0.020
0.002
a. Fraction of body weight (L/Kg) for 70 kg human.
Equation 1 is limited to non-adipose tissue because Kow does not properly estimate the
hydrophobic interactions of chemicals and ionized lipids found in adipose tissue (Poulin and
Theil, 2002). Alternatively, Kob, based on the olive oihbuffer partition coefficient, takes into
account partitioning of nonionized and ionized species:
Pt:p, adipose* = Kob (Vnlt + 0.3Vpht) + [Vwt + 0.7Vphf| (2)
Kob (Vnlp + 0.3Vphp) + [Vwt + 0.7Vphp]
However, experimental data for Kob are much more limited than for Kow. It is therefore
necessary to compute this parameter from Kow (Leo et al., 1971) :
Log Kob = 1.115 x log Kow-1.35, n=104,r=0.99 (3)
Partition Coefficients
September 25,
D5 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Both equations 1 and 2 tend to overestimate the Pt:p to the extent that protein binding in tissue
and plasma is not taken into consideration. According to Poulin and Theil (2002), equation 1
should be multiplied by the ratio of the fraction of unbound protein the plasma to the fraction of
unbound protein in tissue (fup/fut).
Pt:p, nonadipose = Pt:p, nonadipose* x (fup/fut) (4)
The fraction unbound in tissue (fut) may be estimated from fup, based on assumptions of the
relative distribution of typical binding proteins between plasma and tissue (ie., albumin, globulins,
and lipoproteins). According to Poulin and Theil (2000), mammalian systems tend toward a value
of 0.5; hence,
fut = l/(l+{[(l-fup)/fup] x 0.5}) (5)
A 10% protein binding in plasma (fup = 0.9) is assumed to obtain a ratio where fup/fut = 0.95.
Thus, the bias in Pt:p is -5% for 10% protein binding. However, the error for adipose tissue is 1:1
since the adjustment is greater (fup/1) with the assumption of no protein binding in adipose tissue:
Pt:p, adipose = Pt:p, adipose* x fup (6)
The predicted Pt:p may be checked for plausibility, provided that experimental data exist for the
steady state volume of distribution. Each tissue contributes to the total experimental volume of
distribution as follows: Vt x Pt:p. Hence, the predicted volume of distribution (Vdss) may be
computed as follows:
Predicted Vdss = S (Vt x Pt:p) + Vp (7)
All estimates of Pt:p assume no protein binding, hence fup/fut = 1 and fup = 1. Assuming 20%
protein binding in the plasma, these methods overestimate Pt:p by 10% in non-adipose tissue.
2.1 Estimation of log? (loglfl[Kow]) for Malathion and Metabolites
An experimental logP=2.89 was used for malathion (Verschueren, 1983). LogP values for
metabolites of malathion weres estimated using the semiempirical neural network approach (see
http://www.logp.com.) (Table D3). The prediction for malathion using this method was logP= 2.3,
comparable to the experimental value of 2.89.
Partition Coefficients
September 25,
D6 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D3. Physiochemical properties of malathion and its metabolites
Compound
Malathion
Malaoxon
DMPTP
DMTP
DMP
MCA
DCA
MW
330
314
158
142
126
302
274
structure
0
^Q II |
0
0
1
o r-V^
^°-{LsJ
V0^/"
o
S
X
^-J-OH
0
>-^
0
1
S i^-OH
acidic
character
neutral
neutral
monoprotic
acid
monoprotic
acid
monoprotic
acid
monoprotic
acid
diprotic
acid
log?
2.89
2.30
0.96
0.46
0.28
0.02
2.12
1.67
pKa
_
-
1.74
0.92
1.24
3.7
pKal = 2.57
pKa2 = 4.53
Log
(Kob)
1.872
-0.28
-6.5
-7.5
-7.5
-2.7
-7.2
Partition Coefficients
D7
September 25,
2006
-------
Appendix D
Cardiac Output and Partition Coefficients
2.2 Estimation of Kob for Malathion and Metabolites
Kob was estimated for malathion using equation 3 giving a value of Log10[Kob]= 1.87. Equation 3
assumes that the compound is neutral. For acid or base compounds, ionization should be taken into
account for accurate prediction of Kob. Malathion and its common metabolites are neutral,
monoprotic acids, or diprotic acid. Adjustments based on Hendersen-Hasselbalch equations take
into consideration ionized species.
Equation 8 employs the naive Kob and the pKa to estimate Kob for monoprotic acids (see Table
A2).
Log Kob (monoprotic acid) = LogKob - log(l + 10pH-pKa) (8)
For the diprotic acid metabolite (DCA), the following equation was employed:
Log Kob (monoprotic acid) = LogKob - log( 1 + 10 pH-pKal+pH-pKa2) (9)
In equations 8 and 9, a physiological pH = 7.4 was used. The pKa for acid metabolites was
predicted using Pallas software (http://server.ccl.net/ccl/pallas.html). The pKa values of the
compounds are predicted using approximately 300 Hammett and Taft equations.
2.3 Calculation of Partition Coefficients
To calculate the Pt:p for any non-adipose tissue, the calc entry for any tissue is divided by the calc
entry for plasma. To calculate the Pt:p for adipose tissue, the calc entry for adipose is divided by
the calc entry for plasma (D*vo:w). In the following tables, 'calc' refers to the numerator of
equation 1 for all tissues except the adipose tissue, where the numerator of equation 2 is employed.
