Physicologically Based Toxicokinetic Models of Tebuconazole and

Article pubs.acs.org/crt

Physicologically Based Toxicokinetic Models of Tebuconazole and Application in Human Risk Assessment Svava Ó sk Jónsdóttir,*,† Trine Klein Reffstrup, Annette Petersen, and Elsa Nielsen National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark S Supporting Information *

ABSTRACT: A series of physiologically based toxicokinetic (PBTK) models for tebuconazole were developed in four species, rat, rabbit, rhesus monkey, and human. The developed models were analyzed with respect to the application of the models in higher tier human risk assessment, and the prospect of using such models in risk assessment of cumulative and aggregate exposure is discussed. Relatively simple and biologically sound models were developed using available experimental data as parameters for describing the physiology of the species, as well as the absorption, distribution, metabolism, and elimination (ADME) of tebuconazole. The developed models were validated on in vivo half-life data for rabbit with good results, and on plasma and tissue concentration−time course data of tebuconazole after i.v. administration in rabbit. In most cases, the predicted concentration levels were seen to be within a factor of 2 compared to the experimental data, which is the threshold set for the use of PBTK simulation results in risk assessment. An exception to this was seen for one of the target organs, namely, the liver, for which tebuconazole concentration was significantly underestimated, a trend also seen in model simulations for the liver after other nonoral exposure scenarios. Possible reasons for this are discussed in the article. Realistic dietary and dermal exposure scenarios were derived based on available exposure estimates, and the human version of the PBTK model was used to simulate the internal levels of tebuconazole and metabolites in the human body for these scenarios. By a variant of the models where the R(−)- and S(+)-enantiomers were treated as two components in a binary mixture, it was illustrated that the inhibition between the two tebuconazole enantiomers did not affect the simulation results for these realistic exposure scenarios. The developed models have potential as an important tool in risk assessment.

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INTRODUCTION Increased focus has been devoted to the development of computational methods for assessing health risk caused by a combined exposure to many chemicals from food and the environment. Researchers and regulatory professionals have investigated the applicability of the presently available approaches and discussed criteria on how to group chemicals for cumulative risk assessment in the pesticide area.1−5 Physiologically based toxicokinetic (PBTK) modeling is one of the techniques that is considered to hold great promise for higher tier cumulative risk assessment of organic chemicals.2,6−10 The use of PBTK based risk assessment has been recommended by international bodies, including a new report on modern technologies for evaluating human health hazards due to exposure to chemicals.3,4,8,10−12 Another important area where PBTK models can be useful is risk assessment for aggregate exposure via different routes, like dietary intake, skin absorption, and inhalation.10,13 PBTK models use a mathematical description of absorption, distribution, metabolism, or elimination (ADME) and other relevant biological processes of the compound(s) of interest to describe the relationship between exposed dose and the internal blood and tissue dose (concentration) level. The animal or © 2016 American Chemical Society

human is described as a set of tissue compartments, and parameters for describing mammalian physiology, as well as the ADME and physicochemical properties of the chemicals studied are entered.6,14−17 Competitive inhibition of metabolic pathways needs to be considered in PBTK modeling of simultaneous exposure to multiple chemicals. Such interactions may affect the relationship between the exposed dose and the dose delivered to the target site and thereby the resulting adverse effects.18,19 The work presented here is intended to explore the possibilities and limitations in using such models for risk assessment by a specific case study of tebuconazole, including exploring the influence of inhibition between R(−) and S(+) enantiomers of this compound. By performing low-dose computational simulations in humans, PBTK modeling can provide information not otherwise available. Interspecies differences can be simulated by developing models for the same chemical in different species, for example, rats and humans, and using interspecies extrapolation of parameters from one species to another when necessary and feasible.15 Received: August 15, 2015 Published: March 15, 2016 715

DOI: 10.1021/acs.chemrestox.5b00341 Chem. Res. Toxicol. 2016, 29, 715−734

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in the literature, where the applicability, strengths, and limitations of using PBTK modeling as a tool in risk assessment was explored for this particular compound. This work aimed to evaluate the influence of uncertainties associated with model parameters and interspecies extrapolations on the overall simulation results and their applicability for risk assessment purposes. It was also investigated at which exposure levels competitive inhibition between the two enantiomers (R(−)- and S(+)tebuconazole influenced the metabolic capacity and thereby the internal body concentration level of tebuconazole. Finally, simulations using realistic dietary and dermal exposure scenarios that were developed as a part of this work are discussed. Ample information on ADME of tebuconazole can be found in the literature, including on metabolic constants,45 which makes it a suitable compound for this case study. Like other triazoles, tebuconazole acts by damaging fungal growth via the inhibition of the cytochrome P450 CYP51 enzyme.46 However, potential adverse human health effects can be associated with exposure to tebuconazole, including potential endocrine disruptive properties.47−51 An ADI (acceptable daily intake) of 0.03 mg/kg bw/day was established based on a no observed adverse effect level (NOAEL) of 2.9 mg/kg bw/day for histopathological alterations in the adrenal gland in two 52-week toxicity studies in dogs.52

Simulations can be carried out at different exposure levels from those used for validating the model. Such high-dose to low-dose extrapolations are extremely useful. PBTK modeling can be made for relevant routes of exposure (i.e., dietary, inhalation, and dermal) and thereby be used to investigate the influence of aggregate exposure via different routes. A PBTK model can be used to investigate interactions between chemicals and to define the doses at which interactions become significant (the interaction threshold), as well as to predict overload of toxicokinetic pathways which may lead to toxicity.20 Simulations for different age groups can be conducted, given that sufficient data are available,21 and interindividual variability can be incorporated by use of appropriate data, supplemented by probabilistic methods.22 For the above reasons, there is an increased interest in using PBTK models as an assisting tool in the risk assessment of organic chemicals. There is an ongoing debate on how such methods can be used and documented and on how to assess that the simulation results are sufficiently accurate for this purpose. Therefore, it is very important to investigate the sources of uncertainty associated with the PBTK modeling and consider that in the overall risk assessment. General recommendations on the use of such models and a checklist to evaluate the adequacy of specific PBTK models for risk assessment have been proposed.7,9−11,23 The challenge is to incorporate such recommendations into adequate work flows and case studies, and to agree on international standards for the documentation of PBTK models for use in risk assessment.11 Traditionally, PBTK models aim to model the toxicokinetics of the individual compounds very accurately. This requires a large number of experimental data which should either be used to insert as parameters in the model or be used to optimize the parameters or validate the final model. Recently, increased interest has been devoted to the development of more generic models for PBTK based risk assessment. Although less accurate than traditional the PBTK model, such models can be useful for making reasonable estimates of internal concentrations of chemical compounds upon exposure in the lower dose exposure range. Judson et al.24 proposed an interesting high throughput approach combining a simple generic PBTK model based on in vitro methods, defining a new metric for risk assessment. Presently, significant emphasis is put into developing new in vitro assays and standardizing already available methods in order to provide data on ADME, protein binding, and other relevant biological parameters for use in PBTK based risk assessment.10,11,25 Although many PBTK models have been published for pesticides during the past decade,26−32 only a few of these studies consider cumulative effects due to exposure to more than one chemical.33−36 PBTK models have been used for many years in the risk assessment of various types of organic chemicals and drugs, mostly for individual compounds.17,23,37−40 However, several studies where PBTK models were used to simulate exposure to multiple volatile compounds have been published.41 Probabilistic models have been used for cumulative risk assessments of chemicals in food, for example, as a part of the EU FP6 project “SafeFood”.42,43 Furthermore, the European Food Safety Authority (EFSA) has published a guidance on using probabilistic methods in the exposure assessment of single or multiple pesticides residues in food.44 In this study, a series of PBTK models were developed for tebuconazole, a fungicide widely used as a pesticide and biocide. The work was intended as a case study based on data available

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METHODS: DEVELOPMENT OF PBTK MODELS FOR TEBUCONAZOLE

Biological Basis and Architecture of the Basic Models. The PBTK models were developed, assuming that the tissues behave like a well-stirred system where the distribution to the different tissue compartments occurs as an instant equilibrium between the tissue and the blood compartments. This equilibrium occurs between the unbound (free) blood and tissue concentrations of the compounds and not between the parts bound to plasma or tissue proteins. Such models are called flow based PBTK models. The PBTK models were developed for tebuconazole in four different species, rat, rabbit, rhesus monkey, and human. A basic model was developed for rats, and the same model architecture was used in the rhesus monkey and human models (Figure 1a). For model validation purposes, additional tissue compartments were included in the model for rabbits (Figure 1b). The PBTK models were developed in the Berkeley Madonna differential equation solver for dynamic modeling of biological systems (http://www.berkeleymadonna.com/). The species and compound specific parameters and the collection of equations were entered in the format required by Berkeley Madonna. The mathematical description of the developed models is given in the Supporting Information (section 1), and the parameters used are presented in the section below. The numerical integration method “Rosenbrock (stiff)” was used for all the dynamic simulations presented in this article. The integration method chosen does not affect the results. Tebuconazole administered as a single oral dose to rats is rapidly and almost completely absorbed from the gastrointestinal tract (GIT) and rapidly distributed to almost all tissues and organs with the highest contents found in the kidney and liver 1 h after administration.52,53 Less than 1% of the administered dose remained in tissues and organs 72 h after exposure, indicating that no long-term accumulation occurs.52,53 The majority of the administered dose of tebuconazole ([phenyl-UL-14C]-tebuconazole) was excreted in the feces (via the bile) in rats, while the urinary excretion accounted for 15−30% of the administered dose.52 Tebuconazole is documented to practically metabolize completely in rats. Therefore, 100% absorption, 100% metabolism, and 100% excretion within 72 h are assumed in the models.52,54 Tebuconazole binds to plasma proteins. No specific biological properties such as active transport that could be considered in the model have been reported. 716

