- Email: [email protected]
PD model to calculate Data Derived Extrapolation Factors for chlorpyrifos
Accepted Manuscript Use of a probabilistic PBPK/PD model to calculate Data Derived Extrapolation Factors for chlorpyrifos Torka S. Poet, Charles Timchalk, Michael J. Bartels, Jordan N. Smith, Robin McDougal, Daland R. Juberg, Paul S. Price PII:
S0273-2300(17)30035-1
DOI:
10.1016/j.yrtph.2017.02.014
Reference:
YRTPH 3774
To appear in:
Regulatory Toxicology and Pharmacology
Received Date: 9 September 2016 Revised Date:
24 January 2017
Accepted Date: 17 February 2017
Please cite this article as: Poet, T.S., Timchalk, C., Bartels, M.J., Smith, J.N., McDougal, R., Juberg, D.R., Price, P.S., Use of a probabilistic PBPK/PD model to calculate Data Derived Extrapolation Factors for chlorpyrifos, Regulatory Toxicology and Pharmacology (2017), doi: 10.1016/j.yrtph.2017.02.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Use of a Probabilistic PBPK/PD Model to Calculate Data Derived Extrapolation Factors for Chlorpyrifos.
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Torka S. Poet§, Charles Timchalk*, Michael J. Bartels†, Jordan N. Smith*, Robin
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McDougal∆¥, Daland R. Jubergǂ, and Paul S. Price†∞
SC
6 §Summit Toxicology, Richland, WA, USA 99352
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*Battelle, Pacific Northwest Division, Richland, WA, USA 99354
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†Dow Chemical Company, Midland, MI, USA
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∞ Current address - US Environmental Protection Agency, RTP, NC 27711 Work done
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prior to joining EPA. Views expressed do not necessarily reflect the views of EPA or the
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United States Government
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∆
University of Ontario Institute of Technology, Oshawa, ON, Canada
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¥
The AEgis Technologies Group, Huntsville, AL, USA 35806
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ǂ Dow AgroSciences, Indianapolis, IA 46268
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Abstract
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A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model
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combined with Monte Carlo analysis of inter-individual variation was used to assess the
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effects of the insecticide, chlorpyrifos and its active metabolite, chlorpyrifos oxon in
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humans. The PBPK/PD model has previously been validated and used to describe
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physiological changes in typical individuals as they grow from birth to adulthood. This
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model was updated to include physiological and metabolic changes that occur with
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pregnancy. The model was then used to assess the impact of inter-individual variability
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in physiology and biochemistry on predictions of internal dose metrics and quantitatively
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assess the impact of major sources of parameter uncertainty and biological diversity on
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the pharmacodynamics of red blood cell acetylcholinesterase inhibition. These metrics
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were determined in potentially sensitive populations of infants, adult women, pregnant
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women, and a combined population of adult men and women. The parameters primarily
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responsible for inter-individual variation in RBC acetylcholinesterase inhibition were
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related to metabolic clearance of CPF and CPF-oxon. Data Derived Extrapolation
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Factors that address intra-species physiology and biochemistry to replace uncertainty
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factors with quantitative differences in metrics were developed in these same
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populations. The DDEFs were less than 4 for all populations. These data and modeling
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approach will be useful in ongoing and future human health risk assessments for CPF
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and could be used for other chemicals with potential human exposure.
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Introduction
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Evaluating the potential impact of chemical exposures on human health is a complex
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and important task. Physiologically based pharmacokinetic and pharmacodynamic
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(PBPK/PD) modeling is increasingly being used to quantitatively determine the
5
association
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pharmacodynamic outcomes. PBPK/PD models have shown their value to predict a
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priori human dose metrics (US EPA, 2006); the physiological, biological and
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biochemical underpinnings of these models also make them well suited to explore inter-
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individual variation in response.
external
exposure,
internal
dose,
and
the
concomitant
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A PBPK/PD model for the organophosphorus pesticide, chlorpyrifos (O,O-diethyl O-
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3,5,6-trichloro-2-pyridinyl-phosphorothioate: (CPF), has been developed over the past
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decade (Busby-Hjerpe et al., 2010; Garabrant et al., 2009; Lowe et al., 2009; Poet et al.,
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2014; Smith et al., 2014; Timchalk and Poet, 2008; Timchalk et al., 2002). This model
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has been used to compare pharmacokinetic and pharmacodynamic responses in rats
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and humans over life-stages from infant to adult, and following multiple routes of
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exposure (Poet et al., 2014; Smith et al., 2014). The mode of action for adverse effects
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from sufficiently high exposures to CPF is well understood. CPF is activated by
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conversion to chlorpyrifos oxon (O,O-diethyl O-3,5,6-trichloro-2-pyridyl) (CPF-oxon).
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The binding of the oxon metabolite to acetylcholinesterases in the central nervous
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system leading to inhibition of the enzyme is considered the sentinel event by the US
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EPA (2014a). Binding and inhibition of red blood cell (RBC) acetylcholinesterase occurs
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at lower doses than inhibition of acetylcholinesterases in the central nervous system,
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and, therefore RBC cholinesterase inhibition is used as a conservative biomarker of
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exposure (Garabrant et al., 2009; Hinderliter et al., 2011; Timchalk and Poet, 2008).
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Chlorpyrifos and CPF-oxon are both detoxified to 3,5,6-trichloro-2-pyridinol (TCPy).
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The primary source of non-occupational exposures to CPF occurs from dietary residues
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(US EPA, 2014a). Concerns have also been raised over potential drinking water
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exposure to the active oxon metabolite that can be formed during chlorination of
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drinking water containing CPF (U.S. EPA 2014a). The US EPA has calculated an acute
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population adjusted dose (aPad) for chlorpyrifos based on oral exposures predicted to
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result in 10% inhibition of red blood cell (RBC) acetylcholinesterase compared to non-
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exposed (control) individuals (US EPA, 2014a). In the past, aPADs have been
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established using default factors of 10 to address the uncertainty in extrapolation from
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animals to typical humans (interspecies uncertainty) and additional factors of 10 to
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extrapolate from typical to sensitive humans (intraspecies uncertainty). The aPad is
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then described as the dose predicted to result in a 10% reduction in RBC cholinesterase
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activity in the species of interest ÷ the total uncertainty factors, which generally total 10-
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1000. With this assessment, extrapolation from animals is no longer needed, as 10%
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RBC inhibition is calculated directly in human populations.
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The U.S. EPA has published guidance for replacing these default uncertainty factors
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with Data Derived Extrapolation Factors (DDEFs) and similar guidance has been
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published by the World Health Organization (US EPA, 2014b; WHO, 2005). According
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to these documents, PBPK/PD models of inter-individual variation in response are an
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improved method for deriving DDEFs. When the mode of action is understood and can
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be described using a PBPK/PD model, the level of response associated with a given
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dose can be compared across species and between individuals. The U.S. EPA has
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adopted this PBPK model to reduce the intraspecies uncertainty factor from 100 to 40
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(10x default safety factor, 4x for intraspecies variation) for most human populations, but
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has maintained a factor of 100 (10x default safety factor, 10x for intraspecies variation)
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for females between the ages of 13-49 years (US EPA, 2014a).
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The Life-stage model of CPF (Smith et al., 2014) was expanded to create a model of
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inhibition of RBC acetylcholinesterase from oral exposures to CPF and CPF-oxon that
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reflects the impacts of inter-individual variation in physiology and metabolism. Both local
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and global sensitivity were assessed to determine which parameters impact model
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outcomes. Mechanistic aspects of the PBPK/PD model were then used to investigate
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the impact of parameter variability and uncertainty using a Monte Carlo analysis.
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Individual variability in physiological and biochemical parameters were compared to
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both the magnitude in response variation and degree of RBC acetylcholinesterase
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inhibition to evaluate potential susceptibility to CPF in the general population. This
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paper describes the workflow used to comprehensively evaluate potential cholinergic
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effects following exposures to CPF or CPF-oxon in infants, adults, and in pregnant and
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non-pregnant women, and the use of the Monte Carlo methods to assess the impact of
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inter-individual variability and parameter uncertainty on model predictions.
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METHODS
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Populations
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Individuals at different life-stages may be more sensitive to the effects of CPF because
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of differences in intrinsic (biological) factors. Sub-populations of specific interest for CPF
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exposures are infants and pregnant women. Intra-species extrapolation factors
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(DDEFHD) were determined for 4 populations: 1) a general population of adult men and
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women, 2) adult women only, 3) pregnant women, and 4) infants at 6 months of age.
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Pregnancy
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Physiological and anatomical changes that occur during pregnancy include increased
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respiration and cardiac output, increased blood volume (both plasma and RBC),
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increased glomerular filtration, potential changes in metabolism, enlarged uterus,
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breasts, and growth of the fetus. These important changes were added to the CPF life-
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stage model (Smith et al., 2014; Poet et al., 2014). The changes in physiological
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parameters to develop the pregnancy model were made based on relevance to CPF
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and CPF-oxon disposition and pharmacodynamics. Model modifications, which grow
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over the course of pregnancy, included: pregnancy related changes in metabolism,
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uterine, placenta and fetal compartments; changes in volumes of the slowly perfused
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and fat compartments; and changes in blood, including increasing blood volume,
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decreasing hematocrit, and increases in lipids, triglycerides, and cholesterol.
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Pregnancy Tissue Growth
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Certain pregnancy specific physiological structures are minimal, have limited
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importance to pharmacokinetics, or are non-existent in a non-pregnant individual,
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including the uterus, placenta, and fetus. However, to appropriately model CPF
5
systemic exposure in pregnant women, these compartments were added to the life-
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stage model described by Smith et al. (2014) and Poet et al. (2014) for pregnant women
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only. The model descriptions of growth in the fetus were taken from Gentry et al. (2003),
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growth description for uterus were taken from the recent review of Abduljalil et al.
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(2012), and data collated from Abduljalil et al. (2012) were used to fit an equation to
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describe growth of placenta using Graphpad Prism (La Jolla, CA). The uterus was
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included in the rapidly perfused compartment of the model. The placenta is important
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both to support fetal growth and to serve as the conduit between the mother and fetus.
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Model-predicted growth compared to measured increases in these pregnancy specific
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compartments are shown in Appendix A, Figure A1.
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For the pregnancy model, tissues and blood flows that change and that will affect
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biological response were identified. Fat and total fat free mass both increase by 20-25%
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(Kopp-Hoolihan et al., 1999). Growth in the fat compartment was described using the
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equation in Abduljalil et al. (2012) (Appendix A, Figure A2). Fat free mass increase was
19
used to estimate growth in the slow and rapid compartments. Approximately 6 kg of
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additional mass is observed from GD0 through birth (Kopp-Hoolihan et al., 1999); this
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additional mass was included in the slow and rapid compartments (Appendix A, Figure
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A3). The rapid compartment describes the bulk of the cardiac output, so the growth in
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the rapid and slow compartments were described to balance total cardiac output and
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the increase in total fat free mass (Abduljalil et al., 2012; Kopp-Hoolihan et al., 1999; Lu
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et al., 2012). Cardiac output changes are described below.
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The end result of pregnancy specific compartmental growth and changes in general
4
physiology is an increase in body weight between 10 and 20 kg above pre-pregnancy
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body weight (Abduljalil et al., 2012; Corley et al., 2003; Kopp-Hoolihan et al., 1999)
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(Appendix A, Figure A4). Body weight increases in this model are primarily due to
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increases in fetus, slow, fat, and rapid compartments and to increased blood volumes.
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No other compartmental changes were needed to fit pregnancy specific body weight
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changes.
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Blood Compartment
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The remaining physiological changes that are important for the biological response to
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CPF exposures involve blood volume and the cardiovascular system (Abduljalil et al.,
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2012; Corley et al., 2003; Hytten, 1985). Equations of Abduljalil et al. (2012) describing
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increased total blood volume were used to describe blood and plasma volume increase
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over gestation. The increase in cardiac output is a result of changes in tissue volumes
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and blood flow. Cardiac output increases during pregnancy, and pregnancy specific
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changes in plasma volume, tissue volumes and blood flow result in increased cardiac
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output that matches measured rates (Appendix A, Figure A5).
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The cardiovascular system changes meet the increasing demands to support fetal
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development. Among those changes are differences in white and red blood cell
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composition and numbers, blood volume changes (described above), and changes in
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lipid and cholesterol content (Abduljalil et al., 2012; Cunningham, 2010; Lippi et al.,
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2007). In the model, hematocrit is used to determine the proportion of plasma and RBC
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enzymes (e.g., carboxyl, PON1, etc). Inhibition of RBC acetylcholinesterase is sensitive
3
to hematocrit (Arnold et al., 2015), and hematocrit changes over pregnancy are
4
variable, but tend to decrease (Cunningham, 2010). These changes in hematocrit were
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described within the model (Appendix A, Figure A6). The lipophilicity of CPF leads to a
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high fat partitioning and increases in circulating lipids during pregnancy result in
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decreases in fat/blood partitioning (Lowe et al., 2009). Partitioning of both CPF and
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CPF-oxon were described as decreasing over the course of pregnancy which results in
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increased circulating levels of both of CPF and CPF-oxon.
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Metabolism
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The balance between bioactivation and detoxification of CPF and CPF-oxon is an
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integral determinant of RBC inhibition, and model-predicted RBC cholinesterase
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inhibition is sensitive to these parameters (Arnold et al., 2015; see below). Foxenberg et
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al. (2007) determined specific CYP450 enzyme activity toward metabolism of CPF and
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CPF-oxon. Reports of gestational changes in the activities of these specific enzymes
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were compared and relative increases and decreases calculated (Appendix A, Table
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1A). The major enzymes and their relative activities were tabulated and compared to
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estimate percent contribution of each toward total metabolism, then the relative changes
19
in these enzyme activities were multiplied to determine the increase or decrease in
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over-all metabolism (Appendix A, Table 2A). Abduljalil et al. (2012) indicate that P450
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activity changes during pregnancy follow a smooth arch, so equations were fit to
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describe similar arches resulting in a 33% increase in bioactivation and a 25% decrease
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in detoxification over the course of pregnancy (Appendix A, Figure A7A, Table 2A).
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PON1-mediated detoxification of CPF-oxon is another major determinant of RBC
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inhibition following exposures to CPF or CPF-oxon. Metabolism of CPF-oxon (Huen et
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al., 2010) and of the related organophosphate, paraoxon, were measured in plasma
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collected from pregnant women by two different research groups (Huen et al., 2010;
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Sarandöl et al., 2010). Neither PON1-mediated metabolism of paraoxon nor CPF-oxon
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were significantly different in pregnant and non-pregnant women in either study. While
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no significant differences in chlorpyrifos-oxonase activity were determined, since this
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process is so important, the slight measured decrease in mean activity was fit to PON1
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activity in both plasma and liver and a 7% decrease in each was described by week 26
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of pregnancy (Appendix A, Figure A7B, Table 3A). The full model code is available upon
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request.
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Local Sensitivity and Parameter Distributions
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The impact of changes in parameter values on predictions of RBC acetylcholinesterase
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was evaluated using sensitivity analysis and characterization of parameter source and
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biologic diversity. The sensitivity analysis was used to identify the critical parameters to
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include within the Monte Carlo analysis. The sensitivity analysis was performed using
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acslXv3.0.2.1 (The AEgis Technologies Group, Inc, Huntsville, AL) in which the model
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was created, for ages of 6 months (infants) and 30 years (adults). Sensitivity analysis
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was not run in pregnant women. Parameters leading to the critical endpoint (RBC
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acetylcholinesterase inhibition), are sometimes quantitatively changed in pregnant
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women (as shown in Appendix A), but there were no substantial changes in any
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parameter
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pharmacodynamics of chlorpyrifos. No additional parameters will occur in the pregnant
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or
any
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parameter
(eg
uterine
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model.
