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\begin{document}

\title{In Vitro Measurement and In Vivo Prediction of Combination Safety of
Rivaroxaban with Tyrosine Kinase Inhibitors: A Medication Safety
Assessment Study}



\author[1]{tingting Zhao}%
\author[1]{Xuening Li}%
\author[2]{Yanwei Chen}%
\author[1]{Dalong Wang}%
\author[2]{Liyan Wang}%
\author[3]{Shan Zhao}%
\author[1]{Changyuan Wang}%
\author[1]{Qiang Meng}%
\author[1]{Huijun Sun}%
\author[1]{Kexin Liu}%
\author[1]{Jingjing Wu}%
\affil[1]{Dalian Medical University}%
\affil[2]{First Affiliated Hospital of Dalian Medical University}%
\affil[3]{Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
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\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Background and Purpose: Rivaroxaban as an oral anticoagulant is widely
used for the prevention and treatment of thromboembolic disease.
Previous studies revealed cytochrome P450 (CYP)--mediated metabolism of
rivaroxaban mainly involves CYP2J2 and CYP3A4. Imatinib, sunitinib and
gefitinib are three tyrosine kinase inhibitors (TKIs) that are
extensively applied for anti-cancer therapy. Statistical research has
shown cancer patients are at approximately 4-7--fold higher risk of vein
thromboembolism than normal patients. Therefore, rivaroxaban and TKIs
have a profound combination foundation. This study aimed to
comprehensively assess the combination safety of rivaroxaban with TKIs
in vitro. Experimental Approach: First, the inhibitory activity of the
three TKIs was screened. Second, to comprehensively evaluate their
inhibitory potential, the reversible and mechanism-dependent inhibitory
kinetic constants of three TKIs on CYP2J2 and CYP3A4 were determined.
Docking simulation was used to explore the molecular mechanism. Finally,
drug-drug interaction (DDI) risks of the combination were assessed using
pharmacokinetic data of cancer patients. Key Results: Imatinib and
gefitinib exerted significant reversible inhibition of both CYP2J2 and
CYP3A4, while sunitinib only showed reversible inhibition of CYP3A4, not
CYP2J2. Three TKIs also showed time-dependent inactivation of CYP3A4 and
slightly on CYP2J2. Notably, sunitinib had a significantly stronger
inactivation effect on CYP3A4 than the other TKIs, with a 4.14-fold IC50
shift. Imatinib was predicted to cause a 114--244\% increase in
rivaroxaban exposure. Conclusion and Implication: Imatinib showed the
strongest inhibition, which was predicted to have a moderate DDI risk.
These results provide evidence for medication guidance when combining
rivaroxaban with TKIs.%
\end{abstract}%



\sloppy


\textbf{In Vitro Measurement and In Vivo Prediction of Combination
Safety of Rivaroxaban with Tyrosine Kinase Inhibitors: A Medication
Safety Assessment Study}

Tingting Zhao\textsuperscript{a,b \#}, Xuening Li\textsuperscript{a, b,
\#}, Yanwei Chen\textsuperscript{c, \#}, Dalong Wang\textsuperscript{a},
Liyan Wang\textsuperscript{c}, Shan Zhao\textsuperscript{d}, Changyuan
Wang\textsuperscript{a, b}, Qiang Meng\textsuperscript{a, b}, Huijun
Sun\textsuperscript{a, b}, Kexin Liu\textsuperscript{a, b}, Jingjing
Wu\textsuperscript{a, b*}

\textsuperscript{a}Department of Clinical Pharmacology, College of
Pharmacy, Dalian Medical University, Dalian 116044, China.

\textsuperscript{b}Provincial Key Laboratory for Pharmacokinetics and
Transport, Liaoning Dalian Medical University, Dalian, Liaoning, China.

\textsuperscript{c}Department of Pharmacy, the First Affiliated Hospital
of Dalian Medical University, Dalian 116011, China.

\textsuperscript{d}Dalian Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China.

\textsuperscript{\#} These authors contributed equally to this work.

\textbf{* Correspondence:}

Dr. Jingjing Wu

ORCID: 0000-0002-5046-9996

Department of Clinical Pharmacology, College of pharmacy

Dalian Medical University

9 West Section, Lvshun South Road, Lvshunkou District, Dalian 116044,
China

\textbf{Data availability statement}

The data underlying this article will be shared on reasonable request to
the corresponding author.

\textbf{Funding}

This work was supported by the National Natural Science Foundation of
China (81874324, 81922070, 81973286, 81803627, 81702995 and U1608283),
and LiaoNing Revitalization Talents Program (XLYC1907103).

\textbf{Authors' Contributions}

\textbf{Tingting Zhao} : Software, Validation, Formal analysis,
Investigation, Writing-Original Draft. \textbf{Xuening Li} : Software,
Validation, Formal analysis, Investigation, Writing Original
Draft.\textbf{Yanwei Chen:} Software, Validation, Formal analysis,
Investigation, Writing Original Draft. \textbf{Jingjing Wu} :
Conceptualization, Supervision, Writing-review and
editing.\textbf{Dalong Wang:} Supervision, Data Curation. \textbf{Shan
Zhao} : Supervision, Data Curation. \textbf{Liyan Wang} :
Supervision.\textbf{Changyuan Wang} : Project administration.
\textbf{Qiang Meng} : Supervision. \textbf{Huijun Sun} : Supervision.
\textbf{Kexin Liu} : Supervision.

\textbf{Conflict of Interest}

The authors declare that they have no conflict of interest.

\textbf{Ethical Approval}

This article does not contain any studies with human participants or
animals performed by any of the authors.

\textbf{Abstract}

\textbf{Background and Purpose:} Rivaroxaban as an oral anticoagulant is
widely used for the prevention and treatment of thromboembolic disease.
Previous studies revealed cytochrome P450 (CYP)--mediated metabolism of
rivaroxaban mainly involves CYP2J2 and CYP3A4. Imatinib, sunitinib and
gefitinib are three tyrosine kinase inhibitors (TKIs) that are
extensively applied for anti-cancer therapy. Statistical research has
shown cancer patients are at approximately 4-7--fold higher risk of vein
thromboembolism than normal patients. Therefore, rivaroxaban and TKIs
have a profound combination foundation. This study aimed to
comprehensively assess the combination safety of rivaroxaban with TKIs
in vitro.

