Abstract
Solid transplant recipients are at increased risk for invasive aspergillosis. Tacrolimus and Voriconazole is one of the most frequently utilized treatments for those recipients with invasive fungal infections. However, it is difficult to use them properly due to the interaction between them. This study aimed to investigate the potential drug-drug interaction between Tacrolimus and Voriconazole by multiple methods, including in vitro liver microsome method and the PBPK(Physiologically Based Pharmacokinetic) model. Midazolam and testosterone were used as probe substrates to evaluate individual differences in CYP3A4/5 metabolic activity. A comprehensive interaction analysis was also conducted based on the STITCH database and the DD-Inter system. Furthermore, a PBPK model was constructed by the data from the literature to simulate the real metabolic process in vivo. The research employed multiple methodologies to demonstrate that the co-administration of Voriconazole significantly enhances Tacrolimus concentrations, considering genotypes and the activity of CYP3A4/5 genotypes. The findings indicated a decrease in the relative percentages of midazolam and testosterone metabolites with increasing Voriconazole concentration. Moreover, the results for residual Tacrolimus in the 30-minute incubation group revealed that Voriconazole exerts a mild inhibitory effect on the in vitro metabolism of Tacrolimus. The STITCH database and DD-Inter system analysis also suggested that Tacrolimus and Voriconazole share a strong association in liver metabolism, most likely interacting with CYP3A4/5 and CYP2C19. Furthermore, the result of PBPK analysis indicated that Tacrolimus AUC increases with Voriconazole co-therapy. Moreover, the AUC of Tacrolimus in intermediate CYP2C19 metabolizers (IM) was the highest at 10.1 µmol·min/L, followed by poor metabolizers (PM) at 8.13 µmol·min/L, and extensive metabolizers (EM) at 2.18 µmol·min/L. And the genotype of CYP3A5 poor metabolizer (PM) had AUC of Tacrolimus at 3.13µmol·min/L and extensive metabolizer (EM) at 2.18µmol·min/L. Microsomal studies, PBPK models, and multiple other analyses have comprehensively elucidated the impact of Voriconazole on Tacrolimus concentrations. These findings can serve as a valuable point of reference for concurrently administering these two medications. These findings also indicate that the genotypes of CYP2C19 play an important role in the development of DDI during concurrent Tacrolimus and Voriconazole treatment, which may have some guidance for clinical medication.
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Introduction
Solid transplant recipients are at increased risk for invasive aspergillosis (IA), a disease with poor outcomes and a substantial economic burden1,2,3. Treatment is complex due to the interaction between the antifungal and immunosuppressant drugs4. Tacrolimus and Voriconazole are one of the most common combinations. Tacrolimus, a commonly used immunosuppressive drug, is primarily metabolized by liver CYP450 after gastrointestinal absorption and is also a substrate of P-glycoprotein5. Therefore, the blood concentration of Tacrolimus is influenced by polymorphisms of multiple metabolic enzyme genes and drug transporter genes6,7,8. Voriconazole, an antifungal medication used to treat or prevent invasive fungal infections in transplant recipients after renal transplantation, can interfere with Tacrolimus metabolism by inhibiting the activity of the CYP3A4/5 enzyme7,9.
Voriconazole and Tacrolimus are commonly used medications for managing invasive fungal infections and organ transplantation, respectively2,10,11. Both drugs are metabolized in the liver by cytochrome P450 (CYP) enzymes, which are known to exhibit complex drug-drug interactions12,13. CYP3A is a vital member of the cytochrome P450 (CYP) family, with CYP3A4 and CYP3A5 being highly expressed in the liver and essential members of the CYP3A family. These enzymes play a crucial role in the metabolic reactions of various substrates14,15. The interaction between Tacrolimus and Voriconazole occurs because Voriconazole can inhibit the enzyme CYP3A4/5.This can lead to a significant fluctuation in Tacrolimus levels in the blood, which can cause toxicity16,17. Genetic variations in CYP3A4/5 significantly impact clinical outcomes. Individuals with an extensive Metabolizer genotype may experience lower blood concentrations of the drug, necessitating higher dosages to achieve therapeutic efficacy. Conversely, those with a slow metabolizer genotype are at an increased risk of severe adverse reactions due to higher concentrations of the drug remaining in the body for extended periods. These dynamics underscore the importance of genetic polymorphisms in the CYP family to the metabolize of Tacrolimus18,19,20. Drug-drug interactions (DDIs) involving these medications are a significant concern in clinical practice as a result of adverse drug reactions, treatment failure, and increased healthcare costs. The risk of DDIs also increases with polypharmacy and is therefore of particular concern in individuals likely to present comorbidities21,22. Therefore, if Tacrolimus and Voriconazole must be used together, careful monitoring of Tacrolimus blood levels is necessary to avoid toxicity. Dose adjustments of Tacrolimus may also be required, based on the blood levels. Studying the interaction mechanisms between these drugs is crucial for optimizing their therapeutic efficacy and minimizing drug toxicity in transplant recipients. Studies have shown that the genetic polymorphism of CYP2C19 affects the blood concentration of Tacrolimus when co-administered with Voriconazole, which may become a factor affecting DDI23.
