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DRUG METABOLISM AND DISPOSITION
Copyright © 2007 by The American Society for Pharmacology and Experimental Therapeutics
DMD 35:79–85, 2007
Vol. 35, No. 1
11346/3159895
Printed in U.S.A.
A Novel Model for the Prediction of Drug-Drug Interactions in
Humans Based on in Vitro Cytochrome P450 Phenotypic Data
Chuang Lu, Gerald T. Miwa, Shimoga R. Prakash, Liang-Shang Gan,1 and Suresh K. Balani
Drug Metabolism and Pharmacokinetics, Drug Safety and Disposition, Millennium Pharmaceuticals, Inc.,
Cambridge, Massachusetts
Received June 5, 2006; accepted September 29, 2006
ABSTRACT:
was quantitatively determined (reactive phenotyping). Likewise,
the effect of ketoconazole on various P450s was quantitated. Using this information, P450-mediated change in the area under the
curve has been predicted without the need of estimating the inhibitor concentrations at the enzyme active site or the Ki. This approach successfully estimated the magnitude of the clinical DDI of
an investigational compound, MLX, which is cleared by multiple
P450-mediated metabolism. It also successfully predicted the
pharmacokinetic DDIs for several marketed drugs (theophylline,
tolbutamide, omeprazole, desipramine, midazolam, alprazolam,
cyclosporine, and loratadine) with a correlation coefficient (r2) of
0.992. Thus, this approach provides a simple method to more
precisely predict the DDIs for P450 substrates when coadministered with ketoconazole or any other competitive P450 inhibitors in
humans.
Ketoconazole is an antifungal medicine and is also a potent
CYP3A4/5 (CYP3A4) inhibitor in humans. Ketoconazole is widely
used in vitro as a selective CYP3A4 inhibitor at low concentrations
(Pelkonen et al., 1998; Li et al., 1999). However, at high concentrations it also inhibits cytochrome P450 (P450) isozymes other than
CYP3A4 (Venkatakrishnan et al., 2001; Stresser et al., 2004) and
UDP glucuronosyltransferase (Yong et al., 2005), as well as transporters such as sodium taurocholate cotransporting polypeptide, Pglycoprotein (Pgp), breast cancer resistance protein, and multidrug
resistance-associated protein 2 (Azer et al., 1995; Salphati and Benet
1998, Achira et al., 1999; Xia et al., 2005b). For the purpose of
overcoming possible drug resistance, clinical usage of ketoconazole
involves much higher concentrations (⬃25-fold) than its in vitro
efficacious concentration (Schaefer-Korting et al., 1984). For studying
the worst-case scenario of CYP3A4-mediated drug-drug interaction
(DDI) potential on a compound, dosing of ketoconazole at 200 mg
b.i.d. is recommended by the Pharmaceutical Research and Manufac-
turers of America (Bjornsson et al., 2003). At such a high dose, the
maximum plasma concentration (Cmax) could reach as high as 20 ␮M
(Rochlitz et al., 1988; Hsu and Chen, 1997). Other dose regimens,
such as 200 mg/day, have also been reported. These doses showed
plasma Cmax ranging from 3.2 to 10 ␮M with most of the values about
10 ␮M (Schaefer-Korting et al., 1984; Ito et al., 1998; Pelkonen et al.,
1998; Hardman et al., 2001; Blanchard et al., 2004). Ketoconazole
treatment has been found to completely inhibit CYP3A4 activity in
vivo (Obach et al., 2006), but the cross-inhibition to P450s other than
CYP3A4 has not yet been thoroughly shown in vivo as it has been in
vitro. The cross-P450 isozyme inhibition by ketoconazole in vivo is
usually overlooked when predicting DDIs between ketoconazole and
CYP3A4 substrates, and ketoconazole is still treated as a selective
CYP3A4 inhibitor in vivo.
P450-reactive phenotyping is a quantitative measurement of the
relative contributions of each P450 to the overall metabolism of a
drug, when this drug is primarily metabolized by P450s. Several
methods are commonly used in the determination of the reactive
phenotyping that include the relative activity factor (Crespi, 1995;
Venkatakrishnan et al., 2001), chemical inhibitors (Newton et al.,
1995; Bourrie et al., 1996; Lu et al., 2003), and monoclonal antibodies
(mAbs) (Gelboin et al., 1999; Shou et al., 2000; Soars et al., 2003). In
1
Current affiliation: Boehringer-Ingelheim Pharmaceuticals, Inc., Ridgefield,
CT.
Article, publication date, and citation information can be found at
http://dmd.aspetjournals.org.
doi:10.1124/dmd.106.011346.
ABBREVIATIONS: CYP3A4, CYP3A4/5; P450, cytochrome P450; Pgp, P-glycoprotein; DDI, drug-drug interaction; mAb, monoclonal antibody;
AUC, area under the curve; KHB, Krebs-Henseleit buffer; LC/MS/MS, liquid chromatography/tandem mass spectrometry; HPLC, high-performance liquid chromatography; fA, fraction of activity remaining of a given enzyme in the presence of inhibitor; fm,P450, fraction of metabolism by
a given enzyme; MLX, Millennium investigational compound X.
