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Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure
Toxicology Letters 232 (2015) 21–27
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Toxicology Letters journal homepage: www.elsevier.com/locate/toxlet
Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure R.Z. Boerleider a,b , J.D.N. Olie a,c , J.C.H. van Eijkeren d, P.M.J. Bos d , B.G.H. Hof a,e , I. de Vries a , J.G.M. Bessems d,1, J. Meulenbelt a,b,f , C.C. Hunault a, * a
National Poisons Information Center, University Medical Center Utrecht (UMCU), P.O. Box 85500, 3508 GA Utrecht, The Netherlands Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands c Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands d National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 Bilthoven, The Netherlands e Department of Pharmaceutical Sciences, University of Utrecht, P.O. Box 80082, 3508 TB Utrecht, The Netherlands f Department of Intensive Care Medicine, University Medical Center (UMCU), P.O. Box 85500, 3508 GA Utrecht, The Netherlands b
H I G H L I G H T S
In silico simulations of human data on acute inhalation exposure to dichloromethane. Evaluation of three available physiologically based pharmacokinetic models. Assessment of the models’ usefulness in supporting emergency risk assessment. Generic models can be used for screening purposes. A chemical-specific model is more appropriate for a detailed application.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 August 2014 Received in revised form 2 October 2014 Accepted 6 October 2014 Available online 13 October 2014
Introduction: Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen).
Keywords: Dichloromethane PBPK model Acute exposure Risk assessment Intoxication Poisoning
Materials and methods: We assessed the accuracy of the models’ predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1 h. The timecourse of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated. Results: With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52–4.19 mg/L with the Bos model, 1.42–4.04 mg/L with the Mumtaz model, and 1.81–4.31 mg/L with the Jongeneelen model compared to 0.27–5.44 mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4–2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels. Conclusions: The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment. ã 2014 Elsevier Ireland Ltd. All rights reserved.
* Corresponding author. Tel.: +31 88 75 595 42; fax: +31 88 75 556 77. E-mail address: [email protected] (C.C. Hunault). Current address: European Commission, JRC, Institute for Health and Consumer Protection, Unit I.05 Systems Toxicology – T.P. 202, Via E. Fermi 2749; I-21027 Ispra, Italy.
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http://dx.doi.org/10.1016/j.toxlet.2014.10.010 0378-4274/ ã 2014 Elsevier Ireland Ltd. All rights reserved.
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1. Introduction Dichloromethane (DCM) (or methylene chloride) is widely used as an industrial solvent and is also frequently involved in acute chemical incidents, even though its use is strictly regulated e.g., in Annex VII of REACH (EU, 2010) and by OSHA (OSHA, 2014a). It is a lipophilic volatile chemical that exists at room temperature as a colorless liquid with a sweet odor. DCM is used in paint removal, chemical processing, and metal cleaning and has various other solvent-related uses. Inhalation is, by far, the most frequent route of human exposure (SCCS, 2012) and can cause mild to serious health effects, including severe neurological symptoms (loss of consciousness, respiratory depression) resulting in coma, hypoxia, and even death (ATSDR, 2000, 2010). Physiologically based pharmacokinetic models (PBPK) may facilitate emergency risk assessment after single inhalation or oral exposure to a chemical by estimating the dose absorbed in a specific organ (Mumtaz et al., 2012; Scheepers, 2010; Hunault et al., 2014). PBPK models represent the body as a set of functional compartments (e.g., blood, brain, liver, muscle and skin, rest of the body) and simulate the pharmacokinetic profiles of chemicals in animals and humans on the basis of available information on relevant physiological, physico-chemical, and biochemical factors. (Meibohm and Derendorf, 1997; Andersen, 2003). After a chemical incident, when exposure levels in the air are known or can be reasonably estimated, PBPK models may be useful in determining whether conducting biomonitoring can be valuable and what the optimal blood sampling time-frame would be. In addition, when external exposure levels are not (yet) known, PBPK modeling in the reverse dosimetry mode (from a blood concentration to an exposed air concentration) could help estimating the external exposure over time after exposure to chemicals (Clewell et al., 2008). By using such models, it may be useful to determine whether certain individuals are at risk of developing severe symptoms. We therefore aimed to evaluate whether three PBPK models could be used for emergency risk-assessment after acute chemical exposure to DCM. The Bos model is specifically developed for DCM (Bos et al., 2006), the Mumtaz model is a model for volatile organic chemicals (VOC) (Mumtaz et al., 2012), and the Jongeneelen model (Jongeneelen and Ten Berge, 2011) is a semi-generic model for volatile and semi-volatile chemicals. ‘Semi-generic’ means that the model is largely generic, but also uses built-in quantitative structure–activity relationships (QSARs) for setting blood:air partitioning, tissue:blood partitioning, and renal excretion.
Fig. 1. Biotransformation of dichloromethane (modified after OSHA, 2014b).
2.2. The Bos model (Bos et al., 2006) This PBPK model was specifically developed for DCM. The model comprises several body compartments (Fig. 2a) and takes into account the saturable P-450 cytochrome step, the GSHconjugation, as well as the genetic polymorphism of the relevant GST (GSTT) in humans. The model is a combination of two other models, the Andersen and the Reitz PBPK models (Andersen et al., 1991; Reitz et al., 1997). The two main advantages of combining these two was to enable the Bos model to calculate the DCM concentration in brain and the HbCO level resulting from an exposure to DCM. The Bos model has already been verified with two experimental human studies (Åstrand et al., 1975; DiVincenzo and Kaplan, 1981) and runs in ACSL. The code of the Bos model is freely available (in the Appendix of the Bos article).
2. Material and methods 2.3. The Mumtaz model (Mumtaz et al., 2012) 2.1. DCM metabolism DCM is metabolised via two pathways (Fig. 1). The first pathway is P-450 mediated and is saturable. CYP 2E1 is the main isoenzyme involved (SCCS, 2012). Via this pathway, DCM is metabolised to carbon monoxide (CO), leading to increased HbCO levels, causing a decrease in oxygen transport capacity and tissue oxygenation. HbCO can peak hours after exposure ceases, as fat and other tissues continue to release accumulated DCM (Rioux and Myers, 1989; Mahmud and Kales, 1999). The second pathway involves GSH-conjugation via glutathione (GSH) and results in carbon dioxide, with formaldehyde and formic acid as metabolic intermediates. The glutathione S-transferases (GST) pathway is increasingly important at high exposure DCM levels. It is noted that in the human population, non, low, and highconjugators (genetic polymorphism) have been identified (Bogaards et al., 1993; Mainwaring et al., 1996; Sherratt et al., 1997; SCCS, 2012).
