Environmental Health Perspectives Volume
102, Supplement 11, December 1994
[Citation
in PubMed] [Related
Articles]
Physiologically Based Pharmacokinetic Model for the Inhibition of Acetylcholinesterase
by Organophosphate Esters
Jeffery M. Gearhart,1 Gary W. Jepson,2 Harvey
J. Clewell,3 Melvin E. Andersen,4 and Rory B. Conolly5
1ManTech Environmental Technology, Inc., Research Triangle
Park, North Carolina; 2U.S. Air Force, Wright Patterson, Ohio;
3Clement International Corporation, Technology, Inc., Ruston,
Louisiana; 4Duke University, Durham, North Carolina; 5CIIT,
Research Triangle Park, North Carolina
Abstract
Organophosphate (OP) exposure can be lethal at high doses while lower
doses may impair performance of critical tasks. The ability to predict such
effects for realistic exposure scenarios would greatly improve OP risk assessment.
To this end, a physiologically based model for diisopropylfluorophosphate
(DFP) pharmacokinetics and acetylcholinesterase (AChE) inhibition was developed.
DFP tissue/blood partition coefficients, rates of DFP hydrolysis by esterases,
and DFP-esterase bimolecular inhibition rate constants were determined in
rat tissue homogenates. Other model parameters were scaled for rats and
mice using standard allometric relationships. These DFP-specific parameter
values were used with the model to simulate pharmacokinetic data from mice
and rats. Literature data were used for model validation. DFP concentrations
in mouse plasma and brain, as well as AChE inhibition and AChE resynthesis
data, were successfully simulated for a single iv injection. Effects of
repeated, subcutaneous DFP dosing on AChE activity in rat plasma and brain
were also well simulated except for an apparent decrease in basal AChE activity
in the brain which persisted 35 days after the last dose. The psychologically
based pharmacokinetic (PBPK) model parameter values specific for DFP in
humans, for example, tissue/blood partition coefficients, enzymatic and
nonenzymatic DFP hydrolysis rates, and bimolecular inhibition rate constants
for target enzymes were scaled from rodent data or obtained from the literature.
Good agreement was obtained between model predictions and human exposure
data on the inhibition of red blood cell AChE and plasma butyrylcholinesterase
after an intramuscular injection of 33 µg/kg DFP and at 24 hr after
acute doses of DFP (10-54 µg/kg), as well as for repeated DFP exposures.
The PBPK model for DFP was also adapted for the purpose of modeling parathion,
including its metabolism to the toxic daughter product paraoxon. The development
and validation of this PBPK model for two OPs provides a basis for
studying the kinetics and in vivo metabolism of other bioactivated
organophosphate pesticides and their pharmacodynamic effect in humans. --Environ
Health Perspect 102(Suppl 11):000-000 (1994)
Key words: PBPK model, acetylcholinesterase inhibition, organophosphates,
diisopropylfluorophosphate, parathion-paraoxon
This article was presented at the workshop on Pharmacokinetics:
Defining the Dose for Risk Assessment held 4-5 March 1992 at the National
Academy of Sciences in Washington, DC.
This research was conducted under U.S. Department of Defense
Contract no. F33615-85-C-0532. Reprints of this article are identified
by the Armstrong Aerospace Medical Research Laboratory, Wright-Patterson
Air Force Base, Ohio as AL/OE-TR-1994-0123. Portions of this paper have
previously been published as: Gearhart JM, Jepson GW, Clewell III HJ, Andersen
ME, Conolly RB. Physiologically based pharmacokinetic and pharmacodynamic
model for the inhibition of acetylcholinesterase by diisopropylfluorophosphate.
Toxicol Appl Pharmacol 106:295-310 (1990).
The animals used in this study were handled in accordance
with the principles stated in the Guide for the Care and Use of Laboratory
Animals prepared by the Committee on Care and Use of Laboratory Animals
of the Institute of Laboratory Animal Resources, National Research Council,
Department of Health and Human Services, National Institute of Health Publication
#86-23, 1985, and the Animal Welfare Act of 1966, as amended.
Address correspondence to J.M. Gearhart, ICF Kaiser, 1201
Gaines St., Ruston, LA 71270. Telephone (318) 255-4800. Fax (318) 255-4960.
Introduction
A variety of organophosphate (OP) esters inhibit acetylcholinesterase
(AChE) in both central and peripheral nervous tissues resulting in excessive
buildup of acetylcholine at neural receptors (1). AChE accumulation
causes a spectrum of acute toxic effects mediated by the nicotinic, muscarinic,
and central nervous system interactions of acetylcholine. These range, depending
on dose, from subclinical performance decrements to convulsions, asphyxia,
and death (2). A model capable of predicting the relationship between
OP exposure and resultant toxic effects would be useful in risk assessment
and the design of measures protective against exposure to cholinesterase
inhibitors. Ideally, the model should be amenable to cross-species scaling
and accurately predictive outside the dose range over which its primary
validation studies were conducted. Physiologically based pharmacokinetic
(PBPK) models meet these criteria (3-7). They consist of descriptions
of the physiology of the exposed organism and the biochemical processes,
which are quantitatively important determinants of toxicant disposition.