79
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Ketoconazole has generally been used as a standard inhibitor for
studying clinical pharmacokinetic drug-drug interactions (DDIs) of
drugs that are primarily metabolized by CYP3A4/5. However, ketoconazole at therapeutic, high concentrations also inhibits cytochromes P450 (P450) other than CYP3A4/5, which has made the
predictions of DDIs less accurate. Determining the in vivo inhibitor
concentration at the enzymatic site is critical for predicting the
clinical DDI, but it remains a technical challenge. Various approaches have been used in the literature to estimate the human
hepatic free concentrations of this inhibitor, and application of
those to predict DDIs has shown some success. In the present
study, a novel approach using cryopreserved human hepatocytes
suspended in human plasma was applied to mimic the in vivo
concentration of ketoconazole at the enzymatic site. The involvement of various P450s in the metabolism of compounds of interest
80
LU ET AL.
Materials and Methods
Reagents. Pooled human liver microsomes from 50 donors were purchased
from XenoTech LLC (Kansas City, KS). Cryopreserved human hepatocytes
were purchased from In Vitro Technologies, Inc. (Baltimore, MD) and AP
Sciences Inc. (Baltimore, MD). 4-Hydroxytolbutamide, 4-hydroxymephenytoin, dextrorphan, 1⬘-hydroxymidazolam, benzylnirvanol, and azamulin were
purchased from BD Gentest (Woburn, MA). Phenacetin, acetaminophen, tolbutamide, dextromethorphan, testosterone, 6␤-hydroxytestosterone, furafylline, sulfaphenazole, quinidine, ketoconazole, NADPH, and MgCl2 were purchased from Sigma (St. Louis, MO). Human plasma was purchased from
Bioreclamation Inc. (Hicksville, NY). S-Mephenytoin was purchased from
BIOMOL Research Labs., Inc. (Plymouth, PA). Ascites fluids containing
mAbs to CYP1A2 (mAb 26-7-5), CYP2C9 (mAb 763-15-5), CYP2C19 (mAb
1-7-4-8), CYP2D6 (mAb 50-1-3), CYP3A4 (3-29-9), and hen egg white
lysozyme (control mAb HyHel-9) were gifts from Dr. Harry V. Gelboin
(National Cancer Institute, Bethesda, MD).
Ketoconazole P450 Inhibition Determination in Human Hepatocytes.
This study was performed in parallel in human plasma and Krebs-Henseleit
buffer (KHB) fortified with 2 mM salicylamide. The salicylamide was used to
reduce possible phase II conjugation to the precursor phase I metabolites,
which are the analytes in this study (Lu and Li, 2001). Serially diluted
ketoconazole stocks were prepared with the final concentrations of 100, 50, 25,
12.5, 6.25, 3.13, 1.6, and 0 ␮M. Human hepatocytes (pooled from two female
and two male donors) were thawed and prepared as described previously (Li et
al., 1999). The hepatocytes (25 ␮l, final concentration of 1.0 ⫻ 106 hepatocytes/ml, viability ⬎80%) were mixed with 25 ␮l of ketoconazole solution at
various concentrations at room temperature for 20 min, followed by a prewarm
period of 10 min at 37°C. The P450 isozyme-specific substrates (50 ␮l, final
concentration: 30 ␮M phenacetin, 150 ␮M tolbutamide, 100 ␮M S-mephenytoin, 8 ␮M dextromethorphan, or 5 ␮M midazolam) were added to start the
P450 activity assays. The incubations were carried out in a 37°C CO2 (5%)
incubator for 45 min for phenacetin, tolbutamide, dextromethorphan, and
midazolam in KHB, and for phenacetin, dextromethorphan, and midazolam in
human plasma. However, the incubations for tolbutamide in human plasma and
S-mephenytoin in both KHB and human plasma were extended to 90 min
because of the substrates’ low turnover rate. All the incubation conditions were
in a predetermined linear range. The reactions were stopped by adding one
equal or 2 volumes of acetonitrile containing 1 ␮M carbutamide (internal
standard) to the KHB or human plasma samples, respectively. The samples
were kept in a refrigerator for 30 min and then centrifuged at 3000g for 10 min.
The supernatants were analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS) for the amount of metabolite formed. The percentage of
metabolic activity remaining was calculated by comparing the P450 activities
in samples with various concentrations of ketoconazole with their vehicle
controls.
Determination of Extracellular Ketoconazole Concentrations. After the
equilibrium period of ketoconazole in human hepatocytes of 20 min at room
temperature and 10 min at 37°C, the hepatocytes were separated from the
buffer or plasma via a centrifugation method described previously (Shitara et
al., 2003). Ketoconazole concentrations in these extracellular media were
analyzed using LC/MS/MS with standard curves prepared in the same matrixes. To determine whether accountable metabolism occurred during this
equilibrium period, two sets of ketoconazole-hepatocyte samples were prepared. The reaction-terminating solution, acetonitrile containing 1 ␮M carbutamide, was added to one set at 0 min and the second set after the equilibrium
period. The percentage of ketoconazole remaining was determined using
LC/MS/MS. The LC/MS/MS system used to determine ketoconazole and P450
substrate metabolites consisted of an Agilent 1100 high-performance liquid
chromatograph (HPLC), a Leap CTC PAL autosampler, and a SCIEX API
4000 detector. Metabolite separation was achieved on a Phenomenex Synergi
C18 column (75 ⫻ 4.6 mm) with a gradient consisting of 0.1% formic
acid/water (mobile phase A) and 0.1% formic acid/acetonitrile (mobile phase
B) at a flow rate of 1.0 ml/min. Specifically, 5% of mobile phase B was applied
for 0.5 min after injection and increased linearly to 95% B from 0.5 to 3.5 min.
