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DRUG METABOLISM AND DISPOSITION
Copyright ª 2014 by The American Society for Pharmacology and Experimental Therapeutics
http://dx.doi.org/10.1124/dmd.113.053793
Drug Metab Dispos 42:782–795, April 2014
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation by Kinase
Inhibitors and Assessment of Drug–Drug Interaction Potentials s
Yedong Wang, Meiyu Wang, Huixin Qi, Peichen Pan, Tingjun Hou, Jiajun Li, Guangzhao He,
and Hongjian Zhang
College of Pharmaceutical Sciences (Y.W., M.W., H.Q., T.H., J.L., G.H., H.Z.), Institute of Functional Nano and Soft Materials, and
Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices (P.P., T.H.), Soochow University, Suzhou, People’s
Republic of China
Received July 19, 2013; accepted January 29, 2014
ABSTRACT
was preferentially inhibited by others (such as bosutinib). Pathwaydependent inactivation (time-dependent inhibition) was also
observed for a number of kinase inhibitors against CYP3A4 but
not CYP2C8. Further studies showed that axitinib had a KI of
0.93 mM and kinact of 0.0137 min21, and the observed inactivation
toward CYP3A4 was probably due to the formation of reactive
intermediate(s). Using a static model, a reasonably accurate
prediction of drug–drug interactions was achieved by incorporating parallel pathways and hepatic extraction ratio. The
present results suggest that potent and pathway-dependent
inhibition of CYP2C8 and/or CYP3A4 pathways by kinase
inhibitors may alter the ratio of paclitaxel metabolites in vivo,
and that such changes can be clinically relevant as differential
metabolism has been linked to paclitaxel-induced neurotoxicity
in cancer patients.
Introduction
the risk of drug–drug interactions and optimize the safety and effectiveness of combination therapies.
Paclitaxel (taxol) is a cytotoxic agent that is widely used for treating
various tumor types, such as in breast, lung, and ovarian cancer. The
drug is administered via constant-rate intravenous infusion in either
a once every 3- or 1-week regimen. Paclitaxel treatment is often
associated with toxicities such as myelosuppression and neurotoxicity
that exhibit marked intersubject variability (Marupudi et al., 2007).
Research has shown that the severity of paclitaxel-induced neurotoxicity is dose-dependent and may be related to its disposition
mechanisms (Bergmann et al., 2011; Leskelä et al., 2011). Paclitaxel
is metabolized by CYP2C8 to form 6a-hydroxypaclitaxel at the C6
position of the taxane ring and by CYP3A4 to form 39-hydroxypaclitaxel at the phenyl C39-position on the C13 side chain (Monsarrat
et al., 1990; Walle et al., 1993). In human bile samples, 6ahydroxypaclitaxel (;13% of the dose) is about 4-fold higher than
p-39-hydroxypaclitaxel (;3% of the dose), suggesting that CYP2C8
plays a major role in the metabolic clearance of paclitaxel in vivo
(Monsarrat et al., 1993, 1998). In human liver microsomes (HLMs), it
has been reported that levels of 6a-hydroxypaclitaxel are consistently
higher (;2.3-fold) than those of p-39-hydroxypaclitaxel (Taniguchi
Metabolism is an important mechanism that the body employs to
eliminate xenobiotics, such as drugs. In the drug discovery and
development arena, it is important to identify metabolic pathways and
key enzymes involved in drug clearance, as metabolites produced by
different enzymatic pathways may possess different activity or toxicity
profiles. With the increasing use of combination therapies, particularly
among anticancer agents, such as paclitaxel-based combinations, the
potential for drug–drug interaction needs to be carefully examined to
achieve optimal clinical results. For drugs with multiple metabolic
pathways, it is beneficial to understand the sensitivity of each pathway
to inhibition or induction because the selectivity toward a particular
pathway may alter metabolite profiles, hence affecting efficacy and
safety outcomes. Consequently, such information would help reduce
This work was supported by a grant from Soochow University, Suzhou,
People’s Republic of China [Grant Q413200711].
dx.doi.org/10.1124/dmd.113.053793.
s This article has supplemental material available at dmd.aspetjournals.org.
ABBREVIATIONS: AUC, area under the curve; CE, collision energy; CLH, hepatic clearance; EH, hepatic extraction ratio; fhep, fraction of clearance
subject to hepatic blood flow limitation; fm, fraction metabolized; HLM, human liver microsomes; kdeg, enzyme degradation rate constant; Ki,
reversible inhibition constant; KI, inhibitor concentration that results in half-maximal enzyme inactivation; kinact, inactivation rate constant; Ks,
spectral dissociation constant; LC-MS/MS, liquid chromatography–tandem mass spectrometry; MTK, montelukast; P450, cytochrome P450; P-gp,
P-glycoprotein; 08Y, bromergocryptine.
782
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Paclitaxel is often used in combination with small molecule
kinase inhibitors to enhance antitumor efficacy against various
malignancies. Because paclitaxel is metabolized by CYP2C8 and
CYP3A4, the possibility of drug–drug interactions mediated by
enzyme inhibition may exist between the combining agents. In
the present study, a total of 12 kinase inhibitors were evaluated
for inhibitory potency in human liver microsomes by monitoring
the formation of CYP2C8 and CYP3A4 metabolites simultaneously. For reversible inhibition, nilotinib was found to be the
most potent inhibitor against both CYP2C8 and CYP3A4, and the
inhibition potency could be explained by strong hydrogen binding
based on molecular docking simulations and type II binding
based on spectral analysis. Comparison of Ki values revealed
that the CYP2C8 pathway was more sensitive toward some
kinase inhibitors (such as axitinib), while the CYP3A4 pathway
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
Materials and Methods
Paclitaxel and docetaxel were purchased from Sigma-Aldrich (St. Louis,
MO). NADPH was purchased from Roche (Basel, Switzerland). HLM (cat. no.
452161, pooled from 20 different organ donors), 6a-hydroxypaclitaxel, and
p-39-hydroxypaclitaxel were purchased from BD Gentest (Woburn, MA).
Bactosomes containing human CYP3A4 and CYP2C8 were purchased from
Cypex (Dundee, UK). Small molecule kinase inhibitors (.99% purity by highperformance liquid chromatography) were obtained from ChemBest Bioscience, Inc. (Shanghai, People’s Republic of China) and StruChem Co. Ltd.
(Suzhou, People’s Republic of China). Disodium hydrogen phosphate and
sodium dihydrogen phosphate were obtained from SinoPharm Chemical
Reagent Co. (Shanghai, People’s Republic of China). All other reagents and
chemicals were of analytical grade and of the highest quality available
commercially.
Incubations with HLMs. CYP2C8- and CYP3A-mediated paclitaxel hydroxylation in HLM were evaluated based on the formation of 6a-hydroxypaclitaxel
and p-39-hydroxypaclitaxel simultaneously. The linearity of metabolite
formation as a function of time and protein concentration was first examined.
The kinetic parameters were also obtained for the two hydroxylation pathways.
The final experimental procedures were set as follows: the incubation mixture
(200 ml) contained phosphate buffer (100 mM at pH 7.4), 1 mM NADPH, 0.2
mg/ml HLM, and the substrate paclitaxel (5 mM, ;Km), and the reaction was
initiated with the addition of NADPH. After 10 minutes of incubation, a 200-ml
aliquot of cold acetonitrile containing the internal standard (docetaxel) was
added to the reaction mixtures to quench the reaction. The samples were then
centrifuged, and aliquots of 10 ml of the supernatants were subjected to liquid
chromatography–tandem mass spectrometry (LC-MS/MS) analysis. Levels of
6a-hydroxypaclitaxel and p-39-hydroxypaclitaxel were quantified as described
herein.
Inhibition of Paclitaxel Hydroxylation by Kinase Inhibitors in HLMs.