This PBPK model was developed for VOCs. They are organic chemical compounds, such as benzene or formaldehyde, that evaporate at room temperature under normal, indoor atmospheric conditions. VOCs represented 23% of the exposures after acute chemical incidents, in the Netherlands, between 2008 and 2010 (Hunault et al., 2014). The Mumtaz model divides the body into seven compartments (Fig. 2b). Its equations use human metabolic, physiological and physicochemical parameters to estimate the concentration of a chemical in each compartment. The model can be used for oral, inhalation and dermal absorption. After absorption and distribution, chemicals are metabolised by the liver or exhaled into the air. The model is not specifically developed for DCM although DCM was one of the seven volatiles used to construct the model and, therefore, does not make a distinction between the two metabolism pathways of DCM. It cannot deal with the genetic polymorphism of human GSTT and it has already been verified with experimental human
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a) Bos
Cinh
Qalv
Alveolar Space
Calv (Cart/Pb)
Lung Blood
Cart
Fat Tissue Group Cvf
Cvr
Slowly Perfused Tissue Group Rapidly Perfused Tissue Group Brain
Cvb Cvl
Inhaled compound
Liver
Qf Cart Qs Cart Qr
Exhaled compound
Alveolar Air QC CV QR CVR
Cvs
Exhale
Inhale
QC CA
Alveolar blood
Alveolar Space
Qc
Qc Cven
c) Jongeneelen
b) Mumtaz
Qalv
23
QS
Lung blood
QR
Richly Perfused
CA
CA
CVS QF CVF
Heart
QS
Slowly Perfused
QF
Fat
Dermal Load
CA
QSk
QSk
Skin
CVSk
Cart
CVK
Ql
QL
Cart
CVL
Adipose
Venous Blood
Dermal Water QK
Dermis
CA
Cart Qb
Evaporated compound
Brain
QK
Kidney
CA QL
Liver
Arterial Blood
Muscle Oral Intake
Bone + Marrow
CA
Stomach + Intestine Vmax Km,KGSH
Metabolites
K Dose
Metabolism
Liver Kidney Excreted compound/ metabolite in urine Blood flow Metabolites
Fig. 2. Schematic presentation of (a) the Bos PBPK model (modified after Bos et al., 2006), (b) the Mumtaz PBPK model (modified after Mumtaz et al., 2012), and (c) the Jongeneelen model (modified after www.cefic-lri.org).
studies for various volatiles (Mumtaz et al., 2012). In the case of DCM, the experimental data used concerned one subject with an intermittent exposure to DCM and a varying physical activity (Åstrand et al., 1975). The Mumtaz model runs in Berkeley Madonna and its code is freely available in an appendix to the paper. 2.4. The Jongeneelen model (Jongeneelen and Ten Berge, 2011) This PBPK model is also called the “IndusChemFate model” (Fig. 2c) and is freely available (http://www.cefic-lri.org/lritoolbox/induschemfate). It was developed for volatile and semivolatile chemicals. It can estimate concentrations and amounts in blood, urine and organs after inhalation, ingestion or dermal exposure according to user-defined exposure scenarios. The model contains algorithms as QSARs, which allows the estimation of, e.g., absorption upon inhalation or diffusion from blood into tissues. The Jongeneelen and Ten Berge model was evaluated for several compounds (e.g., pyrene, N-methyl-pyrrolidone, methyl-tertbuthylether, heptane, 2-butoxyethanol and ethanol) but not for DCM. The Jongeneelen model runs in MS Excel and its code is written in Visual Basic. 2.5. Human data used The human data were obtained from a prior healthy volunteer study (Van Veen et al., 2002) approved by the Medical Ethical Board of the University Medical Center, Utrecht. In this study, 11 healthy volunteers were individually exposed to DCM for 1 h, with or without an air mask. In this paper, only data from the 6 volunteers who did not wear an air mask during the experiment
were included (Table 1). They brushed a paint stripper containing DCM on a wooden surface of 1.28 m2 in a closed room. Once the application was completed, the volunteers remained in the room. Ten minutes before the end of the 1 h exposure period, they had to remove the paint (with DCM) from the surface with a brush and scraper. At the end of the 1 h exposure, they left the room. The stripped surface and the temporary floor covering were taken out of the room before the start of the next experiment. In between the experiments, the room was thoroughly ventilated until original levels of DCM and CO were reached. The DCM air concentrations in the closed room were measured by a Miran 1B2 infrared spectrophotometer at 1.60 m from the floor. Fig. 3 shows the DCM air concentration in the room over time. Eighteen blood samples were drawn from each volunteer (from T = 1 h up to 48 h post-exposure). DCM concentrations were measured in whole blood using gas chromatography. For the percentage of carboxyhemoglobin (% HbCO), a CO-oximeter was used.
Table 1 Demographics and individual external DCM exposure dose (AUCs) of the healthy volunteers. Participant
Height (cm)
Weight (kg)
Age (y)
Tobacco status
AUC (ppm h)
1 2 3 4 5 6
178 185 178 186.5 190 177
79 81 65 85 83 64
27 23 25 23 26 21
Non-smoker Non-smoker Non-smoker Non-smoker Non-smoker Non-smoker
279 351 386 173 229 194
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Fig. 3. Dichloromethane (DCM) air concentration in the room over time (modified after Van Veen et al., 2002).
2.6. Analyses and simulations We calculated the total amount of external DCM for each participant by computing individual AUCs (area under the curve). These were computed up to the last concentration equal or above the LOQ (limit of quantification), using the trapezoidal rule. The three models were then used to simulate DCM blood concentrations following DCM exposure. For the purposes of simplicity, a ‘block-exposure’ scenario was simulated i.e., an exposure to a constant DCM concentration in air for 1 h. Percentages of HbCO in blood were also simulated with the Bos model. During assessment of the model performances, the simulations of the blood concentrations by the models were visually compared to the observed blood DCM concentrations and % HbCO. Then, we performed a more quantitative assessment according to Mumtaz (Mumtaz et al., 2012). We calculated the observed and predicted AUC values for the DCM blood concentration and % HbCo (using the trapezoidal rule). Predicted AUC values for the % HbCO were calculated only for the Bos model as this was the only model which allowed this simulation. We assessed the performance of each model by calculating the mean ratio (AUCr) between the AUC predicted by one of the three models and the AUC value computed from the observed data (mean of the six individual AUC ratios). We interpreted the ratio as ‘the closer the value is to 1, the better the agreement between observation and model prediction’. We also calculated the mean of the sum of the squared differences (MSSDs) between model predictions and observation. To obtain the MSSDs, we squared the difference between a measured data point and the value of the simulation at the corresponding time. These squared differences were totalled and then divided by the number of data points. We interpreted the MSSD value as ‘the lower the value, the better the fit’. Simulations were performed using the acslX software version 11.8 for the Bos model (www.acslx.com) and the Berkeley Madonna software version 8.3.11 (www.berkeleymadonna.com) for the Mumtaz model. Simulations with the Jongeneelen model v2.00 were run in MS Excel version 2004 on Windows XP Service pack 3. 3. Results The calculated DCM external dose (area under exposure-time curves) ranged between 173 and 386 ppm h (Table 1). The measured Cmax of DCM in blood ranged between 0.27 and 5.44 mg/L at the end of the exposure (1 h) (Fig. 4). The predicted DCM blood concentration after 1 h ranged between 1.52–4.19 mg/L with the Bos model (Fig. 4a), 1.42–4.04 mg/L with the Mumtaz model (Fig. 4b) and 1.81–4.31 mg/L with the Jongeneelen model (Fig. 4c). For all volunteers, the DCM concentration-time profiles
Fig. 4. Observed and predicted dichloromethane (DCM) blood concentrations (mg/ L); (a) simulation using the Bos model, (b) simulation using the Mumtaz model, and (c) simulation using the Jongeneelen model. E: duration of exposure to DCM (60 min).