\textbf{Experimental Approach:} First, the inhibitory activity of the
three TKIs was screened. Second, to comprehensively evaluate their
inhibitory potential, the reversible and mechanism-dependent inhibitory
kinetic constants of three TKIs on CYP2J2 and CYP3A4 were determined.
Docking simulation was used to explore the molecular mechanism. Finally,
drug-drug interaction (DDI) risks of the combination were assessed using
pharmacokinetic data of cancer patients.

\textbf{Key Results:} Imatinib and gefitinib exerted significant
reversible inhibition of both CYP2J2 and CYP3A4, while sunitinib only
showed reversible inhibition of CYP3A4, not CYP2J2. Three TKIs also
showed time-dependent inactivation of CYP3A4 and slightly on CYP2J2.
Notably, sunitinib had a significantly stronger inactivation effect on
CYP3A4 than the other TKIs, with a 4.14-fold IC\textsubscript{50} shift.
Imatinib was predicted to cause a 114--244\% increase in rivaroxaban
exposure.

\textbf{Conclusion and Implication:} Imatinib showed the strongest
inhibition, which was predicted to have a moderate DDI risk. These
results provide evidence for medication guidance when combining
rivaroxaban with TKIs.

\textbf{Keywords:} rivaroxaban, tyrosine kinase inhibitors, combined
medication, CYP2J2, CYP3A4, drug-drug interaction, medication safe

\textbf{Introduction}

Rivaroxaban, an oral anticoagulant, is widely used for the prevention
and treatment of thromboembolic disorders in clinical practice. It
exhibits direct anticoagulant effects by inhibiting coagulation factor
Xa. Although in vivo pharmacokinetic data are stable, bleeding is still
a risk factor that be considered for medication safety. Our previous
studies have shown CYP2J2 and CYP3A4 to be the major isoforms involved
in the metabolic process of rivaroxaban, and importantly CYP2J2 showed
\textasciitilde{}39-fold higher catalytic efficiency than CYP3A4 (Zhao
et al., 2021). Notably, CYP2J2, which dominated the metabolism of
rivaroxaban, is always not considered in routine drug-drug interaction
(DDI) research due to its lower abundance in the liver. It is this lack
of research that may lead to the possibility of severe
rivaroxaban-related clinical DDIs being overlooked.

Cancer-related vein thromboembolism (VTE) is closely associated with
increased morbidity and mortality (Song, Rosovsky, Connors \&
Al-Samkari, 2019). In cancer patients, the presence of tumours is one of
the risk factors for VTE. Additionally, both the hypercoagulability of
cancer patients and the thrombogenicity of anti-cancer agents are
reasons for the high incidence of VTE in cancer patients (Shalhoub et
al., 2017; Zamorano et al., 2016). According to statistics, cancer
patients have an approximately 4--7-fold higher risk of VTE than normal
patients, and cancer patients with VTE account for about 20\% of all VTE
patients (Blom, Doggen, Osanto \& Rosendaal, 2005; Streiff, 2016).
Meanwhile, thrombotic diseases are the leading cause of non-neoplastic
death in cancer patients (Song, Rosovsky, Connors \& Al-Samkari, 2019;
Timp, Braekkan, Versteeg \& Cannegieter, 2013; Zamorano et al., 2016).
Therefore, coagulation prevention or anticoagulant treatment is
unavoidable in cancer patients.

For decades, anticoagulant therapy for cancer-related VTE has been
limited to vitamin K antagonists (VKAs) and heparin drugs. Recently,
direct oral anticoagulants (DOACs) have also become an option (Streiff
et al., 2020). Rivaroxaban is recommended for the treatment of
superficial vein thrombosis and VTE prophylaxis following the discharge
by the National Comprehensive Cancer Network of America in 2020 of the
clinical practice guidelines for cancer-associated venous thromboembolic
disease (Streiff et al., 2020). Similarly, the Chinese Society of
Clinical Orology has also added rivaroxaban as a recommended
anticoagulant for partial tumour patients. Additionally, data from large
randomised clinical trials also suggest that DOCAs combined with
validated risk assessment scores are a reasonable choice for the primary
thromboprophylaxis of cancer patients instead of low molecular weight
heparin (Song, Rosovsky, Connors \& Al-Samkari, 2019). Importantly,
Prins et al. found that rivaroxaban is similar to the standard treatment
in terms of efficacy and safety for cancer patients, and the ease of
taking this medicine improves patient adherence, and thus it may be used
as an alternative to the standard therapy for some cancer patients
(Prins et al., 2013; Sanfilippo \& Wang, 2019).

In addition to the cautious selection of anticoagulation medication to
achieve the best therapy outcomes, anticoagulant adverse effects can
also be severe and alarming, which should be comprehensively prevented.
Compared with non-cancer patients, the recurrence rate of VTE in cancer
patients is 3--4 times higher and the incidence of major bleeding is
also increased by 2--3 times (Douketis, Crowther, Foster \& Ginsberg,
2001; Levitan et al., 1999; Monreal et al., 2006; Prandoni et al.,
2002). In addition to VTE, the risk of atrial fibrillation (AF) for
cancer patients was also extremely high (Onaitis, D'Amico, Zhao, O'Brien
\& Harpole, 2010). An analysis showed that the occurrence of AF for
cancer patients in the 90 days after the cancer diagnosis was much
higher than for normal patients (Saliba, Rennert, Gronich, Gruber \&
Rennert, 2018). And, notably, there is growing awareness that many
cancer-related factors are associated with AF, such as inflammations,
metabolic or electrolyte abnormalities and cancer therapy (Crusz \&
Balkwill, 2015; Diakos, Charles, McMillan \& Clarke, 2014; Farmakis,
Parissis \& Filippatos, 2014; Nattel, Burstein \& Dobrev, 2008; Nattel
\& Harada, 2014). The prevention of stroke and systemic embolism by
using anticoagulants is one of the therapeutic cornerstones of AF
management (January et al., 2019). Therefore, cancer patients may
receive anticoagulant therapy concurrently with anti-cancer therapy.