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Figure 1. Scheme illustrating the single compound PBTK models for tebuconazole in rats, rhesus monkeys, and humans (a) and rabbits (b). It describes the routes of administration implemented in the models (dietary/oral, dermal, and i.v.), distribution in the compartments, liver metabolism, and excretion of metabolites in either feces (via bile) or urine. In order to validate the model with available data for distribution to tissues in rabbits after i.v. administration, more tissue compartments and administration by the i.v. route were added to the model for rabbits. Tebuconazole is slightly lipophilic (logPow = 3.7).55−57 Thus, shortterm deposition in fat tissue after single dose administration, as well as accumulation in fat tissue following repeated exposure for a prolonged period, cannot be excluded. This has been considered in the model. Tebuconazole is primarily metabolized by cytochrome (CYP) mediated oxidation and subsequently conjugated to glucuronides and sulfates. Tebuconazole-1-hydroxy and tebuconazole-carboxylic acid are the major metabolites detected in rat urine and feces, accounting for 16−28% and 14−36% of the identified eliminated metabolites, respectively. For a small fraction of tebuconazole, the triazole moiety is cleaved off to form free triazole, 5.4% of the administered dose in male rats and 1.5% in female rats.52 Tebuconazole-1-hydroxy, tebuconazole1-hydroxy-glucoronate, tebuconazole-carboxylic acid, and tebuconazolecarboxylic acid-glucoronate were identified as the main metabolites found in urine samples from seven agricultural workers during and 24 h after the end of exposure, with mean contributions of 13%, 67%, 7%, and 13%, respectively.58 Tebuconazole-1-hydroxy and its glucuronate are also the primary metabolites in lactating goats. In laying hens, the primary metabolites are the same as those in rats.59 In a different study, it was shown that voriconazole, which is structurally similar to tebuconazole, undergoes similar phase 1 and phase 2 metabolic reactions in the mouse, rat, rabbit, guinea pig, dog, and humans.60 Furthermore, many conazoles are known to be CYP3A4 substrates in humans and undergo similar CYP mediated metabolism in other species.61 Tebuconazole was predicted to be a CYP3A4 substrate in humans by a quantitative structure−activity relationship (QSAR).62 On the basis of these observations, we have assumed that tebuconazole undergoes similar metabolic reactions in other rodents and mammals, including humans. It has been reported that some triazoles are metabolized to 1,2,4-triazole, which can cause cranio-facial malformations during fetal development. Tebuconazole was not found to cause such malformations in a range of in vivo studies where this has been investigated;52 see an overview of these studies in Appendix 1 in ref 62. This is most likely because only a very small fraction of tebuconazole is metabolized to 1,2,4-triazole in female rats. It is therefore not necessary to consider

this metabolite specifically in the development of the PBTK models. No toxicological effects have been reported for the other metabolites of tebuconazole. Therefore, only tebuconazole was modeled as an individual compound, and the combined sum of all metabolites is considered as one entity in the PBTK model. Target organs identified in rats are the liver, the adrenals, and the hematological system.52 Specific compartments were made for the arterial and venous blood, and the liver. It was decided not to include a specific adrenal compartment in the model due to a lack of data on tissue composition of neutral and phospholipids, which is necessary for calculating the adrenal/blood partition coefficient. In order to include the dermal exposure route in the model, a skin compartment was included as well. A specific compartment was made for adipose tissue (fat) as it behaves differently from any other tissue. The rest of the body was described by two compartments, namely, rapidly perfused and slowly perfused tissues. The rapidly perfused tissues include tissues like the kidney, brain, heart, lung, spleen, pancreas, adrenal, and thyroid, and the slowly perfused tissues include muscle and bone.63 Excretion of tebuconazole metabolites from the body, in feces via the bile and in the urine, was described by introducing a separate excretion compartment. It was decided to omit a description of enterohepatic recirculation in the model due to the lack of parameters to describe this process and to avoid making the model complicated by adding compartments for the GIT. By using a technical excretion compartment, we can develop a relatively simple PBTK model that is focused on the target organs of the compound studied, namely, the liver and blood. In this way, a biologically plausible and useful model could be made, without introducing unnecessary uncertainties. Several changes and modifications of the model equations and parameters were made for the model developed for the rabbit compared to the model for rats. The same general mass balance used for the rapidly and slowly perfused compartments in rats was used for the additional tissue compartments in rabbits, entering the appropriate tissue volumes, blood flows, and partition coefficients for each tissue (see Tables 1 and 3). A route for the administration of an i.v. dose was added to the model; see the Supporting Information for details. 717

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Chemical Research in Toxicology Table 1. Physiological Parameters for the PBTK Modelsa parameter

rat

human

rabbit

monkey

body weight (BW) [kg]

0.250

70

2.12 (mean value from 77)

6.2

2.87 rat value rat value

human value human value human value

rat value rat value rat value rat value rat value 33b

human human human human human 73b

rat value rat value rat value

human value human value human value

rat value rat value

human value human value

tissue volumes (Vp,t) as percentage of body weight (tissue volumes Vt [l] = BW × Vp,t/100) 3.66 (±0.65) 2.57 Vp,liver 19.0 (±2.62) 3.71 Vp,skin Vp,rapidly_perf used 0.57 + 0.33 + 0.73 + 0.50 + 0.32 + 2.0 + 0.47 + 0.44 + 0.76 + 0.14 + (= brain + heart + kidney + lung + 0.20+ 0.005 + 0.019 = 2.67 0.26+ 0.03 + 0.02 = 4.12 pancreas + spleen + thyroid + adrenal) 40.43 + 5.0 = 45.43 40.00 + 7.1 = 47.1 Vp,slowly_perf used (= muscle + bone) 7.21 21.42 Vp,fat (= adipose) 5.44 5.14 Vp,venous69 2.72 2.57 Vp,arterial69 8.16 7.71 Vp_blood (venous + arterial) 6.6 312 cardiac output (QC) [l/h]b Organ blood flows (Qp,t) as percentage of cardiac output (organ blood flows Qt [l/h]= QC × Qp,t/100) 18.3 22.7 Qp,liver 5.8 5.8 Qp,skin 2.0 + 5.1 + 14.1 + 2.1 + 0.3 = 23.6 11.4 + 4.0 + 17.5 + 2.5c+ 0.3d = 35.7 Qp,rapidly_perf used (= brain + heart + kidney + lung + adrenal) Qp,slowly_perf used (= muscle + bone) 27.8 + 12.2 = 40.0 19.1 + 4.2 = 23.3 7.0 5.2 Qp,fat (= adipose)

value value value value value

a Data are from ref 63 if not otherwise stated. bExtrapolation of rat value to the other species (s) rabbit and monkey was done by QC(s) = QC(rat)(BWs/BWrat)0.75. cThe organ flow for bronchial tissues is used in the calculation for lungs. dOrgan blood flow for rat adrenals is used.

Figure 2. Scheme illustrating the developed PBTK model for tebuconazole in rat, treated as a binary mixture of R(−)- and S(+)-tebuconazole. It describes input to the model (dietary and dermal), distribution to the compartments, liver metabolism including inhibition caused by the other enantiomer, and excretion of metabolites in either urine or feces. Binary PBTK Model for R(−)- and S(+)-Tebuconazole. In the PBTK model developed for tebuconazole as an individual compound, the observed differences between the ability of the two enantiomers to inhibit the metabolism of one another are not considered. In order to investigate the influences of this on the simulation results, the model was further developed into a binary model where R(−)- and S(+)-tebuconazole were described as two separate compounds. The Michaelis−Menten equation for each enantiomer was modified to include reversible inhibition, inhibition constants were added to the list of parameters, and the metabolic constants of the R(−) and S(+) forms were entered instead of the mean values of those. A binary version of the model was made for both rats and rabbits. A schematic representation of the binary model for rats is shown in Figure 2. The implementation was made such that simulations could be done for tebuconazole treated as an individual compound as well, only by

setting all administration routes to zero for one of the two compounds and to insert the appropriate values for the metabolic constants for the other compound. Thus, simulations were made to make sure that each individual part of the model was correctly implemented, before carrying out simulations for binary mixtures. Parameters for the PBTK Models. The following sections list the parameters assembled for the models. These parameters provide necessary physiology information for the different species and describe the ADME processes of tebuconazole in the body; see Figure 3. Physiological Parameters. Several collections of physiological parameters for adult as well as for young animals and humans have been published,63−65 but no internationally accepted list of reference values of such parameters for use in PBTK modeling has been compiled yet. In this work, it was decided to use the very comprehensive paper by Brown and co-workers63 which provides a valuable overview of the physiological parameters for both rats and humans to be used 718

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Figure 3. Sources for data used in the development of the PBTK models. The physiological parameters were taken from Brown et al.,63 data on fractional absorption and urinary and fecal excretion (elimination) for tebuconazole were found in the FAO/WHO Joint Meeting on Pesticide Residues (JMPR) report and the Draft Assessment Report (DAR) from the European Commission,52,54 distribution to tissues was estimated by tissue/blood partition coefficients calculated based on the experimental octanol/water coefficient for tebuconazole, the fraction unbound in plasma was predicted by QSAR (quantitative structure−activity relationship),62 and metabolic constants were taken from Shen et al.45 implementation of the model.45 A simple mean of the values for the two enantiomers was calculated and used in the single compound version of the model. The corresponding constants measured for either the R or the S form as substrate (compound X-TEB) in the presence of the other enantiomer as an inhibitor (compound Y-TEB) were used to derive the inhibition constants (KI) used in the binary model by the following equation. CI = 10 μM(μmol/1) was the concentration of the inhibitor in these experiments. The in vitro metabolic constants were scaled to in vivo as described in section 1 in the Supporting Information.

in PBTK models, and it has previously been used in several PBTK models.33,66−68 Volumes of arterial and venous blood were taken from Igari et al.69 The physiological parameters used in the PBTK models in this article are listed in Table 1. These are body weight (BW) and tissue volumes as percentage of BW (Vp,t), as well as cardiac output (QC), and organ blood flows as percentage of QC (Qp,t).63 In each case, mean values for a male of the given species was used because the data available for validating the model are on males. The actual tissue volumes and organ blood flows were calculated as shown in Table 1. The blood flow to each compartment is scaled within the PBTK models to ensure that 100% of the cardiac output is accounted for. This was done by summing all the reported values for tissue blood flows together, multiplying the inverse of this sum with the cardiac output to calculate a scaling factor which was implemented in the model. Cardiac output for the rabbit and rhesus monkey were calculated from rat values by interspecies extrapolation (see footnote b in Table 1). The mean of experimental values for control rhesus monkeys listed by Forsyth70 is 77 l/h and corresponds well with the extrapolated value from rats. In the rabbit model, it was assumed that tissue volumes and blood flows were the same percentage of the body weight and cardiac output, respectively, as in rat, with the exception of tissue volume of liver (Vp,liver) where a value for rabbits was available. For the rhesus monkey model, the tissue volumes as percentages of body weight and blood flows as percentages of cardiac output for humans were used and scaled linearly with the body weight and cardiac output for rhesus monkey, respectively. Compound Specific Parameters for Tebuconazole. The compound specific parameters used for each species in the developed models along with the assumptions made are listed in Table 2, and the tissue/blood partition coefficients calculated for each tissue are listed in Table 3. As discussed above, fractional absorption (Fa) for rats was set to 1 based on available data. Detailed information on the oral absorption rate was not available, but it has been estimated by the FAO/ WHO Joint Meeting on Pesticide Residues (JMPR) and the European Commission in a Draft Assessment report (DAR) that all of the tebuconazole was absorbed within a few hours in rats.52,54 In the model, the absorption rate of an administered bolus dose from the GIT to the liver is simulated by a pulse function, using an absorption rate constant (ka) of 1 h−1. It was assumed that both of these parameters are the same for the three other species. Metabolic constants (Vmax and KM) for the depletion (conversion) measured separately for each of the R(−) and S(+) enantiomers of tebuconazole in male rat microsomes were used in the binary