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Table 2 Comparison of the predicted values with the experimental data for DCM blood concentrations and % HbCO (AUC ratios and MSSD values). Metric
Mean (SD) DCM
% HbCO No calibration
% HbCO Calibration #1a
% HbCO Calibration #2b
AUCr Bos model AUC/data AUC Mumtaz model AUC/data AUC Jongeneelen model AUC/data AUC
2.1 (1.8) 2.1 (1.8) 2.8 (2.5)
11.1 (7.1) NAc NAc
3.0 (1.8) NAc NAc
5.0 (3.2) NAc NAc
MSSD Bos model MSSD Mumtaz model MSSD Jongeneelen model MSSD
0.49 (0.1) 0.44 (0.1) 0.59 (0.3)
5.87 (1.0) NAc NAc
0.47 (0.2) NAc NAc
1.22 (0.5) NAc NAc
a b c
Calibration of the Bos model using the individual baseline % HbCO level. Calibration of the Bos model using 0.5% HbCO level as standard baseline % HbCO in non-smokers (Wald et al., 1981). Not applicable.
(shape) were well predicted with both the Bos and Mumtaz models, especially in the elimination phase. The Jongeneelen model showed a somewhat slower systemic clearance than the observed data suggested. The mean AUCr values for the Bos and Mumtaz models were 2.1, indicating a reasonable representation of the observations by model predictions (Table 2). The AUCr value for the Jongeneelen model was 2.8, indicating a somewhat larger over-prediction of the DCM blood concentration. The MSSD values were quite similar for the Bos and Mumtaz models (0.4851 and 0.4361, respectively) and higher for the Jongeneelen model (0.5888) (Table 2). The highest % HbCO in the healthy volunteers was reached between 2 and 3 h after the onset of exposure and ranged between 0.4 and 2.3% (Fig. 5a). % HbCO could only be simulated by the Bos model. The highest predicted % HbCO ranged between 3.1 and 4.2% and was reached between 1.75 and 2.25 h after DCM exposure. The shape of the % HbCO over time resembled the shape of the observed data but the predicted % HbCO were systematically higher. By default, the baseline of the % HbCO is higher in the Bos model than was the baseline % HbCO value measured among the participants. The % HbCO measured at baseline in the healthy volunteers were very low (range: 0–0.5%). We therefore modified the baseline endogenous production of CO and calibrated the Bos model in order to get a baseline % HbCO level equal to the measured individual % HbCO levels. Predictions of the highest % HbCO levels were then satisfactory (1.3–2.3%, Fig. 5b). The AUCr for the % HbCO was 11.1 without calibration and 3 with individual calibration, indicating over-prediction of the % of HbCO (Table 2). The MSSD values for the % of HbCO were the lowest after using the individual baseline % HbCO (Table 2). As baseline % HbCO levels of patients are unknown in case of acute chemical incidents, we predicted the % HbCO again, using the value of 0.5% as standard baseline % HbCO (instead of the individual baseline % HbCO) (Wald et al., 1981). Fig. 5c shows the resulting predictions (range of highest % HbCO: 1.7–2.8%). 4. Discussion The aim of this study was to evaluate the performance of three PBPK models in emergency risk-assessment after single exposure to DCM. Results show that all three models can predict DCM blood concentrations reasonably well. Only marginal differences were observed in the predicted blood concentration by the Bos and Mumtaz models. However, only the Bos model can predict the % HbCO, which is one of the toxic end-points in dichloromethane intoxications.
The Bos and Mumtaz models are more difficult to use than the Jongeneelen model which runs in Excel. Their codes require a minimum of knowledge in kinetics and of practice with acslX or Berkeley Madonna software. To make predictions with the Bos model, we first had to convert the published ACSL code into acslX code as the former is no longer available. The acslX software is a more modern version of the ASCL software. It is not freely available and is highly specific, which limits the number of potential users. It would be interesting to see what the benefits of converting the model code into a general computer language could be. Another possibility could be to reconstruct and parameterise the model, then convert the model code into an optimal computer language by using an easy-to-use web application such as UK HSLs Model Equation Generator ‘MEGen’ (Loizou and Hogg, 2011). This application is a code generator that produces codes such that they can be solved by software package of choice (e.g., conversion option to acslX and Berkeley Madonna is available). The Berkeley Madonna code of the Mumtaz model is available in the Mumtaz paper, and could also be used for other VOCs (e.g., for toluene, benzene). The Jongeneelen model, implemented in Excel, is the easiest to use. The strength of the Mumtaz and Jongeneelen models is that they are applicable for multiple substances. They can therefore be used for a first screening or a first impression, keeping in mind that they may imply possible uncertainties due to the lack of possible chemical-specific kinetic processes (e.g., no equation describing in detail the metabolism pathways). The Bos model can be used for a more detailed application. The models then have a different function, and might be used in the context of a chemical incident in the form of a step by step approach: screening by using e.g., the Jongeneelen model versus a more detailed approach using the Bos model, depending on what is desired. Being able to simulate the experimental data of the healthy volunteers was difficult due to the fact that the exposure conditions varied more than in previous studies used to validate the Bos and Mumtaz models (Åstrand et al., 1975; DiVincenzo and Kaplan, 1981; Mumtaz et al., 2012). The study from which the experimental data were used to assess the models, was originally designed to validate a mathematical paint exposure model, and, therefore, aimed to reflect real life situations. This explains why the participants were not exposed to DCM using breathing masks, but rather had to mimic someone who would normally apply paint stripper on a surface and subsequently remove it with a brush and scraper. This experimental study had been conducted in our department years ago but only the results concerning the modeling of the air DCM concentration in the room have ever been published (Van Veen et al., 2002). However, the available original Case Report Forms and source documents contained data on participants, the
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Fig. 5. Observed and predicted carboxyhemoglobin levels (% HbCO) in blood, using the Bos model: (a) without calibration, (b) with calibration of the model, using the individual baseline % HbCO, (y-axis is a normalised scale, i.e., individual background level set at 0%), and (c) with calibration of the model, using as a baseline % HbCO of 0.5% (Wald et al., 1981). E: duration of exposure to dichloromethane (60 min).