Given the high prevalence of drug interactions in transplant recipients, it is essential to understand the potential interactions between these two drugs and their effects on hepatic microsomal enzymes. Therefore, this study aims to investigate the in vitro metabolism of Voriconazole and Tacrolimus in hepatic microsomes and to evaluate the potential drug-drug interactions between them. Specifically, we hypothesize that Voriconazole will affect the metabolism of Tacrolimus through inhibition of CYP3A4/5 activity, resulting in altered drug exposure and potential adverse effects.
The PBPK model is frequently utilized in vitro to simulate the process of drug metabolism in vivo, aiding in the explore the mechanism of DDIs24. By establishing a PBPK model verified by clinical data, the possible path of the DDIs between Tacrolimus and Voriconazole in vivo can be more accurately illustrated, confirming that these interactions are associated with CYP3A4/5.
Results
Analysis of midazolam metabolite: the production of 1’ -hydroxy midazolam
The effects of midazolam metabolites were measured and compared to a NC (Negative Control) group (Table 1). The metabolite content of midazolam was lowest in the positive control group compared to the negative control group. The activity of midazolam metabolites relative to the NC group was also lowest in the positive control group, with only a 12.2% median. Further analysis investigated the impact of Voriconazole concentrations on midazolam metabolites. Results revealed that when Voriconazole concentrations were below 10.00µmol, the inhibition of the CYP3A4/5 enzyme was limited, as shown by the relative percentages of midazolam metabolites ranging from 94.6 to 121%. However, when Voriconazole concentrations exceeded 50.00µmol, the relative percentages of midazolam metabolites decreased as Voriconazole concentration increased (Fig. 1a).
Inhibition of VRC on CYP3A4/5 metabolism in human liver microsomes. NC Negative control; PC Positive control; VRC Voriconazole.
The median inhibitory concentration was calculated to be 379.5µmol using GraphPad Prism. These findings suggest that Voriconazole may inhibit the activity of midazolam metabolites, particularly at high concentrations, highlighting the complexity and interconnectedness of natural systems.
Analysis of testosterone metabolite: the production of 6-hydroxy testosterone
Testosterone metabolites were observed for natural expression, and their activity relative to the NC group was calculated (Table 2). Compared to the NC group, the PC(Positive Control) group exhibited the lowest content of testosterone metabolites, resulting in the lowest percentage of activity relative to the NC group, with a median of only 12.2%. Voriconazole concentrations ranging from 0.08 to 50.00 µM were utilized, and their effect on the relative ratios of testosterone metabolites was observed. The results revealed that the relative ratios of testosterone metabolites decreased as the concentration of Voriconazole increased, with relative ratios of 111%, 104%, 93.8%, 79.6%, and 43.0%, respectively (Fig. 1). The IC50 value for Tacrolimus was determined to be 38.22 µmol/L (95% CI 28.97 to 50.43 µmol/L). This value was derived from a logistic regression analysis of the dose-response data, and the 95% confidence interval was calculated to reflect the variability in the data. The goodness of fit of the model was assessed with an R² value of 0.9285, indicating a strong fit to the experimental data.
The remaining amount of tacrolimus in three groups
wThe maternal residual amount of Tacrolimus was analyzed and detected. At the start of the reaction (0 min), the mean residual amount of Tacrolimus in the NC group, positive control group, and Voriconazole group were 0.141 µM, 0.123 µM, and 0.119 µM, respectively (Fig. 2).
Maternal residual amount of Tacrolimus incubated for 0 and 30 min. NC Negative control; PC Positive control; VRC Voriconazole.
As the incubation progressed for 30 min, the maternal residual of Tacrolimus in the three groups was monitored. The results were as follows: 0.00612 µmol, 0.0958 µmol, and 0.0157 µmol. The percentage of the maternal residual amount of Tacrolimus and the dose of Tacrolimus in each group were calculated (Table 3).