Tyrosine kinase inhibitors (TKIs) are a class of drugs that reduce the
phosphorylation of tyrosine protein kinases. TKIs compete with tyrosine
protein kinases for ATP phosphorylation sites to achieve targeted
anti-tumour therapy (Jiao, Bi, Ren, Song, Wang \& Wang, 2018). Among
these, imatinib, sunitinib and gefitinib have been the mainstay
treatments for various solid tumours and malignant blood diseases since
their launch in 2000 (Burotto, Manasanch, Wilkerson \& Fojo, 2015; Cheng
et al., 2013; Kuczynski, Lee, Man, Chen \& Kerbel, 2015; Tirumani,
Jagannathan, Krajewski, Shinagare, Jacene \& Ramaiya, 2013; Wertheimer
et al., 2015). Imatinib was almost the first TKI anti-tumour drug to
gain approval by the US Food and Drug Administration (FDA) and has
become a first-line clinical drug for gastrointestinal stromal tumours
(GIST) and chronic myeloid leukaemia (CLM) (O'Brien et al., 2003; von
Mehren \& Widmer, 2011). However, due to the long treatment cycles, the
safety of imatinib in combination with other drugs is particularly
important (Guilhot, 2004; Nebot, Crettol, d'Esposito, Tattam, Hibbs \&
Murray, 2010). As a multitargeted TKI, sunitinib exerts strong
angiogenesis inhibitory activity. It was approved by the FDA in 2006 as
a first-line drug for metastatic renal cell carcinoma, and it was also
used as a second-line drug for imatinib-resistant patients (Kalra, Rini
\& Jonasch, 2015). Gefitinib was the first TKI to gain approval in the
US and Japan for treating advanced non-small-cell lung cancer (NSCLC),
and can significantly prolong the progression-free survival of NSCLC
patients (Dhillon, 2015). It is noteworthy that CYP3A4 accounts for a
considerable proportion of the CYP-mediated metabolism of these three
TKIs, which is similar to rivaroxaban. Indeed, it is the overlap between
the metabolic enzymes for rivaroxaban and these three TKIs that may
produce DDIs.

The status of CYP2J2 in DDI evaluation is very different to that of
CYP3A4, which may relate to the distribution characteristics of CYP2J2.
CYP2J2 is a P450 isoform that is mainly distributed in the heart and
arteries and is responsible for the metabolism of arachidonic acid; its
expression is lower in the liver. However, CYP2J2 was recently
highlighted as an emerging tumour marker. Numerous studies have reported
the high expression of CYP2J2 in various cancer cell lines, tumour
tissues and even in the liver of cancer patients, which may relate to
tumour expansion and metastasis (Allison et al., 2017; Karkhanis, Hong
\& Chan, 2017). Therefore, inhibiting CYP2J2 may be a novel and
effective approach for cancer therapy (Allison et al., 2017). However,
safety assessment data on medication combinations are lacking, so the
related roles of CYP2J2 are unknown.

Imatinib, sunitinib and gefitinib have been widely applied for patients
with solid tumours in clinical practice. Thus, the comitant
administration of rivaroxaban with these three TKIs has a profound
combination foundation in the treatment of cancer patients. However, the
safety of this combination deserves further attention as assessment data
on the safety of rivaroxaban with TKIs is limited: more relevant and
detailed pharmacokinetics measurements are required. The present study
assessed the DDI risk of the combination of rivaroxaban with the three
TKIs by in vitro enzyme assays. Importantly, the investigation was
mainly performed on CYP2J2 and CYP3A4 to comprehensively explore their
reversible and time-dependent inactivation behaviours. Finally, the in
vivo DDI risk of the combination of rivaroxaban with the three TKIs was
estimated according to detailed pharmacokinetic parameters of cancer
patients, producing direct evidence to inform clinical medication safety
assessment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure. 1} Molecular structure of (A) imatinib, (B) sunitinib
and (C) gefitinib.

\textbf{Materials and methods}

\textbf{Materials and chemicals}

Rivaroxaban, sunitinib and NADPH were purchased from Shanghai Yuanye
Bio-Technology Co., Ltd. Imatinib and gefitinib were obtained from
Sigma-Aldrich (Missouri, USA). cDNA-expressed recombinant human CYP3A4
was purchased from Cypex Ltd (Dundee, UK). Pooled human liver microsomes
(HLM) and recombinant human CYP2J2 were purchased from BioreclamationIVT
(Maryland, USA). The pooled HLM and recombined P450 isoforms were stored
at --80  before experimentation. PBS was prepared from dipotassium
bisulphate and potassium dihydrogen sulphate in the appropriate
proportions and stored at 4  until use. All analytical reagent grade
and HPLC grade solvents were from Tedia, Inc. (Ohio, USA).

High-performance liquid chromatography (HPLC) was performed using an
Agilent MSD/MS system controller, two 1260 series pumps, a 1200 series
autosampler and a 1200 series variable wavelength detector. Other
instrumentation included a constant temperature vibrating mixer
(Hangzhou ALLSHENG Instrument Co. Ltd, China), scroll machine,
centrifuge and elite ODS-BP analysis column (4.6 \selectlanguage{ngerman}× 200 mm, 5 \selectlanguage{greek}μ\selectlanguage{english}m).

\textbf{Analytical methods}

The inhibitory effect of the TKIs on the metabolism of rivaroxaban in
recombined P450 isoforms and pooled human liver microsomes were compared
by quantifiably detecting the production of the major metabolite using
HPLC. The mobile phase consisted of 60\% methanol (A pump) and 40\% pure
water with 0.2\% formic acid (B pump) with isocratic elution. Detection
conditions were as follows: column temperature, 30 \selectlanguage{ngerman}°C; autosampler
injection volume, 20 \selectlanguage{greek}μ\selectlanguage{english}L; flow rate, 0.5 mL\selectlanguage{ngerman}·min\textsuperscript{--1};
and, detector wavelength, 240 nm. The amount of major rivaroxaban
metabolite (M1) was determined from the rivaroxaban standard curve, for
which the correlation coefficient was \textgreater{} 0.999.