circumstances of the exposure sessions, and the blood DCM concentration and % of HbCO. Room temperature and the physical activity of scraping off paint remnants varied between the different exposure sessions, e.g., sunshine raised the room temperature at the end of the experiment with Participant 2, also increasing the volatility of DCM and resulting in higher DCM air concentrations. Differences in physical activity of scraping off the paint remnants may have led to a higher heart rate and blood flow through the lungs, and hence to variations in DCM uptake. Another variation is the inter-individual difference in DCM metabolism related to GST polymorphism and inter-individual and time-variable differences in expression of the relevant P-450 (Dankovic and Bailer, 1994; SCCS, 2012). The P450 isoenzym CYP 2E1 is specifically involved in the biotransformation of DCM. It is expressed in human liver and in extra-hepatic tissues as well, inducible by ethanol (SCCS, 2012). Furthermore, its activity may vary, depending on the use of certain drugs such as acetaminophen, which has been shown to induce CYP2E1 in rats, at sub-toxic doses (Kim et al., 2007). As for GST polymorphism, the incidence of non-conjugators has been estimated to be 22% for African Americans and 62% for Asian Americans (Haber et al., 2002; Pemble et al., 1994). Fast metabolizers with a high % HbCO will be more at risk if they have ischemic cardiac diseases, whereas slow metabolisers with high DCM blood concentrations will be more at risk of developing acute CNS effects, if we assume that the parent chemical is the toxic agent in the brain, and not the metabolite. The anesthetic effect of DCM and the hypoxic effect of HbCO may both contribute to CNS depression (Stewart et al., 1972; Hall and Rumack, 1990) sometimes leading to coma and accidental deaths (Zarrabeitia et al., 2001; Macisaac et al., 2013). Genotype was not determined in the experimental study so we could not use this variable as potential cause of inter-individual variability for the simulations. Participant 6 seems to be a relative outlier with low DCM blood concentrations and % HbCO which might be explained by a lower physical activity resulting in a lower inhaled DCM exposure dose, and/or by a more efficient biotransformation of DCM due to genetic polymorphism or enzyme induction. PBPK models may be useful after an acute chemical incident in determining whether conducting biomonitoring is valuable. Such PBPK models may be useful to the Dutch National Poisons Information Center, which provides advice after chemical incidents, including advice on the relevancy of conducting a biomonitoring study after an acute incident and the best time for sampling. In the case of DCM, usually, only the % HbCO can be measured within several hours after the incident, which makes the Bos model the most interesting for our center as it is the only model that can predict the % HbCO. The total amount of HbCO in blood is the sum of endogenous CO production in the blood, pulmonary absorption, and CO produced by the metabolism of DCM. Therefore, the HbCO concentration after DCM exposure is described by these three components in the Bos model. The % HbCO predicted by the Bos model differed from the observed percentage in the healthy volunteer study, very likely due to differences between the single inherent basic endogenous HbCO level at baseline in the Bos model and the actual HbCO level in the healthy volunteers. All healthy volunteers had a very low % HbCO level at baseline so we had to calibrate the Bos model in order to get more satisfactory predictions. The best prediction of % HbCO levels were obtained after using the individual baseline % HbCO levels. As such information is unavailable during acute chemical incidents, we also looked at the Bos model’s performance with 0.5% as a standard plausible low baseline value. This value of 0.5 comes from an epidemiological study (Wald et al., 1981) that included a large number of people (N = 11249 among which 6641 non-smokers and 2083 smokers). Based on this study, we chose to simulate a 0.5% baseline.
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Simulations in this study concern only exposure through inhalation as these were the data available. Inhalation exposure is relevant for single exposure chemical incidents, as shown by a recent review of this kind of incidents in which inhalation represented 80% of the cases (Hunault et al., 2014). This present study is only a first step, which might be continued in the future, for example, by taking account of dermal exposure. The PBPK approach indeed allows the predictions of multiple exposure routes. We could not assess the models’ capability of quantifying the inter-individual differences in DCM metabolism as there was no data available on participants’ genotype in the healthy volunteer study used to assess the performance of the PBPK models. However, this kind of information from exposed people will not be available during an acute chemical incident either. In conclusion, the Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. However, the Bos model was the only model able to predict the formation of HbCO, which is one of the toxic endpoints of acute dichloromethane intoxication. The Jongeneelen and Mumtaz models can be used in emergency risk assessment to get a first impression of the DCM blood concentration whereas the Bos model can be used for a more detailed application, in particular in cases where many individuals are acutely exposed to DCM and for whom HbCO monitoring is not directly available. Conflict of interest The authors declare that there are no conflicts of interest. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgements This research was supported by grant S/660021 from the National Dutch Institute for Public Health and the Environment (RIVM). References Andersen, M.E., Clewell III, H.J., Gargas, M.L., MacNaughton, M.G., Reitz, R.H., Nolan, R.J., Mckenna, M.J., 1991. Physiologically based pharmacokinetic modeling with dichloromethane, its metabolite, carbon monoxide, and blood carboxyhemoglobin in rats and humans. Toxicol. Appl. Pharmacol. 108, 14–27. Andersen, M.E., 2003. Toxicokinetic modeling and its applications in chemical risk assessment. Toxicol. Lett. 138, 9–27. ATSDR, 2000. Toxicological Profile for Methylene ChlorideUS Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry, Atlanta, GA. . (accessed 22.08.14) http://www.atsdr.cdc.gov/ toxprofiles/tp14.pdf. ATSDR, 2010. Addendum to the toxicological profile for methylene chlorideUS Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry, Atlanta, GA. . (accessed 22.08.14) http:// www.atsdr.cdc.gov/toxprofiles/methylene_chloride_addendum.pdf. Åstrand, I., Övrum, P., Carlsson, A., 1975. Exspoure to methylene chloride. I. Its concentration in alveolair air and blood during rest and exercise and its metabolism. Scand. J. Work Environ. Health 1, 78–94. Bogaards, J.J.P., van Ommen, B., van Bladeren, P.J., 1993. Interindividual differences in the in vitro conjugation of methylene chloride with glutathione by cytosolic glutathione S-transferase in 22 human liver samples. Biochem. Pharmacol. 45, 2166–2169. Bos, P.M.J., Zeilmaker, M.J., van Eijkeren, J.C.H., 2006. Application of physiologically based pharmacokinetic modeling in setting acute exposure guideline levels for methylene chloride. Toxicol. Sci. 91, 576–585. Clewell, H.J., Tan, Y.M., Campbell, J.L., Andersen, M.E., 2008. Quantitative interpretation of human biomonitoring data. Toxicol. Appl. Pharmacol. 231, 122–133.
