* Département de Santé Environnementale et Santé au Travail, Faculté de Médecine, Université de Montréal, C. P. 6128, Succursale Centre-ville, Montréal, Québec, Canada H3C 3J7;
Département de Mathématiques et de Statistique and Centre de Recherches Mathématiques, Faculté des Arts et des Sciences, Université de Montréal, C.P. 6128, Succursale Centre-ville, Montréal, Québec, Canada, H3C 3J7; and
Institut National de Santé Publique du Québec, Direction de la Toxicologie Humaine, Centre de Toxicologie, 945 Avenue Wolfe, Ste-Foy, Québec, Canada G1V 5B3
Received December 19, 2002; accepted February 10, 2003
ABSTRACT
A toxicokinetic model is proposed to predict the time evolution of malathion and its metabolites, mono- and dicarboxylic acids (MCA, DCA) and phosphoric derivatives (dimethyl dithiophosphate [DMDTP], dimethyl thiophosphate [DMTP], and dimethyl phosphate [DMP]) in the human body and excreta, under a variety of exposure routes and scenarios. The biological determinants of the kinetics were established from published data on the in vivo time profiles of malathion and its metabolites in the blood and urine of human volunteers exposed by intravenous, oral, or dermal routes. In the model, body and excreta compartments were used to represent the time varying amounts of each of the following: malathion, MCA, DCA, DMDTP, DMTP, and DMP. The dynamic of intercompartment exchanges was described mathematically by a differential equation system that ensured conservation of mass at all times. The model parameters were determined by statistically adjusting the explicit solution of the differential equations to the experimental human data. Simulations provide a close approximation to kinetic data available in the published literature. When simulating a dermal exposure to malathion, the main route of entry for workers, the model predicts that it takes an average of 11.8 h to recover half of the absorbed dose of malathion eventually excreted in urine as metabolites, compared to 3.2 h following an intravenous injection and 4.0 h after oral administration. This shows that following a dermal exposure, the absorption rate governs the urinary excretion rate of malathion metabolites because the dermal absorption rate is much slower than biotransformation and renal clearance processes. The model served to establish biological reference values for malathion metabolites in urine since it allows links to be made between the absorbed dose of malathion and the time course of cumulative amounts of metabolites excreted in urine. From the no-observed-effect level (NOEL) of 0.61 µmol/kg/day derived from the data of Moeller and Rider (1962), the model predicts corresponding biological reference values for MCA, DCA, and phosphoric derivatives of 44, 13, and 62 nmol/kg, respectively, in 24-h urine samples. The latter were used to assess the health risk of workers exposed to malathion in botanical greenhouses, starting from urinary measurements of MCA and DCA metabolites.
Key Words: malathion; monocarboxylic acids; dicarboxylic acids; phosphoric derivatives; toxicokinetics; risk assessment.
Malathion (O,O-dimethyl S-1,2-di(ethoxycarbonyl)ethyl phosphorodithioate) is an organophosphate (OP) insecticide widely used in agriculture and residential settings as well as in public health programs for mosquito-borne disease control (Environnement Québec, 2002; U.S. EPA, 2000
). It is also used in some countries for the treatment of head lice (Roberts, 2002
). Like other OP insecticides, malathion exerts its neurotoxic action in humans, as in insects, through cholinesterase (ChE) inhibition. This results in the accumulation of acetylcholine within synapses leading to over-stimulation of postsynaptic receptors (Liu and Pope, 1998
). In acutely exposed individuals, clinical signs of OP intoxication usually appear at inhibition of 6070% of acetylcholinesterase (AChE) activity in red blood cells (RBC). However, light clinical signs and symptoms were reported in subjects with 3060% reduction in RBCAChE activity (Sidell, 1994
).
The American Conference of Governmental Industrial Hygienists proposed a biological exposure index (BEI®) for AChE inhibiting pesticides to prevent cholinergic health effects (ACGIH, 2002). An RBCAChE activity of 70% of the individuals baseline (i.e., 30% inhibition of the activity) is proposed as a biological reference. However, a BEI® based on the measurement of urinary biomarkers of malathion has not been proposed thus far, although this is desirable since it would give an earlier warning of possible effects than AChE inhibition. Indeed, it is clearly established that urinary biomarkers of exposure to malathion are more sensitive exposure indices, since they can be measured at exposure doses lower than those necessary to induce a measurable inhibition of AChE activity.
Experimental studies in animals and humans show that malathion is oxidized by cytochrome P-450, in small amounts (46% in rats), to malaoxon, which is responsible for ChE inhibition (U.S. EPA, 2000). Both malathion and malaoxon are rapidly detoxified by carboxylesterases to mono-acid and di-acid derivatives, which are excreted mainly in urine; these acids can further be metabolically converted to phosphoric derivatives, also primarily excreted in urine (Ecobichon, 1992
; Feldmann and Maibach, 1974
; Jellinek et al., 2000
; U.S. EPA, 2000
).