\textbf{Enzyme inhibition assays}

The total volume of the incubation system with HLM was 200 \selectlanguage{greek}μ\selectlanguage{english}L, which
contained rivaroxaban (200 \selectlanguage{greek}μ\selectlanguage{english}M), HLM (0.3 mg\selectlanguage{ngerman}·mL\textsuperscript{--1}),
TKIs, NADPH (10 mM final) and PBS. The total volume of the incubation
system with recombined P450 isoforms was 100 \selectlanguage{greek}μ\selectlanguage{english}L, which contained
rivaroxaban (400 \selectlanguage{greek}μ\selectlanguage{english}M), recombined enzymes (CYP2J2 0.4
mg\selectlanguage{ngerman}·mL\textsuperscript{--1}, CYP3A4 0.6 mg·ml\textsuperscript{--1}),
TKIs, NADPH (10 mM final) and PBS. The selection of the rivaroxaban
concentration depended on the K\textsubscript{m} values of the kinetic
studies (22.81, 19.37 and 46.98 \selectlanguage{greek}μ\selectlanguage{english}M for HLM, CYP2J2 and CYP3A4,
respectively), as reported in our previous study (Zhao et al., 2021).
The concentration of CYP3A4 (0.6 mg\selectlanguage{ngerman}·mL\textsuperscript{--1}) was
selected to correspond with the lowest detected concentration of M1. The
detailed method can be found in our previous publication (Zhao et al.,
2021). Briefly, rivaroxaban, TKIs, enzymes and PBS were pre-incubated at
37 \selectlanguage{english} for 5 min, following which NADPH was added to initiate the
reaction. After 60 min, the reaction was terminated by adding an equal
volume of ice-cold methanol. The samples were centrifuged at 2000 x g
for 15 minutes at 4 \selectlanguage{english}. Aliquots of the supernatants were stored at --20
degC until analysis by HPLC.

\textbf{Reversible inhibition of CYP3A4 and CYP2J2 by TIKs}

Rivaroxaban was used as the probe substrate for the determination of
reversible inhibition kinetics values. The incubation system with CYP3A4
included rivaroxaban (0--400 \selectlanguage{greek}μ\selectlanguage{english}M), potential inhibitor (imatinib: 0--10
\selectlanguage{greek}μ\selectlanguage{english}M; gefitinib: 0--10 \selectlanguage{greek}μ\selectlanguage{english}M; sunitinib: 0--20 \selectlanguage{greek}μ\selectlanguage{english}M), NADPH and PBS. The
incubation system with CYP2J2 included rivaroxaban (0--100 \selectlanguage{greek}μ\selectlanguage{english}M),
potential inhibitor (imatinib, gefitinib and sunitinib), NADPH and PBS.
The inhibition constant (\emph{K\textsubscript{i}} ) was determined
using various concentrations of inhibitors and rivaroxaban.
K\emph{\textsubscript{i}}was calculated by three inhibition mode
formulae (competitive, non-competitive and mixed-mode) using Prism v.6.0
(GraphPad, San Diego, CA, USA). Detailed information on the fitting
formulae and related parameters can be found in our previous publication
(Li, Cao, He, Ge, Guo \& Wu, 2018).

\textbf{IC\textsubscript{50}shift assay}

The 30 min pre-incubation of TKIs with NADPH and CYP2J2 preceded the
normal incubation, following which the IC\textsubscript{50} values
(IC\textsubscript{50} shift) were re-determined. These
IC\textsubscript{50} shift values were compared with the
IC\textsubscript{50} values that were determined without the 30 min
pre-incubation, with a more than 1.5-fold decrease considered to be
evidence of time-dependent inactivation. Other reaction conditions were
as mentioned above.

\textbf{Time-dependent inactivation of CYP3A4}

To investigate the time-dependent inactivation of CYP3A4 by sunitinib,
seven gradient concentrations (0--5 \selectlanguage{greek}μ\selectlanguage{english}M) and six time points (0--20 min)
were used. It is worth noting that a higher substrate concentration than
its Michaelis-Menten constant is required to reduce reversible
inhibition. The data were then fitted to a linear regression model,
which reflected the linear relation between `ln remaining activity' and
`inactivation concentration' (\emph{I} ). The negative slope of this
linear relationship reflected the observed inactivation rates
(K\textsubscript{obs}) value, which could be plotted against \emph{I} to
allow the fitting of inactivation kinetic parameters\emph{K}
\textsubscript{I} and \emph{K} \textsubscript{inact} to the nonlinear
least-squares regression based on Eq. 1. Using Prism v.6.0 (GraphPad,
San Diego, CA, USA).

\textbf{(1)}

\textbf{Molecular docking simulations}

The CYP2J2 crystal structure homology model was used to conduct
molecular docking simulations between TKIs and rivaroxaban in SYBYL
(X-1.1). The CYP2J2 model was constructed using the Clustal Omega
webserver (https://www.ebi.ac.uk/Tools/msa/clustalo/), as previously
described (Ning et al., 2019). The crystal structure of CYP3A4 (PDB:
4D7D) was from the crystal structures that bound to a known inhibitor.
The 3D structures of the TKIs were subjected to energy minimisation
using the default Tripos force field parameters, and the
Gasteiger-H\selectlanguage{ngerman}ückel charges were calculated for each compound. The
Surflex-Dock mode was used to generate binding conformations of TKIs
with CYP2J2, from which the optimal conformations were determined by
their empirical functions ChemScore. The PyMOL Molecular Graphics System
v.16.1.0.15350 (DeLano Scientific LLC) was used to visualise the docking
results.

\textbf{Quantitative prediction of DDI risk}

Kinetic constants were included in the mechanistic static model to
explore reversible inhibition and time-dependent inactivation. This
static model was previously developed and refined by Fahmi et al.
(Fahmi, Maurer, Kish, Cardenas, Boldt \& Nettleton, 2008) and
Isoherranen et al. (Isoherranen, Lutz, Chung, Hachad, Levy \&
Ragueneau-Majlessi, 2012) to account for the inhibition of multiple P450
isoforms. In the present study, this model was designed to explore the
contributions of enzyme inhibition in the prediction of DDI risk. The
area under the curve ratio (AUCR) in the presence of a pharmacokinetic
DDI was used as the index, as described by Eq. 2.

\textbf{(2)}

Here, A is the time-dependent inactivation of each P450 isoform that was
observed in the liver, as described by Eq. 3.

\textbf{(3)(4)}

Here, B is the reversible inhibition of each P450 isoform that was
observed in the liver, as described by Eq. 4. The degradation rates
(K\textsubscript{deg}) of CYP2J2 and CYP3A4 were 0.00026 and 0.00032
min\textsuperscript{--1}, respectively (Cheong et al., 2017), where I
represented the in vivo concentration of inhibitors in healthy and solid
tumour patients. Additionally, the fraction of rivaroxaban metabolised
by CYP2J2 or CYP3A4 was input from our previous study (Zhao et al.,
2021), which were 0.95 for CYP2J2 and 0.025 for CYP3A4.