When comparing the maternal residual amount of Tacrolimus at 30 min to the start of the reaction, the mean percentage of residual amount and dose of Tacrolimus differed significantly among the groups. Specifically, the NC group had a mean percentage of 4.33%, the positive control group had a mean percentage of 78.11%, and the Voriconazole group had a mean percentage of 13.2% (Fig. 3). These findings suggest that the maternal residual amounts of Tacrolimus vary depending on the type of treatment administered. An increase in the VRC group was observed to significantly reduce the metabolic rate of Tacrolimus, especially within the first 30 min after administration, with a markedly lower rate in the VRC group compared to the NC and PC groups (Fig. 3).
Result from PBPK model
The simulation results for various dosing regimens, in conjunction with different CYP2C19 and CYP3A5 genotypes, revealed notable differences in the area under the concentration-time curve (AUC) when comparing the co-administration of Tacrolimus and Voriconazole to Tacrolimus monotherapy. The disparity in AUC between the combination therapy and Tacrolimus alone was found to be dose-dependent. Specifically, a Tacrolimus dose of 0.15 mg/kg administered twice daily (bid) resulted in an AUC difference of 0.81 µmol·min/L. This difference increased to 1.09 µmol·min/L when the dose was augmented to 0.2 mg/kg bid and further escalated to 2.89 µmol·min/L at the higher dose of 0.3 mg/kg bid. This means that the Voriconazole and Tacrolimus degree of the occurrence of drug interaction is directly related to the dose of Tacrolimus.
Inhibition of VRC on CYP3A4 metabolism in human liver microsomes. NC Negative control; PC Positive control; VRC Voriconazole.
Additionally, the simulations explored the impact of drug-drug interactions (DDIs) within different CYP2C19 genotypes. The results indicated that individuals with the CYP3A5 poor metabolizer (PM) genotype exhibited an AUC of 3.13 µmol·min/L for Tacrolimus, whereas those characterized as extensive metabolizers (EM) had an AUC of 2.18 µmol·min/L. Among the CYP2C19 genotypes, the intermediate metabolizer (IM) displayed the highest Tacrolimus AUC of 10.1 µmol·min/L, followed by the PM genotype with an AUC of 8.13 µmol·min/L, and the EM genotype with 2.18 µmol·min/L. It is unusual for CYP2C19 intermediate metabolizers(IM) to have the most considerable AUC difference. CYP2C19 poor metabolizers (PM) generally had the most considerable AUC difference, with Extensive metabolizers followed by intermediate metabolizers. The analysis of AUCR (Area Under the Curve Relative Ratio) (Table 4) reveals that the extent of drug interaction diminishes with increasing daily doses of Tacrolimus. Specifically, for CYP2C19 extensive metabolizers (EM), AUCR decreased from 1.97 to 1.20 as the dose increased, On the other hand, poor metabolizers (PM) of CYP2C19 exhibited a significantly higher degree of drug interaction, with an AUCR as high as 20.27, markedly exceeding those of EMs and IMs This stark contrast highlights the profound impact of genetic polymorphisms on drug responses, underscoring the necessity to consider individual genetic profiles in clinical settings to optimize dosing and minimize adverse effects. The reasons for this situation are complicated. The complex interplay between the inhibitory effect of Voriconazole on CYP3A4 and its potential to induce other metabolic enzymes that may significantly affect Tacrolimus levels, leading to the observed pharmacokinetic responses.
In addition, we considered external physiological factors, such as changes in liver function, age, diet, and the presence of other drugs, that may have different effects on drug metabolism in different metabolomes25,26,27.
These findings suggest a correlation between the extent of the DDI and both the CYP2C19 and CYP3A5 genotypes and the plasma concentration of Voriconazole (Fig. 4). This relationship highlights the importance of considering individual genetic makeup when evaluating the pharmacokinetic interactions between Tacrolimus and Voriconazole.
Result from STITCH database
We also performed a search of the STITCH database (http://stitch.embl.de/), using multiple keywords, including “Tacrolimus,” “Voriconazole,” “CYP3A4,” " CYP3A5,” and “CYP2C19,” which resulted in the following outcomes28. The network graph displays the strength of the connection between the nodes, with thicker lines representing stronger associations. Grey nodes represent protein-protein interactions, green nodes indicate chemical-protein interactions and red nodes depict interactions between chemicals. We did not utilize links between chemicals to expand the network. The various line colours reflect the types of evidence supporting the association between the nodes (Figure S1). A high correlation between Tacrolimus and CYP3A4 (correlation coefficient = 0.990) and between Tacrolimus and CYP3A5 (correlation coefficient = 0.952) was observed. Similarly, the result also demonstrated a significant correlation between Voriconazole and CYP3A4 (correlation coefficient = 0.851) and between Voriconazole and CYP3A5 (correlation coefficient = 0.818).