This includes parameters describing tissue solubilities, metabolism, and
binding of the toxicant. A number of PBPK models have been developed for
solvents and relatively stable halogenated compounds (5,8,9). Additionally,
an in vitro kinetic model for diisopropylfluorophosphate (DFP) in
rat brain has been developed (10), and a PBPK model for single dose
pharmacodynamics of soman in rats was recently published (11).
The goal of this study was to develop a quantitative, physiologically
based model of OP pharmacokinetics and AChE inhibition. While AChE inhibition
is not a toxic effect per se, it is an index of the probability that acute
OP toxicity will occur (12). DFP is not itself commercially important,
but was chosen for this study as its mechanism of action is thought to be
representative of other highly toxic OPs (12). Parathion (PA) is
an organophosphate insecticide that owes its toxicity to the oxidized product
of parathion, paraoxon (PO). The metabolism of PA takes place predominantly
in the liver, but it is also thought that some metabolism of parathion occurs
in extrahepatic tissues. The basic model structure described herein for
DFP and parathion should be applicable to other OPs whose acute toxicity
is mediated by inhibition of AChE.
Development of the Physiologically Based Pharmacokinetic
Model
Elements of Model Structure (Overview)
The major determinants of DFP disposition in vivo are its hydrolysis
by esterases, binding to esterases, tissue solubility, and volatility (leading
to exhalation). DFP is metabolized (hydrolyzed) by A esterases (AEST)
to a noninhibitory product, diisopropylphosphoric acid (DIP). This organophosphate
ester also binds to B esterases (BEST) and inhibits their enzymatic activity.
The hydrolysis reaction is governed by Michaelis-Menten kinetics whose Vmax
and Km were determined with in vitro assays
(Appendix 1). Those compartments described in the model as having A esterase
activity (DFPase) are shown in Figure 1 as shaded areas. The binding to
and inhibition of B esterases were modeled as bimolecular reactions and
were also determined from in vitro studies (Tables 1 and 2). All
tissue compartments in the model except fat were described as having B esterase
activity (13). The major determinants of parathion and paraoxon disposition
in the model are metabolism of parathion to paraoxon, hydrolysis of paraoxon
by esterases, binding of paraoxon to esterases, and the tissue solubility
of the parent and daughter compound.
Figure 1. Model
for acetylcholinesterase (AChE) inhibtion, aging, regeneration, synthesis,
and degradation.
Hydrolysis of Diisopropylfluorophosphate
DFP is rapidly hydrolyzed by A esterases (14). These enzymes are
present in virtually all tissues but often have high activity in the blood.
As a result, blood AEST strongly affects the amount of DFP reaching AChE
in target tissues. The activity of AEST was obtained from laboratory studies
in the rat as described below. The Michaelis-Menten constants Km
and Vmax were determined for AEST activity in rat brain,
blood, liver, and kidney. The Km for AEST in each of these
tissues was assumed to be the same for each of the corresponding mouse tissues.
Vmax values for each of the corresponding mouse tissues
were scaled to the 0.7 power of body weight (8). DFP also undergoes
spontaneous hydrolysis in aqueous solutions. A pseudofirst-order rate of
spontaneous hydrolysis was estimated in heat treated (30 min at 60°C)
homogenates of each tissue (10) and was found to be insignificant
(0.0046 min-1 relative to the enzymatic rates of DFP hydrolysis
(15).
Hydrolysis of Paraoxon
The hydrolysis of paraoxon by A esterases was described with Michaelis-Menten
kinetics, using data from Wallace and Dargan (16), Chemnitius et
al. (17), and Pla and Johnson (18).
Diisopropylfluorophosphate Binding to Esterases
The schema for model simulations of free B esterase, in this case AChE,
is shown in Figure 2. The amount of free AChE is determined by the concentration
of DFP and 4 different rate constants. The basal level of AChE results from
a balance between basal degradation of AChE and synthesis of new enzyme.
After exposure to DFP, the amount of free AChE is governed by the balance
between the bimolecular rate of inhibition and the rate at which inhibited
AChE is regenerated. Once AChE is inhibited, it will either return to free
AChE, or age and form a permanently inhibited enzyme.
Figure 2. Diagram
of the physiologically based pharmacokinetic model for diisopropylfluorophosphate
(DFP). The shaded tissue compartments indicate organs within the model in
which DFP-ase activity is described. Arrows in and out of the lung compartment
indicate inhalation and exhalation of DFP.
B esterases (19) have a serine residue at the catalytic site to
which OPs bind, inactivating the enzyme. BESTs include AChE, butyrlcholinesterase
(BChE), and carboxylesterase (CaE). While AChE is the target in acute OP
intoxication, DFP binding to BChE and CaE is without adverse physiological
effect (20). Phosphorylated BESTs undergo spontaneous hydrolysis,
leading to regeneration of the active enzyme (Figure 2). For DFP, however,
this process is relatively slow (21) (Table 3). Phosphorylated BEST
may also undergo aging (22), resulting in a permanent loss of activity
(Figure 2). BEST activities were obtained from Maxwell et al.(13).