Mobile phase B was held at 95% from 3.5 to 3.6 min, and the column was
re-equilibrated to 5% B from 3.6 to 5.0 min. A positive ion spray in the
multiple-reaction monitoring mode was applied with a predetermined parent/
product mass transition ion pairs for ketoconazole and P450 probe substrate
metabolites.
Reactive Phenotyping of MLX Using P450 Selective Chemical Inhibitors. In 96-well plates, microsomes (0.5 mg/ml in 0.1 M potassium phosphate
buffer, pH 7.4) were prewarmed in duplicate with [14C]MLX (10 ␮M) and
P450 selective inhibitors (predetermined concentrations) for 5 min at 37°C.
The reactions were initiated by the addition of NADPH (2.0 mM)/MgCl2 (3
mM) and incubated for 15 min. The reactions were terminated by the addition
of equal volumes of acetonitrile. After storing for 30 min in a refrigerator, the
sample plates were centrifuged at 3000g for 10 min, and the supernatants were
analyzed using reverse-phase HPLC with an on-line ␤-RAM radiochemical
detector. P450 selective substrates were incubated in parallel with microsomes
in the presence of P450 selective inhibitors to show optimal inhibition. This
information was then used to correct the partial inhibition and cross-reactivity
of these inhibitors on the MLX metabolism. The final concentrations of P450
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one recent report, all three methods were applied to determine the
relative P450 contributions to the metabolism of a proteasome inhibitor, Velcade (Millennium Pharmaceuticals, Cambridge, MA) (Uttamsingh et al., 2005). With the introduction of the potent and selective
CYP2C19 and CYP3A4 inhibitors benzylnirvanol and azamulin, respectively (Walsky and Obach 2003; Stresser et al., 2004), using
chemical inhibitors to determine the reactive phenotyping has become
an easy and cost-effective choice.
A major and important effort in the pharmaceutical industry is
predicting the clinical DDI from in vitro data. It is especially
important for drugs that are primarily metabolized by CYP3A4
because many drugs on the market are CYP3A4 inhibitors/substrates. Besides the pharmacokinetic properties of the substrate
drug, two factors that dictate the DDI potential are the inhibitor’s
inhibition constant (Ki) and the enzyme site free concentration of
that inhibitor. The Ki usually can be determined from in vitro
microsomal incubation assays with the consideration of protein
binding. But the enzyme site inhibitor concentration cannot readily
be assessed. Vast efforts have been focused on defining the enzyme
site inhibitor concentration, such as use of the peripheral vein
concentration, the portal vein concentration, the steady-state systemic plasma concentration, a multiple (such as 10⫻) of the plasma
concentration, the maximum plasma concentration, the maximum
hepatic input concentration, or the liver concentration (Blanchard
et al., 2004; Cook et al., 2004; Ito et al., 2004; Bachmann 2006;
Obach et al., 2006). For each possible concentration, there is
always a choice of using the total concentration or the free concentration. Choosing one way to estimate the enzyme site inhibitor
concentration may provide a better DDI prediction for certain
classes of drugs. However, there is no consensus on which concentration represents the enzyme site concentration and therefore
should be used for the DDI prediction of all the classes of drugs.
In the present study, human hepatocytes were incubated in
human plasma in the presence of various concentrations of ketoconazole to determine the ketoconazole cross-reactivity or crossinhibition on five major P450s: CYP1A2, CYP2C9, CYP2C19,
CYP2D6, and CYP3A4. Reactive phenotyping was determined for
the investigational compound MLX using chemical inhibitors and
mAbs. Without the need to consider the enzyme site inhibitor
concentration or the Ki, the DDI potential [the area under the curve
(AUC) changes] was predicted using the combined information
from the ketoconazole cross-reactivity in human hepatocytes and
the reactive phenotyping of MLX. This method was also applied to
a set of marketed drugs and showed a good correlation between the
predicted DDI and the observed clinical DDI. Thus, this method
can help predict DDIs early in the preclinical development stage
(Balani et al., 2005).
81
PREDICTION OF CLINICAL DRUG-DRUG INTERACTIONS
TABLE 1
P450 activity of human hepatocytes in the presence of ketoconazole in KHB buffer
Ketoconazole was equilibrated with human hepatocytes in KHB to allow nonspecific binding. After equilibration, the extracellular concentration of ketoconazole was measured. The
remaining P450 activities in hepatocytes were also determined using the probe substrates.