Evaluation of the sensitivity of CYP2C8- and CYP3A-mediated paclitaxel
hydroxylation toward a given inhibitor was performed by monitoring the
formation of 6a-hydroxypaclitaxel and p-39-hydroxypaclitaxel simultaneously
in the same incubation. For reversible inhibition, Ki values were determined
using the substrate paclitaxel at concentrations of 2.5, 5, and 10 mM, with
inhibitor concentrations ranging from 0 to 100 mM. At the end of incubations,
samples were prepared as described previously, and the levels of 6a-hydroxypaclitaxel
and p-39-hydroxypaclitaxel were quantified.
For the time-dependent inhibition component of this assay, kinase inhibitors
at a concentration of 10 mM were first incubated with HLMs in the presence of
NADPH. After a 30-minute preincubation, enzyme activities were determined
with the addition of paclitaxel for CYP2C8 and CYP3A4 simultaneously. For
compounds that demonstrated a strong reversible inhibition, a 20-fold dilution
was performed after the 30-minute preincubation. After another 10 minutes of
the incubation, the reaction mixtures were processed, and the samples were
subjected to LC-MS/MS analysis for metabolite formation. Separately, enzyme
activities without preincubation were determined with coincubation of
paclitaxel and a given kinase inhibitor for 10 minutes in the presence of
NADPH. By comparing the percentage of enzyme activity remaining between
the above incubation settings, the time-dependent inhibition could be identified.
Because the time-dependent inhibition of CYP3A4 using midazolam as a probe
substrate has been reported for several kinase inhibitors and kinetic parameters
are readily available (Kenny et al., 2012), the inactivation parameters of
CYP3A4 using paclitaxel p-39-hydroxylation were determined for axitinib
only.
In brief, axitinib at concentrations of 0.3125, 0.625, 1.25, 2.5 and 5 mM was
incubated with HLMs (4 mg/ml). At specified time points, the aliquots of the
reaction mixture were transferred into a different set of tubes containing
paclitaxel (5 mM) and the rest of the components including NADPH as
described previously. This step resulted in a 20-fold dilution for the initial
reaction mixture. After a further 10 minutes of incubation, the reaction mixtures
were processed, and the samples were analyzed for the levels of p-39hydroxypaclitaxel.
Identification of Axitinib-Glutathione Adducts in HLMs. Axitinib
(10 mM) was incubated with HLM (1 mg/ml), glutathione (5 mM), and
phosphate buffer (100 mM, pH 7.4) in a final reaction volume of 200 ml. The
mixture was warmed at 37°C for 5 minutes, and the reaction was initiated with
the addition of NADPH (1 mM). Control samples without NADPH were also
prepared. The reaction was terminated at specified time points by adding 200 ml
of ice-cold acetonitrile. The resulting mixture was centrifuged at 13,000g (4°C)
to precipitate the proteins, and a 300-ml aliquot of supernatant was collected
and evaporated to dryness under a stream of nitrogen. The pellet was redissolved in 100 ml of the mobile phase, and a 10-ml aliquot of the reconstituted
solution was subjected to mass spectrometry analysis.
LC-MS/MS Analysis. Quantitation of CYP2C8 and CYP3A4 metabolites
was achieved by a LC-MS/MS system consisting of an API4000 Qtrap mass
spectrometer equipped with a turbo-V ionization source (Applied Biosystems,
Concord, ON, Canada), two LC-20AD pumps with a CBM-20A controller,
DGU-20A solvent degasser, and a SIL-20A autosampler (Shimadzu Scientific
Instruments, Columbia, MD). An Agela Venusil XBP C18 column (50 2.1 mm;
5 mm particle size) was used to achieve high-performance liquid chromatography separation (Bonna-Agela Technologies, Tianjin, People’s Republic of
China). The column temperature was held at 40°C. Gradient elution at a flow
rate of 0.3 ml/min was performed using the following mobile phase: A,
acetonitrile:water (5:95, v:v) and B, acetonitrile:water (95:5, v:v).
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et al., 2005), while a more extensive study using 47 individual HLMs
showed that CYP3A4-mediated paclitaxel p-39-hydroxylation could
be more predominant in subjects receiving phenobarbital treatment
(Sonnichsen et al., 1995). In addition to cytochrome P450 (P450)–
mediated metabolic clearance, efflux transport mediated by P-glycoprotein
(P-gp) may play a role in paclitaxel disposition (Sparreboom et al.,
1997).
Pharmacokinetic variability of paclitaxel in patients has been
thought to be associated with different expressions and activities of
CYP2C8, CYP3A4, and P-gp, and numerous studies have been
conducted to examine the role of polymorphisms in CYP enzymes and
their relationship with intersubject variability in efficacy and
neurotoxicity. Recent reports suggest that while CYP2C8*3 is
associated with a higher rate of clinical responses, it is also correlated
with an increased risk for neurotoxicity (Leskelä et al., 2011; Hertz
et al., 2012, 2013). In addition, the CYP3A4*22 genotype has been
shown to affect paclitaxel-induced neurotoxicity (de Graan et al.,
2013). Furthermore, changes in the clearance of the CYP2C8
metabolite, 6a-hydroxypaclitaxel, is correlated to ABCB1 polymorphism and P-gp activity (Fransson et al., 2011). These findings
indicate that P450-mediated paclitaxel metabolism and possibly
specific metabolites are important factors affecting its clinical efficacy
and toxicity and that drug–drug interactions caused by P450 inhibition
may have important clinical implications for combination therapies.
Paclitaxel-based combination therapies are often used to evaluate
the newer generation of agents such as kinase inhibitors to maximize
antitumor efficacy (see Table 1 for more information). Although
numerous studies have been conducted for such combination
therapies, drug–drug interactions between paclitaxel and kinase
inhibitors have not been fully examined in detail. Therefore, it is of
clinical relevance to investigate the inhibition of P450-mediated
paclitaxel metabolism by kinase inhibitors and evaluate druginteraction potentials between the combining agents. In the present
report, a total of 12 kinase inhibitors representing different structures
(Supplemental Table 1) were obtained commercially, and their
inhibitory potency against CYP2C8- and CYP3A4-mediated paclitaxel hydroxylation was examined in HLM. Data indicate that the
inhibition of paclitaxel metabolism is pathway dependent and that
different metabolic pathways exhibit certain sensitivities toward those
kinase inhibitors, suggesting that the formation and ratio of paclitaxel
metabolites can be altered in the presence of kinase inhibitors. Such
findings would provide valuable information for the understanding of
metabolic interactions between the combining agents, thus leading to
further optimization of paclitaxel-based combination therapies.
783
784
Wang et al.
TABLE 1
Summary of key information for selected kinase inhibitors
Inhibitor
Structure
Cmaxa
(ng/ml)
Combination
with Paclitaxel
DDI
(Yes/No)
Referenceb
26.9
Yes
No
Murakami et al.
(2012)
Vermorken et al.
(2013)
Axitinib
35.4
Yes
No
Martin et al.
(2012)
Bosutinib
215.0a
NA
NA
Hsyu et al.
(2013)
Dasatinib
78.0
Yes
No
Secord et al.
(2012)
Erlotinib
1703.6
Yes
No
Tran et al.
(2011)
Gefitinib
400.0
Yes
No
Miller et al.
(2003)
(continued )
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Afatinib
785
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
TABLE 1—Continued
Cmaxa
(ng/ml)
Combination
with Paclitaxel
DDI
(Yes/No)
Imatinib
6650.0
Yes
No
Pishvaian et al.
(2012)
Lapatinib
4893.0
Yes
NA
Esteva et al.
(2013)
Nilotinib
1595.0
NA
NA
Larson et al.
(2012)
Pazopanib
16,000.0
Yes
Yes
Burris et al.
(2012)
Sorafenib
5900.0
Yes
No
Okamoto et al.
(2010)
Inhibitor
Structure
Referenceb
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(continued )
786
Wang et al.
