RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
Use of a Physiologically Based Pharmacokinetic Model for Quantitative Prediction of Drug–Drug Interactions via CYP3A4 and Estimation of the Intestinal Availability of CYP3A4 Substrates YOKO MANO,1 YUICHI SUGIYAMA,2 KIYOMI ITO3 1
Pharmacokinetics and Metabolism, Drug Safety and Pharmacokinetics Laboratories, Taisho Pharmaceutical Company Ltd., Saitama 331-9530, Japan 2 Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Kanagawa 230-0045, Japan 3 Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo 202-8585, Japan Received 11 February 2015; revised 21 April 2015; accepted 21 April 2015 Published online 4 June 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.24495 ABSTRACT: The purpose of this study was to predict the drug–drug interactions (DDIs) via CYP3A4 by estimating the extent of hepatic CYP3A4 inhibition based on a physiologically based pharmacokinetic (PBPK) model of both substrate and inhibitor and the increase in the intestinal availability (Fg ) due to the enzyme inhibition. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using in vivo Ki values estimated from other clinical DDI studies and predicted in vivo Ki values calculated using the correlation between the log P and the in vivo Ki /in vitro Ki ratio was more accurate than that using in vitro Ki values. Incorporating inhibition of both intestinal and hepatic metabolism resulted in better prediction than that obtained considering inhibition in the liver alone, and all the DDIs (AUC increase by the inhibitor) were predicted within 2-fold accuracy when in vivo Ki values were used. In addition, Fg values were successfully back-calculated from the clinical DDI data based on the present model. In conclusion, the present PBPK model incorporating the in vivo Ki values was found to be useful for quantitative prediction of clinical DDIs and for estimation of the Fg values for CYP3A4 substrates C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci for which intravenous data were not available. 104:3183–3193, 2015 Keywords: clinical trial simulations; cytochrome P450; drug interactions; intestinal metabolism; pharmacokinetics; physiologically based pharmacokinetic modeling
INTRODUCTION CYP3A4 is the major enzyme of the cytochrome P450 family of enzymes, accounting for 82% and 40% of the total P450 enzyme content in the human small intestine and liver, respectively,1 and is the enzyme responsible for the metabolism of more than 50% of the top 200 drugs prescribed in the United States.2 Therefore, drug–drug interactions (DDIs) via the inhibition of
Abbreviations: Cb , drug concentrations in the blood; Ch , drug concentrations in the liver; CLh,int , hepatic intrinsic clearance; clog P, computer-calculated log P; CLr , renal clearance; CLtot , total body clearance; DDIs, drug–drug interactions; F, bioavailability; Fa , fraction absorbed; fb , unbound fraction in the blood; fe , fraction of unchanged drug excreted in urine; Fg , intestinal availability; Fh , hepatic availability; fm , contribution of CYP3A4 to the metabolism of the substrate; fp , unbound fraction in the plasma; Ht , hematocrit; Ig , inhibitor concentration in the enterocytes; k12 , transfer rate constant from the central compartment to the peripheral compartment; k21 , transfer rate constant from the peripheral compartment to the central compartment; ka , absorption rate constant; ke , elimination rate constant; kdeg , the degradation rate constant of CYP3A4; Ki,app , the apparent dissociation constant between CYP3A4 and the inhibitor; kinact , the maximum inactivation rate constant; Kp,h , liver–plasma concentration ratio; PBPK, physiologically based pharmacokinetic; Qh , hepatic blood flow; Rb , blood to plasma concentration ratio; REact,3A4 , ratio of the active enzyme relative to the total CYP3A4; Tlag , time-lag in drug absorption; V1 , distribution volume of the central compartment; Vh , hepatic volume; Xg , amounts of the drugs in the intestine compartment; Xsub , amounts of the drugs in the peripheral compartment. Correspondence to: Yoko Mano (Telephone: +81-48-669-3036; Fax: +81-48652-7254; E-mail: [email protected]) This article contains supplementary material available from the authors upon request or via the Internet at http://wileylibrary.com. Journal of Pharmaceutical Sciences, Vol. 104, 3183–3193 (2015) C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association
CYP3A4 sometimes result in serious problems in clinical practice. Recently, attempts have been made to predict DDIs taking into account interactions in the intestine as well as those in the liver, as the first-pass effect in the intestine greatly contributes to the interactions via CYP3A4.3–5 A previous study suggested that in vivo Ki values estimated from the plasma concentration–time profiles of the substrate in the presence and absence of inhibitor in clinical DDI studies were lower than the in vitro Ki values, and that for DDIs resulting from reversible CYP inhibition, the prediction using in vivo Ki values was more accurate than that using in vitro Ki values.6 In this study, clinical DDI studies involving nine CYP3A4 substrates, which are listed as “in vivo sensitive CYP3A substrates” in the DDI draft guidance published by the United States Food and Drug Administration (US FDA),7 were searched for using the Metabolism Transport Drug Interaction Database (DIDB) established by the University of Washington. Using a physiologically based pharmacokinetic (PBPK) model incorporating the inhibition of both intestinal and hepatic metabolism, the AUC ratios were predicted for interactions involving the six substrates for which intravenous data were available, and the accuracy was compared among the results obtained using the in vivo Ki values estimated from clinical DDI studies, predicted in vivo Ki values and in vitro Ki values. For the remaining three substrates for which intravenous data were not available, the Fg values were back-calculated from the plasma concentration–time profiles obtained with or without inhibitors.
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METHODS Collection of the Pharmacokinetic Data Among the 31 drugs listed as “in vivo sensitive CYP3A substrates” in the DDI draft guidance published by US FDA,7 we have randomly selected nine drugs (aprepitant, dronedarone, eplerenone, ticagrelor, tolvaptan, vardenafil, dasatinib, everolimus, and quetiapine) for which PBPK modeling analysis for prediction of DDI had not been reported to the best of our knowledge. The bioavailability (F), fraction of unchanged drug excreted in urine (fe ), unbound fraction in the plasma (fp ), blood to plasma concentration ratio (Rb ), and the contribution of CYP3A4 to the metabolism of the substrate (fm ) were obtained from the literature (Table 1 and Supplementary Table S1). If no information on the Rb was available, it was estimated by Eq. (1) using the fp and a hematocrit (Ht ) value of 0.45.8 ⎛
0.617·log
Rb = ⎝10
(1−f p ) fp
⎞
+0.208
· f p − 1⎠ · Ht + 1
(1)
The fm was evaluated from in vitro inhibition studies conducted using a specific CYP inhibitor and a value of unity was assumed if no information was available, in consideration of the substrate being a “sensitive in vivo CYP3A substrate”. Calculation of the Pharmacokinetic Parameters for the CYP3A4 Substrates The pharmacokinetic parameters of the six substrates for which intravenous data were available (aprepitant, dronedarone, eplerenone, ticagrelor, tolvaptan, and vardenafil) were estimated as follows. The plasma–concentration profiles of these substrates were obtained from the literature and the plasma concentrations were converted to blood concentrations using the Rb values. Fixing F at the reported value, the substrate blood concentration profiles after a single oral administration was fitted to a one- or two-compartment model to obtain the optimized values of the elimination rate constant (ke ) and distribution volume of the central compartment (V1 ) using Phoenix WinNonlin (Version 6.2; Pharsight, Mountain View, California). The total body clearance (CLtot ) and renal clearance (CLr ) were then calculated using Eqs. (2) and (3), respectively: CLtot = ke · V1
(2)
CLr = CLtot · f e
(3)
from the literature or calculated by the method reported by Wakayama et al.17 . For everolimus, Fa was assumed to be 1, because the reported results of a preclinical study suggested its high intestinal absorption.18 The unbound fraction in the blood (fb ) was calculated using Eq. (6): fb =
fp Rb
(6)
The liver-plasma concentration ratio (Kp,h ) was estimated based on Eq. (7):19 K p,h
P × (0.0348 + 0.3 × 0.0252) + 1 × (0.751 + 0.7 × 0.0252) P × (0.0035 + 0.3 × 0.00225) + 1 × (0.945 + 0.7 × 0.00225)
=
×
fp fh
(7)
where P is the octanol–water partition ratio and was estimated using Eq. (8) from the computer-calculated log P (clog P) obtained using Chem draw (Version 12.0) or ACD/logD (Version 12.01): P = 10c log P
(8)
fh is the hepatic unbound fraction for specific binding to albumin, globulins, and lipoproteins, and was calculated using Eq. (9), assuming a value of 0.5 for the tissue interstitial fluid-to-plasma concentration ratios of albumin, globulins, and lipoproteins: fh =
1 1+
1−f p fp
(9)
× 0.5
Fitting Analyses of the Blood Concentration–Time Profiles of the CYP3A4 Substrates Based on a PBPK Model Figure 1 shows a simple PBPK model consisting of the central compartment, liver compartment, peripheral compartment, and intestine compartment.6 According to this model, the concentration profiles of the substrate can be expressed by the
Hepatic availability (Fh ) and intestinal availability (Fg ) were calculated using Eqs. (4) and (5), respectively: Fh = 1 −
(CLtot − CLr ) Qh
Fg =
F Fh · Fa
(4)
(5)
where the value of the hepatic blood flow (Qh ) was fixed at 96.6 L/h6 and the fraction absorbed (Fa ) was either obtained
Figure 1. Physiologically based pharmacokinetic model.6
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DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
Fg
0.04
0.69
0.36
0.56
0.145
CLr (L/h)
8.35 0.388 6.59 0.558 1.55
Dronedarone
Eplerenone
Ticagrelor
Tolvaptan
Vardenafil
Inhibitord
Erythromycin Fluconazole Indinavir Ketoconazole Itraconazole
1.00 0.948 1.00 1.00 1.00
25.8
14.4
8.26
64.2
0.956 0.950 1.00 1.00 0.885
Fa
0
0
0.00896
0
1.54
0
CLr (L/h)
0.16 0.89 0.4 0.01 0.002
fp
0.321
0.733
0.851
0.914
0.351
0.957
Fh
1 1 1 0.632 0.58
Rb
0.503
0.952
0.469
0.788
0.114
0.744
Fg
0.438 0.714 0.529 5.84 6.67
Kp,h
0.9
0.802
0.901
0.958
0.996
0.828
Fa
356 0.118 114 1100 10800
CLh,int (L/h)
0.0534
0.02
0.006
0.41
0.003
0.0035
fp
b
Calculated using Eq. (1). Assumed to be 1 in consideration of being a sensitive in vivo CYP3A4 substrates. c Optimized by fitting analysis [fitted value (calculated S.E.)]. d Parameters registered in DDI Simulator Ver. 2.1.
