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. 2026 May 26;20:579360. doi: 10.2147/DDDT.S579360

An Evaluation of the Inhibitory Effect of Apatinib on Ilaprazole Metabolism: in vitro, in vivo and in silico Study

Ruibin Li 1,2,*, Shiqi Jiang 1,2,*, Yan Chen 1, Peiqi Wang 1, Haoxin Fu 1, Jun Wu 1, Wenzhi Wu 3,✉
PMCID: PMC13222014  PMID: 42221355

Abstract

Background

Ilaprazole is a novel proton pump inhibitor (PPI) approved orally in China and Korea for the treatment of gastric ulcers, duodenal ulcers (DU) and erosive esophagitis. It is mainly metabolized to ilaprazole sulfone by CYP3A4.

Methods

The quantitative determination method of ilaprazole and its metabolite ilaprazole sulfone was optimized and fully validated by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). In addition, the effect of apatinib on ilaprazole and its metabolite ilaprazole sulfone was evaluated by in vitro and in vivo experiments using rat liver microsomes (RLM), human liver microsomes (HLM), molecular docking and Sprague-Dawley (SD) rats.

Results

The results of bioanalytical methodology showed that ilaprazole and ilaprazole sulfone had good linear relationship in plasma samples, and their selectivity, accuracy, precision, stability, matrix effect, recovery, carry-over, dilution integrity and re-injection reproducibility all met the needs of quantitative analysis of samples. In vitro, apatinib inhibited the metabolism of ilaprazole with half-maximum inhibitory concentration (IC50) values of 1.60 and 16.28 μM in RLM and HLM, respectively, and was a mixed inhibitor (non-competitive + competitive) in both systems. In addition, in both systems, the metabolic stability results indicated that apatinib inhibited the metabolism of ilaprazole. In vivo, under the action of apatinib, the AUC(0-t), AUC(0-∞), Cmax of ilaprazole and the AUC(0-t), AUC(0-∞), t1/2, Cmax of its metabolite ilaprazole sulfone were all significantly increased, while the CLz/F of both were significantly decreased. In vitro and in vivo studies showed that apatinib inhibited the metabolism of ilaprazole. Finally, molecular docking was performed as a supportive visualization tool and suggested possible spatial overlap within the CYP3A4 active site.

Conclusion

In vitro systems and SD rats, apatinib exhibited an inhibitory effect on the metabolism of ilaprazole. In addition, molecular docking provided structural context for the observed interaction. However, whether the same phenomenon exists in humans remains to be further investigated.

Keywords: ilaprazole, apatinib, drug-drug interaction, UPLC-MS/MS, molecular docking

Introduction

Duodenal ulcer (DU) is a chronic disease characterized by remission and recurrence. The tendency to relapse is unpredictable due to individual differences and may persist for many years and even for life in most patients.1 The main cause of occurrence and recurrence of DU is Helicobacter pylori infection. To date, the incidence and prevalence of DU remain high, imposing a considerable economic burden on healthcare systems.2,3 Gastric acid hypersecretion is a key pathogenic mechanism of DU.4 Acid suppression therapy is crucial in the treatment of DU. The introduction of PPIs represented a major therapeutic breakthrough, as PPIs are more effective than histamine H2-receptor antagonists in promoting ulcer healing.5,6 Omeprazole, one of the earliest proton pump inhibitors (PPIs), has been widely used in clinical practice with proven efficacy.7 However, omeprazole requires a higher dose to achieve the same acid inhibitory effect compared to other PPIs and may have adverse effects on hematological characteristics and renal function.8–11 Therefore, the current research on PPIs will focus on more effective, lower dose and better safety.12

Ilaprazole is a novel PPI approved orally in China and Korea for the treatment of gastric ulcers, DU and erosive esophagitis.13 Although first- and second-generation PPIs (such as omeprazole, lansoprazole, and esomeprazole) are generally safe and effective, treatment still presents many challenges, including significant individual variability and short duration of acid suppression.13,14 Studies have shown that ilaprazole provides stronger inhibition of acid secretion, longer half-life, higher bacteriostasis and similar safety profile in a dose-dependent manner compared to omeprazole.15 Compared with esomeprazole, ilaprazole can achieve the same level of intragastric acid suppression with a lower dose and less frequent administration.16 In addition, several clinical trials have compared the efficacy of ilaprazole with other PPIs in the treatment of DU, and the results demonstrated that ilaprazole achieved a higher 4-week healing rate.17 Therefore, ilaprazole is a drug with good therapeutic effect and high safety, showing unique advantages in clinical practice.

CYP3A4 is responsible for the metabolism of half of all clinically used drugs, and alterations in its catalytic activity are critically important for bioavailability and drug-drug interaction (DDI).18 Inhibition of CYP3A4 can lead to substantial increases in systemic exposure to sensitive substrates, potentially enhancing pharmacological effects or increasing the risk of adverse reactions.19 Currently, CYP3A4-mediated DDI are widely recognized as a primary source of clinically relevant interactions.19 In human liver microsomes (HLM) incubations, the addition of a CYP3A-selective inhibitor (such as ketoconazole) or anti-CYP3A4 immunoglobulin significantly increased ilaprazole concentrations, indicating that its metabolism is markedly inhibited by CYP3A4 activity.20 Studies have shown that ilaprazole is mainly metabolized by CYP3A4 to form the main metabolite ilaprazole sulfone.21,22 Therefore, co-administration of ilaprazole with CYP3A4 inhibitors may alter its therapeutic efficacy and increase the potential risk of adverse effects, such as diarrhea, headache, dizziness, abnormal liver function, rash, urticaria, etc.20