Ketoconazole in
Incubation
Ketoconazole
Extracellular
100 ␮M
50 ␮M
25 ␮M
12.5 ␮M
6.25 ␮M
3.13 ␮M
1.6 ␮M
0
19.3
9.23
3.98
1.48
0.640
0.263
0.153
0
fA (%)a
CYP1A2
CYP2C9
CYP2C19
CYP2D6
CYP3A4
2
16
41
55
63
77
91
100
1
4
17
31
54
72
80
100
2
9
31
59
71
82
83
100
9
36
60
78
88
101
101
100
0
0
0
1
3
5
8
100
␮M
a
fA (%) ⫽ fraction of enzyme activity remaining in the presence of ketoconazole.
of interest, was not considered for this study. Equation 4 (see under
Discussion for details) was used to calculate AUC changes, assuming linear
pK and a Cmax of 10 ␮M at 200 mg b.i.d. or q.d. dose of ketoconazole
(Chien et al., 2006). For example, for omeprazole, with a ketoconazole dose
of 200 q.d. and a Cmax of 10 ␮M, the fA3A4 and fA2C19 were calculated to
be 0.006 and 0.604, respectively (Table 2). Together with fm3A4 of 0.40 and
fm2C19 of 0.60 from literature, and assuming clearance only caused by
metabolism (i.e., fm,hep ⫽ 1), a prediction value of 2.74-fold increase in
AUC was calculated. A similar calculation was done for another omeprazole study in which a low ketoconazole dose of 50 mg q.d. was reported
(University of Washington DDI database, 2004). This predicted for a
1.74-fold change in AUC (Table 5). For theophylline and alprazolam, the
renal clearance had marginal contribution to the total clearance of theophylline and alprazolam, but the information on ketoconazole’s effect on
renal clearance was not available. Therefore, a range of values was predicted such that the low value considers the renal clearance contribution in
the calculation, whereas the high value does not. One can imagine the low
value is similar to the situation in which renal clearance is not affected by
ketoconazole, whereas the high value is similar to the situation in which
renal clearance is totally blocked by ketoconazole.
Results
The inhibition of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and
CYP3A4 by ketoconazole in human hepatocytes in KHB or human
plasma is presented in Tables 1 and 2. Figure 1 shows the plot of
extracellular ketoconazole concentration versus the P450 activity
remaining from the data in Table 2. For both Tables, column 1
represents the concentrations in the incubation—the initial concentration of ketoconazole. The “Keto Extracellular” (column 2) represents the extracellular concentration of ketoconazole remaining
in the KHB buffer or plasma after the equilibration period. Com-
TABLE 2
P450 activity of human hepatocytes in the presence of ketoconazole in human plasma
Ketoconazole was equilibrated with human hepatocytes in human plasma to allow nonspecific binding. After equilibration, the extracellular concentration of ketoconazole was measured.
The remaining P450 activities in hepatocytes (fA) were also determined using the probe substrates.
Ketoconazole in
Incubation
Ketoconazole
Extracellular
fA (%)a
CYP1A2
CYP2C9
CYP2C19
CYP2D6
CYP3A4
79
81
83
89
92
92
97
100
60
71
72
75
80
84
79
100
32
42
57
63
83
90
89
100
96
84
99
91
99
100
97
100
0
0
0
1
5
11
12
100
␮M
100 ␮M
50 ␮M
25 ␮M
12.5 ␮M
6.25 ␮M
3.13 ␮M
1.6 ␮M
0
a
71.8
34.0
14.1
7.30
4.04
2.22
0.857
0
fA (%) ⫽ fraction of enzyme activity remaining in the presence of ketoconazole inhibition.
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selective inhibitors and probe substrates were 100 ␮M furafylline and 30 ␮M
phenacetin for CYP1A2, 5 ␮M sulfaphenazole and 150 ␮M tolbutamide for
CYP2C9, 20 ␮M benzylnirvanol and 100 ␮M S-mephenytoin for CYP2C19, 5
␮M quinidine and 8 ␮M dextromethorphan for CYP2D6, and 2 ␮M azamulin
and 50 ␮M testosterone for CYP3A4. The analyses of the P450 substrate
metabolites were the same as described earlier. The analysis of MLX metabolites was performed using an HPLC system consisting of an Agilent 1100
binary pump (Palo Alto, CA) coupled to a IN/US ␤-RAM detector (IN/US
Systems, Inc., Tampa, FL). A 60-min gradient with 1.0 ml/min flow rate was
applied to separate the metabolites from their parent peak using a Synergi
Fusion-RP column (4.6 ⫻ 250 mm, 4 ␮m, 80 Å) purchased from Phenomenex
Inc. (Torrance, CA). The peak areas of MLX and its metabolites were detected
using an on-line ␤-RAM detector with 1:1 (v/v) infusion of the In-Flow
Scintillator (IN/US Systems, Inc.). The sample injection volume was 100 ␮l.
Mobile phases A and B were water and acetonitrile, respectively, each supplemented with 0.1% v/v formic acid.