TABLE 1—Continued
Inhibitor
Structure
Sunitinib
Cmaxa
(ng/ml)
58.1
Combination
with Paclitaxel
DDI
(Yes/No)
Yes
No
Referenceb
Kozloff et al.
(2010)
DDI, drug–drug interaction; NA, not available.
a
Plasma concentrations (Cmax wherever possible) were from clinical studies described by respective reports. These values were used for the prediction of drug interactions with paclitaxel without
any modifications.
b
For some kinase inhibitors such as gefitinib, lapatinib, and sorafenib, there are numerous reports on combinational use with paclitaxel. Only those with pharmacokinetic drug-interaction data were
selected as references in the table.
Macromodel in Schrödinger. After that, the inhibitors were prepared with the
Ligprep module, and the protonated states were generated at pH = 7.0 6 2.0.
Molecular docking simulations were performed with the Glide module in
Schrödinger. Before docking, the binding grid box of size 10 Å 10 Å 10 Å
was generated and centered on the ligand in the active pocket. In ligand
docking, the extra-precision docking mode was adopted to generate the
minimized pose with Glide program.
Spectral Analysis of the Binding of Kinase Inhibitors to CYP3A4 and
CYP2C8. To further investigate the binding type of kinase inhibitors with P450
enzymes, UV-visible absorbance of human CYP3A4 or CYP2C8 was recorded
with a Thermo Scientific Multiskan Go spectrophotometer (Waltham, MA) in
the split-beam mode. The experiments were performed by 1-nm step-scanning
with a wavelength range encompassing 350 to 500 nm. Spectral analysis in the
presence or absence of inhibitors was performed in matched microquartz
cuvettes (2 mm 10 mm, internal dimension) containing 0.2 mM CYP3A4 or
CYP2C8 and 20% glycerol (v/v) in 50 mM tris-acetate buffer (pH 7.4) (Dahal
et al., 2012). Stock solutions of kinase inhibitors were diluted to obtain a final
concentration of 20 mM in the sample cuvettes, with the same amount of
solvents added to the reference cuvette containing the same amount of enzymes
(total volume of solvent added was ,1%, v/v). Preliminary experiments using
ketoconazole (a typical type II inhibitor) and nilotinib indicated that the mixture
containing 0.2 mM P450 enzyme and 20 mM inhibitor yielded optimal spectral
results. When applicable, values of spectral dissociation constant (Ks) were
determined for kinase inhibitors with strong interactions (Isin and Guengerich,
2006).
Kinetic Data Analysis. Inhibition potency (Ki) for each metabolic pathway
by selected kinase inhibitors in the reversible inhibition setting was calculated using Enzyme Kinetics Modules of Sigma Plot 12.3 (Systat Software,
San Jose, CA) based on the Dixon equations (Dixon, 1972). For the timedependent inhibition of CYP3A4 by axitinib, kinetic parameters such as
apparent KI and kinact were obtained using nonlinear regression (Prism 5.0;
GraphPad Software, San Diego, CA). The average values of three separate incubations are presented.
Calculation of AUC Changes Based on In Vitro Inhibition Data.
Prediction of drug–drug interactions using in vitro inhibition data has been well
described in the literature (Ito et al., 2005; Obach et al., 2006; Fahmi et al.,
2008; Lu et al., 2008). The area under the curve (AUC) ratio (AUC9/AUC) of
a victim drug is inversely related to the hepatic clearance ratio (CLH/CLH9) in
the presence and absence of an inhibitor, assuming little or no renal clearance.
Because parallel pathways (CYP2C8 and CYP3A4) are involved in paclitaxel
metabolism, AUC changes can be simply described using equations as follows
(Ito et al., 2005; Lu et al., 2008):
Equation 1 for reversible inhibition of both CYP2C8 and CYP3A4:
AUCratio ¼
1
fm;2C8
1þI=Ki;2C8
f
m;3A4
þ 1þI=K
i;3A4
ð1Þ
Equation 2 for reversible (CYP2C8) and time-dependent inhibition
(CYP3A4):
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For MS/MS quantitation, the API4000 Qtrap mass spectrometer was
operated in the electrospray ionization positive mode with multiple reactionmonitoring to detect paclitaxel metabolites and the internal standard with
a dwell time set to 100 milliseconds. The ion transitions monitored were 6ahydroxypaclitaxel, 892.3 (M + Na) → 308.1; p-39-hydroxypaclitaxel, 892.3 (M +
Na) → 324.1; and docetaxel (IS), 830.2 (M + Na) → 549.4. After setting the
mass transition and collision energy for each metabolite and the internal
standard, all other parameters were optimized for the best sensitivity. Data were
collected and processed using the Analyst 1.5.2 data collection and integration
software (AB Sciex, Framingham, MA).
The identification of the axitinib-glutathione adduct was achieved using an
ACQUITY ultra-performance liquid chromatography system coupled with
a quadruple time-of-flight mass spectrometer (Waters Corp., Milford, MA) with
an electrospray ionization source. The chromatographic separation was done on
an ACQUITY BEH C18 column (2.1 mm 100 mm ID., 1.8 mm). The mobile
phase was a mixture of 5 mM ammonium acetate aqueous solution containing
0.05% formic acid (A) and acetonitrile (B). At a flow rate of 0.4 ml/min, the
mobile phase was held at 5% B for 2 minutes, and then the gradient started
from 5% to 35% B in 8 minutes and increased linearly to 99% B. After
maintaining for 1 minute at 99% B, mobile phase B was returned back to 5%
for column equilibration. MS detection was conducted in the negative ionization mode.
The major operating parameters for the quadruple time-of-flight mass
spectrometer were set as follows: capillary voltage, 4 kV; source temperature,
100°C; desolvation temperature, 350°C; collision gas, argon; desolvation gas
(nitrogen) flow rate, 800 l/h; data acquisition range, m/z 80–1000 Da; and data
format, centroid. Data were acquired under the MSE mode, in which two
separate scan functions were programmed independently with low and high
collision energies (CEs). The mass spectrometer switched rapidly between
these two functions during data acquisition. As a result, information on intact
precursor ions as well as fragment ions was obtained from one liquid
chromatography run. In this study, one scan function used a low CE setting
(5 eV of trap CE and 3 eV of transfer CE), and the other scan function used a
high CE setting (ramped trap CE from 10 to 20 eV and 18 eV of transfer CE).
Data analysis and instrument control were performed using the MassLynx
4.1 software (Waters Corp.). MetaboLynx, a subroutine of the MassLynx
software, was used to identify metabolite ions.
Molecular Docking Simulations of Kinase Inhibitors with CYP3A4 and
CYP2C8. To examine structural features for inhibitor-enzyme binding,
molecular docking simulations were performed. The crystal structures of
CYP3A4 (PDB ID 3UA1) (Sevrioukova and Poulos 2012) and CYP2C8 (PDB
ID 2NNI) (Schoch et al., 2008) were obtained from the RCSB Brookhaven
Protein Data Bank (Berman et al., 2000). The Protein Preparation module in
Schrödinger 9.0 (Schrödinger, Portland, OR) was then used to remove the
crystallographic water molecules, add hydrogen atoms, assign partial charges
using the OPLS-2005 force field, assign protonation states, and minimize the
structures (Kaminski et al., 2001). The minimization was terminated when the
root-mean-square deviation reached a maximum value of 0.3 Å. The structures
of the inhibitors were all sketched in Maestro and were minimized with
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
AUCratio ¼
1
fm;2C8
1þI=Ki;2C8
þ
ð2Þ
fm;3A4
1þk
Ipkinact
deg ðIþKI Þ
B
@
fm;2C8
1
1þI=Ki;2C8
þfm;3A4
1
1þI=Ki;3A4
þð1 2 fm Þ
C
A
ð3Þ
Equation 4 is for reversible (CYP2C8) and time-dependent inhibition
(CYP3A4):
1
1
AUCratio ¼
0
1=EH
B
0
1 C
C
B
B
B
B
B
B
B
B
fhep B
1
B
B 1
BðEH 2 1ÞB
B
@
B
B
1
fm;2C8
þfm;3A4
B
1þI=Ki;2C8
@
C
C
!