Apatinib is a novel inhibitor of VEGFR-2 tyrosine kinase and has been approved as an antiangiogenic drug for the treatment of advanced or metastatic gastric cancer (GC).23,24 Gastrointestinal reactions are common adverse reactions in cancer treatment. Treatment with tyrosine kinase inhibitors (TKIs) often causes gastrointestinal symptoms such as nausea, vomiting, and abdominal pain, which may lead to abnormal acid secretion and DU.25–27 This greatly affects the quality of life of patients, so ilaprazole may be needed to alleviate the symptoms caused by the treatment of these drugs. Moreover, helicobacter pylori infection is the main causative factor of DU and GC, and DU can progress to GC under the condition of increased reactive oxygen species.27,28 Therefore, these two symptoms may coexist clinically and require treatment with symptomatic drugs. In addition, studies have shown that ilaprazole can inhibit T cell-derived protein kinases and show potential anti-cancer effects.29 Therefore, apatinib in combination with ilaprazole may be a viable clinical treatment option. Apatinib is a substrate and inhibitor of CYP3A4.30 Given that ilaprazole is mainly metabolized by CYP3A4, a potential DDI may occur when both are used in combination. The potential metabolic DDI between ilaprazole and apatinib has not been systematically investigated, particularly with respect to inhibitory potency, underlying mechanisms, and consistency between in vitro and in vivo systems.

In this study, we established and validated an ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous determination of ilaprazole and its metabolite ilaprazole sulfone. We investigated the inhibitory effect of apatinib on the metabolism of ilaprazole in vitro with rat liver microsomes (RLM) and HLM and explored the inhibitory mechanism of apatinib on ilaprazole. In addition, the metabolic stability of apatinib on ilprazole was investigated. Sprague-Dawley (SD) rats were used to study the interaction between apatinib and ilaprazole in vivo. Finally, molecular docking was performed as an auxiliary visualization tool to qualitatively illustrate the potential binding orientations of ilaprazole and apatinib within the CYP3A4 active site. This study is expected to provide basic data for clinical application of ilaprazole.

Materials and Methods

Chemicals and Reagents

Ilaprazole and ilaprazole sulfone were purchased from Shanghai Canspec Scientific & Technology Co., Ltd. (Shanghai, China). Apatinib was kindly provided by Jiangsu Hengrui Pharmaceutical Co., Ltd. (Jiangsu, China). Omeprazole (internal standard, IS) and ketoconazole were purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China). HLM was obtained from iPhase Pharmaceutical Services Co., Ltd. (Beijing, China), while RLM was prepared by our own team.31 Nicotinamide adenine dinucleotide phosphate (NADPH) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). All the unmentioned chemicals and reagents in this experiment were of analytical grade or higher.

Equipment and Operating Conditions

UPLC-MS/MS equipped with a Waters Acquity UPLC BEH C18 column (2.1 mm × 50 mm, 1.7 μm; Waters Corp., Millipore, Bedford, MA, USA) was used to quantify ilaprazole and its metabolite ilaprazole sulfone. Analytes were quantified by multiple reaction monitoring (MRM) in positive ion mode using a Waters XEVO TQ-S triple quadrupole mass spectrometer. 0.1% formic acid solution (A) and acetonitrile (B) were used as the mobile phase, eluting with a gradient flow rate of 0.40 mL/min for 2 min. The column temperature was set at 40°C and the autosampler was set at 10°C. The main operating parameters of ilaprazole, ilaprazole sulfone and IS for mass spectrometers were summarized in Table 1.

Table 1.

Ion Transitions and the Main Operating Parameters of Ilaprazole, Ilaprazole Sulfone, and Omeprazole (IS) in the Mass Spectrometer

Analytes Parent (m/z) Daughter (m/z) Cone (V) Collision (eV)
Ilaprazole 367.1 184.4 10 20
Ilaprazole sulfone 383.3 184.1 10 20
IS 345.9 198.2 10 20
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Standard Solutions, Calibration Standards, and Quality Control (QC) Sample Preparation

Stock solutions of ilaprazole, ilaprazole sulfone, and IS were prepared in methanol at a concentration of 1.0 mg/mL. These stock solutions were used to prepare calibration standards and quality control (QC) samples. The concentration ranges of the calibration curves were selected based on the plasma concentrations observed in rats and were 1–1000 ng/mL for ilaprazole and 0.5–100 ng/mL for ilaprazole sulfone. Working solutions at different concentrations were obtained by serial dilution with methanol. Three QC levels were prepared for each analyte. The QC concentrations for ilaprazole were 2 ng/mL (LQC), 400 ng/mL (MQC), and 800 ng/mL (HQC), while those for ilaprazole sulfone were 1 ng/mL (LQC), 40 ng/mL (MQC), and 80 ng/mL (HQC). The lower limits of quantification (LLOQ) were 1 ng/mL for ilaprazole and 0.5 ng/mL for ilaprazole sulfone, respectively. The IS working solution was prepared by diluting the stock solution with methanol to a final concentration of 200 ng/mL. All solutions were stored at −80 °C for subsequent use.

Method Verification

To ensure the accuracy and reliability of the analytical method, method validation was performed in accordance with the latest U.S. Food and Drug Administration (FDA) guidance for bioanalytical method validation.32 The validation included the assessments of selectivity, accuracy, precision, recovery, matrix effect, stability, carry-over, dilution reliability and re-injection reproducibility, and all evaluation parameters should be within the acceptable range.

Determination of Half-Maximum Inhibitory Concentration (IC50) in vitro

An in vitro liver microsome incubation system with a volume of 200 μL was established for the metabolism of ilaprazole in RLM and HLM. The system was contained of PBS (0.1 M), ilaprazole (1, 2, 5, 10, 20, 50, 100, 150 μM), RLM or HLM (0.3 mg/mL), and NADPH (1 mM). The above system was used to determine the Michaelis-Menten constant (Km) of ilaprazole in RLM and HLM, respectively. To determine the IC50 value of apatinib for inhibiting the metabolism of ilaprazole, the concentrations of apatinib were designed to be 0.01, 0.1, 1, 10, 25, 50, and 100 μM, while the concentration of ilaprazole was the corresponding Km value of the system. Additionally, the potent CYP3A4 inhibitor ketoconazole was used as a positive control at concentrations of 0.01, 0.1, 1, 10, 25, 50, and 100 μM.33 The constructed system was pre-incubated at 37°C for 5 min, and then NADPH was added to initiate the reaction. After the system was incubated at 37°C for 30 min, it was transferred to –80°C. Then, 400 μL of acetonitrile and 20 μL of IS working solution (200 ng/mL) were added. After vortexing for 2 min and centrifugation at 13000 rpm for 10 min, 100 μL of supernatant was obtained and injected into the injection vial for UPLC-MS/MS analysis.