For plasma tissue, the denominator of equation 1 is employed except for plasma (D*vo:w), where
the denominator of equation 2 is employed.
Partition Coefficients
September 25,
D8 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D4. Partition coefficient calculations for malathion
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
(D*vo:w)
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho-
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
74.5
776.0
776.0
776.0
776.0
776.0
776.0
776.0
776.0
776.0
776.0
776.0
74.5
calc
59.11
58.14
53.56
42.33
13.56
20.64
33.65
5.24
20.92
25.36
21.02
4.19
1.26
P.=o
46.99
13.88
12.79
10.11
3.24
4.93
8.04
1.25
5.00
6.06
5.02
Vdss:
vt*p
Vl "t:D
5.62
1.19
0.26
0.17
0.02
0.02
0.21
0.01
2.00
0.23
0.01
9.770
Partition Coefficients
D9
September
2006
25,
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D5. Partition coefficient calculations for malaoxon
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho-
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
0.53
9.12
9.12
9.12
9.12
9.12
9.12
9.12
9.12
9.12
9.12
9.12
0.53
calc
0.60
1.12
1.43
1.22
0.92
1.03
1.16
0.87
1.00
1.02
1.04
0.98
0.95
P.=o
0.63
1.14
1.45
1.24
0.93
1.04
1.17
0.88
1.02
1.03
1.06
Vdss:
vt*p
Vl "t:D
0.075
0.097
0.029
0.021
0.004
0.005
0.030
0.007
0.407
0.038
0.003
0.759
Partition Coefficients
D10
September
2006
25,
-------
Appendix D
Cardiac Output and Partition Coefficients
(D*vo:w)
Partition Coefficients
September 25,
Dll 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D6. Partition coefficient calculations for DMTPT
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
(D*vo:w)
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
3.18e-07
2.88
2.88
2.88
2.88
2.88
2.88
2.88
2.88
2.88
2.88
2.88
3.18e-07
calc
0.18
0.65
1.01
0.88
0.82
0.87
0.89
0.83
0.84
0.82
0.88
0.96
0.95
P.=o
0.19
0.68
1.05
0.92
0.85
0.91
0.93
0.87
0.88
0.85
0.91
Vdss
vt*p
Vl "t:D
0.023
0.058
0.021
0.016
0.004
0.004
0.024
0.007
0.350
0.032
0.002
0.584
Partition Coefficients
D12
September
2006
25,
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D7. Partition coefficient calculations for DMP
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
(D*vo:w)
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho-
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
3.25e-08
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
1.05
3.25e-08
calc
0.18
0.52
0.88
0.79
0.79
0.82
0.81
0.82
0.79
0.76
0.83
0.95
0.95
P.:o
0.19
0.54
0.93
0.83
0.83
0.86
0.85
0.87
0.83
0.80
0.87
Vdss:
Vt*P
Vl "t:D
0.023
0.047
0.019
0.014
0.004
0.004
0.022
0.007
0.333
0.030
0.002
0.546
Partition Coefficients
D13
September 25,
2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D8. Partition coefficient calculations for MCA
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
(D*vo:w)
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho-
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
1.92e-03
132
132
132
132
132
132
132
132
132
132
132
1.92e-03
calc
0.18
10.24
9.77
7.79
2.94
4.17
6.35
1.57
4.19
4.91
4.24
1.50
0.95
P.=D
0.19
6.84
6.52
5.21
1.97
2.78
4.24
1.05
2.80
3.28
2.83
Vdss:
vt*p
Vl "t:D
0.023
0.586
0.130
0.089
0.009
0.012
0.110
0.008
1.119
0.122
0.007
2.258
Partition Coefficients
D14
September 25,
2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Table D9. Partition coefficient calculations for DCA
Tissue
Adipose
(D*vo:w)
Bone
Brain
Gut
Heart
Kidney
Liver
Lung
Muscle
Skin
Spleen
Plasma
Plasma
(D*vo:w)
Tissue
(Vt)
0.1196
0.0856
0.0200
0.0171
0.0047
0.0044
0.0260
0.0076
0.4000
0.0371
0.0026
0.0424
0.0424
Water
(Vw)
0.1800
0.4390
0.7700
0.7180
0.7580
0.7830
0.7510
0.8110
0.7600
0.7180
0.7880
0.9450
0.9450
Neutral
lipid
(Vnl)
0.7900
0.0740
0.0510
0.0487
0.0115
0.0207
0.0348
0.0030
0.0238
0.0284
0.0201
0.0035
0.0035
Phospho-
lipids
(Vpl)
0.0020
0.0011
0.0565
0.0163
0.0166
0.0162
0.0252
0.0090
0.0072
0.0111
0.0198
0.0023
0.0023
Ko:w
6.49e-08
46.8
46.8
46.8
46.8
46.8
46.8
46.8
46.8
46.8
46.8
46.8
6.49e-08
calc
0.1814
3.9160
3.9880
3.2360
1.5400
1.9900
2.7500
1.0840
1.9790
2.2100
2.0200
1.1420
0.9466
P.:o
0.19
3.43
3.49
2.83
1.35
1.74
2.41
0.95
1.73
1.94
1.77
Vdss:
vt*p
Vl "t:D
0.023
0.294
0.070
0.048
0.006
0.008
0.063
0.007
0.693
0.072
0.005
1.331
Partition Coefficients
D15
September
2006
25,
-------
Appendix D
Cardiac Output and Partition Coefficients
2.4 References
Haddad S, Poulin P, and Krishnan K. 2000. "Relative lipid content as the sole mechanistic determinant of the adipose tissue:blood
partition coefficients of highly lipophilic organic chemicals." Chemosphere 839-843.