(A) Tacrolimus dose of 0.15 mg/kg bid, 0.2 mg/kg bid and 0.3 mg/kg predicted plasma concentration, (B) Effects of different Tacrolimus doses on DDIs, (C) The effect of different CYP2C19 genotypes on the plasma concentration of Tacrolimus in the presence of Voriconazole concentration, (D) The effect of different CYP3A5 genotypes on the plasma concentration of Tacrolimus in the presence of Voriconazole concentration.
Result from DD-Inter system
Upon retrieval of the DD-Inter system developed by Xiong, G. et al.29. ADMETlab2.0 was utilized to predict the metabolic data of Voriconazole and Tacrolimus (Table S1)30. The effect of Voriconazole and Tacrolimus on various CYP enzymes was assessed. The findings revealed a 91.30% probability of Voriconazole inhibiting CYP3A4 enzyme activity, an 85.4% probability of inhibiting CYP2C19, and an 86.10% probability of Tacrolimus inhibiting CYP3A4 enzyme activity. The likelihood of inhibiting CYP2C19 was found to be 67.6%. Both Voriconazole and Tacrolimus were identified as inhibitors and substrates of CYP3A4, as well as inhibitors or substrates of CYP2C19, with interaction probabilities exceeding than 64.9%.
Discussion
This research utilized in vitro liver microsomes investigation and integrated a PBPK model to simulate diverse Tacrolimus dosing protocols and account for variability in CYP2C19 and CYP3A4 genotypes. This comprehensive methodology provided a nuanced analysis of the influence of Voriconazole on Tacrolimus concentration, thereby contributing invaluable insights into the co-administration of these medications in a clinical context. The potential for drug-drug interactions between Tacrolimus and Voriconazole has been reported in several studies, indicating that the concentration of Tacrolimus fluctuates wildly in vivo when combined with Voriconazole10,16,31,32,33,34. This study aimed to investigate the inhibitory effects of Voriconazole on the activity of CYP450 enzymes involved in drug metabolism and the interaction between Voriconazole and Tacrolimus using human liver microsomes.
The results of this hepatic microsome study demonstrate that Voriconazole has an inhibitory effect on the activity of midazolam and testosterone metabolites, particularly at high concentrations. The median inhibitory concentrations were calculated to be 379.5µM and 38.3µM for midazolam and testosterone, respectively. These findings support the notion that Voriconazole can significantly impact the activity of cytochrome P450 enzymes involved in drug metabolism. The result is consistent with previous studies35,36. In addition, the increase in midazolam activity might be related to the inhibitory effects of Voriconazole on the CYP3A enzyme. As an inhibitor of CYP3A, Voriconazole could indirectly increase midazolam activity by slowing down its metabolic rate37,38. Besides, experimental conditions such as microsomal protein concentration, NADPH concentration, and temperature, all of which could also influence drug metabolism.
It is imperative to recognize the role of ketoconazole as a potent CYP3A4 inhibitor used in our study as a positive control. This established a benchmark against which other inhibitors were compared. Voriconazole, although primarily metabolizing through CYP2C19, also demonstrates inhibitory actions on CYP3A4. The differential inhibitory capacity of ketoconazole and Voriconazole on CYP3A4 underscores a crucial aspect of their pharmacological profile, which is significant in the context of drug interactions and enzyme inhibition.
Furthermore, the inclusion of midazolam and testosterone, both metabolized via CYP3A4, aimed to provide a detailed analysis of how different inhibitors impact CYP3A4 activity. Notably, testosterone displayed more pronounced effects in our assessments of CYP3A4 inhibition, suggesting a more precise interaction profile with the inhibitors tested. Although the results from midazolam were not as robust, its inclusion was vital. Midazolam does not share pharmacological targets with Voriconazole, thereby serving as an independent indicator of CYP3A4 activity. This approach allowed for a broader understanding of the implications of drug interactions beyond those solely involving midazolam’s metabolic pathways.
Our analysis clearly shows a significant interaction between Voriconazole and Tacrolimus, where increased Voriconazole levels reduce Tacrolimus metabolism with a median inhibitory concentration of 54.8µmol. This interaction is evident from our data and aligns with other studies39,40. However, further study is still needed. At the same time, these findings suggest that Voriconazole has an inhibitory effect on the maternal residual amount of Tacrolimus, with higher concentrations resulting in a greater reduction.