Inhibitory Mechanism of Apatinib on Ilaprazole in vitro

To explore the potential mechanism of inhibition of ilaprazole by apatinib, the concentrations of apatinib and ilaprazole were set according to the IC50 value of apatinib and the Km value of ilaprazole, respectively. In RLM, the concentrations of apatinib were 0, 0.80, 1.60, and 3.20 μM, and the concentrations of ilaprazole were 2.05, 4.10, 8.20, and 16.40 μM. In HLM, the concentrations of apatinib were 0, 4.07, 8.14, and 16.28 μM, and the concentrations of ilaprazole were 10.94, 21.88, 43.76, and 87.52 μM. Finally, the samples were processed as described above. Four inhibition models (competitive, noncompetitive, uncompetitive, and mixed) were simultaneously fitted to the experimental data using nonlinear regression in GraphPad Prism 9.5. Model selection was performed based on the Akaike Information Criterion (AIC), and the model with the lowest AIC value was considered to provide the best fit. ΔAIC values were calculated relative to the model with the minimum AIC. The coefficient of determination (R2) was further used to evaluate the goodness-of-fit.

Metabolic Stability

To verify the metabolic stability of ilaprazole, we constructed a system (200 μL) consisting of 1.0 μM ilaprazole, 0.3 mg/mL RLM or HLM, 1 mM NADPH, 0.1 M PBS and apatinib (in RLM: 1.60 μM; in HLM: 16.28 μM). The group without apatinib in the incubation system served as the control. In RLM, the incubation system was pre-incubated for 5 min, then NADPH was added and incubated for 0, 20, 30, 40, 50, 60, 80, and 100 min. In HLM, the incubation system was incubated for 0, 5, 15, 20, 25, 30, 35 and 50 min. The post-treatment method was the same as that of the above enzyme reaction.

In vivo Experiments

Ten Sprague-Dawley (SD) male rats were purchased from the Laboratory Animal Center of The First Affiliated Hospital of Wenzhou Medical University to study the interaction between apatinib and ilaprazole. Rats were housed in SPF-grade animal facilities with free access to standard chow and water, and were fasted for 12 h prior to the pharmacokinetic study. The housing environment was maintained under controlled conditions, with a temperature of 18–29 °C (diurnal fluctuation ≤3 °C), relative humidity of 40%–70%, and a 12 h light/12 h dark cycle. Healthy male SD rats within the predefined body weight range were included, and animals with abnormal health status, behavior, or body weight during acclimatization were excluded prior to dosing. Apatinib is clinically administered at a daily dose of 500 mg, and ilaprazole is used at daily doses of 10, 20, and 40 mg, all of which have demonstrated good safety profiles in clinical practice.14,34 The corresponding animal doses were converted based on body surface area normalization. In addition, the dosing regimens of ilaprazole and apatinib in SD rats have been reported in previous studies.35,36 Therefore, doses of 3 mg/kg for ilaprazole and 40 mg/kg for apatinib were selected for the present animal experiment. Blinding was not implemented during sample collection and analysis. The sample size (n = 5 per group) was determined based on prior pharmacokinetic and DDI studies conducted in rats, which indicated that this sample size would be sufficient to detect biologically meaningful differences while minimizing animal use.37

SD rats were randomly divided into two groups (n = 5 per group). The control group (ilaprazole alone) was received a single oral dose of 3 mg/kg ilaprazole, and the treatment group (ilaprazole with apatinib) was received a single oral dose of 3 mg/kg ilaprazole and 40 mg/kg apatinib. The rats were fasted for 12 h before the experiment, but had free access to water. During the experiment, the treatment group was orally administered apatinib dissolved in corn oil. After 30 min, both groups were orally administered ilaprazole dissolved in 0.5% carboxymethylcellulose sodium (CMC-Na) solution. Then, blood samples (0.3 mL) were collected from the tail vein of the rats at 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, and 12 h. Plasma was separated by centrifugation at 13,000 rpm for 10 min. Finally, plasma supernatant (100 μL) was stored at −80 °C for further analysis. All experimental procedures were supervised and approved by the Institutional Animal Care and Use Committee (IACUC) of the First Affiliated Hospital of Wenzhou Medical University, and the review ethics number was WYYY-IACUC-AEC-2025-057. This experiment was conducted in accordance with the Guidelines for Ethical Review of the Welfare of Laboratory Animals (GB/T 35892-2018), issued by the National Standardization Administration of China. Animal welfare was monitored throughout the study. Rats were observed daily for general health status, behavior, body weight changes, and signs of pain or distress, and no animals met the predefined humane endpoint criteria. After the experiment, all rats were euthanized by intravenous injection of 150 mg/kg phenobarbital.

Molecular Docking

The sdf structures of apatinib and ilaprazole were obtained from the PubChem database and converted to pdb format for molecular docking. The three-dimensional structure of CYP3A4 (PDB ID: 5ET8) was obtained from the protein database. These structures were optimized by removing water molecules, ligands, and other heteroatoms using PyMOL software. AutoDock Tools 1.5.6 was used for the pre-processing of target proteins and ligands prior to molecular docking, including the addition of polar hydrogens and the calculation of Gasteiger charges. Subsequently, molecular docking was performed using AutoDock 1.5.6 with a grid box centered on the active site of CYP3A4 and dimensions of 50 Å × 50 Å × 50 Å. Representative docking conformations were visualized using PyMOL to illustrate possible ligand-enzyme interactions.