Laskowski DA. 2002 "Physical and chemical properties of pyrethroids." Rev. Environ. Contain. Toxicol. 174: 49-170.
Leo A, Hansh C, and Elkins D.1971. "Partition coefficients and their uses." Chem Rev 71: 525-615.
Meylen WM and Howard PH. 2001. Log n-octanol:water partition coefficients. KOWWIN database. Syracuse Research Corporation,
Environmental Science Center, Syracuse, NY 13210.
Poulin P andTheil FP. 2000. "A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically
based pharmacokinetic models in drug discovery."/. Phartn. Sci. 89: 16-35
Poulin P and Theil FP. 2002. "Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of
distribution."/. Pharm. Sci. 91: 129-156.
Poulin P and Krishnan K. 1996. "A mechanistic algorithm for predicting blood: air partition coefficients of organic chemicals with the
consideration of reversible binding in hemoglobin." Toxicol. Appl. Pharmacol. 136: 131-137.
Verschueren K. 1983. Handbook of Environmental Data on Organic Chemicals. 2nd ed. New York, NY: Van Nostrand Reinhold., pp.
799-803.
Partition Coefficients
September 25,
D16 2006
-------
Appendix D
Cardiac Output and Partition Coefficients
Partition Coefficients
September 25,
D17 2006
-------
Appendix E
Results for the 3 Year Old Female
12 H our C om plete Washo
.O
"5s
O
O
1E-4 -
1E-S
16 Zt 32 *0 «
Time (Hours)
CD
03
c
0,1 -
0.01 -=
1E-3 -
O
O
1E-S
\12 Hour Complete Washoff
- Malathion
Malaoxon
1 i ' I 'I 'I 'I 'I 'I ' !
0 B 16 24 32 40 4,8 56 61
Time (Hours)
Figure El. Concentration of malathion and malaoxon in
venous blood following exposure to 6 pet on scalp of the 3-
year-old female.
Figure E2. Concentration of malathion and malaoxon in brain
following exposure to 6 pet on scalp of the 3-year-old female.
Results for the 3-Year-Old Female
El
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
OJ
c.
O.QQQ4. -
0)
en
£2
o
*4—
O
0) O.OOOt -
"cS
tr
0.0000
12 Hour
J^—^C om plete Wash off
2i 32 40 IB
Tim e (Hours)
64.
0.010 -
D
_c
I
° 0.008'
o
g, 0.004.
05
J=
O
"S 0.002-
OJ
"cS
cr
0.000
12 H our Com plete Wash off
8 16 2t 32 40 18
Time (Hours)
56 64
Figure E3. Rate of urine elimination of DMTP, DMDTP, and
DMP following exposure of the 3-year-old female to 6 pet Fi§ure E4' Rate of urine elimination of MCA and DCA
malathion on scalp. following exposure of the 3-year-old female to 6 pet malathion
on scalp.
Results for the 3-Year-Old Female
E2
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
O)
•§ 0.01
ID
OI
£,
"E 1 E-3 -
O
45 lE-i
I E-5
12 Hour complete Washoff
24 32 40 48
Time (Hours)
E
"c~
13
O
CD
"5
O
1 E-3 -
I E-4.
• 12 Hour Complete Washoff
8 16 Zi 32 40 18 56 61
Tim e (Hours)
Figure E5. Cumulative elimination of DMTP, DMDTP, and Figure E6. Cumulative elimination of MCA and DCA in urine
DMP in urine following exposure of the 3-year-old female to 6 following exposure of the 3-year-old female to 6 pet malathion
pet malathion on scalp. on scalp.
Results for the 3-Year-Old Female
E3
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
T3
O
° 0.1 -
m
en
o
m
0.01 -
ch
£
c
O
c
O
O
1E-1 •
HourCompleteWashoff
Malathion
Malaoxon
I 'I * I 'I
I 'I
16 2i 32 iO iS
Tim e (Hours)
ns
m
(Z
0.1 -
.^ 0.0 1
•g
'c
CD
5 1E-M
lE-i .
1 2 H our C om plete Washoff
Malathion
Malaoxon
16 24 32 *0 48
Tirn e (Hours)
56 'it
Figure E7. Concentration of malathion and malaoxon in
venous blood following exposure to 50 pet on the scalp of the
3-year-old female.
Figure E8. Concentration of malathion and malaoxon in
brain following exposure to 50 pet on the scalp of the 3-year-
old female.
Results for the 3-Year-Old Female
E4
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
Results for the 3-Year-Old Female September 25,
E5 2006
-------
Appendix E
Results for the 3 Year Old Female
0.006 -
0.004.