Additionally, the results from the STITCH database indicate a robust association between Tacrolimus and Voriconazole in liver microsomal metabolism, implying potential interaction during metabolism in vivo. In addition, using the DD-Inter system, we found that both compounds are substrates (or inhibitors) of various CYP enzymes, with CYP3A4 and CYP2C19 being the most likely enzymes to interact with them. The DD-Inter analysis reveals a common target between Voriconazole and Tacrolimus, suggesting that their interaction is highly complex and may involve not only CYP3A4/5 but also CYP2C19.
In recent years, A number of studies have demonstrated that a well-constructed PBPK model can reasonably predict the blood drug concentration of patients, so as to give a reliable basis for the adjustment of drug dose24,41,42,43. The results indicate that when Voriconazole and Tacrolimus were taken according to the label, the blood concentration of Tacrolimus increased significantly. However, there are limited data on the absorption, distribution and elimination of Tacrolimus in specific organs in the body, and the plasma protein binding rate of Tacrolimus is up to 99%, making it challenging to accurately simulate the actual blood concentration of Tacrolimus in the human body. Our study revealed that the degree of drug-drug interactions between Tacrolimus and Voriconazole is highly dependent not only on the genotype of the direct metabolic enzyme CYP3A4/5 but also on the genotype of CYP2C19. The likely reason for this is that the CYP2C19 genotype is related to the concentration of Voriconazole, and Voriconazole inhibits the activity of the CYP3A4/5 enzyme in a concentration-dependent manner23.
A good PBPK model can accurately predict the course of a drug in vivo44. In this study, we adopted a combination of top-down and bottom-up approaches to build the model. We obtained the fundamental physicochemical properties of the drugs by reviewing the literature and using the clinical study data to identify the model’s parameters. After testing, our model has good predictive ability on clinical data. At the same time, a drug’s in vivo process involves many factors that the PBPK model cannot fully consider. We must conduct a more advanced exploration of our research to describe the in vivo process of drugs in as much detail as possible and to accurately explore the possible mechanisms of drug interactions. This will enhance the model’s predictive accuracy and relevance to real-world scenarios. Meanwhile, we believe that our findings, he noted limitations, still provide valuable insights into how CYP2C19 genotypes can significantly influence drug interactions, a factor corroborated by existing literature45,46.
Despite the abundance of DDI clinical data on Tacrolimus and Voriconazole, there is still a lack of large-scale data with defined metabolic enzyme genotypes and stable doses. Due to the complexity of the factors affecting the interaction between these two drugs, the effect of individual variation in these clinical data is very large, which may not be a good way to show how different metabolic enzyme genotypes affect the pharmacokinetics of the two drugs. Therefore, it is necessary to establish a suitable PBPK model for the study of those complex drug interaction mechanisms.
In our study, we recognize the absence of direct in vivo sample validation as a key limitation in confirming the absolute accuracy of our PBPK model. Despite this constraint, we have implemented several robust alternative methods to ensure the reliability of our predictions. Further reinforcement of our model’s credibility is provided by comparative analyses with established PBPK models that exhibit a high concordance in predictions of drug absorption and metabolism47,48. Additionally, the employment of the well-validated PK-Sim version 11.2 software, based on widely accepted pharmacokinetic and pharmacodynamic principles, bolsters our confidence in the model’s predictions49While these strengths significantly support the model’s predictive capability, future research to further enhance the model’s robustness by including in vivo validation is also needed50.
In the study examining the correlation between Tacrolimus dosage and its interaction levels, analysis of the AUCR data indicated that the AUCR at higher doses of Tacrolimus was lower compared to medium and low doses. This reduction may be attributed to the inhibitory effects of high-dose Tacrolimus on CYP3A4/5 enzyme activity, which in turn weakens the pharmacological impact of Voriconazole. This finding suggests that there is a competitive interaction at the enzyme level51. The PBPK model predictions indicate that as Tacrolimus dosage increases, the magnitude of its interaction with Voriconazole (AUCR) decreases. We hypothesize that this phenomenon is primarily due to the self-inhibitory effect of Tacrolimus on CYP3A4.Tacrolimus is primarily metabolized by CYP3A4 and is also a known inhibitor of this enzyme. At lower Tacrolimus doses, Voriconazole, a strong CYP3A4 inhibitor, significantly reduces Tacrolimus metabolism, leading to an increased AUCR. However, as the Tacrolimus dose increases, its inhibitory effect on CYP3A4 may reach saturation. At this saturation point, CYP3A4 activity is already maximally suppressed by Tacrolimus itself, limiting the additional inhibitory effect of Voriconazole. Consequently, the drug interaction effect diminishes, leading to a decreased AUCR that approaches 1.This phenomenon is similar to the dose-dependent metabolic saturation observed with other CYP3A4 substrate drugs, such as Schizandrol B, where the inhibitory effect reaches a plateau at higher