a
65.6
0.59
Aprepitant
4.14
F
Substrate
CLtot (L/h)
Table 1. Pharmacokinetic Parameters of the Substrates and Inhibitors
5.09 0.628
0.628a 0.923a
0.434 0.861 2.13 1.08 0.234
ka (1/h)
0.817
0.51
45.0 60.1 41.4 33.0 215
V1 (L)
1.50
5.12
3.27
5.06
0.615a
0.652
Kp,h
Rb
– – – – 0.0797
k12 (1/h)
0.9
1b
0.95
0.657
1b
0.98
fmCYP3A4
– – – – 0.0492
k21 (1/h)
760 (37) 20,800 (60,200) 20.2 (0.5) 1860 (366) 890 (40) 3040 (169)
CLh,int c (L/h)
2910 411 10.6 132
Ki,in vitro (:g/L)
0.818 (0.160) 0.791 (0.625) 1.74 (0.14) 0.491 (0.291) 0.876 (0.134) 1.96 (0.46)
ka c (1/h)
5270 0.264 0.821 0.282
Ki,in vivo (:g/L)
85.8 (4.8) 415 (696) 36.2 (1.2) 49.3 (31.9) 108 (10) 206 (17)
V1 c (L)
4.8
kinact (1/h)
–
0.191 (0.160) –
0.239 (0.321) –
–
k12 c (1/h)
35,500
Ki,app (:g/L)
–
0.0899 (0.0686) –
0.0238 (0.0498) –
–
k21 c (1/h)
–
–
–
0.922 (0.061) 1.09 (0.15) –
Tlag (1/h)
15,16
14
13
12
11
9,10
Reference
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
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following differential equations:
V1
Vh,s
Q h · Ch,s · Rb,s dCh,s = Q h · Cb,s − dt K p,h,s
Q h · Ch · Rb dCb = −Q h · Cb + dt K p,h −k12 · V1 · Cb + k21 · X sub · Cb − CLr · Cb
+ ka,s · X g,s (t) − f m,3A4,s · (10) − (1 − f m,3A4,s ) ·
Vh
Q h · Ch · Rb Ch dCh = Q h · Cb − + ka · X g − CLh,int · · f u,p dt K p,h K p,h
dX g (t) = −ka · X g (t) where at t = Tlag , dt
X g Tlag = Fa · Fg · Dose
(13)
V1,i
(14)
(15)
(16)
(17)
(18)
Q h · Ch,i · Rb,i dCh,i = Q h · Cb,i − dt K p,h,i + ka,i · X g,i (t) − CLh,int,i ·
Ch,i · f u,p,i K p,h,i
dX sub,i = k12,i · Cb,i · V1,i − k21,i · X sub,i dt
(19)
(20)
dX g,i (t) = −ka,i · X g,i (t) where at t = 0, dt X g,i (0) = Fa,i · Fg,i · Dosei
(21)
For each DDI, three values of Ki were used for the enzyme inhibition in the liver: in vitro Ki values calculated from metabolic inhibition experiments, in vivo Ki values estimated from other clinical DDI studies, and predicted in vivo Ki values calculated by Eq. (22) using the correlation between the log P and the in vivo Ki /in vitro Ki ratio.6
r Reversible inhibition (inhibitors except for erythromycin)
+ k21,s · X sub,s · Cb,s − CLr,s · Cb,s
CLh,int,s · Ch,s · f u,p,s K p,h,s
Q h · Ch,i · Rb,i dCb,i = −Q h · Cb,i + − k12,i · V1,i · Cb,i dt K p,h,i
Vh,i
Simulation of the Concentration Profiles of the CYP3A4 Substrates in Interaction Studies with Inhibitors
Q h · Ch,s · Rb,s dCb,s = −Q h · Cb,s + − k12,s · V1,s · Cb,s V1,s dt K p,h,s
Ch,s · f u,p,s K p,h,s
+ k21,i · X sub,i · Cb,i − CLr,i · Cb,i
where Cb and Ch are the drug concentrations in the blood and liver, respectively, Xsub and Xg are the amounts of the drugs in the peripheral and intestine compartment, respectively, k12 is the transfer rate constant from the central compartment to the peripheral compartment, k21 is the transfer rate constant from the peripheral compartment to the central compartment, ka is the absorption rate constant, CLh,int is the hepatic intrinsic clearance, and Tlag is the time-lag in drug absorption. The substrate blood concentration profiles after a single oral administration were fitted into Eqs. (10)–(13) to obtain the optimized values of CLh,int , ka , V1 , k12 , k21 , and Tlag using Phoenix WinNonlin. The hepatic volume (Vh ) was fixed at 1.5 L.
Method 1: Considering only Inhibition in the Liver
·
dX g,s (t) = −ka,s · X g,s (t) where at t = Tlag,s , dt
X g,s Tlag,s = Fa,s · Fg,s · Doses
(12)
The changes in the concentrations of the substrates due to DDIs were predicted by simulating the concentration profiles of both substrates and inhibitors based on the PBPK model shown in Figure 1. According to this model, the concentration profiles of the substrate and the inhibitor can be expressed by the following differential equations, depending on the inhibition mechanism (reversible or time-dependent) and on whether the intestinal inhibition is incorporated or not. The parameters with subscripts “s” and “i” denote those for the substrate and inhibitor, respectively.
1+
f u,p,i ·Ch,i K p,h,i ·K i,3A4
dX sub,s = k12,s · Cb,s · V1 − k21,s · X sub,s dt
(11)
dX sub = k12 · Cb · V1 − k21 · X sub dt
CLh,int,s
Predicted in vivo K i = in vitro K i · 4.4525 · e−1.339·log P
(22)
For fluconazole, with a log P value of 0.993, the in vitro Ki value was used as the predicted in vivo Ki value, based on the report by Kato et al.6 that the difference between the in vivo and in vitro Ki values of an inhibitor with a log P value of less than 1 is relatively small.
Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
DOI 10.1002/jps.24495
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
r Time-dependent inhibition (erythromycin) For DDIs involving erythromycin, a time-dependent inhibitor of CYP3A4, Eq. (23) was used instead of Eq. (15): Vh,s
Q h · Ch,s · Rb,s dCh,s = Q h · Cb,s − + ka,x · X g,s (t) dt K p,h,s − f m,3a4,s · REact,3A4 (t) · CLh,int,s · − (1 − f m,3A4,s ) ·
Ch,s · f u,p,s K p,h,s
CLh,int,s · Ch,s · f u,p,s K p,h,s
(23)
where REact,3A4 represents the ratio of the active enzyme relative to the total CYP3A4, expressed by Eq. (24) using the apparent dissociation constant between CYP3A4 and the inhibitor (Ki,app ), the maximum inactivation rate constant (kinact ) and the degradation rate constant of CYP3A4 (kdeg ). The value of kdeg was fixed at 0.0158 h−1 .20
The metabolic Inhibition in the liver was estimated in the same manner as that for method 1. Plasma concentration–time profiles of the substrates with and without co-administration of inhibitors were simulated by the above two methods, and the AUC ratio (AUC increase by the inhibitor) was calculated for each case using DDI Simulator (Version 2.1; Fujitsu Kyushu Systems, Fukuoka, Japan). All the parameters for the substrates and inhibitors used in the analyses are summarized in Table 1. The inhibitor parameters, including the in vivo Ki values, were those registered in DDI Simulator. Comparison of the Model Predictability The predictability of the AUC ratio was compared based on the geometric mean-fold error (GMFE, Eq. (28), the root mean square error (RMSE, Eq. (29) and % of the AUC ratios predicted within 2-fold accuracy. GMFE = 10
C
kinact,3A4 · Ch,i · f u,p,i · K h,i dREact,3A4 p,h,i =− Ch,i dt K i,app,3A4 + f u,p,i · K p,h,i
+ kdeg,3A4 · 1 − REact,3A4
RMSE =
Method 2: Considering Inhibition of both Intestinal and Hepatic Metabolism The increase in the value of Fg,s due to intestinal CYP3A4 inhibition was estimated with Eq. (25) using the inhibitor concentration in the enterocytes ([Ig ]) calculated using Eq. (26):22 Fg,s (+inhibitor) Fg,s (control) 1 1 1+[I]g /K i
×
kdeg kdeg +(kinact ×[I]g /K i,app +[I]g )
× 1 − Fg,s + Fg,s (25)
[I]g =
Dose · ka,i · Fa,i Qg
(26)
In Eq. (25), the in vitro Ki for hepatic CYP3A4 was used for Ki , and the time-dependent inhibition parameters (kinact , and Ki,app ) were also assumed to be the same between the intestine and the liver. The kdeg for CYP3A4 in the intestine and the blood flow rate through the enterocytes were fixed at 0.029 h−121 and 14.9 L/h,22 respectively. The estimated increase in the value of Fg,s was then incorporated into the PBPK model to accommodate the effect of metabolic inhibition in the intestine, using Eq. (27) as a substitute for Eq. (17): dX g,s (t) = −ka,s · X g,s (t) where at t = Tlag,s , dt
Fg,s (+inhibitor) · Doses X g,s Tlag,s = Fa,s · Fg,s Fg,s (control) DOI 10.1002/jps.24495
AUC ratio
log Predicted Observed AUC ratio N
(28)
1 (Predicted AUC ratio − Observed AUC ratio)2 N (29)
(24)
=−
3187
Fitting Analyses of the Blood Concentration–Time Profiles of the CYP3A4 Substrates Based on a PBPK Model with or Without Inhibitor for the Estimation of Fg For the three substrates (dasatinib, everolimus, and quetiapine) for which the intravenous data were not available, the Fg value was back-calculated from the plasma concentration– time profiles with or without inhibitors. To validate this method Fg estimation, the Fg values of 12 substrates (aprepitant, dronedarone, ticagrelor, tolvaptan, vardenafil, midazolam, triazolam, zolpidem, diazepam, methylprednizolone, alprazolam, and alfentanil) for which intravenous data were available were first back-calculated from the concentration–time profiles obtained with or without inhibitors as follows: The substrate blood concentration profiles with or without inhibitors (ketoconazole or itraconazole) were fitted into Eq. (14) based on the simultaneous analysis of Eqs. (14)–(21), and (25)–(27), fixing the hepatic Ki values at those registered as the in vivo Ki in DDI simulator (Table 1), to obtain the optimized values of Tlag,s , CLh,int,s , ka,s , V1,s , k12,s , k21,s and Fg,s . Phoenix WinNonlin was used and the differential equations were numerically solved by using the Livermore Solver for ordinary differential equations for the non-stiff system, with the weight value fixed at 1/y or 1/y 2 . Because the back-calculated Fg values for the 12 substrates were comparable to those calculated from the intravenous data using Eq. (5) (see Results section), this method for estimating the Fg was applied for dasatinib, everolimus, and quetiapine.