^ 0.003 -
03
CD
C
J5 0.002 -
o
o
0.001 -
o:
0.0 DO
12Hour
Complete Washoff
16 24 32 10
Tim e (Hours)
X
f
o
CD
03
O
o
CD
03
cc
D.06 -
0.04.
0.02
D.DD
2 Hour Complete Washoff
16 24, 32 iO 4.8
Time (Hours)
Figure E9. Rate of urine elimination of DMTP, DMDTP, and Figure E10. Rate of urine elimination of MCA and DCA
DMP following exposure of the 3-year-old female to 50 pet following exposure of the 3-year-old female to 50 pet
malathion on scalp. malathion on scalp.
Results for the 3-Year-Old Female
E6
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
QJ
5
"3
~±
Z3
O
£
CD
s
3
I
o
1 -a
0.1 -
1 E-i
12 Hour Complete Washoff
24
^
o
(D
.£
15
^
3
O
0.1 -
0.01 -
1E-3
12 Hour Complete Washoff
Time (Hours)
16 24 32 40 48 56 64 72
Time (Hours)
Figure Ell. Cumulative elimination of DMTP DMDTP DMP Fi§«re E12. Cumulative elimination of MCA and DCA in
in urine following exposure of the 3-year-old female to 50 pet «rine following exposure of the 3-year-old female to 50 pet
malathion on scalp. malathion on scalp.
Results for the 3-Year-Old Female
E7
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
T3
O
_0
m
to
E Q.Qi -
o
03
c
CD
O
O
1 E-t
Malathion
Malaoxon
16 2t 32 40 48
Tim e (Hours)
56
10 -
CO
m
g
o
"cc
o
O
0.01 -.
1 E-i
32 40
Tim e (Hours)
M alathion
M alaoxon
Figure E13. Concentration of malathion and malaoxon in Figure E14. Concentration of malathion and malaoxon in
venous blood following oral exposure of the 3-year-old female brain following oral exposure of the 3-year-old female to 5 mg
to 5 mg per kg dose. per kg dose.
Results for the 3-Year-Old Female
E8
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
<
o
m
a
tc
.c
O
"o
Q)
tc
0.
Q.OOO
030 -
32 10 18
6i 72
Ol
o
E 0.15
III
Ol
c
ra
0.10 -
0 DOS -
"re
ODD
Time (Hours)
32 ID i8
Time (Hours)
Figure E15. Rate of Urine Elimination of DMTP, DMDTP, Figure E16. Rate of Urine Elimination of MCA and DCA
and DMP following oral exposure of the 3-year-old female to following oral exposure of the 3-year-old female to 5 mg per
5 mg per kg dose. kg dose.
Results for the 3-Year-Old Female
E9
September 25,
2006
-------
Appendix E
Results for the 3 Year Old Female
CO
.E
5
<
CO
JS 0.01 -
1
=3
o
32 40 18
Tim e (Hours)
cu
c
ID -
o
E
CO
IB O.I
15
E
o
32 tD IB
Time (Hours)
56 6t
Figure E17. Cumulative elimination DMTP, DMDTP, and Figure E18. Cumulative elimination MCA and DCA in urine
DMP in urine following oral exposure of the 3-year-old female following oral exposure of the 3-year-old female to 5 mg per
to 5 mg per kg dose. kg dose.
Results for the 3-Year-Old Female
E10
September 25,
2006
-------
Appendix E
Results for 3 Year Old Male
T3
O
.2 OH I -
m
m
3
o
•- 1 E-3 -
O
"cS
o
O
I E-i -
1 E-5
1 2 Hour Complete Washoff
Malathion
• Malaoxon
32 tO 18
Tim e (Hours)
56 64
CO
CD
en
.E
1 2 Hour Com plete Washoff
0.01
C
co
u
o
O 1E-4-:
1E-5
Malathion
• Malaoxon
n 'I ' I ' n
16 24 32 4D 48 56 64
Time (Hours)
I
72
Figure E19. Concentration of malathion and malaoxon in
venous blood following exposure to 6 pet on scalp of the 3-year-
old male.
Figure E20. Concentration of malathion and malaoxon in
brain following exposure to 6 pet on scalp of the 3-year-old
male.
Results for the 3 Year Old Male
Ell
July 5, 2006
-------
Appendix E
Results for 3 Year Old Male
CO
C
5
C
±
"a
o
co
en
O
<*—
o
CO
•*—'
as
cr
OJOQOB -
0 JO 006
0JOQ02 -
0 JO ODD
12 Hour
C omplete Washoff
I i I I i
18 24 32 40 4B
CD
.£
5
c
I
"ra
£
zs
o
CD
01
C
OJ
.C
o
CD
•g
ct
0.010 -,
0.008 -
3.006 -
Q.QOt -
0.000
2 Hour C omplete W ash off
Time (H ours)
16 24. 32 40 45
Time (Hours)
Figure Ell. Rate of urine elimination of DMTP, DMDTP, and Figure E22. Rate of urine elimination of MCA and DCA
DMP following exposure of the 3-year-old male to 6 pet following exposure of the 3-year-old male to 6 pet malathion
malathion on scalp. on scalp.