1
Ipkinact
1þ
kdeg ðIþKI Þ
þð1 2 fm Þ
C
C C
C C
C þ 1 2 fhep
Cþ1C
C
C C
A C
C
C
A
ð4Þ
where EH is the hepatic extraction ratio of paclitaxel and fhep is the fraction of
clearance subject to the hepatic blood flow limitation (Kirby and Unadkat,
2010).
Among variables that were used for the AUC ratio calculation, the Ki values
were experimentally obtained for the 12 kinase inhibitors by monitoring
CYP2C8- and CYP3A4-mediated paclitaxel hydroxylation. The values of kinact
and KI for axitinib were also determined experimentally. The EH of paclitaxel
after intravenous administration was calculated by using the equation:
EH ¼ CLH =ðQH þ CLH Þ
which is based on literature data (Smorenburg et al., 2003). For inhibitor
concentration I, the plasma maximal concentrations at clinically relevant doses
were used (Table 1). The literature value of kdeg (0.019 h21) was assumed for
CYP3A4 (Fahmi et al., 2008), and a value of 1 was assigned for fhep (Kirby and
Unadkat, 2010). The fraction of the dose metabolized (fm) was estimated to be
0.963 based on paclitaxel human absorption, distribution, metabolism, and
excretion (ADME) data (Monsarrat et al., 1993, 1998).
Results
Formation of 6a-Hydroxypaclitaxel and p-39-Hydroxypaclitaxel in Pooled HLMs. The activity of individual cytochrome P450
enzymes in HLM preparations varies markedly among different HLM
sources. To maintain consistency and avoid complications due to
source variability, the pooled HLM from the same batch were used
throughout the present study. Under the current experimental conditions (0.2 mg/ml HLM and 5 mM paclitaxel), the formation of p-39hydroxypaclitaxel (CYP3A4) was linear over the 60-minute incubation
period, and the formation of 6a-hydroxypaclitaxel (CYP2C8) was
linear during the initial time points with the rate decreasing over time
(data not shown). For subsequent experiments, the incubation time
was set at 10 minutes to ensure the linear condition for the formation
of both metabolites.
Kinetic studies revealed that Km and Vmax values for CYP3A4 and
CYP2C8 pathways were 5.1 mM and 25.1 pmol/min/mg, and 5.2 mM
and 222.1 pmol/min/mg, respectively. By comparing the ratio of Vmax/
Km, the formation of 6a-hydroxypaclitaxel was almost 9 times faster
than that of p-39-hydroxypaclitaxel, suggesting that CYP2C8-mediated
paclitaxel hydroxylation was more predominant in the current batch of
pooled HLM. The Km and Vmax values observed in the present study
were within 2-fold to 3-fold of the published results; this is not
surprising given that laboratory-to-laboratory variability is often seen for
in vitro metabolism studies (Polasek et al., 2004; Zhang et al., 2008).
The differences in Km and Vmax values might be due to different HLM
sources as well as the incubation conditions. It is also interesting to note
that sigmoidal kinetics was observed for the p-39-hydroxylpaclitaxel
formation in HLM-H40 (Polasek et al., 2004).
Based on the above findings and the fm value from the literature
(Monsarrat et al., 1993, 1998), the values of in vivo fm,2C8 and fm,3A4
were estimated to be 0.865 and 0.098, respectively, assuming that
CYP2C8 and CYP3A4 were the only enzymes responsible for
paclitaxel metabolism under current conditions. These fm values were
used for the subsequent drug-drug interaction prediction.
Sensitivity of CYP2C8 and CYP3A4 Pathways to Reversible
Inhibition. As a well accepted probe substrate for CYP2C8, the
formation of 6a-hydroxypaclitaxel is frequently used to evaluate the
inhibitory potency of test compounds against CYP2C8 activity in
HLM. Because paclitaxel is also metabolized by CYP3A4 and is often
used in combination therapies, it was thus necessary to determine the
inhibition of these two separate metabolic pathways by a given
inhibitor simultaneously.
In the present study, the inhibitory potency of 12 kinase inhibitors
was first determined in the reversible inhibition setting by monitoring
the formation of 6a-hydroxypaclitaxel (CYP2C8) and p-39-hydroxypaclitaxel (CYP3A4). Judging from the Ki values (Table 2), the studied
kinase inhibitors displayed a wide range of inhibitory potency, among
which nilotinib was the most potent inhibitor against both CYP2C8
(Ki = 0.10 mM) and CYP3A4 (Ki = 0.28 mM). This finding was
consistent to a recent report (Kim et al., 2013). It was interesting to
note that the selected kinase inhibitors displayed pathway-dependent
inhibition toward CYP2C8- and CYP3A4-mediated paclitaxel hydroxylation. Based on the Ki ratios (CYP2C8 versus CYP3A4) and using
2-fold as an arbitrary cutoff, CYP2C8-mediated paclitaxel hydroxylation was more sensitive to inhibition by axitinib, lapatinib, nilotinib,
and sorafenib, whereas CYP3A4-mediated paclitaxel hydroxylation was
preferentially inhibited by bosutinib, dasatinib, erlotinib, pazopanib,
and sunitinib. Afatinib, gefitinib, and imatinib displayed similar inhibitory potency against these two separate pathways (Table 2).
Sensitivity of CYP2C8 and CYP3A4 Pathways to TimeDependent Inhibition. The time-dependent inhibition of CYP2C8and CYP3A4-mediated paclitaxel metabolism by kinase inhibitors was
subsequently investigated. In this setting, a simpler approach was first
adapted where the values of the percentage of enzyme activity remaining
with or without a 30-minute preincubation of kinase inhibitors in the
presence of NADPH were obtained at an inhibitor concentration of
10 mM. For inhibitors with a strong reversible inhibition at 10 mM,
a 20-fold dilution assay was performed. Table 3 summarizes enzyme
activity remaining with or without 30-minute preincubation in the
presence of the studied kinase inhibitors.
Based on the percentage of enzyme activity remaining, there was no
obvious time-dependent inhibition of CYP2C8-mediated paclitaxel
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
where fm,2C8 or fm,3A4 is the fraction metabolized by CYP2C8 or CYP3A4, I is
the inhibitor concentration (plasma Cmax is used in this case), Ki is the
reversible inhibition constant, kinact is the inactivation rate constant, kdeg is the
enzyme degradation rate constant, and KI is the inhibitor concentration that
results in half-maximal enzyme inactivation.
Although considerable success has been achieved using the mathematical
(static) models described above, these models assumed a very low hepatic
extraction ratio (EH) and were best suited for orally administered victim drugs
(Kirby and Unadkat, 2010). For comparison purposes and with the expectation
to increase the accuracy for prediction, a recently described equation for
intravenously administered drugs (Kirby and Unadkat, 2010) was adapted to
calculate the AUC ratios in the presence or absence of a given kinase inhibitor.
Equation 3 is for reversible inhibition (both CYP2C8 and CYP3A4):
1
0
1
AUCratio ¼
1=EH
0
1 C
B
B
C
B
C
fhep B
1
Aþ1C
1 BðEH
C þ 1 2 fhep
2 1Þ@
787
788
Wang et al.