RESULTS Fitting Analyses of the Concentration–Time Profiles of the CYP3A4 Substrates
(27)
The blood concentration profiles of the CYP3A4 substrates were fitted into Eqs. (10)–(13) based on the simple PBPK model (Fig. 1) to obtain the optimized values of CLh,int , ka , V1 , k12 ,
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and k21 . All the pharmacokinetic parameters for the substrates used in the analyses are summarized in Table 1 together with those estimated by the fitting analyses. As shown in supplementary Figure 1, the fitted curves for the substrate concentration profiles were close to the observed profiles. Simulation of the CYP3A4 Substrates Concentration Profiles in Interaction Studies with Inhibitors The substrate concentration changes due to DDIs were predicted by simulating the concentration profiles of both the substrates and inhibitors using Eqs. (14)–(27) based on the PBPK model shown in Figure 1, and the AUC ratios were calculated. Figure 2 shows the plasma concentration profiles of aprepitant and vardenafil with or without co-administration of ketoconazole simulated by methods 1 and 2. These two substrates were chosen because their concentration profiles with or without coadministration of typical strong inhibitor, ketoconazole, were available and also the profiles have been more successfully reproduced as compared to other substrates. For both aprepitant and vardenafil, the concentration profiles simulated by method 2, incorporating the inhibition of both intestinal and hepatic metabolism, were closer to the observed concentration profiles than those simulated by method 1, which only considered inhibition in the liver.
Table 2 summarizes the AUC ratios predicted by methods 1 and 2 using the in vitro Ki , predicted in vivo Ki and in vivo Ki values, and the relationship between the observed and predicted AUC ratios is shown in Figure 3. Method 1, which ignored the inhibition of intestinal metabolism, underestimated the observed AUC ratios in comparison to method 2. The smaller the original Fg of the substrate, the larger the difference tended to be between the observed AUC ratio and that predicted by method 1, and also the larger the prediction improvement by method 2. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using the predicted in vivo Ki values and in vivo Ki values estimated from clinical DDI studies was more accurate than that using the in vitro Ki values, for both methods 1 and 2. Table 3 shows the comparison of the predictability of the AUC ratios for DDIs resulting from reversible inhibition of CYP3A4. Both the GMFE values (2.11–3.59 and 1.31–1.99 for method 1 and 2, respectively) and RMSE values (6.65–8.14 and 3.02–4.81 for method 1 and 2, respectively) were lower for method 2 than for method 1. These values were lower when predicted in vivo Ki values or in vivo Ki values estimated from clinical DDI studies were used than when in vitro Ki values were used, for both methods 1 and 2. The AUC ratios for 12.5%–50.0% and 25.0%–100% of the eight studies were predicted within a 2-fold accuracy based on method 1 and 2, respectively, and the percentage was higher when predicted
Figure 2. Plasma concentration profiles of aprepitant (a) and vardenafil (b) in the interaction study with ketoconazole. (a) (1) Reported aprepitant concentration profiles after a single oral dose of aprepitant (125 mg, day 5) with ketoconazole (400 mg p.o., QD, day 1–10, open circles) or placebo (closed circles).9 (2) Simulated aprepitant concentration profiles, assuming the same dosing regimen as in (1). The solid line represents the profiles without ketoconazole; the dashed line with ketoconazole by method 1; the dotted line with ketoconazole by method 2. (b) (1) Reported vardenafil concentration profiles after a single oral dose of vardenafil (normalized to 5 mg, day 4) with ketoconazole (200 mg p.o., QD, day 1–4, open circles) or placebo (closed circles).15 (2) Simulated vardenafil concentration profiles, assuming the same dosing regimen as in (1). The solid line represents the profiles without ketoconazole; the dashed line with ketoconazole by method 1; the dotted line with ketoconazole by method 2. In vivo Ki was used for the simulation. Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
3.48 23.7 – 2.20 2.99 5.28 4.34 – 12.9 11.0 2.59 2.73 – 1.83 2.36 2.48 4.13 – 6.48 5.51 4.10 24.9 – 2.68 3.17 5.66 5.10 – 8.06 12.3 3.05 2.87 – 2.22 2.50 2.66 4.85 – 4.06 6.19 2.06 15.9 1.78 2.68 2.03 3.38 2.16 3.64 5.60 4.93 9 11 23 23 23 13 14 15 15 15 Aprepitant Dronedarone Eplerenone Eplerenone Eplerenone Ticagrelor Tolvaptan Vardenafil Vardenafil Vardenafil
125 200 100 100 100 90 30 20/5 10 20/5
Single dose (day 5) Single dose (day 8) Single dose (day 7) Single dose (day 6) Single dose (day 6) Single dose (day 4) Single dose (day 2) Single dose (day 4) Single dose (day 7) Single dose (day 4)
Ketoconazole Ketoconazole Erythromycin Fluconazole Ketoconazole Ketoconazole Ketoconazole Erythromycin Indinavir Ketoconazole
400 200 500 200 200 200 200 500 800 200
4.78 16.8 2.87 2.24 5.39 7.32 5.40 4.02 16.3 9.95 QD (day1–10) QD (day 1–8) BID (day 1–7) QD (day 1–6) BID (day 1–6) BID (day 1–10) QD (day 1–3) TID (day 1–4) TID (day 1–7) QD (day 1–4)
1.53 1.83 1.41 2.22 1.60 1.59 2.06 1.86 2.82 2.48
Method 2 Method 1 Method 2 Method 1 Method 2 Method 1 Reference Schedule Dose (mg)
3189
in vivo Ki values or in vivo Ki values estimated from clinical DDI studies were used than when in vitro Ki values were used, for both methods 1 and 2. Estimation of Fg for the CYP3A4 Substrates
Schedule Dose (mg)
Predicted in vivo Ki Inhibitor Substrate
Table 2. The Observed AUC Ratios and Those Predicted by the PBPK Model
Observed
In vitro Ki
Predicted
AUC Ratio
In vivo Ki
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
The Fg values of the CYP3A4 substrates for which intravenous data were available were back-calculated from their concentration–time profiles obtained with or without inhibitors (ketoconazole or itraconazole) using the parameters listed in Table 1 and Supplementary Table S1. As shown in Figure 4 and Supplementary Table S2, the back-calculated Fg values were comparable to those calculated from the intravenous data using Eq. (5). For the three substrates (dasatinib, everolimus, and quetiapine) for which intravenous data were not available, the substrate blood concentration profiles obtained with or without inhibitors (ketoconazole or erythromycin) were fitted into Eq. (14) based on the simultaneous analysis of Eqs. (14)–(16), (18)–(21), and (23)–(27) to obtain the optimized values of Tlag,s , CLh,int,s , ka,s , V1,s , k12,s , k21,s , and Fg,s . The parameters fixed and optimized in the analyses are summarized in Supplementary Tables S1 and S4, respectively. The fitted curves were close to the observed profiles, as shown in Figure 5 (e.g., for the case of everolimus), and the Fg values of dasatinib, everolimus, and quetiapine were estimated as 0.744, 0.219–0.391, and 0.538– 0.800, respectively. The values of Fg in the presence of inhibitor, calculated by multiplying those under the control condition and the increase estimated using Eq. (25), reached nearly 1 for all the cases, indicating complete inhibition of the intestinal metabolism in the presence of inhibitor. A larger contribution of inhibition in the intestine in the overall interaction was estimated in the case of a smaller Fg value under the control condition.
DISCUSSION The present study investigated whether the DDIs via CYP3A4 can be quantitatively predicted by estimating the extent of hepatic CYP3A4 inhibition based on simple PBPK modeling of both the substrate and inhibitor and incorporating the increase in the Fg due to the intestinal enzyme inhibition. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using predicted in vivo Ki values and in vivo Ki values was more accurate than that using in vitro Ki values (Table 2 and Fig. 3). The fold difference in the predicted and observed AUC ratios ranged from 0.55 (DDI between eplerenone and ketoconazole) to 1.41 (DDI between dronedarone and ketoconazole) using method 2 with the in vivo Ki , which we think is acceptable. Considering that ketoconazole is involved as an inhibitor in both of the above DDIs, the differences might be associated with the substrate characteristics, such as low bioavailability of dronedarone (4%), but the reasons are unknown.11 Because the prediction using in vitro Ki values underestimated the observed AUC ratios, either predicted in vivo Ki values or in vivo Ki values should be used for a more accurate prediction of DDIs. When the possibility of a candidate compound being a CYP3A4 inhibitor is to be evaluated and its in vivo Ki value is unavailable, use of predicted in vivo Ki values, estimated from the log P and the in vitro Ki value, is recommended. The AUC ratios for 12.5%–50.0% and 37.5%–100% of the DDI studies were predicted within 2-fold accuracy based on method Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
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Figure 3. Relationship between the predicted and observed AUC ratios. (a) method 1: The prediction assuming only inhibition in the liver, (b) method 2: the prediction assuming inhibition of both intestinal and hepatic metabolism. Open triangles, open circles, and closed circles represent predictions using in vitro Ki , predicted in vivo Ki , and in vivo Ki , respectively. Solid line represents unity. Dashed lines represent 2-fold boundary around the line of unity.
Table 3. Predictability of the Method 1 and Method 2 In vitro Ki
GMFE RMSE % within 2-fold error
Predicted in vivo Ki
In vivo Ki
Method 1
Method 2
Method 1
Method 2
Method 1
Method 2
3.59 8.14 12.5
1.99 4.81 25.0
2.11 7.00 50.0
1.37 4.29 87.5
2.20 6.65 50.0
1.31 3.02 100
Method 1, the prediction assuming only inhibition in the liver; method 2, the prediction assuming inhibition of both intestinal and hepatic metabolism; GMFE, the geometric mean fold error; RMSE, the root mean square error.