Reactive Phenotyping of MLX Using Anti-P450 mAbs. Similar to the
chemical inhibitor assay, predetermined dilutions of mAbs were preincubated
with microsomes (0.5 mg/ml) at 37°C for 10 min. The [14C]MLX (10 ␮M) and
NADPH (2.0 mM)/MgCl2 (3 mM) were then added to start the incubations at
37°C for 15 min. The hen egg white lysozyme (control protein in the same
-fold of dilutions) was included in the study as control for the 100% P450
activities. The dilutions of mAbs that generated the optimal inhibition were
CYP1A2 (mAb 26-7-5, 1:800), CYP2C9 (mAb 763-15-5, 1:240), CYP2C19
(mAb 1-7-4-8, 1:240), CYP2D6 (mAb 50-1-3, 1:40), and CYP3A4 (3-29-9,
1:20). Percent inhibition at these dilutions was determined in an earlier study
using the prototypical P450 substrates.
Calculation of AUC Changes from in Vitro Data. Because the P450
contents in the gut are only a small fraction of that in the liver (Obach et
al., 2006), the contribution of metabolism by gut to the overall metabolism
in humans may generally be considered as low. Thus, Fg⬘/Fg factor (Ito et
al., 2005; Galetin et al., 2006), which is hardly available for the compounds
82
LU ET AL.
FIG. 1. Relationship of the extracellular plasma concentration of ketoconazole and
P450 activity remaining in human hepatocytes in an incubation of ketoconazole in
human hepatocytes suspended in human plasma.
TABLE 3
Results were presented after correcting the partial inhibition and cross-reactivity. See
Table 4 and text for details.
fm,P450 (%)a
Chemical inhibitors
Monoclonal antibodies
a
CYP1A2
CYP2C9
CYP2C19
CYP2D6
CYP3A4
4.8
0
0
0
0
3.2
21
3.2
87
63
fm,P450 (%) ⫽ fractional contribution of specific P450 to the metabolism of MLX.
paring column 1 with column 2, there was less than 20% of
ketoconazole remaining in the KHB incubation, whereas in plasma
incubation there was about 70% of ketoconazole remaining. The
lower extracellular concentrations attribute to nonspecific binding
to the hepatocytes. Ketoconazole, on incubation with KHB in the
traditional way, showed a dose-dependent inhibition of all five
P450s with the rank order of CYP3A4 ⬎⬎ CYP2C9 ⬎ CYP2C19 ⬎
CYP1A2 ⬎ CYP2D6. When human hepatocytes were incubated in
human plasma instead, a system aiming to mimic the in vivo
condition, ketoconazole showed lower inhibition, whereas the rank
order remained similar: CYP3A4 ⬎⬎ CYP2C19 ⬎ CYP2C9 ⬎
CYP1A2 ⬎ CYP2D6. In contrast to the incubation in KHB, it was
noticed that at high concentrations, ketoconazole tended to cause
more inhibition of CYP2C19 than CYP2C9. Within the concentration range tested in this study, dose-dependent inhibition of keto-
Discussion
Cryopreserved hepatocyte suspension is a valid model for studying drug metabolism and DDIs. Hepatocyte incubations are usually
carried out in KHB, which is an essential but minimum salt buffer
TABLE 4
Substrate specificity of prototypical P450 inhibitors in human liver microsomes
Inhibitor
CYP1A2
Phenacetin
CYP2C9
Tolbutamide
CYP2C19
S-Mephenytoin
Furafylline
Sulfaphenazole
Benzylnirvanol
Quinidine
Azamulin
85.5
5.4
⫺0.2
⫺44.5
⫺75.3
6.2
92.8
43.6
22.4
15.3
53.1
13.8
97.0
28.3
17.6
CYP2D6
Dextromethorphan
CYP3A4
Testosterone
MLXa
5.3
15.0
33.3
23.2
89.9
8.54
3.57
23.4
30.4
77.6
average % inhibitionb
CYP1A2
CYP2C9
CYP2C19
CYP2D6
CYP3A4
a
7.5
19.5
10.5
66.6
7.5
The relative contribution of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 to the metabolism of MLX was calculated by the following equations:
8.54
3.57
23.4
30.4
77.6
⫽
⫽
⫽
⫽
⫽
0.855xA ⫹ 0.062xB ⫹ 0.531xC ⫹ 0.075xD ⫹ 0.053xE
0.054xA ⫹ 0.928xB ⫹ 0.138xC ⫹ 0.195xD ⫹ 0.15xE
0xA ⫹ 0.436xB ⫹ 0.970xC ⫹ 0.105xD ⫹ 0.333xE
0xA ⫹ 0.224xB ⫹ 0.283xC ⫹ 0.666xD ⫹ 0.232xE
0xA ⫹ 0.153xB ⫹ 0.176xC ⫹ 0.075xD ⫹ 0.899xE
where A, B, C, D, and E are the percentage contributions of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively, to the metabolism of MLX.
b
A negative number may suggest a stimulation of the metabolism. Negative values of quinidine and azamulin inhibition on CYP1A2 were also used in the calculation, and the results were similar
to those in Table 3.
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Relative contributions of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4
to the metabolism of MLX in human liver microsomes
conazole on CYP2D6 was not observed in the presence of plasma.