For each model parameter, sensitivity was measured in terms of a sensitivity
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coefficient, defined as the change in peak RBC acetylcholinesterase inhibition divided
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by the change in the parameter. In this analysis a small change (1%) was made in the
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parameter value and the impact on peak RBC acetylcholinesterase inhibition was
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determined. A value of 1 indicates that the 1% change in parameter produces a 1%
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change in RBC inhibition. Values greater than 1 indicate a greater than 1% change in
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predicted output. Values close to zero indicate that acetylcholinesterase activity
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predictions were not affected by changes in the parameter.
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The absolute sensitivity coefficients were identified for all parameters for each age and
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dose. Parameters accounting for 95% of the total sensitivity were identified, and
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included in the next step of the workflow. The cutoff for continued analysis of any given
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parameter was determined by the following criterion:
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0.95 =
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where SCp are the sensitivity coefficients for the parameters that were further evaluated,
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and SCi are the sensitivity coefficients for all parameters. This determination was
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performed separately for each of the two doses and the two ages.
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The confidence in each of the sensitive parameters was characterized using criteria that
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considered both variability and uncertainty (Meek et al., 2013). Variability was defined
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as the inter-individual differences in the parameter due to inherent factors (e.g. body
20
composition or metabolic competency) while uncertainty is due to the use of
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assumptions, extrapolations, or experimental data interpretation (Nestorov, 2001). Most
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of the parameters in this model are based on measured human data, so uncertainty is
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far out-weighed by variability and the a priori values were investigated to determine the
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extent of potential variability in humans. To facilitate describing the analysis of the
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sensitive parameters, they were placed into three related groups: physiological
4
parameters governing distribution; metabolic rates governing clearance of CPF or CPF-
5
oxon; and RBC acetylcholinesterase biochemical constants.
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The extent of physiological variability in body weight, hepatic blood flow, hepatic
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volume, and hematocrit were determined in adult and infant populations using the
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Physiological Parameters for PBPK Modeling software (Price et al., 2003). The mean
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and CV (coefficient of variation) for the physiological parameters were determined by
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generating 5,000 sets of values for infants (ages 4-8 months), and adults (ages 25-35).
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It has been suggested that transfer rates describing oral absorption into the liver have a
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high degree of uncertainty (Bois, 2000; Chiu et al., 2014), but the sensitive parameter
13
here describes transfer from the stomach to the intestine, which should be
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approximated by gastric emptying, so physiological estimates for gastric emptying in
15
adults (Ziessman et al., 2004) and children (Singh et al., 2006) were used to determine
16
variation (Table 1).
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Physiological parameters that were not varied include hepatic partitioning and plasma
18
protein binding. Any physiological input that alters hepatic partitioning would alter
19
solubility in other tissues as well as blood. Partitioning of unbound chemicals such as
20
CPF or CPF-oxon is primarily dictated by lipid fractions in the plasma and tissues.
21
Blood:tissue partitioning of CPF and CPF-oxon were based on published QSAR
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techniques. These techniques were used to calculate values from levels of water and
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neutral/phospholipids in blood or plasma and the relevant tissue. Prior researchers have 12
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shown that blood lipid levels are highly correlated with liver lipids (Carpentier et al,
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2008). Plasma binding was conservatively estimated from an in vitro study (Lowe et al.,
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2009). When incubated with 20 mg/ml protein, CPF was found to be >99% bound. Since
4
the reference range for serum albumin is 34-50 mg/mL, binding is unlikely to ever be
5
less than 99%. While measuring CPF-oxon binding is not possible due to rapid
6
hydrolysis by plasma albumin (Lowe et al., 2009; Sogorb et al., 2008; Sultatos et al.,
7
1984), based on the structural similarity between CPF and CPF-oxon, CPF-oxon is also
8
likely to be >99% bound.
9
Hepatic carboxyl esterase is responsible for removal of CPF-oxon that is produced in
10
the liver. In the life-stage model, basal activity of hepatic carboxyl esterase was
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calculated from Pope et al. (2005) and cytochrome P450 intestinal metabolic rates were
12
extrapolated from rats (Poet et al., 2003); therefore no measure of human variation was
13
available. In their study, Obach et al. (2001) assessed testosterone metabolism in whole
14
human small intestine, and their measured variability was used in the Monte Carlo
15
description. The variability in human testosterone metabolism was consistent with the
16
variability of CPF metabolism measured in human hepatic microsomes (Smith, et al.,
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2011: Table 1).
18
Coefficients of variation for metabolic rates for hepatic and plasma PON1 and hepatic
19
CYP450 metabolism of the parent CPF were calculated from measured in vitro rates
20
(Smith et al., 2011). In the liver, CPF is bioactivated via CYP450s to the active CPF-
21
oxon. PON1 in the liver and plasma then mediate the detoxification of CPF-oxon to
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TCPy. CYP450s in the liver also directly detoxify CPF to TCPy. There were no age-
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related changes in metabolism on a per mg protein basis in vitro, and data from all 30
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samples were used to define the mean and CV for these inputs. Thus, variability was
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maximized by including all in vitro measurements. Metabolic rates were scaled by tissue
3
size, so total metabolism was reduced in infants compared to adults.
4
Genetic variation in the PON1 gene results in polymorphisms that alter the catalytic
5
efficiency toward organophosphorus compounds (Albers et al., 2010; Costa et al.,
6
2013). However, differences in the metabolism of CPF-oxon mediated by the three
7
phenotypes are modest or non-existent at relevant environmental contaminant levels
8
(Garabrant et al., 2009, Smith et al., 2011,Coombes et al., 2014). The lack of phenotype
9
impact on chlorpyrifos-oxonase activity is also suggested by the relatively narrow range
10
of activity toward this substrate compared to paraoxon reported by Huen et al., (2012)
11
(34-fold and 165-fold for CPF-oxon and paraoxon, respectively). Due to this lack of
12
differentiation, a single log normal distribution was used to describe PON1 activity.
13
The correlation between activities of the critical enzymes in liver (PON1 and CYP450)
14
were empirically investigated since data on activities of these three enzyme mediated
15
processes (CYP450 activation, CYP450 deactivation, and PON1 deactivation) were
16
measured in the same liver tissue samples (Smith et al., 2011). The data show that low
17
to moderate correlations occurred between the rates of metabolism of CPF to CPF-oxon
18
and both the metabolism of CPF to TCPy (R2=0.6) and CPF-oxon to TCPy (R2 = 0.3). In
19
the Monte Carlo PBPK/PD model neither correlation was included. Bukowski et al.
20
(1995) reported that low to moderate R2 (less than 0.5) have little impact on models.
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Correlating rates of conversion of CPF to CPF-oxon with conversion rates to TCPy will
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result in a reduction in the prediction of inter-individual variation in response (i.e.,
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increases in activation of CPF would occur in individuals with increased levels of
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detoxification). Therefore to be conservative, the inputs were treated as independent.
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PON1 is synthesized and secreted in the liver (Fuhrman, 2012). This suggests that
4
individuals with low levels of PON1 activity in the liver should also have low levels in
5
their plasma resulting in less detoxification of CPF-oxon in both compartments. The
6
impact of the correlation would tend to be an increased variation in RBC
7
acetylcholinesterase inhibition since individuals deficient in PON1 in the liver would also
8
be deficient in PON1 in the plasma. While we are unaware of any empirical data on the
9
correlation of liver and plasma PON1, the model includes the assumption that the
10
variations in plasma and liver PON1 are completely correlated. The inter-individual
11
variation reported by Smith et al. (2011) for liver PON1 is larger than variation reported
12
for plasma PON1 and was used to describe PON1 variation.
13
The final group of sensitive parameters described RBC acetylcholinesterase inhibition,
14
degradation, and reactivation. Since these are all proximate to the biomarker of interest
15
(RBC acetylcholinesterase inhibition), model sensitivity is to be expected. The variability
16
from studies described by Mason et al. (2000) was used to assign CV for reactivation of
17
RBC acetylcholinesterase. In this study, human erythrocytes were exposed to the
18
organophosphates and half-lives for aging and reactivation were measured.
19
Degradation was estimated based on RBC turnover rate (Chapman and McDonald,
20
1968), and the inhibition rate variance was obtained from Ditriadis and Syrmos (2011).
21
RBC acetylcholinesterase degradation and reactivation show inter-individual differences
22
and were varied according to Chapman and McDonald (1968) and Mason et al., (2000),
23
respectively. Erythrocyte acetylcholinesterase aging rate is a measure of the rate that
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the CPF-oxon acetylcholinesterase complex moves from a reversible to an irreversible
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form. This is driven by a chemical reaction between the two molecules and is only
3
dependent upon the chemical structure and characteristics of the CPF-oxon and the
4
specific acetylcholinesterase. As a result, there is no expectation of inter-individual
5
variation in this parameter.
6
In summary, 16 of the 20 sensitive parameters were varied across individuals. The
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means and CVs for the distributions describing the inter-individual variation in the 16
8
parameters were identified from the literature cited above and are presented in Table 1.
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Global Sensitivity Screening using Morris and eFast analyses.
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Because each parameter identified in the local sensitivity assessment has its own
11
unique range in variability, parameters were further evaluated using global screening.
12
These
13
acetylcholinesterase. Morris and eFast analyses were performed to quantitatively
14
determine the impact of the different sources of input variation. The Morris test, a
15
pseudo-global sensitivity approach that uses the average of a number of local sensitivity
16
analyses taken at a variety of model states (King and Perera, 2010; McNally et al.,
17
2011) was used to rank the parameters that have the highest influence on other
18
parameters over time (McNally et al., 2011). The extended Fourier Amplitude Sensitivity
19
Test (eFAST) was then used to precisely determine which of the high influence
20
parameters identified by the Morris test had a high potential to impact predictions of
21
RBC acetylcholinesterase inhibition. The parameters identified by these two global
22
assessments are noted in Table 1.
show
how
these
sensitive
parameters
jointly
affect
RBC
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tests
16
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Monte Carlo Simulations
2
The Monte Carlo PBPK/PD model was created using acslX. The 16 parameters, of the
3
top 20 that account for > 95% of total variation and can be expected to demonstrate
4
inter-individual variation (Table 1), were randomly sampled from their distributions and
5
the impact of these distributions on peak RBC acetylcholinesterase inhibition were
6
assessed. Distributions of the 16 parameters were described as either normal (tissue
7
volumes and blood flows) or lognormal (biochemical parameters such as rate constants)
8
(Nestorov, 2001; Thomas et al., 1996). All log normal distributions were truncated at the
9
lower end by 1% of the mean value. The upper ends were not truncated. With the
10
exception of values for plasma and liver PON1 rates all inputs are assumed to be
11
independent.
12
Initially, the impact on RBC acetylcholinesterase inhibition was determined after
13
simulating one to five days of exposure in adults or infants. The model was run for 1000
14
infants (age six months) and adults at 3 µg/kg/day, the cPad (chronic population
15
adjusted dose) that was recommended prior to having an available PBPK model (US
16
EPA, 2000). No effects on RBC acetylcholinesterase inhibition were seen, so the impact
17
of five daily oral doses of 300 µg/kg/day of CPF were assessed. This dose was chosen
18
since it produces slight inhibition in the mean individual and a large distribution in
19
magnitude of acetylcholinesterase inhibition in the population assessments. This 300
20
µg/kg/day dose is > 600-fold above median dietary intake levels estimated by EPA
21
(USEPA, 2014a). Finally, the model was run assuming once daily oral exposures of 300
22
µg/kg/day CPF for all other simulations. Simulation of a single-day dose was run since
23
longitudinal models of dietary exposures to pesticide residues reveal that daily
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exposures to CPF are punctuated by a single day’s dose that is significantly higher than
2
all other day’s exposures (Juberg et al., 2012); thus a single acute dose will describe
3
the worst-case scenario for any real-world exposures. All single dose analyses were
4
conducted on populations of 3000 infants or adults.
5
The Monte Carlo PBPK/PD model described thus far includes all potential inter-
6
individual parameter complexity. Progressing through the workflow as described was
7
designed to identify all possible physiological and biochemical parameters important to
8
model output. Subsets of the 16 parameters were varied in a stepwise manner to
9
determine which would have the greatest effect on the magnitude of inter-individual
10
variation in RBC acetylcholinesterase inhibition. The subsets of parameters investigated
11
were based on the three groups of parameters (biochemistry, physiology, and
12
metabolism) and on the top parameters determined in the eFast assessment. The
13
predictions of the magnitude of variation in RBC acetylcholinesterase inhibition when
14
subsets of parameters were varied were compared to predictions resulting from varying
15
all parameters. Results from these steps to reduce model complexity and known human
16
variability in PON1 activity was then evaluated to determine shifts in response (i.e. peak
17
RBC inhibition) with different input parameters.
18
Uncertainty in Distributions of Metabolic Parameters (Bootstrap)
19
The initial mean and CV for the two CYP450 mediated activities and liver PON1 were
20
obtained from in vitro metabolic data using samples from 30 individuals (Smith et al.,
21
2011). Because in vitro studies on which the distributions of inter-individual variation in
22
human metabolism were based have a finite sample size, there is an uncertainty in the
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estimate of the values of the means and CVs of these distributions. A parametric
2
bootstrap technique (Cullen and Frey, 1999) was used to characterize the impact of the
3
finite sample size on the means and CVs of metabolic parameters. Means and CV from
4
the bootstrapped datasets were then applied to the Monte Carlo model in groups of 500
5
individuals for each new matched set of metabolic parameters.
6
Deriving DDEF values using the PBPK/PD model
7
The Monte Carlo PBPK/PD model was used to derive DDEFs for acute oral exposures
8
to CPF for populations of: 1) men and women, 2) women only, 3) pregnant women in
9
the most sensitive (3rd) trimester, and 4) infants. The 3rd trimester was determined to be
10
most sensitive in the median individual pregnant woman based on the exposure dose of
11
CPF that lead to 10% RBC acetylcholinesterase inhibition. Monte Carlo simulations
12
were
13
acetylcholinesterase inhibition with this PBPK/PD model. Input parameters were
14
randomly selected as described above. The approach used follows the recent EPA and
15
WHO guidance (WHO, 2001, EPA, 2014).
16
The value of DDEFHD is defined as:
SC
address
the
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to
impact
of
inter-individual
variation
on
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used
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17
DDEFHD = 18 19 20 21
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PODH PODSH
where, PODH is the dose resulting in the response (10% RBC inhibition) in the median individual, and PODSH is the dose that causes the same response in a sensitive individual. 19
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The median value of ED10 in a population of simulated individuals was used to define
2
PODH. The 1st percentiles of ED10 in simulated populations were used as the basis of
3
the sensitive individual for the DDEFHD for each population. For the infant population,
4
infants at 6 months of age were simulated using the Monte Carlo PBPK model. This age
5
was selected due to confidence of model parameters at this age and since six month
6
old infants begin to consume solid foods.
7
The values of the median and 1st percentiles of ED10 used for the DDEF values were
8
determined using Monte Carlo simulations of the different populations. For the
9
enzymatic processes (PON1 in liver and plasma and P450 activities in liver) values
10
were taken from random selected bootstrapped distributions. The resulting model thus
11
includes both sources of variation and parameter uncertainty. Values of the medians
12
and 1st percentiles of each population were determined using the following process.
13
Each simulated individual was assigned randomly generated oral doses from a log
14
uniform distribution with a minimum and maximum value of 0.03 to 3 mg/kg. Because of
15
the log normal distribution, individuals exhibiting peak RBC activity that was 80-95% of
16
control were tabulated and used to determine the median and 1st percentiles. The
17
medians and the 1st percentiles for each of the populations were determined and
18
resulting values of the measures were used to estimate the one-sided 95 percent lower
19
and upper confidence limits of the measures.
20
The proposed approach used here is based on a Monte Carlo model of combined
21
uncertainty and variation. Appendix B shows that the uncertainty in key parameters has
22
minimal impact on the estimate of the median and 1st percentiles (about a 10%
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1
reduction). As a result, a combined Monte Carlo model of both uncertainty and variation
2
is expected to give the same result as a two-dimensional approach.