doses52. Furthermore, the AUCR data highlighted the significant influence of CYP2C19 genotypes on the pharmacokinetic characteristics of Tacrolimus when co-administered with Voriconazole, emphasizing the pivotal role of CYP2C19 genetic polymorphisms in regulating the metabolic dynamics of these drugs in combination therapy. About the result that the CYP2C19 IMs group shows a higher AUC value compared to the PMs, the complexities of drug metabolism involving Voriconazole and Tacrolimus are explored, mainly focusing on the interaction between Voriconazole’s CYP2C19-mediated metabolism and its inhibitory effect on CYP3A4. Recent pharmacokinetic studies have been integrated25,26,27,53,54, providing evidence on how genetic variations in the CYP2C19 and CYP3A4 enzymes can influence the metabolism and clearance of these drugs, as well pointed out the important role of CYP2C19 in Tacrolimus drug interactions53. Additionally, the impact of external factors such as concurrent medication use, dietary influences, and individual variations in liver and kidney function, which could significantly affect metabolic pathways and drug exposure, is considered55. A proposed mechanism, based on pharmacogenetic interactions, suggests that reduced Voriconazole clearance coupled with altered Tacrolimus metabolism due to CYP3A4 inhibition could explain the observed variations in AUC among different metabolizer groups. When voriconazole concentrations are elevated, the inhibition of CYP3A4 becomes more pronounced, thereby shifting a greater proportion of tacrolimus metabolism to CYP3A5. Given that CYP3A5 is less susceptible to voriconazole-mediated inhibition, tacrolimus may undergo more rapid clearance in individuals classified as CYP2C19 poor metabolizers (PMs), leading to a less pronounced increase in the area under the concentration-time curve (AUC) compared to intermediate metabolizers (IMs). Furthermore, evidence suggests that, under conditions of CYP3A4 saturation—particularly when tacrolimus concentrations in vivo are excessively high—alternative metabolic pathways, such as glucuronidation via UDP-glucuronosyltransferases (UGTs), may contribute to tacrolimus metabolism56,57,58. Furthermore, future research directions are necessary to include a more comprehensive examination of population pharmacokinetics across diverse patient groups.
Overall, the results showed that Voriconazole had a significant inhibitory effect on the activity of midazolam and testosterone metabolites, with testosterone serving as a more effective probe for detecting CYP3A4/5 activity. Moreover, as the Voriconazole concentration increased, the maternal residual amount of Tacrolimus decreased, indicating that Voriconazole has an inhibitory effect on the maternal residual amount of Tacrolimus, with higher concentrations resulting in a more significant reduction. The results from the STITCH database and the DD-Inter system analysis also suggested that the two drugs may interact during metabolism in vivo, involving not only CYP3A4/5 but also CYP2C19.
Despite the promising results of the study, some limitations need to be addressed in future research. Firstly, the study was conducted in vitro, and therefore, the observed inhibitory effects may not be representative of the in vivo situation. Further, well-designed studies are required to confirm the drug-drug interaction and determine the exact interaction mechanism in vivo. Additionally, the study only examined the inhibitory effect of Voriconazole on the activity of cytochrome P450 enzymes involved in drug metabolism. Other potential drug-drug interactions involving Tacrolimus and Voriconazole, such as their effects on CYP2C19 genotype, CYP2B6, drug transporters, P-glycoprotein (P-gp) or the immune system, were not investigated23,46,59,60,61,62. Another limitation of this study is that it could not examine the influence of specific gene polymorphisms of liver drug enzymes on the interaction between Voriconazole and Tacrolimus since obtaining specific genotypes of liver drug enzymes is costly and challenging. However, the influence of genetic variability on drug metabolism and interactions cannot be ignored. Therefore, further research is needed to evaluate the full extent of the interaction between these drugs. In the context of anti-Aspergillus therapy following organ transplantation, the combination of Tacrolimus and Voriconazole is commonly used but their interaction may be influenced by several other medications. Mycophenolate mofetil, another widely employed immunosuppressant, may also play a role in modulating the pharmacokinetics of Tacrolimus in vivo, thereby impacting its therapeutic efficacy. Furthermore, the role of mycophenolate mofetil in anti-Aspergillus therapy warrants attention. Given that the combination therapy of Tacrolimus and Voriconazole is susceptible to the effects of many drugs, therapeutic drug monitoring is essential during clinical use to help develop appropriate personalized medication regimens. This monitoring not only aids in optimizing drug efficacy but also prevents potential drug interactions, ensuring both the safety and effectiveness of the treatment30,63,64.At the same time, establishing a good PBPK model requires high-quality clinical data, which is difficult to achieve. In the process of PBPK model establishment, many factors affect the pharmacokinetic behaviour of drugs, such as age, diet and the application of other drugs, making it difficult to accurately describe the pharmacokinetic behaviour in vivo. More studies are needed to perfect and verify the reliability of the PBPK model, improve its generalization ability, and further improve its robustness.