Basal activities of AChE, BChE, and CaE were defined by zero-order synthesis
rates (moles binding sites/hour) and first-order loss rates (hr-1;
Table 3). First-order loss of rat brain AChE was obtained from Wenthold
(23), who used radiolabled precursors of AChE to determine enzyme
degradation. Synthesis of rat plasma and brain AChE was determined from
model optimization of the data of Michalek (24), Traina and Serpietri
(25). The zero-order synthesis rates and first-order loss rates for
BESTs in rat plasma and in mice were estimated by fitting simulations to
data describing the rate of return of enzyme activity to normal levels over
time after DFP dosing (see "Results"). The synthesis and loss
rates used for AChE were also used for BChE and CaE since the rate of increase
of these activities after OP inhibition is close to that of AChE (25,26).
Paraoxon Binding to Esterases
The activity of esterases which are inhibited by paraoxon were obtained
from Maxwell (13).
Metabolism of Parathion to Paraoxon
The metabolism of parathion to its toxic daughter product paraoxon was
described in the model to occur in the liver and the kidney by Michaelis-Menten
kinetics. The initial Km and Vmax for
this metabolism were obtained from the work of Wallace and Dargan (16).
These rates of metabolism were optimized to provide better predictions of
parathion and paraoxon pharmacokinetics.
Specification of Compartments
The PBPK model for DFP consisted of both organ-specific and lumped compartments
(Figure 1; Appendix 2; Table 4). Organ-specific compartments were used to
describe those tissues directly involved in acute DFP toxicity (e.g., brain,
lung, diaphragm) or those expected to significantly influence DFP pharmacokinetics
(e.g., blood, fat). Blood, though not a target organ, is an important site
of DFP metabolism, and blood AChE is a useful reflection of AChE activity
in less accessible organs. Separate venous and arterial compartments were
used because it has been shown experimentally that there can be significant
arterial-venous differences in DFP concentrations (27). The kidney
and liver are also sites of enzymatic hydrolysis of DFP for which tissue-specific
metabolic parameters were readily obtainable. Lumped compartments (fat,
rapidly perfused, etc.) were used to describe remaining tissues. The rapidly
perfused lumped compartment represented viscera not explicitly described
elsewhere. The slowly perfused compartment denoted mainly muscle tissue
distributed throughout the organism. Use of lumped compartments helped to
preserve a balance between parsimony of model structure, to maintain chemical
mass balance, and to describe explicitly mammalian physiology and biochemistry
that determine the pharmacokinetic behavior of OPs.
The PBPK model consisting of mass-balance differential equations and
associated computer programs that describe the time-dependent pharmacokinetics
of DFP and inhibition of AChE were written in Advanced Continuous Simulation
Language (ACSL)(Mitchell and Gauthier Associates, Inc., Concord, MA). Simulations
were run on a VAX 8530 (Digital Equipment Co., Maynard, MA).
Laboratory Estimation of Parameter Values
Male Fischer-344 rats (200-250 g at time of use) were obtained from Charles
River Breeding Laboratories (Wilmington, MA). They were housed in plastic
shoe box cages with hardwood chip bedding, two per cage, on a 12-hr light
and dark cycle. Food (Purina Formulab #5008) and water were available ad
libitum. Animals were quarantined for 2 weeks to allow for acclimatization
and for quality assurance procedures designed to ascertain animal health.
Quantitation of Diisopropylfluorophosphate Metabolism
Enzymatic Hydrolysis. Vmax and Km
estimates were obtained by measuring fluoride ion appearance in rat
tissue-saline homogenates (1:9 w/v) stirred constantly at 37°C (Table
3). Standards were prepared in distilled water. Fluoride was measured with
an Orion 701 digital Ionalyzer equipped with an Orion 96-09 combination
fluoride electrode (10). Heat-treated (30 min at 60°C) homogenates
of each tissue were used to measure nonenzymatic DFP hydrolysis. Vmax
and Km were estimated from Lineweaver-Burk plots
of the data (28). Vmax values for DFP metabolism
determined in rat tissues were scaled for mice and humans by 0.7(body weight)
(8,29). The rat Km was used without adjustment
for mouse and human simulations.
Bimolecular Inhibition Rate Constants. The rate of reaction
of DFP with AChE, BChE, and CaE, corresponding to the rate of esterase inhibition,
was obtained by measuring decreases in tissue homogenate esterase activity
after addition of DFP (Table 1). AChE activity was assayed with a modification
of the method of Ellman (30). Assays were initiated by adding 20
µ of tissue-saline homogenate (1:3 w/v) to 2.0 ml of substrate solution
(0.3 mM acetylthiocholine [Calbiochem, San Diego, CA], 0.3 mM dithionitrobenzoic
acid [Calbiochem, San Diego, CA] in 0.05 M potassium phosphate buffer [Pfaltz
and Bauer, Stanford, CT]) that had been previously warmed in a water bath
at 37°C. After rapid mixing, 0.3 ml of the homogenate/substrate mixture
was immediately added to a temperature-controlled cuvette (37) in
a Gilford 2600 spectrophotometer (Oberlin, OH). The cuvette was held for
1 min and then absorbence was read at 0.1-min intervals for 1 min. DFP (Sigma
Chemical Co., St. Louis, MO) was then added and the absorbence read at 412
nm at 0.1-min intervals for 5 min. The bimolecular inhibition rate constant
was evaluated as
[1]
where ki is the bimolecular rate constant (M-1min-1);
[S] is the substrate concentration (M); V is the reaction
velocity (mole/min/g tissue), Vo is the AChE activity at time zero
(mole/min/g tissue); t is the duration (min) of incubation; [DFP]
is the concentration of DFP (M), Km is the Michaelis-Menten
constant (M). The appropriate volume of DFP was added and there was a plot
of absorbence at 412 nm versus time obtained. Slopes of tangents to the
curve provided the velocity of the substrate hydrolysis reaction. The bimolecular
rate constants for BChE and CaE were determined as for AChE, except that
the substrates were butyrylthiocholine and p-nitrophenylacetate,
respectively. The concentration of tissues used in tissue homogenates was
not a factor in these calculations as inhibition reaction rates were determined
per gram of tissue.