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3
RESULTS
5
Pregnancy
6
The CPF life-stage PBPK/PD (Smith et al., 2014; Poet et al. 2014) was modified to
7
include physiological and anatomical changes that occur during pregnancy, including:
8
increased respiration and cardiac output, increased blood volume (both plasma and
9
RBC), increased glomerular filtration, potential changes in metabolism, enlarged uterus,
10
breasts, and growth of the fetus. These modifications were consistent with other PBPK
11
models of pregnancy (see references in Appendix A). Overall, these model additions
12
were also shown to provide appropriate fits to literature data for physiological and
13
biochemical changes (Appendix A). As a result, DDEF values were obtained for both
14
non-pregnant and pregnant human populations, as described below.
15
Local Sensitivity
16
The parameters with sensitivity coefficients ≥ 0.3 account for 90% of all summed model
17
variability, while 20 of the approximately 160 model parameters have sensitivity
18
coefficients ≥ 0.1 and account for > 95% of the summed local sensitivity. The small
19
changes in the remaining parameters have a negligible impact on model predictions.
20
The default values used for all model parameters in all of the analyses are given in
21
Smith et al., (2014) and Poet et al., (2014).
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Screening of Multiple Parameters using eFAST and Morris Test.
2
The top nine ranked influential parameters based on the Morris test were all related to
3
metabolism of CPF and CPF-oxon (Table 1). Of the 20 most sensitive parameters only
4
RBC degradation and hematocrit were not directly related to metabolism. Hematocrit is
5
used in the model to calculate the volume of RBC and therefore both of these are
6
directly related to the amount of RBC acetylcholinesterase which was the endpoint
7
investigated. Further screening using eFAST highlighted the importance of metabolic
8
clearance of both CPF and CPF-oxon. PON1 is a high capacity enzyme with a very high
9
Vmax and therefore P450-mediated conversion of CPF to CPF-oxon in the liver will
10
facilitate overall clearance since the oxon metabolite is rapidly cleared to TCPy prior to
11
systemic circulation. Thus, metabolic clearance of CPF and CPF-oxon are dependent
12
on hepatic blood flow to deliver the chemicals to the liver. In the first 2 days of a
13
repeated exposure, the most influential parameters in the eFAST analysis were directly
14
related to hepatic extraction of CPF and CPF-oxon (CYP, PON1 and blood flow). By
15
day 5, plasma PON1 also became influential.
16
Monte Carlo Simulation of Human Variation in Response
17
Results from a controlled human exposure study where five adult volunteers received
18
an oral dose of 0.5 mg/kg CPF have been used to calibrate the life-stage model
19
(Timchalk et al., 2002; Smith et al., 2014, Nolan et al., 1984). The study-specific inter-
20
individual variation in the five volunteers varied by up to a factor of seven, with peak
21
inter-individual differences in RBC inhibition occurring six hours after the administered
22
dose. Figure 1 presents a plot of the individual responses over time and the 95 percent
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confidence limits from 500 simulated individuals using the Monte Carlo PBPK/PD
2
model.
3
The Monte Carlo model was used to simulate RBC cholinesterase inhibition that occurs
4
after five days of repeated oral CPF doses (300 ug/kg/day) or CPF-oxon doses (30
5
µg/kg/day), respectively. After the CPF dose in both infants and adults, median peak
6
RBC acetylcholinesterase inhibition was found to be ~60% of control (Figure 2). The
7
small difference in CPF-oxon clearance rate begins to impact infants (Smith et al.,
8
2014), resulting in a slightly greater predicted inhibition in infants compared to adults
9
after 2 days at these two doses. Following a dose of 30 ug/kg/day of CPF-oxon, the 14
10
relevant sensitive parameters were considered in the same groupings (the same 16
11
parameters from the CPF model, less the CPF-specific metabolic rates) were varied,
12
and maximum inhibition in the median adult or infant was less than 2% after a single
13
day (Figure 2C: for simplicity, only results in infants are shown, but modeling results for
14
adults were indistinguishable.). After five days, the 5th and 95th percentile range in
15
inhibited RBC acetylcholinesterase was 97-33% of control in infants following a CPF
16
dose. Since direct exposures to CPF-oxon bypasses CYP450-mediated bioactivation,
17
overall variation is less following exposure to the metabolite. Variation is still driven
18
primarily by metabolic clearance (i.e., PON1-mediated metabolism). In each case,
19
consistent with previous findings (Price et al., 2011, Juberg et al. 2012), after five days
20
of exposure, the variation in response was primarily explained by metabolism.
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Determining the relative contribution of variation in subsets of the sensitive parameters.
2
The eFAST analysis indicated a link between the five metabolic parameters and the
3
prediction of variation in RBC acetylcholinesterase inhibition. Correlations between
4
these individual metabolic input values and responses were determined. Whilie
5
distributions
6
acetylcholinesterase inhibition is bounded at the top by 0% (100% of control RBC
7
acetylcholinesterase activity), with the median individual only showing ~5% inhibition,
8
resulting in a skewing of the curves (Fig 3), which has been demonstrated previously
9
(Hinderliter et al., 2011). The R2 over the entire response is therefore low, but the
10
Pearson’s coefficient (p) shows a strong linear correlation for each of these input
11
distributions. As expected, CPF bioactivation (CYP450-mediated bioactivation of CPF to
12
the CPF-oxon and hepatic blood flow) are positively correlated with the percent of
13
inhibition and detoxification (P450-mediated metabolism of CPF to TCPy and PON1-
14
mediated detoxification of the oxon) have negative slopes (Figure 3).
15
To further investigate the importance of subsets of parameters in concert, a series of
16
Monte Carlo assessments were run where subsets of the inputs were varied (and the
17
remaining parameters were held at their mean values). Predictions of human RBC
18
acetylcholinesterase inhibition, when subsets of parameters were included, were
19
compared to the prediction resulting from varying all parameters (Figure 4). Following a
20
3000 µg/kg CPF dose, the variation in RBC acetylcholinesterase inhibition when all 16
21
sensitive parameters were distributed is closely approximated when only parameters
22
describing metabolic clearance are varied (hepatic blood flow, CYP450 bioactivation to
23
CPF-oxon, CYP40 detoxification to TCPy and PON1 detoxification of the CPF-oxon in
the
metabolic
input
parameters
were
lognormal,
RBC
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liver and blood). CYP450 activity, when varied on its own, has only a small impact on
2
total model variation, but when PON1 is added, the impact is more than additive (Figure
3
4A).
4
The primary driver of variation for CPF-oxon pharmacokinetics, following oral intake, is
5
PON1 detoxification. Hepatic blood flow is also shown for comparison, and has little
6
impact on the magnitude of the variation in response on its own (Figure 4B). However,
7
varying hepatic blood flow and PON1 in concert results in a greater magnitude of
8
variation than when all 14 parameters are included (the same 16 parameters from the
9
CPF model, less the 2 CPF-specific metabolic rates). This is likely because of
10
magnifying or diminishing hepatic clearance of the oxon without other parameters that
11
may have counter-active effects, such as faster oral absorption or decreasing inhibition
12
rate.
13
To further determine the impact of using metabolic clearance as a surrogate for varying
14
all parameters identified in Table 1, the three related groups of sensitive parameters
15
(physiological
16
bioactivation/detoxification,
17
acetylcholinesterase activity) were assessed (Figure 5). Varying only those parameters
18
associated with RBC acetylcholinesterase biochemistry or whole body physiology
19
resulted in very little variation in RBC acetylcholinesterase peak inhibition, despite the
20
high relative variation of the input parameters. The largest total variation CV for any one
21
group of input parameters was associated with metabolic processes, and that is
22
reflected in the CV when they are varied together (Figure 5A). The total CV for the
23
lumped metabolic parameters is greater than all variation. When all 16 parameters are
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governing
EP
parameters
biochemical
metabolic
parameters
rates related
governing to
RBC
AC C
and
distribution,
25
ACCEPTED MANUSCRIPT
varied,
in
some
parameters
will
result
in
decreases
in
RBC
2
acetylcholinesterase while others cause increases, resulting in a lower CV for the
3
response. Biochemistry related directly to RBC acetylcholinesterase has minimal impact
4
on variation in model output.
5
While the known genetic polymorphism for PON1 has little or no effect on CPF-oxon
6
metabolism at environmental levels (exposures well below the ED10), it is acknowledged
7
that inter-individual differences in PON1 metabolism can be a significant driver in
8
variation in response following theoretical high dose exposures (Furlong et al., 2010;
9
Huen et al., 2012), and the evidence presented thus far indicate that PON1-mediated
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10
clearance
11
acetylcholinesterase inhibition after exposure to either CPF or CPF-oxon at sufficiently
12
high doses. When all sensitive parameters are varied, the population distribution
13
indicates most individuals will exhibit 0-3% inhibition in RBC acetylcholinesterase after
14
exposure to 300 µg/kg CPF, but when only PON1 is varied, this distribution shifts
15
toward greater inhibition, with most individuals exhibiting about 2-5% inhibition (Figure
16
5B). The estimated arithmetic mean occupational exposures (combined dermal and
17
inhalation routes) is 350 µg/kg/day (US EPA, 2016), but these routes by-pass first pass
18
metabolism and will result in even less cholinesterase inhibition than these oral
19
situations.
20
The range of values in parameters related to CPF activation and CPF and CPF-oxon
21
metabolic clearance used in this analysis are significantly wider than the values in Smith
22
et al. 2011 (60-100 fold vs. 6-30 fold, respectively) (Table 1). This occurs because the
23
parameters were modeled using log normal distributions that are fitted to the in vitro
CPF-oxon
is
an
important
determinant
of
variation
in
RBC
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of
26
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measurements. Thus the distributions included values well outside the range of the 30
2
measurements in the original study. With larger donor-pool evaluations of liver CYP
3
activity (up to 200 donors), the activity values generally vary less than 50-fold (Crespi
4
2009, Parkinson 2004, Zhang 2015), which is consistent with the variation used in this
5
modeling; thus the use of such a large CV results in a representative and conservative
6
calculation for DDEF factors (below). The range used to describe plasma PON1 values
7
was also increased by the assumption that inter-individual variation in the plasma levels
8
were correlated to the variation in liver PON1 levels, which showed considerably more
9
variation than did the plasma samples. The CV used to describe variation in PON1
10
(0.57) herin was slightly greater than that used for a similar analysis for parathion
11
(Gentry et al., 2002).
12
The distributions of response in peak RBC acetylcholinesterase inhibition for
13
populations of 3000 individual infants and adults receiving an oral dose of 300 µg/kg of
14
CPF are presented in Figure 6. Figure 6B presents the results of 20 populations of
15
infants, which were produced with 20 sets of values for means and CVs for the
16
metabolic parameters produced using a bootstrap of the Smith et al. (2012) data. The
17
range of predicted RBC inhibition for the 95th percentiles of the populations (the most
18
sensitive individuals) was ~58% of baseline in the model and ranged from ~55 to ~85%
19
in the 20 iterations.
20
In summary, metabolism (bioactivation and detoxification) alone and in combination had
21
a greater impact on RBC acetylcholinesterase inhibition than physiology and non-
22
metabolic biochemistry. Because of the importance of these parameters, a bootstrap
23
technique was used on the in vitro data from Smith et al. (2011), This bootstrap resulted
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in the inclusion of metabolic values that were 3.5 to 10 times wider than the ranges in
2
the raw data (Table 2), and nearly 2x greater than found in a large cohort of over 200
3
people (Huen et al., 2012).
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Data Derived Extrapolation Factors
6
The understanding of inter-individual variation in physiology and biochemistry described
7
above was used to derive an intraspecies DDEF for CPF and CPF-oxon exposures. All
8
sensitive parameters were varied (Table 1), and the bootstrap of metabolic variation
9
was included in the 2 dimensional Monte Carlo analysis. For each population (men and
10
women, women only, pregnant women, and infants) 20,000 individuals were simulated.
11
RBC acetylcholinesterase inhibition shows a typical S-shaped dose-response after
12
exposure to a range of doses of CPF or CPF-oxon; the predictions from the Monte
13
Carlo simulations appear as a cloud reflecting the variation in response across
14
individuals (Figure 7). The horizontal width of the clouds gives the range of doses
15
associated with a given level of response. A visual inspection of the width of the dose
16
response clouds indicates that the doses which cause a 10% reduction in background
17
values range from 80 µg/kg in the most sensitive individuals to 2,400 µg/kg for CPF and
18
from 30 to 900 µg/kg for CPF-oxon.
19
The 1st and median percentiles of simulated individuals exhibiting peak RBC activity 80-
20
95% of control were used to determine the DDEF (Figure 7: Table 3). Inter-individual
21
sensitivity was assessed by comparing the median individual to the most sensitive 1st
22
percentile. All human populations were compared to the median ED10 for the population
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of combined adult men and women. Following CPF exposures, the median ED10 in
2
these simulated populations varied from 0.39 to 0.52 mg/kg, the lowest median
3
exposure dose was observed in pregnant women, and the least sensitive group (highest
4
ED10) were infants (Table 3). The DDEF, however, is lowest in pregnant women, and
5
highest in infants. This is due to the larger difference in the 1st percentile in infants
6
compared to pregnant women. Figure 8 shows a comparison of pregnant and non-
7
pregnant women following exposures to CPF.
8
For all populations, DDEFHD were less than 4 for CPF and less than 2.5 for CPF-oxon
9
(Tables 3 and 4). Figure 9 summarizes the percentile distributions from the data shown
10
in Figures 7 and 8. DDEF values are consistently 2.9-3.6 for CPF and 1.8-2.1 for CPF-
11
oxon (Tables 4 and 5).
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DISCUSSION
2
A quantitative understanding of biological and biochemical variability is critically
3
important to understand potential risk to sensitive individuals following CPF exposures.
4
The ability to describe pharmacokinetic and pharmacodynamic processes provides
5
invaluable insight into internal dose to address uncertainty in extrapolation from typical
6
to sensitive humans (intra-species uncertainty) as well as from animals to typical
7
humans (inter-species uncertainty). Inter-individual variability in biological and
8
biochemical processes were assessed using Monte Carlo methodology where a large
9
number of model runs were conducted with parameter values randomly selected from
10
distributions that reflected interindividual variation or a combination of interindividual
11
variation and uncertainty (enzyme activities).
12
The biological and biochemical basis of these PBPK models make them well suited to
13
assess, analyze, and predict population pharmacokinetics and response (Bois et al.,
14
2010; EPA, 2014). Physiological and biochemical changes associated with life-stages
15
and pregnancy have been well-characterized and validated within this model (see
16
Appendix A for pregnancy physiological and biochemical changes and Smith et al.,
17
2014 for life-stage related changes).
18
Monte Carlo PBPK/PD model represents the state of the art methodology to predict
19
biological response following exposures across species and in sensitive populations.
20
Model
21
pharmacodynamic processes), the confidence in model parameterization/calibration,
22
and both model uncertainty and population variability can be characterized using local
23
and global sensitivity techniques and Monte Carlo analyses (Barton et al., 2007; Meek 30
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structure
(including
mathematical
representation
of
physiological
and
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et al., 2013). The extensive development of an a priori CPF PBPK/PD model, which
2
includes human parameterization and optimization against human data, was leveraged
3
to create a model capable of predicting true population responses.
4
Using these Monte Carlo techniques permits the assessment of variability in human
5
physiology and biochemistry concomitantly, rather than in isolation. For example,
6
hepatic volume and blood flow were varied across populations at the same time that
7
metabolic rates were varied on a per tissue basis, resulting in a higher potential
8
magnitude of hepatic metabolic differences between subjects.
9
CPF is extensively bioactivated to CPF-oxon, primarily in the liver, followed by
10
substantial hydrolysis of CPF-oxon in the liver and blood, resulting in only low levels of
11
CPF and trace levels of CPF-oxon becoming systemically bioavailable. Thus the inputs
12
that have the most influence on inter-species or intra-species variation are factors
13
relating to absorption in the gut, binding to acetylcholinesterase, and metabolic
14
bioactivation and clearance. Parameters related to metabolic clearance of both CPF
15
and CPF-oxon had a large impact on model predictions. These are the same
16
parameters that, according to the eFAST results, have a joint impact when evaluated
17
together. Thus, multiple lines of investigation suggest that metabolic processes are
18
important determinants of RBC acetylcholinesterase inhibition in the human population.