KM(X − TEB ,with inhibitor Y − TEB) ⎛ ⎞ CI ⎟⎟ = KM(X − TEB ,pure enantiomer)⎜⎜1 + KI(Y − TEB , X − TEB) ⎠ ⎝ First order elimination rate constants for excretion through the urine and feces were determined by curve-fit to available data on the cumulative excretion of tebuconazole in the rat52,54 and rhesus monkey. (See Scheme S2 in the Supporting Information for details.) The experimentally derived metabolic constants and elimination rate constants were extrapolated to the other species by using allometric scaling, and the equations used and the resulting numerical values are shown in Table 2. The use of interspecies extrapolation of the metabolic constants is supported by evidence presented in the Biological Basis and Architecture of the Basic Models section that tebuconazole undergoes similar metabolic reactions in all four species. The use of interspecies extrapolation was validated to the extent possible as discussed in the first two sections in Results and Discussion regarding the metabolic constants, and Scheme S2 in the Supporting Information and the second to last paragraph in the Simulations for the Proposed Dermal Exposure Scenarios section regarding the elimination constants. Dermal absorption is both dependent on the concentration of the active compound and the type of formulation of the product used. The Danish EPA provided us with an estimate in humans for a solvent-based formulation with an active compound concentration of 0.5−0.6% w/w in solution.62 In cases where no experimental data are available for specific physicochemical and kinetic parameters for a compound, and the corresponding data for other structurally similar compounds are available, QSAR modeling can in some cases be used for predicting the missing parameters.10,71,72 For two properties, the fraction unbound in plasma and the volume of distribution, new QSAR models were developed based on available experimental data for other triazoles and imidazoles from human studies. The resulting models were used to 719

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ka Pt:b

oral absorption rate constant tissue/blood partition coefficients (solubility ratio part) fraction unbound in plasma

[μM] [μmol/h] [μM] [μM] [μM] [l/kg]

KM Vmax

KM KI

KI

Vd

720

a

1.0 (2)

human 1.0 (2)

rabbit 1.0 (2)

monkey

12.2 3.6

12.2 ± 2.7 3.6

not applied

feces kef = 0.039 urine keu = 0.0078 0.0468 (4)

1.2

0.17 0.40 15.7 (6)

0.19

0.77

6.3

14.8 3034

14.8 ± 2.2 44.3 ± 3.7

6.3

1.0 4642

1.0 67.8 ± 3.2

not applied

0.080

rat value

6.3

12.2 3.6

14.8 220

1.0 337

0.095 (dermal) 0.21 (i.v.) (5) not applied

feces + urine: ke = 0.10 0.10

human value

6.3

12.2 3.6

14.8 493

1.0 754

1.0 (3) 1.0 1.0 1.0 rat values in Table 3 human values in rat values in human values in Table 3 Table 3 Table 3 0.53 ± 0.2 0.34 ± 0.1 rat value human value

1.0 (1)

rat

predicted by QSAR (human value) and by interspecies extrapolation (rat value)62,92,93 obtained by curve-fit to cumulative excretion data for rat and monkey, respectively;52,54 see Scheme S2. (4) The fecal + urinary value for rats was extrapolated to the other species. ke(s) = ke(rat)(BWs/BWrat)0.25 (5) The value for monkey, curve-fit to data from dermal and i.v. studies, extrapolated to human as well ke(s) = ke(monkey)(BWs/BWmonkey)0.25 (6) estimated for humans for 0.5% w/w solution; data provided by Danish EPA62

(1) assumption: complete absorption based on refs 52,54 (2) assumption: complete absorption for other species (3) Tebuconazole is documented to absorb within a few hours.52 The calculated solubility ratio for each tissue is based on tissue composition and Pow. predicted by QSAR (human value) and by interspecies extrapolation (rat value)62,91 It is assumed that tebuconazole does not bind to tissue proteins rat value measured in liver microsomes extrapolated by allometric scaling:45 Vmax(s) = Vmax(rat)(BWs/BWrat)0.75 Allometic scaling of KM implies using the rat value for all the species.45 rat value extrapolated to other species; see explanation for R-TEB;45 scaling of Vmax to whole liver; see Scheme S1 rat value used for all species45 based on data from ref 45; allometric scaling of KI implies using the rat value for all the species. based on data from ref 45; rat value used for all the species.

estimated/measured/calculated/fitted/assumed

See also Figure 3. Vmax and KM of the racemic mixture were calculated as the mean of the values of the pure entantiomers. The tissue/blood partition coefficients are listed in Table 3.

[%]

[h−1]

[h−1]

[h−1]

[μmol/h]

[h−1]

unit

f u,t Vmax

elimination rate constants by curve- ke fit to excretion data elimination rate constant (total) ke (rat → human/ rabbit/monkey) elimination rate constant (total) ke (monkey→ human) dermal absorption Dabs

fraction unbound in tissue maximum velocity R(−) form (R-TEB) Michaelis−Menten const. R-TEB maximum velocity S(+) form (S-TEB) Michaelis−Menten const. S-TEB inhibition constant R-TEB inhib. by S-TEB inhibition constant S-TEB inhib. by R-TEB volume of distribution

Fa

fractional absorption

f u,b

symbol

parameter

Table 2. Overview of the Compound Specific Parameters Used and Data Sources for These Parametersa

Chemical Research in Toxicology Article

DOI: 10.1021/acs.chemrestox.5b00341 Chem. Res. Toxicol. 2016, 29, 715−734

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Chemical Research in Toxicology

in the equation above for evaluating Pt. Pvow was calculated with the following correlation from Poulin and Haddad:76

Table 3. Estimated Solubility Ratios for Tissue/Blood (Pt:b) Contribution to the Partition Coefficients for Tebuconazole Based on the Tissue Fractional Volume Composition Given in Table 3.4 in Ref 62 and the Octanol−Water Partition Coefficient (Pow)a

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RESULTS AND DISCUSSION Simple Validation of the Developed Models Based on Half-Life Data for Rats and Rabbits. Zhu et al.77 estimated the half-lives of the R(−) and the S(+) enantiomers of tebuconazole to 123 and 88 min, respectively, based on in vivo data measured in male rabbits after i.v. administration of 30 mg/kg bw racemic tebuconazole. The mean of these two measured values is 104 min, which we estimate as the half-life of the racemic mixture in rabbits). No corresponding in vivo data are available in rats. However, half-lives determined from the in vitro study in male rat liver microsomes have been published, where the values for the R(−) and S(+) forms of tebuconazole are 49 and 22 min, respectively,45 and the mean is 36 min. Both the in vivo data from the rabbit study and the in vitro data measured in rat microsomes show that in the presence of metabolic enzymes, conversion of tebuconazole to the different metabolites occurs very fast. One should be aware that the metabolic constants and half-lives in rats are derived from the same measurements, and thus the only independent validation, is the one in rabbits in vivo. Half of the administered dose of racemic tebuconazole was metabolized 112 min after i.v. administration, according to a simulation by the single compound model in rabbits (not considering inhibition), and after 135 min when simulated as a binary solution of the R(−) and S(+) forms of tebuconazole (taking inhibition into account) (see Table 4). Thus, there is

partition coefficients (Pt:b) adipose (Pvow) adipose (Pow) bone brain gut heart kidney liver lung muscle skin spleen blood rapidly perfusedc slowly perfusedd

rat

human

rabbitb

monkeyb

249 394 13.0 25.6 15.5 8.2 9.7 10.6 12.1 5.9 13.6 5.5 1.0 13.5 5.9

145 189 17.8 16.3 12.8 4.0 6.1 10.2 1.4 6.2 7.6 6.3 1.0 10.1 6.2

249 394 13.0 25.6 15.5 8.2 9.7 10.6 12.1 5.9 13.6 5.5 1.0 13.5 5.9

145 189 17.8 16.3 12.8 4.0 6.1 10.2 1.4 6.2 7.6 6.3 1.0 10.1 6.2

a

For adipose tissue (fat), partition coefficients have both been calculated based on Pvow and Pow. bRat values were used for rabbits and human values for monkeys. cAverage for brain, hearth, kidney, lung, and spleen, weighted by tissue volumes.62 dMuscle value was used for slowly perfused tissue. predict the corresponding properties for tebuconazole in humans and for the volume of distribution for the most significant metabolites as well. Values for the rat were obtained by interspecies extrapolation, based on the QSAR predictions from the human models and correlative equations from the literature.62 A detailed description of the development and validation of the QSAR models, as well as human to rat extrapolation, is published elsewhere.62 For both of these properties and the tissue/blood partition coefficients discussed below, human values were assumed for the other primate, rhesus monkey, and rat values for the other rodent, rabbit. As tebuconazole undergoes relatively fast excretion from the body, we consider it unlikely that it binds to tissue proteins. It is thus assumed that tebuconazole only binds to plasma proteins and that the fraction unbound in tissue was set to one in all four species (f u,t = 1). The tissue/blood partition coefficients (Pt) were calculated as the ratio between the solubility of the compound in tissue, t, and blood, b, (Pt:b), respectively, multiplied by the ratio between fraction of unbound compound in blood (f u,b) and in tissue (f u,t) as described by Poulin and Theil;73 see the equation below. The solubility was calculated based on the experimentally determined octanol−water partition coefficient value for tebuconazole at 25 °C (logPow = 3.7)55−57 and the fractional volume content of water (Fw), neutral lipids (Fnl), and phospholipids (Fph) in blood or wet tissue, respectively. The phospholipids were considered as 30% of the hydrophilic phase and 70% of the lipophilic phase. The fractional volume contents for the majority of tissue were taken from the paper by Poulin and Theil,73 who have made a comprehensive analysis and a revised list of values based on available handbooks and scientific papers. Values for human whole blood and rat erythrocytes were taken from Poulin and Krishnan.74,75 The calculated Pt:b values are listed in Table 3. Pt = Pt : b ×

fu , b fu , t

=

(Pow(Fnl , t + 0.3Fph , t ) + (Fw , t + 0.7Fph , t )) (Pow(Fnl , b + 0.3Fph , b) + (Fw , b + 0.7Fph , b))