To assess exposure to malathion in field studies, malathion mono- and dicarboxylic acids (MCA and DCA) in urine are thus used as specific biomarkers (Adgate et al., 2001; MacIntosh et al., 1999
; Márquez et al., 2001
) while the phosphoric derivatives dimethyl dithiophosphate (DMDTP), dimethyl thiophosphate (DMTP) and dimethyl phosphate (DMP) serve as nonspecific urinary biomarkers (Cocker et al., 2002
; Coye et al., 1986
; Fenske, 1988
; Fenske and Leffingwell, 1989
).
The use of these urinary biomarkers to assess risk is dependent on the establishment of a link between their measurements and critical biological effects. Various authors have established a relationship between the exposure dose and urinary biomarkers in controlled human studies (Feldmann and Maibach, 1974; Jellinek et al., 2000
), while others have studied links between the exposure dose and the inhibition of AChE activity in volunteers (Moeller and Rider, 1962
). In some reports, attempts were also made to link urinary biomarkers to the appearance of early biological effects under experimental conditions, but the administered doses were not sufficient to induce an inhibition of AChE activity (Dennis and Lee, 1999
; Jellinek et al., 2000
). In field studies with exposed workers, these relationships cannot be established directly on the basis of measurements of external doses (ambient air concentrations or skin deposits). In the context of malathion exposure in occupational settings, which occurs mainly through dermal contact (ACGIH, 2002
; Tuomainen et al., 2002
), knowledge of the kinetics is essential since dermal absorption is subject to large variations among individuals, and depending on the exposed skin site (Bronaugh and Maibach, 1999
; Cohen and Rice, 2001
; Feldmann and Maibach, 1974
).
If links can be established between the cumulative amounts of urinary biomarkers and the absorbed dose of malathion, whatever the exposure route (oral, dermal, pulmonary) or scenario (single, repeated, intermittent or continuous exposure), and between the absorbed dose and the appearance of biological effects, it then becomes possible to use urinary biomarkers to assess risk in workers exposed to malathion. These links can be made through toxicokinetic modeling, provided appropriate experimental data on the metabolism and disposition of malathion and its metabolites are available. A physiologically based pharmacokinetic model has been developed to describe the human dermal absorption, metabolism, and excretion of malathion (Rabovsky and Brown, 1993). This model did not, however, seek to describe the urinary excretion kinetics of specific metabolites, and thus does not allow reconstruction of the absorbed dose starting from urinary measurements.
The objectives of this study were (i) to develop a toxicokinetic model for humans, linking the dose of malathion absorbed under different exposure routes and scenarios to the time courses of malathion metabolites in urine and (ii) to use this model as a framework for relating published human no-observed-effect level(s) (NOEL) to associated amounts of metabolites in urine, which are to be proposed as convenient biological reference values to prevent health effects.
MATERIALS AND METHODS
Model development: Conceptual and functional representation.
The disposition kinetics of malathion and its metabolites, following intravenous, oral, or dermal exposure, were modeled using a multi-compartment dynamical system, described mathematically by a mass balance differential equation system (see Appendix). The model conceptual and functional representation was designed to describe the data provided by Feldmann and Maibach (1974) and Jellinek et al. (2000)
on the kinetics of malathion and its metabolites in human volunteers.
The model conceptual representation is depicted in Figure 1. Symbols and abbreviations used in this study are described in Table 1
. The model uses a specific body compartment for the malathion burden in blood and tissues in dynamical equilibrium with blood, i.e., tissues that rapidly reach and maintain a fixed ratio with blood (referred to later as the blood compartment B(t) for simplicity). Another compartment regroups the malathion stored in tissues S(t), i.e., malathion in lipids or bound to tissue proteins. A compartment, M(t), is also used to describe the whole-body burden of total metabolites. In addition, different compartments are introduced for cumulative amounts of each specific malathion metabolite in either urine or feces, that is for malathion mono-carboxylic acids (MCA), dicarboxylic acids (DCA), and for the phosphoric derivatives: dimethyl dithiophosphate (DMDTP), dimethyl thiophosphate (DMTP), and dimethyl phosphate (DMP). The route-of-entry, gastro-intestinal tract or skin, is also represented as a separate input compartment. In the model, all amounts are initially expressed on a mole basis.
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In the model, the rates of change in the amounts of a substance in a given compartment are described mathematically as the difference between compartment rates of uptake and loss. Exchange rates between compartments represent either the physical transfer of the same substance or the transfer (on a mole to mole basis) through biotransformation of malathion to its metabolites or one metabolite to another. Solving the system of differential equations yields the mathematical function of the time courses of malathion and its metabolites in the different compartments.