As expected, ketoconazole was found to be stable in the hepatocyte
incubations in KHB or plasma during the 30-min period of equilibration (data not shown); therefore, the lower extracellular concentrations of ketoconazole concentrations were caused by binding
instead of metabolism. Table 3 shows the reactive phenotyping
results for MLX using chemical inhibitors, suggesting MLX is
primarily metabolized by CYP3A4, followed by CYP2D6 and to a
lesser extent by CYP1A2. These results were similar to those
generated using mAbs, except the contribution of CYP2D6 in the
chemical inhibitor assay was much higher. In our experience, using
quinidine and dextromethorphan tended to overestimate CYP2D6
contribution compared with that using the relative activity factor or
the mAb method (Uttamsingh et al., 2005). A hypothesis that may
help explain this observation is that quinidine at low concentrations is known to stimulate CYP3A4 metabolism, but at high
concentrations it appears to show inhibition (Ludwig et al., 1999;
Ngui et al., 2000). This stimulation by quinidine could reduce the
estimates for net CYP3A4 inhibition, thereby leading to underestimation of the cross-reactivity of quinidine on CYP3A4, which in
turn would lead to an overestimation of CYP2D6 contribution.
Table 4 presents the cross-reactivity of the P450 inhibitors used in
this study and a method of correcting for this cross-reactivity for
obtaining the reactive phenotyping results of MLX. For example,
the observed inhibition of MLX metabolism by furafylline (Table
4, the rightmost column) is attributed to the sum of partial inhibition of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 by
furafylline, measured using P450 probe substrates. An equation
can then be derived for each inhibitor. By solving the five equations simultaneously, the contribution of the five P450s (the five
variables) to the metabolism of MLX can be solved, and the results
are shown in Table 3. Table 5 lists DDI predictions for the
investigational compound (MLX) and a set of marketed compounds. Also included is a comparison between the values predicted from this study and the values observed in clinical trials. A
correlation coefficient (r2) of 0.992 (Fig. 2) was derived from the
data in Table 5. A slope close to unity (0.980) suggested a good
prediction of clinical DDIs from the in vitro data.
83
PREDICTION OF CLINICAL DRUG-DRUG INTERACTIONS
TABLE 5
Marketed compounds: ketoconazole DDI prediction using the hepatocytes in plasma model
-Fold of AUC Change
Compound
Reference
Predicted
MLX
Theophylline
Desipramine
Midazolam
Tolbutamide
Omeprazole
Loratadine
Cyclosporine
Alprazolam
a
8.33
1.13–1.16
1.00
16.7
1.43
1.74–2.74
2.48
3.45
2.75–4.91
Observed
frenalb
ⴱ
1.11
1.02
17.0
1.77
1.36–2.05
3.47
4.39
3.98
0.18
0.70
⬍0.01
0.001
⬍0.01
Negligible
⬍0.01
0.20
Unpublished
Obach 2005c,d,e
Obach 2005c,d,e
Obach 2005;d Galetin 2006e
Obach 2005;d Soars 2003e
U of Washington DDI database;d
U of Washington DDI database;d
U of Washington DDI database;d
U of Washington DDI database;d
Soars 2003e
Galetin 2006e,f
Galetin 2006e,f
Galetin 2006e,f
Predictions are based on our model assuming 200 mg b.i.d. doses of ketoconazole have plasma Cmax of 10 ␮M and linear pK.
Information on renal clearance was from Hardman et al. (2001), except that of desipramine was from Physicians Desk Reference (2001).
Assume substrates are selective (theophylline, desipramine for CYP1A2 and CYP2D6, respectively).
d
References for observed AUC changes.
e
References of fm.
f
The ketoconazole inhibition to the metabolism routes other than CYP3A4 was not considered because the information was not available.
* Within 10% of the predicted value.
a
b
c
for hepatocytes to maintain their metabolic function. Several researchers (Shibata et al., 2002; Bachmann et al., 2003) have tried
to add human serum into hepatocyte incubations to better mimic
the in vivo clearance by correcting the effect of protein binding on
drug metabolism. There are concerns that protein binding complicates the determination of enzyme kinetic parameters in hepatocyte
incubations (Tucker et al., 2001; Bjornsson et al., 2003), apart from
introducing additional challenges for bioanalysis, because of lower
turnover rate resulting from the lower free concentration of a
substrate. With the improvements in the sensitivity of mass spectrometers in recent years, the bioanalytical sensitivity no longer
appears to be a challenge. In this study, the P450 inhibition in
hepatocytes was measured over a wide range of concentrations of
ketoconazole to cover all the possible in vivo plasma Cmax values.