Materials and methods
Study design
Human liver microsomes incubation
The study design for this research consisted of two parts. In the first part, we aimed to investigate the effect of Voriconazole on CYP3A4/5 enzyme activity in vitro. To achieve this, we prepared Voriconazole standard solutions with concentrations of 0.08, 0.4,2,10,50 µM. We used midazolam and testosterone as CYP3A4/5 substrates and detected the formation of 1’-hydroxy midazolam or 6-hydroxytestosterone using chromatography. The human liver microsomal incubation conditions were as follows: microsomal protein concentration of 0.5 mg/mL, NADPH concentration of 1.0 mM, and incubation temperature of 37.0 °C. The incubation process involved two steps: initially, the test compound was incubated with human liver microsomes for 15 min, followed by adding NADPH and CYP450 substrates, and the incubation continued for an additional 30 min. Negative control (NC) and positive control (PC) groups were included to ensure accuracy. The NC group consisted of an incubation system without the test compound, containing only the CYP450 substrate and NADPH cofactor. The PC group included the incubation system with enzyme inhibitors (such as CYP3A inhibitor Ketoconazole), CYP450 substrate, and NADPH co-factor.
The second part of the study aimed to explore the inhibitory effect of Voriconazole on Tacrolimus metabolism in human liver microsomes. We prepared the solution with 200µL phosphate buffer (pH7.4) containing 5 mmol magnesium chloride and 0.5 mg/mL liver microsomal protein to achieve this Inhibitors Ketoconazole (N = 3), Voriconazole (N = 3), and blank buffer (N = 3) were added to the mixture. The inhibitors were incubated with human liver microsomes for 15 min at 37℃. Coenzyme NADPH, UDPGA, and Tacrolimus were added, and the test concentration of Tacrolimus and Voriconazole was 0.5µmol/L and 40µmol/L. The incubation was continued for 0 and 30 min. At the end of the incubation, we added 200µL of ice acetonitrile and centrifuged the samples at 14,000 g for 10 min.
PBPK model construction
PBPK model was constructed with PK-Sim® ( PK-Sim®11 Update 2Version 11.2 -Build 142) based on literature data48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66. Based on the published models, the parameters for establishing the PBPK model were obtained by importing the literature data and identifying the parameters closer to the clinical data67,68,69. Some physicochemical properties of these drugs were derived from calculated using Advanced Chemistry Development (ACD/Labs) Software V11.02 (© 1994–2023 ACD/Labs) predictions.
Simulations were conducted using the established PBPK model to assess the blood concentration-time curves of orally administered Voriconazole at a dosage of 200 mg twice daily (400 mg on day 1), Tacrolimus at a dosage of 0.15 mg/kg/day twice daily. Meanwhile, the metabolism of Tacrolimus and Voriconazole exhibits gene specificity. Therefore, the effect of CYP3A5 and CYP2C19 were also considered in our study. Different Tacrolimus doses and genotypes of CYP2C19 and CYP3A5 were also explored to determine the extent of drug interactions. Specifically, we selected oral Tacrolimus dosages of 0.15 mg/kg bid, 0.2 mg/kg bid, and 0.3 mg/kg bid to explore the doses effects and under the condition of 0.15 mg/kg bid Tacrolimus to explore different effects of genotypes of CYP2C19 and CYP3A5.
Detection of chromatographic conditions
This study used different chromatographic methods to analyze the target compounds. The chromatographic conditions for the metabolite of midazolam (1’ - hydroxy midazolam) were as follows: the column temperature was set at 25℃ and the column used was Diamond C18 (250 × 4.6 mm). The mobile phase was a mixture of methanol and water (10mM potassium phosphate) in a ratio of 54:46, with a flow rate of 1 ml/min. UV detection was performed at a wavelength of 278 nm. For the metabolite of testosterone (6 - hydroxy testosterone), the following chromatography conditions were used: the column temperature was maintained at 25℃, and the Diamond C18 column with dimensions of 250 × 4.6 mm was used. The mobile phase consisted of water (0.05% H3PO4), acetonitrile, and methanol in a ratio of 8:15:5 for mobile phase A, and water (0.05% H3PO4PO) and acetonitrile in a ratio of 50:50 for mobile phase B. The flow rate was set at 1 mL/min, and detection was performed at a wavelength of 245 nm. For Tacrolimus, the following chromatographic conditions were used: an Agilent Eclipse XDB-C18 column (3.5 μm, 2.1 mm×100 mm) was used, and the mobile phase consisted of a 2 mmol·L−1 ammonium acetate aqueous solution and methanol (5∶95, v/v) at a flow rate of 0.3 mL/min.