Model development: Determination of parameters.
The model parameters were determined from the in vivo time profiles provided by Feldmann and Maibach (1974) and Jellinek et al. (2000)
. The parameters were determined by statistical best-fits of the explicit solutions of differential equations to experimental human time-course data, or by log-linear regression on the time-profile data (using in both cases reported average values). A professional edition of MathCad software was used for this purpose (MathSoft, Inc., Cambridge, MA).
Extensive use was made of the different time scales involved in the biological processes to simplify differential equations, using quasi-steady state approximations (QSSA) (Segel, 1988; Segel and Slemrod, 1989
). This enabled no more than two model parameters to be estimated per fit. In short, QSSA predicts that compartments with rapid attrition reach a dynamic equilibrium with their slow varying "feeder" compartment. For instance, the output transfer rate, kBM + kBS, of malathion in blood B(t) to total body metabolites M(t) and to storage tissues S(t) was considered large compared to the rates of change of its feeder compartments: R(t) for absorption or S(t) for storage release, because the burden of malathion in blood is quickly depleted under an oral dose (Jellinek et al., 2000
).
The transfer rate constant of total metabolites in the body to urine, kMU, was estimated by least-square-fit adjustment of the model general solution to the data of Feldmann and Maibach (1974) on the urinary excretion time course of average total 14C in male human volunteers (n = 6) exposed intravenously (iv) to 1 µCi of 14C-labeled malathion (dose in mole or g not specified). These authors reported the time course of total 14C urinary excretion rate (% of the administered dose per h). For the fitting of experimental data and to determine the kMU parameter, the experimental values were converted to cumulative burdens expressed as a fraction of the administered dose. Log-linear regression on the time course of average total 14C urinary excretion rate reported by Feldmann and Maibach (1974)
during the 24- to 120-h period following the iv injection also served to determine the low transfer rate constant kSB of malathion from storage tissues to blood.
The transfer rate constants of malathion in blood to total body metabolites, kBM, and to storage tissues, kBS, could not be determined precisely for lack of available detailed time profile data of malathion in blood shortly following malathion exposure in human volunteers. However, the sum of kBM + kBS could be approximated from the data of Jellinek et al.(2000). These authors showed that 1 h following a single oral dose of 15 mg/kg of malathion in human volunteers, blood concentrations of malathion were below the limit of detection (102 ng/ml). This corresponds to an attrition half-life of blood malathion of at most 12 min, which is consistent with a very rapid distribution or biotransformation of malathion. These findings indicate that the rate value of the sum of kBM + kBS must be greater or equal to
or 3.47. The individual values of kBM and kBS parameters were determined by adjustment of the numerical solution of the system of differential equations to the data of Feldmann and Maibach (1974)
in volunteers exposed iv to 14C-malathion.
The data of Jellinek et al.(2000) were used to determine the fractions of total body metabolites recovered as each of the five metabolites: MCA, DCA, DMDTP, DMTP, and DMP (fMU-MCA, fMU-DCA, fMU-DMDTP, fMU-DMTP, and fMU-DMP, respectively). These authors reported mean urinary excretion of these metabolites (as a percentage of the administered dose) for the 012-, 1224-, and 2448-h periods following a single oral malathion dose of 0.5, 1.5, 10, and 15 mg/kg of body weight in male human volunteers as well as 15 mg/kg in female volunteers (n = 5 per group). The fractions of specific to total body metabolites were thus obtained from the molar ratio of specific metabolites excreted in urine to total metabolites in urine. This amounts to assuming that the metabolites are produced in different proportions, but are excreted in urine at the same kMU rate. This simplification was necessary, given the lack of detailed blood and urinary excretion-time profiles for each metabolite and the paucity of data on the conjugation reactions of malathion metabolites and their renal clearance. However, the data of Feldmann and Maibach (1974)
, from which the kMU was derived, together with the data of Jellinek et al.(2000)
show that elimination of the metabolites to urine is almost complete 12 to 24 h following an iv or oral exposure. It is thus reasonable to assume that the renal clearance of the different conjugated malathion metabolites is more or less the same.
The oral absorption fraction used in simulations was that reported by Jellinek et al. (2000). The experimental data did not allow for the exact determination of the oral absorption rate constant; a range of values was tested, consistent with data from studies on related compounds (Carmichael et al., 1989
; Nolan et al., 1984
), together with the duration of intestinal transit up to the main absorption site of malathion (jejunum). For dermal exposures, the absorption fraction and the absorption-rate constant were estimated by least-square-fit adjustment of the model general solution to the data of Feldmann and Maibach (1974)
on the urinary excretion time course of average total 14C in male human volunteers (n = 6) exposed dermally to 1 µCi of 14C-labeled malathion on the forearms.
Model development: Model simulations.