\textbf{Results}

\textbf{Initial screening of TKI inhibitory activity}

The inhibitory effects of the three TKIs, imatinib, sunitinib and
gefitinib, on rivaroxaban metabolism with HLM, CYP3A4 and CYP2J2 were
initially investigated at three TKI concentrations (1, 10, and 100 \selectlanguage{greek}μ\selectlanguage{english}M).
Generally, imatinib and gefitinib showed strong inhibition of the
metabolism of rivaroxaban, but sunitinib only exerted inhibitory
activity in the incubation with HLM and CYP3A4. As shown in Figure 2A,
imatinib had the most potent inhibitory effect on rivaroxaban metabolism
with HLM, inhibiting approximately 50\% of catalytic activity at 1 \selectlanguage{greek}μ\selectlanguage{english}M.
Imatinib also showed the strongest inhibitory effect on CYP3A4-mediated
rivaroxaban metabolism, with undetectable formation of M1 in the
incubation with 100 \selectlanguage{greek}μ\selectlanguage{english}M imatinib (Figure 2B). Imatinib and gefitinib
showed similar inhibition at 1 and 10 \selectlanguage{greek}μ\selectlanguage{english}M towards the metabolism mediated
by CYP2J2 (Figure 2C). While sunitinib had minimal inhibitory effect on
the metabolism of rivaroxaban by CYP2J2, the residual enzyme activity
was more than 70\% at 100 \selectlanguage{greek}μ\selectlanguage{english}M (Figure 2C).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 2.} The initial screening of imatinib, sunitinib and
gefitinib inhibition with: (A) HLM, (B) CYP2J2 and (C) CYP3A4. The TKI
concentrations were 1, 10 and 100 \selectlanguage{greek}μ\selectlanguage{english}M. Results are shown as the mean \selectlanguage{ngerman}±
S.D. of at least three determinations. N.D.: not detectable.

\textbf{Determination of IC\textsubscript{50} values}

The concentration range was determined according to the results obtained
from the initial screening. Both imatinib and gefitinib showed a strong
inhibitory effect on the rivaroxaban metabolism mediated by CYP3A4 and
CYP2J2, with IC\textsubscript{50} values below 10 \selectlanguage{greek}μ\selectlanguage{english}M (Figure 3B, C and
Table 1). Additionally, sunitinib exerted a potent inhibitory effect
against CYP3A4-mediated metabolism, while the effect on CYP2J2-mediated
metabolism was not obvious with an IC\textsubscript{50} value of 397.70
\selectlanguage{greek}μ\selectlanguage{english}M (Figure 3E, F). Notably, in CYP3A4-mediated rivaroxaban metabolism,
imatinib showed the strongest inhibitory activity with an
IC\textsubscript{50} value of 4.57 \selectlanguage{greek}μ\selectlanguage{english}M, while gefitinib showed the
strongest inhibitory effect on metabolism by CYP2J2 with an
IC\textsubscript{50} value of 3.71 \selectlanguage{greek}μ\selectlanguage{english}M. In the metabolism associated with
HLM incubation, imatinib was also the strongest inhibitor of the three
TKIs with an IC\textsubscript{50} of 1.69 \selectlanguage{greek}μ\selectlanguage{english}M (Figure 3A). The detailed
IC\textsubscript{50} values are shown in Table 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 3} Dose-response curves of TKI inhibition with (A, D)
HLM, (B, E) CYP2J2 and (C, F) CYP3A4. Results are shown as the mean from
at least three experiments.

\textbf{Table 1.} IC\textsubscript{50} values of TKI inhibition of
rivaroxaban metabolism.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \textbf{HLM} & \textbf{CYP2J2} &
\textbf{CYP3A4}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 1.69 \selectlanguage{ngerman}± 0.130 & 5.23 \selectlanguage{ngerman}± 0.0777 & 4.57 \selectlanguage{ngerman}±
0.234\tabularnewline
\textbf{Sunitinib} & 79.56 \selectlanguage{ngerman}± 7.01 & 397.70 \selectlanguage{ngerman}± 105 & 11.30 \selectlanguage{ngerman}±
0.561\tabularnewline
\textbf{Gefitinib} & 7.39 \selectlanguage{ngerman}± 0.203 & 3.71 \selectlanguage{ngerman}± 0.131 & 7.72 \selectlanguage{ngerman}±
0.492\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

Data are reported as \selectlanguage{greek}μ\selectlanguage{english}M and were obtained from three independent
experiments. All data represent the mean \selectlanguage{ngerman}± S.D.

\textbf{Reversible inhibition behaviour of TKIs on CYP3A4 and CYP2J2}

The reversible inhibition of CYP3A4 and CYP2J2 was investigated with
various concentrations of rivaroxaban in the presence and absence of the
TKIs (Figures 4, 5). The concentrations of rivaroxaban covered the
1/5K\textsubscript{m}--5K\textsubscript{m} range and the TKI
concentrations covered the IC\textsubscript{50} values. The
K\emph{\textsubscript{i}} value of the TKIs was fitted from the kinetic
curve and the R\textsuperscript{2} values and inhibition modes are shown
in Tables 2 and 3. As sunitinib did not exert more than 50\% inhibition
even at 250 \selectlanguage{greek}μ\selectlanguage{english}M, the K\emph{\textsubscript{i}} value was not measured.
All three TKIs showed non-competitive inhibition on CYP3A4- and
CYP2J2-mediated rivaroxaban metabolism with good correlation. This was
corroborated by the respective Dixon and Lineweaver--Burk plots, which
also showed that the TKIs inhibited rivaroxaban in the non-competitive
mode. As with the IC\textsubscript{50} results, imatinib showed the
strongest inhibition on CYP3A4-mediated rivaroxaban metabolism with a
K\emph{\textsubscript{i}} value of 1.92 \selectlanguage{greek}μ\selectlanguage{english}M. The
K\emph{\textsubscript{i}} values of sunitinib and gefitinib with CYP3A4
were 13.24 and 4.91 \selectlanguage{greek}μ\selectlanguage{english}M, respectively, which were similar to their
IC\textsubscript{50} values. Imatinib and gefitinib showed similar
inhibitory effects on CYP2J2, with K\emph{\textsubscript{i}} values of
3.53 and 2.99 \selectlanguage{greek}μ\selectlanguage{english}M, respectively. The detailed parameters are given in
Tables 1 and 2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 4.} Reversible inhibition of CYP2J2 by imatinib and
gefitinib. Lineweaver--Burk plots for the inhibition of (A) imatinib and
(C) gefitinib (C) on CYP2J2-mediated rivaroxaban metabolism; (B) and (D)
are the corresponding Dixon plots. The data represent the mean \selectlanguage{ngerman}± S.D. of
triplicate experiments.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 5.} Reversible inhibition of CYP3A4 by imatinib,
gefitinib and sunitinib. Lineweaver--Burk plots for the inhibition of
(A) imatinib, (C) gefitinib and (E) sunitinib on CYP3A4-mediated
rivaroxaban metabolism; (B), (D) and (F) are the corresponding Dixon
plots. The data represent the mean \selectlanguage{ngerman}± S.D. of triplicate experiments.