TABLE 2
Inhibition potency of selected kinase inhibitors against the formation of
6a-hydroxypaclitaxel and p-39-hydroxypaclitaxel
Reversible Kia (mM)
Inhibitors
6a-Paclitaxel Hydroxylation
(CYP2C8)
Afatinib
Axitinib
Bosutinib
Dasatinib
Erlotinib
Gefitinib
Imatinib
Lapatinib
Nilotinib
Pazopanib
Sorafenib
Sunitinib
94.83
0.17
1.94
6.31
4.02
8.69
11.28
1.43
0.10
3.72
1.59
91.51
6
6
6
6
6
6
6
6
6
6
6
6
p-39-Paclitaxel Hydroxylation
(CYP3A4)
27.05
0.07
0.24
1.44
0.56
0.47
0.97
0.73
0.01
0.49
0.38
27.38
53.38
1.94
0.14
2.29
1.28
4.80
7.68
3.83
0.28
0.97
4.11
20.35
6
6
6
6
6
6
6
6
6
6
6
6
19.76
1.10
0.06
1.63
0.07
5.04
2.29
0.81
0.34
0.26
2.13
8.74
hydroxylation by those kinase inhibitors with the exception of afatinib,
erlotinib, gefitinib, lapatinib, and sorafenib, which showed a modest
time-dependent inhibition (10–30% activity changes).
In contrast, most of the selected kinase inhibitors (except sorafenib)
showed strong time-dependent inhibition against CYP3A4-mediated
paclitaxel metabolism, consistent with the literature reports (Li et al.,
2009a; Teng et al., 2010; Filppula et al., 2012; Kenny et al., 2012). For
the subsequent drug-drug interaction prediction, inactivation parameters (KI and kinact) of CYP3A4 by those kinase inhibitors were
adapted from the literature reports (Li et al., 2009a; Teng et al., 2010;
Filppula et al., 2012; Kenny et al., 2012). Because there were no data
available for axitinib, we further investigated the kinetics and mechanisms of CYP3A4 inactivation by this drug.
As illustrated in Fig. 1, the inactivation of CYP3A4 by axitinib
displayed characteristic time and concentration dependency. Kinetic
analysis revealed that axitinib exhibited a KI of 0.93 mM and kinact of
TABLE 3
Inhibition of the formation of 6a-hydroxypaclitaxel and p-39-hydroxypaclitaxel by
selected kinase inhibitors after a 30-minute preincubation
Time-Dependent Inhibition
(% of Enzyme Activity Remaining)a
Inhibitors
6a-Paclitaxel Hydroxylation
(CYP2C8)
0 min
Afatinib
Axitinib
Bosutinib
Dasatinib
Erlotinib
Gefitinib
Imatinib
Lapatinib
Nilotinib
Pazopanib
Sorafenib
Sunitinib
75.8
40.4
60.9
62.7
110.4
45.2
68.6
96.0
22.1
48.0
96.6
95.2
6
6
6
6
6
6
6
6
6
6
6
6
0.5
3.1b
0.6
2.8
5.8b
1.7
10.6
3.9b
1.1b
0.9
4.6b
4.6
30 min
55.9
65.4
62.9
66.9
85.7
34.8
59.6
76.1
19.3
54.6
66.6
84.6
6
6
6
6
6
6
6
6
6
6
6
6
13.9
2.7b
2.1
1.8
4.4b
0.4
0.3
3.2b
0.4b
3.0
0.3b
1.8
p-39-Paclitaxel Hydroxylation
(CYP3A4)
0 min
36.5
86.2
9.4
37.6
90.8
41.1
31.2
97.1
65.0
15.9
73.5
67.0
6
6
6
6
6
6
6
6
6
6
6
6
6.9
3.3b
1.2
14.5
5.7b
12.5
6.6
4.5b
1.9b
6.8
3.3b
29.1
30 min
22.3
59.0
5.4
3.5
60.5
9.6
8.8
63.5
39.4
5.7
77.4
38.7
6
6
6
6
6
6
6
6
6
6
6
6
6.2
6.3b
0.23
1.2
2.7b
0.8
0.2
7.0b
8.2b
1.6
10.0b
2.7
a
The 30-minute preincubations were conducted at an inhibitor concentration of 10 mM. The
percentage (%) of enzyme activity remaining was calculated individually as compared with the
solvent control with or without preincubation. The average values of three separate incubations
are reported.
b
For inhibitors showing a strong reversible inhibition at 10 mM, a 20-fold dilution assay was
performed (see Materials and Methods).
Fig. 1. Kinetic investigation on CYP3A4 inactivation by axitinib. (A) Time- and
concentration-dependent inhibition of the formation of p-39-hydroxypaclitaxel. (B)
Nonlinear regression analysis of CYP3A4 inactivation by axitinib.
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
a
Reversible inhibitory Ki values were determined as described in Materials and Methods. The
inhibitor concentrations used were 0.04, 0.2, 0.5, 1, 2, 5, 10, 50, and 100 mM, along with
paclitaxel concentrations of 2.5, 5, and 10 mM. The average of three separate incubations was
reported.
0.0137 min21 against CYP3A4-mediated paclitaxel hydroxylation.
The kinact/KI ratio of axitinib against CYP3A4 was similar to that of
dasatinib but smaller than that of nilotinib, suggesting an intermediate
inhibition potency (Kenny et al., 2012).
The CYP3A4 inactivation by axitinib might be caused by a reactive
intermediate (epoxide) formed during the catalysis, as an axitinibglutathione adduct was detected in the presence of NADPH. As shown
in Fig. 2, two glutathione adducts were detected. Adduct-I had a
retention time of 5.57 minutes with a molecular mass of 694.2, and
adduct-II was eluted at 4.66 minutes with a molecular mass of 710.2,
showing a difference of 16 Dalton. Additional experiments showed
that adduct-I could be formed in the absence of NADPH, suggesting
a direct conjugation of glutathione to axitinib. Based on the fragmentation pattern and accurate mass analysis, it was postulated that adduct-I
(694.2188 → 565.171 → 387.127 → 356.087) was formed via a direct
conjugation to the double bond, whereas adduct-II (710.2126 →
581.165 → 550.128 → 403.122 → 372.080) was formed at the same
site, albeit after the formation of an epoxide intermediate in the presence of NADPH.
Molecular Docking Simulations of Kinase Inhibitors with
CYP3A4 and CYP2C8. To examine the structural features that
could explain the inhibitor-enzyme binding, we performed molecular
docking simulations using the observed reversible Ki values. First,
inhibitors bromergocryptine (08Y, a type I ligand) and montelukast
(MTK) were redocked into CYP3A4 and CYP2C8, respectively, to
evaluate the docking performance of Glide. According to the Glide
docking results, the predicted structure of the 08Y-CYP3A4 complex
agreed very well with 3UA1 crystal (Sevrioukova and Poulos 2012),
but the predicted structure of MTK-CYP2C8 complex did not with
2NNI (Schoch et al., 2008). The root-mean-square deviation between
the Glide-calculated pose and the experimentally-determined structure
of 08Y in 3UA1 was 1.09 Å and that of MTK in 2NNI was 2.82 Å. It
appeared that Glide could not accurately characterize some interactions between MTK and CYP2C8.
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
789
Based on the above observations, the studied kinase inhibitors were
docked into the crystal structures of CYP3A4 and CYP2C8,
respectively. The best conformation of each protein-ligand complex
was determined by the lowest predicted binding free energies of the
docked ligands. Figure 3 illustrates correlations between the best Glide
extra-precision docking scores and the experimental Ki values of
CYP3A4 and CYP2C8. It is of note that the docking scores for
CYP3A4 exhibited a moderate correlation with experimental data
(r2 = 0.64), but the docking scores of CYP2C8 showed a relatively
lower linear correlation (r2 = 0.51). The results of the docking analysis
suggest that different structural features of the studied kinase inhibitors
might have different binding conformations, and thus further refinement is needed to characterize the interactions between the enzymeinhibitor complexes.