Once the parameters were estimated as described above, numerical solution of the complete model was performed by the Runge-Kutta method on the differential equation system. Simulations were conducted again using the professional edition of MathCad software from MathSoft, Inc. The model predicts the amounts of malathion and its metabolites in the body and in excreta at any time point postexposure for any route of exposure and scenario.
Simulations of exposure scenarios, where continuous or repeated intermittent doses are administered through time, were performed by introducing a nonhomogenous term, g(t), describing these varying inputs (see Appendix).
Model validation.
The model, developed using the previously mentioned data, was validated using different sets of experimental data: Maibach et al.(1971), Wester et al.(1983)
, and Dennis and Lee (1999)
. Only in the study of Dennis and Lee (1999)
were the data presented in graphical form. Those graphs were thus scanned and the data were read using Sigma Plot Graphing Software (Jandel Corporation, San Rafael, CA).
Determination of biological reference values.
Biological reference values for MCA, DCA, and phosphoric derivatives amounts in 24-h urine samples are proposed here based on model predictions and the data of Moeller and Rider (1962). These authors exposed male human volunteers repeatedly, once a day, to gelatin capsules containing malathion. The dosage regimen was 8 mg/day (0.1 mg/kg/day) for 32 days, 16 mg/day (0.2 mg/kg/day) for 47 days, or 24 mg/day (0.3 mg/kg/day) for 56 days (n = 5 per exposure group). Plasma and erythrocyte cholinesterase activities were measured twice weekly before, during, and after administration. No clinical signs or symptoms of toxicity were observed during the study period at any dose level. The exposure doses NOEL and lowest-observed-effect level (LOEL) were estimated by Moeller and Rider (1962)
to be 0.2 and 0.3 mg/kg/day, respectively, based on a >10% inhibition of both plasma and erythrocyte cholinesterases as compared to baseline values. We reanalyzed the data of Moeller and Rider (1962)
, considering as significant an inhibition of
19% plasma and
12% erythrocyte cholinesterases in two successive measurements, as proposed by several authors (Gage, 1967
; Heath and Vale, 1992
; Larsen et al., 1982
). Though we used a different criterion, the NOEL and LOEL arrived at are identical to those of Moeller and Rider (1962)
. From the NOEL value of 0.2 mg/kg/day, the corresponding absorbed dose was estimated, and, using the model, associated urinary amounts of MCA, DCA, and phosphoric derivatives were obtained.
To determine biological reference values starting from the amounts of MCA, DCA, and phosphoric derivatives accumulated in urine 024 h following the onset of exposure, the model input conditions were set to generate biological reference values with a safety margin. This was achieved with an 8-h/day absorbed NOEL dose, using the slowest absorption rate constant found compatible with the various experimental data available. The fractions of total metabolites recovered in urine as MCA, DCA, and phosphoric derivatives were also attributed the smallest individual values determined using data from the study of Krieger and Dinoff (2000), who were the only authors to report individual values of MCA, DCA, and phosphoric derivatives in urine. These input conditions ensure a "minimal" urinary excretion of the metabolites for a given dose. This provides a safety margin through a possible overestimation of the dose absorbed when starting from urinary measurements.
Application.
The proposed biological reference values were applied to assess health risk in workers exposed to malathion. Unpublished urinary excretion data from botanical garden workers, collected by the Institut National de Santé Publique du Québec (INSPQ), Direction de la Toxicologie Humaine (Québec, Canada), were used. Twenty-four-hour urine samples were collected in male and female subjects (n = 9 and 2, respectively) following a 2- to 3-h work shift in greenhouses that had been sprayed with malathion 12 to 15 h earlier. The workers studied had not participated in the application of malathion.
Samples were analyzed for - and ß-MCA and DCA metabolite contents at the INSPQ. Urinary levels were determined by capillary gas chromatography/mass spectrometry (GC/MS) after acidification, extraction on C18 micro-columns, and derivatization with diazomethane. Analysis was performed using a model 5890 gas chromatography system (Hewlett Packard), a model 5972 mass spectrometer (Hewlett Packard) and a fused silica capillary column HP-5MS (30 m x 0.32 mm, 0.25 µm). The limits of detection for MCA and DCA were 2 and 1 µg/l, respectively. Average recovery of urine samples spiked with 10 µg/l of authentic reference standards was 113 and 108% for MCA and DCA, respectively (n = 10 samples). Inter-day coefficient of variation for replicate analysis of the same urine sample spiked with 10 µg/l of reference standard was 5.6 and 4.8% for MCA and DCA, respectively (n = 10 days).