19
These processes are also some of the most heterogeneous within the population.
20
Because of the importance of metabolism, a careful assessment of the uncertainty in
21
our understanding of the inter-individual variation for these parameters, including the
22
impact of potential correlations between the parameters and if such correlations could
23
result in increases in the range of sensitivity in adults and infants, was included. This led
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to the adoption of the conservative assumption that plasma and liver PON1 are
2
correlated.
3
The known genetic polymorphism for PON1 has little or no effect on CPF-oxon
4
metabolism at environmental levels (Garabrant et al., 2009, Smith et al., 2011,Coombes
5
et al., 2014). The PON1 (chlorpyrifos-oxonase) activity in cord blood reported by Huen
6
et al. (2012) ranged 34-fold (n=236), with lesser variation in adult women (8.3-fold;
7
n=219). Other researchers have estimated CYP450 variations in activity for CPF
8
metabolism of 16-fold (Foxenberg, et al., 2007). Importantly, the endogenous functions
9
of PON1 as an antioxidant (Cole et al., 2005; Ferré et al., 2001; Fuhrman, 2012)
10
suggest that PON1 activity is necessary, so the range bounded here at 1% is clearly
11
conservative. Further, the in vitro studies from which the hepatic parameters were
12
obtained included 8 individuals below the age of one year (Smith et al., 2011).
13
PON1 is a high capacity enzyme, and polymorphisms or decreases in PON1 activity are
14
generally described as reducing its efficiency (Bois et al., 2010; Jansen et al., 2009).
15
Consistent with this, distributing PON1 activity in the Monte Carlo analysis resulted in a
16
shifted frequency of predicted RBC inhibition towards an increase for CPF exposures.
17
This is partially due to bounding at 100% activity, which cannot be increased. The shift
18
is also due to the importance of clearance. Because of the high Km and Vmax for these
19
enzymes toward oxon metabolism, the clearance behavior is essentially first-order, and
20
oxon is generally cleared as fast as it is produced from the slower CYP450-mediated
21
metabolism of CPF.
22
The ED10 for all populations was between 0.39 and 0.52 mg/kg. The median pregnant
23
female represented the most sensitive population, with an ED10 of 0.39 mg/kg, ~20% 32
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lower than male and female adults. The most sensitive, 1st percentile pregnant woman
2
was predicted to be no more sensitive than any other human population, however
3
(Table 3, Figure 9).
4
The POD in human populations are similar to the POD in rats. The rat POD obtained
5
from the BMDL10 values for 10% RBC inhibition in neonatal rats, the most sensitive
6
animal studies, is 0.36 mg/kg (Reiss et al., 2012). The DDEFAD is a comparison of the
7
median dose leading to the ED10 in the most sensitive animal species to the dose
8
leading to an ED10 in the human populations. For the final DDEF, this is unneeded since
9
a POD was obtained directly in humans. However, comparing the ED10 POD in humans
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to rats, to the ED10 for the human populations, DDEFAD is less than 1.
11
The U.S. EPA has traditionally suggested the use of default values of 10 for both the
12
intra- and inter-species uncertainty factors. The use of values of 10 for these factors has
13
a long history, dating back more than 60 years (Lehman and Fitzhugh, 1953). The use
14
of a value of 10 for both factors is recognized as a conservative assumption that may be
15
justified for a small fraction of chemicals, but is not required for all compounds (Baird et
16
al., 1996; Gaylor and Kodell, 2002; WHO IPCS, 2014). The PBPK/PD modeling results
17
indicate that the combined DDEF values for both CPF and oxon are ≤ 4 (versus 10 for
18
the default uncertainty factor) which suggest that there are only minimal differences in
19
response between animals and humans and across humans for both compounds. In all
20
cases the median values of ED10 are similar indicating that age, gender, and pregnancy
21
status had minimal effect on sensitivity to chlorpyrifos and that age had minimal effects
22
on CPF-oxon. In addition, the use of 10% RBC acetylcholinesterase inhibition as the
23
POD for a risk assessment for CPF, which has been suggested by US EPA (2014a),
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1
has a built-in safety factor since the 10% RBC acetylcholinesterase inhibition level is
2
protective
3
acetylcholinesterase in brain and other tissues.
4
This state of the art PBPK/PD Monte Carlo model quantitatively describes the internal
5
metrics following exposures to CPF in the human population. It demonstrates the use of
6
such a model to fully describe the effect of parameter distribution on an internal dose
7
metric (RBC acetylcholinesterase inhibition). This model can be used to inform
8
decisions for exposure safety assessment in sensitive individuals.
possible
apical
effects
associated
with
the
inhibition
of
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Acknowledgements
2
Funding for this research was provided by Dow AgroSciences.
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Figures
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Figure 1. Data from Nolan et al., 1994. Five Subjects (A-E) were given 0.5 mg/kg CPF
3
orally. A) RBC cholinesterase inhibition. B) Plasma cholinesterase inhibition. Solid lines
4
are the median and dotted lines show upper and lower 95 percent confidence limits from
5
500 runs of the model while varying the 16 sensitive parameters.
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Figure 2. Population variation in RBC ChE activity. A) After five daily doses of 300
7
µg/kg/day of chlorpyrifos (CPF) in adults. B) After five daily doses of 300 µg/kg/day of
8
CPF in infants, C) After five daily doses of 30 µg/kg/day of CPF-oxon in infants (adults
9
not shown). Solid lines are median, lighter lines show upper and lower 95th percentile of
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1500 runs with all 16 parameters distributed, dotted lines show the upper and lower
11
95th percentile when only metabolism is varied.
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Figure 3. Correlation between parameters identified in the eFAST analysis and peak
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RBC inhibition after 1 day of dosing with 300 µg/kg/day CPF in 6 month old infants. A
14
Pearson’s T test indicates a significant correlation (P< 0.0001) for each of these
15
parameters and RBC ChE inhibition, but R2 values indicate that the magnitude of the
16
correlations between inhibition and the parameters are not strong. For the parameters
17
distributed using a lognormal function (A-D), the Pearson’s T test was conducted by
18
comparing the parameter to ln (RBC inhibition).
19
Figure 4. Variation in RBC AChE activity in adult men and women after A) a single 300
20
µg/kg oral dose of CPF, or B) a single 100 µg/kg oral dose of CPF- oxon (the dose was
21
increased from 30 µg/kg for this analysis to elicit a larger response). Lighter solid line
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shows the median when all 16 (CPF) or 14 (oxon) parameters are varied, the dotted
2
lines show the 5th percentile when different subgroups of parameters are varied.
3
Figure 5. A) Comparison of variability in model output and model input for different
4
parameter distributions in a population of adult men and women. Lines show the total
5
coefficient of variation normalized to the mean of the distribution for the output
6
parameter (RBC cholinesterase) compared to the total CV for the input parameters for
7
3000 MC simulations (line). Physiology: body weight (tissue size), tissue blood flows,
8
hematocrit, oral absorption rate; Biochemistry: total cholinesterase amount, and
9
degradation, inhibition and reactivation rates. Metabolism, CYP450 and PON1 activities
10
in intestine, liver, and plasma (See Table 1). B) RBC cholinesterase inhibition
11
distribution for potential sensitive adult humans, showing the number of individuals
12
exhibiting peak inhibition from 0-15%. There is a shift to the right (predicting more
13
inhibition) when only PON1 activity is varied. Note- only up to 15% percent inhibition
14
shown for clarity.
15
Figure 6. A) Population variation in the maximum RBC Cholinesterase inhibition in 3000
16
adults or infants resulting from a 300 µg/kg/day oral dose of chlorpyrifos (CPF) when all
17
16 sensitive parameters are varied . B) Population variation in the maximum RBC
18
cholinesterase inhibition in infants resulting from a 300 µg/kg/day oral dose of CPF
19
following the bootstrap analysis of four key metabolic parameters. The bootstrap was
20
run 20 times.
21
Figure 7. Peak RBC cholinesterase inhibition, expressed as percent of background, A)
22
in a population of 20,000 simulated adult men and women exposed to single oral bolus
23
doses of chlorpyrifos (CPF) (black x) or CPF-oxon (gray +) ranging from 3 to 3,000 47
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µg/kg, or B) in a population of 20,000 simulated infants exposed to single oral bolus
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doses of CPF (black x) or CPF-oxon (gray +) ranging from 3 to 3,000 µg/kg (log scale).
3
Note, only the first 10,000 simulations for each population are shown for clarity.
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Figure 8. Peak RBC cholinesterase inhibition, expressed as percent of background, in a
6
population of twenty bootstraps of 1000 runs each (20,000 total individuals: the results
7
of the first 10,000 simulations for clarity) of adult non-pregnant women (black x) or
8
pregnant women in their 3rd trimester (gray +) exposed to single oral bolus doses of
9
chlorpyrifos (CPF) from 3 to 3,000 µg/kg (log scale). The lines show the 50th percentile
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Figure 9. Percentile distribution of individuals showing peak RBC inhibition of 10% (90%
12
activity). These are the results from twenty bootstraps of 1000 runs each (20,000 total
13
individuals) for each population. See Figures 7 and 8.
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Total Blood Volume
1.18
Plasma PON1
1.18
Hepatic Blood Flow (L/hr×kg tissue)
1.18
50
RBC ChE Inhibition Rate (l/µmol×hr)
1.15
100
Hepatic PON1 (µmol/hr×kg tissue) Unbound fraction of oxon (%) Hematocrit (%)
Hepatic Carboxyl Basal Activity Rate (l/hr/kg tissue) Hepatic Carboxyl Reactivation Rate (l/hr)
0.03ǂ 0.53* 0.27 0.17
0.57*
0.01
NA¥
0.45
0.068
0.01
0.14
690
0.59
0.62
1.5e3
0.53
0.49
0.014
0.36
0.48
82
0.52†
0.43
53
0.52†
0.41
0.31
0.26‡
0.28
0.0113
NA¥
0.27
1.17e7
NA¥
0.26
AgeDependent∞
0.03ǂ
0.25
1.27e6
0.36
0.10
0.014
0.36
0.98
0.63 0.62
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Volume of the Liver
NA¥
1.5 e5
1.02
0.45
RBC ChE Degradation Rate (l/hr) Hepatic P450 Bioactivation to Oxon (µmol/hr×kg tissue) Hepatic P450 Detoxification to TCPy (µmol/hr×kg tissue) RBC ChE Reactivation Rate (l/hr) Intestinal CYP Bioactivation to Oxon (µmol/hr×kg tissue) Intestinal CYP Detoxification to TCPy (µmol/hr×kg tissue) Transfer Rate to Intestine (hr-1) RBC ChE Aging Rate (l/hr) Total Carboxyl Enzyme (hr/active site)
12.8 AgeDependent∞ AgeDependent∞
P3M; Price et al., 2003 Smith et al, 2011 Materne et al, 2000 Dimitriadis and Syrmos, 2011 Smith et al, 2011
SC
1.25
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Table 1. Parameters showing a sensitivity coefficient greater than 0.1 in the local sensitivity analysis after a 1 mg/kg/day dose Morris eFast Variability Sensitivity Mean Top 9 Top Parameter CV Reference value Coefficient• 6§
P3M; Price et al., 2003 Chapman et al., 1968 Smith et al, 2011 Smith et al, 2011 Mason et al 2000 Obach et al., 2001 Obach et al., 2001 Singh, et al. 2006
P3M; Price et al., 2003 Pope et al., 2005 Mason et al 2000
3
1
2
3
1
2
9 4
4
5
5
6
6
8 7
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Parameters are listed in descending order of sensitivity. The sensitivity of most parameters was greater in infants than adults. For clarity only infants are shown. These 20 parameters accounted for >95% of all model sensitivity. CV – Coefficient of Variation. • Local Sensitivity Analysis following a repeated 3 µg/kg/day dose in both infants and adults resulted in no parameters more sensitive than 0.001.This higher dose was chosen to approximate the ED10, and be high enough to result in sensitivity of the maximum number of parameters. ∞ Age-specific parameters are calculated using a polynomial equation, see Smith et al., 2014. ¥ Not Applicable. See text for justification. ǂ The sensitivity of blood and liver volume were assessed based on the first term in an agedependent calculation formula that predicts total volume normalized to body weight for an individual of a specific age. These parameters are likely more sensitive than the final parameter since small changes in the first term will be carried through the equation. Despite this, to be conservative, the total blood and liver volume were varied. The variability shown here is for the parameter prior to multiplying by body weight. *Plasma and hepatic PON1 were linked in the final analyses using the hepatic CV, see text. † Based on intestinal metabolism of testosterone.
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Table 2. Ratios of the maximum value to minimum value in the raw data, model output and bootstrap model simulations for the critical enzyme activities. CYP450 to CYP450 to Oxon Hepatic PON1 Plasma PON1 TCPy Range in in vitro data 12 28 11 6 (Smith et al., 2011) Range in parametric 33* 26 34 33* distribution Range in 20 74 98 58* 58* parametric bootstraps * PON1 in liver and plasma were assumed to be correlated and thus have the same variation.
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Table 3. Data use in deriving the values of the Data Derived Extrapolation Factors for intra-species extrapolation (DDEFHD):
ED10 (mg/kg) DDEFHD
0.14
1st percentile
0.52
0.13
3.4
Non-Pregnant Female Median (50th 1st percentile) percentile 0.46
3.6
0.14
Pregnant Female§ Median (50th 1st percentile) percentile 0.39
3.4
Pregnant cohort is 3rd trimester, based on most sensitive group.
SC
§
0.47
Infants Median (50th percentile)
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Adult Male and Female Median (50th 1st percentile) percentile
Table 4. Data use in deriving the values of the Data Derived Extrapolation Factors for intra-species extrapolation (DDEFHD) for CPF-oxon: CPF-oxon (infants) Median (50th percentile 0.089
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CPF-oxon (male and female) Median (50th percentile First percentile 0.061 0.033 1.8
First percentile 0.029 2.1
0.16 2.9
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Plasma ChE (Percent of Control)
B
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A
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Figure 2.
Metabolic Activity (µmol/hr)
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P450
10000
oxon
R2=0.10 p<0.0001
1000 100
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10 1 0
20
40
60
80
100
TCPy R2=0.31 p<0.0001
100000
10000
1000 0
20
40
60
80
100
20 10 0
0
20
40
60
RBC ChE (percent inhibited)
Figure 3.
TCPy
R2=0.30 p<0.0001
100000
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Plasma PON1
10000
1000
0
20
40
60
RBC ChE (percent inhibited)
R2=0.14 p<0.0001
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Hepatic Blood Flow (L/hr)
RBC ChE (percent inhibited)
1000000
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80
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Figure 4.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
200
0
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RBC ChE (percent inhibited)
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Figure 5.
B
400 All Parameters PON1 only
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RBC ChE (Percent of Control)
Total Coefficient of Variation (%)
Frequency
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Figure 6.
B) Infants 100
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Single Oral Dose (mg/kg)
Figure 7.
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Variability in metabolic clearance has the greatest impact on response Data Derived Extrapolation Factors should be used to replace default UF DDEF can be determined for potentially sensitive populations using MC techniques The DDEF for Chlorpyrifos is ≤4, less than half of the default uncertainty factors
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• • • •
S0273-2300(17)30035-1
DOI:
10.1016/j.yrtph.2017.02.014
Reference:
YRTPH 3774
To appear in:
Regulatory Toxicology and Pharmacology
Received Date: 9 September 2016 Revised Date:
24 January 2017
Accepted Date: 17 February 2017
Please cite this article as: Poet, T.S., Timchalk, C., Bartels, M.J., Smith, J.N., McDougal, R., Juberg, D.R., Price, P.S., Use of a probabilistic PBPK/PD model to calculate Data Derived Extrapolation Factors for chlorpyrifos, Regulatory Toxicology and Pharmacology (2017), doi: 10.1016/j.yrtph.2017.02.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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1 2
Use of a Probabilistic PBPK/PD Model to Calculate Data Derived Extrapolation Factors for Chlorpyrifos.