Figure 4. Relationship between the back-calculated and observed Fg values of the CYP3A4 substrates. The Fg values of CYP3A4 substrates were back-calculated from their concentration–time profiles with or without inhibitors (ketoconazole or itraconazole) and plotted against the observed Fg values calculated from intravenous data using Eq. (5).
1 and 2, respectively, indicating that the prediction by method 2 was more accurate than that by method 1 (Table 3). It is important for quantitative predictions of DDIs via CYP3A4 to take into account the interaction in the intestine as well as that in
the liver. In the present study, the increase in the values of Fg due to the intestinal enzyme inhibition was estimated using a static model and incorporated into the PBPK model, whereas the ADAM (Advanced Dissolution Absorption and Metabolism) model is used in other prediction models such as the Simcyp Simulator (Simcyp Limited, Sheffield, UK) for dynamic prediction of intestinal absorption, transport, metabolism and DDIs.26 Although the ADAM model allows for estimation of the intestinal absorption of low-solubility compounds and also the DDI predictions considering the differential distribution of CYP3A4 along the intestine, it requires a large number of input parameters, some of which are not necessarily possible to estimate with accuracy. From that viewpoint, our prediction model with Fg estimation using a static model has the advantage of requiring much fewer parameters. Four of the CYP3A4 substrates analyzed in the present study (aprepitant, 9 ticagrelor,13 tolvaptan,14,27 and vardenafil28 ) are also reported as substrates of P-glycoprotein, and two of the CYP3A4 inhibitors (erythromycin29 and ketoconazole29 ) are also known to inhibit P-glycoprotein. Although DDIs via Pglycoprotein were not incorporated in the present model, the AUC ratios for DDIs involving these compounds were predicted within 2-fold accuracy by method 2 using in vivo Ki values (Table 3), suggesting a minor contribution of Pglycoprotein inhibition. In addition, membrane transport is unlikely to be a rate-limiting step in the intestinal absorption of a highly permeable and highly soluble drug such as vardenafil.15
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DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
–
–
0.326 (0.086) 0.0749 (0.0317)
–
0.493 (0.101) 0.408 (0.037) –
0.0993 (0.0164) 0.0925 (0.0170) –
1.47 (0.15) – – –
136 (10) 15.3 (2.3) 26.7 (1.8) 130 (13) 181 (13) 200
500
Ketoconazole
Erithromycin
25
200
Quetiapine
Quetiapine
b
35 2.29
34 6.22
25 4.52 500 Erithromycin 2 Everolimus
a
1.25 0.995
1.86 0.999
2.48 0.970
4.55 0.999 24 15.3 200 Ketoconazole 2 Everolimus
Optimized by fitting analysis [fitted value (calculated S.E.)]. The values of Fg in the presence of inhibitor, calculated by multiplying those under the control condition and the increase estimated using Eq. (25).
6.00 (27.50) 3.25 (0.55) 6.00 (1.03) 6.00 (4.66) 6.00 (2.70) 3360 (375) 69.5 (7.7) 96.9 (14.7) 178 (16) 147 (9) 1.34 1.00
0.744 (0.099) 0.219 (0.020) 0.391 (0.024) 0.538 (0.060) 0.800 (0.098) 33 4.84
BID (day 1–6) BID (day 1–5) TID (day 1–9) QD (day 1–4) TID (day 1–4) 200 Ketoconazole
QD (day 1–6) Single dose (day 5) Single dose (day 5) Single dose (day 4) Single dose (day 4) 20 Dasatinib
Fa g References Observed AUC ratio Schedule Dose (mg) Dose (mg)
Schedule
Inhibitor Substrate
Table 4. Estimation of Fg for the CYP3A4 Substrates for which Intravenous Data were Unavailable
Fg b with Inhibitor
Fold Change in Fg
CLh,int a (L/h)
ka a (l/h)
V1 a (L)
k12 a (l/h)
k21 a (l/h)
Tlag a (L/h)
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For the three substrates (dasatinib, everolimus, and quetiapine) for which intravenous data were not available, the Fg values were back-calculated from the plasma concentration– time profiles obtained with or without inhibitors. To validate this method for Fg estimation, the Fg values of 12 substrates for which intravenous data were back-calculated from the concentration–time profiles obtained with or without inhibitors (ketoconazole or itraconazole). We found that the backcalculated Fg values were comparable to those calculated from the intravenous data (Fig. 4). Several methods of predicting Fg have been reported as follows: Nishimuta et al.30 developed a method for predicting Fg that is applicable to low-permeability compounds based on metabolic clearance from metabolic stability assays using human liver microsomes for CYP3A4 substrates, and the permeability values obtained by parallel artificial permeability assay (PAMPA). Gertz et al.31 reported that Fg can be estimated from interaction studies with grapefruit juice, assuming complete inhibition of CYP3A4-mediated intestinal metabolism and no effects on the hepatic enzymes or the fraction absorbed of the drug. Hisaka et al.32 evaluated the Fg from the changes in the AUC and elimination half-life of the drug caused by DDIs. Because it is important for quantitative predictions of DDIs via CYP3A4 to take into account the increase in the values of Fg , the present method for estimating Fg would be useful for CYP3A4 substrates for which intravenous data are not available. The Fg values of dasatinib, everolimus, and quetiapine were estimated as 0.744, 0.219– 0.391, and 0.538–0.800, respectively (Table 4). The Fg values of cyclosporin and midazolam have been estimated to be 0.4– 0.79 and 0.29–0.68, respectively, based on intravenous and oral data obtained from different clinical studies,3 both showing 2fold variation. Therefore, the 1.79-fold and 1.49-fold variation in the Fg values of everolimus and quetiapine, respectively, estimated from different clinical DDI studies, may be regarded as acceptable. In conclusion, the present PBPK model for both CYP3A4 substrates and inhibitors, incorporating the increase in the Fg value due to the intestinal enzyme inhibition, was found to be useful for quantitative prediction of clinical DDIs when combined with the in vivo Ki values. Based on the present PBPK model, the Fg values for CYP3A4 substrates for which intravenous data were not available were also successfully backcalculated using the plasma concentration profiles obtained from clinical DDI studies.
REFERENCES 1. Paine MF, Hart HL, Ludington SS, Haining RL, Rettie AE, Zeldin DC. 2006. The human intestinal cytochrome P450 “pie”. Drug Metab Dispos 348(5):880–886. 2. Williams JA, Hyland R, Jones BC, Smith DA, Hurst S, Goosen TC, Peterkin V, Koup JR, Ball SE. 2004. Drug–drug interactions for UDPglucuronosyltransferase substrates: A pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios. Drug Metab Dispos 32(11):1201–1208. 3. Galetin A, Gertz M, Houston JB. 2008. Potential role of intestinal first-pass metabolism in the prediction of drug–drug interactions. Expert Opin Drug Metab Toxicol 4(7):909–922. 4. Fahmi OA, Hurst S, Plowchalk D, Cook J, Guo F, Youdim K, Dickins M, Phipps A, Darekar A, Hyland R, Obach RS. 2009. Comparison of different algorithms for predicting clinical drug-drug interactions, based on the use of CYP3A4 in vitro data: Predictions of compounds as precipitants of interaction. Drug Metab Dispos 37(8):1658–1666. Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
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Figure 5. Plasma concentration profiles of everolimus in the interaction studies. (a) Everolimus concentration profiles after a single oral dose of everolimus (2 mg, day 4) with ketoconazole (200 mg p.o., BID, day 1–8, open circles) or placebo (closed circles).24 The dashed line and the solid line indicate the fitted curves for the time courses with and without ketoconazole, respectively. In vivo Ki was used for the fitting analysis. (b) Everolimus concentration profiles after a single oral dose of everolimus (2 mg, day 5) with erythromycin (200 mg p.o., TID, day 1–9, open circles) or placebo (closed circles).25 The dashed line and the solid line indicate the fitted curves for the time courses with and without erythromycin, respectively. For both panels (a) and (b), the reported blood concentrations were converted to plasma concentrations using the Rb .
5. Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A. 2010. Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and timedependent enzyme inhibition by parent compound and its metabolite in both liver and gut – the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci 39(5):298–309. 6. Kato M, Shitara Y, Sato H, Yoshisue K, Hirano M, Ikeda T, Sugiyama Y. 2008. The quantitative prediction of CYP-mediated drug interaction by physiologically based pharmacokinetic modeling. Pharm Res 25(8):1891–1901. 7. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER). Guidance for industry, drug interaction studies—study design, data analysis, implications for dosing, and labeling recommendations. February 2012. 8. Uchimura T, Kato M, Saito T, Kinoshita H. 2010. Prediction of human blood-to-plasma drug concentration ratio. Biopharm Drug Dispos 31(5–6):286–297. 9. Emend Drug approval package. 10. Sanchez RI, Wang RW, Newton DJ, Bakhtiar R, Lu P, Chiu SH, Evans DC, Huskey SE. 2004. Cytochrome P450 3A4 is the major enzyme involved in the metabolism of the substance P receptor antagonist aprepitant. Drug Metab Dispos 32(11):1287–1292. 11. Multaq. Drug approval package. 12. Eplerenone. Drug approval package. 13. Brilinta. Drug approval package. 14. Samsca. Drug approval package. 15. Levitra. Drug approval package. 16. Varma MV, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ, El-Kattan A, Troutman MD. 2010. Physicochemical space for optimum oral bioavailability: Contribution of human intestinal absorption and first-pass elimination. J Med Chem 53(3):1098–108. 17. Wakayama N, Ciloy JM, Kobashi A, Matsushita M, Ito K, Yamashita F, Maeda K. (unpublished results). In silico prediction of human intestinal drug absorption from chemical structures using logistic regression. 18. AFINITOR. Drug approval package. 19. Poulin P, Theil FP. 2002. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci 91(1):129–156.
