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In vitro to in vivo extrapolation and physiologically based modeling of cytochrome P450 mediated metabolism in beagle dog gut wall and liver Aki T Heikkinen, Stephen Fowler, Lynn Gray, Jia Li, Ying Peng, Preeti Yadava, Aruna Railkar, and Neil Parrott Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/mp300692k • Publication Date (Web): 25 Feb 2013 Downloaded from http://pubs.acs.org on March 12, 2013

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Molecular Pharmaceutics

In vitro to in vivo extrapolation and physiologically based modeling of cytochrome P450 mediated metabolism in beagle dog gut wall and liver Aki T. Heikkinen1, 2, Stephen Fowler2, Lynn Gray3, Jia Li4, Ying Peng4, Preeti Yadava4, Aruna Railkar4, Neil Parrott2* 1

University of Eastern Finland, Faculty of Health Sciences, School of Pharmacy, Kuopio, Finland; 2 F.

Hoffmann-La Roche AG, pRED, Pharma Research & Early Development, Non-Clinical Safety, Basel, Switzerland; 3 F. Hoffmann-La Roche Ltd, pRED, Pharma Research & Early Development, NonClinical Safety, Nutley, NJ, USA; 4 F. Hoffmann-La Roche Ltd, pRED, Pharma Research & Early Development, Pharmaceutical and Analytical R & D, Nutley, NJ, USA *

CORRESPONDING AUTHOR

Neil Parrott F. Hoffmann-La Roche AG pRED, Pharma Research & Early Development, Non-Clinical Safety Grenzacherstrasse 124, B70/R130 CH-4070 Basel Switzerland Tel: +41 61 68 80813 E-mail: [email protected] ACS Paragon Plus Environment

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For Table of Contents Use Only

In vitro to in vivo extrapolation and physiologically based modeling of cytochrome P450 mediated metabolism in beagle dog gut wall and liver

Aki T. Heikkinen, Stephen Fowler, Lynn Gray, Jia Li, Ying Peng, Preeti Yadava, Aruna Railkar, Neil Parrott

PBPK model for intestinal absorption Enzyme abundance based IVIVE of metabolism in the gut wall and liver

ACS Paragon Plus Environment

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ABSTRACT

Beagle dog is a widely used in vivo model to guide clinical formulation development and to explore the potential for food effects. However, the results in dog are often not directly translatable to human. Consequently, a physiologically based modeling strategy has been proposed, using the dog as a validation step to verify model assumptions before making predictions in human. One current weakness in this strategy is the lack of validated tools to incorporate gut wall metabolism into the dog model. In this study, in vitro to in vivo extrapolation factors for CYP2B11 and CYP3A12 mediated metabolism were established based on tissue enzyme abundance data reported earlier. Thereafter, physiologically based modeling of intestinal absorption in beagle dog was conducted in GastroPlus™ using Vmax and Km determined in recombinant enzymes as inputs for metabolic turnover. The predicted fraction of absorbed dose escaping the gut wall metabolism (Fg) of all 5 reference compounds studied (domperidone, felodipine, nitrendipine, quinidine and sildenafil) were within a 2 fold range of the value estimated from in vivo data at single dose levels. However, further in vivo studies and analysis of the dose dependent pharmacokinetics of felodipine and nitrendipine showed that more work is required for robust forecasting of non-linearities. In conclusion, this study demonstrates an approach for prediction of the gut wall extraction of CYP substrates in the beagle dog, thus enhancing the value of dog studies as a component in a strategy for prediction of human pharmacokinetics. KEYWORDS IVIVE, PBPK, ACAT, First pass metabolism, Bioavailability


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to integrate quantitatively in silico and in vitro data for simulation and prediction of in vivo pharmacokinetics (PK). The popularity and acceptance of PBPK modeling from both pharmaceutical industry and regulators has increased significantly in recent years

1-4

. However, quantitative extrapolation from in vitro data to in

vivo (IVIVE) is still prone to a number of uncertainties 5, 6 and several workers have found that building PBPK models for animal species and performing a preliminary verification against in vivo observations leads to improved predictions of clinical PK 7-9. According to this strategy of “pre-clinical verification” poor agreement between animal simulations and in vivo data leads to further experiments and data analysis until the reasons for discrepancies are identified. In practical application, when mechanistic explanations are elusive an empirical correction factor may be derived from animal data and applied for human

10, 11

. Furthermore, this “pre-clinical verification” serves to highlight the uncertainties in the

human prediction and thus the confidence in the predicted outcome. However, to support this approach, suitable anatomical and physiological data and in vitro to in vivo scaling factors for the animal species are needed. Beagle dog is a species often used for in vivo formulation testing, exploring food effects on oral PK as well as for toxicological experimentation. Consequently, there has been an interest in developing PBPK models for beagle dog, especially focusing on intestinal absorption

12, 13

. One potential determinant of

intestinal absorption, first pass metabolism in the gut wall, has been a gap in dog PBPK models; a major obstacle being the lack of in vitro to in vivo scaling factors for metabolic turnover. Therefore, we recently quantified the abundance of several cytochrome P450 (CYP) enzymes along the beagle dog intestine, as well as in the liver, using a mass spectrometry based assay

14

. CYP3A12, followed by

CYP2B11, were observed to be the most abundant CYP enzymes in the dog gut. The other CYP enzymes quantifiable in the liver were below the assay limit of quantification in the intestinal samples. The data generated allowed estimation of abundance of CYP3A12 and CYP2B11 in intact tissue in different segments of the intestine and in the liver which should serve as the basis for in vitro to in vivo ACS Paragon Plus Environment

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scaling factors for both tissues. The purpose of this study was to implement intestinal and hepatic CYP3A12 and CYP2B11 mediated metabolism in a beagle dog PBPK model and show the utility of the scaling factors established for prediction of first pass gut wall metabolism using a set of commercially available compounds as references.