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Torka S. Poet§, Charles Timchalk*, Michael J. Bartels†, Jordan N. Smith*, Robin
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McDougal∆¥, Daland R. Jubergǂ, and Paul S. Price†∞
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6 §Summit Toxicology, Richland, WA, USA 99352
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*Battelle, Pacific Northwest Division, Richland, WA, USA 99354
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†Dow Chemical Company, Midland, MI, USA
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∞ Current address - US Environmental Protection Agency, RTP, NC 27711 Work done
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prior to joining EPA. Views expressed do not necessarily reflect the views of EPA or the
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United States Government
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∆
University of Ontario Institute of Technology, Oshawa, ON, Canada
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¥
The AEgis Technologies Group, Huntsville, AL, USA 35806
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ǂ Dow AgroSciences, Indianapolis, IA 46268
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Abstract
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A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model
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combined with Monte Carlo analysis of inter-individual variation was used to assess the
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effects of the insecticide, chlorpyrifos and its active metabolite, chlorpyrifos oxon in
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humans. The PBPK/PD model has previously been validated and used to describe
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physiological changes in typical individuals as they grow from birth to adulthood. This
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model was updated to include physiological and metabolic changes that occur with
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pregnancy. The model was then used to assess the impact of inter-individual variability
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in physiology and biochemistry on predictions of internal dose metrics and quantitatively
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assess the impact of major sources of parameter uncertainty and biological diversity on
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the pharmacodynamics of red blood cell acetylcholinesterase inhibition. These metrics
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were determined in potentially sensitive populations of infants, adult women, pregnant
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women, and a combined population of adult men and women. The parameters primarily
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responsible for inter-individual variation in RBC acetylcholinesterase inhibition were
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related to metabolic clearance of CPF and CPF-oxon. Data Derived Extrapolation
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Factors that address intra-species physiology and biochemistry to replace uncertainty
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factors with quantitative differences in metrics were developed in these same
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populations. The DDEFs were less than 4 for all populations. These data and modeling
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approach will be useful in ongoing and future human health risk assessments for CPF
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and could be used for other chemicals with potential human exposure.
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Introduction
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Evaluating the potential impact of chemical exposures on human health is a complex
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and important task. Physiologically based pharmacokinetic and pharmacodynamic
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(PBPK/PD) modeling is increasingly being used to quantitatively determine the
5
association
6
pharmacodynamic outcomes. PBPK/PD models have shown their value to predict a
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priori human dose metrics (US EPA, 2006); the physiological, biological and
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biochemical underpinnings of these models also make them well suited to explore inter-
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individual variation in response.
external
exposure,
internal
dose,
and
the
concomitant
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A PBPK/PD model for the organophosphorus pesticide, chlorpyrifos (O,O-diethyl O-
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3,5,6-trichloro-2-pyridinyl-phosphorothioate: (CPF), has been developed over the past
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decade (Busby-Hjerpe et al., 2010; Garabrant et al., 2009; Lowe et al., 2009; Poet et al.,
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2014; Smith et al., 2014; Timchalk and Poet, 2008; Timchalk et al., 2002). This model
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has been used to compare pharmacokinetic and pharmacodynamic responses in rats
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and humans over life-stages from infant to adult, and following multiple routes of
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exposure (Poet et al., 2014; Smith et al., 2014). The mode of action for adverse effects
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from sufficiently high exposures to CPF is well understood. CPF is activated by
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conversion to chlorpyrifos oxon (O,O-diethyl O-3,5,6-trichloro-2-pyridyl) (CPF-oxon).
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The binding of the oxon metabolite to acetylcholinesterases in the central nervous
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system leading to inhibition of the enzyme is considered the sentinel event by the US
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EPA (2014a). Binding and inhibition of red blood cell (RBC) acetylcholinesterase occurs
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at lower doses than inhibition of acetylcholinesterases in the central nervous system,
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and, therefore RBC cholinesterase inhibition is used as a conservative biomarker of
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exposure (Garabrant et al., 2009; Hinderliter et al., 2011; Timchalk and Poet, 2008).
2
Chlorpyrifos and CPF-oxon are both detoxified to 3,5,6-trichloro-2-pyridinol (TCPy).
3
The primary source of non-occupational exposures to CPF occurs from dietary residues
4
(US EPA, 2014a). Concerns have also been raised over potential drinking water
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exposure to the active oxon metabolite that can be formed during chlorination of
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drinking water containing CPF (U.S. EPA 2014a). The US EPA has calculated an acute
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population adjusted dose (aPad) for chlorpyrifos based on oral exposures predicted to
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result in 10% inhibition of red blood cell (RBC) acetylcholinesterase compared to non-
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exposed (control) individuals (US EPA, 2014a). In the past, aPADs have been
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established using default factors of 10 to address the uncertainty in extrapolation from
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animals to typical humans (interspecies uncertainty) and additional factors of 10 to
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extrapolate from typical to sensitive humans (intraspecies uncertainty). The aPad is
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then described as the dose predicted to result in a 10% reduction in RBC cholinesterase
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activity in the species of interest ÷ the total uncertainty factors, which generally total 10-
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1000. With this assessment, extrapolation from animals is no longer needed, as 10%
16
RBC inhibition is calculated directly in human populations.
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The U.S. EPA has published guidance for replacing these default uncertainty factors
18
with Data Derived Extrapolation Factors (DDEFs) and similar guidance has been
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published by the World Health Organization (US EPA, 2014b; WHO, 2005). According
20
to these documents, PBPK/PD models of inter-individual variation in response are an
21
improved method for deriving DDEFs. When the mode of action is understood and can
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be described using a PBPK/PD model, the level of response associated with a given
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dose can be compared across species and between individuals. The U.S. EPA has
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adopted this PBPK model to reduce the intraspecies uncertainty factor from 100 to 40
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(10x default safety factor, 4x for intraspecies variation) for most human populations, but
3
has maintained a factor of 100 (10x default safety factor, 10x for intraspecies variation)
4
for females between the ages of 13-49 years (US EPA, 2014a).
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The Life-stage model of CPF (Smith et al., 2014) was expanded to create a model of
6
inhibition of RBC acetylcholinesterase from oral exposures to CPF and CPF-oxon that
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reflects the impacts of inter-individual variation in physiology and metabolism. Both local
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and global sensitivity were assessed to determine which parameters impact model
9
outcomes. Mechanistic aspects of the PBPK/PD model were then used to investigate
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the impact of parameter variability and uncertainty using a Monte Carlo analysis.
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Individual variability in physiological and biochemical parameters were compared to
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both the magnitude in response variation and degree of RBC acetylcholinesterase
13
inhibition to evaluate potential susceptibility to CPF in the general population. This
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paper describes the workflow used to comprehensively evaluate potential cholinergic
15
effects following exposures to CPF or CPF-oxon in infants, adults, and in pregnant and
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non-pregnant women, and the use of the Monte Carlo methods to assess the impact of
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inter-individual variability and parameter uncertainty on model predictions.
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METHODS
2
Populations
3
Individuals at different life-stages may be more sensitive to the effects of CPF because
4
of differences in intrinsic (biological) factors. Sub-populations of specific interest for CPF
5
exposures are infants and pregnant women. Intra-species extrapolation factors
6
(DDEFHD) were determined for 4 populations: 1) a general population of adult men and
7
women, 2) adult women only, 3) pregnant women, and 4) infants at 6 months of age.
8
Pregnancy
9
Physiological and anatomical changes that occur during pregnancy include increased
10
respiration and cardiac output, increased blood volume (both plasma and RBC),
11
increased glomerular filtration, potential changes in metabolism, enlarged uterus,
12
breasts, and growth of the fetus. These important changes were added to the CPF life-
13
stage model (Smith et al., 2014; Poet et al., 2014). The changes in physiological
14
parameters to develop the pregnancy model were made based on relevance to CPF
15
and CPF-oxon disposition and pharmacodynamics. Model modifications, which grow
16
over the course of pregnancy, included: pregnancy related changes in metabolism,
17
uterine, placenta and fetal compartments; changes in volumes of the slowly perfused
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and fat compartments; and changes in blood, including increasing blood volume,
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decreasing hematocrit, and increases in lipids, triglycerides, and cholesterol.
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Pregnancy Tissue Growth
2
Certain pregnancy specific physiological structures are minimal, have limited
3
importance to pharmacokinetics, or are non-existent in a non-pregnant individual,
4
including the uterus, placenta, and fetus. However, to appropriately model CPF
5
systemic exposure in pregnant women, these compartments were added to the life-
6
stage model described by Smith et al. (2014) and Poet et al. (2014) for pregnant women
7
only. The model descriptions of growth in the fetus were taken from Gentry et al. (2003),
8
growth description for uterus were taken from the recent review of Abduljalil et al.
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(2012), and data collated from Abduljalil et al. (2012) were used to fit an equation to
10
describe growth of placenta using Graphpad Prism (La Jolla, CA). The uterus was
11
included in the rapidly perfused compartment of the model. The placenta is important
12
both to support fetal growth and to serve as the conduit between the mother and fetus.
13
Model-predicted growth compared to measured increases in these pregnancy specific
14
compartments are shown in Appendix A, Figure A1.
15
For the pregnancy model, tissues and blood flows that change and that will affect
16
biological response were identified. Fat and total fat free mass both increase by 20-25%
17
(Kopp-Hoolihan et al., 1999). Growth in the fat compartment was described using the
18
equation in Abduljalil et al. (2012) (Appendix A, Figure A2). Fat free mass increase was
19
used to estimate growth in the slow and rapid compartments. Approximately 6 kg of
20
additional mass is observed from GD0 through birth (Kopp-Hoolihan et al., 1999); this
21
additional mass was included in the slow and rapid compartments (Appendix A, Figure
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A3). The rapid compartment describes the bulk of the cardiac output, so the growth in
23
the rapid and slow compartments were described to balance total cardiac output and
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the increase in total fat free mass (Abduljalil et al., 2012; Kopp-Hoolihan et al., 1999; Lu
2
et al., 2012). Cardiac output changes are described below.
3
The end result of pregnancy specific compartmental growth and changes in general
4
physiology is an increase in body weight between 10 and 20 kg above pre-pregnancy
5
body weight (Abduljalil et al., 2012; Corley et al., 2003; Kopp-Hoolihan et al., 1999)
6
(Appendix A, Figure A4). Body weight increases in this model are primarily due to
7
increases in fetus, slow, fat, and rapid compartments and to increased blood volumes.
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No other compartmental changes were needed to fit pregnancy specific body weight
9
changes.
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Blood Compartment
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The remaining physiological changes that are important for the biological response to
12
CPF exposures involve blood volume and the cardiovascular system (Abduljalil et al.,
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2012; Corley et al., 2003; Hytten, 1985). Equations of Abduljalil et al. (2012) describing
14
increased total blood volume were used to describe blood and plasma volume increase
15
over gestation. The increase in cardiac output is a result of changes in tissue volumes
16
and blood flow. Cardiac output increases during pregnancy, and pregnancy specific
17
changes in plasma volume, tissue volumes and blood flow result in increased cardiac
18
output that matches measured rates (Appendix A, Figure A5).
19
The cardiovascular system changes meet the increasing demands to support fetal
20
development. Among those changes are differences in white and red blood cell
21
composition and numbers, blood volume changes (described above), and changes in
22
lipid and cholesterol content (Abduljalil et al., 2012; Cunningham, 2010; Lippi et al.,
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2007). In the model, hematocrit is used to determine the proportion of plasma and RBC
2
enzymes (e.g., carboxyl, PON1, etc). Inhibition of RBC acetylcholinesterase is sensitive
3
to hematocrit (Arnold et al., 2015), and hematocrit changes over pregnancy are
4
variable, but tend to decrease (Cunningham, 2010). These changes in hematocrit were
5
described within the model (Appendix A, Figure A6). The lipophilicity of CPF leads to a
6
high fat partitioning and increases in circulating lipids during pregnancy result in
7
decreases in fat/blood partitioning (Lowe et al., 2009). Partitioning of both CPF and
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CPF-oxon were described as decreasing over the course of pregnancy which results in
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increased circulating levels of both of CPF and CPF-oxon.
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Metabolism
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The balance between bioactivation and detoxification of CPF and CPF-oxon is an
12
integral determinant of RBC inhibition, and model-predicted RBC cholinesterase
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inhibition is sensitive to these parameters (Arnold et al., 2015; see below). Foxenberg et
14
al. (2007) determined specific CYP450 enzyme activity toward metabolism of CPF and
15
CPF-oxon. Reports of gestational changes in the activities of these specific enzymes
16
were compared and relative increases and decreases calculated (Appendix A, Table
17
1A). The major enzymes and their relative activities were tabulated and compared to
18
estimate percent contribution of each toward total metabolism, then the relative changes
19
in these enzyme activities were multiplied to determine the increase or decrease in
20
over-all metabolism (Appendix A, Table 2A). Abduljalil et al. (2012) indicate that P450
21
activity changes during pregnancy follow a smooth arch, so equations were fit to
22
describe similar arches resulting in a 33% increase in bioactivation and a 25% decrease
23
in detoxification over the course of pregnancy (Appendix A, Figure A7A, Table 2A).
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PON1-mediated detoxification of CPF-oxon is another major determinant of RBC
2
inhibition following exposures to CPF or CPF-oxon. Metabolism of CPF-oxon (Huen et
3
al., 2010) and of the related organophosphate, paraoxon, were measured in plasma
4
collected from pregnant women by two different research groups (Huen et al., 2010;
5
Sarandöl et al., 2010). Neither PON1-mediated metabolism of paraoxon nor CPF-oxon
6
were significantly different in pregnant and non-pregnant women in either study. While
7
no significant differences in chlorpyrifos-oxonase activity were determined, since this
8
process is so important, the slight measured decrease in mean activity was fit to PON1
9
activity in both plasma and liver and a 7% decrease in each was described by week 26
10
of pregnancy (Appendix A, Figure A7B, Table 3A). The full model code is available upon
11
request.
12
Local Sensitivity and Parameter Distributions
13
The impact of changes in parameter values on predictions of RBC acetylcholinesterase
14
was evaluated using sensitivity analysis and characterization of parameter source and
15
biologic diversity. The sensitivity analysis was used to identify the critical parameters to
16
include within the Monte Carlo analysis. The sensitivity analysis was performed using
17
acslXv3.0.2.1 (The AEgis Technologies Group, Inc, Huntsville, AL) in which the model
18
was created, for ages of 6 months (infants) and 30 years (adults). Sensitivity analysis
19
was not run in pregnant women. Parameters leading to the critical endpoint (RBC
20
acetylcholinesterase inhibition), are sometimes quantitatively changed in pregnant
21
women (as shown in Appendix A), but there were no substantial changes in any
22
parameter
23
pharmacodynamics of chlorpyrifos. No additional parameters will occur in the pregnant
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(eg
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will
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model.
For each model parameter, sensitivity was measured in terms of a sensitivity
2
coefficient, defined as the change in peak RBC acetylcholinesterase inhibition divided
3
by the change in the parameter. In this analysis a small change (1%) was made in the
4
parameter value and the impact on peak RBC acetylcholinesterase inhibition was
5
determined. A value of 1 indicates that the 1% change in parameter produces a 1%
6
change in RBC inhibition. Values greater than 1 indicate a greater than 1% change in
7
predicted output. Values close to zero indicate that acetylcholinesterase activity
8
predictions were not affected by changes in the parameter.
9
The absolute sensitivity coefficients were identified for all parameters for each age and
10
dose. Parameters accounting for 95% of the total sensitivity were identified, and
11
included in the next step of the workflow. The cutoff for continued analysis of any given
12
parameter was determined by the following criterion:
13
0.95 =
14
where SCp are the sensitivity coefficients for the parameters that were further evaluated,
15
and SCi are the sensitivity coefficients for all parameters. This determination was
16
performed separately for each of the two doses and the two ages.