20. Yang J, Liao M, Shou M, Jamei M, Yeo KR, Tucker GT, RostamiHodjegan. 2008. A Cytochrome p450 turnover: Regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions. Curr Drug Metab 9(5):384– 394. 21. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. 2007. Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 8(7):676–684. 22. Rostami-Hodjegan A, Tucker G. 2004. ‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug-drug interactions. Drug Discov Today Technol 1(4):441–448. 23. Cook CS, Berry LM, Burton E. 2004. Prediction of in vivo drug interactions with eplerenone in man from in vitro metabolic inhibition data. Xenobiotica 34(3):215–228. 24. Kovarik JM, Beyer D, Bizot MN, Jiang Q, Shenouda M, Schmouder RL. 2005. Blood concentrations of everolimus are markedly increased by ketoconazole. J Clin Pharmacol 45(5):514–518. 25. Kovarik JM, Beyer D, Bizot MN, Jiang Q, Shenouda M, Schmouder RL. 2005. Effect of multiple-dose erythromycin on everolimus pharmacokinetics. Eur J Clin Pharmacol 61(1):35–38. 26. Sinha VK, Snoeys J, Osselaer NV, Peer AV, Mackie C, Heald D. 2012. From preclinical to human−prediction of oral absorption and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: A workflow by using case example. Biopharm Drug Dispos 33(2):111– 121. 27. Shoaf SE, Ohzone Y, Ninomiya S, Furukawa M, Bricmont P, Kashiyama E, Mallikaarjun S. 2011. In vitro P-glycoprotein interactions and steady-state pharmacokinetic interactions between tolvaptan and digoxin in healthy subjects. J Clin Pharmacol 51(5):761–769. 28. Choi MK, Song IS. 2012. Characterization of efflux transport of the PDE5 inhibitors, vardenafil and sildenafil. J Pharm Pharmacol 64(8):1074–1083. 29. Ekins S1, Kim RB, Leake BF, Dantzig AH, Schuetz EG, Lan LB, Yasuda K, Shepard RL, Winter MA, Schuetz JD, Wikel JH, Wrighton SA. 2002. Three-dimensional quantitative structure–activity relationships of inhibitors of P-glycoprotein. Mol Pharmacol 61(5):964–973. 30. Nishimuta H, Sato K, Yabuki M, Komuro S. 2011. Prediction of the intestinal first-pass metabolism of CYP3A and UGT substrates in humans from in vitro data. Drug Metab Pharmacokinet 26(6):592–601.
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31. Gertz M, Davis JD, Harrison A, Houston JB, Galetin A. 2008. Grapefruit juice–drug interaction studies as a method to assess the extent of intestinal availability: Utility and limitations. Curr Drug Metab 9(8):785–795. 32. Hisaka A, Nakamura M, Tsukihashi A, Koh S, Suzuki H. 2014. Assessment of intestinal availability (FG) of substrate drugs of cytochrome p450s by analyzing changes in pharmacokinetic properties caused by drug-drug interactions. Drug Metab Dispos 42(10):1640– 1645.
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33. SPRYCEL. Drug approval package. 34. Grimm SW, Richtand NM, Winter HR, Stams KR, Reele SB. 2006. Effects of cytochrome P450 3A modulators ketoconazole and carbamazepine on quetiapine pharmacokinetics. Br J Clin Pharmacol 61(1):58–69. 35. Li KY, Li X, Cheng ZN, Zhang BK, Peng WX, Li HD. 2005. Effect of erythromycin on metabolism of quetiapine in Chinese suffering from schizophrenia. Eur J Clin Pharmacol 60(11):791– 795.
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Use of a Physiologically Based Pharmacokinetic Model for Quantitative Prediction of Drug–Drug Interactions via CYP3A4 and Estimation of the Intestinal Availability of CYP3A4 Substrates YOKO MANO,1 YUICHI SUGIYAMA,2 KIYOMI ITO3 1
Pharmacokinetics and Metabolism, Drug Safety and Pharmacokinetics Laboratories, Taisho Pharmaceutical Company Ltd., Saitama 331-9530, Japan 2 Sugiyama Laboratory, RIKEN Innovation Center, RIKEN Research Cluster for Innovation, RIKEN, Kanagawa 230-0045, Japan 3 Research Institute of Pharmaceutical Sciences, Musashino University, Tokyo 202-8585, Japan Received 11 February 2015; revised 21 April 2015; accepted 21 April 2015 Published online 4 June 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.24495 ABSTRACT: The purpose of this study was to predict the drug–drug interactions (DDIs) via CYP3A4 by estimating the extent of hepatic CYP3A4 inhibition based on a physiologically based pharmacokinetic (PBPK) model of both substrate and inhibitor and the increase in the intestinal availability (Fg ) due to the enzyme inhibition. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using in vivo Ki values estimated from other clinical DDI studies and predicted in vivo Ki values calculated using the correlation between the log P and the in vivo Ki /in vitro Ki ratio was more accurate than that using in vitro Ki values. Incorporating inhibition of both intestinal and hepatic metabolism resulted in better prediction than that obtained considering inhibition in the liver alone, and all the DDIs (AUC increase by the inhibitor) were predicted within 2-fold accuracy when in vivo Ki values were used. In addition, Fg values were successfully back-calculated from the clinical DDI data based on the present model. In conclusion, the present PBPK model incorporating the in vivo Ki values was found to be useful for quantitative prediction of clinical DDIs and for estimation of the Fg values for CYP3A4 substrates C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci for which intravenous data were not available. 104:3183–3193, 2015 Keywords: clinical trial simulations; cytochrome P450; drug interactions; intestinal metabolism; pharmacokinetics; physiologically based pharmacokinetic modeling
INTRODUCTION CYP3A4 is the major enzyme of the cytochrome P450 family of enzymes, accounting for 82% and 40% of the total P450 enzyme content in the human small intestine and liver, respectively,1 and is the enzyme responsible for the metabolism of more than 50% of the top 200 drugs prescribed in the United States.2 Therefore, drug–drug interactions (DDIs) via the inhibition of
Abbreviations: Cb , drug concentrations in the blood; Ch , drug concentrations in the liver; CLh,int , hepatic intrinsic clearance; clog P, computer-calculated log P; CLr , renal clearance; CLtot , total body clearance; DDIs, drug–drug interactions; F, bioavailability; Fa , fraction absorbed; fb , unbound fraction in the blood; fe , fraction of unchanged drug excreted in urine; Fg , intestinal availability; Fh , hepatic availability; fm , contribution of CYP3A4 to the metabolism of the substrate; fp , unbound fraction in the plasma; Ht , hematocrit; Ig , inhibitor concentration in the enterocytes; k12 , transfer rate constant from the central compartment to the peripheral compartment; k21 , transfer rate constant from the peripheral compartment to the central compartment; ka , absorption rate constant; ke , elimination rate constant; kdeg , the degradation rate constant of CYP3A4; Ki,app , the apparent dissociation constant between CYP3A4 and the inhibitor; kinact , the maximum inactivation rate constant; Kp,h , liver–plasma concentration ratio; PBPK, physiologically based pharmacokinetic; Qh , hepatic blood flow; Rb , blood to plasma concentration ratio; REact,3A4 , ratio of the active enzyme relative to the total CYP3A4; Tlag , time-lag in drug absorption; V1 , distribution volume of the central compartment; Vh , hepatic volume; Xg , amounts of the drugs in the intestine compartment; Xsub , amounts of the drugs in the peripheral compartment. Correspondence to: Yoko Mano (Telephone: +81-48-669-3036; Fax: +81-48652-7254; E-mail: [email protected]) This article contains supplementary material available from the authors upon request or via the Internet at http://wileylibrary.com. Journal of Pharmaceutical Sciences, Vol. 104, 3183–3193 (2015) C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association
CYP3A4 sometimes result in serious problems in clinical practice. Recently, attempts have been made to predict DDIs taking into account interactions in the intestine as well as those in the liver, as the first-pass effect in the intestine greatly contributes to the interactions via CYP3A4.3–5 A previous study suggested that in vivo Ki values estimated from the plasma concentration–time profiles of the substrate in the presence and absence of inhibitor in clinical DDI studies were lower than the in vitro Ki values, and that for DDIs resulting from reversible CYP inhibition, the prediction using in vivo Ki values was more accurate than that using in vitro Ki values.6 In this study, clinical DDI studies involving nine CYP3A4 substrates, which are listed as “in vivo sensitive CYP3A substrates” in the DDI draft guidance published by the United States Food and Drug Administration (US FDA),7 were searched for using the Metabolism Transport Drug Interaction Database (DIDB) established by the University of Washington. Using a physiologically based pharmacokinetic (PBPK) model incorporating the inhibition of both intestinal and hepatic metabolism, the AUC ratios were predicted for interactions involving the six substrates for which intravenous data were available, and the accuracy was compared among the results obtained using the in vivo Ki values estimated from clinical DDI studies, predicted in vivo Ki values and in vitro Ki values. For the remaining three substrates for which intravenous data were not available, the Fg values were back-calculated from the plasma concentration–time profiles obtained with or without inhibitors.