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MATERIALS AND METHODS SELECTION OF REFERENCE COMPOUNDS AND ESTIMATION OF COMPONENTS OF ORAL BIOAVAILABILITY IN VIVO The reference compounds for this study were selected based on in vivo data available in the literature. A search was made for beagle dog PK studies performed with compounds subject to CYP3A mediated intestinal first pass metabolism and including both intravenous (IV) and oral dosing in cross over design. The fraction escaping hepatic metabolism (Fh) was calculated according to Equation 1 and, subsequently, the fraction of absorbed dose escaping gut wall metabolism (Fg) was estimated using Equation 2 15.

1

:

Equation 1

Equation 2

Qh is the liver blood flow in dog (taken as 43 ml/min/kg

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) and B:P is the blood to plasma

concentration ratio. Renal excretion in dog has been reported to be negligible for all the selected compounds

17-21

. Therefore, total plasma clearance (CLtot) after IV dosing is used as the estimate of

hepatic plasma clearance (CLh). The fraction of dose absorbed (Fa) and the absolute bioavailability (F) were taken directly from the literature reports together with the other PK data. CHEMICALS All chemicals were obtained from commercial sources, unless otherwise noted, and were of analytical grade or better. MEASUREMENT OF METABOLIC TURNOVER IN VITRO Metabolic turnover was measured in vitro using dog intestinal and liver microsomes (DIM and DLM, respectively), which were prepared as described in

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, and in recombinantly expressed dog CYP2B11

and CYP3A12 (rdCYP) (Cypex Ltd, Dundee, Scotland) as these 2 CYP enzymes have already been shown to be present and active in the dog gut wall 14, 22. ACS Paragon Plus Environment

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DLM, DIM and rdCYPs were diluted for incubations with 100 mM potassium phosphate buffer, pH 7.4, and the test substrate in DMSO stock solution was added. After 10 minutes pre-incubation at 37°C the reaction was started by addition of pre-heated NADPH solution to reach a concentration of 1 mM NADPH. The enzyme concentration in the incubations with recombinant enzymes was 100 pmol/ml, based on spectrally determined total P450 content data provided by the manufacturer. The total protein concentration in rdCYP incubations was adjusted to 0.3 mg/ml by adding blank control bactosomes where necessary. The protein concentration in the microsome incubations was 0.5 mg/ml. The DMSO concentration in all incubations was a constant 0.5%. The samples were withdrawn at time points 1, 3, 6, 9, 15, 25, 35 and 45 minutes and quenched with equal volume of ice cold acetonitrile. The free fractions in the incubations were determined by equilibrium dialysis to enable correction of results for unspecific protein binding. All the incubations were run in duplicate, as a minimum. The substrate concentrations were quantified using standard HPLC-MS techniques. Incubations with DIM and DLM were done using a single substrate concentration (2 µM) and apparent clearance (CLapp) was determined from the disappearance of the parent. The incubations with rdCYPs were performed at several initial substrate concentrations and the maximum rate of metabolism per amount of enzyme (Vmax) and the unbound concentration resulting in 50% of Vmax (Km,u) by the major CYP enzymes present in the beagle dog gut wall were estimated by fitting Equation 3 to the substrate concentration-time profiles. Fitting was done using WinNonLin software (version 5, Pharsight, Mountain View, CA) with a weighting of .

∗ ∗ $

∗,!"#

%, &∗,!"#

Equation 3

where MCYP is the amount of CYP in the incubation, C is the concentration of the parent compound and fu,inc is the unbound fraction in the incubations. PREDICTION OF GUT WALL PERMEABILITY FROM IN VITRO CACO-2 PERMEABILITY Permeability of the reference compounds was measured in vitro using a Caco-2 assay. Caco-2 cells were seeded in collagen coated 24-transwell plates at a density of 4.55 x 105 cells/cm2 and maintained in ACS Paragon Plus Environment

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medium for 7-9 days. Cells with transepithelial electrical resistance of over 500 ohm were used for the assay. The pH values of 1% DMSO/ HBSS (Hanks’ Balanced Salt Solution) buffer in the apical and basolateral compartments were 6.5 and 7.4, respectively. Compounds at a target concentration of 50 µM were applied to the donor wells. Experiments were run in triplicate and samples were taken from the receiver and donor sides at 60 min. Concentrations were determined by mass spectrometry. Known high and low permeability standard drugs (propranolol and furosemide) as recommended in the FDA guidance 23 were run as controls. The apparent permeability (Papp) was calculated using Equation 4: '((

)

* ∗+

Equation 4

Where C is the receiver drug concentration, t is time, V is the volume of the receiver chamber, C0 is the initial drug concentration in the donor chamber and A is the surface area of the cell monolayer. Effective duodenal permeability (Peff) in dog was predicted from Caco-2 Papp in two steps. First, a correlation between measured human Peff

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and Caco-2 Papp of 19 primarily passively absorbed

compounds established under exactly the same experimental conditions as used for the reference compounds (data not shown) was used to scale from Caco-2 permeability to human Peff. Thereafter, the scaling function built into GastroPlus™, version 8.0, was used to convert human Peff to dog Peff. This interspecies scaling has been established by Simulations Plus dog by Dr. G. Amidon

13

25

based on unpublished observations in

and it assumes an approximately 3-fold higher transcellular permeability in

dog than in human and accounts for the species difference in paracellular permeability. The screening assay for human P-glycoprotein (P-gp) at Roche flagged domperidone, quinidine and sildenafil as possible substrates (data not shown) and there is also literature evidence for these compounds interacting with human P-gp 26-28. Therefore, the Caco-2 Papp for these compounds was determined both with and without 2 µM elacridar, a potent inhibitor of P-gp 29. The data in the presence of elacridar was