Results for the 3 Year Old Male
E12
July 5, 2006
-------
Appendix E
Results for 3 Year Old Male
CD
E
5
•a
e
o
3
O
D.01 -
1 E-3
1 E-i -
12 H our C om plete Washoff
I \ I n
16 2i 32 40
Time (Hours)
CD
C51
•e
3
o
CD
|
O
I E-3 -
1E-1
'12 Hour Complete Washoff
n •l !i !l
2* 32 40
Tim e (Hours)
•H
Figure E23. Cumulative elimination of DMTP, DMDTP, and Figure E24. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 3-year-old male to 6 urine following exposure of the 3-year-old male to 6 pet
pet malathion on scalp. malathion on scalp.
Results for the 3 Year Old Male
E13
July 5, 2006
-------
Appendix E
Results for 3 Year Old Male
O
o
W
3
O
0.1 -
c 0.01
01
.E
c
o
"§ 1E-3-
C
O
O
2 Hour Complete Washoff
Malathion
Malaoxon
I 'I 'I 'I
I 'I
H 32 10 19
Tim e (Hours)
56 i:4
m
CO
0.1
0.01 -
-------
Appendix E
Results for 3 Year Old Male
Hi
c
O.OOfi -
S> 0.005 -
g 0.001-
co
"o 0.003 -
CD
C
J5 0.002 -
O
N—
o
jli 0.001 -
"tc
cc
12 Hour
Complete vwshoff
-i|i|i|if
0 6 16 21 3S
iO
Time (Hours)
^C
5 0.08 -
en
E
O
-------
Appendix E
Results for 3 Year Old Male
D.I -
is
O
12 H our C om plete Washoff
32 to IB
Tim e (Hours)
OP
c
o
0.1 -
DIM d
^
13
O
1E-3
2 H our C om plete Washoff
21 32 id K
Tim e (Hours)
Figure E29. Cumulative elimination of DMTP, DMDTP, and Figure E30. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 3-year-old male to 50 urine following exposure of the 3-year-old male to 50 pet
pet malathion on scalp. malathion on scalp.
Results for the 3 Year Old Male
E16
July 5, 2006
-------
73
O
_o
CD
w
>
•&
E,
c
O
^*—t
05
"E
C
O
O
Appendix E
Results for 3 Year Old Male
0,1 -
0.01 -
1E-3 -
Malathion
Malaoxon
32 10
Time (Hours
i8
56
•it
O5
m
O
"JS
O)
O
O
O
10 -
1 ,
0.1 ,
OBI -
1&3 •=
1&4-
Malathion
Malaoxon
16
24
32
40
48
58
64
72
Tim e (Hours)
Figure E31. Concentration of malathion and malaoxon in
venous blood following oral exposure of the 3-year-old
male to 5 mg per kg dose.
Figure E32. Concentration of malathion and malaoxon
in brain following oral exposure of the 3-year-old male to
5 mg per kg dose.
Results for the 3 Year Old Male
E17
July 5, 2006
-------
Appendix E
Results for 3 Year Old Male
CD
C
"L—
D
c
.-^.
E
o
<
OS
en
O
<*»»
o
CD
to
cr
OJ005 -
ODOO
1 'I '1 ' F
16 24 32 10 45
Time (Hours)
CD
c
0,30 -|
.C 0.25 -
I
O
CD
OB
ce
O
E o.is -
3,10 -
3,05 -
16 2t 32 ID
Time (Hours)
Figure E33. Rate of urine elimination of DMTP, DMDTP, and
BMP following oral exposure of the 3-year-old male to 5 mg per
kg dose.
Figure E34. Rate of urine elimination of MCA and DCA
following oral exposure of the 3-year-old male to 5 mg per
kg dose.
Results for the 3 Year Old Male
E18
July 5, 2006
-------
Appendix E
Results for 3 Year Old Male
ra
~ 0.1 -
o
OJ
IS 0.01 -
3
E
zs
O
10 -
2» 32 40
Tim e (Hours)
O)
c
i
> o.i -
3
O
21 32 to t8
Time (Hours)
Figure E35. Cumulative elimination of DMTP, DMDTP, and
DMP in urine following oral exposure of the 3-year-old male
to 5 mg per kg dose.
Figure E36. Cumulative elimination of MCA and DCA in
urine following oral exposure of the 3-year-old male to 5 mg
per kg dose.
Results for the 3 Year Old Male
E19
July 5, 2006
-------
Appendix E
Results for 9 Year Old Male
O
m
CO
3
O
cc
•E
'3.1
O
O
I EnS -
I E-7 ^
12 Hour Complete Washoff
i 'i
16 2t 32 tQ 18
Time (Hours)
56 fit
O.I •,
0 JD 1 ,
c
O lE-i.
c
CD
o
C
O
O
1 E-S -
1 E-6 -
1 E-7
12 Hour Complete Washoff
0 8 16 2* 32 *0 48 56 61
Time (Hours)
Figure E37. Concentration of malathion and malaoxon in Figure E38. Concentration of malathion and malaoxon in
venous blood following exposure to 6 pet on scalp of the 9- brain following exposure to 6 pet on scalp of the 9-year-old
year-old male. male.