RESULTS
Model Development
Table 2 presents parameter values of the model determined using the data of Feldmann and Maibach (1974)
and Jellinek et al.(2000)
. Figure 2
shows that with these parameter values, the model reproduces closely the data of Feldmann and Maibach (1974)
on the cumulative urinary excretion time course of total 14C in human volunteers exposed intravenously and dermally to 14C-labeled malathion. The model, with the same parameters, also simulates well the data of Jellinek et al. (2000)
on the cumulative urinary excretion of malathion metabolites 012, 024, and 048 h following an oral exposure to malathion in human volunteers (Table 3
). Asymptotically, the model predicts that 90% of the absorbed dose is excreted in urine; the rest is predicted to be excreted in feces, which is compatible with the rat data of Lechner and Abdel-Rahman (1986)
.
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From the cumulative urinary-excretion time courses of malathion metabolites (Fig. 6B), the model predicts that 32, 51, and 83% of total amounts recovered in urine are excreted during the first 8, 12, and 24 h, respectively, following a dermal exposure. Asymptotically, urinary MCA, DCA, and phosphoric derivatives represent 3.45, 0.83, and 2.06% of exposure dose (48.9, 11.8, and 29.2% of the absorbed dose; these latter excretions are identical to those after oral exposure, as should be). These simulations show that following a dermal exposure to malathion, the absorption-rate constant governs the overall urinary excretion rate of the metabolites, because the dermal absorption rate, kabs-dermal, is small compared to the biotransformation rate and renal clearance (represented in the model by kBM and kMU, respectively). According to model predictions, to recover half of the absorbed dose of malathion eventually excreted in urine as metabolites takes 11.8 h following a single dermal application, compared to 3.2 h following an intravenous injection and 4.0 h after oral exposure. Furthermore, as mentioned previously, the dermal absorption rate constant varies among individuals and depends on the skin region exposed to malathion. Table 4
shows that, for dermal exposure to malathion, varying the absorption rate constant markedly affects the cumulative urinary excretion time course of total metabolites (the sum of MCA, DCA, and phosphoric derivatives).
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Application
The model was used to assess the health risk related to malathion exposure in botanical-garden workers, starting from measurements of the amounts of MCA and DCA metabolites in 24-h urine samples (as described in the Materials and Methods section). The workers exhibited excretion values of MCA and DCA in their 24-h urine samples corresponding to a range of 0.017 to 0.210 times the biological reference value proposed for MCA and a range of 0.011 to 0.324 times the value proposed for DCA, indicating a negligible health risk for those workers (Table 6).
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A heuristic toxicokinetic model was developed, which integrates a wealth of experimental in vivo time-profile data, to uncover the critical biological determinants of the kinetics of malathion and its metabolites in humans. The model provides a good understanding of the time evolution of malathion and its metabolites in the body and in urine, under different exposure scenarios (single, repeated, continuous, or intermittent exposure). With this model, it is also possible to assess the effect of the route of entry on time-course data. In the context of biological monitoring, the model facilitates the interpretation of urinary measurements collected over different time periods. It can also be used to propose optimum sampling strategies for routine biological monitoring.
The model showed that, upon absorption, malathion in blood is either readily biotransformed or transferred to storage tissues, though only a small fraction of the absorbed dose of malathion is in fact transferred to storage tissues. Metabolites are also rapidly excreted in urine and feces. Consequently, the exposure route determines the time-dependent urinary excretion rate of malathion metabolites, because different routes-of-entry imply different absorption rates. For example, when simulating a single oral exposure, 8089% of the absorbed dose is eliminated from the body within 12 h, whereas, after a single dermal application, only between 2953% of the absorbed dose is excreted during the same time period (these value ranges are obtained by considering the lowest and highest absorption rate constant used to simulate the various available data). In workers who are typically exposed to malathion mainly through the skin, the absorption rate thus limits the output rates of metabolites in urine, because dermal absorption is much slower than biotransformation rate and renal clearance. This is not the case in individuals exposed by the oral or pulmonary routes, where the absorption rate is not a limiting step in the excretion kinetics.
Considering a continuous 8-h dermal exposure during a workday, the model predicts that 5280% of the absorbed dose will be eliminated from the body within the 24-h period following the onset of exposure, as compared to 8498% within 48 h. Consequently, for the biological monitoring of worker exposure to malathion through measurements of the amounts of urinary metabolites, collection of 24-h urine samples, starting at the beginning of the work shift, appears as an adequate sampling strategy. In the case of workers subjected to exposure day after day, malathion is predicted by the model to build up in storage tissues during the course of a workweek, thus resulting in a progressive increase in total body burden. Repeating the exposure from week to week does not, however, cause any significant increase in maximum and minimum body burdens. In this situation, it is best to collect the 24-h urine samples on the last day of a workweek. Measurements should be repeated periodically when exposure levels are suspected to vary significantly with time.