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Hyperbaric oxygen for methylene chloride poisoning: report on two cases. Ann. Emerg. Med. 18, 691–695. SCCS, 2012. Scientific Community on Consumer Safety. Opinion on Dichloromethane http://ec.europa.eu/health/scientific_committees/ consumer_safety/docs/sccs_o_118.pdf (accessed 22.08.14). Scheepers, P.T., 2010. Application of biological monitoring for exposure assessment following chemical incidents: a procedure for decision making. J. Expo. Sci. Environ. Epidemiol. 21, 247–261. Sherratt, P.J., Pulford, D.J., Harrison, D.J., GreenT, Hayes, J.D., 1997. Evidence that human class Theta glutathione S-transferase T1–1 can catalyse the activation of dichloromethane, a liver and lung carcinogen in the mouse. Comparison of the tissue distribution of GST T1–1 with that of classes Alpha, Mu and Pi GST in human. Biochem. J. 326, 837–846. Stewart, R.D., Fisher, T.N., Hosko, M.J., Peterson, J.E., Baretta, E.D., Dodd, H.C., 1972. Experimental human exposure to methylene chloride. Arch. Environ. 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Toxicology Letters journal homepage: www.elsevier.com/locate/toxlet
Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure R.Z. Boerleider a,b , J.D.N. Olie a,c , J.C.H. van Eijkeren d, P.M.J. Bos d , B.G.H. Hof a,e , I. de Vries a , J.G.M. Bessems d,1, J. Meulenbelt a,b,f , C.C. Hunault a, * a
National Poisons Information Center, University Medical Center Utrecht (UMCU), P.O. Box 85500, 3508 GA Utrecht, The Netherlands Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands c Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands d National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 Bilthoven, The Netherlands e Department of Pharmaceutical Sciences, University of Utrecht, P.O. Box 80082, 3508 TB Utrecht, The Netherlands f Department of Intensive Care Medicine, University Medical Center (UMCU), P.O. Box 85500, 3508 GA Utrecht, The Netherlands b
H I G H L I G H T S
In silico simulations of human data on acute inhalation exposure to dichloromethane. Evaluation of three available physiologically based pharmacokinetic models. Assessment of the models’ usefulness in supporting emergency risk assessment. Generic models can be used for screening purposes. A chemical-specific model is more appropriate for a detailed application.
A R T I C L E I N F O
A B S T R A C T
Article history: Received 25 August 2014 Received in revised form 2 October 2014 Accepted 6 October 2014 Available online 13 October 2014
Introduction: Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen).
Keywords: Dichloromethane PBPK model Acute exposure Risk assessment Intoxication Poisoning
Materials and methods: We assessed the accuracy of the models’ predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1 h. The timecourse of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated. Results: With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52–4.19 mg/L with the Bos model, 1.42–4.04 mg/L with the Mumtaz model, and 1.81–4.31 mg/L with the Jongeneelen model compared to 0.27–5.44 mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4–2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels. Conclusions: The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment. ã 2014 Elsevier Ireland Ltd. All rights reserved.
* Corresponding author. Tel.: +31 88 75 595 42; fax: +31 88 75 556 77. E-mail address: [email protected] (C.C. Hunault). Current address: European Commission, JRC, Institute for Health and Consumer Protection, Unit I.05 Systems Toxicology – T.P. 202, Via E. Fermi 2749; I-21027 Ispra, Italy.
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http://dx.doi.org/10.1016/j.toxlet.2014.10.010 0378-4274/ ã 2014 Elsevier Ireland Ltd. All rights reserved.
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1. Introduction Dichloromethane (DCM) (or methylene chloride) is widely used as an industrial solvent and is also frequently involved in acute chemical incidents, even though its use is strictly regulated e.g., in Annex VII of REACH (EU, 2010) and by OSHA (OSHA, 2014a). It is a lipophilic volatile chemical that exists at room temperature as a colorless liquid with a sweet odor. DCM is used in paint removal, chemical processing, and metal cleaning and has various other solvent-related uses. Inhalation is, by far, the most frequent route of human exposure (SCCS, 2012) and can cause mild to serious health effects, including severe neurological symptoms (loss of consciousness, respiratory depression) resulting in coma, hypoxia, and even death (ATSDR, 2000, 2010). Physiologically based pharmacokinetic models (PBPK) may facilitate emergency risk assessment after single inhalation or oral exposure to a chemical by estimating the dose absorbed in a specific organ (Mumtaz et al., 2012; Scheepers, 2010; Hunault et al., 2014). PBPK models represent the body as a set of functional compartments (e.g., blood, brain, liver, muscle and skin, rest of the body) and simulate the pharmacokinetic profiles of chemicals in animals and humans on the basis of available information on relevant physiological, physico-chemical, and biochemical factors. (Meibohm and Derendorf, 1997; Andersen, 2003). After a chemical incident, when exposure levels in the air are known or can be reasonably estimated, PBPK models may be useful in determining whether conducting biomonitoring can be valuable and what the optimal blood sampling time-frame would be. In addition, when external exposure levels are not (yet) known, PBPK modeling in the reverse dosimetry mode (from a blood concentration to an exposed air concentration) could help estimating the external exposure over time after exposure to chemicals (Clewell et al., 2008). By using such models, it may be useful to determine whether certain individuals are at risk of developing severe symptoms. We therefore aimed to evaluate whether three PBPK models could be used for emergency risk-assessment after acute chemical exposure to DCM. The Bos model is specifically developed for DCM (Bos et al., 2006), the Mumtaz model is a model for volatile organic chemicals (VOC) (Mumtaz et al., 2012), and the Jongeneelen model (Jongeneelen and Ten Berge, 2011) is a semi-generic model for volatile and semi-volatile chemicals. ‘Semi-generic’ means that the model is largely generic, but also uses built-in quantitative structure–activity relationships (QSARs) for setting blood:air partitioning, tissue:blood partitioning, and renal excretion.
Fig. 1. Biotransformation of dichloromethane (modified after OSHA, 2014b).
2.2. The Bos model (Bos et al., 2006) This PBPK model was specifically developed for DCM. The model comprises several body compartments (Fig. 2a) and takes into account the saturable P-450 cytochrome step, the GSHconjugation, as well as the genetic polymorphism of the relevant GST (GSTT) in humans. The model is a combination of two other models, the Andersen and the Reitz PBPK models (Andersen et al., 1991; Reitz et al., 1997). The two main advantages of combining these two was to enable the Bos model to calculate the DCM concentration in brain and the HbCO level resulting from an exposure to DCM. The Bos model has already been verified with two experimental human studies (Åstrand et al., 1975; DiVincenzo and Kaplan, 1981) and runs in ACSL. The code of the Bos model is freely available (in the Appendix of the Bos article).
2. Material and methods 2.3. The Mumtaz model (Mumtaz et al., 2012) 2.1. DCM metabolism DCM is metabolised via two pathways (Fig. 1). The first pathway is P-450 mediated and is saturable. CYP 2E1 is the main isoenzyme involved (SCCS, 2012). Via this pathway, DCM is metabolised to carbon monoxide (CO), leading to increased HbCO levels, causing a decrease in oxygen transport capacity and tissue oxygenation. HbCO can peak hours after exposure ceases, as fat and other tissues continue to release accumulated DCM (Rioux and Myers, 1989; Mahmud and Kales, 1999). The second pathway involves GSH-conjugation via glutathione (GSH) and results in carbon dioxide, with formaldehyde and formic acid as metabolic intermediates. The glutathione S-transferases (GST) pathway is increasingly important at high exposure DCM levels. It is noted that in the human population, non, low, and highconjugators (genetic polymorphism) have been identified (Bogaards et al., 1993; Mainwaring et al., 1996; Sherratt et al., 1997; SCCS, 2012).