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assumed to better predict the passive permeability through the gut wall and was thus used for prediction of dog Peff, unless otherwise indicated. IN VIVO DOG PK STUDIES To explore the non-linearity of oral PK of felodipine and nitrendipine, cross-over PK studies with a single intravenous dose level (0.2 mg/kg; 0.4 mg/ml felodipine or nitrendipine in 30 % PEG400, 30 % ethanol and 40 % distilled water) and three oral solution (gavage) dose levels (0.2, 0.7 and 2 mg/kg; 0.1, 0.35 or 1 mg/ml felodipine in 30 % PEG400, 15 % ethanol, 0.5 % Tween80 and 54.5 % distilled water; 0.1, 0.35 or 1 mg/ml nitrendipine in 30 % PEG400, 30 % ethanol, 0.5 % Tween80 and 39.5 % distilled water) were conducted in 3 female beagle dogs (mean body weight 7.5 kg). To avoid adverse events which might compromise the safety of the experimental animals and the interpretability of the PK data, the dose range tested was limited at the upper end to be similar to the doses used in the literature. The lower end of the dose range was set based on simulated plasma concentrations and sensitivity of routine bioanalytics to enable robust establishment of plasma concentration versus time profiles at all dose levels. 1 week washout periods were allowed between doses. The dogs were fasted overnight prior to dosing and were treated with 6 µg/kg intramuscular injection of pentagastrin approximately 45 min before dosing. The dogs were fed approximately 4 hours post dosing and water was available ad libitum throughout the study. Blood samples (maximum 0.5 ml) were collected at 5, 15, 30 minutes, 1, 2, 4, and 7 hours post dose (intravenous) or at 15, 30 minutes, 1, 2, 4, 7 and 12 hours post dose (oral solution) into tubes containing potassium EDTA as an anticoagulant and were kept cold (on ice or refrigeration) prior to centrifugation to prevent decomposition. The plasma was separated after cold centrifugation, transferred into labeled amber screw cap vials and stored in a -70° C freezer. Felodipine and nitrendipine concentrations in plasma were quantified using HPLC-MS techniques. PHYSIOLOGICAL MODELING OF GUT WALL METABOLISM AND SIMULATION OF ORAL PHARMACOKINETICS IN BEAGLE DOG The ACAT model in GastroPlus™ software (version 8.0, Simulations Plus, Lancaster, CA) was used for physiological modeling of intestinal absorption and gut wall first pass metabolism in the beagle dog. ACS Paragon Plus Environment

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The default beagle dog ACAT model in GastroPlus™ with some modifications published earlier 13 was the basis for an updated ACAT model used in this study (Table 1). The model parameters for the enzyme abundance profile along the intestine (Figure 1) and in the liver were set based on the geometric mean of experimentally determined enzyme abundance in the gut wall and in liver 14. The background and assumptions for the parameterization of this updated ACAT model are further described in the supporting material. The theoretical background of the GastroPlus™ ACAT model may be found in existing publications

5, 25, 30

. Solubility and dissolution were modeled as described in 5. Unless stated

otherwise, the default GastroPlus™ settings for compound precipitation were used, i.e. the mean precipitation time was set to 900 sec and precipitation was assumed to form round particles with 25 µm radius. The absorption rate coefficient in different gut segments was calculated from dog Peff using the “Theoretical SA/V” absorption scaling factor model of GastroPlus™. This model accounts for differences in the surface area available for absorption between the segments

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and assumes no impact

of gut lumen pH on permeability, which is consistent with the assumption that the microclimate pH on the surface of the gut wall is not directly regulated by the luminal pH

31, 32

. In addition to the

transcellular absorption pathway, paracellular absorption which bypasses the enterocyte compartment has been included separately in GastroPlus™ version 8. The Zhimin hindered diffusion model 33, which utilizes two molecular radii to account for the ellipsoidal shape of molecules, was used in simulation of the paracellular pathway. The parameter values for molecular radii were predicted from molecular structures using ADMET predictor (version 6.0) module of GastroPlus™. Beagle dog oral PK were simulated in GastroPlus™ using the updated ACAT model in combination with compartmental models describing the disposition PK. Distribution kinetics were assumed to be linear and the distribution parameters, i.e. compartment volume(s) and inter-compartment clearances were estimated by fitting compartmental models to the plasma concentration versus time profiles obtained after IV dosing at a single dose level in cross-over design with the oral dosing. Fitting was done using the PKPlus module in GastroPlus™ with weighting of , and 1, 2 or 3 compartment model was selected based on Akaike information criterion. Saturable metabolism in the liver and gut ACS Paragon Plus Environment

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wall were assumed as the only elimination routes and were modeled according to Michaelis-Menten kinetics and the free drug hypothesis. Vmax for CYP2B11 and CYP3A12 mediated metabolism in each gut wall segment and the whole liver were extrapolated based on enzyme abundance (Table 1) from Vmax

determined in rdCYPs (Table 2) assuming equal specific enzyme activity in rdCYPs and intact

tissues. Consequently, time and concentration dependent metabolic rate (Rmetab) in each gut wall segment and in liver is calculated according to Equation 5. -./ ∑8

)%1,! ∗234,! ∗,5!667 $%,,! &,5!667

Equation 5

Where i is the index for ith enzyme, MCYP is the amount of enzyme in the given intestinal segment or whole liver, Vmax and Km,u are parameters determined in rdCYPs (Equation 3, Table 2). Cu,tissue represents the free concentration available for metabolism and the Cu,tissue versus time profile for each intestinal segment and liver were simulated using the updated ACAT model and the liver model built in GastroPlus™ which treats the liver as a well stirred tank, respectively. The fraction unbound in the enterocytes (fu,ent) was set to 1, unless otherwise indicated. Cu,tissue in the enterocyte compartments is controlled by the effective volume of the compartment and the balance between permeation rate from the gut lumen and the sum of the permeation rate from enterocytes to portal circulation and the metabolism rate. For liver, Cu,tissue is assumed to be equal to the free concentration in the hepatic blood and is thus dependent of unbound fraction in plasma (fu,p), blood to plasma concentration ratio (B:P), the liver blood flow, metabolism rate and the effective volume of the liver. However, the detailed description of these models, particularly the treatment of the apparent volume of the liver and the permeation of the drug through the basolateral membrane from the enterocytes to portal circulation, are proprietary information owned by SimulationsPlus 25. GastroPlus™ provides Fa and F as simulation outputs and uses the fraction of dose permeating through the apical membrane of enterocytes

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as the definition of Fa

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. Additionally, Fg, and Fh were

calculated from GastroPlus™ outputs according to Equation 6 and Equation 7, respectively.