Partition Coefficients
Partition coefficients for DFP (Table 5) were determined by the vial
equilibration technique (31,32). DFP was injected into a closed vial
containing heat-treated (60°C, 1 hr) rat blood or tissue homogenate
at 37°C. After equilibration (2 hr), head space DFP space was quantitated
by gas chromatography. Reference vials containing saline were treated identically.
Tissue-blood partition coefficients were calculated by dividing the tissue-air
value by that for blood-air. Partition coefficients determined for rat tissues
were also used for simulations of mouse and human data. The muscle-blood
partition was used for the lumped, slowly perfused compartment and the liver-blood
partition for the lumped richly perfused compartment. The partitioning of
parathion in the brain compartment was estimated from the data of Eigenberg
et al. Parathion and paraoxon partitions in other tissues were determined
in vitro by equilibrium dialysis (33).
Results
Diisopropylfluorophosphate Pharmacokinetics and Acetylcholinesterase
Inhibition in Mice
Several published studies of DFP pharmacokinetics and AChE inhibition
in mice and rats were analyzed with the simulation model. This included
both single and multiple dosing regimens. Martin (34) injected Dublin
ICR male mice (Dominion Laboratories, Dublin, VA) via the tail vein with
1 mg tritium [3H]DFP/kg. To measure free, bound, and metabolized
[3H]DFP, tissues from treated animals were homogenized, centrifuged,
and extracted with ethyl acetate. The ethyl acetate extracts were then either
counted with liquid scintillation for total radioactivity, concentrated
under nitrogen (N2), and subjected to thin-layer chromatography,
or the aqueous portion that remained after extraction was solubilized and
counted for radioactivity.
A dominant characteristic of DFP pharmacokinetics well illustrated by
this study is rapid clearance of free DFP from blood and brain. Free DFP
in plasma had largely disappeared within 1 min of iv injection (Figure 3a).
In brain, free DFP at approximately 1 min post injection had fallen to a
small fraction of its peak concentration (Figure 3b). The PBPK model
accurately simulated these data (Figure 3a,b). At time points greater
than 5 min though, a less rapid disappearance of DFP from blood and brain
than was predicted by the model was measured experimentally (Figure 3c,d).
However, the area under the plasma and brain concentration-time curves at
these later time points represented less than 3% of the total free DFP dose
to these tissues. It is possible that the apparent failure of the model
to accurately track the free DFP concentration at later time points may
be due to the measurement of 3H activity as a surrogate for the
actual concentration of free DFP. Some 3H activity inferred to
be free DFP may, in fact, have been a DFP metabolite or products of covalent
binding.
Figure 3. Time-course
of free diisopropylfluorophosphate concentration (DFP) (milligrams per liter)
in plasma and brain in male mice (Dublin ICR) after tail vein injection
of 1 mg DFP/kg. Each datum represents the mean (±1 SD) of five animals.
Data from Martin (34). Solid line depicts computer simulation generated
with the physiologically based pharmacokinetic model.
Martin (34) also measured AChE inhibition in mice after iv injection
of DFP. The nadir of AChE activity occurred within 1 min in plasma and at
about 5 min in brain (Figures 4a,5a). After optimization of
AChE resynthesis, PBPK model simulations of these data were reasonably accurate
from the time of injection through 24 hr (Figures 4b,5b).
The PBPK model was initially structured so that plasma and brain tissue
AChE activities would return to 100% of control levels in about 3.75 days.
Martin (34) found, however, that 7 days after a single iv injection
of 1 mg DFP/kg, brain AChE activity in the mouse was only about 80% of the
preexposure level (Figure 5c).
Figure 4. Time-course
of plasma acetylcholinesterase (AChE) activity in male mice (Dublin ICR)
after tail vein injection of 1 mg diisopropylfluorophosphate/kg. Data are
expressed as a fraction of control activity. Each datum represents the mean
(±1 SD) of five animals. Data from Martin (34). Solid line
depicts computer simulation.
Figure 5. Time-course
of brain acetylcholinesterase (AChE) activity in male mice (Dublin ICR)
after tail vein injection of 1 mg diisopropylfluorophosphate/kg, expressed
as a fraction of control (AChE) activity. Each datum represents the mean
(±1 SD) of five animals. Data from Martin (34). Solid line
depicts computer simulation.