This PBPK model was developed for VOCs. They are organic chemical compounds, such as benzene or formaldehyde, that evaporate at room temperature under normal, indoor atmospheric conditions. VOCs represented 23% of the exposures after acute chemical incidents, in the Netherlands, between 2008 and 2010 (Hunault et al., 2014). The Mumtaz model divides the body into seven compartments (Fig. 2b). Its equations use human metabolic, physiological and physicochemical parameters to estimate the concentration of a chemical in each compartment. The model can be used for oral, inhalation and dermal absorption. After absorption and distribution, chemicals are metabolised by the liver or exhaled into the air. The model is not specifically developed for DCM although DCM was one of the seven volatiles used to construct the model and, therefore, does not make a distinction between the two metabolism pathways of DCM. It cannot deal with the genetic polymorphism of human GSTT and it has already been verified with experimental human
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a) Bos
Cinh
Qalv
Alveolar Space
Calv (Cart/Pb)
Lung Blood
Cart
Fat Tissue Group Cvf
Cvr
Slowly Perfused Tissue Group Rapidly Perfused Tissue Group Brain
Cvb Cvl
Inhaled compound
Liver
Qf Cart Qs Cart Qr
Exhaled compound
Alveolar Air QC CV QR CVR
Cvs
Exhale
Inhale
QC CA
Alveolar blood
Alveolar Space
Qc
Qc Cven
c) Jongeneelen
b) Mumtaz
Qalv
23
QS
Lung blood
QR
Richly Perfused
CA
CA
CVS QF CVF
Heart
QS
Slowly Perfused
QF
Fat
Dermal Load
CA
QSk
QSk
Skin
CVSk
Cart
CVK
Ql
QL
Cart
CVL
Adipose
Venous Blood
Dermal Water QK
Dermis
CA
Cart Qb
Evaporated compound
Brain
QK
Kidney
CA QL
Liver
Arterial Blood
Muscle Oral Intake
Bone + Marrow
CA
Stomach + Intestine Vmax Km,KGSH
Metabolites
K Dose
Metabolism
Liver Kidney Excreted compound/ metabolite in urine Blood flow Metabolites
Fig. 2. Schematic presentation of (a) the Bos PBPK model (modified after Bos et al., 2006), (b) the Mumtaz PBPK model (modified after Mumtaz et al., 2012), and (c) the Jongeneelen model (modified after www.cefic-lri.org).
studies for various volatiles (Mumtaz et al., 2012). In the case of DCM, the experimental data used concerned one subject with an intermittent exposure to DCM and a varying physical activity (Åstrand et al., 1975). The Mumtaz model runs in Berkeley Madonna and its code is freely available in an appendix to the paper. 2.4. The Jongeneelen model (Jongeneelen and Ten Berge, 2011) This PBPK model is also called the “IndusChemFate model” (Fig. 2c) and is freely available (http://www.cefic-lri.org/lritoolbox/induschemfate). It was developed for volatile and semivolatile chemicals. It can estimate concentrations and amounts in blood, urine and organs after inhalation, ingestion or dermal exposure according to user-defined exposure scenarios. The model contains algorithms as QSARs, which allows the estimation of, e.g., absorption upon inhalation or diffusion from blood into tissues. The Jongeneelen and Ten Berge model was evaluated for several compounds (e.g., pyrene, N-methyl-pyrrolidone, methyl-tertbuthylether, heptane, 2-butoxyethanol and ethanol) but not for DCM. The Jongeneelen model runs in MS Excel and its code is written in Visual Basic. 2.5. Human data used The human data were obtained from a prior healthy volunteer study (Van Veen et al., 2002) approved by the Medical Ethical Board of the University Medical Center, Utrecht. In this study, 11 healthy volunteers were individually exposed to DCM for 1 h, with or without an air mask. In this paper, only data from the 6 volunteers who did not wear an air mask during the experiment
were included (Table 1). They brushed a paint stripper containing DCM on a wooden surface of 1.28 m2 in a closed room. Once the application was completed, the volunteers remained in the room. Ten minutes before the end of the 1 h exposure period, they had to remove the paint (with DCM) from the surface with a brush and scraper. At the end of the 1 h exposure, they left the room. The stripped surface and the temporary floor covering were taken out of the room before the start of the next experiment. In between the experiments, the room was thoroughly ventilated until original levels of DCM and CO were reached. The DCM air concentrations in the closed room were measured by a Miran 1B2 infrared spectrophotometer at 1.60 m from the floor. Fig. 3 shows the DCM air concentration in the room over time. Eighteen blood samples were drawn from each volunteer (from T = 1 h up to 48 h post-exposure). DCM concentrations were measured in whole blood using gas chromatography. For the percentage of carboxyhemoglobin (% HbCO), a CO-oximeter was used.
Table 1 Demographics and individual external DCM exposure dose (AUCs) of the healthy volunteers. Participant
Height (cm)
Weight (kg)
Age (y)
Tobacco status
AUC (ppm h)
1 2 3 4 5 6
178 185 178 186.5 190 177
79 81 65 85 83 64
27 23 25 23 26 21
Non-smoker Non-smoker Non-smoker Non-smoker Non-smoker Non-smoker
279 351 386 173 229 194
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Fig. 3. Dichloromethane (DCM) air concentration in the room over time (modified after Van Veen et al., 2002).
2.6. Analyses and simulations We calculated the total amount of external DCM for each participant by computing individual AUCs (area under the curve). These were computed up to the last concentration equal or above the LOQ (limit of quantification), using the trapezoidal rule. The three models were then used to simulate DCM blood concentrations following DCM exposure. For the purposes of simplicity, a ‘block-exposure’ scenario was simulated i.e., an exposure to a constant DCM concentration in air for 1 h. Percentages of HbCO in blood were also simulated with the Bos model. During assessment of the model performances, the simulations of the blood concentrations by the models were visually compared to the observed blood DCM concentrations and % HbCO. Then, we performed a more quantitative assessment according to Mumtaz (Mumtaz et al., 2012). We calculated the observed and predicted AUC values for the DCM blood concentration and % HbCo (using the trapezoidal rule). Predicted AUC values for the % HbCO were calculated only for the Bos model as this was the only model which allowed this simulation. We assessed the performance of each model by calculating the mean ratio (AUCr) between the AUC predicted by one of the three models and the AUC value computed from the observed data (mean of the six individual AUC ratios). We interpreted the ratio as ‘the closer the value is to 1, the better the agreement between observation and model prediction’. We also calculated the mean of the sum of the squared differences (MSSDs) between model predictions and observation. To obtain the MSSDs, we squared the difference between a measured data point and the value of the simulation at the corresponding time. These squared differences were totalled and then divided by the number of data points. We interpreted the MSSD value as ‘the lower the value, the better the fit’. Simulations were performed using the acslX software version 11.8 for the Bos model (www.acslx.com) and the Berkeley Madonna software version 8.3.11 (www.berkeleymadonna.com) for the Mumtaz model. Simulations with the Jongeneelen model v2.00 were run in MS Excel version 2004 on Windows XP Service pack 3. 3. Results The calculated DCM external dose (area under exposure-time curves) ranged between 173 and 386 ppm h (Table 1). The measured Cmax of DCM in blood ranged between 0.27 and 5.44 mg/L at the end of the exposure (1 h) (Fig. 4). The predicted DCM blood concentration after 1 h ranged between 1.52–4.19 mg/L with the Bos model (Fig. 4a), 1.42–4.04 mg/L with the Mumtaz model (Fig. 4b) and 1.81–4.31 mg/L with the Jongeneelen model (Fig. 4c). For all volunteers, the DCM concentration-time profiles
Fig. 4. Observed and predicted dichloromethane (DCM) blood concentrations (mg/ L); (a) simulation using the Bos model, (b) simulation using the Mumtaz model, and (c) simulation using the Jongeneelen model. E: duration of exposure to DCM (60 min).