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9:

Equation 6

FDp is the fraction of dose reaching the portal vein.

9:

Equation 7

EVALUATION OF PREDICTION PERFORMANCE The fold error (fe) of predictions was calculated using Equation 8 =>67?@7A B8 C/D.EF.G(E.8H. :?7A!#57A :?7A!#57A B8 C/D.EF.I(E.8H. < =>67?@7A

.;67?@7A

∑" U OPQR S

Equation 9


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RESULTS SELECTION OF REFERENCE COMPOUNDS Based on the literature search, a set of 5 suitable compounds covering a wide range of Fg (0.4-0.9) were identified. The PK parameters estimated from literature in vivo data and the physicochemical properties of the reference compounds are summarized in Table 3 and Table 4, respectively. The in vivo Fa estimates were based on mass balance studies with radiolabelled compound reported in the literature (domperidone 17, sildenafil 19 and nitrendipine 35) and may be underestimates if there is biliary excretion of radiolabelled material. Therefore, in vivo estimates of Fa and Fg should be considered as lower and higher bound estimates, respectively. For felodipine the experimental approach to conclude complete absorption in vivo after oral solution dosing was not explicitly described 36. An in vivo Fa estimate for quinidine was not found. However, the high solubility and permeability of quinidine, combined with the high observed bioavailability, suggest no significant limitations in Fa. Consequently, complete absorption of quinidine was assumed. Overall, it can be summarized that the Fa of all the selected compounds with the doses and dosage forms used (Table 3) was high (>0.7) and F (range 0.2-0.7) is mainly limited due to first pass metabolism in the gut wall and liver.

IN VITRO METABOLISM AND PERMEABILITY Vmax and Km,u determined in rdCYPs are presented in Table 2. Felodipine and nitrendipine were metabolized by both rdCYP2B11 and rdCYP3A12 whereas domperidone, quinidine and sildenafil did not show detectable turnover in incubations with rdCYP2B11. Microsomal clearance of each reference compound in 4 batches of both DIM and DLM was predicted by scaling from rdCYP based Vmax and Km,u to microsomal clearance at 2µM substrate concentration and accounting for enzyme abundance in each microsome batch and differences in fu,inc in the incubations (Figure 2) (see supporting material for details of calculations). Attempts to correct for possible differences in specific enzyme activity in rdCYPs, DIM and DLM based on metabolic reactions of diazepam as markers for CYP2B11 and CYP3A12 activity

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tended towards overprediction of microsomal clearance (data in supporting ACS Paragon Plus Environment

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material) whereas assuming equal specific enzyme activity in rdCYPs and microsomes resulted in better overall accordance between predicted and observed microsomal clearance (Figure 2, mfe = +1.5, gmfe = 2.2). Mfe calculated individually for each compound was outside the range from -2 to 2 only for sildenafil (mfe = +6.3). Caco-2 Papp and the corresponding predicted dog Peff used in the simulations are listed in Table 2 and Table 5. Caco-2 Papp of domperidone and quinidine in presence of 2 µM elacridar was 10 and 2.4 fold higher than without elacridar, respectively, whereas elacridar did not significantly affect Caco-2 Papp of sildenafil.

PREDICTION OF BIOAVAILABILITY AND PLASMA EXPOSURE Effect of elacridar on Caco-2 Papp and on the consequent predicted dog Peff had only a small impact on simulations of quinidine and sildenafil whereas the impact on domperidone simulations was more significant (Table 5). For these three compounds only the simulations using Caco-2 data in the presence of elacridar have been used when summarizing the overall simulation results in Figure 3, Figure 4 and in the text below. Figure 3 summarizes the performance of predictions of oral bioavailability, broken into three contributing components, Fa, Fg and Fh, against literature in vivo data at single dose levels. Predictions of Fa, Fg and Fh, as well as the overall F, for all compounds were within 1.5 fold from the in vivo estimated counterparts. Comparison of predicted vs observed AUC is shown in Figure 4 including both literature data as well as the felodipine and nitrendipine data generated in the current study. Overall, the majority of simulated AUCs were within 2 fold from the observations (mfe=-0.1, gmfe=1.8). AUCs of quinidine and sildenafil after both IV and PO dosing were overpredicted and underpredicted, respectively, more than 2 fold, whereas there was generally lesser tendency towards over or underprediction with other reference compounds. NON-LINEAR PK OF FELODIPINE AND NITRENDIPINE


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Simulations for both felodipine and nitrendipine suggested non-linearity in gut wall extraction, but not in the hepatic first pass, within the experimentally tested dose range (Figure 5). Additionally, simulations suggested that both felodipine and nitrendipine dosed in solution would precipitate in the GI tract at all the dose levels tested in vivo (not shown). However, due to rapid permeation and predicted re-dissolution of the compound in the intestine the simulated Fa is generally unaffected and only slightly limited at the highest nitrendipine dose (Figure 5). In contrast, precipitation had an impact on the predicted saturation of gut wall metabolism, exemplified by simulations showing sensitivity to solubility and dissolution rate (data in supporting material). Assuming fu,gut= fu,p, instead of fu,gut = 1 in the simulations, did not change simulated AUC of felodipine and nitrendipine at low, non-saturating, doses (below the range of experimentally tested doses). In contrast, decreasing fu,gut shifted the saturation of gut wall metabolism towards higher doses (Figure 5) resulting in lower predicted AUC. In addition, lowering fu,gut had an impact also on the shape of simulated plasma concentration time profiles (Figure 6) by slowing down and prolonging the absorption phase from the enterocytes to circulation. In accordance with predicted non-linearity in first pass elimination, the dose normalized observed AUC of felodipine and nitrendipine after oral solution dosing suggest dose non-linearity within the dose range studied (Figure 5). However, quantitatively the AUC of felodipine at the middle dose (0.7 mg/kg) was overpredicted (3.3 fold) more than the high (2 mg/kg, 1.3 fold) and low (0.2 mg/kg, 2.1 fold) doses. Correspondingly, the AUC of nitrendipine at the high dose was underpredicted (2.0 fold) more than at middle (1.3 fold) and low (1.4 fold) doses.