17
The confidence in each of the sensitive parameters was characterized using criteria that
18
considered both variability and uncertainty (Meek et al., 2013). Variability was defined
19
as the inter-individual differences in the parameter due to inherent factors (e.g. body
20
composition or metabolic competency) while uncertainty is due to the use of
21
assumptions, extrapolations, or experimental data interpretation (Nestorov, 2001). Most
22
of the parameters in this model are based on measured human data, so uncertainty is
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far out-weighed by variability and the a priori values were investigated to determine the
2
extent of potential variability in humans. To facilitate describing the analysis of the
3
sensitive parameters, they were placed into three related groups: physiological
4
parameters governing distribution; metabolic rates governing clearance of CPF or CPF-
5
oxon; and RBC acetylcholinesterase biochemical constants.
6
The extent of physiological variability in body weight, hepatic blood flow, hepatic
7
volume, and hematocrit were determined in adult and infant populations using the
8
Physiological Parameters for PBPK Modeling software (Price et al., 2003). The mean
9
and CV (coefficient of variation) for the physiological parameters were determined by
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generating 5,000 sets of values for infants (ages 4-8 months), and adults (ages 25-35).
11
It has been suggested that transfer rates describing oral absorption into the liver have a
12
high degree of uncertainty (Bois, 2000; Chiu et al., 2014), but the sensitive parameter
13
here describes transfer from the stomach to the intestine, which should be
14
approximated by gastric emptying, so physiological estimates for gastric emptying in
15
adults (Ziessman et al., 2004) and children (Singh et al., 2006) were used to determine
16
variation (Table 1).
17
Physiological parameters that were not varied include hepatic partitioning and plasma
18
protein binding. Any physiological input that alters hepatic partitioning would alter
19
solubility in other tissues as well as blood. Partitioning of unbound chemicals such as
20
CPF or CPF-oxon is primarily dictated by lipid fractions in the plasma and tissues.
21
Blood:tissue partitioning of CPF and CPF-oxon were based on published QSAR
22
techniques. These techniques were used to calculate values from levels of water and
23
neutral/phospholipids in blood or plasma and the relevant tissue. Prior researchers have 12
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shown that blood lipid levels are highly correlated with liver lipids (Carpentier et al,
2
2008). Plasma binding was conservatively estimated from an in vitro study (Lowe et al.,
3
2009). When incubated with 20 mg/ml protein, CPF was found to be >99% bound. Since
4
the reference range for serum albumin is 34-50 mg/mL, binding is unlikely to ever be
5
less than 99%. While measuring CPF-oxon binding is not possible due to rapid
6
hydrolysis by plasma albumin (Lowe et al., 2009; Sogorb et al., 2008; Sultatos et al.,
7
1984), based on the structural similarity between CPF and CPF-oxon, CPF-oxon is also
8
likely to be >99% bound.
9
Hepatic carboxyl esterase is responsible for removal of CPF-oxon that is produced in
10
the liver. In the life-stage model, basal activity of hepatic carboxyl esterase was
11
calculated from Pope et al. (2005) and cytochrome P450 intestinal metabolic rates were
12
extrapolated from rats (Poet et al., 2003); therefore no measure of human variation was
13
available. In their study, Obach et al. (2001) assessed testosterone metabolism in whole
14
human small intestine, and their measured variability was used in the Monte Carlo
15
description. The variability in human testosterone metabolism was consistent with the
16
variability of CPF metabolism measured in human hepatic microsomes (Smith, et al.,
17
2011: Table 1).
18
Coefficients of variation for metabolic rates for hepatic and plasma PON1 and hepatic
19
CYP450 metabolism of the parent CPF were calculated from measured in vitro rates
20
(Smith et al., 2011). In the liver, CPF is bioactivated via CYP450s to the active CPF-
21
oxon. PON1 in the liver and plasma then mediate the detoxification of CPF-oxon to
22
TCPy. CYP450s in the liver also directly detoxify CPF to TCPy. There were no age-
23
related changes in metabolism on a per mg protein basis in vitro, and data from all 30
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samples were used to define the mean and CV for these inputs. Thus, variability was
2
maximized by including all in vitro measurements. Metabolic rates were scaled by tissue
3
size, so total metabolism was reduced in infants compared to adults.
4
Genetic variation in the PON1 gene results in polymorphisms that alter the catalytic
5
efficiency toward organophosphorus compounds (Albers et al., 2010; Costa et al.,
6
2013). However, differences in the metabolism of CPF-oxon mediated by the three
7
phenotypes are modest or non-existent at relevant environmental contaminant levels
8
(Garabrant et al., 2009, Smith et al., 2011,Coombes et al., 2014). The lack of phenotype
9
impact on chlorpyrifos-oxonase activity is also suggested by the relatively narrow range
10
of activity toward this substrate compared to paraoxon reported by Huen et al., (2012)
11
(34-fold and 165-fold for CPF-oxon and paraoxon, respectively). Due to this lack of
12
differentiation, a single log normal distribution was used to describe PON1 activity.
13
The correlation between activities of the critical enzymes in liver (PON1 and CYP450)
14
were empirically investigated since data on activities of these three enzyme mediated
15
processes (CYP450 activation, CYP450 deactivation, and PON1 deactivation) were
16
measured in the same liver tissue samples (Smith et al., 2011). The data show that low
17
to moderate correlations occurred between the rates of metabolism of CPF to CPF-oxon
18
and both the metabolism of CPF to TCPy (R2=0.6) and CPF-oxon to TCPy (R2 = 0.3). In
19
the Monte Carlo PBPK/PD model neither correlation was included. Bukowski et al.
20
(1995) reported that low to moderate R2 (less than 0.5) have little impact on models.
21
Correlating rates of conversion of CPF to CPF-oxon with conversion rates to TCPy will
22
result in a reduction in the prediction of inter-individual variation in response (i.e.,
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increases in activation of CPF would occur in individuals with increased levels of
2
detoxification). Therefore to be conservative, the inputs were treated as independent.
3
PON1 is synthesized and secreted in the liver (Fuhrman, 2012). This suggests that
4
individuals with low levels of PON1 activity in the liver should also have low levels in
5
their plasma resulting in less detoxification of CPF-oxon in both compartments. The
6
impact of the correlation would tend to be an increased variation in RBC
7
acetylcholinesterase inhibition since individuals deficient in PON1 in the liver would also
8
be deficient in PON1 in the plasma. While we are unaware of any empirical data on the
9
correlation of liver and plasma PON1, the model includes the assumption that the
10
variations in plasma and liver PON1 are completely correlated. The inter-individual
11
variation reported by Smith et al. (2011) for liver PON1 is larger than variation reported
12
for plasma PON1 and was used to describe PON1 variation.
13
The final group of sensitive parameters described RBC acetylcholinesterase inhibition,
14
degradation, and reactivation. Since these are all proximate to the biomarker of interest
15
(RBC acetylcholinesterase inhibition), model sensitivity is to be expected. The variability
16
from studies described by Mason et al. (2000) was used to assign CV for reactivation of
17
RBC acetylcholinesterase. In this study, human erythrocytes were exposed to the
18
organophosphates and half-lives for aging and reactivation were measured.
19
Degradation was estimated based on RBC turnover rate (Chapman and McDonald,
20
1968), and the inhibition rate variance was obtained from Ditriadis and Syrmos (2011).
21
RBC acetylcholinesterase degradation and reactivation show inter-individual differences
22
and were varied according to Chapman and McDonald (1968) and Mason et al., (2000),
23
respectively. Erythrocyte acetylcholinesterase aging rate is a measure of the rate that
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the CPF-oxon acetylcholinesterase complex moves from a reversible to an irreversible
2
form. This is driven by a chemical reaction between the two molecules and is only
3
dependent upon the chemical structure and characteristics of the CPF-oxon and the
4
specific acetylcholinesterase. As a result, there is no expectation of inter-individual
5
variation in this parameter.
6
In summary, 16 of the 20 sensitive parameters were varied across individuals. The
7
means and CVs for the distributions describing the inter-individual variation in the 16
8
parameters were identified from the literature cited above and are presented in Table 1.
9
Global Sensitivity Screening using Morris and eFast analyses.
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Because each parameter identified in the local sensitivity assessment has its own
11
unique range in variability, parameters were further evaluated using global screening.
12
These
13
acetylcholinesterase. Morris and eFast analyses were performed to quantitatively
14
determine the impact of the different sources of input variation. The Morris test, a
15
pseudo-global sensitivity approach that uses the average of a number of local sensitivity
16
analyses taken at a variety of model states (King and Perera, 2010; McNally et al.,
17
2011) was used to rank the parameters that have the highest influence on other
18
parameters over time (McNally et al., 2011). The extended Fourier Amplitude Sensitivity
19
Test (eFAST) was then used to precisely determine which of the high influence
20
parameters identified by the Morris test had a high potential to impact predictions of
21
RBC acetylcholinesterase inhibition. The parameters identified by these two global
22
assessments are noted in Table 1.
show
how
these
sensitive
parameters
jointly
affect
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Monte Carlo Simulations
2
The Monte Carlo PBPK/PD model was created using acslX. The 16 parameters, of the
3
top 20 that account for > 95% of total variation and can be expected to demonstrate
4
inter-individual variation (Table 1), were randomly sampled from their distributions and
5
the impact of these distributions on peak RBC acetylcholinesterase inhibition were
6
assessed. Distributions of the 16 parameters were described as either normal (tissue
7
volumes and blood flows) or lognormal (biochemical parameters such as rate constants)
8
(Nestorov, 2001; Thomas et al., 1996). All log normal distributions were truncated at the
9
lower end by 1% of the mean value. The upper ends were not truncated. With the
10
exception of values for plasma and liver PON1 rates all inputs are assumed to be
11
independent.
12
Initially, the impact on RBC acetylcholinesterase inhibition was determined after
13
simulating one to five days of exposure in adults or infants. The model was run for 1000
14
infants (age six months) and adults at 3 µg/kg/day, the cPad (chronic population
15
adjusted dose) that was recommended prior to having an available PBPK model (US
16
EPA, 2000). No effects on RBC acetylcholinesterase inhibition were seen, so the impact
17
of five daily oral doses of 300 µg/kg/day of CPF were assessed. This dose was chosen
18
since it produces slight inhibition in the mean individual and a large distribution in
19
magnitude of acetylcholinesterase inhibition in the population assessments. This 300
20
µg/kg/day dose is > 600-fold above median dietary intake levels estimated by EPA
21
(USEPA, 2014a). Finally, the model was run assuming once daily oral exposures of 300
22
µg/kg/day CPF for all other simulations. Simulation of a single-day dose was run since
23
longitudinal models of dietary exposures to pesticide residues reveal that daily
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exposures to CPF are punctuated by a single day’s dose that is significantly higher than
2
all other day’s exposures (Juberg et al., 2012); thus a single acute dose will describe
3
the worst-case scenario for any real-world exposures. All single dose analyses were
4
conducted on populations of 3000 infants or adults.
5
The Monte Carlo PBPK/PD model described thus far includes all potential inter-
6
individual parameter complexity. Progressing through the workflow as described was
7
designed to identify all possible physiological and biochemical parameters important to
8
model output. Subsets of the 16 parameters were varied in a stepwise manner to
9
determine which would have the greatest effect on the magnitude of inter-individual
10
variation in RBC acetylcholinesterase inhibition. The subsets of parameters investigated
11
were based on the three groups of parameters (biochemistry, physiology, and
12
metabolism) and on the top parameters determined in the eFast assessment. The
13
predictions of the magnitude of variation in RBC acetylcholinesterase inhibition when
14
subsets of parameters were varied were compared to predictions resulting from varying
15
all parameters. Results from these steps to reduce model complexity and known human
16
variability in PON1 activity was then evaluated to determine shifts in response (i.e. peak
17
RBC inhibition) with different input parameters.
18
Uncertainty in Distributions of Metabolic Parameters (Bootstrap)
19
The initial mean and CV for the two CYP450 mediated activities and liver PON1 were
20
obtained from in vitro metabolic data using samples from 30 individuals (Smith et al.,
21
2011). Because in vitro studies on which the distributions of inter-individual variation in
22
human metabolism were based have a finite sample size, there is an uncertainty in the
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estimate of the values of the means and CVs of these distributions. A parametric
2
bootstrap technique (Cullen and Frey, 1999) was used to characterize the impact of the
3
finite sample size on the means and CVs of metabolic parameters. Means and CV from
4
the bootstrapped datasets were then applied to the Monte Carlo model in groups of 500
5
individuals for each new matched set of metabolic parameters.
6
Deriving DDEF values using the PBPK/PD model
7
The Monte Carlo PBPK/PD model was used to derive DDEFs for acute oral exposures
8
to CPF for populations of: 1) men and women, 2) women only, 3) pregnant women in
9
the most sensitive (3rd) trimester, and 4) infants. The 3rd trimester was determined to be
10
most sensitive in the median individual pregnant woman based on the exposure dose of
11
CPF that lead to 10% RBC acetylcholinesterase inhibition. Monte Carlo simulations
12
were
13
acetylcholinesterase inhibition with this PBPK/PD model. Input parameters were
14
randomly selected as described above. The approach used follows the recent EPA and
15
WHO guidance (WHO, 2001, EPA, 2014).
16
The value of DDEFHD is defined as:
SC
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the
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The median value of ED10 in a population of simulated individuals was used to define
2
PODH. The 1st percentiles of ED10 in simulated populations were used as the basis of
3
the sensitive individual for the DDEFHD for each population. For the infant population,
4
infants at 6 months of age were simulated using the Monte Carlo PBPK model. This age
5
was selected due to confidence of model parameters at this age and since six month
6
old infants begin to consume solid foods.
7
The values of the median and 1st percentiles of ED10 used for the DDEF values were
8
determined using Monte Carlo simulations of the different populations. For the
9
enzymatic processes (PON1 in liver and plasma and P450 activities in liver) values
10
were taken from random selected bootstrapped distributions. The resulting model thus
11
includes both sources of variation and parameter uncertainty. Values of the medians
12
and 1st percentiles of each population were determined using the following process.
13
Each simulated individual was assigned randomly generated oral doses from a log
14
uniform distribution with a minimum and maximum value of 0.03 to 3 mg/kg. Because of
15
the log normal distribution, individuals exhibiting peak RBC activity that was 80-95% of
16
control were tabulated and used to determine the median and 1st percentiles. The
17
medians and the 1st percentiles for each of the populations were determined and
18
resulting values of the measures were used to estimate the one-sided 95 percent lower
19
and upper confidence limits of the measures.
20
The proposed approach used here is based on a Monte Carlo model of combined
21
uncertainty and variation. Appendix B shows that the uncertainty in key parameters has
22
minimal impact on the estimate of the median and 1st percentiles (about a 10%
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1
reduction). As a result, a combined Monte Carlo model of both uncertainty and variation
2
is expected to give the same result as a two-dimensional approach.
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RESULTS
5
Pregnancy
6
The CPF life-stage PBPK/PD (Smith et al., 2014; Poet et al. 2014) was modified to
7
include physiological and anatomical changes that occur during pregnancy, including:
8
increased respiration and cardiac output, increased blood volume (both plasma and
9
RBC), increased glomerular filtration, potential changes in metabolism, enlarged uterus,
10
breasts, and growth of the fetus. These modifications were consistent with other PBPK
11
models of pregnancy (see references in Appendix A). Overall, these model additions
12
were also shown to provide appropriate fits to literature data for physiological and
13
biochemical changes (Appendix A). As a result, DDEF values were obtained for both
14
non-pregnant and pregnant human populations, as described below.
15
Local Sensitivity
16
The parameters with sensitivity coefficients ≥ 0.3 account for 90% of all summed model
17
variability, while 20 of the approximately 160 model parameters have sensitivity
18
coefficients ≥ 0.1 and account for > 95% of the summed local sensitivity. The small
19
changes in the remaining parameters have a negligible impact on model predictions.