×

log Pvow = 1.099log Pow − 1.31

Table 4. Validation of the Developed Models Based on Experimental and Predicted in Vivo Half-Lives in Rabbit, as Well as a Simple Evaluation of Simulated in Vivo Half-Life in Rat in Relation to the Observed in Vitro Half-Life in Rat Liver Microsomes45,77 a species

experiment

rat R(−)/S(+)-tebuconazole, in vitro rabbit R(−)/S(+)-tebuconazole, in vivo

t1/2 (exp.) [min] t1/2 (pred.) [min] 36 104

52−57 112−135

a

The simulations were carried out with the single compound and binary models for tebuconazole.

overall good agreement between the predicted and experimental in vivo half-lives in rabbits. According to simulations by the single compound and binary PBTK models for rats, half of the tebuconazole had been converted to metabolites 53−57 min after the administration of 20 mg/kg bw as an oral bolus dose. The corresponding simulated in vivo half-life obtained following an oral bolus dose of 2 mg/kg bw was 52 min (Table 4). It is important to point out that half-lives obtained from in vitro measurements cannot be compared directly with the corresponding in vivo values. One would obviously expect the metabolic conversion to be faster in a well-stirred in vitro system, where the whole applied dose of tebuconazole is available to the metabolic enzymes from the beginning, compared to that in rats in vivo, where an orally administered dose reaches the liver over a period of time. Optimally, in vitro half-lives should be scaled in vivo in order to make a comparison. However, as an oral dose of tebuconazole is estimated to be absorbed and delivered to the liver within a few hours after administration, we justify this

fu , b fu , t

Because of the special properties of adipose tissue (fat), which is particularly rich in neutral lipids, it is recommended to use the vegetable oil−water partition coefficient (logPvow) rather than logPow 721

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Chemical Research in Toxicology comparison as additional and approximate evidence to support the pure in vivo validation in rabbits. These results indicate that the overall metabolic conversion rate of tebuconazole is sufficiently accurately simulated with the PBTK models for both rat and rabbit. According to this simple investigation of half-lives, the experimental metabolic constants seem to be reliable, and the interspecies extrapolation of the metabolic constants from rats to rabbits gives overall half-lives of acceptable accuracy compared to the experimental data. This fast conversion of tebuconazole is illustrated in Figure 4 by a simulation of an oral bolus dose of 20 mg/kg bw (16 μmol)

Figure 5. Simulated blood concentration of tebuconazole compared to experimental plasma concentrations after the administration of an i.v. dose of 30 mg/kg bw to rabbits. Simulations were made with the single compound PBTK model using the mean of the metabolic constants for R(−)- and S(+)-tebuconazole (dashed line) and the binary model for a racemic mixture of R(−)- and S(+)-tebuconazole with the influence of the reversible inhibition implemented (solid line).

see Figure 5. Slight influence of inhibition is seen for the simulated tissue concentrations for this dose group (results not shown). For a corresponding simulation at 3 mg/kg bw dose, no influence of inhibition is seen on blood and tissue concentrations (simulation results not shown). As the predicted partition coefficient between plasma and erythrocytes is estimated to be close to one (Perythrocytes:plasma = 1.3 in rat), it is assumed that the plasma and blood concentrations are practically the same. Thus, plasma level data were used for validating blood concentrations simulated by the model. There is an overall acceptable agreement between the simulated blood concentration curves and the experimental plasma concentrations, with the simulated blood concentrations underestimated by around 40−50% at three time points, 15, 30, and 60 min after i.v. administration (Figure 5). All of the simulated curves for tissue concentrations shown in Figure 6 are made with the binary implementation of the model, including the effect of inhibition. Acceptable agreement is seen between predicted and experimental tissue concentrations for the heart, kidney, brain, muscle, and adipose tissue. The simulated curves for the heart and kidney generally underestimate the experimental data points by a factor of 2, and the simulated curve for the brain overestimates the experimental concentration values by about 50%. The simulated curve for adipose tissue agrees well with the experimental data points, except at the highest time point where the simulation indicates around six times longer clearance time for tebuconazole compared to that of the experiment. Considering the relatively high experimentally measured partition to adipose tissue, the experimental clearance time of 500 min (8.3 h) is strangely short. The experimental data for adipose tissue indicate that saturation might occur in this case. Unfortunately, two tissues fall outside the limit of acceptable accurate predictions for use in risk assessment discussed below. The peak concentration in the liver is underestimated by at least a factor of 7 by the model compared to the experimental data (Figure 6), and the simulated concentration levels in the lung are also significantly underestimated by the model (data are in ref 62). It is noted, that the experimental concentration levels in the liver are similar to those in the kidney and the other rapidly

Figure 4. Simulated total amount of tebuconazole and metabolites present in rat blood and tissues following the administration of an oral bolus dose of 20 mg/kg bw. On this graph, only the fraction of tebuconazole that has been absorbed from the GIT at each time point is considered. The total amount of tebuconazole that has been metabolized in the liver is shown as well, both the metabolites present in the body and those already eliminated. The simulation was carried out with and without inhibition considered, but only the tebuconazole concentration was influenced at this dose.

administered to the PBTK model for rats. It can be seen that according to the simulation a peak level of 1.5−2 μmol total amount of tebuconazole distributed in the rat body is reached 1.3 h after administration. The peak level is calculated as the total amount of tebuconazole in the body subtracted by the amount of tebuconazole not yet absorbed from the GIT at each time point. For comparison, a peak level of 13 μmol of different tebuconazole metabolites in the rat body was reached 3.7 h after administration. It is also seen that around 99% of the metabolites have been excreted from the rat body 72 h after administration. Validation and Analysis Based on Available Data in Rabbits. Data on the concentrations of tebuconazole in plasma and in several tissues at various time points after i.v. administration (30 mg/kg bw) to male rabbits77 was the only data set on distribution to tissues over time available to us. This data set was used as the primary validation set for the model, using the rabbit version of the model described in the methods section. As mentioned in the previous section, simulations were both made for a racemic mixture employing the mean metabolic constants for the R(−) and the S(+) enantiomer (e.g., without considering inhibition) and for a binary mixture of two enantiomers (e.g., considering inhibition between the two enantiomers). Only minimal influence of inhibition is seen for simulated blood concentrations after 30 mg/kg bw i.v. administration; 722

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rabbits, discussed in the previous section, indicates that the overall metabolism rate in rabbit is simulated accurately. In the absence of tissue concentration data following oral administration specifically in rabbits and corresponding data for other species, we consider it unwise to alter the present implementation of the model to reproduce the experimental data for liver concentration levels more accurately. Therefore, we conclude that one needs to be aware of this uncertainty introduced into the model, at least upon simulation of nonoral administration/exposure scenarios. This needs to be investigated better in future work, both by studying other compounds with similar properties and by acquiring more data for tebuconazole. For this purpose, it would be helpful have more in vivo half-life and tissue concentration data for different exposure scenarios in different species, as well as in vitro determined metabolic constants and partitioning coefficients in different tissues in relevant species. Zhu et al. pointed out that the tebuconazole was stockpiled in the lung due to the lung first-pass effect.77 A simple flow based model assuming that no binding to lung tissue occurs might not be sufficient to describe the distribution to the lung. Implementation of an additional biological mechanism to the model would require solid biological evidence for the occurrence of specific processes. In the absence of such data, and considering that the lung is not a target organ and normally lumped into rapidly perfused compartment, this was not attempted. The first data point at 15 min post-administration is significantly lower than the data points measured at higher time points in all of these data sets, indicating a long lag time for the tebuconazole to distribute to the different tissues. A flow regulated PBTK model does not capture such a lag time, as it assumes instantaneous distribution within the body, which is governed by a thermodynamic equilibrium modeled by the tissue/blood partition coefficients implemented in the model. It is possible that it takes some time to establish equilibrium in a living system, although the data show surprisingly long lag times compared to those of other i.v. studies.77−80 It should be mentioned that each data point is based on one animal only77 and that the oddity of this data point might also be caused by the genetic variability of one of the animals or other experimental problems. The flow-regulated well stirred model approach can be considered appropriate for all but the lowest time point. As the model is overestimating rather than underestimating the experimental levels at this early time point, it is not considered a problem with respect to the application of the model for risk assessment. The overall conclusion for the validation in rabbits is that the simulated concentration levels of tebuconazole in blood and all tissues, except the liver and lung, are within a factor of 2, which has been defined as the acceptable deviation for PBTK simulation results to be used for risk assessment.9 This degree of uncertainty can be expected for such a fully predictive PBTK model that uses simple estimates of the tissue/blood partition coefficients. It should be mentioned that the parameters used in the PBTK models are experimentally determined or calculated values that have not been adjusted to any blood or tissue concentration data, and many of the parameters used have been extrapolated from rats. The influence of uncertainties in the model parameters on the simulation results is discussed in greater detail in ref 62 and in the following section, and the deviations seen for the liver and lung compartments are discussed above.