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DISCUSSION In this study the beagle dog ACAT model in GastroPlus™ was updated with previously established data on CYP2B11 and CYP3A12 enzyme abundance in the beagle dog gut wall and liver. The utility of this updated model for IVIVE and prediction of drug metabolism in the gut wall and liver was shown by simulations of oral and IV PK of 5 reference compounds which are primarily eliminated via CYP mediated metabolism. Recombinant enzymes were selected as the in vitro model for IVIVE of metabolism and in vitro data generated in DIM and DLM were used to gain insights on extrapolation of metabolic turnover from rdCYPs to microsomes in which the enzymes are in their native membrane environment. In this extrapolation one of the assumptions was that the turnover of all the compounds in DIM and DLM is not contributed to by any other enzymes than CYP2B11 and CYP3A12. Current data cannot completely rule out turnover by additional enzymes in microsomes. However, the tendency towards overprediction of microsomal clearance rather than underprediction, when extrapolating from recombinant enzymes, suggest that significant contribution of additional enzymes is unlikely. Moreover, due to low expression of additional CYP enzymes in DIM

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, the possibility for involvement of additional enzymes seems

more relevant in DLM than in DIM. Significant involvement of additional enzymes in DLM would be observed as a difference in prediction performance when extrapolating from rdCYPs to microsomal clearance (more underprediction of turnover in DLM than in DIM). As the results do not show such behavior for any of the compounds (Figure 2) we conclude that additional CYP enzyme contribution is minor. Substantial differences in the specific enzyme activity of human CYP enzymes in recombinant systems and in human liver microsomes have been reported and these differences may depend on the recombinant expression system, assay conditions and the metabolic reaction followed 37. Consequently, it has been proposed that use of intersystem extrapolation factors (ISEF) established for each experimental setting is appropriate 38, 39. However, ISEFs cannot be determined for metabolic reactions which are mediated by more than one enzyme and so, a common approach is to establish ISEFs using one or more single enzyme probe substrates and apply the mean ISEF for a given enzyme to scaling of ACS Paragon Plus Environment

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the metabolism for the compound of interest 39, 40. In the current study, the ISEF values for rdCYP2B11 and rdCYP3A12 based on diazepam-N-demethylation and diazepam-3-hydroxylation, respectively, were available. However, using these ISEFs tended to overpredict microsomal clearance in both DIM and DLM (data in supporting material) whereas an approach assuming equal specific enzyme activity in rdCYPs and microsomes resulted in generally good predictions of both microsomal clearance as well as in vivo metabolism. Nonetheless, overprediction of microsomal clearance of sildenafil (Figure 2) was seen and was also reflected in an underprediction of AUC after both IV and oral dosing (Figure 4). Taken together, these observations reinforce the view that using ISEF based on a single probe reaction may be misleading, and point out that quantitative comparison of metabolic turnover in recombinant enzymes and in microsomes is recommended as it does allow probing for possible sources of error in IVIVE. The main aim of the current study was to evaluate the performance of IVIVE and PBPK modeling of first pass metabolism in the beagle dog gut wall. Validation using literature in vivo data for 5 reference compounds at single dose levels resulted in excellent results (Figure 3). However, it must be pointed out that the intestinal first pass of 2 of the reference compounds (quinidine and sildenafil) is minor and simply neglecting it for these compounds would not significantly impact the overall simulations of oral PK. Additionally, all of the compounds were metabolized by CYP3A12 and only two compounds (felodipine and nitrendipine) were observably metabolized also by CYP2B11. Consequently, the current data set is limited regarding the evaluation of IVIVE scaling factors for CYP2B11. A larger set of compounds including also specific CYP2B11 substrates would ideally be needed for more thorough analysis to be carried out. However, in the current study the identification of suitable reference compounds was primarily based on the availability of suitable literature data. Possibly extensive experimental effort that would have been required for identifying additional suitable CYP2B11 substrates was not considered to be in the scope of current work. Also, it is acknowledged the sample sizes in the in vivo studies used as the reference to evaluate the predictions are small and it is possible that those data do not represent the typical behavior over the whole beagle population. However, 3-4 ACS Paragon Plus Environment

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dogs is a typical sample size used in drug development for exploratory formulation and food effect studies when there is no specific justification for a larger sample. Such as high inter-individual variability observed in the previous experiments with the same compound. In the current study we decided we could not justify use of additional experimental animals. Despite these potential weaknesses, this study provides the first systematic attempt to validate IVIVE scaling factors for dog based upon recombinantly expressed activity and abundance data. It is hoped that additional data to support a fuller validation will become available with time, especially if the proposed approach to parameterize gut wall and hepatic metabolism in dog PBPK models is utilized in the drug development programs. Simulations suggested a dose non-linearity due to saturation of gut wall metabolism, but not hepatic metabolism, at the dose level used in the literature in vivo studies of felodipine and nitrendipine. Therefore, an additional in vivo dog PK study at three different dose levels was performed to probe the predictions of dose non-linearity. As predicted by simulations, dose non-linearity was observed in the pharmacokinetics of both felodipine and nitrendipine. However, the observed non-linearity was not fully captured and so accuracy of exposure predictions varied between dose levels (Figure 5). Accurate and precise prediction of non-linear gut wall metabolism requires valid IVIVE of metabolic capacity and saturability as well as accurate simulation of local drug concentrations exposed to the enzymes in different segments of the intestine. Simple models such as the ‘Qgut model’