20
The default values used for all model parameters in all of the analyses are given in
21
Smith et al., (2014) and Poet et al., (2014).
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Screening of Multiple Parameters using eFAST and Morris Test.
2
The top nine ranked influential parameters based on the Morris test were all related to
3
metabolism of CPF and CPF-oxon (Table 1). Of the 20 most sensitive parameters only
4
RBC degradation and hematocrit were not directly related to metabolism. Hematocrit is
5
used in the model to calculate the volume of RBC and therefore both of these are
6
directly related to the amount of RBC acetylcholinesterase which was the endpoint
7
investigated. Further screening using eFAST highlighted the importance of metabolic
8
clearance of both CPF and CPF-oxon. PON1 is a high capacity enzyme with a very high
9
Vmax and therefore P450-mediated conversion of CPF to CPF-oxon in the liver will
10
facilitate overall clearance since the oxon metabolite is rapidly cleared to TCPy prior to
11
systemic circulation. Thus, metabolic clearance of CPF and CPF-oxon are dependent
12
on hepatic blood flow to deliver the chemicals to the liver. In the first 2 days of a
13
repeated exposure, the most influential parameters in the eFAST analysis were directly
14
related to hepatic extraction of CPF and CPF-oxon (CYP, PON1 and blood flow). By
15
day 5, plasma PON1 also became influential.
16
Monte Carlo Simulation of Human Variation in Response
17
Results from a controlled human exposure study where five adult volunteers received
18
an oral dose of 0.5 mg/kg CPF have been used to calibrate the life-stage model
19
(Timchalk et al., 2002; Smith et al., 2014, Nolan et al., 1984). The study-specific inter-
20
individual variation in the five volunteers varied by up to a factor of seven, with peak
21
inter-individual differences in RBC inhibition occurring six hours after the administered
22
dose. Figure 1 presents a plot of the individual responses over time and the 95 percent
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confidence limits from 500 simulated individuals using the Monte Carlo PBPK/PD
2
model.
3
The Monte Carlo model was used to simulate RBC cholinesterase inhibition that occurs
4
after five days of repeated oral CPF doses (300 ug/kg/day) or CPF-oxon doses (30
5
µg/kg/day), respectively. After the CPF dose in both infants and adults, median peak
6
RBC acetylcholinesterase inhibition was found to be ~60% of control (Figure 2). The
7
small difference in CPF-oxon clearance rate begins to impact infants (Smith et al.,
8
2014), resulting in a slightly greater predicted inhibition in infants compared to adults
9
after 2 days at these two doses. Following a dose of 30 ug/kg/day of CPF-oxon, the 14
10
relevant sensitive parameters were considered in the same groupings (the same 16
11
parameters from the CPF model, less the CPF-specific metabolic rates) were varied,
12
and maximum inhibition in the median adult or infant was less than 2% after a single
13
day (Figure 2C: for simplicity, only results in infants are shown, but modeling results for
14
adults were indistinguishable.). After five days, the 5th and 95th percentile range in
15
inhibited RBC acetylcholinesterase was 97-33% of control in infants following a CPF
16
dose. Since direct exposures to CPF-oxon bypasses CYP450-mediated bioactivation,
17
overall variation is less following exposure to the metabolite. Variation is still driven
18
primarily by metabolic clearance (i.e., PON1-mediated metabolism). In each case,
19
consistent with previous findings (Price et al., 2011, Juberg et al. 2012), after five days
20
of exposure, the variation in response was primarily explained by metabolism.
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Determining the relative contribution of variation in subsets of the sensitive parameters.
2
The eFAST analysis indicated a link between the five metabolic parameters and the
3
prediction of variation in RBC acetylcholinesterase inhibition. Correlations between
4
these individual metabolic input values and responses were determined. Whilie
5
distributions
6
acetylcholinesterase inhibition is bounded at the top by 0% (100% of control RBC
7
acetylcholinesterase activity), with the median individual only showing ~5% inhibition,
8
resulting in a skewing of the curves (Fig 3), which has been demonstrated previously
9
(Hinderliter et al., 2011). The R2 over the entire response is therefore low, but the
10
Pearson’s coefficient (p) shows a strong linear correlation for each of these input
11
distributions. As expected, CPF bioactivation (CYP450-mediated bioactivation of CPF to
12
the CPF-oxon and hepatic blood flow) are positively correlated with the percent of
13
inhibition and detoxification (P450-mediated metabolism of CPF to TCPy and PON1-
14
mediated detoxification of the oxon) have negative slopes (Figure 3).
15
To further investigate the importance of subsets of parameters in concert, a series of
16
Monte Carlo assessments were run where subsets of the inputs were varied (and the
17
remaining parameters were held at their mean values). Predictions of human RBC
18
acetylcholinesterase inhibition, when subsets of parameters were included, were
19
compared to the prediction resulting from varying all parameters (Figure 4). Following a
20
3000 µg/kg CPF dose, the variation in RBC acetylcholinesterase inhibition when all 16
21
sensitive parameters were distributed is closely approximated when only parameters
22
describing metabolic clearance are varied (hepatic blood flow, CYP450 bioactivation to
23
CPF-oxon, CYP40 detoxification to TCPy and PON1 detoxification of the CPF-oxon in
the
metabolic
input
parameters
were
lognormal,
RBC
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liver and blood). CYP450 activity, when varied on its own, has only a small impact on
2
total model variation, but when PON1 is added, the impact is more than additive (Figure
3
4A).
4
The primary driver of variation for CPF-oxon pharmacokinetics, following oral intake, is
5
PON1 detoxification. Hepatic blood flow is also shown for comparison, and has little
6
impact on the magnitude of the variation in response on its own (Figure 4B). However,
7
varying hepatic blood flow and PON1 in concert results in a greater magnitude of
8
variation than when all 14 parameters are included (the same 16 parameters from the
9
CPF model, less the 2 CPF-specific metabolic rates). This is likely because of
10
magnifying or diminishing hepatic clearance of the oxon without other parameters that
11
may have counter-active effects, such as faster oral absorption or decreasing inhibition
12
rate.
13
To further determine the impact of using metabolic clearance as a surrogate for varying
14
all parameters identified in Table 1, the three related groups of sensitive parameters
15
(physiological
16
bioactivation/detoxification,
17
acetylcholinesterase activity) were assessed (Figure 5). Varying only those parameters
18
associated with RBC acetylcholinesterase biochemistry or whole body physiology
19
resulted in very little variation in RBC acetylcholinesterase peak inhibition, despite the
20
high relative variation of the input parameters. The largest total variation CV for any one
21
group of input parameters was associated with metabolic processes, and that is
22
reflected in the CV when they are varied together (Figure 5A). The total CV for the
23
lumped metabolic parameters is greater than all variation. When all 16 parameters are
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governing
EP
parameters
biochemical
metabolic
parameters
rates related
governing to
RBC
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distribution,
25
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varied,
in
some
parameters
will
result
in
decreases
in
RBC
2
acetylcholinesterase while others cause increases, resulting in a lower CV for the
3
response. Biochemistry related directly to RBC acetylcholinesterase has minimal impact
4
on variation in model output.
5
While the known genetic polymorphism for PON1 has little or no effect on CPF-oxon
6
metabolism at environmental levels (exposures well below the ED10), it is acknowledged
7
that inter-individual differences in PON1 metabolism can be a significant driver in
8
variation in response following theoretical high dose exposures (Furlong et al., 2010;
9
Huen et al., 2012), and the evidence presented thus far indicate that PON1-mediated
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clearance
11
acetylcholinesterase inhibition after exposure to either CPF or CPF-oxon at sufficiently
12
high doses. When all sensitive parameters are varied, the population distribution
13
indicates most individuals will exhibit 0-3% inhibition in RBC acetylcholinesterase after
14
exposure to 300 µg/kg CPF, but when only PON1 is varied, this distribution shifts
15
toward greater inhibition, with most individuals exhibiting about 2-5% inhibition (Figure
16
5B). The estimated arithmetic mean occupational exposures (combined dermal and
17
inhalation routes) is 350 µg/kg/day (US EPA, 2016), but these routes by-pass first pass
18
metabolism and will result in even less cholinesterase inhibition than these oral
19
situations.
20
The range of values in parameters related to CPF activation and CPF and CPF-oxon
21
metabolic clearance used in this analysis are significantly wider than the values in Smith
22
et al. 2011 (60-100 fold vs. 6-30 fold, respectively) (Table 1). This occurs because the
23
parameters were modeled using log normal distributions that are fitted to the in vitro
CPF-oxon
is
an
important
determinant
of
variation
in
RBC
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measurements. Thus the distributions included values well outside the range of the 30
2
measurements in the original study. With larger donor-pool evaluations of liver CYP
3
activity (up to 200 donors), the activity values generally vary less than 50-fold (Crespi
4
2009, Parkinson 2004, Zhang 2015), which is consistent with the variation used in this
5
modeling; thus the use of such a large CV results in a representative and conservative
6
calculation for DDEF factors (below). The range used to describe plasma PON1 values
7
was also increased by the assumption that inter-individual variation in the plasma levels
8
were correlated to the variation in liver PON1 levels, which showed considerably more
9
variation than did the plasma samples. The CV used to describe variation in PON1
10
(0.57) herin was slightly greater than that used for a similar analysis for parathion
11
(Gentry et al., 2002).
12
The distributions of response in peak RBC acetylcholinesterase inhibition for
13
populations of 3000 individual infants and adults receiving an oral dose of 300 µg/kg of
14
CPF are presented in Figure 6. Figure 6B presents the results of 20 populations of
15
infants, which were produced with 20 sets of values for means and CVs for the
16
metabolic parameters produced using a bootstrap of the Smith et al. (2012) data. The
17
range of predicted RBC inhibition for the 95th percentiles of the populations (the most
18
sensitive individuals) was ~58% of baseline in the model and ranged from ~55 to ~85%
19
in the 20 iterations.
20
In summary, metabolism (bioactivation and detoxification) alone and in combination had
21
a greater impact on RBC acetylcholinesterase inhibition than physiology and non-
22
metabolic biochemistry. Because of the importance of these parameters, a bootstrap
23
technique was used on the in vitro data from Smith et al. (2011), This bootstrap resulted
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in the inclusion of metabolic values that were 3.5 to 10 times wider than the ranges in
2
the raw data (Table 2), and nearly 2x greater than found in a large cohort of over 200
3
people (Huen et al., 2012).
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Data Derived Extrapolation Factors
6
The understanding of inter-individual variation in physiology and biochemistry described
7
above was used to derive an intraspecies DDEF for CPF and CPF-oxon exposures. All
8
sensitive parameters were varied (Table 1), and the bootstrap of metabolic variation
9
was included in the 2 dimensional Monte Carlo analysis. For each population (men and
10
women, women only, pregnant women, and infants) 20,000 individuals were simulated.
11
RBC acetylcholinesterase inhibition shows a typical S-shaped dose-response after
12
exposure to a range of doses of CPF or CPF-oxon; the predictions from the Monte
13
Carlo simulations appear as a cloud reflecting the variation in response across
14
individuals (Figure 7). The horizontal width of the clouds gives the range of doses
15
associated with a given level of response. A visual inspection of the width of the dose
16
response clouds indicates that the doses which cause a 10% reduction in background
17
values range from 80 µg/kg in the most sensitive individuals to 2,400 µg/kg for CPF and
18
from 30 to 900 µg/kg for CPF-oxon.
19
The 1st and median percentiles of simulated individuals exhibiting peak RBC activity 80-
20
95% of control were used to determine the DDEF (Figure 7: Table 3). Inter-individual
21
sensitivity was assessed by comparing the median individual to the most sensitive 1st
22
percentile. All human populations were compared to the median ED10 for the population
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of combined adult men and women. Following CPF exposures, the median ED10 in
2
these simulated populations varied from 0.39 to 0.52 mg/kg, the lowest median
3
exposure dose was observed in pregnant women, and the least sensitive group (highest
4
ED10) were infants (Table 3). The DDEF, however, is lowest in pregnant women, and
5
highest in infants. This is due to the larger difference in the 1st percentile in infants
6
compared to pregnant women. Figure 8 shows a comparison of pregnant and non-
7
pregnant women following exposures to CPF.
8
For all populations, DDEFHD were less than 4 for CPF and less than 2.5 for CPF-oxon
9
(Tables 3 and 4). Figure 9 summarizes the percentile distributions from the data shown
10
in Figures 7 and 8. DDEF values are consistently 2.9-3.6 for CPF and 1.8-2.1 for CPF-
11
oxon (Tables 4 and 5).
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DISCUSSION
2
A quantitative understanding of biological and biochemical variability is critically
3
important to understand potential risk to sensitive individuals following CPF exposures.
4
The ability to describe pharmacokinetic and pharmacodynamic processes provides
5
invaluable insight into internal dose to address uncertainty in extrapolation from typical
6
to sensitive humans (intra-species uncertainty) as well as from animals to typical
7
humans (inter-species uncertainty). Inter-individual variability in biological and
8
biochemical processes were assessed using Monte Carlo methodology where a large
9
number of model runs were conducted with parameter values randomly selected from
10
distributions that reflected interindividual variation or a combination of interindividual
11
variation and uncertainty (enzyme activities).
12
The biological and biochemical basis of these PBPK models make them well suited to
13
assess, analyze, and predict population pharmacokinetics and response (Bois et al.,
14
2010; EPA, 2014). Physiological and biochemical changes associated with life-stages
15
and pregnancy have been well-characterized and validated within this model (see
16
Appendix A for pregnancy physiological and biochemical changes and Smith et al.,
17
2014 for life-stage related changes).
18
Monte Carlo PBPK/PD model represents the state of the art methodology to predict
19
biological response following exposures across species and in sensitive populations.
20
Model
21
pharmacodynamic processes), the confidence in model parameterization/calibration,
22
and both model uncertainty and population variability can be characterized using local
23
and global sensitivity techniques and Monte Carlo analyses (Barton et al., 2007; Meek 30
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structure
(including
mathematical
representation
of
physiological
and
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et al., 2013). The extensive development of an a priori CPF PBPK/PD model, which
2
includes human parameterization and optimization against human data, was leveraged
3
to create a model capable of predicting true population responses.
4
Using these Monte Carlo techniques permits the assessment of variability in human
5
physiology and biochemistry concomitantly, rather than in isolation. For example,
6
hepatic volume and blood flow were varied across populations at the same time that
7
metabolic rates were varied on a per tissue basis, resulting in a higher potential
8
magnitude of hepatic metabolic differences between subjects.
9
CPF is extensively bioactivated to CPF-oxon, primarily in the liver, followed by
10
substantial hydrolysis of CPF-oxon in the liver and blood, resulting in only low levels of
11
CPF and trace levels of CPF-oxon becoming systemically bioavailable. Thus the inputs
12
that have the most influence on inter-species or intra-species variation are factors
13
relating to absorption in the gut, binding to acetylcholinesterase, and metabolic
14
bioactivation and clearance. Parameters related to metabolic clearance of both CPF
15
and CPF-oxon had a large impact on model predictions. These are the same
16
parameters that, according to the eFAST results, have a joint impact when evaluated
17
together. Thus, multiple lines of investigation suggest that metabolic processes are
18
important determinants of RBC acetylcholinesterase inhibition in the human population.
19
These processes are also some of the most heterogeneous within the population.
20
Because of the importance of metabolism, a careful assessment of the uncertainty in
21
our understanding of the inter-individual variation for these parameters, including the
22
impact of potential correlations between the parameters and if such correlations could
23
result in increases in the range of sensitivity in adults and infants, was included. This led
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to the adoption of the conservative assumption that plasma and liver PON1 are
2
correlated.