It is interesting to note that, based on the data of Jellinek et al. (2000), cumulative amounts of metabolites in urine (expressed as a percentage of the malathion exposure dose) appear in the following order: MCA > phosphoric derivatives > DCA. These findings, combined with the fact that the MCA metabolite is specific to malathion exposure (Márquez et al., 2001
; Tuomainen et al., 2002
), suggest that MCA in urine is the most useful individual biological indicator of exposure to malathion. Of course, measurements of phosphoric derivatives can also be of interest as nonspecific bio-indicators of exposure to organophosphate compounds (Cocker et al., 2002
; Coye et al., 1986
).
The model enables links to be established at all times between the dose, the body burden of malathion and that of its metabolites, and the amounts of specific metabolites excreted in urine. The model can thus be used to reconstruct, through back calculations, the absorbed dose of malathion following oral or dermal exposure, starting from measurements of cumulative amounts of a specific metabolite excreted in urine over a given period of time. Reconstructing the absorbed dose from metabolites in urine avoids unnecessary assumptions or approximations about the absorption fraction, which is known to be subject to large inter-individual variations and, in the case of dermal exposure, to vary according to anatomical skin regions (ACGIH, 2002; Cohen and Rice, 2001
; Feldmann and Maibach, 1974
).
It was also possible, with the model, to propose biological reference values with a margin of safety by simulating a dermal exposure scenario typical of a daily worker exposure, and using a combination of kinetic parameters that overestimates the reconstructed absorbed dose, starting from urinary measurements. The establishment of biological reference values, based on a dermal-exposure scenario rather than an oral or pulmonary exposure, contributes to safe estimates whatever the exposure route. Indeed, in an individual exposed to malathion mainly by inhalation or ingestion, the urinary excretion of metabolites is more rapid than following a dermal exposure and hence urinary excretion values in 24-h urine samples collected at the beginning of an exposure period are more important. It should also be reminded that the biological reference values were determined with the model, using the slowest absorption rate found compatible with the available literature data on excretions, together with the lowest individual values found for the fractions of total metabolites recovered in urine as MCA, DCA, or phosphoric derivatives. This was to consider the variations in the absorption rate among individuals and depending on the exposed skin regions (Feldmann and Maibach, 1974) as well as the reported variations from one study to another in the fractions of total metabolites recovered in urine as MCA, DCA, and phosphoric derivatives (Bradway and Shafik, 1977
; Jellinek et al., 2000
; Krieger and Dinoff, 2000
). However, the study of Jellinek et al.(2000)
showed that over the 0.5 to 15 mg/kg oral dose range, the fraction of total metabolites recovered in urine as MCA, DCA, and phosphoric derivatives was independent of the dose.
To assess the risk of exposure to malathion, the amounts of MCA, DCA, and phosphoric derivatives in the 24-h urine samples of exposed subjects can be compared to the proposed reference values. Ideally, it is best to establish the risk from measurements of the sum of acids rather than MCA or DCA individually, for a specific but more precise estimate. Otherwise, the risk can be estimated from measurements of the sum of all metabolites in urine, which, despite not being entirely specific to malathion exposure, allows the bypass of uncertainties about the relative weights of each metabolite in total excretion. Also, when 24-h urine samples cannot be collected for practical reasons, collection periods can be shortened; Table 5 can be used to estimate the 24-h urinary excretion in exposed subjects starting from measurements over other urine collection periods. For a "safe-side" estimation, excretion values in Table 5
obtained from the slowest absorption rate should be used. However, to minimize the effects of inter-individual and inter-site variations of absorption rates on urinary outputs, it is best to collect urine samples over the longest possible time period.
The model and proposed biological reference values were used in this study to assess the risk for workers exposed to malathion in a botanical garden. The risk was predicted to be negligible given that the measurements of MCA and DCA in 24-h urine samples were lower than the proposed biological reference values. In general, these workers appear to be less exposed than the greenhouse workers of a recent Spanish report (Márquez et al., 2001) where MCA was measured in the urine of three individuals following applications of malathion. The total amounts of MCA excreted during the 24-h period following applications were calculated to be 134, 182, and 671 µg, corresponding to 31.7, 8.6, and 6.3 nmol/kg, assuming a body weight of 70 kg. These values represent respectively 0.72, 0.20, and 0.14 of the proposed biological reference value.