41

cannot attempt this and

more sophisticated models such as the ACAT model in GastroPlus™ or similar models 42-44 have to be used. However, while such models do allow prediction of non-linear gut wall metabolism, published studies have usually considered only a single dose level per compound or have performed parameter optimization against in vivo data thus providing limited insight on the capability to forecast dose dependency. Simulated drug concentration in solution in gut lumen has an impact on the simulated concentration in the enterocytes. Therefore, it is possible that errors in predicting precipitation and re-dissolution may have contributed to the errors in predicting non-linear gut wall first pass of felodipine and nitrendipine. However, quantitative forecasting of intestinal drug precipitation based on in vitro measurements is ACS Paragon Plus Environment

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challenging and the recent advancements are understandably focused on predicting precipitation of compound upon entry into the small intestine

45, 46

rather than predicting precipitation in stomach of a

compound dosed in solution. Moreover, direct validation of such predictions would require sampling of contents of gastrointestinal tract. The in vitro data available in the current study do not enable refinement of precipitation rate predictions nor does the data allow for direct evaluation if, and to what extent, precipitation and re-dissolution takes place in vivo. Therefore, definitive conclusions on the possible role of precipitation in the current study cannot be drawn and the default settings for simulating precipitation in GastroPlus™ were used in the current study. One other possible factor in the saturation of gut wall metabolism is binding of the compound to proteins and lipids of intestinal tissue. However, it is currently not clear how such unspecific binding actually affects gut wall extraction. The literature reports no conclusive experimental data and simulation studies can give conflicting results depending on the model assumptions concerning the relevant concentrations in the enterocyte (free or total) which drive the metabolic rate and diffusion from intestinal tissue to circulation

5, 47

. In accordance with the free drug hypothesis, the GastroPlus™

ACAT model assumes that free drug concentration in the enterocytes drives both metabolic rate and the active and passive transfer of compound through cell membranes. Given these assumptions, the fu,gut parameter simply affects the apparent volume of the enterocyte compartments and influences simulated Fg only in non-linear conditions by modulating the simulated time course of the drug concentration in the enterocytes and, thus, affecting the saturation of metabolism 5. Lowering fu,gut (i.e. increasing the effective volume of enterocyte compartment) in the simulations reduces the predicted saturation of metabolism, decreases Fg and shifts the appearance of predicted dose non-linearity towards higher doses (Figure 5). In addition, decreasing fu,gut in simulations causes a delay between predicted absorption of the compound from intestinal lumen to enterocytes and appearance in the circulation (Figure 6). Another assumption affecting the simulated drug concentration within the enterocytes, and consequently the saturation of metabolism and active transport, concerns permeation resistances of the apical and basolateral cell membranes of the enterocytes 26, 48. Generally, it is thought that the apical cell ACS Paragon Plus Environment

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membrane is a stronger barrier to permeation than the basolateral membrane

49

. Consequently, some

published models for prediction of gut wall metabolism have made the approximation that effectively only the apical membrane poses a permeation barrier and the clearance of the compound from enterocytes to portal circulation is limited solely by blood flow

42, 50

. This assumption is probably

appropriate for high permeability compounds but may be less so for compounds with low permeability. Therefore, several published models incorporate both membrane permeability and blood flow to enterocytes as factors limiting the flux from the enterocytes to portal circulation

41, 44, 51-53

. This results

in simulations where low permeability compounds exhibit lower Fg than high permeability compounds. Experimental determination of permeation resistance at different membranes of a cellular barrier and the fraction of intestinal blood flow reaching the enterocytes is not a trivial task 48, 53. Therefore, it would be challenging to obtain direct experimental support for the quantitative assumptions made in this regards for simulation of the flux of the drug to and from the gut wall. Empirical optimization of models may therefore be a justified approach for improvement of predictions of non-linear gut wall extraction. In some publications, the exact assumptions made regarding the relative contribution of apical and basolateral membranes and the intestinal blood flow are not fully reported 5, 44. This applies also for the current study, as in GastroPlus™ the assumptions involved and the exact mathematical description of the compound flux through the basolateral membrane of enterocytes are not disclosed to the end user 25. In any case, whatever the approach taken for the parameterizing the PBPK models, greater transparency on assumptions made would be preferable in order to allow scientific community to utilize the published work for further development and applications 54. In addition to solubility and dissolution issues, passive permeation through the intestinal tissue and metabolic turnover in the gut wall, active transport may have an impact on the oral absorption kinetics. Caco-2 data generated in this study showed that quinidine and domperidone are transported by human P-gp, whereas significant involvement of active transport on Caco-2 permeation of the other compounds was not indicated. Regarding canine P-gp, quinidine has been shown to be a transported substrate 55 but no definitive data on domperidone could be found in the literature. Caco-2 data both with and without ACS Paragon Plus Environment

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active P-gp were used as inputs in exploratory simulations of quinidine, domperidone and sildenafil and showed a notable impact on simulated Fa and Fg of domperidone but negligible impact for quinidine and sildenafil (Table 5). However, these simulations may not enable conclusions on the impact of active transport by P-gp on oral PK in vivo because 1) there are probably quantitative differences in P-gp activity (abundance) between Caco-2 and intestinal tissue 2) there are possible species differences in substrate selectivity between human (Caco-2) and canine P-gp, and 3) the approach used for IVIVE is mechanistically incorrect for P-gp mediated transport since permeability through both apical and basolateral membranes of enterocytes are derived from the same Peff input located at the apical membrane on enterocytes