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Table 2 Comparison of the predicted values with the experimental data for DCM blood concentrations and % HbCO (AUC ratios and MSSD values). Metric
Mean (SD) DCM
% HbCO No calibration
% HbCO Calibration #1a
% HbCO Calibration #2b
AUCr Bos model AUC/data AUC Mumtaz model AUC/data AUC Jongeneelen model AUC/data AUC
2.1 (1.8) 2.1 (1.8) 2.8 (2.5)
11.1 (7.1) NAc NAc
3.0 (1.8) NAc NAc
5.0 (3.2) NAc NAc
MSSD Bos model MSSD Mumtaz model MSSD Jongeneelen model MSSD
0.49 (0.1) 0.44 (0.1) 0.59 (0.3)
5.87 (1.0) NAc NAc
0.47 (0.2) NAc NAc
1.22 (0.5) NAc NAc
a b c
Calibration of the Bos model using the individual baseline % HbCO level. Calibration of the Bos model using 0.5% HbCO level as standard baseline % HbCO in non-smokers (Wald et al., 1981). Not applicable.
(shape) were well predicted with both the Bos and Mumtaz models, especially in the elimination phase. The Jongeneelen model showed a somewhat slower systemic clearance than the observed data suggested. The mean AUCr values for the Bos and Mumtaz models were 2.1, indicating a reasonable representation of the observations by model predictions (Table 2). The AUCr value for the Jongeneelen model was 2.8, indicating a somewhat larger over-prediction of the DCM blood concentration. The MSSD values were quite similar for the Bos and Mumtaz models (0.4851 and 0.4361, respectively) and higher for the Jongeneelen model (0.5888) (Table 2). The highest % HbCO in the healthy volunteers was reached between 2 and 3 h after the onset of exposure and ranged between 0.4 and 2.3% (Fig. 5a). % HbCO could only be simulated by the Bos model. The highest predicted % HbCO ranged between 3.1 and 4.2% and was reached between 1.75 and 2.25 h after DCM exposure. The shape of the % HbCO over time resembled the shape of the observed data but the predicted % HbCO were systematically higher. By default, the baseline of the % HbCO is higher in the Bos model than was the baseline % HbCO value measured among the participants. The % HbCO measured at baseline in the healthy volunteers were very low (range: 0–0.5%). We therefore modified the baseline endogenous production of CO and calibrated the Bos model in order to get a baseline % HbCO level equal to the measured individual % HbCO levels. Predictions of the highest % HbCO levels were then satisfactory (1.3–2.3%, Fig. 5b). The AUCr for the % HbCO was 11.1 without calibration and 3 with individual calibration, indicating over-prediction of the % of HbCO (Table 2). The MSSD values for the % of HbCO were the lowest after using the individual baseline % HbCO (Table 2). As baseline % HbCO levels of patients are unknown in case of acute chemical incidents, we predicted the % HbCO again, using the value of 0.5% as standard baseline % HbCO (instead of the individual baseline % HbCO) (Wald et al., 1981). Fig. 5c shows the resulting predictions (range of highest % HbCO: 1.7–2.8%). 4. Discussion The aim of this study was to evaluate the performance of three PBPK models in emergency risk-assessment after single exposure to DCM. Results show that all three models can predict DCM blood concentrations reasonably well. Only marginal differences were observed in the predicted blood concentration by the Bos and Mumtaz models. However, only the Bos model can predict the % HbCO, which is one of the toxic end-points in dichloromethane intoxications.
The Bos and Mumtaz models are more difficult to use than the Jongeneelen model which runs in Excel. Their codes require a minimum of knowledge in kinetics and of practice with acslX or Berkeley Madonna software. To make predictions with the Bos model, we first had to convert the published ACSL code into acslX code as the former is no longer available. The acslX software is a more modern version of the ASCL software. It is not freely available and is highly specific, which limits the number of potential users. It would be interesting to see what the benefits of converting the model code into a general computer language could be. Another possibility could be to reconstruct and parameterise the model, then convert the model code into an optimal computer language by using an easy-to-use web application such as UK HSLs Model Equation Generator ‘MEGen’ (Loizou and Hogg, 2011). This application is a code generator that produces codes such that they can be solved by software package of choice (e.g., conversion option to acslX and Berkeley Madonna is available). The Berkeley Madonna code of the Mumtaz model is available in the Mumtaz paper, and could also be used for other VOCs (e.g., for toluene, benzene). The Jongeneelen model, implemented in Excel, is the easiest to use. The strength of the Mumtaz and Jongeneelen models is that they are applicable for multiple substances. They can therefore be used for a first screening or a first impression, keeping in mind that they may imply possible uncertainties due to the lack of possible chemical-specific kinetic processes (e.g., no equation describing in detail the metabolism pathways). The Bos model can be used for a more detailed application. The models then have a different function, and might be used in the context of a chemical incident in the form of a step by step approach: screening by using e.g., the Jongeneelen model versus a more detailed approach using the Bos model, depending on what is desired. Being able to simulate the experimental data of the healthy volunteers was difficult due to the fact that the exposure conditions varied more than in previous studies used to validate the Bos and Mumtaz models (Åstrand et al., 1975; DiVincenzo and Kaplan, 1981; Mumtaz et al., 2012). The study from which the experimental data were used to assess the models, was originally designed to validate a mathematical paint exposure model, and, therefore, aimed to reflect real life situations. This explains why the participants were not exposed to DCM using breathing masks, but rather had to mimic someone who would normally apply paint stripper on a surface and subsequently remove it with a brush and scraper. This experimental study had been conducted in our department years ago but only the results concerning the modeling of the air DCM concentration in the room have ever been published (Van Veen et al., 2002). However, the available original Case Report Forms and source documents contained data on participants, the
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Fig. 5. Observed and predicted carboxyhemoglobin levels (% HbCO) in blood, using the Bos model: (a) without calibration, (b) with calibration of the model, using the individual baseline % HbCO, (y-axis is a normalised scale, i.e., individual background level set at 0%), and (c) with calibration of the model, using as a baseline % HbCO of 0.5% (Wald et al., 1981). E: duration of exposure to dichloromethane (60 min).