Acetylcholinesterase Inhibition in Rat Plasma and Brain after Repeated
Dosing with Diisopropylfluorophosphate
Michalek (24) injected male Wistar rats sc with DFP every second
day for 22 days. The first dose was 1.1 mg DFP/kg, and subsequent doses
were 0.7 mg/kg. Five rats (four DFP and one vehicle exposed) were sacrificed
for measurement of brain AChE activity at 1.5 and 24 hr after the 1st, 2nd,
4th, 6th, 9th, and 12th DFP doses and at various intervals (48 and 72 hr;
days 7, 14, 28, and 35) after the last dose. Traina and Serpietri (25)
measured the effect of DFP on plasma AChE activity using the same rat strain
and dosing regimen as Michalek (24).
Traina and Serpietri (25) found that plasma AChE activity in the
rat fell rapidly after sc injection of DFP (Figure 6a). AChE activity
was about 15% of control 1.5 hr after the initial dosing and about 70% of
control at 24 hr. Simulations of these data (Figure 6a) were obtained
after optimization of the rate of absorption of DFP into blood following
a single sc injection. Simulation of the initial fall in plasma AChE activity
was particularly sensitive to this parameter. The rate of AChE synthesis
and first-order loss were also visually optimized against these data. This
was critical to successful simulation of the return of AChE activity towards
control levels obtained after a single sc injection. Once obtained in this
manner the model was used for simulation of the multiple dosing scenario
(Figure 6b).
Figure 6. Plasma
acetylcholinesterase (AChE) activity in male rats (Wistar) injected sc with
diisopropyl fluorophosphate (DFP) (a first dose of 1.1 mg DFP/kg, then 0.7
mg/kg every other day for 22 days). Data are expressed as a fraction of
the control AChE activity. Plasma AChE activity was assayed at 1.5 and 24
hr after each dose of DFP. Each datum represents the mean (±1 SD)
of four animals. Data from Traina and Serpietri (25). Solid line
depicts computer simulation.
Plasma AChE activities measured (25) after the last subcutaneous
dose of DFP (day 22) are suggestive of an overshoot of the control level.
Plasma AChE activity had returned to a stable 100% of the control level
by 5 to 6 days after the last DFP dose (Figure 6b). There was no
provision in the PBPK model for simulation of such an overshoot, though
this phenomenon has been modeled for other systems, as in the case of glutathione
replacement after depletion by toxicant exposure (35,36).
The pattern of brain AChE activity after subcutaneous DFP dosing (24)
was similar to that seen in plasma (25), though the extent of recovery
between doses was smaller (Figure 7a,b). The recovery of brain AChE
activity after termination of DFP dosing was prolonged. For example, activity
was only about 65% of control 28 days after the last dose of DFP. When the
basal rates of AChE degradation and synthesis were optimized for acceptable
simulation of brain AChE activity during dosing (Figure 7a), simulation
of AChE activity after termination of dosing predicted a more rapid return
than was observed (Figure 7B). Model structure dictated a relatively
rapid and monophasic return of brain AChE activity to the original control
level. The data suggest, however, that a full return of brain AchE activity
to the control level is a multiphasic process.
Figure 7. Brain
acetylcholinesterase (AChE) in male rats (Wistar) injected sc with diisopropylfluorophosphate
(DFP) (a first dose of 1.1 mg DFP/kg, then 0.7 mg/kg every other day for
22 days). Data are expressed as a fraction of control AChE activity. Brain
AChE activity was assayed at 1.5 and 24 hr after DFP dosing. Each datum
represents the mean (±1 SD) of six animals. Data from Michalek et
al. (24). Solid line depicts computer simulation.
Acetylcholinesterase Inhibition in Humans
Simulations of both the acute and repeated DFP exposures provide predictions
of AChE and BChE inhibition that are very near the experimentally determined
human values. The time-course of AChE and BChE inhibition after an acute
dose of 33 µg DFP/kg bw resulted in a maximum AChE inhibition at 24
hr and maximum BChE inhibition at 4 hr (Figure 8a). This difference
in the degree and time to maximum enzyme inhibition is a direct reflection
of the 20-fold difference in the bimolecular inhibition rate constants for
the two enzymes. The simulation provided a good prediction of AChE inhibition
for all the acute time-course inhibition data. Simulations of red blood
cell (RBC)-AChE and plasma BChE inhibition in four people injected at four
different doses of DFP covering 10 to 54 µg DFP/kg bw compared favorably
to the actual enzyme activity measured in these individuals at 24 hr (Figure
8b). The greatest discrepancy between the simulations and data occurred
for the RBC-AChE at 54 µg/kg dose and the 10 µg/kg plasma BChE
values (Figure 8b).
Figure 8. (a)
Time-course of red blood cell acetylcholinesterase (AChE) and plasma butyrlcholinesterase
(BChE) activities in a human after an im injection with 33 µg diisopropylfluorophosphate
(DFP)/kg. (b) Twenty-four hour values for red blood cell AChE and
plasma BChE activities in four humans after an im injection with from 10
to 54 µg DFP/kg. (c) Average values for the time-course of
red blood cell AChE and plasma BChE activities in 35 human subjects after
daily im injections with an average of 20 µg DFP/kg/day. (d) Predicted
inhibition of human brain AChE activity after an acute im dose of 20 µg
DFP/kg/day. Data for a, b, and c are expressed as a fraction
of control activity. Solid lines depict computer simulation. Data from Grob
et al. (47).