56

25

whereas P-gp is actually

. Ideally, active transport should be included into the

model as a separate mechanism based on in vitro data. This was not possible as the abundance of drug transporters in the dog gut wall is currently not known and in vitro models for measuring transport rate by dog transporters are, although published 55, not readily available. However, although the impact of P-gp was neglected, the results of this study do not indicate a major bias. CONCLUSION The current study established in vitro to in vivo scaling factors for CYP2B11 and CYP3A12 mediated metabolism in the beagle dog gut wall and liver based on previously published tissue enzyme abundance. The utility of these scaling factors for IVIVE of metabolic turnover from an in vitro recombinant enzyme system was demonstrated using a physiologically based intestinal absorption and liver model and good prediction of oral PK in beagle dog was shown for 5 reference compounds as a validation set. Additionally, this study illustrated some of the challenges involved in predicting dose dependent oral PK of intestinally metabolized compounds and suggested that further improvement of the mechanistic basis of physiologically based intestinal absorption models might be required for improving quantitative forecasting of saturation of gut wall metabolism. The in vitro to in vivo scaling factors established and the ACAT model updated in the current study are expected to be useful in the analysis of formulation studies of intestinally metabolized compounds conducted in beagle dog. ACS Paragon Plus Environment

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ACKNOWLEDGMENT This work has been partially presented as a poster, Heikkinen AT et al: Physiologically Based Modeling of First Pass Metabolism in the Gut Wall – Establishment and Preliminary Validation of In Vitro-In Vivo Scaling Factors for Intestinal Metabolism in Beagle Dog, in the 19th MDO Meeting and 12th European ISSX Meeting, June 17-21, 2012, Noordwijk aan Zee, the Netherlands The authors thank their colleagues in the DMPK laboratories of F. Hoffmann-La Roche both in Basel, Switzerland, and Nutley, NJ, for their contributions on generating the experimental data. Furthermore, helpful discussions with Viera Lukacova, Michael B. Bolger, Hans Lennernäs and Erik Sjögren are gratefully acknowledged. We thank SimulationsPlus (Lancaster, CA) for providing the School of Pharmacy, Faculty of Health Sciences, Univeristy of Eastern Finland, with an academic license for GastroPlus™ software. This work was funded by Roche Postdoc Fellowship (RPF) program.


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SUPPORTING INFORMATION AVAILABLE Supporting information is available on descriptions of 1) extrapolation of in vitro metabolism from recombinant enzymes to microsomes 2) parameterization of the updated beagle dog ACAT model and 3) sensitivity of felodipine and nitrendipine simulations of dose dependence on solubility and precipitation time inputs. This information is available free of charge via the Internet at http://pubs.acs.org/


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FIGURES

Figure 1. A) Experimentally determined tissue abundance of CYP2B11 (dashed line, open symbols) and CYP3A12 (solid line, filled symbols) along the intestine per post mortem length of intestine 14. Individual symbols represent measurements from individual donors and line goes through the geometric mean of the small intestine segments 1 through 4, small intestine 5, small intestine 6, caecum and colon. B) The enzyme amount in each segment of the updated ACAT model was set based on the experimentally determined geometric mean abundance in the corresponding portions of the intestine. See the supporting material for further details on calculations. The in vivo length of the intestine is assumed approximately 2.5 fold shorter than the post mortem length resulting in higher CYP amount per in vivo length (B) than per post mortem length (A). Further details available in the supporting material.


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1000 CL, scaled from recombinants (µl/min/mg microsomal protein)

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100

10

1

0.1 0.1

Domperidone Felodipine Nitrendipine Quinidine Sildenafil 1 10 100 CL, observed in microsomes (µl/min/mg microsomal protein)

1000

Figure 2. Observed vs predicted apparent clearance in DIM (open symbols) and DLM (closed symbols) at 2µM substrate concentration. Specific enzyme activity was assumed equal in rdCYPs, DIM and DLM. Different shaped symbols refer to compounds as indicated in the figure. Solid and dashed lines indicate the line of unity and 2-fold error interval, respectively.


26

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Figure 3. In vivo estimated vs predicted Fa, Fg, Fh and F. The in vivo estimates are based on literature data (Table 3). Solid and dashed lines indicate the line of unity and 1.5-fold error interval, respectively.

100000 AUC, predicted (µg*h/L)

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10000

1000

100

10 10

Domperidone Felodipine Nitrendipine Quinidine Sildenafil

100 1000 10000 AUC, observed (µg*h/L)

100000

Figure 4. Observed vs predicted AUC after IV (closed symbols) and PO (open symbols) dosing. Different shaped symbols refer to compounds as indicated in the figure. Solid and dashed lines indicate the line of unity and 2-fold error interval, respectively.


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Figure 5. Dose dependent PK of felodipine (A, B, C) and nitrendipine (D, E, F) oral solution in beagle dog. A, D, dose dependence of dose normalized AUC. Symbols represent the mean±SD of observations. The solid and dashed curves illustrate the GastroPlus™ simulations at various dose levels assuming fu,gut=1 and fu,gut=fu,p, respectively. Panels B, C, E and F summarize the simulated dose dependence of different components of oral bioavailability with fu,gut=1 (B, E) and fu,gut=fu,p (E, F).


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Figure 6. Mean ± SD felodipine (A) and nitrendipine (B) plasma concentrations in beagle dog after oral solution doses of 0.2mg/kg (triangles), 0.7 mg/kg (squares) and 2 mg/kg (diamonds). Curves are the simulated profiles assuming fu,ent=1 (solid lines) or fu,ent= fu,p (dashed lines).