Results for the 9 Year Old Male
E20
July 5, 2006
-------
Appendix E
Results for 9 Year Old Male
01
c
c
I
E
o
<
CD
Ol
c.
CD
6
*»—
o
-------
Appendix E
Results for 9 Year Old Male
o
3
O
0.01
1E-3
lE-i
IE-
12 Hour
Complete Washoff
16 2t 32 tO 48 56 64
m
c
5
en
£
tD
>
E
^
o
12 Hour
Complete Washoff
Time (Hours)
16 2t 32 40 46
Time (Hours)
Figure E41. Cumulative elimination of DMTP, DMDTP, and Figure E42. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 9-year-old male to 6 urine following exposure of the 9-year-old male to 6 pet
pet malathion on scalp. malathion on scalp.
Results for the 9 Year Old Male
E22
July 5, 2006
-------
Appendix E
Results for 9 Year Old Male
13
O
_o
m
o
c
OS 1E-3 -i
"a 1E~* ~
g
c
o
(0
'E
-------
Appendix E
Results for 9 Year Old Male
CD
C
^> 0.005.
C
I
0.00 i -
0.003-
"5
CD
CO
.c
O
"S O.DOt -
CD
ts
ce
0.000
12 Hour
C omplete Wash off
16 2i 32 tO
T irn e ( H o u rs )
CD
c
.!= 0.06 -
E
o
—
o
CD
O>
C
(5
JZ
O
•s
-------
Appendix E
Results for 9 Year Old Male
CD 0.1 -
o
0.01 -
O
1E-3 -
12 Hour Complete Washoff
I 'I 'I 'I
I'I
CD
C
5
c
O O.I
CD
5
3
^ Ofll -
1 2 Hour Complete Washoff
i i
i I
Tim e (Hours)
8 18 21 32 10 48
Time (Hours)
Figure E47. Cumulative elimination of DMTP, DMDTP, and Figure E48. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 9-year-old male to 6 urine following exposure of the 9-year-old male to 6 pet
pet malathion on scalp. malathion on scalp.
Results for the 9 Year Old Male
E25
July 5, 2006
-------
Appendix E
Results for 9 Year Old Male
O
_O
CD
en
Bl IE-3-
e
o
o
o
lE-fi
16
32 tD
Tiim e (Hours)
m
c
c
o
c
o
O
O.I ^
0.01 -
1 E-i -
I E-6
iO
i8
Time (Hours)
Figure E49. Concentration of malathion and malaoxon in Figure E50. Concentration of malathion and malaoxon in
venous blood following oral exposure of the 9-year-old male to brain blood following oral exposure of the 9-year-old male
5 mg per kg dose. to 5 mg per kg dose.
Results for the 9 Year Old Male
E26
July 5, 2006
-------
Appendix E
Results for 9 Year Old Male
-------
Appendix E
Results for 9 Year Old Male
CO
c
=5
E
o
E
CO
13
=5
IS
O
1 -
D.1 -
0.01 -
24 32 40 48 56 64
Time (Hours)
I
72
o
CD
.£
"CP
1
O
10 -
1 -
0.1
I
24
I
32
\
40
48
I
56
I
64
72
Time (Hours)
Figure E53. Cumulative elimination of DMTP, DMDTP, and
DMP in urine following oral exposure of the 9-year-old male
to 5 mg per kg dose.
Figure E54. Cumulative elimination of MCA and DCA in urine
following oral exposure of the 9-year-old male to 5 mg per kg
dose.
Results for the 9 Year Old Male
E28
July 5, 2006
-------
Appendix E
Results for 18 Year Old Female
-a OD1 -
o
Q
CD
CO
§ IE-3 -J
C
lE-i -
CO
O
'•*—I
03
o
o
IE-?
12 Hour Com plete Washoff
i
16
t
32
I
JO
I
w
Time (Hours)
0.1 -;
001 -:
_T 1 E-3 -.
O
"cc
•£ 1 E-5 -5
dl
O 1M^
I E-I -.
12 Hour Complete Washoff
»8
Time (Hours)
Figure E55. Concentration of malathion and malaoxon in
venous blood following exposure to 50 pet on scalp of the 18-
year-old female.
Figure E56. Concentration of malathion and malaoxon in
brain following exposure to 50 pet on scalp of the 18-year-old
female.
Results for the 18 Year Old Female
E29
July 5, 2006
-------
Appendix E
Results for 18 Year Old Female
0) O.OOS -|
.E
D
O.QQt
"a
E
g 0.003 -
E
"S
en 0.002 -
c
w
6
2 0.001-
15
CE
0.000
12 Hour
Complete Washoff
OJ 006-
C
-
0.03 -
C
I
E
o
O
OJ
a
c
CO
JZ
O
0 o.oi H
0>
"cc
tr
12 Hour
Com plete Washoff
Time (Hours)
24 32 *0
Time (Hours)
Figure E57. Rate of urine elimination of DMTP, DMDTP, and Figure E58. Rate of urine elimination of MCA DCA following
DMP following exposure of the 18-year-old female to 50 pet exposure of the 18-year-old female to 50 pet malathion on
malathion on scalp. scalp.