Besides carrying out the incubation in KHB, 100% of human
plasma was used with hepatocytes to closely mimic the in vivo
conditions. Cryopreserved hepatocytes are known to preserve the
P450 activities, as well as most of the phase II enzymes (Li et al.,
1999; Madan et al., 1999). Uptake transporters, such as organic
anion transporting polypeptide and sodium taurocholate cotransporting polypeptide, are also preserved in the cryopreserved human
hepatocytes (Shitara et al., 2003), but some efflux transporters,
such as Pgp, multidrug resistance-associated protein 2, and breast
CL ⫽ fm共 fm1A2CL ⫹ fm2C9CL ⫹ fm2C19CL ⫹ fm2D6CL ⫹ fm3A4CL. . .兲
⫹ fotherCL
(1)
where fm is the fraction of clearance by metabolism, which often
attributes to gut and liver metabolism (fm ⫽ fm,gut⫻ fm,hep), and
fother is the fraction of clearance by other routes such as renal or
biliary excretion (fm ⫹ fother ⫽ 1). The fm1A2CL is the fraction of
metabolic clearance by CYP1A2, and so on. The “. . . ” in eq. 1
refers to the enzymes other than the major five P450s. The fm could
be simplified as a fraction of hepatic metabolism (fm,hep) if the
substrate is administered i.v. or metabolism by intestinal or other
organs is insignificant compared with that of the liver. Similar to
what was proposed by Ito et al. (2005), in the presence of an
inhibitor, eq. 1 can be rewritten as:
CL1 ⫽ fm,hep
冢
冣
fm3A4CL
fm2C9CL
fotherCL
⫹
⫹... ⫹
I
I
I
1⫹
1⫹
1⫹
Ki3A4
Ki2C9
Ki,other
(2)
The DDI potential is usually expressed as the ratio of AUCI (AUC
with the inhibitor) over the AUC of control (without inhibitor), which
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 14, 2017
FIG. 2. Correlation of the observed clinical and the predicted DDI (-fold of AUC
change) using eq. 4. The data are from Table 5. Average values of theophylline,
omeprazole, and alprazolam were used in the plot instead of a range.
cancer resistance protein, are internalized in hepatocyte suspension
(Liu et al., 1999; Xia et al., 2005a). Nevertheless, ketoconazole
also inhibits these efflux transporters (Salphati and Benet 1998;
Achira et al., 1999; Xia et al., 2005b); thus, they are rendered not
functional in both in vitro and in vivo situations. Therefore, it is
reasonable to assume that when the ketoconazole concentration in
the extracellular plasma in the in vitro model is comparable with
the plasma concentration in vivo, the concentration of the inhibitor
at the enzyme site in the in vitro model is also comparable with that
of the enzyme site in vivo. In this way, estimating the actual
enzyme site inhibitor concentration becomes unnecessary. This
approach should apply to most reversible P450 inhibitors. The
effect of inhibitors and test compounds as substrates and inhibitors
of transporters is yet to be fully evaluated. The current approach
works with the CYP3A inhibitor ketoconazole, which is not a
substrate but an inhibitor of Pgp. Also, if a test compound is
primarily metabolized by phase II enzymes, the salicylamide
should not be used to block phase II conjugations.
In general, a simple model for the prediction of DDIs could be
illustrated as the total clearance equals the sum of clearances by
different routes:
84
LU ET AL.
has an inverse relationship with the clearance ratio in the absence or
presence of the inhibitor.
AUC1 CL
⫽
AUC CL1
1
⫽
fm,hep
冤冢
冣冥 冢
fm3A4
fm2C9
⫹
⫹...
I
I
1⫹
1⫹
Ki3A4
Ki2C9
⫹
fother
I
1⫹
Ki,other
冣
(3)
Thus, the AUC ratio for a compound primarily cleared by metabolism
can be expressed as a function of the remaining activity of each
enzyme toward the metabolism of this compound, or the remaining
clearance routes.
AUC1 CL
1
⫽
⫽
AUC CL1 fm,hep共 fm3A4 fA3A4 ⫹ fm2C9 fA2C9 ⫹ . . .兲 ⫹ fother fA,other
(4)
f A ⫽ Minimum activity ⫹
共Maximum activity ⫺ Minimum activity兲
1 ⫹ 10共 x⫺log EC50兲*Hill slope
(5)
Practically, to determine the percentage of enzyme activity remaining
fA at an inhibitor concentration x requires a well fitted sigmoidal
curve. From our data, within the physiological concentration range,
ketoconazole only partially inhibits common P450s in human hepatocytes in the presence of human plasma. Thus, a linear model is more
practical for estimating the percentage remaining of enzyme activity
fA at an inhibitor concentration x. This linear model assumes that
within a narrow range (one concentration above and one concentration
below the targeted x, Cmax), the inhibition response is linear. Therefore, the P450 activity remaining can be interpreted between these
points:
f A ⫽ AL ⫺ 共x ⫺ CL兲
共 AL ⫺ AH兲
共CH ⫺ CL兲
(6)
where AH, AL and CH, CL are the activities and concentrations above
and below the target concentration x, respectively.
MLX is an investigational compound. Through qualitative enzyme
mapping and metabolite profiling studies, MLX was found to be
metabolized mainly by P450 isozymes. Furthermore, quantitative
Downloaded from dmd.aspetjournals.org at ASPET Journals on June 14, 2017
where fm,P450 is again the relative contribution of an enzyme to the
total metabolism of a compound (without inhibitor), and fA is the
fraction of enzyme activity remaining in the presence of an inhibitor
[i.e., fA3A4 ⫽ 1/(1 ⫹ I / Ki3A4)]. Whereas fm,P450 could be determined
in several ways (Uttamsingh et al., 2005), including using the selective chemical inhibitors (Table 3), the fA in vivo can be estimated from
the fA obtained from the in vitro data such as those in Table 2. Thus,
DDIs can be predicted by combining the information from the reactive
phenotyping study and the inhibitor’s cross-reactivity study (eq. 4 if a
compound is primarily cleared by metabolism or the other clearance
route is known).