Figure 6. (a and b) Curves showing simulated tissue concentrations of tebuconazole after the administration of an i.v. dose of 30 mg/kg bw to rabbits compared to experimental data points (symbols). The simulations were carried out with the binary PBTK model for a racemic mixture of R(−)- and S(+)-tebuconazole with the influence of reversible inhibition implemented.

perfused tissues. The calculated tissue/blood partition coefficients for these organs are similar too, indicating that tebuconazole should distribute similarly to these tissues. According to this, it does not seem likely that the liver/blood partition coefficient is less accurately determined than the other tissue/blood partition coefficients. The fast metabolism of tebuconazole was seen to be a major factor in lowering the concentration level in the liver in the simulation. A possible explanation for this large deviation between simulation and experiment is that this is due to small accumulation or detention of tebuconazole in the liver tissue. It is seen that the levels in the liver are slightly higher than those for the other tissues at 72 h after administration in rats.54 Also, it could be caused by the interplay between the fast metabolism of this particular compound and the equilibrium-based description of distribution to tissue in the model. The liver is assumed to be the only site of metabolism in the model. However, in case metabolism would occur in other organs as well, this would not affect the metabolism rate in the liver but only enhance the overall metabolism rate in the organism. However, addition of a storage compartment to incorporate diffusion limitations for the liver would reduce the metabolism rate in the liver to some degree. It should be mentioned, that the simple validation by comparing simulated half-lives with experimental in vivo half-lives in 723

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Chemical Research in Toxicology Uncertainties of the Model Parameters and Sensitivity of That on the Simulation Results. Species dependent parameters like body weight, tissue volumes, cardiac output, and blood flow rates in tissues are well established for an average male rat or human. Each of the developed models represents an average male individual of the given species, and neither the influence of genetic variability nor the influence of different life stages and gender were considered in this work. The uncertainties in the simulations for blood and tissue concentrations are mainly considered to be associated with the compound specific parameters describing the ADME properties of tebuconazole. Sensitivity analysis of the compound specific parameters in the model was conducted. A local analysis was carried out by varying one parameter at a time as described by Chiu et al.,81 but instead of changing each parameter by 1%, it was varied by a factor of 2. In such a way, each parameter was multiplied by a factor of 2 (100% increase) or divided by a factor of 2 (50% decrease), simulations were made, and the change in the chosen dose metric was recorded in each case. We have chosen the variation by a factor of 2 to reflect the evaluated uncertainty levels for some of the compound specific parameters. Maximum blood and tissue concentration levels were chosen as dose metrics, and the normalized sensitivity coefficient (SC) was calculated by the following equation. Herein, P is the original parameter value (as listed in Tables 2 and 3), P′ is the altered parameter value, C and C′ are the corresponding maximum blood or tissue concentrations, respectively, and the subscript x is i when the parameter value is increased and d when it is decreased.

Table 5. Sensitivity Analysis for the Rat Model, Simulating an Oral Bolus Dose of 1 mg/kg bw, and the Human Model, Simulating Dietary Dose of 0.001 mg/kg bw/day or Absorbed Dermal Dose of 0.001 mg/kg bw/daya species/exposure scenario parameter/ symbol ka Fa

Pt (liver) Pt (rperf) Pt (sperf) Pt

(adipose) Pt (skin)

f u.b f u.t (liver)

Vmax KM

ln(C′/C) SCx = ln(P′/P)

keu(rat) kef(rat)

The results from the sensitivity analysis for three different scenarios are listed in Table 5. One of these scenarios uses an oral bolus dose in rat, as the rat is the species with the most experimental data available for describing the ADME of tebuconazole. Then, a dietary and a dermal exposure scenario in humans were analyzed as well, as the sensitivity of the parameters in humans is important for evaluating the uncertainties associated with the human model. We used the same exposure time as that for the dietary and dermal exposure scenarios presented in the previous section, but the same dose (exposure) was used for both scenarios in the sensitivity analysis. The normalized sensitivity coefficients measure the relative change in the chosen dose metric (blood concentration in most of the cases) as a function of the relative change in the parameter considered. SC = 1 means that the relative change in the dose metric scales with the relative change of the parameter. A smaller value of SC means that it scales less than the relative change of the parameter, and a higher value of SC means that it scales more. The rule of thumb is that this parameter should not exceed one, to avoid amplification of uncertainties in parameter values in the model output. The tissue/blood partition coefficients and fraction unbound in plasma are the parameters that govern the modeling of distribution to tissue. It is seen that the normalized sensitive coefficients for the tissue/blood partition coefficients was generally around one, irrespective of the administration route. This means that the maximum concentrations in the respective tissues generally scaled linearly with the increase or decrease in the tissue/blood partition coefficients. An exception to this is

ke Dabs

rat/oral bolus 1 mg/kg bw

dose metric

SCi

SCd

blood conc. max liver conc. max blood conc. max liver conc. max rperfused conc. max sperfused conc. max adipose conc. max skin conc. max blood conc. max blood conc. max liver conc. max blood conc. max blood conc. max blood conc. max blood conc. max blood conc. max blood conc. max

0.64

0.70

human/ human/ dietary 0.001 dermal 0.001 mg/kg bw/day mg/kg bw/day SCi

SCd

1.0

1.0

1.0

1.0

SCi

SCd

0.96

0.98

1.0

1.0

1.0

1.0

0.96

0.98

1.0

1.0

0.97

0.98

0.66

0.77

0.91

0.98

0.55

0.78

0.22

0.31

0.10

0.17

0.08

0.13

0.50

0.63

0.99

1.00

0.96

0.99

−1.4

−1.3

−1.1

−1.1

−1.3

−1.1

0.0

0.0

0.01

−0.96

−1.0

−1.0

−0.91 −0.83 −0.97

−0.94

−0.10

−0.15

0.81

0.88

0.94

0.97

0.15

0.10

0.0

0.0

0.0

0.0 0.0

0.0

0.0

0.0

1.0

1.0

a The normalized sensitivity coefficients (SC) are calculated for a twofold (100%) increase (SCi) or a two-fold (50%) decrease (SCd) in each parameter using the algorithm: SCx = ln(C′/C)/ln(P′/P)), where P is the initial parameter value, and P′ is the modified parameter value. C is the initial output of the model. C′ is the output for the modified parameter value P′. The symbols for the parameters are shown in Table 2, and conc. max stands for maximum concentration.

adipose tissue, which is less sensitive to variation in the partition coefficient. The maximum blood concentration scaled inversely and close to linearly with the fraction unbound in plasma, rendering SC in the range −1.1 to −1.3 for the different administration routes. Similarly, the SC value became −1.0 when the fraction unbound in liver tissue was reduced from the value of one (assuming no binding to liver proteins) to 0.5 (assuming that half of the tebuconazole reaching the liver binds to liver proteins). It should be mentioned that the fraction unbound in plasma or tissue can vary less than most of the other parameters in the model (only between zero and one), but the uncertainty is slightly amplified for fraction unbound in plasma. Whereas the maximum blood concentration varied inversely linearly with changes in Vmax (SC around −1) and linearly with changes in KM (SC around one) upon oral and dietary administration, these metabolic constants were much less 724

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The method used to calculate approximate estimate of the tissue/blood partition coefficients is useful in the absence of experimental data. Whereas, identical experimental values for the octanol/water partition coefficient were found in three publications, the fractions of water and lipids in the tissues are taken from various sources, including older handbooks, and those data are considered to be a source of some of the uncertainties in the estimated tissue/blood partition coefficients. There might also be uncertainties due to the method used for predicting the partition coefficients. An analysis that compared predictions for selected nonionic chemical compounds82 indicates that overall similar results are obtained with different equations from the literature,76,82−85 including the relatively simple equation that was used in this work. Tebuconazole is neutral at blood pH levels (result obtained by pKa prediction in ACD Laboratories ACD/ToxSuite 2.95 (http://www.acdlabs. com/products/admet/tox/)). However, better availability of in vitro data on tissue partitioning is essential to improve parametrization of distribution to tissue in PBTK modeling.10 As mentioned above, two parameters were predicted by QSAR models developed based on structurally similar compounds. One of these parameters, the fraction unbound in plasma, has substantial influence on the simulated blood and tissue concentrations of tebuconazole. The QSAR models were validated using data points not included in the model development, and by analyzing these validation results, we estimated the uncertainty of the predicted fraction unbound in humans to be ±0.1. A prediction for rats was obtained by interspecies extrapolation, and the uncertainty of the predicted fraction unbound in rats was estimated to be twice the value for the human prediction, e.g., ± 0.2.62 According to the sensitivity analysis above, these uncertainty estimates are slightly amplified in the simulated blood concentrations. We conclude that the parameters that seem to be the main source of uncertainty according to our validation results are the tissue/blood partition coefficients and that some uncertainty is also linked to the fraction unbound in plasma. The interspecies extrapolation of the metabolic constants from rats to humans was also evaluated to be a significant source of uncertainty as well. The other parameters were evaluated to contribute significantly less to the uncertainties of the model simulations. Influence of Inhibition between R(−)- and S(+)Tebuconazole in Rats. In order to have an idea at which exposure levels the influence of inhibition might be significant, a series of simulations were carried out using the PBTK models for rat. These simulations were used to evaluate the importance of inhibition for simulations carried out for real exposure scenarios presented in the following section, among others. An experimental value for the racemic mixture, implicitly including the effect of the inhibition, was not provided. Thus, as the metabolic constants for tebuconazole were measured for each of the entantiomers, considering the influence of inhibition explicitly, the binary implementation of the model must be considered to be more correct than the single compound implementation. It is, however, only necessary to consider the inhibition at higher dose levels, and thus, it is practical to use the simpler single compound implementation of the model for simulations at lower dose levels. This investigation illustrates when the binary implementation of the developed models is needed. Although, these are two enantiomers of the same compound, they inhibit each other in a similar way and other and more dissimilar molecules that compete for the same metabolic enzymes. The unusually detailed data available from this study