Statistical Analysis

The Michaelis-Menten, IC50, Lineweaver-Burk plots and mean plasma concentration–time curves were calculated and plotted using GraphPad Prism 9.5. Drug and Statistics (DAS) software (version 3.0, Mathematical Pharmacology Professional Committee of China, Shanghai, China) was employed to establish a non-compartmental model for determining the pharmacokinetic parameters of ilaprazole. Statistical analyses were performed using SPSS (version 27.0; SPSS Inc., Chicago, IL, USA). The normality of data distribution was assessed using the Shapiro–Wilk test, and the homogeneity of variances was evaluated using Levene’s test. When both normality and equal variance assumptions were satisfied, differences between two groups were analyzed using the Student’s t-test. If the assumption of equal variances was violated, Welch’s t-test was applied. For data that did not follow a normal distribution, the Mann–Whitney U-test was used. p-value < 0.05 was regarded as statistically significant. Data are represented as mean ± standard deviation (SD).

Results

Method Verification

Selectivity

An UPLC–MS/MS method was developed for the simultaneous measurement of ilaprazole and ilaprazole sulfone in rat plasma. This method was sensitive, reliable, and efficient. The retention times of ilaprazole, ilaprazole sulfone, and IS were 1.19, 1.25 and 1.18 min, respectively. The two analytes and IS were well separated and showed no interference from endogenous substances. The selectivity of the method was assessed by comparing blank rat plasma, blank plasma spiked with the analytes at LLOQ, and plasma samples collected after oral administration of ilaprazole (as shown in Figure 1). No apparent endogenous interference was observed at the retention times of the analytes or IS, demonstrating that this method had good selectivity.

Figure 1.

Three chromatograms showing ilaprazole, ilaprazole sulfone and omeprazole retention times in plasma samples. Image A displays three chromatograms labeled MRM of 3 Channels ES plus. Chromatogram 1 shows ilaprazole sulfone with retention times at 1.28 and 1.29 minutes. Chromatogram 2 shows ilaprazole with retention times at 1.25 and 1.37 minutes. Chromatogram 3 shows omeprazole with retention times at 1.18 and 1.19 minutes. Image B also shows three chromatograms labeled MRM of 3 Channels ES plus. Chromatogram 1 shows ilaprazole sulfone at 1.25 minutes. Chromatogram 2 shows ilaprazole at 1.19 minutes. Chromatogram 3 shows omeprazole at 1.18 minutes. Image C repeats the same pattern as Image B, with ilaprazole sulfone at 1.25 minutes, ilaprazole at 1.19 minutes and omeprazole at 1.18 minutes. The x-axis is labeled Time in minutes and the y-axis is labeled percentage. The chromatogram in Image A represents a blank plasma sample without analytes and IS. The chromatograms in Images B and C represent different plasma samples containing analytes and IS.

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Representative chromatograms of ilaprazole, ilaprazole sulfone, and IS. (A) Blank plasma sample (no analyte, no IS); (B) Blank plasma sample containing the analytes and IS; (C) Rat plasma sample containing the analytes and IS.

The Linearity of the Standard Curve

The calibration curves of ilaprazole and ilaprazole sulfone exhibited good linearity over the concentration ranges of 1–1000 ng/mL and 0.5–100 ng/mL, respectively. Calibration curves were generated using 1/x2 weighted linear least squares regression by plotting the peak area ratios of the analytes to IS versus nominal concentrations. The corresponding regression equations were y = (0.041853*x + 0.012187, r2 = 0.996) for ilaprazole and y = (0.058114*x + 0.010038, r2 = 0.993) for ilaprazole sulfone, respectively.

Precision, Accuracy, Recovery and Matrix Effect

The accuracy and precision of each analyte were evaluated using QC samples at four concentration levels. The results showed that the accuracy of ilaprazole was below 3.0% and precision below 2.9%; for ilaprazole sulfone, accuracy was consistently below 5.1% and precision below 4.8%. At the LLOQ level, both accuracy and precision met the ± 20% acceptance criteria. The recovery and matrix effect results satisfied the acceptance limits of ± 15%. All validation results are summarized in Tables S1 and S2.

Stability

Stability studies were conducted under four conditions: room temperature storage for 3 h, storage in an autosampler at 10°C for 4 h, exposure to three complete freeze-thaw cycles, and storage at −80°C for 3 weeks. Under all conditions, QC samples of ilaprazole and ilaprazole sulfone at various concentration levels demonstrated excellent stability, with results meeting the acceptance criteria for bioanalytical method validation established by the U.S. FDA. Relevant data are summarized in Table S3.

Carry-Over

Carry-over was evaluated by injecting blank plasma samples immediately following the upper limit of quantification (ULOQ) of ilaprazole and ilaprazole sulfone. The analyte signals detected in the blank samples were below 20% of the LLOQ, and the IS response was below 5% of its nominal value. As shown in Table S4, the results indicated that no significant carry-over effect was observed, ensuring accurate quantification of the subsequent samples.

Dilution Integrity

Dilution integrity was evaluated by diluting dilution QC (DQC) samples 5- and 10-fold with blank plasma to assess the accuracy and precision of each analyte. The nominal concentrations of the DQC samples were 4000 ng/mL for ilaprazole and 400 ng/mL for ilaprazole sulfone. The results are summarized in Table S5. The data demonstrated that biological samples with concentrations between the ULOQ and the DQC levels could be accurately and precisely quantified after 5-fold and 10-fold dilution.

Re-Injection Reproducibility

Re-injection reproducibility was evaluated by re-analyzing QC samples (LQC, MQC, HQC) after storage in the autosampler for 24 h. The results are summarized in Table S6, indicating that the samples were stable and could be reliably re-injected for analysis.