Results for the 18 Year Old Female
E30
July 5, 2006
-------
Appendix E
Results for 18 Year Old Female
0.1 -=
.E 0 .0 1
13
g 1 E-3
CD
03
O
I E-S
12Hour
Complete Vtfashoff
16 21 32
40
Time (Hours)
m
c
5
0,1 -
OBI
^
o
I E-3
12 Hour Complete Washoff
16
32 ID
Tim e (Hours)
Figure E59. Cumulative elimination of DMTP, DMDTP, and
DMP in urine following exposure of the 18-year-old female to
50 pet malathion on scalp.
Figure E60. Cumulative elimination of MCA and DCA in
urine following exposure of the 18-year-old female to 50 pet
malathion on scalp.
Results for the 18 Year Old Female
E31
July 5, 2006
-------
Appendix E
Results for 18 Year Old Female
1 ,
O
O
DQ
m
3
O
C
OP
0.01 -
OJ
O
c 1 E-4 -
o
O
1E-5
Malathion
Malaoxon
16 24 32 40 48 56 64
Tiime (Hours)
i
72
10 -,\
03
m
C31
JE,
c
o
03
S
o
O
0.1 -=
D.D1 -=
1E-4-=
1E-6 •
Malathion
Malaoxon
16 24 32 40 48 56 64 72
Tiime (Hours)
Figure E61. Concentration of malathion and malaoxon in Figure E62. Concentration of malathion and malaoxon in
venous blood following oral exposure of the 18-year-old female brain flowing oral exposure of the 18-year-old female to 5
to 5 mg per kg dose. mg per kg dose
Results for the 18 Year Old Female
September 25,
E32
2006
-------
Appendix E
Results for 18 Year Old Female
Results for the 18 Year Old Female September 25,
E33 2006
-------
Appendix E
Results for 18 Year Old Female
-------
Appendix E
Results for 18 Year Old Female
O
40
48
58
64
05
E
0)
_>
CP
3
E
3
O
10 -
I -
0.1
I
16
I
I ' I
40 18
I
•54
Time (Hours)
Tim e (Hours)
Figure E65. Cumulative elimination DMTP, DMDTP, and
DMP in urine following oral exposure of the 18-year-old
female to 5 mg per kg dose.
Figure E66. Cumulative elimination MCA and DC A in urine
following oral exposure of the 18-year-old female to 5 mg per
kg dose.
Results for the 18 Year Old Female
September 25,
E35
2006
-------
Appendix E
Results for 18 Year Old Female
tft
O
"co
'c
-------
Appendix E
Results for 18 Year Old Female
0.0006 -,
O)
c
0.0005 -
1UJ
"c
=5
CD
J3IO.OD02
CB
-C
O
B o.oooi
CD
"S
cr
0.0000
12Hour
"Complete Washoff
It 32 iO
Time (Hours)
.£ 0 .006 -
s
~3i
. 0 .005 -
O O.OOt -
<
>*—
0 0 .003 -
o
**—
o
o
DC
0.001 -
12 Hour
Complete Washoff
It 32 tO
Time (Hours)
Figure E70. Rate of urine elimination of MCA and DCA
Figure E69. Rate of urine elimination of DMTP, DMDTP, and following exposure of the 18-year-old female to 6 pet
DMP following exposure of the 18-year-old female to 6 pet malathion on scalp.
malathion on scalp.
Results for the 18 Year Old Female
September 25,
E37
2006
-------
Appendix E
Results for 18 Year Old Female
CD
E
13
O
^>
"w
O
1 E-3 -
1 E-l -
IE-6 -
1 E-6
12 Hour
Com plete Wash off
I ' I 'i '1 ' I * i
-------
Appendix E
Results for 18 Year Old Male
5 o.oi
o
CD
01
1E-3 -
£ 1 E-4 -
2
"05
1 1E'5 '
o
o
O
12 Hour Complete Washoff
16
-Malathion
Malaoxon
I
24
I
32
I
40
I
48
I
58
I
64
72
80
Time (Hours)
12 Hour Complete Washoff
Time (Hours)
Figure E73. Concentration of malathion and malaoxon in
venous blood following exposure to 50 pet on scalp of the 18-
year-old male.
Figure E74. Concentration of malathion and malaoxon in
brain following exposure to 50 pet on scalp of the 18-year-old
male.
Results for the 18 Year Old Male
E39
July 5, 2006
-------
Appendix E
Results for 18 Year Old Male
0 ,005 -i
CD
c
5
c
I"
E
^ 0 .003 -
O
E
CO
O 0.001 -
i—
o
CD
"co
cr
o.ooo
12 Hour
Complete Washoff
16 21 32 tO t?
Time (Hours)
0.06 -,
c O.OS
I
"3t
O.Oi -
O
0.03 -
O
cu
ca
O
•s
'3.1
"re
Q-
0.01 -
12 Hour
" C o m p lete W a sho f f
nj![III|I|I|
0 8 16 2* 32 40 IS
Time (Hours)
Figure E75. Rate of urine elimination of DMTP, DMDTP, and Fi§ure E76- Rate of urine elimination of MCA and DCA
DMP following exposure of the 18-year-old male to 50 pet following exposure of the 18-year-old male to 50 pet
malathion on scalp. malathion on scalp.