\textbf{Table 2.} Reversible inhibition kinetic parameters for
rivaroxaban metabolism mediated by CYP2J2.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \selectlanguage{greek}\textbf{K\textsubscript{i} (μM)}\selectlanguage{english} & \textbf{Type} &
\textbf{R\textsuperscript{2}}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 3.53 \selectlanguage{ngerman}± 0.221 & Non-competitive &
0.9514\tabularnewline
\textbf{Sunitinib} & N.D. & N.D. & N.D.\tabularnewline
\textbf{Gefitinib} & 2.99 \selectlanguage{ngerman}± 0.123 & Non-competitive &
0.9784\tabularnewline
\bottomrule
\end{longtable}

The data are reported as the mean \selectlanguage{ngerman}± S.D. of three incubations. N.D.: not
detectable.

\textbf{Table 3.}Reversible inhibition kinetic parameters of TKIs on
rivaroxaban metabolism mediated by CYP3A4.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \selectlanguage{greek}\textbf{K\textsubscript{i} (μM)}\selectlanguage{english} & \textbf{Type} &
\textbf{R\textsuperscript{2}}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 1.92 \selectlanguage{ngerman}± 0.779 & Non-competitive &
0.9795\tabularnewline
\textbf{Sunitinib} & 13.24 \selectlanguage{ngerman}± 0.756 & Non--competitive &
0.9581\tabularnewline
\textbf{Gefitinib} & 4.91 \selectlanguage{ngerman}± 0.254 & Noncompetitive &
0.9562\tabularnewline
\bottomrule
\end{longtable}

The data are reported as the mean \selectlanguage{ngerman}± S.D. of three incubations.

\textbf{IC\textsubscript{50}-shift assays on CYP2J2 and CYP3A4}

To comprehensively explore the inhibitory effects of the TKIs on the two
P450 isoforms, their mechanism-dependent inhibition was investigated.
IC\textsubscript{50} shift assays of CYP2J2 and CYP3A4 were performed.
Compared with direct inhibition (IC\textsubscript{50}: 5.23 and 3.71 \selectlanguage{greek}μ\selectlanguage{english}M
for imatinib and gefitinib, respectively), the 30-min pre-incubation
with NADPH in the CYP2J2 incubation did not significantly change the
inhibitory effect of imatinib and gefitinib (Figure 6A, C, and Table 4),
while the inhibitory effect of sunitinib was slightly increased after
the 30-min pre-incubation with NADPH in the CYP2J2 incubation with the
IC\textsubscript{50} decreasing 1.27-fold (Figure 6B and Table 4).
Although the 30-min pre-incubation slightly increased its inhibitory
activity, the inhibition of CYP2J2 by sunitinib was still weaker than by
imatinib and gefitinib, with an IC\textsubscript{50} of 312.90 \selectlanguage{greek}μ\selectlanguage{english}M (Table
4). In brief, the 30-min pre-incubation did not significantly affect the
inhibition of CYP2J2 by the three TKIs, with all
IC\textsubscript{50}shifts being less than 1.5-fold. Additionally, all
IC\textsubscript{50}values for the inhibition of CYP3A4 by the TKIs
decreased by more than 1.5-fold, which indicated that time-dependent
inactivation of CYP3A4 by all three TKIs had occurred (Table 5.).
Notably, sunitinib showed the largest change in IC\textsubscript{50}
shift (Figure 6E), with an IC\textsubscript{50} value following the 30
min pre-incubation of 2.73 \selectlanguage{greek}μ\selectlanguage{english}M.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 6.} Effects of imatinib, sunitinib and gefitinib on
rivaroxaban metabolism by (A, B and C) CYP2J2 and (D, E and F) CYP3A4
with or without a 30-min pre-incubation in the presence of NADPH. Data
points are from three independent experiments.

\textbf{Table 4.} IC\textsubscript{50} shifts initiated by
pre-incubation of the three TKIs with NADPH in CYP2J2 incubations.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \selectlanguage{greek}\textbf{IC\textsubscript{50} (μM)}\selectlanguage{english} &
\textbf{IC\textsubscript{50}-shift} & \textbf{Fold
decrease}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 5.23 \selectlanguage{ngerman}± 0.0777 & 7.43 \selectlanguage{ngerman}± 0.208 & 0.70\tabularnewline
\textbf{Sunitinib} & 397.70 \selectlanguage{ngerman}± 105 & 312.90 \selectlanguage{ngerman}± 9.93 & 1.27\tabularnewline
\textbf{Gefitinib} & 3.71 \selectlanguage{ngerman}± 0.131 & 3.51 \selectlanguage{ngerman}± 0.0930 & 1.06\tabularnewline
\bottomrule
\end{longtable}

Data are reported as the mean \selectlanguage{ngerman}± S.D. of three incubations.