Effect of Apatinib on the Metabolism of Ilaprazole in vitro

The Michaelis–Menten curves and IC50 curves of ilaprazole in RLM and HLM are shown in Figure 2. In RLM and HLM, the Km values of ilaprazole were 8.20 ± 0.30 and 43.76 ± 3.17 μM, respectively. The IC50 values of apatinib for inhibiting ilaprazole metabolism were 1.60 ± 0.02 and 16.28 ± 0.57 μM, respectively. For ketoconazole, the IC50 values for inhibition of ilaprazole metabolism in RLM and HLM were 0.27 ± 0.01 and 2.30 ± 0.92 μM, respectively. Nonlinear regression was used to analyze four inhibition models. Model comparison was performed using the AIC. The model with the lowest AIC was considered the best-fitting model, and ΔAIC values were calculated relative to this minimum AIC. As shown in Table 2 and Table 3, the mixed inhibition model (combining competitive and noncompetitive inhibition) had the lowest AIC (ΔAIC = 0) in RLM and HLM, indicating the best fit among the tested models. Models with higher ΔAIC values showed progressively poorer fits. Consistently, the mixed inhibition model also exhibited the highest R2 value, further supporting its suitability as the best-fitting model for describing the observed inhibition kinetics. In RLM and HLM, Table 2 and Table 3 show that under the mixed inhibition model, the αKi values were 0.86 μM and 28.57 μM, respectively, while the Ki values were 0.35 μM and 7.30 μM, respectively. The nonlinear regression curves of inhibition kinetics analyzed using the Michaelis–Menten equation and the mixed inhibition model are shown in Figure 3. The kinetic parameters (Km and Vmax) derived from the Michaelis–Menten equation are summarized in Table 4. In the RLM and HLM systems, with increasing concentrations of apatinib, the metabolism of ilaprazole showed an increase in Km and a decrease in Vmax. This kinetic profile was consistent with the characteristics of mixed-type inhibition, involving both competitive and noncompetitive inhibition. To facilitate data visualization, Lineweaver–Burk plots were also generated (Figure S1). However, the primary mechanistic evidence was derived from nonlinear regression and model comparison, whereas the Lineweaver–Burk plots served only a supplementary and illustrative role.

Figure 2.

Two panels showing Michaelis-Menten and IC50 curves for ilaprazole in RLM and HLM systems. Image A displays two graphs for RLM. The first is a Michaelis-Menten curve with Ilaprazole concentration (micromolar) on the x-axis and reaction velocity (picomol/min/µg) on the y-axis, showing a Km of 8.20 ± 0.30 µM. The second is an IC50 curve with Log(Apatinib) concentration (micromolar) on the x-axis and control activity (%) on the y-axis, with an IC50 of 1.60 ± 0.02 µM. Image B shows two graphs for HLM. The first is a Michaelis-Menten curve with the same axes as Image A, showing a Km of 43.76 ± 3.17 µM. The second is an IC50 curve with the same axes as Image A, with an IC50 of 16.28 ± 0.57 µM. Additional IC50 curves for ketoconazole in RLM and HLM systems show values of 0.27 ± 0.01 µM and 2.30 ± 0.92 µM, respectively.

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The Michaelis-Menten curves and IC50 curves of ilaprazole in RLM (A) and HLM (B). Data are expressed as mean ± SD, n = 3.

Table 2.

Evaluation of Inhibition Models and Selection of the Best-Fit Model in RLM

Inhibition Model (RLM) Ki (μM) αKi (μM) AIC ΔAIC R2
Competitive 0.15 – −704.5 40.7 0.971
Noncompetitive 0.60 −735.8 9.4 0.985
Uncompetitive – 0.37 −684.2 61.0 0.955
Mixed 0.35 0.86 −745.2 0.0 0.988
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Notes: ΔAIC, difference in AIC calculated as AIC – AICmin, where AICmin represents the lowest AIC among the tested models. The model with ΔAIC = 0 was considered the best-fitting model. Ki and αKi represent the dissociation constants of the inhibitor binding to the free enzyme and the enzyme–substrate complex, respectively.

Abbreviations: AIC, Akaike Information Criterion; R2, coefficient of determination.

Table 3.

Evaluation of Inhibition Models and Selection of the Best-Fit Model in HLM

Inhibition Model (HLM) Ki (μM) αKi (μM) AIC ΔAIC R2
Competitive 5.25 – −751.4 1.3 0.962
Noncompetitive 12.29 – −750.2 2.5 0.961
Uncompetitive – 5.05 −720.2 32.5 0.930
Mixed 7.30 28.57 −752.7 0 0.964
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Notes: ΔAIC, difference in AIC calculated as AIC – AICmin, where AICmin represents the lowest AIC among the tested models. The model with ΔAIC = 0 was considered the best-fitting model. Ki and αKi represent the dissociation constants of the inhibitor binding to the free enzyme and the enzyme–substrate complex, respectively.

Abbreviations: AIC, Akaike Information Criterion; R2, coefficient of determination.

Figure 3.

Two graphs showing ilaprazole inhibition kinetics in RLM and HLM using mixed model and Michaelis-Menten analyses. The image A shows two graphs depicting the inhibition kinetics of ilaprazole in RLM using mixed model inhibition and Michaelis-Menten analyses. The x-axis is labeled 'Ilaprazole (μM)' and the y-axis is labeled 'V (pmol/min/μg RLM)'. The graphs display curves for apatinib at concentrations of 0, 0.8, 1.6 and 3.2 μM. The image B shows two graphs illustrating the inhibition kinetics of ilaprazole in HLM using mixed model inhibition and Michaelis-Menten analyses. The x-axis is labeled 'Ilaprazole (μM)' and the y-axis is labeled 'V (pmol/min/μg HLM)'. The graphs display curves for apatinib at concentrations of 0, 4.07, 8.14 and 16.28 μM. Each graph compares the enzyme activity of ilaprazole at different apatinib concentrations, showing the relationship between ilaprazole concentration and enzyme activity in both RLM and HLM systems.

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Inhibitory kinetics of ilaprazole on enzyme activity in RLM (A) and HLM (B): Mixed model inhibition and Michaelis-Menten analyses. Data are expressed as Mean ± SD, n = 3.

Table 4.