The binding mechanisms of two molecules (nilotinib and sunitinib)
were further analyzed for their inhibitory potency. The predicted
binding free energies of nilotinib and sunitinib were 29.81 and 27.11
kcal/mol for CYP3A4, and 29.94 and 26.10 kcal/mol for CYP2C8,
respectively. Data suggest that nilotinib exhibited much higher
binding affinities than sunitinib in both CYP3A4 and CYP2C8
systems, which is consistent with the inhibitory potency observed for
nilotinib (Table 2). Figure 4A illustrates the binding mode of the
CYP3A4-nilotinib complex. It can be noted that one of the three
fluorine atoms in nilotinib formed a hydrogen bond with the hydroxyl
group of residue Ser119. Moreover, a strong arene-cation interaction
was observed between the pyrimidine ring of nilotinib and residue
Arg106. For the CYP3A4-sunitinib complex, a similar arene-cation
interaction between the benzene ring of indole in sunitinib and residue
Arg106 was also observed (Fig. 4B), but no hydrogen bond was found
in the complex. In the CYP2C8-nilotinib complex (Fig. 4C), the
fluorine atoms in nilotinib also contributed markedly to ligand binding
by forming three hydrogen bonds with residues Thr240, Val237, and
Asn204. In addition, another hydrogen bond between the amino
hydrogen of methylimidazole in nilotinib and the carbonyl oxygen of
residue Asp293 was observed. In contrast, sunitinib formed one
hydrogen bond between its carbonyl oxygen and the hydroxyl
hydrogen of residue Ser103 and was located far from the heme center
of CYP2C8 as compared with nilotinib (Fig. 4D).
Spectral Analysis of Inhibitor and CYP Enzyme Binding. To
characterize the binding types between kinase inhibitors and P450
enzymes, absorbance differences of CYP3A4 or CYP2C8 in the
presence and absence of kinase inhibitors were recorded from 350 to
500 nm using a UV-visible spectrophotometer. As summarized in
Table 4, ketoconazole, a typical type II inhibitor, displayed characteristic binding spectra for both CYP3A4 and CYP2C8. For CYP2C8,
most of these kinase inhibitors exhibited characteristic type II binding
spectra, except bosutinib, dasatinib, and sunitinib. For CYP3A4,
a majority of kinase inhibitors exhibited type II or type II-like binding
spectra, except afatinib, erlotinib, lapatinib, and sunitinib for which
a defined spectrum could not be obtained due to weak binding.
Interestingly, sorafenib was the only inhibitor that displayed a type I
binding with CYP3A4. Based on the available Ks values (Table 4), a
good correlation was observed between Ks and Ki, respectively, for
CYP3A4 (r2 = 0.807) and CYP2C8 (r2 = 0.950).
Prediction of Paclitaxel AUC Changes in the Presence of Kinase
Inhibitors. The potential of drug-drug interactions between paclitaxel
and selected kinase inhibitors was examined by predicting AUC
changes in paclitaxel exposure (the victim drug). First, the interactions
were predicted based on reversible inhibition of both CYP2C8 and
CYP3A4 pathways by kinase inhibitors, and the results are
summarized in Table 5. By using the simple (11I/Ki) values, some
of these selected kinase inhibitors could elicit significant drug-drug
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
Fig. 2. Identification of axitinib-glutathione adducts in human liver microsomes. (A) Extracted ion chromatograms of glutathione adducts in human liver microsomes. (B)
Fragmentation patterns of axitinib-glutathione adducts.
790
Wang et al.
interactions with paclitaxel by inhibiting either the CYP2C8 or
CYP3A4 pathway, such as nilotinib, pazopanib, and sorafenib for
CYP2C8, and bosutinib, nilotinib, and pazopanib for CYP3A4.
Further analysis using the static equation (eq. 1) incorporating
CYP2C8 and CYP3A4 parallel pathways but without the consideration of paclitaxel EH suggested that nilotinib, pazopanib, and
sorafenib could cause marked drug-drug interactions (more than 6-fold
increases in AUC) with paclitaxel (Table 5). Using eq. 3 and with the
consideration of paclitaxel EH (0.78) and parallel pathways, the predicted drug-interactions dramatically decreased for nilotinib, pazopanib,
and sorafenib. In this case, the maximal drug-drug interaction predicted was about 3.8-fold that could be caused by nilotinib, a potent
inhibitor for both CYP2C8 and CYP3A4 pathways (Table 5).
Simulations with varying EH values of paclitaxel revealed that the
magnitude of interactions predicted for these kinase inhibitors was
reduced with increasing EH. For example, nilotinib could cause more
than 12-fold interaction when a theoretical EH of 0.1 was used for
prediction. In contrast, the fold change in AUCs decreased to 2.3-fold
using a theoretical paclitaxel EH of 0.9 (Table 5). A similar trend was
observed for other kinase inhibitors such as lapatinib, pazopanib, and
sorafenib, suggesting that the EH of paclitaxel might have a profound
effect on the prediction accuracy. Although clinical interaction data
are limited between paclitaxel and the combining kinase inhibitors, it
appears that the equation (eq. 3) described by Kirby and Unadkat
(2010) achieved a better performance in predicting drug-drug interactions mediated by reversible inhibition by those kinase inhibitors, if
a 2-fold rule was applied (Guest et al., 2011).
Because most of those kinase inhibitors have been shown to be
time-dependent inactivators of CYP3A4, their interaction potential
with paclitaxel was predicted based on reversible inhibition of
CYP2C8 and time-dependent inactivation of CYP3A4 simultaneously.
For this purpose, eq. 4 with the consideration of paclitaxel EH (0.78)
was used, as eq. 2 (ignoring EH) resulted in a marked overprediction as described earlier (data not shown). As shown in Table 6, an
increased propensity of drug-drug interactions was noted when timedependent inactivation of CYP3A4 was incorporated for prediction, as
is evident by the larger AUC changes. As stated previously, HLMs
from some donors exhibited higher CYP3A4 activities, leading to
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
Fig. 3. Correlation between the Glide docking scores and the
experimental logKi. (A) CYP3A4. (B) CYP2C8.
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
791
a higher fm,3A4 in paclitaxel metabolism; thus, we examined the effect
of fm,3A4 on drug interactions.
As expected, increases in CYP3A4 contribution to paclitaxel
metabolism (fm,3A4 from 0.1 to 0.9) resulted in bigger AUC changes.
For nilotinib, a marked interaction with paclitaxel could be anticipated
as the compound is a potent reversible inhibitor of CYP2C8 and
a potent time-dependent inactivator of CYP3A4. The low interaction
potential predicted for axitinib with paclitaxel could be due to its low
plasma concentrations (Martin et al., 2012), even though the drug was
a potent CYP2C8 and CYP3A4 inhibitor based on in vitro data. It
appears that ignoring the EH of paclitaxel (Table 5) and the higher
contribution of CYP3A4 metabolism (higher fm,3A4, Table 6) results in
overprediction of the interactions between paclitaxel and those kinase
inhibitors.
Discussion
The activity of individual cytochrome P450 enzymes in HLMs
varies markedly due to factors such as the organ donors’ genetic
background, health condition, and medication history. With respect to
CYP2C8 and CYP3A4, different ratios in the formation of 6ahydroxypaclitaxel (CYP2C8) versus p-39-hydroxypaclitaxel (CYP3A4)
have been reported (Sonnichsen et al., 1995). In our present study, the
formation of 6a-hydroxypaclitaxel was about 9 times higher than that
of p-39-hydroxypaclitaxel based on the observed Vmax/Km ratio, indicating that CYP2C8-mediated paclitaxel hydroxylation was more
predominant in the current batch of HLMs. The predominance of 6ahydroxypaclitaxel formation, up to 13-fold higher as compared with
p-39-hydroxypaclitaxel, was also observed in some individual livers
(Václavíková et al., 2003; Taniguchi et al., 2005). In vivo, the level of
6a-hydroxypaclitaxel was about 4 times higher than that of p-39hydroxypaclitaxel in bile and urine samples, suggesting that CYP2C8
played a major role in paclitaxel metabolism in human (Monsarrat
et al., 1998). Recent reports indicate that paclitaxel-induced neurotoxicity is correlated with CYP2C8 activity and the level of 6ahydroxypaclitaxel (Leskelä et al., 2011; Hertz et al., 2012, 2013). In
addition, CYP3A4*22 carriers with decreased enzyme activity are
associated with an increased risk of neurotoxicity (de Graan et al.,
2013). These findings suggest that P450-mediated paclitaxel metabolism and possibly specific metabolites are important factors affecting
its clinical efficacy, thus highlighting the clinical relevance of drugdrug interactions and associated changes in metabolic clearance for
paclitaxel-based combination therapies.