sensitive to changes in these parameters for dermal administration. The sensitivity of Vmax on the model output is illustrated for a dermal exposure scenario in Figure S3 in the Supporting Information. The normalized sensitivity coefficient was slightly below one for the oral absorption rate constant and took the value of one for the fractional absorption and the dermal absorption. Neither blood nor tissue concentrations were influenced by changes in the elimination rate constant (SC of zero). Graphs illustrating the sensitivity to some of these parameters are found in ref 62. The half-life of tebuconazole is governed by the conversion rate and thereby the metabolic constants. Lowering Vmax by a factor of 2 increased the half-life around 21% (SC = −0.27), but increasing Vmax by a factor of 2 lowered the half-life by around 10% (SC = −0.15) in rats after a single dose oral administration of 1 mg/kg bw. Correspondingly, in rabbits after i.v. administration of 30 mg/kg bw, lowering Vmax by a factor of 2 increased the half-life around 58% (SC = −0.66), but increasing Vmax by a factor of 2 lowered the half-life by around 27% (SC = −0.45). For an i.v. dose of 3 mg/kg bw, the corresponding SC values were −0.49 and −0.30, respectively. Uncertainties associated with the different parameters have generally similar influence on the simulated concentration levels, and thus the uncertainties of the overall simulations depend on the uncertainty of the individual parameters. It is argued based on the validation results presented in the previous sections that the absorption rate constant, the metabolic constants, and the elimination rate constants in rats are accurately determined based on experimental data. The absorption rate constant is based on experimental information for rats and is only used in oral dose simulations in rats in this article. Similarly, the dermal absorption is based on human data and is only used for simulating human exposure scenarios. The fractional oral absorption is experimentally determined in rats and it is assumed that all of the tebuconazole is absorbed in the other species as well, and thereby it is ensured that the risk is not underestimated. It is also argued that interspecies extrapolations of these parameters seem to give adequate results in cases where this could be validated. See validation of the scaling of the metabolic constants from rats to rabbits above, as well as validation of elimination rate constants from rats to rhesus monkeys in Scheme S2, and from rats to humans in the “Simulations for the Proposed Dermal Exposure Scenarios” section. Moreover, the simulated blood and tissue concentration levels are not sensitive to variability in the elimination rate constant. The metabolic constants are, however, a source of uncertainty in species where the extrapolation could not be validated. The extrapolation of the metabolic constants from rats to humans could not be adequately validated. In the absence of a validation, an estimate of the potential uncertainties associated with this extrapolation was made. This was done by comparing the difference between metabolic constants measured in human liver microsomes and the corresponding metabolic constants extrapolated based on measured values in rat liver microsomes for compounds similar to tebuconazole. The level of uncertainty was estimated to be around factor of 2 in humans; see details on this analysis in Scheme S3 is the Supporting Information. The level of uncertainty for the estimated tissue/blood partition coefficients was estimated to be generally within a factor of 2 based on the validation results in rabbits, and we evaluate similar uncertainty levels for these parameters in all four species. 725

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Chemical Research in Toxicology thus also gives an indication of at which dose levels inhibition might generally be important in a simulation of a binary mixture of different compounds. The influence of inhibition was explored at different orally and dermally administered single doses. For this purpose, simulations were made with the single compound PBTK model for tebuconazole and the binary version of the model. The single compound version uses the mean metabolic constants measured for the R(−) and S(+) forms and treats tebuconazole as one compound. In the binary implementation of the model, R(−)- and S(+)-tebuconazole are treated as two interacting compounds, where the measured metabolic constants for each of the two enantiomers and the inhibition constant due to the inhibition by the other enantiomer are used (Table 2). When blood and tissue concentrations predicted with these two versions of the model are compared, practically no difference is seen at an oral bolus dose of 1 mg/kg bw (0.5 mg/kg bw of each enantiomer in the binary simulation). The influence of inhibition is gradually increased and a significant influence of inhibition is seen at a dose of 10 mg/kg bw (5 mg/kg bw of each enantiomer) (Figure 7). Thus, it is seen that competitive inhibition influences the tebuconazole level in the higher dose group, whereas for the lower dose group inhibition did not show any effect. Corresponding simulations were carried out for the dermal exposure. In this case, no influence of inhibition was seen at a dose of 10 mg/kg bw absorbed through the skin, but at 100 mg/kg bw an effect of inhibition was seen (Figure 8). This corresponds to substantially higher exposed doses, as only a fraction of the tebuconazole applied to the skin surface is actually absorbed. As a compound absorbed dermally is distributed in the body before reaching the liver, a lower concentration of tebuconazole is distributed to the liver at each time point, and thus the capacity of the metabolic enzymes is less affected than in case of an oral administration. In both simulations, it was assumed that tebuconazole was absorbed within an hour. Predictions for Specific Exposure Criteria in Humans. Simulations for the Proposed Dietary Exposure Scenarios. An estimate of dietary exposure of tebuconazole was taken from a report about pesticide residues detected in samples from commodities of food (fruit, vegetables, cereals, etc.) sold on the Danish market from 2004 to 2011.86 The mean daily exposure of an average consumer to each pesticide was based on samples collected by the pesticide monitoring program under The Danish Veterinary and Food Administration. Only results for fruits and vegetables from conventional production are included. According to this report, the mean intake among consumers in Denmark is 0.013 μg tebuconazole/kg bw/day.86 Exposure of consumers that eat more than 550 g/day of fruit and vegetable is assumed to be the double of the exposure of an average consumer according to the report, namely, 0.026 μg/kg bw/day. It should be mentioned that according to the yearly reports on pesticide residues found in the monitoring program, residues of tebuconazole were only found in fruits and vegetables and not in other commodities like, e.g., cereals.87 When implemented in the model, the daily dietary dose is divided equally over 12 h (7−19 h), with the remaining 12 h as resting time with no exposure. Figure 9 shows the simulation for repeated dietary exposure of 0.026 μg/kg bw/day during 19 days, followed by a 14 day exposure free period. The simulation illustrates the very rapid conversion of tebuconazole to metabolites that are eliminated

Figure 7. Simulated blood concentrations in rat for the single compound PBTK model using the mean of the metabolic constants for R(−)- and S(+)-tebuconazole (marked No inhibition) and the binary model for a racemic mixture of R(−)- and S(+)-tebuconazole with the influence of reversible inhibition implemented (marked Inhibition). Simulation after an oral dose administration of (a) 1 mg/kg bw (0.5 mg/kg bw of each enantiomer in the binary simulation) and (b) 10 mg/kg bw (5 mg/kg bw of each enantiomer in the binary simulation).

within days. The peak level (amount) of tebuconazole in the body is smaller than that of the metabolites, with the simulated total amount of tebuconazole in the body up to a level of 0.0009 μmol reached after 9 days of repeated exposure and the simulated level of metabolites is 0.0023 μmol. The simulated blood, liver, and skin concentrations of tebuconazole are lower than 0.01 nmol/L. Whereas clearance of tebuconazole from tissues was generally fast according to this simulation, some accumulation is seen in adipose tissue after repeated exposure, reaching a steady peak level of 0.046 nmol/L after 12 days. Also, a 14-day exposure free period was needed to completely clear this accumulated tebuconazole from adipose tissue and the body according to the simulation. A simulation was performed on a scenario where the dietary dose was set equal to the ADI of tebuconazole, i.e., 0.03 mg/kg bw/day, in order to generate an internal assessment value that corresponds to the ADI value for external exposure. The peak level of tebuconazole in the body was 1.0 μmol, reached after 20 days of repeated exposure, and the simulated peak level of 726

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Figure 9. (a) Predicted curves for the total amount of tebuconazole and metabolites in the human body after oral dietary administration of 0.026 μg/kg bw/day, simulated for 19 days, followed by an exposure free 14 day simulation. (b) Predicted concentration curves for tebuconazole in human blood, liver, skin, and adipose tissue in the same simulation.

Figure 8. Simulated blood concentrations in rat for the single compound PBTK model using the mean of the metabolic constants for R(−)- and S(+)-tebuconazole (marked No inhibition) and the binary model for the racemic mixture of R(−)- and S(+)-tebuconazole with the influence of reversible inhibition implemented (marked Inhibition). Simulation after exposure to a dermal dose of (a) 10 mg/kg bw and (b) 100 mg/kg bw absorbed through the skin.

It is important to state that the developed PBTK models were only validated on data obtained from single dose exposure experiments. Therefore, uncertainties are associated with the simulated tissue and body levels for such repeated dose exposure scenarios. However, indication of accumulations in adipose tissue was also seen in experimental data in rabbits after single dose administration, and our simulations illustrate a possible tendency of a low degree of accumulation of tebuconazole in adipose tissue during long-term exposure. Simulations for the Proposed Dermal Exposure Scenarios. We have primarily focused on dermal exposure to tebuconazole used as pesticide and biocide for professional use. In this work, an exposure scenario due to professional industrial treatment of timber is analyzed. Similar analysis for other dermal exposure scenarios are shown elsewhere.62 The Danish EPA provided us with an estimate of tebuconazole exposure of a professional working with industrial wood treatment. A solvent-based formulation was considered in this work, as the active compound was absorbed in larger quantities, posing a greater risk, compared to when a water-based formulation is used. An active compound concentration of 0.5% w/w and a dermal absorption of 15.7% were assumed for this product. Penetration through clothes was set to 10% and mitigation by gloves set to 100%. (See Table A9.3 in ref 62 for information on the exposure models.) Biocide exposure due to industrial wood

metabolites was 2.6 μmol. These values are a factor of 1000 higher than the corresponding values for the exposure scenario above, as the ADI is 1000 times higher than the estimated dose. Although the PBTK simulation output does not provide additional information to perform risk assessment based on internal dose instead of external exposure when studying a single compound at low exposure level, the model gives extra information on the amounts of the parent compound and the metabolites in the body. Also as shown in the following section, PBTK simulations allow for useful comparison of internal dose levels between the dermal and the dietary routes. The internal body levels of tebuconazole and concentration levels of tebuconzole in blood and tissue are clearly influenced by the uncertainty in the rat to human extrapolation of the metabolic constants. In fact, these levels scale linearly or inversely linearly with variation in the metabolic constants upon dietary exposure as illustrated in the sensitivity analysis shown in Table 5. Therefore, significant uncertainties are associated with the numerical values prensented above, up to a factor of 4 in each direction. The simulations shown should be considered valuable as they illustrate interesting trends. 727