Effect of Apatinib on Kinetic Parameters (Km and Vmax) of Ilaprazole Metabolism in RLM and HLM

System Inhibitor Concentration Km (μM) Vmax (pmol/min/μg) R2
RLM 0 3.718 ± 0.313 0.0156 ± 0.0009 0.962
0.5 IC50 5.536 ± 0.622 0.0075 ± 0.0001 0.959
IC50 7.496 ± 1.998 0.0055 ± 0.0002 0.924
2 IC50 13.120 ± 4.089 0.0048 ± 0.0004 0.937
HLM 0 48.960 ± 1.640 0.0228 ±0.0006 0.995
0.25 IC50 64.160 ± 0.397 0.0165 ± 0.0003 0.999
0.5 IC50 75.080 ± 9.539 0.0159 ± 0.0015 0.996
IC50 83.710 ± 5.943 0.0151 ± 0.0009 0.988
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Abbreviation: R2, coefficient of determination.

Metabolic Stability

The metabolic stability of ilaprazole was evaluated in RLM and HLM in the absence and presence of apatinib. As shown in Figure 4, linear regression was performed between the natural logarithm of the remaining percentage of ilaprazole and incubation time to obtain the slope (k). The following equations were used to calculate the in vitro half-life (t1/2) and intrinsic clearance rate (CLint): t1/2 = 0.693/k; V (mL/mg) = incubation volume/protein in incubation; CLint (mL/min/mg protein) =V × 0.693/t1/2.38 In RLM, the t1/2 of ilaprazole was increased from 45.89 ± 0.82 min to 182.70 ± 4.84 min in the presence of apatinib, representing a 3.98-fold increase. Correspondingly, the CLint was decreased from 0.0503 ± 0.0009 to 0.0126 ± 0.0003 mL/min/mg, representing an 75% reduction. Similarly, in HLM, apatinib prolonged the t1/2 of ilaprazole from 80.80 ± 2.67 min to 139.35 ± 17.39 min (a 1.72-fold increase), while the CLint was decreased from 0.0286 ± 0.0010 to 0.0166 ± 0.0019 mL/min/mg (an 42% reduction). The metabolic stability results (including t1/2 and CLint) of the co-incubation of apatinib and ilaprazole are shown in Table 5.

Figure 4.

Two graphs showing ilaprazole stability in RLM and HLM with and without apatinib over time. The image A shows a graph labeled RLM with the x-axis as Time (minutes) and the y-axis as Ln of percentage remaining ilaprazole. Two curves are plotted: one for Ilaprazole plus Apatinib and another for Ilaprazole alone. The Ilaprazole plus Apatinib curve shows a slower decline compared to the Ilaprazole alone curve over 100 minutes. The image B shows a graph labeled HLM with the x-axis as Time (minutes) and the y-axis as Ln of percentage remaining ilaprazole. Similar to the first graph, two curves are plotted: one for Ilaprazole plus Apatinib and another for Ilaprazole alone. The Ilaprazole plus Apatinib curve shows a slower decline compared to the Ilaprazole alone curve over 50 minutes.

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The metabolic stability curves of ilaprazole in the presence or absence of apatinib in RLM (A) and HLM (B). Data are expressed as Mean ± SD, n = 3.

Table 5.

Linear Regression Equations for the Linear Part of t1/2 and CLint in the Presence or Absence of Apatinib

System Group Linear Regression Equation t1/2 (min) CLint (mL/min/mg)
RLM Ilaprazole Y = −0.01510*X + 4.434 45.89 ± 0.82 0.0503 ± 0.0009
Ilaprazole + Apatinib Y = −0.003793*X + 4.607 182.70 ± 4.84 0.0126 ± 0.0003
HLM Ilaprazole Y = −0.008577*X + 4.601 80.80 ± 2.67 0.0286 ± 0.0010
Ilaprazole + Apatinib Y = −0.004973*X + 4.635 139.35 ± 17.39 0.0166 ± 0.0019
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Effect of Apatinib on the Metabolism of Ilaprazole in vivo

The mean plasma concentration-time curves of ilaprazole and ilaprazole sulfone, and their corresponding pharmacokinetic parameters are shown in Figure 5, Table 6 and Table 7, respectively. When ilaprazole was co-administered with apatinib, significant changes were observed in the pharmacokinetic parameters of ilaprazole. Specifically, the AUC(0–t) and AUC(0–∞) of ilaprazole were increased by 7.29- and 7.45-fold, respectively, and Cmax was increased by 2.70-fold. But, CLz/F was markedly reduced. Although no statistically significant differences were found in t1/2 and Tmax, both values showed a tendency to be prolonged. For the metabolite ilaprazole sulfone, the AUC(0–t) and AUC(0–∞) were increased by 8.03- and 8.49-fold, respectively, Cmax was increased by 2.53-fold, and t1/2 was correspondingly prolonged. However, CLz/F was significantly decreased. No statistically significant difference was observed in Tmax. These findings demonstrated that apatinib markedly inhibited the metabolism of ilaprazole in SD rats.

Figure 5.

Two graphs showing ilaprazole and ilaprazole sulfone concentration over time with and without apatinib. The image A shows a graph of ilaprazole concentration over time. The x-axis is labeled 'Time (h)' and the y-axis is labeled 'Concentration (ng per mL)'. Two curves are plotted: one for ilaprazole alone and another for ilaprazole with apatinib. The ilaprazole with apatinib curve shows higher concentrations, peaking around 800 ng per mL at 1 hour, then gradually decreasing. The ilaprazole alone curve peaks lower, around 400 ng per mL and decreases more rapidly. The image B shows a graph of ilaprazole sulfone concentration over time. The x-axis is labeled 'Time (h)' and the y-axis is labeled 'Concentration (ng per mL)'. Two curves are plotted: one for ilaprazole alone and another for ilaprazole with apatinib. The ilaprazole with apatinib curve peaks around 60 ng per mL at 3 hours, while the ilaprazole alone curve peaks lower, around 30 ng per mL and decreases more rapidly.