Inhibitory potency of a given drug against individual P450 enzymes
is often characterized based on specific probe reactions (Bjornsson
et al., 2003; Huang et al., 2007). Although data obtained using this
approach are valuable for the characterization of drug interaction
potentials and the design of adequate clinical investigations, it is also
important to know whether a kinase inhibitor (often in chronic
administration) will interfere with the metabolic clearance of the combining agent such as paclitaxel. For this purpose, our present study
was undertaken to evaluate the inhibition of CYP2C8- and CYP3A4-
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
Fig. 4. The inhibitor–protein interaction diagram. (A) CYP3A4-nilotinib complex. (B) CYP3A4–sunitinib complex. (C) CYP2C8–nilotinib complex. (D) CYP2C8–sunitinib
complex.
792
Wang et al.
TABLE 4
UV-visible spectral differences of CYP3A4 and CYP2C8 in the presence of selected kinase inhibitors
Spectral analysis was performed out as described in Materials and Methods. The binding type was defined based on Soret maximum and
trough absorbance (Dahal et al., 2012): type I, maximum 385–390 nm, trough 415–420 nm; type II, maximum 420–435 nm, trough
390–405 nm. Ketoconazole, a typical type II inhibitor, was used as a positive control for system validation.
CYP2C8
CYP3A4
Inhibitors
Max (nm)
Trough (nm)
Binding Type
Ks(mM)
Max (nm)
Trough (nM)
Binding Type
Ks (mM)
431
419
421
389
404
400
—a
—a
399
399
410
417
401
401
389
—a
II
II
II
NA
NA
2.2
NA
NA
12.6
NA
23.5
NA
0.5
NA
1.7
NA
429
390
—a
410
413
419
—a
412
410
—a
404
409
420
—a
II
NA
NA
4.5
NA
NA
NA
NA
18.1
NA
2.2
0.8
3.0
NA
Ketoconazle
Afatinib
Axitinib
Bosutinib
Dasatinib
Erlotinib
Gefitinib
Imatinib
Lapatinib
Nilotinib
Pazopanib
Sorafenib
Sunitinib
417
410
429
429
429
411
411
II
II?
II
II?
II
II?
II
428
429
428
424
424
427
429
390
II
II?
II?
II?
II
II
II
I
mediated paclitaxel metabolism simultaneously by selected kinase
inhibitors. Based on the observed Ki values, a pathway-dependent reversible inhibition was observed against CYP2C8- or CYP3A4-mediated
paclitaxel metabolism, suggesting that some kinase inhibitors could
selectively alter metabolic pathways of paclitaxel. The alteration of
CYP2C8 and/or CYP3A4 activity may lead to changes in metabolite
ratios, thus affecting the efficacy and safety outcomes of paclitaxelbased combination therapies, as paclitaxel-induced neurotoxicity has
been correlated to CYP2C8 or CYP3A4 activity (Leskelä et al., 2011;
Hertz et al., 2012; de Graan et al., 2013). Inhibition of CYP2C8- and/
or CYP3A4-mediated paclitaxel metabolism in HLMs also has been
reported for efflux-reversing agents and phenolic compounds (Desai
et al., 1998; Václavíková et al., 2003).
With respect to time-dependent inhibition, the selected kinase inhibitors did not show marked effects against CYP2C8 with the exception of afatinib, erlotinib, gefitinib, lapatinib, and sorafenib, for which
a modest time-dependent inhibition was noted. Further investigation
of this finding is under way to delineate CYP2C8 inactivation
mechanisms. For the CYP3A4 pathway, time-dependent inhibition
was observed for the majority of selected kinase inhibitors, consistent
with literature reports where midazolam is used as a probe substrate
(Li et al., 2009a,b, 2010; Teng et al., 2010; Kenny et al., 2012). For
some of those kinase inhibitors, inactivation mechanisms were also
investigated (Teng et al., 2010; Takakusa et al., 2011). As observed
for axitinib in our present study, a majority of those compounds have
the potential to form reactive intermediates during P450 catalysis, as
evident by the formation of glutathione adducts (Kenny et al., 2012). It
thus appears that enzyme inactivation is a common mechanism that
may be associated with CYP3A4-mediated drug interactions caused
by small molecule kinase inhibitors.
The kinase inhibitors investigated herein represent different core
structures and could be categorized into quinazoline, quinoline,
aminopyrimidine, aminothiazole, and others based on the binding
mode with the ATP-active site of different kinases. In the docking
TABLE 5
Prediction of drug-drug interactions between paclitaxel and kinase inhibitors based on reversible inhibition
Inhibitors
Afatinib
Axitinib
Bosutinib
Dasatinib
Erlotinib
Gefitinib
Imatinib
Lapatinib
Nilotinib
Pazopanib
Sorafenib
Sunitinib
a
R2C8 (1+I/Ki,2C8)
R3A4 (1+I/Ki,3A4)
AUCratio
(Ignoring EH)a
1.00
1.54
1.21
1.03
1.99
1.10
2.00
4.70
31.12
10.83
6.83
1.00
1.00
1.04
3.90
1.07
4.10
1.19
2.47
2.38
11.76
38.70
3.25
1.01
1.04
1.52
1.35
1.07
2.17
1.15
2.12
4.43
27.64
12.13
6.37
1.04
AUCratiob
EH = 0.1d
EH = 0.5d
EH = 0.9d
EH = 0.78e
1.00
1.40
1.26
1.03
1.91
1.10
1.87
3.54
12.57
7.70
4.76
1.00
1.00
1.22
1.14
1.01
1.51
1.05
1.48
2.41
7.43
4.72
3.09
1.00
1.00
1.04
1.03
1.00
1.10
1.01
1.10
1.28
2.29
1.74
1.42
1.00
1.00
1.10
1.06
1.01
1.22
1.02
1.21
1.61
3.78
2.61
1.90
1.00
Observed AUCratioc
0.86
1.04
—
0.86
0.97
—
—
—
—
43% increase in Cmax
—
1.30
A static model incorporating parallel pathways was used (Ito et al., 2005; Lu et al., 2008). The EH of paclitaxel was not incorporated.
A static model described by Kirby and Unadkat (2010) was used by incorporating parallel pathways and with the consideration of paclitaxel EH.
Observed AUCratio was estimated based on literature reports (Table 1) where AUC values were available.
d
Arbitrary EH values were assigned and used for comparison purposes.
e
The EH value of paclitaxel was estimated based on literature data (Smorenburg et al., 2003) as described in Materials and Methods.
b
c
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
NA, data not available (Ks values could not be accurately determined due to weak binding and less-defined spectra).
a
A well defined spectrum could not be obtained due to weak binding.