The Cmax of ketoconazole at the steady state can be used to assess
the worst-case inhibitory effect, although the Cmax may vary from
person to person because of interindividual variability in pK or from
study to study because of different dosing regimens. For an individual
Cmax or a comparable in vitro concentration x in our model, the
fraction of enzyme activity remaining (fA) can be calculated from the
titration curves (Fig. 1 or Table 2). Theoretically, the fA and x follow
a sigmoidal relationship:
P450 phenotyping also found that this compound was mainly metabolized by CYP3A4 with some contributions from CYP2D6 and
CYP1A2. Considering that CYP2D6 contribution may be overestimated, ketoconazole at a concentration of 10 to 20 ␮M inhibits 87%
of the CYP3A4 contribution (⬃100% inhibition of the 87% CYP3A4
contribution) (Tables 2 and 3) and 1% of the CYP1A2 contribution
(⬃20% cross-reactivity of the 4.8% CYP1A2 contribution) (Tables 2
and 3). Thus, using the approach of DDI prediction illustrated in eq.
4, an 8.33-fold increase in AUC was predicted for MLX coadministration with ketoconazole. A clinical DDI study using 200 mg b.i.d. of
ketoconazole showed an increase of mean AUC of MLX within 10%
of the predicted 8.33-fold (unpublished data).
There is limited literature available on compounds with information
on both clinical DDIs with ketoconazole and quantitative reactive
phenotyping. Table 5 lists several compounds found in recent literature (University of Washington DDI database handout, 2004; Galetin
et al., 2006; Obach et al., 2006) with the observed clinical DDI.
Although there is no reactive phenotyping information available for
theophylline and desipramine, these two compounds have been treated
as prototypical substrates for CYP1A2 and CYP2D6 for retroactive
prediction based on our method. Midazolam is widely considered as
an exclusive CYP3A4 substrate. Omeprazole was found to be metabolized by CYP2C19 (60%) and CYP3A4 (40%), whereas tolbutamide
was found to be metabolized by CYP2C9 (72%) and CYP2C19 (28%)
(Soars et al., 2003). CYP3A4 was reported to contribute 71, 80, and
60% to the metabolism of cyclosporine, alprazolam, and loratadine,
respectively (Galetin et al., 2006), but the rest of the clearance routes
have not been documented. It is also known that cyclosporine is
partially cleared renally and that CYP2D6 contributes toward the
metabolism of loratadine (Yumibe et al., 1996). In this study, the
prediction of these three compounds was based on the assumption that
ketoconazole does not affect the non-CYP3A4 metabolic clearance
routes. In vivo human plasma ketoconazole concentration values vary
in the literature, most being around 10 ␮M with 20 ␮M being the
highest at 200 mg b.i.d.; in practice, it is assumed to be 10 ␮M. The
observed clinical values were adapted from Obach et al. (2006) and
the DDI database (handout) from the University of Washington
(2004). Using this information and the ketoconazole cross-reactivity
data in Table 2, the prediction of AUC changes is presented in Table
5. It is noteworthy that these predictions compare very closely with
the observed clinical values (r2 ⫽ 0.992 and slope of 0.980) (Fig. 2).
For most of the compounds, renal clearance plays a minor role (1% or
less) (Table 5); thus, renal clearance contribution was not included in
the calculation. For theophylline and alprazolam, predictions were
made with and without considering the renal clearance contribution.
Because the effect of ketoconazole on the renal clearance of these two
compounds is unknown, a range of predictions was reported such that
the low value shows ketoconazole has no effect on renal clearance,
whereas the high value shows that ketoconazole totally blocked the
renal clearance. For Fig. 2, average values were used for data presented as ranges in Table 5. Desipramine was treated as a selective
CYP2D6 substrate. Ketoconazole was predicted to have no effect on
its metabolic clearance based on our hepatocyte cross-reactivity study.
Prediction of ketoconazole effect on its renal clearance was not
explored. For loratadine, cyclosporine, and alprazolam, only fm3A4
information was available (Galetin et al., 2006); hence, the interaction
could be underestimated.
In summary, the DDI prediction approach presented in this study
involves using two in vitro determinations: the reactive phenotyping
(the relative contributions of enzymes to the overall metabolism of a
drug, an intrinsic property of the drug) and the P450 cross-reactivity
of an inhibitor at close to the physiological condition (hepatocyte
PREDICTION OF CLINICAL DRUG-DRUG INTERACTIONS
incubation in human plasma). Ketoconazole is used in this study as an
example. However, most inhibitors of interest, except those that are
efflux pump substrates that can be pumped out of hepatocytes leading
to reduced hepatocyte concentration and hence reduced potency, can
be applied. In most cases, the pharmaceutical industry likes to show
the worst-case scenario for DDI potential for drugs that are mainly
metabolized by a few isozymes; therefore, a handful of known inhibitors, such as ketoconazole for CYP3A4 and quinidine for CYP2D6,
are often used. Thus, this approach provides a simple way of predicting the DDI early in the preclinical development stage, with the main
advantage being not having to estimate the actual inhibitor concentration at the enzyme site.
Acknowledgments. We thank Dr. Frank W. Lee for valuable
discussion, Judith Ianelli for proofreading of the manuscript, and
Richard Gallegos for technical support.
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