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intake at the ADI level, an observation that would be worthwhile investigating further. Although NOAEL has been determined for tebuconazole following dermal administration in rabbits,52 unfortunately no information was available on the dermal absorption of tebuconazole in the administered emulsion formulation. As dermal absorption can vary significantly between different formulations, we do not have information to convert the dermal NOAEL in rabbits to an internal dose. Instead, a simulation was performed using the AOEL (acceptable operator effect level) of tebuconazole of 0.03 mg/kg bw/day (which is of the same size as the dietary ADI) but keeping all other settings the same as in the simulation for the dermal exposure scenario above. This resulted in peak levels of 6.2 μmol for tebuconazole and 3.1 μmol for the metabolites. Thus, the internal body levels of tebuconazole in the dermal simulation at AOEL level are six times larger than the ones obtained in the dietary simulation at ADI. Considering the uncertainties associated with the human model, the simulated internal body levels corresponding to AOEL might be overestimated. However, since the internal body level in this scenario is much larger than the internal body level corresponding to the ADI it would be worth further investigation. The simulation also showed a trace of the chemicals continuously present in the body of a professional while subject to repeated dermal dosing on a daily basis. This trace was not fully eliminated during 2 days without exposure (corresponding to a weekend). Our results indicate that a trace of tebuconazole and metabolites would remain in the body in the absence of days without exposure. In fact, according to the simulation, a 14 day exposure free period was needed to remove all of the tebuconazole from the body, due to the accumulation in adipose tissues. The simulated concentrations of tebuconazole in blood, liver, skin, and adipose tissue during the simulation showed relatively larger skin concentration and lower liver concentration, compared to those in the dietary exposure (Figures 9 and 10). After dietary exposure, the concentration level in the skin, liver, and blood was at a similar level. In contrast to this, dermal exposure resulted in four times larger concentration in the skin than in blood, and the liver concentration was correspondingly lower. The simulated tissue concentrations are all below 45 nmol/L during the simulation. Some accumulation of tebuconazole is seen in adipose tissue upon repeated exposure; however, tebuconazole was cleared from other tissues within 24 h according to the simulation. As discussed above, the liver concentration is significantly underestimated by the model in the case of nonoral exposure, and the actual liver concentration is probably similar to the concentration in the other rapidly perfused tissue such as the kidney and heart. The body levels of tebuconazole and concentration levels of tebuconazole in blood and tissue are not particularly sensitive to uncertainties associated with the metabolic constants upon dermal exposure, as illustrated by the sensitivity analysis (see Table 5). Even 4-fold larger metabolic capacity would only affect these levels to a smaller degree as illustrated in Figure S3 in the Supporting Information. According to a biomonitoring study of the dermal exposure to tebuconazole in a group of winegrowers, it was shown that the largest fraction of the two most important metabolites was recovered from urinary samples in a period of 24 h after exposure but that significant fractions were recovered during and 25−48 h post-exposure as well.88 A simulation was made

treatment is assumed to take place 4 h/day (12−16 h), 5 days a week, and the dermally absorbed daily exposure of each employee was estimated to be 292 μg/day. The simulated body levels due to the 292 μg/day of tebuconazole (4.2 μg/kg bw/day for a 70 kg human) estimated by the exposure model gave peak levels of tebuconazole and metabolites in the body around 0.83 and 0.42 μmol, respectively (Figure 10). Thus, the metabolite level is a similar

Figure 10. (a) Predicted curves for the total amount of tebuconazole and metabolites in the human body after dermal exposure of 292 μg/day (4.2 μg/kg bw/day) during 4 h of industrial treatment of timber, simulated for 19 days assuming exposure during the 5 day working week, and no exposure on the weekend, followed by 14 days of an exposure free period. (b) Predicted concentration curves for tebuconazole in human blood, liver, skin, and adipose tissue for the same simulation.

fraction of the exposed dose following both dermal and dietary exposure, where the corresponding tebuconazole level is a five times larger fraction of the exposed dose following dermal exposure compared to the dietary exposure scenarios. These results illustrate that the compound metabolizes more slowly when exposed via the dermal route compared to the dietary one. This is because of the first pass effect from the GIT to the liver, which enhances the conversion rate of compounds after a dietary exposure. An interesting observation is that the simulated peak levels of tebuconazole for this dermal scenario (0.83 μmol) are only slightly below the peak levels of tebuconazole of 1.0 μmol obtained from the simulation for a dietary dose of 0.03 mg/kg bw/day corresponding to the dietary ADI. However, this result is an indication that professional workers might have internal levels of tebuconazole close to those resulting from dietary 728

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dietary exposure was 6.4% of the total exposure, a small contribution was seen from dietary exposure in the simulated curve for the metabolites. A dietary exposure level of 0.29 μg/kg bw/day is more than 1000 times below the exposure level where no effect of inhibition was seen on blood concentration levels in a rat simulation (Figure 7). Similarly, dermal exposure of 4.2 μg/kg bw/day is more than 1000 times lower than the dermal exposure level where no effect of inhibition was seen in the rat simulation (Figure 8). Thus, inhibition does not need to be considered for these exposure scenarios, and the individual compound implementation of the PBTK model can be used.

for one work day (7.5 h) and 23 h post-treatment for a 90 kg professional worker at an exposure level of 0.712 mg/work day and dermal absorption of 13%. These numbers match the median time frame and exposure level of the first work shift. The total amount of excreted metabolites was 0.075 μmol (23 μg tebuconazole equivalents) during the treatment and 0.17 μmol (52 μg tebuconazole equivalents) during 23 h post-treatment according to the simulation. The median tebuconazole equivalents of measurements of urine samples from the subjects is 19 μg during treatment and 57 μg 23 h post-treatment. Considering the uncertainties in these measurements and variability between test subjects, the simulation and the experiment must be considered to render comparable results. In a recent study, the total actual exposure of the body, head, and hands to tebuconazole was estimated to be 1.02 mg/work day based samples from clothes, headware, and water from hand washing.89 This corresponds to an absorbed dose of 0.133 mg/work day, using a dermal exposure of 13% given by these authors. This exposure level is similar to the one in the scenario proposed above. Simulations for the Combined Dietary and Dermal Exposure Scenario. Simulations were then carried out assuming aggregate exposure to tebuconazole (dietary and dermal), as the professionals working with applying biocides and pesticides, are also exposed to pesticides through their diet. We used the biocide industrial treatment scenario as an example of dermal exposure (292 μg/day). For a consumer eating more than 550 g fruits and vegetables a day (0.026 μg/kg bw/day = 1.8 μg/day), the dietary intake would be 0.6% of the combined dermal and dietary exposure. An additional scenario was made in order to illustrate an example of aggregate exposure where the dietary contribution is significant compared to the dermal one. Thus, a dietary exposure scenario was established by calculating the sum of the mean daily exposures to 18 triazoles and related compounds detected in samples of fruits and vegetables sold in Denmark (0.29 μg/kg bw/day corresponding to 20 μg/day).62 This is a fictive scenario assuming that all triazole residues are tebuconazole and not the real exposure to tebuconazole. The simulation was repeated assuming exposure to tebuconazole corresponding to this combined exposure. In this simulation (Figure 11), where the

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QUALITY AND APPLICABILITY OF THE DEVELOPED PBTK MODELS Quality of the Developed PBTK Models, Strengths, and Weaknesses. In the development of the PBTK models, much work was devoted to creating as good, accurate, and relatively simple PBTK models for tebuconazole as possible, in order to simulate blood and tissue concentration levels that are sufficiently accurate for use in risk assessment based on available data. A major strength of the developed models is that the used compound specific parameters are based on available experimental data on ADME properties of tebuconazole, where the necessary in vitro to in vivo extrapolations of the metabolic constants were performed. No parameters were adjusted to experimental blood and tissue concentration data. The main weakness is that it was not possible to validate the developed models at every relevant condition, and the human versions of the PBTK model could only be validated on relatively uncertain in vivo elimination data following dermal exposure. Another shortcoming of the developed models is that no data to validate blood and tissue concentrations following multiple dose exposure were available. Thus, uncertainties are associated with simulations of repeated exposure. Essentially, we used the possibility to do extrapolation of parameters from one species to another to utilize data from four species for developing and validating the models and to test the adequacy of the interspecies extrapolations used. This does not guarantee that the simulations in humans are sufficiently accurate, but it provides a plausible argument on the validity of the models and the extrapolations performed. In the absence of validation of extrapolation of metabolic constants from rats to humans, the uncertainty associated with the resulting metabolic constants was estimated based on available data for similar compounds. See the section about uncertainties of model parameters for a detailed discussion on this, as well as Scheme S3 in the Supporting Information. The developed models are flow regulated PBTK models that assume instantaneous equilibrium between levels in blood and tissues and do not incorporate diffusion limitations in tissue uptake, and are thus not adequate for modeling of long-term accumulation in tissue. Such flow based models may have some limitations at very low tissue concentrations,90 but our study showed that even the low concentration levels in the terminal phase in rabbit were adequately predicted. Applicability of the Developed PBTK Models in Risk Assessment. If PBTK models should have practical uses in risk assessment, it is imperative to describe the model building, selection of parameters, and validation of the model in a sufficiently detailed and transparent way, such that the quality of the model predictions can be evaluated. The limitations of

Figure 11. Simulated curves for the total amount of tebuconazole and metabolites in the human body after dermal exposure of 4.2 μg/kg bw/day (292 μg/day) during 4 h of industrial treatment of timber each day and dietary exposure of 0.29 μg/kg bw/day (20 μg/day). The simulation was carried out for 9 days, with industrial treatment relevant on work days, but dietary exposure considered every day. 729

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Chemical Research in Toxicology the models should also be discussed.10,11 In this work we have put much emphasis in documenting and evaluating the developed models. Presently, general requirements to PBTK models intended for use in risk assessment are being debated.8−11 Recommendations proposed by the US EPA and others were discussed in a recent EFSA report.10 First, it was pointed out that species and life stages implemented in the model need to be relevant for the specific risk assessment. Second, that the transparency and adequacy of the structure and parameters of each model need to be evaluated and peer-reviewed. Third, that the concentration of the toxic moiety should be adequately simulated in target organs for relevant exposure route(s) and time course. Bessems et al. proposed development of specific standard reporting formats for PBTK models intended for use in risk assessment.11 These authors also recommended the use of the WHO IPCS checklist for evaluating PBTK model for risk assessment purposes.9 We used the WHO IPCS checklist for evaluating the quality and applicability of the model developed in this work, thereby considering a whole range of questions regarding the underlying biological basis, reliability, and applicability of the model, and the model’s ability to simulate data.9 This evaluation is found as Scheme S4 in the Supporting Information. It is concluded that appropriate biological basis was established for the developed models, which are suitable for carrying out simulation for an average adult male in each of the species. It was demonstrated that the models could simulate kinetics under various conditions, including inhibition effects. As the liver is a target organ, it is a drawback that the simulated concentrations levels in the liver after nonoral administration were significantly underestimated by the models, that otherwise render predictions of acceptable accuracy, according to our investigations. If the developed models were used for risk assessment, the best advice we can give is to multiply the simulated blood and tissue concentration levels by a factor of 2. When dermal or i.v. administration is applied, according to the discussion above it would be better to assume a liver concentration at the same level as the other rapidly perfused tissues than using the simulated concentration levels for the liver. In the example shown in the “Predictions for Specific Exposure Criteria in Humans” section, the dietary exposure scenario resulted in internal doses a factor 1000 below the simulated internal doses resulting from a dietary intake corresponding to ADI. However, it was seen that the simulated internal dose of tebuconazole following dermal exposure for an employee working with industrial wood treatment was slightly below the simulated internal dose at a dietary intake corresponding to ADI. Even considering the uncertainties associated with the human model, this is an important observation. In some cases, a refinement of the risk assessment may be required, i.e., using a relevant NOAEL as a point of departure instead of ADI. The ADI generally is calculated from the NOAEL by applying a default factor of 10 to take into account variability between species and a factor of 10 to take into account variability between human individuals. In relation to the use of PBTK modeling in risk assessment, these 10-fold default factors can be divided to allow for the incorporation of toxicokinetic and toxicodynamic differences and thereby replace or minimize the relevant part of the overall default assessment factor.11 On the basis of the evaluation of all the data fed into the models, we evaluate that it is acceptable to make predictions