Pathway-Dependent Inhibition of Paclitaxel Hydroxylation
TABLE 6
Prediction of drug–drug interactions between paclitaxel and kinase inhibitors by
incorporating time-dependent inactivation of CYP3A4
AUCratio (as a Function of fm,CYP3A4)a
Inhibitors
Afatinib
Axitinib
Bosutinib
Dasatinib
Erlotinib
Gefitinib
Imatinib
Lapatinib
Nilotinib
Pazopanib
Sorafenib
Sunitinib
0.102c
0.50c
0.90c
NA
1.13
NA
1.02
1.24
1.04
1.24
1.76
4.10
2.62
NA
1.01
NA
1.27
NA
1.15
1.54
1.16
1.54
2.24
4.49
3.23
NA
1.03
NA
1.56
NA
1.53
2.96
1.47
3.00
3.71
4.99
4.46
NA
1.05
Observed AUCratiob
0.86
1.04
—
0.86
0.97
—
—
—
—
43% increase in Cmax
—
1.30
simulation with CYP3A4 and CYP2C8, a moderate correlation was
found between the Glide docking scores and the experimental Ki
values of CYP3A4, but the docking scores and the experimental Ki
values of CYP2C8 showed a weak linear correlation. These results
suggest that there is need for further improvements in the characterization of interactions between the model inhibitors (08Y and MTK)
and respective P450 enzymes.
Spectral analysis between kinase inhibitors and CYP3A4 or
CYP2C8 can provide insight on the observed inhibitory effects.
These kinase inhibitors contain heterocyclic nitrogen atoms that may
bind to the heme-iron of P450 enzymes, resulting in enzyme activity
inhibition. Our study revealed that most of the kinase inhibitors
display characteristic spectra of type II binding to CYP2C8 and/or
CYP3A4, as exemplified by nilotinib (Table 4). For most of these type
II binders, strong inhibition was observed, with a few exceptions such
as afatinib for CYP2C8. It is interesting to note that the strong
CYP2C8 inhibitor bosutinib did not show the type II binding
spectrum. Comparison of available Ks and Ki values reveals a good
correlation for both CYP3A4 and CYP2C8. As shown previously for
compounds that contain heterocyclic nitrogen, the binding types and
affinity can be affected by the availability of such nitrogen for iron
coordination due to steric hindrance and ring substitution (Chiba et al.,
2001; Dahal et al., 2012). As such, ongoing efforts aim to characterize
the contribution of various interactions (iron coordination, hydrogen
binding, hydrophobic interaction, etc.) to the observed inhibitory
effects.
Prediction of drug-drug interactions using in vitro data has been
extensively explored using both static and dynamic models, and
considerable success has been achieved (Ito et al., 2005; Obach et al.,
2006; Lu et al., 2008; Fahmi et al., 2008; Rowland Yeo et al., 2010).
For intravenously administered drugs, it has been suggested that this
type of modeling can be used for prediction assuming a very low
hepatic EH (Kirby and Unadkat, 2010). In our study, the AUC ratios
of paclitaxel (an intravenous drug) are predicted by ignoring the
hepatic EH of paclitaxel as well as by considering EH (Kirby and
Unadkat, 2010). When compared with available clinical reports on
pharmacokinetic interactions between paclitaxel and kinase inhibitors
(Table 1 and Table 5), the current analysis shows that the EH of
paclitaxel had a marked impact on the fold of predicted interactions, as
a lower extraction ratio; ignoring the EH is linked to overprediction for
several kinase inhibitors. Furthermore, overprediction is also noted
when the time-dependent inhibition component of CYP3A4 is incorporated, which is in turn affected by the f m,3A4 of paclitaxel
(Table 6). Overprediction of drug–drug interactions has been reported
for CYP3A4 inactivation by various kinase inhibitors (Kenny et al.,
2012). For experimentally determined kinetic parameters (such as Ki),
nonspecific binding and albumin tend to decrease “effective concentrations” of some drugs and lead to overestimation of parameters obtained
from HLM incubations (Margolis and Obach 2003; Wattanachai et al.,
2011; Nagar and Korzekwa 2012). The decreased inhibitory potency due
to overestimation of Ki values may result in underprediction of drug-drug
interaction potential, a phenomenon was not seen during our analysis. In
our study, a reasonably accurate prediction was obtained using Ki values
generated with nominal inhibitor concentrations in HLMs, inhibitor
plasma Cmax reported in the literature, and the incorporation of the EH of
paclitaxel (the victim drug).
Clinically significant drug interactions between paclitaxel and kinase
inhibitors based on AUC changes have not been reported, even though
paclitaxel is frequently combined with various kinase inhibitors for the
treatment of a variety of tumor types. From the limited clinical data and
current prediction, it appears that the likelihood of a strong interaction is
relatively low for a majority of kinase inhibitors tested. Because actual
drug-drug interactions in the clinical setting depend on multiple factors
such as dose levels, dose regimen (timing of drug administration), and
the patient’s genetic background, potential drug-drug interactions between paclitaxel and kinase inhibitors (such as nilotinib) via strong
CYP2C8 and/or CYP3A4 inhibition should not be ignored. Although
pharmacokinetic drug interactions are often described by AUC changes
in parent drugs, exposure alteration in key metabolites is also worthwhile to describe. Because changes in the CYP3A4 (de Graan et al.,
2013) and/or CYP2C8 (Hertz et al., 2012, 2013) metabolism of paclitaxel
are correlated with an increased risk of neurotoxicity, differential
metabolism (or metabolite ratio) in the presence of an inhibitor may be
clinically relevant. Therefore, the availability of in vivo information on
pharmacokinetic changes in parent drugs as well as key metabolites
would be valuable not only to confirm the utility of predictive models but
also to define the underlying mechanisms.
In conclusion, pathway-dependent inhibition (CYP2C8 versus
CYP3A4) and time-dependent inhibition (CYP3A4) of paclitaxel
metabolism by selected kinase inhibitors were observed. Molecular
docking simulations revealed that potent inhibition of CYP2C8 and
CYP3A4 by nilotinib can be explained by the strong binding between
nilotinib and respective enzymes. In addition, the type II binding to
underlying P450 enzymes was evident for the majority of kinase
inhibitors. With the consideration of EH of the victim drug paclitaxel,
a reasonably accurate prediction of drug-drug interactions between paclitaxel and kinase inhibitors is achieved. Although clinically significant
drug interactions have not been reported, the strong in vitro inhibition of
CYP2C8- and CYP3A4-mediated paclitaxel metabolism by some
kinase inhibitors warrants further evaluation. In particular, alteration
in metabolite formation and its relationship to paclitaxel-induced neurotoxicity needs to be addressed. It can be anticipated that further investigations in vivo will lead to safer and more effective use of paclitaxel-based
combination therapies.
Acknowledgments
The authors thank Xingxing Diao and Dr. Xiaoyan Chen at the Shanghai
Institute of Materia Medica for technical assistance with the identification of
axitinib-glutathione adducts using ultra-performance liquid chromatography
quadruple time-of-flight accurate mass spectrometry.
Downloaded from dmd.aspetjournals.org at ASPET Journals on August 9, 2017
NA, data not available (compounds are not time-dependent inhibitor of CYP3A4 based on
data in Table 3).
a
A static model described by Kirby and Unadkat (2010) was used by incorporating parallel
pathways (Ito et al., 2005; Lu et al., 2008) and with the consideration of EH.
b
Observed AUCratio was estimated based on literature reports (Table 1) where AUC values
were available. The values are the same as described in Table 5.
c
The value of fm,CYP3A4 (0.102) was estimated based on literature reports on total fm
(Monsarrat et al., 1993, 1998) and the current experimental data. The values of 0.50 and 0.90
were assigned arbitrarily for comparison purposes.
793
794
Wang et al.
Authorship Contributions
Participated in research design: Y. Wang, He, Zhang.
Conducted experiments: Y. Wang, M. Wang, Qi, Li, He, Pan, Hou.
Performed data analysis: Y. Wang, M. Wang, Li, Zhang.
Wrote or contributed to the writing of the manuscript: Y. Wang, Hou,
Zhang.
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Address correspondence to: Dr. Hongjian Zhang, Soochow University, College
of Pharmaceutical Sciences, Suzhou, 215123, People’s Republic of China. E-mail:
[email protected]
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