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Mean plasma concentration-time curves of ilaprazole (A) and ilaprazole sulfone (B) in the control group (ilaprazole alone) and the treatment group (ilaprazole with apatinib). Data are expressed as mean ± SD, n = 5.

Table 6.

The Main Pharmacokinetic Parameters of Ilaprazole in Two Groups of SD Rats

Parameters Ilaprazole Ilaprazole + Apatinib
AUC(0-t) (ng/mL*h) 283.66 ± 64.28 2350.85 ± 670.88**
AUC(0-∞) (ng/mL*h) 286.17 ± 66.79 2419.50 ± 681.26**
t1/2 (h) 1.35 ± 0.64 2.12 ± 1.11
Tmax (h) 1.00 ± 0.00 1.10 ± 0.22
CLz/F (L/h/kg) 11.02 ± 2.90 1.33 ± 0.41**
Cmax (ng/mL) 186.25 ± 54.34 688.93 ± 325.84*
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Notes: Data are expressed as Mean ± SD, n = 5. Compared with the control Group, *p < 0.05; **p < 0.01.

Abbreviations: AUC, area under the plasma concentration-time curve; t1/2, elimination half time; Tmax, peak time; CLz/F, plasma clearance; Cmax, maximum plasma concentration.

Table 7.

The Main Pharmacokinetic Parameters of Ilaprazole Sulfone in Two Groups of SD Rats

Parameters Ilaprazole Ilaprazole + Apatinib
AUC(0-t) (ng/mL*h) 28.16 ± 8.88 254.28 ± 85.77**
AUC(0-∞) (ng/mL*h) 28.19 ± 8.86 267.48 ± 82.74***
t1/2 (h) 0.69 ± 0.14 2.42 ± 0.75**
Tmax (h) 1.00 ± 0.00 2.00 ± 1.17
CLz/F (L/h/kg) 116.44 ± 41.00 12.14 ± 3.92**
Cmax (ng/mL) 16.69 ± 5.22 58.90 ± 23.31**
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Notes: Data are expressed as Mean ± SD, n = 5 Compared with the control Group, **p < 0.01; *** p < 0.001.

Abbreviations: AUC, area under the plasma concentration-time curve; t1/2, elimination half time; Tmax, peak time; CLz/F, plasma clearance; Cmax, maximum plasma concentration.

Molecular Docking

To provide a supportive visualization of how ilaprazole and apatinib might interact with the CYP3A4 enzyme, molecular docking was employed as a complementary tool to the kinetic data. As shown in Figure 6, ilaprazole exhibited an optimal binding energy of −8.20 kcal/mol and formed two hydrogen bonds with residues LEU-216 (3.0 Å) and HEM-601 (2.4 Å). Apatinib exhibited an optimal binding energy of −9.12 kcal/mol, forming two hydrogen bonds with residues LEU-216 (3.1 Å) and ASP-214 (3.5 Å).

Figure 6.

Molecular docking of ilaprazole and apatinib with CYP3A4, showing interactions and hydrogen bonds. The image shows molecular docking of ilaprazole and apatinib with CYP3A4. On the left, a superimposed three-dimensional structural model illustrates the enzyme. On the right, an inset details interactions: ilaprazole is represented in cyan, apatinib in purple and residues in gray. Hydrogen bonds are depicted as yellow dashed lines. Ilaprazole forms two hydrogen bonds with residues LEU-216 at 3.0 angstroms and HEM-601 at 2.4 angstroms. Apatinib forms two hydrogen bonds with residues LEU-216 at 3.1 angstroms and ASP-214 at 3.5 angstroms. The visualization supports understanding of how these compounds interact with the enzyme.

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Molecular docking of ilaprazole and apatinib with CYP3A4. Superimposed three-dimensional structural model and interactions (inset): ilaprazole = cyan; apatinib = purple; Residues = gray; Hydrogen bonds = yellow dashed lines.

Discussion

DU is a common digestive system disease with a high incidence worldwide.17 Helicobacter pylori infection is a major risk factor for DU, accounting for 90% of cases.39 Ilaprazole is a new-generation benzimidazole PPI that is safe and effective in the treatment of DU. It is metabolized primarily by CYP3A4, suggesting that drugs that affect the activity of CYP3A4 may alter the plasma exposure and tissue distribution of ilaprazole and its metabolite ilaprazole sulfone.

Apatinib is a novel small molecule selective TKI that has been shown to prolong progression-free survival and overall survival in advanced GC.40 It is mainly metabolized by CYP3A4 and is an inhibitor of CYP3A4.30,41 Given its inhibitory effect on CYP3A4, it is meaningful to investigate whether apatinib affects the metabolism of ilaprazole. Studies have shown that the plasma concentrations of venlafaxine, tramadol, and buspirone were significantly elevated when co-administered with apatinib, thereby increasing the risk of adverse reactions.42–44 Helicobacter pylori infection is a major causal factor for DU and GC, and DU may progress to GC.45 Additionally, in clinical practice, GC patients often experience acid-related gastrointestinal symptoms during TKIs therapy (such as apatinib), which may necessitate the use of PPIs for symptom relief. Therefore, ilaprazole and apatinib may be used in combination.46