with the developed models. Some uncertainty estimates for the rat, rabbit, and human models are discussed in the section on uncertainty of the model parameters above. Corresponding estimates for the human models are difficult to make at present. The models developed in this project can be considered as the first step in developing an applicable toolbox to provide input to risk assessment of pesticides, biocides, and other chemicals. This project provides valuable information for developing a more generic toolbox for cumulative risk assessment, where similar compounds are grouped together and assuming similar ADME for the compounds. Another application area is aggregate exposure from different routes as illustrated above in the exposure scenario with combined dietary and dermal exposure of tebuconazole.

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CONCLUSIONS In this article, we present new PBTK models for tebuconazole, treated as a single compound and as a binary mixture of the R(−) and S(+) enantiomers. The models were developed in four species, rat, rabbit, rhesus monkey, and human. The model development was based on a thorough investigation of the ADME, toxicology and a broad range of biological and physicochemical parameters available in the literature for tebuconazole and related compounds. The biological basis of the models is thus considered adequate. The PBTK models were carefully developed, validated to the extent it was possible, and analyzed. A simple validation using in vivo half-life data in rabbits indicated that the rabbit versions of the model simulates the metabolic conversion rate accurately. The validations and analysis of tebuconazole in rabbits after i.v. administration indicate that the blood and tissue levels are generally predicted within a factor of 2 from the experimental data but unfortunately underestimated by a factor of about seven for the liver, which is one of the target organs. This is a significant shortcoming of an otherwise good model. It was seen from our simulations for the binary mixture that inhibition of metabolic enzymes did not have to be considered at lower exposure levels. Simulations in rats showed that inhibition did not influence the blood and tissue levels after administration of a single oral dose below 1 mg/kg bw or for the absorbed fraction of a dermal dose below 10 mg/kg bw. Realistic exposure scenarios for humans, like those analyzed in this work, are at more than 1000 times lower dose than these thresholds. There is sufficient metabolic capacity to metabolize the compounds in the low exposure simulations, such that the two enantiomers do not inhibit the metabolism of one another. The exposure scenarios constructed based on detected pesticide residues in fruits and vegetable resulted in very low body levels of tebuconazole in our simulation. For a dermal exposure scenario based on exposure assessment for professional workers doing industrial wood treatment, the estimated daily exposure was 160 times larger than the estimated dietary exposure. This gave significant internal body levels of tebuconazole that were further enhanced by the slower metabolism after dermal exposure compared to that of dietary exposure. Our simulation showed that at repeated daily exposure, the metabolism and excretion of tebuconazole did not have the capacity to remove the active compound and its metabolites before the organism was exposed again. Furthermore, the simulation suggests some accumulation of tebuconazole in adipose tissue. Thus, it was important with exposure free days to clear the body according to our results. These simulations show a clear 730

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trend, and although the numerical values of the concentration level in adipose tissue might not be exact, the overall trend shown is important. This illustrates how PBTK models can be applied to simulate situations that have not been tested experimentally and provide input to experimental studies. Unfortunately, we do not have experimental data to verify these findings at present. The developed models were shown to be very useful for exploring trends in ADME, interactions between compounds at lower and higher concentration levels, and the influence of aggregate exposure. It was evaluated that such models can be useful for providing input to risk assessment at lower exposure levels, well below NOAEL. In such cases, potential uncertainties in the model due to the parameters used and extrapolations are considered as well, and the models need to be documented appropriately. The credibility of the PBTK models is crucial for a spreading of their use in risk assessment. This can only be achieved by a high degree of transparency and thorough documentation of the developed models including considerations concerning the model structure and equations as well of the choice of parameters and their origin.

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REFERENCES

(1) Hadrup, N., Taxvig, C., Pedersen, M., Nellemann, C., Hass, U., and Vinggaard, A. M. (2013) Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro. PLoS One 8, e70490. (2) EFSA (2013) International Framework Dealing with Human Risk Assessment of Combined Exposure to Multiple Chemicals. EFSA J. 11, 1−69. (3) EFSA (2008) Scientific Opinion of the Panel on Plant Protection Products and Their Residues (PPR Panel) on a request from the EFSA evaluate the suitability of existing methodologies and, if appropriate, the identification of new approaches to assess cumulative and synergistic risks from pesticides to human health with a view to set MRLs for those pesticides in the frame of Regulation (EC) 396/2005. EFSA J. 704, 1−84. (4) Kortenkamp, A., Evans, R., Faust, M., Kalberlah, F., Scholtze, M., Schuhmacher-Wolz, U. (2012) Investigation of the State of the Science on Combined Action of Chemicals in Food through Dissimilar Modes of Action and Proposal for Science-Based Apprach for Performing Related Cumulative Assessment, pp 1−233, EFSA, Parma, Italy. (5) Nielsen, E., Nørhede, P., Boberg, J., Isling, L. K., Kroghsbo, S., Hadrup, N., Bredsdorff, L., Mortensen, A., Larsen, J. C. (2012) Identification of Cumulative Assessment Groups of Pesticides, External Scientific Report Submitted to EFSA, Question No. Q-2009-01092, EFSA, Parma, Italy. (6) Reffstrup, T. K., Larsen, J. C., and Meyer, O. (2010) Risk assessment of mixtures of pesticides. Current approaches and future strategies. Regul. Toxicol. Pharmacol. 56, 174−192. (7) Meek, M. E., Barton, H. A., Bessems, J. G., Lipscomb, J. C., and Krishnan, K. (2013) Case study illustrating the WHO IPCS guidance on characterization and application of physiologically based pharmacokinetic models in risk assessment. Regul. Toxicol. Pharmacol. 66, 116−129. (8) U.S. EPA (2006) Exposure Related Dose Estimating Model (ERDEM). A Physiologically-Based Pharmacokinetic and Pharmacodynamic (PBPK/PD) Model for Assessing Human Exposure and Risk (Blancato, J. N., Power, F. W., Brown, R. N., Dary, C. C., Eds.) pp 1− 151, U.S. EPA, Las Vegas, NV. (9) IPCS (2010) Characterization and Application of Physiologically Based Pharmacokinetic Models in Risk Assessment, pp 1−91, World Health Organization and IOMC (Inter-Organization Programme for the Sound Management of Chemicals), Geneva, Switzerland. (10) EFSA (2014) Modern methodologies and tools for human hazard assessment of chemicals. EFSA J. 12, 3638. (11) Bessems, J., Coecke, S., Gouliarmou, V., Whelan, M., Worth, A. (2015) EURL ECVAM Strategy for Achieving 3Rs Impact in the Assessment of Toxicokinetics and Systemic Toxicity, pp 1−20, European Commission, Luxembourg. (12) Utembe, W., and Gulumian, M. (2015) Challenges and research needs for risk assessment of pesticides for registration in Africa. Hum. Ecol. Risk Assess. 21, 1518−1541. (13) Beamer, P. I., Canales, R. A., Ferguson, A. C., Leckie, J. O., and Bradman, A. (2012) Relative pesticide and exposure route contribution to aggregate and cumulative dose in young farmworker children. Int. J. Environ. Res. Public Health 9, 73−96. (14) U.S. EPA (2006) Approaches for the Application of Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment (Krishnan, K., Barton, H., Chiu, W., DeWoskin, R., Foureman, G., Lipscomb, J., Schlosser, P., Sonawane, B., Thompson, C., Eds.) pp 1−123, National Center for Environmental Assessment. Office of Research and Development, Washington, DC. (15) Campbell, J. L., Jr., Clewell, R. A., Gentry, P. R., Andersen, M. E., and Clewell, H. J., III. (2012) Physiologically based pharmacokinetic/toxicokinetic modeling. Methods Mol. Biol. 929, 439−499. (16) Rietjens, I. M., Louisse, J., and Punt, A. (2011) Tutorial on physiologically based kinetic modeling in molecular nutrition and food research. Mol. Nutr. Food Res. 55, 941−956.

ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemrestox.5b00341. Detailed mathematical description of the developed PBTK models, in depth information on conversion and uncertainties of the metabolic constants and optimization and validation of elimination constants, as well as evaluation of the developed models with respect to the WHO IPCS guidance on PBTK models to be used in risk assessment (PDF)

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Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Address †

insilTox ApS, Sverigesvej 20B, 2800 Kgs. Lyngby, Denmark.

Funding

This work was funded by a grant from the Danish Environmental Protection Agency for the project “Optimization of the cumulative risk assessment of pesticides and biocides using computational techniques: Pilot project”, who also provided data for the dermal exposure scenario, and by the National Food Institute, Technical University of Denmark. Notes

The authors declare no competing financial interest.

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ABBREVIATIONS PBTK, Physiologically based toxicokinetic; ADME, absorption, distribution, metabolism and elimination; EU, European Union; FP6, Sixth Research Framework Program; EFSA, European Food Safety Authority; ADI, acceptable daily intake; NOAEL, no observed adverse effect level; AOEL, acceptable operator effect level; GIT, gastrointestinal tract; FAO, Food and Agricultural Organization; WHO, World Health Organization; JMPR, Joint Meeting on Pesticide Residues; DAR, Draft Assessment Report; Danish EPA, Danish Environmental Protection Agency; QSAR, quantitative structure−activity relationship; IPCS, International-Organization Programme for Sound Management of Chemicals 731

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