The model predictions of AChE and BChE inhibition resulting from repeated
im injections of 20 µg DFP/kg bw were representative of the overall
trend of enzyme inhibition (Figure 8c). During the first 5 days of
repeated dosing, the actual AChE activity dropped rapidly from 100% of baseline
to approximately 50% activity and then at a much slower rate to a final
enzyme activity of 30%. The simulation of these data predicted a inhibition
of approximately 40% during the initial 5 days of dosing that was sustained
throughout the rest of the exposure period. BChE activity dropped immediately
during the first 5 days to 15% of the basal enzyme activity and remained
at this level during the exposure period (Figure 8c). The simulation
predicted a 10 to 20% greater initial drop in enzyme activity then was actually
measured.
Parathion and Paraoxon Kinetics in the Rat
Eigenberg (37) measured concentrations of PA and PO in brain,
liver, and blood, as well as PA in fat, following an iv injection of PA
at a dose of 3 mg/kg. Simulation of PA and PO kinetics in brain, liver,
and blood after iv injection of 3.0 mg PA/kg were in general agreement with
the published data (Figure 10,12). The simulation of PA kinetics in fat
tissue (Figure 11) required the addition of diffusion limitation to this
compartment to achieve agreement with the experimental data.
Figure 9. Simulated
concentration of diisopropylfluorophosphate in human brain (µg/l)
after a 5-min inhalation at 50 ppm. The level of plasma AEST for curve A
was 1.5 times the scaled activity, for curve B it was the scaled level of
activity, and for C, it was 50% of the scaled activity.
Figure 10. Time-course
of parathion in blood, liver, and brain in (µg/ml) for 8 hr after
an iv injection of parathion at a dose of 3 mg/kg. Each data point represents
the mean of four rats. Data from Eigenberg et al. (37).
Figure 11. Time-course
of parathion in fat (µg/ml) for 24 hr after an iv injection of paraoxon
at a dose of 3 mg/kg. Each data point represents the mean of four rats.
Data from Eigenberg et al. (37).
Figure 12. Time-course
of paraoxon in liver, brain, and blood in (µg/ml) 1 to 3 hr after
an iv injection of parathion at a dose of 3 mg/kg. Each data point represents
the mean of four rats. Data from Eigenberg et al. (37).
Discussion
The Michaelis-Menten parameters used to describe DFP hydrolysis and the
bimolecular inhibition rate constants describing BEST inhibition were obtained
in vitro. The success of simulations of in vivo DFP pharmacokinetics
data (Figure 3a,b) suggests that the parameter values measured in
vitro are reasonable estimates of the corresponding in vivo values.
In vitro estimation of in vivo parameter values was also used
by Dedrick (3). They developed a PBPK model for arabinofuranosylcytosine
(ARA-C) and its metabolite, arabinofuranosyluracil in four species and used
rates of ARA-C metabolism measured in vitro. However, Reitz (38)
found significant in vitro and in vivo differences in the
rates of methylene chloride metabolism. Clearly, this problem must be considered
on a case-by-case basis. Good agreement between in vitro and in
vivo metabolic rates might be expected when the enzyme is soluble and
its activity is not directly dependent on cofactor concentration (e.g.,
phosphorylphosphatases) (17). In such cases it should be relatively
easy to create an in vitro milieu functionally similar to the in
vivo milieu. On the other hand, some enzymes are embedded in cellular
membranes, have specific orientations with respect to other enzymes that
preprocess substrates, and have activities highly dependent on cofactor
concentrations (e.g., cytochrome P450) (39). Activities of the latter
type may be greatly affected by tissue homogenization and dilution in buffer.
The results obtained in this study and in the studies of Dedrick (3)
and Reitz (38) are consistent with this expectation.
The rate of AChE inhibition is a function of the bimolecular rate constant,
ki. The ki for AChE was determined in rat
brain homogenate and this value was then used in all model compartments
having this enzyme activity. The rat ki for AChE was also
used for mice. This approach was also used for the DFP-esterase inhibition
rate constants for CaE and BChE (see section on model development). The
ability of the PBPK model to simulate AChE inhibition in mice and rats (Figures
4-7) indicates that the variance in these rate constants among tissues and
species is not large. Andersen (40) found the rates of inhibition
for solubilized AChE from the cerebral cortex of mice was within 20% of
the rate for rats.
One goal of this research effort was to predict the kinetics and AChE
inhibition not only after an acute exposure to DFP, but also after repeated
doses. A good simulation of the experimental data for plasma and brain AChE
inhibition in rats during repeated subcutaneous administration of DFP was
achieved (Figures 6,7). The experimentally measured return of brain AChE
activity was much slower than the rate of enzyme resynthesis that was used
in the model. If the brain AChE resynthesis rate was decreased in the model
to agree with the rate of experimentally determined postexposure synthesis,
then predictions of brain AChE activity during DFP dosing would be underestimated.
The experimentally determined activity of AChE in brain after each DFP
dose was reproducible, yet the levels of AChE at the 48- and 72-hr time
points at the end of all repeated dosing indicated there was an alteration
in the mechanisms controlling enzyme synthesis. For the model to predict
the return of brain AChE to normal levels of activity after repeated dosing,
it would be necessary to address the effects of down regulation of AChE
receptors on AChE enzyme synthesis rates. The simulation of AChE resynthesis
used the preexposed level of AChE activity as a reference in setting the
rate of resynthesis. This assumed that the receptor affinity and impetus
to return AChE to control tissue levels was unaltered. If the receptor numbers
and affinities are affected by repeated dosing with DFP, it would alter
the rates of AChE resynthesis experimentally, which would require a change
in the model parameter controlling AChE synthesis. This effect is believed
to be a response to excessive tissue concentrations of acetylcholine after
AChE inhibition (41).