\textbf{Table 5.}IC\textsubscript{50} shifts initiated by pre-incubation
of the three TKI drugs with NADPH in CYP3A4 incubations.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \selectlanguage{greek}\textbf{IC\textsubscript{50} (μM)}\selectlanguage{english} &
\textbf{IC\textsubscript{50}-shift} & \textbf{Fold
decrease}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 4.57 \selectlanguage{ngerman}± 0.234 & 2.55 \selectlanguage{ngerman}± 0.370 & 1.79\tabularnewline
\textbf{Sunitinib} & 11.30 \selectlanguage{ngerman}± 0.5606 & 2.73 \selectlanguage{ngerman}± 0.149 & 4.14\tabularnewline
\textbf{Gefitinib} & 7.72 \selectlanguage{ngerman}± 0.492 & 3.03 \selectlanguage{ngerman}± 0.218 & 2.55\tabularnewline
\bottomrule
\end{longtable}

Data are reported as the mean \selectlanguage{ngerman}± S.D. of three incubations.

\textbf{Time-dependent inactivation of CYP3A4 by sunitinib}

Given the 4.14-fold IC\textsubscript{50} shift of sunitinib on CYP3A4
following pre-incubation with NADPH for 30 min, various concentrations
and pre-incubation times were used to determine the time-dependent
inactivation constants (Figure 7A). The maximum inactivation rate
(K\emph{\textsubscript{inact})} and the inhibitor concentration needed
to cause half of K\textsubscript{inact} (K\textsubscript{I}) were fitted
using the non-linear regression method. As shown in Figure 7B, the
K\textsubscript{inact} and K\textsubscript{I} values of sunitinib were
0.0339 min\textsuperscript{--1} and 2.901 \selectlanguage{greek}μ\selectlanguage{english}M, respectively. The
K\textsubscript{inact} value indicated that approximately 3.4\% of
CYP3A4 was inactivated per minute when it was incubated with the
saturating concentration of sunitinib.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 7.} (A) Time- and concentration-dependent inactivation by
sunitinib of CYP3A4-mediated rivaroxaban metabolism. (B) Observed
inactivation rates (K\emph{\textsubscript{obs}} ) were plotted against
the sunitinib concentration to calculate the inactivation kinetic
constants K\emph{\textsubscript{I}} and K\emph{\textsubscript{inact}} .
Each point in Figure 7A was obtained from duplicate experiments.

\textbf{Molecular docking simulations}

Molecular docking simulations were used to elucidate the binding
conformations for the interactions between the TKIs and CYP2J2 or
CYP3A4. The corresponding ChemScore values of three TKIs are shown in
Figure. 8. In the docking simulation between CYP2J2 and the TKIs,
gefitinib had the lowest ChemScore value, followed by imatinib and then
sunitinib. This ChemScore ranking was consistent with the inhibitory
intensity of the TKIs on CYP2J2. Additionally, imatinib had the lowest
ChemScore value in the docking simulation with CYP3A4, followed by
gefitinib and then sunitinib, which was also consistent with the
inhibitory intensity of these TKIs on CYP3A4.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 8.} Molecular docking simulations of imatinib (A),
sunitinib (B) and gefitinib (C) with CYP2J2, and (D, E and F) with
CYP3A4.

\par\null

\textbf{Estimation of the DDI risk between rivaroxaban and the three
TKIs}

According to the inhibition constants of the TKIs for CYP2J2- and
CYP3A4-mediated rivaroxaban metabolism, the AUC fold-changes when the
TKIs were combined with rivaroxaban were predicted. All predictions were
performed based on the C\textsubscript{max} of patients with solid
tumours after administering recommended TKI doses (Gschwind et al.,
2005; Scheffler, Di Gion, Doroshyenko, Wolf \& Fuhr, 2011; Sparano et
al., 2009). For patients with solid tumours, imatinib was predicted to
influence rivaroxaban metabolism in vivo, leading to a 2.14--3.44-fold
change in the AUC, which was predicted to result in a maximum
rivaroxaban exposure increase of 244\% (Table 6). Sunitinib and
gefitinib were predicted to result in maximum rivaroxaban exposure
increases of 2\% and 11\%, respectively.

\textbf{Table 6.} Prediction of drug interaction risk in vivo arising
from inhibition of CYP2J2 and CYP3A4.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{TKI} & \textbf{I (nM)\textsuperscript{a}} & \textbf{AUC
ratio\textsuperscript{b}} & \textbf{AUC increase (\%)}\tabularnewline
\midrule
\endhead
\textbf{Imatinib} & 4,173--9,283 & 2.14--3.44 & 114--244\tabularnewline
\textbf{Sunitinib} & 62.99--69.52 & 1.02 & 2\tabularnewline
\textbf{Gefitinib} & 190.2--355.8 & 1.06--1.11 & 6--11\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a}I (\selectlanguage{greek}μ\selectlanguage{english}M) represents the C\textsubscript{max} of
patients with solid tumours, which were cited from (Di Gion et al.,
2011; Gschwind et al., 2005; Sparano et al., 2009) for imatinib, (Di
Gion et al., 2011) for sunitinib and (Scheffler, Di Gion, Doroshyenko,
Wolf \& Fuhr, 2011) for gefitinib.

\textsuperscript{b} The AUC ratio was calculated based on Equations
(2)--(4).

\textbf{Discussion}

There is extensive clinical application of rivaroxaban in combination
with TKIs, and cancer patients are one of the main groups that receive
rivaroxaban treatment. Due to the hypercoagulability of cancer patients
and the thrombogenicity of anti-cancer agents, anticoagulant therapy has
become an essential treatment for cancer patients. Increasing evidence
has indicated that DOACs are safer and more convenient than classical
anticoagulation medications, which is reflected in increasing clinical
guidelines recommending that DOACs replace classical anticoagulation
medications for cancer patient VTE therapy. In our previous study,
CYP2J2, but not CYP3A4, was found to be dominant in the P450 metabolism
of rivaroxaban with a contribution of 41.1\%. However, few published
reports have targeted CYP2J2 to explore the safety of rivaroxaban
combinations, and even fewer of these have looked at rivaroxaban in
combination with TKIs. Therefore, the estimation of DDI risk between
TKIs and rivaroxaban based on CYP2J2 and CYP3A4 is necessary and
meaningful for clinical practice. This was the focus of the present
study.