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METHODS Collection of the Pharmacokinetic Data Among the 31 drugs listed as “in vivo sensitive CYP3A substrates” in the DDI draft guidance published by US FDA,7 we have randomly selected nine drugs (aprepitant, dronedarone, eplerenone, ticagrelor, tolvaptan, vardenafil, dasatinib, everolimus, and quetiapine) for which PBPK modeling analysis for prediction of DDI had not been reported to the best of our knowledge. The bioavailability (F), fraction of unchanged drug excreted in urine (fe ), unbound fraction in the plasma (fp ), blood to plasma concentration ratio (Rb ), and the contribution of CYP3A4 to the metabolism of the substrate (fm ) were obtained from the literature (Table 1 and Supplementary Table S1). If no information on the Rb was available, it was estimated by Eq. (1) using the fp and a hematocrit (Ht ) value of 0.45.8 ⎛
0.617·log
Rb = ⎝10
(1−f p ) fp
⎞
+0.208
· f p − 1⎠ · Ht + 1
(1)
The fm was evaluated from in vitro inhibition studies conducted using a specific CYP inhibitor and a value of unity was assumed if no information was available, in consideration of the substrate being a “sensitive in vivo CYP3A substrate”. Calculation of the Pharmacokinetic Parameters for the CYP3A4 Substrates The pharmacokinetic parameters of the six substrates for which intravenous data were available (aprepitant, dronedarone, eplerenone, ticagrelor, tolvaptan, and vardenafil) were estimated as follows. The plasma–concentration profiles of these substrates were obtained from the literature and the plasma concentrations were converted to blood concentrations using the Rb values. Fixing F at the reported value, the substrate blood concentration profiles after a single oral administration was fitted to a one- or two-compartment model to obtain the optimized values of the elimination rate constant (ke ) and distribution volume of the central compartment (V1 ) using Phoenix WinNonlin (Version 6.2; Pharsight, Mountain View, California). The total body clearance (CLtot ) and renal clearance (CLr ) were then calculated using Eqs. (2) and (3), respectively: CLtot = ke · V1
(2)
CLr = CLtot · f e
(3)
from the literature or calculated by the method reported by Wakayama et al.17 . For everolimus, Fa was assumed to be 1, because the reported results of a preclinical study suggested its high intestinal absorption.18 The unbound fraction in the blood (fb ) was calculated using Eq. (6): fb =
fp Rb
(6)
The liver-plasma concentration ratio (Kp,h ) was estimated based on Eq. (7):19 K p,h
P × (0.0348 + 0.3 × 0.0252) + 1 × (0.751 + 0.7 × 0.0252) P × (0.0035 + 0.3 × 0.00225) + 1 × (0.945 + 0.7 × 0.00225)
=
×
fp fh
(7)
where P is the octanol–water partition ratio and was estimated using Eq. (8) from the computer-calculated log P (clog P) obtained using Chem draw (Version 12.0) or ACD/logD (Version 12.01): P = 10c log P
(8)
fh is the hepatic unbound fraction for specific binding to albumin, globulins, and lipoproteins, and was calculated using Eq. (9), assuming a value of 0.5 for the tissue interstitial fluid-to-plasma concentration ratios of albumin, globulins, and lipoproteins: fh =
1 1+
1−f p fp
(9)
× 0.5
Fitting Analyses of the Blood Concentration–Time Profiles of the CYP3A4 Substrates Based on a PBPK Model Figure 1 shows a simple PBPK model consisting of the central compartment, liver compartment, peripheral compartment, and intestine compartment.6 According to this model, the concentration profiles of the substrate can be expressed by the
Hepatic availability (Fh ) and intestinal availability (Fg ) were calculated using Eqs. (4) and (5), respectively: Fh = 1 −
(CLtot − CLr ) Qh
Fg =
F Fh · Fa
(4)
(5)
where the value of the hepatic blood flow (Qh ) was fixed at 96.6 L/h6 and the fraction absorbed (Fa ) was either obtained
Figure 1. Physiologically based pharmacokinetic model.6
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DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
Fg
0.04
0.69
0.36
0.56
0.145
CLr (L/h)
8.35 0.388 6.59 0.558 1.55
Dronedarone
Eplerenone
Ticagrelor
Tolvaptan
Vardenafil
Inhibitord
Erythromycin Fluconazole Indinavir Ketoconazole Itraconazole
1.00 0.948 1.00 1.00 1.00
25.8
14.4
8.26
64.2
0.956 0.950 1.00 1.00 0.885
Fa
0
0
0.00896
0
1.54
0
CLr (L/h)
0.16 0.89 0.4 0.01 0.002
fp
0.321
0.733
0.851
0.914
0.351
0.957
Fh
1 1 1 0.632 0.58
Rb
0.503
0.952
0.469
0.788
0.114
0.744
Fg
0.438 0.714 0.529 5.84 6.67
Kp,h
0.9
0.802
0.901
0.958
0.996
0.828
Fa
356 0.118 114 1100 10800
CLh,int (L/h)
0.0534
0.02
0.006
0.41
0.003
0.0035
fp
b
Calculated using Eq. (1). Assumed to be 1 in consideration of being a sensitive in vivo CYP3A4 substrates. c Optimized by fitting analysis [fitted value (calculated S.E.)]. d Parameters registered in DDI Simulator Ver. 2.1.
a
65.6
0.59
Aprepitant
4.14
F
Substrate
CLtot (L/h)
Table 1. Pharmacokinetic Parameters of the Substrates and Inhibitors
5.09 0.628
0.628a 0.923a
0.434 0.861 2.13 1.08 0.234
ka (1/h)
0.817
0.51
45.0 60.1 41.4 33.0 215
V1 (L)
1.50
5.12
3.27
5.06
0.615a
0.652
Kp,h
Rb
– – – – 0.0797
k12 (1/h)
0.9
1b
0.95
0.657
1b
0.98
fmCYP3A4
– – – – 0.0492
k21 (1/h)
760 (37) 20,800 (60,200) 20.2 (0.5) 1860 (366) 890 (40) 3040 (169)
CLh,int c (L/h)
2910 411 10.6 132
Ki,in vitro (:g/L)
0.818 (0.160) 0.791 (0.625) 1.74 (0.14) 0.491 (0.291) 0.876 (0.134) 1.96 (0.46)
ka c (1/h)
5270 0.264 0.821 0.282
Ki,in vivo (:g/L)
85.8 (4.8) 415 (696) 36.2 (1.2) 49.3 (31.9) 108 (10) 206 (17)
V1 c (L)
4.8
kinact (1/h)
–
0.191 (0.160) –
0.239 (0.321) –
–
k12 c (1/h)
35,500
Ki,app (:g/L)
–
0.0899 (0.0686) –
0.0238 (0.0498) –
–
k21 c (1/h)
–
–
–
0.922 (0.061) 1.09 (0.15) –
Tlag (1/h)
15,16
14
13
12
11
9,10
Reference
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
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RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
following differential equations:
V1
Vh,s
Q h · Ch,s · Rb,s dCh,s = Q h · Cb,s − dt K p,h,s
Q h · Ch · Rb dCb = −Q h · Cb + dt K p,h −k12 · V1 · Cb + k21 · X sub · Cb − CLr · Cb
+ ka,s · X g,s (t) − f m,3A4,s · (10) − (1 − f m,3A4,s ) ·
Vh
Q h · Ch · Rb Ch dCh = Q h · Cb − + ka · X g − CLh,int · · f u,p dt K p,h K p,h
dX g (t) = −ka · X g (t) where at t = Tlag , dt
X g Tlag = Fa · Fg · Dose
(13)
V1,i
(14)
(15)
(16)
(17)
(18)
Q h · Ch,i · Rb,i dCh,i = Q h · Cb,i − dt K p,h,i + ka,i · X g,i (t) − CLh,int,i ·
Ch,i · f u,p,i K p,h,i
dX sub,i = k12,i · Cb,i · V1,i − k21,i · X sub,i dt
(19)
(20)
dX g,i (t) = −ka,i · X g,i (t) where at t = 0, dt X g,i (0) = Fa,i · Fg,i · Dosei
(21)
For each DDI, three values of Ki were used for the enzyme inhibition in the liver: in vitro Ki values calculated from metabolic inhibition experiments, in vivo Ki values estimated from other clinical DDI studies, and predicted in vivo Ki values calculated by Eq. (22) using the correlation between the log P and the in vivo Ki /in vitro Ki ratio.6
r Reversible inhibition (inhibitors except for erythromycin)
+ k21,s · X sub,s · Cb,s − CLr,s · Cb,s
CLh,int,s · Ch,s · f u,p,s K p,h,s
Q h · Ch,i · Rb,i dCb,i = −Q h · Cb,i + − k12,i · V1,i · Cb,i dt K p,h,i
Vh,i
Simulation of the Concentration Profiles of the CYP3A4 Substrates in Interaction Studies with Inhibitors
Q h · Ch,s · Rb,s dCb,s = −Q h · Cb,s + − k12,s · V1,s · Cb,s V1,s dt K p,h,s
Ch,s · f u,p,s K p,h,s
+ k21,i · X sub,i · Cb,i − CLr,i · Cb,i
where Cb and Ch are the drug concentrations in the blood and liver, respectively, Xsub and Xg are the amounts of the drugs in the peripheral and intestine compartment, respectively, k12 is the transfer rate constant from the central compartment to the peripheral compartment, k21 is the transfer rate constant from the peripheral compartment to the central compartment, ka is the absorption rate constant, CLh,int is the hepatic intrinsic clearance, and Tlag is the time-lag in drug absorption. The substrate blood concentration profiles after a single oral administration were fitted into Eqs. (10)–(13) to obtain the optimized values of CLh,int , ka , V1 , k12 , k21 , and Tlag using Phoenix WinNonlin. The hepatic volume (Vh ) was fixed at 1.5 L.
Method 1: Considering only Inhibition in the Liver
·
dX g,s (t) = −ka,s · X g,s (t) where at t = Tlag,s , dt
X g,s Tlag,s = Fa,s · Fg,s · Doses
(12)
The changes in the concentrations of the substrates due to DDIs were predicted by simulating the concentration profiles of both substrates and inhibitors based on the PBPK model shown in Figure 1. According to this model, the concentration profiles of the substrate and the inhibitor can be expressed by the following differential equations, depending on the inhibition mechanism (reversible or time-dependent) and on whether the intestinal inhibition is incorporated or not. The parameters with subscripts “s” and “i” denote those for the substrate and inhibitor, respectively.
1+
f u,p,i ·Ch,i K p,h,i ·K i,3A4
dX sub,s = k12,s · Cb,s · V1 − k21,s · X sub,s dt
(11)
dX sub = k12 · Cb · V1 − k21 · X sub dt
CLh,int,s
Predicted in vivo K i = in vitro K i · 4.4525 · e−1.339·log P
(22)
For fluconazole, with a log P value of 0.993, the in vitro Ki value was used as the predicted in vivo Ki value, based on the report by Kato et al.6 that the difference between the in vivo and in vitro Ki values of an inhibitor with a log P value of less than 1 is relatively small.
Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
DOI 10.1002/jps.24495
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
r Time-dependent inhibition (erythromycin) For DDIs involving erythromycin, a time-dependent inhibitor of CYP3A4, Eq. (23) was used instead of Eq. (15): Vh,s
Q h · Ch,s · Rb,s dCh,s = Q h · Cb,s − + ka,x · X g,s (t) dt K p,h,s − f m,3a4,s · REact,3A4 (t) · CLh,int,s · − (1 − f m,3A4,s ) ·
Ch,s · f u,p,s K p,h,s
CLh,int,s · Ch,s · f u,p,s K p,h,s
(23)
where REact,3A4 represents the ratio of the active enzyme relative to the total CYP3A4, expressed by Eq. (24) using the apparent dissociation constant between CYP3A4 and the inhibitor (Ki,app ), the maximum inactivation rate constant (kinact ) and the degradation rate constant of CYP3A4 (kdeg ). The value of kdeg was fixed at 0.0158 h−1 .20
The metabolic Inhibition in the liver was estimated in the same manner as that for method 1. Plasma concentration–time profiles of the substrates with and without co-administration of inhibitors were simulated by the above two methods, and the AUC ratio (AUC increase by the inhibitor) was calculated for each case using DDI Simulator (Version 2.1; Fujitsu Kyushu Systems, Fukuoka, Japan). All the parameters for the substrates and inhibitors used in the analyses are summarized in Table 1. The inhibitor parameters, including the in vivo Ki values, were those registered in DDI Simulator. Comparison of the Model Predictability The predictability of the AUC ratio was compared based on the geometric mean-fold error (GMFE, Eq. (28), the root mean square error (RMSE, Eq. (29) and % of the AUC ratios predicted within 2-fold accuracy. GMFE = 10
C
kinact,3A4 · Ch,i · f u,p,i · K h,i dREact,3A4 p,h,i =− Ch,i dt K i,app,3A4 + f u,p,i · K p,h,i
+ kdeg,3A4 · 1 − REact,3A4
RMSE =
Method 2: Considering Inhibition of both Intestinal and Hepatic Metabolism The increase in the value of Fg,s due to intestinal CYP3A4 inhibition was estimated with Eq. (25) using the inhibitor concentration in the enterocytes ([Ig ]) calculated using Eq. (26):22 Fg,s (+inhibitor) Fg,s (control) 1 1 1+[I]g /K i
×
kdeg kdeg +(kinact ×[I]g /K i,app +[I]g )
× 1 − Fg,s + Fg,s (25)
[I]g =
Dose · ka,i · Fa,i Qg
(26)
In Eq. (25), the in vitro Ki for hepatic CYP3A4 was used for Ki , and the time-dependent inhibition parameters (kinact , and Ki,app ) were also assumed to be the same between the intestine and the liver. The kdeg for CYP3A4 in the intestine and the blood flow rate through the enterocytes were fixed at 0.029 h−121 and 14.9 L/h,22 respectively. The estimated increase in the value of Fg,s was then incorporated into the PBPK model to accommodate the effect of metabolic inhibition in the intestine, using Eq. (27) as a substitute for Eq. (17): dX g,s (t) = −ka,s · X g,s (t) where at t = Tlag,s , dt
Fg,s (+inhibitor) · Doses X g,s Tlag,s = Fa,s · Fg,s Fg,s (control) DOI 10.1002/jps.24495
AUC ratio
log Predicted Observed AUC ratio N
(28)
1 (Predicted AUC ratio − Observed AUC ratio)2 N (29)
(24)
=−
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Fitting Analyses of the Blood Concentration–Time Profiles of the CYP3A4 Substrates Based on a PBPK Model with or Without Inhibitor for the Estimation of Fg For the three substrates (dasatinib, everolimus, and quetiapine) for which the intravenous data were not available, the Fg value was back-calculated from the plasma concentration– time profiles with or without inhibitors. To validate this method Fg estimation, the Fg values of 12 substrates (aprepitant, dronedarone, ticagrelor, tolvaptan, vardenafil, midazolam, triazolam, zolpidem, diazepam, methylprednizolone, alprazolam, and alfentanil) for which intravenous data were available were first back-calculated from the concentration–time profiles obtained with or without inhibitors as follows: The substrate blood concentration profiles with or without inhibitors (ketoconazole or itraconazole) were fitted into Eq. (14) based on the simultaneous analysis of Eqs. (14)–(21), and (25)–(27), fixing the hepatic Ki values at those registered as the in vivo Ki in DDI simulator (Table 1), to obtain the optimized values of Tlag,s , CLh,int,s , ka,s , V1,s , k12,s , k21,s and Fg,s . Phoenix WinNonlin was used and the differential equations were numerically solved by using the Livermore Solver for ordinary differential equations for the non-stiff system, with the weight value fixed at 1/y or 1/y 2 . Because the back-calculated Fg values for the 12 substrates were comparable to those calculated from the intravenous data using Eq. (5) (see Results section), this method for estimating the Fg was applied for dasatinib, everolimus, and quetiapine.
RESULTS Fitting Analyses of the Concentration–Time Profiles of the CYP3A4 Substrates
(27)
The blood concentration profiles of the CYP3A4 substrates were fitted into Eqs. (10)–(13) based on the simple PBPK model (Fig. 1) to obtain the optimized values of CLh,int , ka , V1 , k12 ,
Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
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and k21 . All the pharmacokinetic parameters for the substrates used in the analyses are summarized in Table 1 together with those estimated by the fitting analyses. As shown in supplementary Figure 1, the fitted curves for the substrate concentration profiles were close to the observed profiles. Simulation of the CYP3A4 Substrates Concentration Profiles in Interaction Studies with Inhibitors The substrate concentration changes due to DDIs were predicted by simulating the concentration profiles of both the substrates and inhibitors using Eqs. (14)–(27) based on the PBPK model shown in Figure 1, and the AUC ratios were calculated. Figure 2 shows the plasma concentration profiles of aprepitant and vardenafil with or without co-administration of ketoconazole simulated by methods 1 and 2. These two substrates were chosen because their concentration profiles with or without coadministration of typical strong inhibitor, ketoconazole, were available and also the profiles have been more successfully reproduced as compared to other substrates. For both aprepitant and vardenafil, the concentration profiles simulated by method 2, incorporating the inhibition of both intestinal and hepatic metabolism, were closer to the observed concentration profiles than those simulated by method 1, which only considered inhibition in the liver.
Table 2 summarizes the AUC ratios predicted by methods 1 and 2 using the in vitro Ki , predicted in vivo Ki and in vivo Ki values, and the relationship between the observed and predicted AUC ratios is shown in Figure 3. Method 1, which ignored the inhibition of intestinal metabolism, underestimated the observed AUC ratios in comparison to method 2. The smaller the original Fg of the substrate, the larger the difference tended to be between the observed AUC ratio and that predicted by method 1, and also the larger the prediction improvement by method 2. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using the predicted in vivo Ki values and in vivo Ki values estimated from clinical DDI studies was more accurate than that using the in vitro Ki values, for both methods 1 and 2. Table 3 shows the comparison of the predictability of the AUC ratios for DDIs resulting from reversible inhibition of CYP3A4. Both the GMFE values (2.11–3.59 and 1.31–1.99 for method 1 and 2, respectively) and RMSE values (6.65–8.14 and 3.02–4.81 for method 1 and 2, respectively) were lower for method 2 than for method 1. These values were lower when predicted in vivo Ki values or in vivo Ki values estimated from clinical DDI studies were used than when in vitro Ki values were used, for both methods 1 and 2. The AUC ratios for 12.5%–50.0% and 25.0%–100% of the eight studies were predicted within a 2-fold accuracy based on method 1 and 2, respectively, and the percentage was higher when predicted
Figure 2. Plasma concentration profiles of aprepitant (a) and vardenafil (b) in the interaction study with ketoconazole. (a) (1) Reported aprepitant concentration profiles after a single oral dose of aprepitant (125 mg, day 5) with ketoconazole (400 mg p.o., QD, day 1–10, open circles) or placebo (closed circles).9 (2) Simulated aprepitant concentration profiles, assuming the same dosing regimen as in (1). The solid line represents the profiles without ketoconazole; the dashed line with ketoconazole by method 1; the dotted line with ketoconazole by method 2. (b) (1) Reported vardenafil concentration profiles after a single oral dose of vardenafil (normalized to 5 mg, day 4) with ketoconazole (200 mg p.o., QD, day 1–4, open circles) or placebo (closed circles).15 (2) Simulated vardenafil concentration profiles, assuming the same dosing regimen as in (1). The solid line represents the profiles without ketoconazole; the dashed line with ketoconazole by method 1; the dotted line with ketoconazole by method 2. In vivo Ki was used for the simulation. Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
3.48 23.7 – 2.20 2.99 5.28 4.34 – 12.9 11.0 2.59 2.73 – 1.83 2.36 2.48 4.13 – 6.48 5.51 4.10 24.9 – 2.68 3.17 5.66 5.10 – 8.06 12.3 3.05 2.87 – 2.22 2.50 2.66 4.85 – 4.06 6.19 2.06 15.9 1.78 2.68 2.03 3.38 2.16 3.64 5.60 4.93 9 11 23 23 23 13 14 15 15 15 Aprepitant Dronedarone Eplerenone Eplerenone Eplerenone Ticagrelor Tolvaptan Vardenafil Vardenafil Vardenafil
125 200 100 100 100 90 30 20/5 10 20/5
Single dose (day 5) Single dose (day 8) Single dose (day 7) Single dose (day 6) Single dose (day 6) Single dose (day 4) Single dose (day 2) Single dose (day 4) Single dose (day 7) Single dose (day 4)
Ketoconazole Ketoconazole Erythromycin Fluconazole Ketoconazole Ketoconazole Ketoconazole Erythromycin Indinavir Ketoconazole
400 200 500 200 200 200 200 500 800 200
4.78 16.8 2.87 2.24 5.39 7.32 5.40 4.02 16.3 9.95 QD (day1–10) QD (day 1–8) BID (day 1–7) QD (day 1–6) BID (day 1–6) BID (day 1–10) QD (day 1–3) TID (day 1–4) TID (day 1–7) QD (day 1–4)
1.53 1.83 1.41 2.22 1.60 1.59 2.06 1.86 2.82 2.48
Method 2 Method 1 Method 2 Method 1 Method 2 Method 1 Reference Schedule Dose (mg)
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in vivo Ki values or in vivo Ki values estimated from clinical DDI studies were used than when in vitro Ki values were used, for both methods 1 and 2. Estimation of Fg for the CYP3A4 Substrates
Schedule Dose (mg)
Predicted in vivo Ki Inhibitor Substrate
Table 2. The Observed AUC Ratios and Those Predicted by the PBPK Model
Observed
In vitro Ki
Predicted
AUC Ratio
In vivo Ki
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
The Fg values of the CYP3A4 substrates for which intravenous data were available were back-calculated from their concentration–time profiles obtained with or without inhibitors (ketoconazole or itraconazole) using the parameters listed in Table 1 and Supplementary Table S1. As shown in Figure 4 and Supplementary Table S2, the back-calculated Fg values were comparable to those calculated from the intravenous data using Eq. (5). For the three substrates (dasatinib, everolimus, and quetiapine) for which intravenous data were not available, the substrate blood concentration profiles obtained with or without inhibitors (ketoconazole or erythromycin) were fitted into Eq. (14) based on the simultaneous analysis of Eqs. (14)–(16), (18)–(21), and (23)–(27) to obtain the optimized values of Tlag,s , CLh,int,s , ka,s , V1,s , k12,s , k21,s , and Fg,s . The parameters fixed and optimized in the analyses are summarized in Supplementary Tables S1 and S4, respectively. The fitted curves were close to the observed profiles, as shown in Figure 5 (e.g., for the case of everolimus), and the Fg values of dasatinib, everolimus, and quetiapine were estimated as 0.744, 0.219–0.391, and 0.538– 0.800, respectively. The values of Fg in the presence of inhibitor, calculated by multiplying those under the control condition and the increase estimated using Eq. (25), reached nearly 1 for all the cases, indicating complete inhibition of the intestinal metabolism in the presence of inhibitor. A larger contribution of inhibition in the intestine in the overall interaction was estimated in the case of a smaller Fg value under the control condition.