3
The known genetic polymorphism for PON1 has little or no effect on CPF-oxon
4
metabolism at environmental levels (Garabrant et al., 2009, Smith et al., 2011,Coombes
5
et al., 2014). The PON1 (chlorpyrifos-oxonase) activity in cord blood reported by Huen
6
et al. (2012) ranged 34-fold (n=236), with lesser variation in adult women (8.3-fold;
7
n=219). Other researchers have estimated CYP450 variations in activity for CPF
8
metabolism of 16-fold (Foxenberg, et al., 2007). Importantly, the endogenous functions
9
of PON1 as an antioxidant (Cole et al., 2005; Ferré et al., 2001; Fuhrman, 2012)
10
suggest that PON1 activity is necessary, so the range bounded here at 1% is clearly
11
conservative. Further, the in vitro studies from which the hepatic parameters were
12
obtained included 8 individuals below the age of one year (Smith et al., 2011).
13
PON1 is a high capacity enzyme, and polymorphisms or decreases in PON1 activity are
14
generally described as reducing its efficiency (Bois et al., 2010; Jansen et al., 2009).
15
Consistent with this, distributing PON1 activity in the Monte Carlo analysis resulted in a
16
shifted frequency of predicted RBC inhibition towards an increase for CPF exposures.
17
This is partially due to bounding at 100% activity, which cannot be increased. The shift
18
is also due to the importance of clearance. Because of the high Km and Vmax for these
19
enzymes toward oxon metabolism, the clearance behavior is essentially first-order, and
20
oxon is generally cleared as fast as it is produced from the slower CYP450-mediated
21
metabolism of CPF.
22
The ED10 for all populations was between 0.39 and 0.52 mg/kg. The median pregnant
23
female represented the most sensitive population, with an ED10 of 0.39 mg/kg, ~20% 32
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lower than male and female adults. The most sensitive, 1st percentile pregnant woman
2
was predicted to be no more sensitive than any other human population, however
3
(Table 3, Figure 9).
4
The POD in human populations are similar to the POD in rats. The rat POD obtained
5
from the BMDL10 values for 10% RBC inhibition in neonatal rats, the most sensitive
6
animal studies, is 0.36 mg/kg (Reiss et al., 2012). The DDEFAD is a comparison of the
7
median dose leading to the ED10 in the most sensitive animal species to the dose
8
leading to an ED10 in the human populations. For the final DDEF, this is unneeded since
9
a POD was obtained directly in humans. However, comparing the ED10 POD in humans
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to rats, to the ED10 for the human populations, DDEFAD is less than 1.
11
The U.S. EPA has traditionally suggested the use of default values of 10 for both the
12
intra- and inter-species uncertainty factors. The use of values of 10 for these factors has
13
a long history, dating back more than 60 years (Lehman and Fitzhugh, 1953). The use
14
of a value of 10 for both factors is recognized as a conservative assumption that may be
15
justified for a small fraction of chemicals, but is not required for all compounds (Baird et
16
al., 1996; Gaylor and Kodell, 2002; WHO IPCS, 2014). The PBPK/PD modeling results
17
indicate that the combined DDEF values for both CPF and oxon are ≤ 4 (versus 10 for
18
the default uncertainty factor) which suggest that there are only minimal differences in
19
response between animals and humans and across humans for both compounds. In all
20
cases the median values of ED10 are similar indicating that age, gender, and pregnancy
21
status had minimal effect on sensitivity to chlorpyrifos and that age had minimal effects
22
on CPF-oxon. In addition, the use of 10% RBC acetylcholinesterase inhibition as the
23
POD for a risk assessment for CPF, which has been suggested by US EPA (2014a),
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1
has a built-in safety factor since the 10% RBC acetylcholinesterase inhibition level is
2
protective
3
acetylcholinesterase in brain and other tissues.
4
This state of the art PBPK/PD Monte Carlo model quantitatively describes the internal
5
metrics following exposures to CPF in the human population. It demonstrates the use of
6
such a model to fully describe the effect of parameter distribution on an internal dose
7
metric (RBC acetylcholinesterase inhibition). This model can be used to inform
8
decisions for exposure safety assessment in sensitive individuals.
possible
apical
effects
associated
with
the
inhibition
of
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1
Acknowledgements
2
Funding for this research was provided by Dow AgroSciences.
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US EPA, 2014a. Chlorpyrifos: Revised Human Health Risk Assessment for Registration
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Review.
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US EPA, 2014b. Guidance for Applying Quantitative Data to Develop Data-Derived
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Extrapolation Factors for Interspecies and Intraspecies Extrapolation. Washington, DC.
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US EPA Office of Prevention, Pesticides and Toxic Substances, Office of Pesticide
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Programs, 2000. Human Health Risk Assessment: Chlorpytifos, Phase 4.
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US EPA, 2011a. Exposure Factors Handbook 2011 Edition (Final): EPA/600/R-09/052F.
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US EPA., Office of Prevention, Pesticides and Toxic Substances, Office of Pesticide
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Programs
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Government Printing Office: Washington, DC:
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US EPA (2016), “Chlorpyrifos: Revised Human Health Risk Assessment for Registration
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Review, Appendix E”, https://www.regulations.gov/document?D=EPA-HQ-OPP-2015-
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0653-0452, Accession date: Jan 16, 2017.
19
WHO, 2005. Chemical-Specific Adjustment Factors For Interspecies Differences And
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Human Variability: Guidance Document For Use Of Data In Dose/Concentration–
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Response Assessment. Geneva, 2005.
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2006. Reregistration Eligibility Decision (RED) for Chlorpyrifos; U.S.
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WHO IPCS, 2014. Guidance Document on Evaluating and Expressing Uncertainty in
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Hazard
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www.who.int/about/licensing/copyright_form/en/index.html
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Zhang, H., Gao, N., Tian, X., Liu, T., Fang, Y., Zhou, J., Wen, Q., Xu, B., Qi, B., Gao, J.,
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Li, H., Jia, L., Qiao, H., 2015. Content and activity of human liver microsomal protein
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and prediction of individual hepatic clearance in vivo. Sci. Rep. 5, 17671.
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Ziessman, H.A., Fahey, F.H., Atkins, F.B., Tall, J., 2004. Standardization and
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quantification of radionuclide solid gastric-emptying studies. J. Nucl. Med. 45, 760–4.
Geneva,
Switzerland.
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Characterization.
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Figures
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Figure 1. Data from Nolan et al., 1994. Five Subjects (A-E) were given 0.5 mg/kg CPF
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orally. A) RBC cholinesterase inhibition. B) Plasma cholinesterase inhibition. Solid lines
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are the median and dotted lines show upper and lower 95 percent confidence limits from
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500 runs of the model while varying the 16 sensitive parameters.
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Figure 2. Population variation in RBC ChE activity. A) After five daily doses of 300
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µg/kg/day of chlorpyrifos (CPF) in adults. B) After five daily doses of 300 µg/kg/day of
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CPF in infants, C) After five daily doses of 30 µg/kg/day of CPF-oxon in infants (adults
9
not shown). Solid lines are median, lighter lines show upper and lower 95th percentile of
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1500 runs with all 16 parameters distributed, dotted lines show the upper and lower
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95th percentile when only metabolism is varied.
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Figure 3. Correlation between parameters identified in the eFAST analysis and peak
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RBC inhibition after 1 day of dosing with 300 µg/kg/day CPF in 6 month old infants. A
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Pearson’s T test indicates a significant correlation (P< 0.0001) for each of these
15
parameters and RBC ChE inhibition, but R2 values indicate that the magnitude of the
16
correlations between inhibition and the parameters are not strong. For the parameters
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distributed using a lognormal function (A-D), the Pearson’s T test was conducted by
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comparing the parameter to ln (RBC inhibition).
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Figure 4. Variation in RBC AChE activity in adult men and women after A) a single 300
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µg/kg oral dose of CPF, or B) a single 100 µg/kg oral dose of CPF- oxon (the dose was
21
increased from 30 µg/kg for this analysis to elicit a larger response). Lighter solid line
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shows the median when all 16 (CPF) or 14 (oxon) parameters are varied, the dotted
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lines show the 5th percentile when different subgroups of parameters are varied.
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Figure 5. A) Comparison of variability in model output and model input for different
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parameter distributions in a population of adult men and women. Lines show the total
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coefficient of variation normalized to the mean of the distribution for the output
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parameter (RBC cholinesterase) compared to the total CV for the input parameters for
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3000 MC simulations (line). Physiology: body weight (tissue size), tissue blood flows,
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hematocrit, oral absorption rate; Biochemistry: total cholinesterase amount, and
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degradation, inhibition and reactivation rates. Metabolism, CYP450 and PON1 activities
10
in intestine, liver, and plasma (See Table 1). B) RBC cholinesterase inhibition
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distribution for potential sensitive adult humans, showing the number of individuals
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exhibiting peak inhibition from 0-15%. There is a shift to the right (predicting more
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inhibition) when only PON1 activity is varied. Note- only up to 15% percent inhibition
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shown for clarity.
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Figure 6. A) Population variation in the maximum RBC Cholinesterase inhibition in 3000
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adults or infants resulting from a 300 µg/kg/day oral dose of chlorpyrifos (CPF) when all
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16 sensitive parameters are varied . B) Population variation in the maximum RBC
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cholinesterase inhibition in infants resulting from a 300 µg/kg/day oral dose of CPF
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following the bootstrap analysis of four key metabolic parameters. The bootstrap was
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run 20 times.
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Figure 7. Peak RBC cholinesterase inhibition, expressed as percent of background, A)
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in a population of 20,000 simulated adult men and women exposed to single oral bolus
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doses of chlorpyrifos (CPF) (black x) or CPF-oxon (gray +) ranging from 3 to 3,000 47
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µg/kg, or B) in a population of 20,000 simulated infants exposed to single oral bolus
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doses of CPF (black x) or CPF-oxon (gray +) ranging from 3 to 3,000 µg/kg (log scale).
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Note, only the first 10,000 simulations for each population are shown for clarity.
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Figure 8. Peak RBC cholinesterase inhibition, expressed as percent of background, in a
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population of twenty bootstraps of 1000 runs each (20,000 total individuals: the results
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of the first 10,000 simulations for clarity) of adult non-pregnant women (black x) or
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pregnant women in their 3rd trimester (gray +) exposed to single oral bolus doses of
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chlorpyrifos (CPF) from 3 to 3,000 µg/kg (log scale). The lines show the 50th percentile
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Figure 9. Percentile distribution of individuals showing peak RBC inhibition of 10% (90%
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activity). These are the results from twenty bootstraps of 1000 runs each (20,000 total
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individuals) for each population. See Figures 7 and 8.
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Total Blood Volume
1.18
Plasma PON1
1.18
Hepatic Blood Flow (L/hr×kg tissue)
1.18
50
RBC ChE Inhibition Rate (l/µmol×hr)
1.15
100
Hepatic PON1 (µmol/hr×kg tissue) Unbound fraction of oxon (%) Hematocrit (%)
Hepatic Carboxyl Basal Activity Rate (l/hr/kg tissue) Hepatic Carboxyl Reactivation Rate (l/hr)
0.03ǂ 0.53* 0.27 0.17
0.57*
0.01
NA¥
0.45
0.068
0.01
0.14
690
0.59
0.62
1.5e3
0.53
0.49
0.014
0.36
0.48
82
0.52†
0.43
53
0.52†
0.41
0.31
0.26‡
0.28
0.0113
NA¥
0.27
1.17e7
NA¥
0.26
AgeDependent∞
0.03ǂ
0.25
1.27e6
0.36
0.10
0.014
0.36
0.98
0.63 0.62
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NA¥
1.5 e5
1.02
0.45
RBC ChE Degradation Rate (l/hr) Hepatic P450 Bioactivation to Oxon (µmol/hr×kg tissue) Hepatic P450 Detoxification to TCPy (µmol/hr×kg tissue) RBC ChE Reactivation Rate (l/hr) Intestinal CYP Bioactivation to Oxon (µmol/hr×kg tissue) Intestinal CYP Detoxification to TCPy (µmol/hr×kg tissue) Transfer Rate to Intestine (hr-1) RBC ChE Aging Rate (l/hr) Total Carboxyl Enzyme (hr/active site)
12.8 AgeDependent∞ AgeDependent∞
P3M; Price et al., 2003 Smith et al, 2011 Materne et al, 2000 Dimitriadis and Syrmos, 2011 Smith et al, 2011
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Table 1. Parameters showing a sensitivity coefficient greater than 0.1 in the local sensitivity analysis after a 1 mg/kg/day dose Morris eFast Variability Sensitivity Mean Top 9 Top Parameter CV Reference value Coefficient• 6§
P3M; Price et al., 2003 Chapman et al., 1968 Smith et al, 2011 Smith et al, 2011 Mason et al 2000 Obach et al., 2001 Obach et al., 2001 Singh, et al. 2006
P3M; Price et al., 2003 Pope et al., 2005 Mason et al 2000
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1
2
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1
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9 4
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Rank order at the end of 21 days.
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Parameters are listed in descending order of sensitivity. The sensitivity of most parameters was greater in infants than adults. For clarity only infants are shown. These 20 parameters accounted for >95% of all model sensitivity. CV – Coefficient of Variation. • Local Sensitivity Analysis following a repeated 3 µg/kg/day dose in both infants and adults resulted in no parameters more sensitive than 0.001.This higher dose was chosen to approximate the ED10, and be high enough to result in sensitivity of the maximum number of parameters. ∞ Age-specific parameters are calculated using a polynomial equation, see Smith et al., 2014. ¥ Not Applicable. See text for justification. ǂ The sensitivity of blood and liver volume were assessed based on the first term in an agedependent calculation formula that predicts total volume normalized to body weight for an individual of a specific age. These parameters are likely more sensitive than the final parameter since small changes in the first term will be carried through the equation. Despite this, to be conservative, the total blood and liver volume were varied. The variability shown here is for the parameter prior to multiplying by body weight. *Plasma and hepatic PON1 were linked in the final analyses using the hepatic CV, see text. † Based on intestinal metabolism of testosterone.
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Table 2. Ratios of the maximum value to minimum value in the raw data, model output and bootstrap model simulations for the critical enzyme activities. CYP450 to CYP450 to Oxon Hepatic PON1 Plasma PON1 TCPy Range in in vitro data 12 28 11 6 (Smith et al., 2011) Range in parametric 33* 26 34 33* distribution Range in 20 74 98 58* 58* parametric bootstraps * PON1 in liver and plasma were assumed to be correlated and thus have the same variation.
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Table 3. Data use in deriving the values of the Data Derived Extrapolation Factors for intra-species extrapolation (DDEFHD):
ED10 (mg/kg) DDEFHD
0.14
1st percentile
0.52
0.13
3.4
Non-Pregnant Female Median (50th 1st percentile) percentile 0.46
3.6
0.14
Pregnant Female§ Median (50th 1st percentile) percentile 0.39
3.4
Pregnant cohort is 3rd trimester, based on most sensitive group.
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Infants Median (50th percentile)
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Table 4. Data use in deriving the values of the Data Derived Extrapolation Factors for intra-species extrapolation (DDEFHD) for CPF-oxon: CPF-oxon (infants) Median (50th percentile 0.089
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ED10 (mg/kg) DDEFHD
CPF-oxon (male and female) Median (50th percentile First percentile 0.061 0.033 1.8
First percentile 0.029 2.1
0.16 2.9
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B
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Figure 2.
Metabolic Activity (µmol/hr)
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P450
10000
oxon
R2=0.10 p<0.0001
1000 100
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10 1 0
20
40
60
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TCPy R2=0.31 p<0.0001
100000
10000
1000 0
20
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60
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0
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RBC ChE (percent inhibited)
Figure 3.
TCPy
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40
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RBC ChE (percent inhibited)
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400 All Parameters PON1 only
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Total Coefficient of Variation (%)
Frequency
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Figure 6.
B) Infants 100
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Figure 7.
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Variability in metabolic clearance has the greatest impact on response Data Derived Extrapolation Factors should be used to replace default UF DDEF can be determined for potentially sensitive populations using MC techniques The DDEF for Chlorpyrifos is ≤4, less than half of the default uncertainty factors
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• • • •