circumstances of the exposure sessions, and the blood DCM concentration and % of HbCO. Room temperature and the physical activity of scraping off paint remnants varied between the different exposure sessions, e.g., sunshine raised the room temperature at the end of the experiment with Participant 2, also increasing the volatility of DCM and resulting in higher DCM air concentrations. Differences in physical activity of scraping off the paint remnants may have led to a higher heart rate and blood flow through the lungs, and hence to variations in DCM uptake. Another variation is the inter-individual difference in DCM metabolism related to GST polymorphism and inter-individual and time-variable differences in expression of the relevant P-450 (Dankovic and Bailer, 1994; SCCS, 2012). The P450 isoenzym CYP 2E1 is specifically involved in the biotransformation of DCM. It is expressed in human liver and in extra-hepatic tissues as well, inducible by ethanol (SCCS, 2012). Furthermore, its activity may vary, depending on the use of certain drugs such as acetaminophen, which has been shown to induce CYP2E1 in rats, at sub-toxic doses (Kim et al., 2007). As for GST polymorphism, the incidence of non-conjugators has been estimated to be 22% for African Americans and 62% for Asian Americans (Haber et al., 2002; Pemble et al., 1994). Fast metabolizers with a high % HbCO will be more at risk if they have ischemic cardiac diseases, whereas slow metabolisers with high DCM blood concentrations will be more at risk of developing acute CNS effects, if we assume that the parent chemical is the toxic agent in the brain, and not the metabolite. The anesthetic effect of DCM and the hypoxic effect of HbCO may both contribute to CNS depression (Stewart et al., 1972; Hall and Rumack, 1990) sometimes leading to coma and accidental deaths (Zarrabeitia et al., 2001; Macisaac et al., 2013). Genotype was not determined in the experimental study so we could not use this variable as potential cause of inter-individual variability for the simulations. Participant 6 seems to be a relative outlier with low DCM blood concentrations and % HbCO which might be explained by a lower physical activity resulting in a lower inhaled DCM exposure dose, and/or by a more efficient biotransformation of DCM due to genetic polymorphism or enzyme induction. PBPK models may be useful after an acute chemical incident in determining whether conducting biomonitoring is valuable. Such PBPK models may be useful to the Dutch National Poisons Information Center, which provides advice after chemical incidents, including advice on the relevancy of conducting a biomonitoring study after an acute incident and the best time for sampling. In the case of DCM, usually, only the % HbCO can be measured within several hours after the incident, which makes the Bos model the most interesting for our center as it is the only model that can predict the % HbCO. The total amount of HbCO in blood is the sum of endogenous CO production in the blood, pulmonary absorption, and CO produced by the metabolism of DCM. Therefore, the HbCO concentration after DCM exposure is described by these three components in the Bos model. The % HbCO predicted by the Bos model differed from the observed percentage in the healthy volunteer study, very likely due to differences between the single inherent basic endogenous HbCO level at baseline in the Bos model and the actual HbCO level in the healthy volunteers. All healthy volunteers had a very low % HbCO level at baseline so we had to calibrate the Bos model in order to get more satisfactory predictions. The best prediction of % HbCO levels were obtained after using the individual baseline % HbCO levels. As such information is unavailable during acute chemical incidents, we also looked at the Bos model’s performance with 0.5% as a standard plausible low baseline value. This value of 0.5 comes from an epidemiological study (Wald et al., 1981) that included a large number of people (N = 11249 among which 6641 non-smokers and 2083 smokers). Based on this study, we chose to simulate a 0.5% baseline.
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Simulations in this study concern only exposure through inhalation as these were the data available. Inhalation exposure is relevant for single exposure chemical incidents, as shown by a recent review of this kind of incidents in which inhalation represented 80% of the cases (Hunault et al., 2014). This present study is only a first step, which might be continued in the future, for example, by taking account of dermal exposure. The PBPK approach indeed allows the predictions of multiple exposure routes. We could not assess the models’ capability of quantifying the inter-individual differences in DCM metabolism as there was no data available on participants’ genotype in the healthy volunteer study used to assess the performance of the PBPK models. However, this kind of information from exposed people will not be available during an acute chemical incident either. In conclusion, the Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. However, the Bos model was the only model able to predict the formation of HbCO, which is one of the toxic endpoints of acute dichloromethane intoxication. The Jongeneelen and Mumtaz models can be used in emergency risk assessment to get a first impression of the DCM blood concentration whereas the Bos model can be used for a more detailed application, in particular in cases where many individuals are acutely exposed to DCM and for whom HbCO monitoring is not directly available. Conflict of interest The authors declare that there are no conflicts of interest. Transparency document The Transparency document associated with this article can be found in the online version. Acknowledgements This research was supported by grant S/660021 from the National Dutch Institute for Public Health and the Environment (RIVM). References Andersen, M.E., Clewell III, H.J., Gargas, M.L., MacNaughton, M.G., Reitz, R.H., Nolan, R.J., Mckenna, M.J., 1991. Physiologically based pharmacokinetic modeling with dichloromethane, its metabolite, carbon monoxide, and blood carboxyhemoglobin in rats and humans. Toxicol. Appl. Pharmacol. 108, 14–27. Andersen, M.E., 2003. Toxicokinetic modeling and its applications in chemical risk assessment. Toxicol. Lett. 138, 9–27. ATSDR, 2000. Toxicological Profile for Methylene ChlorideUS Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry, Atlanta, GA. . (accessed 22.08.14) http://www.atsdr.cdc.gov/ toxprofiles/tp14.pdf. ATSDR, 2010. Addendum to the toxicological profile for methylene chlorideUS Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry, Atlanta, GA. . (accessed 22.08.14) http:// www.atsdr.cdc.gov/toxprofiles/methylene_chloride_addendum.pdf. Åstrand, I., Övrum, P., Carlsson, A., 1975. Exspoure to methylene chloride. I. Its concentration in alveolair air and blood during rest and exercise and its metabolism. Scand. J. Work Environ. Health 1, 78–94. Bogaards, J.J.P., van Ommen, B., van Bladeren, P.J., 1993. Interindividual differences in the in vitro conjugation of methylene chloride with glutathione by cytosolic glutathione S-transferase in 22 human liver samples. Biochem. Pharmacol. 45, 2166–2169. Bos, P.M.J., Zeilmaker, M.J., van Eijkeren, J.C.H., 2006. Application of physiologically based pharmacokinetic modeling in setting acute exposure guideline levels for methylene chloride. Toxicol. Sci. 91, 576–585. Clewell, H.J., Tan, Y.M., Campbell, J.L., Andersen, M.E., 2008. Quantitative interpretation of human biomonitoring data. Toxicol. Appl. Pharmacol. 231, 122–133.
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