In this study, we investigated the inhibitory effect of apatinib on the metabolism of ilaprazole in vitro and in vivo. Ketoconazole is a potent inhibitor of CYP3A4, and the metabolism of affected drugs can be significantly altered. Therefore, ketoconazole was used as a positive control, with IC50 values of 0.27 and 2.30 μM in RLM and HLM, respectively. The results indicated that inhibition of CYP3A-related pathways can significantly affect the metabolism of ilaprazole. The IC50 values of apatinib on ilaprazole in RLM and HLM were 1.60 and 16.28 μM, respectively. When the IC50 value falls between 1 and 50 μM, it indicates moderate inhibition.47 Based on our IC50 results, apatinib exhibited moderate inhibition in both systems (particularly in RLM). In both systems, apatinib prolonged the t1/2 of ilaprazole and reduced its CLint, indicating that apatinib inhibited the metabolism of ilaprazole. Studies have shown that in HLM, the CYP3A4 inhibitor ketoconazole increases ilaprazole concentrations, consistent with our findings.48 Moreover, to investigate the inhibition mechanism, ilaprazole was used as the substrate and apatinib as the inhibitor in both RLM and HLM. The results showed that, in RLM and HLM, the inhibition kinetics of apatinib on ilaprazole metabolism were best described by a mixed-type inhibition model (noncompetitive + competitive), which was supported by nonlinear regression analysis and model selection based on the AIC. In addition, the kinetic parameters (Km and Vmax) were obtained using the Michaelis–Menten equation. In this study, increasing concentrations of apatinib resulted in an increase in the Km and a decrease in the Vmax, which was consistent with the characteristics of mixed-type inhibition. Ki is the dissociation constant for the binding of the inhibitor to the enzyme. A lower Ki value indicates a stronger inhibitory effect.49 The Ki values in RLM and HLM were 0.35 and 7.30 μM, respectively. In contrast, apatinib exhibited a greater inhibitory potential toward ilaprazole in RLM. The IC50 value, Ki value, and metabolic stability experiments all consistently indicated stronger inhibitory effects in RLM. However, this observation may be attributable to species differences.50,51

The results of in vivo and in vitro experiments were consistent. When co-administered with apatinib in rats, the bioavailability of ilaprazole was significantly increased. The AUC(0–t) of ilaprazole was increased from 283.66 ± 64.28 to 2350.85 ± 670.88 ng/mL h, and the AUC(0–∞) was likewise significantly increased from 286.17 ± 66.79 to 2419.50 ± 681.26 ng/mL h. A significant increase in Cmax was also observed. Additionally, CLz/F was reduced to 1.33 ± 0.41 L/h/kg. The increased exposure of ilaprazole likely resulted from the inhibition of its metabolism by apatinib. Interestingly, compared with the single-administration group, the AUC(0–t) and AUC(0–∞) of ilaprazole sulfone were significantly elevated, and t1/2 was prolonged to 2.42 ± 0.75 h. In addition, Cmax was markedly increased, whereas CLz/F was significantly decreased. This result may be due to the increased bioavailability of ilaprazole in the presence of apatinib, which would mean that more ilaprazole was converted to ilaprazole sulfone. A similar phenomenon has been reported in studies of the co-administration of elagolix with omeprazole, in which the exposures of both the parent drug and its sulfone metabolite were increased, consistent with our findings.52 Taken together, these findings indicated that apatinib inhibited ilaprazole metabolism in vitro and in vivo.

Based on published data, the plasma Cmax of apatinib was reached to 1521 ng/mL (3.56 μM) on the first day after oral administration of a 500 mg dose in patients.53 Considering the reported plasma unbound fraction (fu) of apatinib (0.074), the unbound peak plasma concentration (Cmax, u), calculated using the equation: Cmax, u = fu × Cmax, total, was approximately 0.26 μM, which represents the free inhibitor concentration (I) available to exert inhibitory effects in vivo.54,55 In the present study, the Ki value of apatinib determined in HLM was 16.33 μM. Accordingly, the inhibition index [I]/Ki was calculated to be approximately 0.016. When this ratio is below 0.02, the likelihood of clinically significant CYP450 inhibition is generally considered to be low.56 However, such predictions are inherently uncertain and may be influenced by multiple factors, including intestinal wall metabolism and transporter-mediated processes. Therefore, clinical data are required to further verify these findings.57

Docking results suggested that both ilaprazole and apatinib may form hydrogen bond interactions with residues in the active site of CYP3A4. The representative docking conformation also suggested partially overlapping binding regions, including possible interactions involving residue LEU-216. These findings provided qualitative in silico insights into the spatial proximity of the two compounds within CYP3A4. It should be noted that the alignment results served only as a supportive visualization tool. Therefore, the docking results should be interpreted with caution and cannot be regarded as definitive evidence of the underlying mechanism. Further experimental studies are required to confirm the precise binding mode and molecular mechanism of inhibition.

Our results demonstrated that apatinib significantly inhibited the metabolism of ilaprazole in RLM, HLM, and in SD rats. However, this study has several limitations that should be acknowledged. First, the sample size in the in vivo experiments was relatively small (n = 5 per group), which may limit statistical power and increase inter-individual variability. However, this sample size was commonly adopted in preliminary pharmacokinetic and DDI studies, and the observed trends were consistent across animals, supporting the reliability of the findings. Nevertheless, studies with larger sample sizes are warranted to further validate and strengthen these results. Second, the mechanistic investigations were primarily based on liver microsome systems, and no experiments using recombinant CYP3A4 were conducted. Therefore, the specific contributions of individual enzymes could not be fully distinguished. Future studies incorporating recombinant enzyme systems would help to better characterize enzyme-specific interactions. Finally, this study was limited to in vitro and animal models and cannot be directly extrapolated to humans. Therefore, the clinical translational relevance of these findings remains to be further validated. Given the inherent interspecies differences in drug metabolism, clinical studies are necessary to confirm the potential risk of DDI in humans.

Conclusion

In this study, a rapid and sensitive UPLC-MS/MS method for the determination of ilaprazole and its metabolite ilaprazole sulfone was established. In addition, the UPLC-MS/MS method was also used to study the DDI of apatinib on ilaprazole in vivo and in vitro. The results showed that apatinib inhibited the metabolism of ilaprazole in vitro and in vivo. In vitro, the inhibition mechanism of apatinib on ilaprazole metabolism was mixed (non-competitive and competitive). Molecular docking was used as a supportive visualization approach to provide structural context for the experimentally observed interaction. These findings indicated a potential interaction between apatinib and ilaprazole in rats, but the relevance to humans remained uncertain, and further clinical studies would be required to determine whether such interactions occur in vivo.

Disclosure

The authors report no conflicts of interest in this work.

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