DISCUSSION The present study investigated whether the DDIs via CYP3A4 can be quantitatively predicted by estimating the extent of hepatic CYP3A4 inhibition based on simple PBPK modeling of both the substrate and inhibitor and incorporating the increase in the Fg due to the intestinal enzyme inhibition. For the DDIs resulting from reversible inhibition of CYP3A4, the prediction using predicted in vivo Ki values and in vivo Ki values was more accurate than that using in vitro Ki values (Table 2 and Fig. 3). The fold difference in the predicted and observed AUC ratios ranged from 0.55 (DDI between eplerenone and ketoconazole) to 1.41 (DDI between dronedarone and ketoconazole) using method 2 with the in vivo Ki , which we think is acceptable. Considering that ketoconazole is involved as an inhibitor in both of the above DDIs, the differences might be associated with the substrate characteristics, such as low bioavailability of dronedarone (4%), but the reasons are unknown.11 Because the prediction using in vitro Ki values underestimated the observed AUC ratios, either predicted in vivo Ki values or in vivo Ki values should be used for a more accurate prediction of DDIs. When the possibility of a candidate compound being a CYP3A4 inhibitor is to be evaluated and its in vivo Ki value is unavailable, use of predicted in vivo Ki values, estimated from the log P and the in vitro Ki value, is recommended. The AUC ratios for 12.5%–50.0% and 37.5%–100% of the DDI studies were predicted within 2-fold accuracy based on method Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
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Figure 3. Relationship between the predicted and observed AUC ratios. (a) method 1: The prediction assuming only inhibition in the liver, (b) method 2: the prediction assuming inhibition of both intestinal and hepatic metabolism. Open triangles, open circles, and closed circles represent predictions using in vitro Ki , predicted in vivo Ki , and in vivo Ki , respectively. Solid line represents unity. Dashed lines represent 2-fold boundary around the line of unity.
Table 3. Predictability of the Method 1 and Method 2 In vitro Ki
GMFE RMSE % within 2-fold error
Predicted in vivo Ki
In vivo Ki
Method 1
Method 2
Method 1
Method 2
Method 1
Method 2
3.59 8.14 12.5
1.99 4.81 25.0
2.11 7.00 50.0
1.37 4.29 87.5
2.20 6.65 50.0
1.31 3.02 100
Method 1, the prediction assuming only inhibition in the liver; method 2, the prediction assuming inhibition of both intestinal and hepatic metabolism; GMFE, the geometric mean fold error; RMSE, the root mean square error.
Figure 4. Relationship between the back-calculated and observed Fg values of the CYP3A4 substrates. The Fg values of CYP3A4 substrates were back-calculated from their concentration–time profiles with or without inhibitors (ketoconazole or itraconazole) and plotted against the observed Fg values calculated from intravenous data using Eq. (5).
1 and 2, respectively, indicating that the prediction by method 2 was more accurate than that by method 1 (Table 3). It is important for quantitative predictions of DDIs via CYP3A4 to take into account the interaction in the intestine as well as that in
the liver. In the present study, the increase in the values of Fg due to the intestinal enzyme inhibition was estimated using a static model and incorporated into the PBPK model, whereas the ADAM (Advanced Dissolution Absorption and Metabolism) model is used in other prediction models such as the Simcyp Simulator (Simcyp Limited, Sheffield, UK) for dynamic prediction of intestinal absorption, transport, metabolism and DDIs.26 Although the ADAM model allows for estimation of the intestinal absorption of low-solubility compounds and also the DDI predictions considering the differential distribution of CYP3A4 along the intestine, it requires a large number of input parameters, some of which are not necessarily possible to estimate with accuracy. From that viewpoint, our prediction model with Fg estimation using a static model has the advantage of requiring much fewer parameters. Four of the CYP3A4 substrates analyzed in the present study (aprepitant, 9 ticagrelor,13 tolvaptan,14,27 and vardenafil28 ) are also reported as substrates of P-glycoprotein, and two of the CYP3A4 inhibitors (erythromycin29 and ketoconazole29 ) are also known to inhibit P-glycoprotein. Although DDIs via Pglycoprotein were not incorporated in the present model, the AUC ratios for DDIs involving these compounds were predicted within 2-fold accuracy by method 2 using in vivo Ki values (Table 3), suggesting a minor contribution of Pglycoprotein inhibition. In addition, membrane transport is unlikely to be a rate-limiting step in the intestinal absorption of a highly permeable and highly soluble drug such as vardenafil.15
Mano, Sugiyama, and Ito, JOURNAL OF PHARMACEUTICAL SCIENCES 104:3183–3193, 2015
DOI 10.1002/jps.24495
DOI 10.1002/jps.24495
–
–
0.326 (0.086) 0.0749 (0.0317)
–
0.493 (0.101) 0.408 (0.037) –
0.0993 (0.0164) 0.0925 (0.0170) –
1.47 (0.15) – – –
136 (10) 15.3 (2.3) 26.7 (1.8) 130 (13) 181 (13) 200
500
Ketoconazole
Erithromycin
25
200
Quetiapine
Quetiapine
b
35 2.29
34 6.22
25 4.52 500 Erithromycin 2 Everolimus
a
1.25 0.995
1.86 0.999
2.48 0.970
4.55 0.999 24 15.3 200 Ketoconazole 2 Everolimus
Optimized by fitting analysis [fitted value (calculated S.E.)]. The values of Fg in the presence of inhibitor, calculated by multiplying those under the control condition and the increase estimated using Eq. (25).
6.00 (27.50) 3.25 (0.55) 6.00 (1.03) 6.00 (4.66) 6.00 (2.70) 3360 (375) 69.5 (7.7) 96.9 (14.7) 178 (16) 147 (9) 1.34 1.00
0.744 (0.099) 0.219 (0.020) 0.391 (0.024) 0.538 (0.060) 0.800 (0.098) 33 4.84
BID (day 1–6) BID (day 1–5) TID (day 1–9) QD (day 1–4) TID (day 1–4) 200 Ketoconazole
QD (day 1–6) Single dose (day 5) Single dose (day 5) Single dose (day 4) Single dose (day 4) 20 Dasatinib
Fa g References Observed AUC ratio Schedule Dose (mg) Dose (mg)
Schedule
Inhibitor Substrate
Table 4. Estimation of Fg for the CYP3A4 Substrates for which Intravenous Data were Unavailable
Fg b with Inhibitor
Fold Change in Fg
CLh,int a (L/h)
ka a (l/h)
V1 a (L)
k12 a (l/h)
k21 a (l/h)
Tlag a (L/h)
RESEARCH ARTICLE – Pharmacokinetics, Pharmacodynamics and Drug Transport and Metabolism
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For the three substrates (dasatinib, everolimus, and quetiapine) for which intravenous data were not available, the Fg values were back-calculated from the plasma concentration– time profiles obtained with or without inhibitors. To validate this method for Fg estimation, the Fg values of 12 substrates for which intravenous data were back-calculated from the concentration–time profiles obtained with or without inhibitors (ketoconazole or itraconazole). We found that the backcalculated Fg values were comparable to those calculated from the intravenous data (Fig. 4). Several methods of predicting Fg have been reported as follows: Nishimuta et al.30 developed a method for predicting Fg that is applicable to low-permeability compounds based on metabolic clearance from metabolic stability assays using human liver microsomes for CYP3A4 substrates, and the permeability values obtained by parallel artificial permeability assay (PAMPA). Gertz et al.31 reported that Fg can be estimated from interaction studies with grapefruit juice, assuming complete inhibition of CYP3A4-mediated intestinal metabolism and no effects on the hepatic enzymes or the fraction absorbed of the drug. Hisaka et al.32 evaluated the Fg from the changes in the AUC and elimination half-life of the drug caused by DDIs. Because it is important for quantitative predictions of DDIs via CYP3A4 to take into account the increase in the values of Fg , the present method for estimating Fg would be useful for CYP3A4 substrates for which intravenous data are not available. The Fg values of dasatinib, everolimus, and quetiapine were estimated as 0.744, 0.219– 0.391, and 0.538–0.800, respectively (Table 4). The Fg values of cyclosporin and midazolam have been estimated to be 0.4– 0.79 and 0.29–0.68, respectively, based on intravenous and oral data obtained from different clinical studies,3 both showing 2fold variation. Therefore, the 1.79-fold and 1.49-fold variation in the Fg values of everolimus and quetiapine, respectively, estimated from different clinical DDI studies, may be regarded as acceptable. In conclusion, the present PBPK model for both CYP3A4 substrates and inhibitors, incorporating the increase in the Fg value due to the intestinal enzyme inhibition, was found to be useful for quantitative prediction of clinical DDIs when combined with the in vivo Ki values. Based on the present PBPK model, the Fg values for CYP3A4 substrates for which intravenous data were not available were also successfully backcalculated using the plasma concentration profiles obtained from clinical DDI studies.
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Figure 5. Plasma concentration profiles of everolimus in the interaction studies. (a) Everolimus concentration profiles after a single oral dose of everolimus (2 mg, day 4) with ketoconazole (200 mg p.o., BID, day 1–8, open circles) or placebo (closed circles).24 The dashed line and the solid line indicate the fitted curves for the time courses with and without ketoconazole, respectively. In vivo Ki was used for the fitting analysis. (b) Everolimus concentration profiles after a single oral dose of everolimus (2 mg, day 5) with erythromycin (200 mg p.o., TID, day 1–9, open circles) or placebo (closed circles).25 The dashed line and the solid line indicate the fitted curves for the time courses with and without erythromycin, respectively. For both panels (a) and (b), the reported blood concentrations were converted to plasma concentrations using the Rb .
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