In summary, a toxicokinetic model was developed that accounts for the constraints related to significant variations in some of the parameters: (i) variations in the dermal absorption fraction and the rate of absorption among individuals and according to the exposed skin regions and (ii) variations from one report to another in the fractions of total metabolites recovered in urine as MCA, DCA, and phosphoric derivatives. In this study, the model was used to reconstruct the absorbed dose starting from measurements of urinary biomarkers; the effect of varying absorption fractions was thus bypassed. Variations in the absorption rate and relative proportions of the different metabolites in urine can impair accurate estimation of the absorbed dose starting from amounts of biomarkers in urine. Under these conditions, to calculate biological reference values with a margin of safety, the smallest values for the latter parameters afforded by the literature were used. This leads to a possible overestimation of the corresponding value for the reconstructed dose. The model also assumes the absence of saturation in the metabolism and clearance processes; over the exposure dose range modeled in this study, the available data in volunteers and workers were accurately predicted without having to introduce saturation. The model cannot, however, be used to predict the kinetics in the saturating exposure dose ranges, as expected in the case of intoxicated subjects. The available literature data remained sufficient to build a robust model to better understand the kinetics of malathion and its metabolites and to propose convenient biological reference values together with sampling strategies. These can be of use immediately for the biological monitoring of malathion exposure through measurements of metabolites in urine.
APPENDIX
First order linear differential equations for each compartment. From Figure 1, the following differential equations are obtained (see Table 1
for definitions of symbols and abbreviations):
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where g(t) is the absorbed oral or dermal dose Dabs per unit of time.
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where fabs is the absorption fraction and Dexp is the exposure dose.
For intravenous injection, g(t) = 0 for t > 0 and at time t = 0: B(0) = Dabs.
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where: i {MCA-C, DCA-C, DMDTP-C, DMTP-C, DMP-C} = 1, and Mi(t) = M(t).
ACKNOWLEDGMENTS
This study was funded by the Institut de Recherche en Santé et Sécurité du Travail du Québec. Authors wish to thank Pierre Dumas of the Institut national de Santé Publique du Québec for the analysis of malathion metabolites in urine.
NOTES
1 To whom correspondence should be addressed. Fax: (514) 343-2200. E-mail: gaetan.carrier{at}umontreal.ca.
REFERENCES
ACGIH (2002). Documentation of the Threshold Limit Values and Biological Exposure Indices, 7th ed. American Conference of Governmental Industrial Hygienists, Cincinnati, OH.
Adgate, J. L., Barr, D. B., Clayton, C. A., Eberly, L. E., Freeman, N. C. G., Lioy, P. J., Needham, L. L., Pellizzari, E. D., Quackenboss, J. J., Roy, A., and Sexton, K. (2001). Measurement of childrens exposure to pesticides: Analysis of urinary metabolite levels in a probability-based sample. Environ. Health Perspect. 109, 583590.[ISI][Medline]
Bradway, D. E., and Shafik, T. M. (1977). Malathion exposure studies. Determination of mono-and dicarboxylic acids and alkyl phosphates in urine. J. Agric. Food Chem. 25, 13421344.[ISI][Medline]
Bronaugh, R. L., and Maibach, H. I. (1999). Percutaneous Absorption. DrugsCosmeticsMechanismsMethodology, 3rd ed. Marcel Dekker, New York.
Carmichael, N. G., Nolan, R. J., Perkins, J. M., Davies, R., and Warrington, S. J. (1989). Oral and dermal pharmacokinetics of triclopyr in human volunteers. Hum. Toxicol. 8, 431439.[ISI][Medline]
Cocker, J., Mason, H. J., Garfitt, S. J., and Jones, K. (2002). Biological monitoring of exposure to organophosphate pesticides. Toxicol. Lett. 134, 97103.[CrossRef][ISI][Medline]
Cohen, D. E., and Rice, R. H. (2001). Toxic responses of the skin. In Casarett & Doulls Toxicology: The Basic Science of Poisons, 6th ed. (C. D. Klaassen, Ed.), pp. 653671. McGraw-Hill, New York.
Coye, M. J., Lowe, J. A., and Maddy, K. J. (1986). Biological monitoring of agricultural workers exposed to pesticides: II. Monitoring of intact pesticides and their metabolites. J. Occup. Med. 28, 628636.[ISI][Medline]
Dennis, G. A., and Lee, P. N. (1999). A phase-1 volunteer study to establish the degree of absorption and effect on cholinesterase activity of four head lice preparations containing malathion. Clin. Drug Invest. 18, 105115.[ISI]
Ecobichon, D. J. (1992). The Basis of Toxicity Testing, 2nd ed. CRC Press, Boca Raton, FL.
Environnement Québec (2002). Bilan des Ventes de Pesticides au Québec en 1997. Gouvernement du Québec, Québec, Canada.