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TABLES Table 1. Parametrization of beagle dog fasted state ACAT model

Segment

pH

Transit

Length

Radius

time (h)

(cm)

(cm)

SEFa

Bile

Pore

Salt

radius

(mM)

(Å)

Porosity/

CYP2B11

CYP3A12

Pore length (1/cm)

Relative

c

(pmol)

Relative

c

(pmol)

3

0.1/0.25b

NA

NA

1

0

2.2

2.58

0

0

0

0

Duodenum

6.2

0.15

12

0.42

6.946

10

9.879

278

2.08*10-3

1 832

2.38*10-3

3 446

Jejunum1

6.3

0.41

33.25

0.42

6.353

8.84

9.428

231.6

5.76*10-3

5 076

6.59*10-3

9 549

Jejunum2

6.4

0.41

33.25

0.42

5.483

6.72

8.765

172.7

5.76*10-3

5 076

6.59*10-3

9 549

Jejunum 3

6.5

0.41

33.25

0.42

4.613

4.54

8.102

124.2

5.08*10-3

4 483

5.75*10-3

8 343

Jejunum 4

6.6

0.41

33.25

0.42

3.742

2.64

7.439

84.9

2.31*10-3

2 034

3.15*10-3

4 562

Ileum

6.7

0.05

4

0.42

3.255

1.38

7.068

66.7

1.59*10-4

140

2.92*10-4

422

Caecum

6.75

3.81

2

0.64

1.63

0

7.008

32.8

9.80*10-5

86

1.45*10-4

210

Asc. Colon

6.45

8.19

4

0.88

1.7

0

6.948

33.7

1.68*10-4

148

1.35*10-4

196

Stomach

a

Surface expansion factors (SEF) represent the fold increase in epithelial surface area over a smooth cylinder due to

intestinal folds and villi. b Stomach transit time of 0.1 and 0.25 h were used for solution and solid dosage forms, respectively. c

CYP expression levels in the beagle dog intestinal segments are given as relative to estimated typical enzyme expression in

a whole liver (0.88 µmol CYP2B11 and 1.45 µmol CYP3A12). This approach is as used in the human ACAT model 5.


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Table 2. Input parameter values used for metabolism in GastroPlus™ modeling.

Domperidone

Vmax CYP3A12 (pmol/min/pmolCYP)

Km,u CYP3A12 (µM)

Vmax CYP2B11 (pmol/min/pmolCYP)

Km,u CYP2B11 (µM)

Caco-2 Papp (10-6 cm/s)

Predicted Dog Peff (µm/s)

20.9

5.8

-a

-a

4b

2.7 4.9

Felodipine

4.4

0.07

4.4

0.07

8.9

Nitrendipine

7.8

0.81

1.5

0.27

26.3

10.8

Quinidine

6.1

32

-a

-a

6.4b

4.2

Sildenafil

14.1

3.4

-a

-a

21.4b

9.2

a

b

Negligible turnover at all tested substrate concentrations. in presence of 2 µM elacridar to inhibit P-gp


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Table 3. Summary of in vivo pharmacokinetic parameters obtained from the literature and the in vivo estimated components of oral bioavailability.

a

IV dose (mg/kg)

IV Cltot (l/h/kg)

B:P

PO dose (mg/kg)

Oral dosage form

Fa,in vivo

Fg,in vivo

Fh,in vivo

Fin vivo

Domperidone

2.5

0.9

0.71

2.5

Capsule

0.91

0.42

0.52

0.20

Felodipine

0.076

1.2

0.68

1.92

Solution

1

0.54

0.32

0.17

Nitrendipine

3.4

1.3

1.46

3.2

Solution

0.73

0.61

0.65

0.29

Quinidine

1

0.4

0.87

7.7a

Tablet

1

0.87

0.84

0.73

Sildenafil

1

0.7

0.97b

20

Solution

0.84

0.90

0.71

0.54

References

17, 57, 58 36 35, 59 60, 61 19

100 mg quinidine tablet dosed to 13kg (mean weight) dogs. bPredicted using ADMET predictor version 6.0.


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Table 4. Physicochemical properties of the reference compounds. Molecular weight Domperidone Felodipine

452.9 384.3

LogP

fu,p(%)

4.2

7

3.86

0.48

Aqueous

Reference pH

Biorelevant

solubility

for aqueous

solubility

(mg/ml)

solubility

(mg/ml)

0.29

6.5

0.55a

6.5

0.19

b b

0.003

Nitrendipine

360.4

3.5

2

0.003

6.5

0.02

Quinidine

324.4

2.64

13

2.09

6.5

2.26c

Sildenafil

474.6

3.18

14

0.72 a

7.8 a

NAd

a

pKa

References

Base 8.1

17, 57, 62, 63

Neutral

44, 64, 65

Neutral

35, 59, 62, 65

Base 8.66, Base

62, 66

4.43 Acid 9.12, Base

19, 67

6.78

Solubility in aqueous buffer and in FaSSIF (Fasted State Simulated Intestinal Fluid) predicted using ADMET predictor

(version 6.0) module of GastroPlus™ . bExperimental solubility in fasted state dog intestinal fluid. cExperimental solubility in FaSSIF.d Impact of bile salts on sildenafil solubility assumed negligible.


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Table 5. Impact of 2 µM elacridar on apparent Caco-2 permeability of domperidone, quinidine and sildenafil, and on consequent simulations of bioavailability With elacridar a

Papp (10-6 cm/s) 4 ± 0.4

Dog Peff (µm/s) 2.7

Fa,pred

Fg,pred

Fh,pred

F,pred

1.00

0.60

0.47

0.28

Quinidine

6.4 ± 0.7

4.2

1.00

0.96

0.92

Sildenafil

21.4 ± 1.6

9.2

1.00

0.96

0.48

Domperidone

a

Without elacridar a

Papp (10-6 cm/s) 0.4 ± 0.1

Dog Peff (µm/s) 0.6

Fa,pred

Fg,pred

Fh,pred

F,pred

0.68

0.30

0.47

0.10

0.88

2.7 ± 0.6

2.4

0.99

0.92

0.92

0.85

0.46

18.7 ± 0.9

8.4

1.00

0.95

0.47

0.45

The apparent permeability in Caco-2 represents the mean ± standard deviation of a triplicate measurement


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