All three TKIs were found to have a remarkable inhibitory effect on
CYP3A4-mediated rivaroxaban metabolism. Imatinib showed the strongest
reversible inhibitory effect towards CYP3A4 with a
K\emph{\textsubscript{i}} value of 1.92 \selectlanguage{greek}μ\selectlanguage{english}M. The inhibition of CYP3A4 by
sunitinib or gefitinib was also potent, with
K\emph{\textsubscript{i}}values of 13.24 and 4.91 \selectlanguage{greek}μ\selectlanguage{english}M, respectively. In
addition to reversible inhibition, the three TKIs also effected
time-dependent inactivation of CYP3A4, especially sunitinib, which
greatly increased the DDI risk in combination with rivaroxaban. Compared
with reversible inhibition, mechanism-dependent inactivation is always
more frequently related to unfavourable DDIs in clinical practice
(Kalgutkar, Obach \& Maurer, 2007). More importantly, the expression of
CYP3A4---which is abundantly expressed P450 isoform in the liver in most
individuals---showed more than 100-fold population variability (Zanger
\& Schwab, 2013). And meanwhile, it has been reported that sunitinib has
a 10-fold higher concentration in the liver than in blood (Lau et al.,
2015). Thus, being cautious about the CYP3A4 enzyme expression in
patients who take both rivaroxaban and TKIs is necessary.

The present study found that CYP2J2, which dominates the metabolism of
rivaroxaban, was inhibited by the TKIs. Notably, the three TKIs showed
different inhibitory activity: imatinib and gefitinib were potent
inhibitors of CYP2J2 with K\textsubscript{\emph{i}} values of 3.53 and
2.99 \selectlanguage{greek}μ\selectlanguage{english}M, respectively, but sunitinib had almost no inhibitory effect on
CYP2J2. Additionally, there was no irreversible inhibition of CYP2J2 by
the three TKIs, with the results showing IC\textsubscript{50} shifts of
less than 1.5-fold. The basic principle of mechanism-based inactivation
is the bioactivation of inhibitors, which is achieved by their
metabolism by the inhibited enzymes (Kalgutkar, Obach \& Maurer, 2007).
Therefore, the results might suggest that the three TKIs were not or
were rarely metabolised by CYP2J2.

Although the three TKIs did not irreversibly inactivate CYP2J2-mediated
rivaroxaban metabolism, the DDI risk produced by inhibiting CYP2J2
cannot be ignored. The distribution of CYP2J2 in vivo is a potential
factor that may increase the DDI risk of rivaroxaban in combination with
TKIs. CYP2J2 was first detected in the liver but was since identified as
an enzyme that is mainly distributed in the heart (Das, Weigle, Arnold,
Kim, Carnevale \& Huff, 2020); indeed, the mRNA levels of CYP2J2 in the
cardiovascular system exceed those of other detected isozymes by 3
million to 62 times (Michaud, Frappier, Dumas \& Turgeon, 2010; Wu,
Moomaw, Tomer, Falck \& Zeldin, 1996). Although CYP2J2 is not usually
considered to be a DDI target due to its lower content in the liver, if
the heart is set as the organ for potential DDIs, there may be an
extremely high risk of DDI between rivaroxaban and TKIs.

In addition to its physiological distribution in the cardiovascular
system, CYP2J2 has also recently been found to be highly expressed in
various tumour tissues (Das, Weigle, Arnold, Kim, Carnevale \& Huff,
2020; Jiang et al., 2005; Karkhanis, Hong \& Chan, 2017). Due to the
overexpression of receptors in tumours (such as PDGFR and EGFR), TKIs
would accumulate in the tumour tissue. Therefore, the DDI risks of
rivaroxaban in combination with TKIs for cancer patients may be higher
than in our predictions, and CYP2J2 may need to become a target for DDI
assessments involving rivaroxaban.

Imatinib was predicted to have moderate DDI risk when combined with
rivaroxaban. Based on the inhibitory constants of imatinib on CYP2J2 and
CYP3A4, and the metabolic contributions of two isoforms, imatinib was
predicted to yield at most a 2.44-fold rivaroxaban AUC increase.
Therefore, according to the FDA's guidelines for the relative risk of
DDIs, a moderate DDI risk may exist for the combination of rivaroxaban
and imatinib (Table 6). This result was predicted from the
pharmacokinetic data of patients with solid tumours who were received
TKIs. Notably, cancer patients have poorer metabolic function than
normal patients, which may directly lead to higher plasma concentrations
of TKIs for cancer patients. Therefore, using pharmacokinetic data from
cancer patients may make our prediction more accurate. However,
metabolic enzyme activity in the normal population is subject to
individual variability, so factors such as hepatic blood flow and
genetic polymorphism cannot be neglected (Zanger \& Schwab, 2013).
Factors that influence the enzyme activity of cancer patients may be
more difficult to determine due to the more complicated in vivo
environment. All these factors would affect the accuracy of our
prediction in clinical practice. Additionally, the plasma concentrations
of anti-cancer drugs in patients with solid tumours that are refractory
to standard therapy are much higher than those of normal patients, which
may result from large doses of anti-cancer drugs and metabolic function
disorders (Scheffler, Di Gion, Doroshyenko, Wolf \& Fuhr, 2011).
Furthermore, it has been reported that lower plasma concentrations of
TKIs (\textless{} 1100 ng\selectlanguage{ngerman}·ml\textsuperscript{--1}) are closely related
to the much faster development of progressive disease and lower
objective response rates for GIST patients (von Mehren \& Widmer, 2011).
All these factors would produce great diversity in the pharmacokinetic
data of TKIs in cancer patients. Thus, a rough in vitro prediction may
neglect to indicate the risk of severe DDI in some individuals.
Therefore, an individual physiologically-based pharmacokinetic model for
cancer patients would be encouraged in the prediction of the
pharmacokinetic behaviour of rivaroxaban in combination with TKIs.

In summary, all three TKIs (imatinib, sunitinib and gefitinib) showed
inhibitory effects on CYP2J2- and CYP3A4-mediated rivaroxaban
metabolism. Imatinib and gefitinib exerted significant reversible
inhibition of CYP2J2 and CYP3A4, while sunitinib only showed reversible
inhibition of CYP3A4. The three TKIs also demonstrated time-dependent
inactivation of CYP3A4, with this effect being slight on CYP2J2.
Furthermore, the combination of rivaroxaban with imatinib was predicted
to constitute a moderate DDI risk. Our results provide data for the
clinical safety assessment of the combination of rivaroxaban with
imatinib, sunitinib and gefitinib in cancer patients, and also give new
insights for DDI assessment involving rivaroxaban.

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