Feldmann, R. J., and Maibach, H. I. (1974). Percutaneous penetration of some pesticides and herbicides in man. Toxicol. Appl. Pharmacol. 28, 126132.[ISI][Medline]
Fenske, R. A. (1988). Correlation of fluorescent tracer measurements of dermal exposure and urinary metabolite excretion during occupational exposure to malathion. Am. Ind. Hyg. Assoc. J. 49, 438444.[ISI][Medline]
Fenske, R. A., and Leffingwell, J. T. (1989). Method for the determination of dialkyl phosphate metabolites in urine for studies of human exposure to malathion. J. Agric. Food Chem. 37, 995998.[ISI]
Gage, J. C. (1967). The significance of blood cholinesterase activity measurements. Residue Rev. 18, 159173.[Medline]
Heath, A. J. W., and Vale, J. A. (1992). Clinical presentation and diagnosis of acute organophosphorus insecticide and carbamate poisoning. In Clinical and Experimental Toxicology of Organophosphates and Carbamates (B. Ballantyne and T. C. Marrs, Eds.), pp. 513519. Butterworth-Heinemann, Oxford.
Jellinek, Schwartz, and Connolly, Inc. (2000). The Effects and Pharmacological Disposition of a Single Oral Dose of Malathion Administered to Human Volunteers. Cheminova, Lemvig, Denmark. Unpublished report, CHA Doc. No.: 299 FYF Amdt-4. Submitted to EPA on 5/17/00, MRID 45125601.
Krieger, R. I., and Dinoff, T. M. (2000). Malathion deposition, metabolite clearance, and cholinesterase status of date dusters and harvester in California. Arch. Environ. Contam. Toxicol. 38, 546553.[CrossRef][ISI][Medline]
Larsen, K.-O., and Hanel, H. K. (1982). Effect of exposure to organophosphorus compounds on cholinesterase in workers removing poisonous depots. Scand. J. Environ. Health 8, 222228.[ISI]
Lechner, D. W., and Abdel-Rahman, M. S. (1986). Kinetics of carbaryl and malathion in combination in the rat. J. Toxicol. Environ. Health 18, 241256.[ISI][Medline]
Liu, J, and Pope, C. N. (1998). Comparative pre-synaptic neurochemical changes in rat striatum following exposure to chlorpyrifos or parathion. J. Toxicol. Environ. Health, Part A 53, 531544.[CrossRef][Medline]
MacIntosh, D. L., Needham, L. L., Hammerstrom, K. A., and Ryan, P. B. (1999). A longitudinal investigation of selected pesticide metabolites in urine. J. Exp. Anal. Environ. Epidemiol. 9, 494501.[CrossRef][ISI][Medline]
Maibach, H. I., Feldmann, R. J., Milby, T. H., and Serat, W. F. (1971). Regional variation in percutaneous penetration in man: Pesticides. Arch. Environ. Health 23, 208211.[ISI][Medline]
Márquez, M. C., Arrebola, F. J., Egea González, F. J., Castro-Cano, M. L., and Martínez Vidal, J. L. (2001). Gas chromatographic-tandem mass spectrometric analytical method for the study of inhalation, potential dermal, and actual exposure of agricultural workers to the pesticide malathion. J. Chromatogr. A 939, 7989.[CrossRef][ISI][Medline]
Moeller, H. C., and Rider, J. A. (1962). Plasma and red blood cholinesterase activity as indications of the threshold of incipient toxicity of ethyl-p-nitrophenyl thionobenzene-phosphonate (EPN) and malathion in human beings. Toxicol. Appl. Pharmacol. 4, 123130.[CrossRef][ISI][Medline]
Nolan, R. J., Rick, D. L., Freshour, N. L., and Saunders, J. H. (1984). Chlorpyrifos pharmacokinetics in human volunteers. Toxicol. Appl. Pharmacol. 73, 815.[CrossRef][ISI][Medline]
Rabovsky, J., and Brown, J. P. (1993). Malathion metabolism and disposition in mammals. J. Occup. Med. Toxicol. 2, 131168.
Roberts, R. J. (2002). Head lice. New Engl. J. Med. 346, 16451650.
Segel, L. A. (1988). On the validity of the steady-state approximation of enzyme kinetics. Bull. Math. Biol. 50, 579593.[ISI][Medline]
Segel, L. A., and Slemrod, M. (1989). The Quasi-Steady State Approximation: A case study in pertubation. SIAM Rev. 31, 446476.[ISI]
Sidell, F. R. (1994). Clinical effects of organophosphorus cholinesterases inhibitors. J. Appl. Toxicol. 14, 111113.[ISI][Medline]
Tuomainen, A., Kangas, J. A., Meuling, W. J. A., and Glass, R. C. (2002). Monitoring of pesticide applicators for potential dermal exposure to malathion and biomarkers in urine. Toxicol. Lett. 134, 125132.[CrossRef][ISI][Medline]
U.S. EPA (2000). Malathion. Office of Prevention, Pesticides, and Toxic Substances, Environmental Protection Agency, Washington, DC.
Wester, R. C., Maibach, H. I., Bucks, D. A. W., and Guy, R. H. (1983). Malathion percutaneous absorption after repeated administration to man. Toxicol. Appl. Pharmacol. 68, 116119.[CrossRef][ISI][Medline]