Clarification of the mechanism of this tolerance has involved the measurement
of receptor binding and numbers. Binding of quinuclidinyl benzilate, a muscarinic
affinity label, has been shown to decrease in affinity and density in the
striatum of rats following chronic cholinesterase inhibition with DFP (42).
Yamada (43) was able to demonstrate a correlation between and a dose-dependent
decrease in muscarinic receptors, AChE activity, and choline uptake in regions
of the brain and gastrointestinal tract of guinea pigs treated repeatedly
with DFP. This effect was antagonized by physostigmine and atropine.
Simulations of the 24-hr recovery of brain AChE after repeated DFP dosing
were well within one standard deviation of the data during the series of
DFP doses and only failed to predict brain AChE levels after dosing had
ceased. This could be due to the experimental protocol chosen by Michalek
(24), where 48 hr were allowed between each successive dose of DFP.
Ehlert (42) has shown the half-time for the loss of muscarinic binding
sites to be approximately 1.6 days. The difference between the frequency
of DFP dosing Michalek (24) used (i.e., which we have simulated)
and the rate of change in receptor numbers and affinity may have been lessened
by the repeated dosing until after the completion of dosing.
While there was a good prediction of DFP pharmacokinetics shortly after
injection that accounted for greater than 95% of the material injected,
the simulation and data became more divergent at longer times after dosing.
After the initial injection of DFP, the major model parameter affecting
the disappearance of compound from the animal is AEST hydrolysis activity.
It would be expected that enzymatic hydrolysis of DFP would continue to
decrease blood and tissue concentrations until there is total disappearance
of the compound. The experimental data showed no difference in plasma DFP
concentrations between 15 and 30 min after injection and only a decrease
in DFP concentration of 32% in brain. Those BEST enzymes with the highest
affinity for DFP already would have become inhibited at this time and their
low molar concentration then would have very little effect on the overall
kinetics of DFP. Because the model and data both predict the amount of DFP
present in blood and brain to be in the picogram per milligram range within
minutes of injection, it is possible that the difference between the data
and simulations are due to inexact compound identification involved in using
the combination of radiolabel and chemical extraction to quantitate very
low concentrations of DFP.
The PBPK model initially developed to describe the kinetics of DFP and
inhibition of AChE in the rodent was successfully scaled to simulate data
from humans repeatedly treated therapeutically with DFP. It was possible
to predict the time course of inhibition of RBC AChE and plasma BChE in
a male human injected intramuscularly with a 33 µg/kg dose of DFP
and these enzyme activities at 24 hr in four different humans injected with
DFP at doses from 10 to 54 µg/kg. By using the data from human studies
to validate the model, it is possible to simulate the amount of AChE inhibition
in other target organs for which there are no human data (Figure 12). This
is significant, because it provides a means of predicting OP effects in
the target organs of humans for which there will probably never be data.
The development of this model to predict the human response to organophosphate
exposure provides a method of modeling possible therapeutic or prophylactic
approaches for organophosphate exposure in humans.
The model was exercised for a hypothetical inhalation exposure of humans
to 50 ppm DFP for 5 min. Because CaE has been shown to be an important detoxification
route for soman (20), a simulation of human exposure to DFP was conducted
with the activity of CaE decreased by a factor of 10, which is the activity
expected in humans (44). The effect of this alteration of CaE levels
on the kinetics and inhibition of AChE was negligible due to the much lower
affinity of CaE for DFP relative to the other enzymes present (Table 1).
The parameter that did have an effect on DFP blood and brain kinetics was
AEST activity in the blood. The levels of AEST in blood were decreased by
one-half and increased by one-half over the amount that was scaled by (body
weight)0.7 from rats to humans. A 50% decrease in AEST activity
in blood caused a 16% increase in the peak concentration of DFP in brain.
In contrast, increasing DFP AEST activity by 50% caused an 9.5% decrease
of DFP in blood and a 13% decrease in brain.
The PBPK model for DFP was successfully modified to describe the time-course
of parathion and paraoxon in blood, liver, and brain, and the time-course
for parathion in fat. A preliminary validation of the model was conducted
with data obtained from the literature and collected in laboratory studies.
It was possible to provide a reasonably good simulation of in vivo
data obtained from the literature, but this required adjustment of enzymatic
values obtained in the literature. With further validation of the model,
it will be possible to predict the inhibition of AChE in the different tissue
compartments of the target organs and use this model to provide predictions
of the degree of AChE inhibition in occupational situations where humans
may be exposed. This will then provide a basis for using the model to perform
risk assessments of different exposure scenarios.
The PBPK model for OPs is in effect a quantitative hypothesis specifying
the factors controlling OP pharmacokinetics in rats, mice, and humans. Once
its parameters have been set to realistic values, the ability of the model
to simulate real data becomes a test of the hypothesis. Failure to accurately
simulate data suggests that model structure, the hypothesis, needs refinement.