Results for the 18 Year Old Male
E40
July 5, 2006
-------
Appendix E
Results for 18 Year Old Male
OJ
C
0.01 -=
1 E-3 -
-5
O
.>
to
1ES
_j 1 E-3 -=|
O
1 E-6
12Hour
Complete Washoff
16 2^ 32 to te
Tim e (Hours)
12 Hour Complete Washoff
16
21 32 40 I*
Time (Hours)
Figure E77. Cumulative elimination of DMTP, DMDTP, and Figure E78. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 18-year-old male to urine following exposure of the 18-year-old male to 50 pet
50 pet malathion on scalp. malathion on scalp.
Results for the 18 Year Old Male
E41
July 5, 2006
-------
Appendix E
Results for 18 Year Old Male
T3
O
O
m
o
I
0.0 I -.
O 1 E-3 -
03
(J
C
O
O
1 E-l
1 E-5
Malathion
Malaoxon
Tim e (Hours)
64
10 ,
c
're
m
C 0,1 -
£ OJ01 -,
IE-.-
O
O
2( 32 10 4-8
Tim e (H ours)
Figure E79. Concentration of malathion and malaoxon in Figure E80. Concentration of malathion and malaoxon in
venous blood following oral exposure of the 18-year-old male brain following oral exposure of the 18-year-old male to 5 mg
to 5 mg per kg dose. per kg dose.
Results for the 18 Year Old Male
E42
July 5, 2006
-------
Appendix E
Results for 18 Year Old Male
QJ 0.08 -
1 .0 -,
24 32 W tfl
Tim e (Hours)
Q)
^C
5
•3,
o
E
<
**—
o
O)
O
•s
O)
IS
cc
0.6 -
o.o.
0 8 18
32 iO
Time (Hours)
Figure E81. Rate of urine elimination of DMTP, DMDTP, and Figure E82. Rate of urine elimination of MCA and DCA
DMP following oral exposure of the 18-year-old male to 5 mg following oral exposure of the 18-year-old male to 5 mg per kg
per kg dose. dose.
Results for the 18 Year Old Male
E43
July 5, 2006
-------
Appendix E
Results for 18 Year Old Male
-a
3
O
O.I
0.0 I -
O
IS
c
g
"E
O
<
_>
_oj
Z3
O
IO -
0.1
2i
iO
48
Time (Hours)
Time (Hours)
Figure E83. Cumulative elimination of DMTP, DMDTP,
and DMP in urine following oral exposure of the 18-year-
old male to 5 mg per kg dose.
Figure E84. Cumulative elimination of MCA and DCA in
urine following oral exposure of the 18-year-old male to 5 mg
per kg dose.
Results for the 18 Year Old Male
E44
September 25, 2006
-------
Appendix E
Results for 18 Year Old Male
12 Hour C om plete Washoff
o
OS
Q
O
1E-5 -
1E-6 -
16
24 32
to is
Time (Hours)
.£=
03
DO
OD1 -
1E-4 -,
c. 1 E-5 ,
O
1 E-6 -.
o 1 E-7 -,
O H
1E-8 -
1E-8
12 Hour Complete Washoff
16 24
32
40 48 66 64
72
Tim e (H ours)
Figure E85. Concentration of malathion and malaoxon in
venous blood following exposure to 6 pet on scalp of the 18-
year-old male.
Figure E86. Concentration of malathion and malaoxon in
brain following exposure to 6 pet on scalp of the 18-year-old
male.
Results for the 18 Year Old Male
E45
September 25, 2006
-------
Appendix E
Results for 18 Year Old Male
0.0006 -i
OS
c
5
I
"5
o.ooo* -
o
E Q.QOB3 -
O
CD
|=? 0.0002 -
CB
.C
O
0 0.000 i -
CD
"co
cr
o.oooo
12 Hour Complete Washoff
16 li 32 tO 48
Time (Hours)
CD
C
£E.
•£
Z5
O
O
CD
Ol
c
CD
JC
O
•4—
O
"CD
CC
OJJ06 -
ODOO
12 Hour
Complete Washoff
24 32 *0 4.8
Time (Hours)
•34
Figure E87. Rate of urine elimination of DMTP, DMDTP, and Figure E88. Rate of urine elimination of MCA and DCA
DMP following exposure of the 18-year-old male to 6 pet following exposure of the 18-year-old male to 6 pet malathion
malathion on scalp. on scalp.
Results for the 18 Year Old Male
E46
September 25, 2006
-------
Appendix E
Results for 18 Year Old Male
0.0 I -
O 1E-3
E
0)
li
3
O |E-i
12 Hour
Complete Washoff
16 2i 32 4-Q 48
(D
c
5
"
o
E
s
=5
O
O.I -
0.01 -
1E-3 -
lE-t
12 Hour
Complete Washoff
Tim e (Hours)
I6 24. 32 *0
Tim e (Hours)
6i
Figure E89. Cumulative elimination of DMTP, DMDTP, and Figure E90. Cumulative elimination of MCA and DCA in
DMP in urine following exposure of the 18-year-old male to 6 urine following exposure of the 18-year-old male to 6 pet
pet malathion on scalp. malathion on scalp.
Results for the 18 Year Old Male
E47
September 25, 2006
-------
-------
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Agency
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