Iterations of this process (i.e., model refinement, predictive simulation,
laboratory experimentation) can be exploited to advance the understanding
of the biological determinates of OP biodistribution. This general paradigm
has recently been discussed by Clewell and Andersen (45).
The present model for OPs does not simulate toxicity per se. Instead,
AChE inhibition is simulated as an index of the likelihood of toxic effect
(2). The acute toxicities of some OP agents may not be due to acetylcholine
overload but rather to actions at sites other than AChE (46). In
these cases, simulation of the concentration of free OP in target tissues
would be expected to provide an index correlated with toxicity. For OPs
whose primary mechanism of acute toxicity is not AChE inhibition, it would
be desirable to extend the current PBPK model to explicitly describe the
OP-tissue interaction most directly correlated with toxicity.
PBPK models are useful in risk assessment because their structure is
amenable to cross-species scaling or to simulate exposure scenarios that
cannot be tested otherwise. A PBPK model validated for experimental animals
and appropriately scaled to humans is theoretically capable of simulating
pharmacokinetic behavior in humans. This assumes that the model structure
appropriate for the experimental species is also appropriate for humans.
Physiological parameters such as organ volumes, organ blood flows, and pulmonary
ventilation rates are well characterized for humans as well as for common
experimental animals. Scaling of toxicant-specific parameters can be more
problematical. Some success in scaling of PBPK models has been achieved
by assuming that metabolic rates [e.g., Vmax , scale in
proportion to body surface area (8)]. With respect to the DFP model,
it is not clear a priori how the DFP-esterase bimolecular inhibition
rate constants should scale from rodents to humans. The ability demonstrated
in rats and mice to successfully use in vitro estimates of parameter
values for simulation of in vivo data suggested the same approach
would work in the human version of the model. Moreover, the similarity of
inhibition rates for solubilized AChE from the cerebral cortex of mice and
rats (40) implies that some parameter values may be relatively insensitive
to large changes in body weight.
In summary, the model structure used in the present study is clearly
relevant to humans for a variety of OPs (2) and the data required
for scaling is explicitly specified by this structure. Application of the
present model to other OPs of concern for human exposure may, therefore,
be a relatively straightforward task.
Appendix I: Conversion of In Vitro Measurements
for In Vivo Modeling
The two calculations shown here provide an example of the dimensional
analysis to convert in vitro DFPase values of Vmax
and Km for extrapolation to in vivo modeling. The
values in Table 3 for Vmax are reported in milligrams
per hour. These values were determined in vitro in grams per minute
per gram of tissue. The in vitro value for Km was
determined in nanomoles per liter. An example of converting in vitro
values to in vivo values for the liver is, for Vmax ,
9.02 mole DFP/min/g liver x 60 min/hr 40 g liver/kgbw x 0.184 g/µmole
x mg/103g = 3983 mg/hr x kg and, for Km, 1.29
mmoles/l x 184 mg/mmole = 237.36 mg/l.
Appendix II: Description of Pharmacokinetic Model
The mass balance differential equation for DFP in brain is VBr x
dCBr/dt = QBr x (CABr - CVBr) - (Vmax
Br x CVBr)/(KmBr + CVBr)
- KAChE x CAEBr x CBr - KCaE x CCEBr x
CBr - KBChE x CBEBr x CBr, where VBr
= volume of brain (l); CBr = concentration of DFP in the brain compartment
(mg/l); QBr = blood flow to the brain (l/hr); CABr = DFP in
arterial brain (mg/l); CVBr = DFP in venous blood leaving brain (mg/l);
Vmax Br = maximum rate of DFPase hydrolysis (mg/hr);
KmBr = Michaelis-Menten constant for DFPase in brain (mg/l);
KAChE = bimolecular rate constant for DFP reaction with AChE (M-hr)-1;
CAEBr = AChE concentration in brain (M); KCaE = bimolecular
rate constant for DFP reaction with CaE (M-hr)-1; CCEBr
= CaE concentration in brain (M); KBChE = bimolecular rate constant
for DFP reaction with BChE (M-hr)-1; and CBEBr = BChE
concentration in brain (M). This equation is also used to describe DFP in
the liver, kidney, rapidly perfused, as well as venous and arterial tissue
compartments. In the remaining compartments (lungs, fat, slowly perfused,
and diaphragm), the differential equation for DFP is VT x dCT/dt
= QT x (CAB - CT/PT), where VT = volume of tissue
(l); dCT/dt = change in tissue concentration with time (mg/hr); QT
= blood flow to tissue (l/hr); CAB = arterial blood concentration
(mg/l); CT = tissue concentration (mg/l); and PT = tissue-to-blood
partition coefficient.
The differential equation to calculate inhibited AChE activity in the
different tissue compartments is VBr x dAEBR/dt = (KAChE
x CAEBR x CBRM) - (KRABR x AEBR - (KAABR
x AEBR), where VBr = volume of brain (l); KAChE
= bimolecular inhibition rate constant of ACHE (M-hr)-1; CAEBR
= free AChE in brain (M); CBRM = DFP in brain (M); KRABR =
rate of regeneration of inhibited AChE (hr-1); AEBR =
inhibited AChE (M); and KAABR = rate of aging of inhibited AChE (hr-1).
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