In this study, we utilized specific concentrations of Voriconazole and Tacrolimus to evaluate their pharmacokinetic interactions and Voriconazole’s inhibitory effect on Tacrolimus’s metabolism. The selection of drug concentrations was carefully calculated based on their clinical relevance and binding characteristics.
Voriconazole’s clinical plasma trough concentrations range from 1 to 5.5 µg/mL, with a plasma protein binding rate of approximately 50.34 ± 10.98%. Based on these figures, the free concentrations of Voriconazole were derived by applying the formula: Free Concentration (C_free) = Total Concentration (C_total) × (1 - Binding Rate). This calculation resulted in free concentrations of approximately 0.5 µg/mL at a total concentration of 1 µg/mL and approximately 2.73 µg/mL at 5.5 µg/mL. Converting these values to micromolar units, given that 1 µg/mL is equivalent to 2.87 µM, resulted in a range of free concentrations from 1.43 µM to 7.83 µM. To ensure a comprehensive evaluation covering potential clinical exposure levels, Voriconazole concentrations for the experiments were set at 0.08 µM, 2 µM, and 10 µM, representing low, medium, and high pharmacokinetic levels.
Tacrolimus is typically administered with a clinical plasma trough concentration ranging from 5 to 15 ng/mL, and the maximum concentration (Cmax) has been reported as approximately 20.3 ± 8.3 ng/mL. Considering its high plasma protein binding rate of 99%, a concentration of 0.5 µM (equivalent to approximately 0.4 µg/mL or 400 ng/mL) was chosen for the experiments. This concentration was selected to simulate a sufficiently high drug exposure level to evaluate the interaction with Voriconazole, ensuring clear visibility of its metabolic inhibition in vitro70.
These chromatographic conditions were optimized for separating and detecting each compound in the samples analyzed in this study. The mobile phases and column temperatures were selected based on their ability to provide good separation of the target analytes. At the same time, the flow rates and detection wavelengths were optimized for sensitivity and selectivity. Using different columns and mobile phases for different compounds allowed for better separation and detection of each analyte.
Data and statistical analysis
For the first part of the study, the formation rate of 1’-hydroxy midazolam and 6-hydroxytestosterone was calculated using the peak area ratio of metabolites to the internal standards. The inhibitory effect of Voriconazole on CYP3A4/5 activity was evaluated by comparing the formation rate of metabolites in the presence and absence of Voriconazole. The IC50 values were calculated using logistic regression analysis with GraphPad Prism software (Version 8.0). The model used to fit the data was a log(inhibitor) vs. normalized response curve. Statistical analysis included the calculation of 95% confidence intervals for IC50 values to account for data variability. For all comparisons, multiple comparison corrections were applied where appropriate to control for a false discovery rate. For the second part of the study, the residual amount of Tacrolimus was determined using the peak area ratio of Tacrolimus to internal standard. The inhibitory effect of Voriconazole on Tacrolimus metabolism was evaluated by comparing the residual amount of Tacrolimus in the presence and absence of Voriconazole.
The statistical software programs GraphPad Prism 8.0 and SPSS 25.0 were utilized to analyze the relevant data. Statistical significance was considered present when the P-value was less than 0.05. Maternal residual Tacrolimus was detected by analysis, and the percentage of maternal residual Tacrolimus between different groups was calculated. The formula was: maternal residual Tacrolimus/total Tacrolimus ×100%. The activity of midazolam or testosterone relative to the NC group was calculated according to the determination results. The calculation formula was: the relative amount of metabolites generated in the test group/the relative amount of metabolites generated in the NC group ×100%. The median inhibitory concentration (IC50) Y = 100/(1 + 10^((X-LogIC50)) was calculated by GraphPad Prism. All data were expressed as mean ± standard deviation (SD).
In the PBPK modeling section of our study, we conducted simulations across a cohort of 50 diverse individuals, differentiated by varying weights and age brackets. We undertook a rigorous statistical analysis of the AUCR data and C_maxR derived from these simulations, from which we subsequently calculated confidence intervals to assess variability and reliability of our findings. The statistical significance of differences between groups was analyzed using one-way analysis of variance (ANOVA) followed by Dunnett’s test. A P value of less than 0.05 was considered statistically significant.
Conclusions
Microsomal studies, PBPK model, and multiple other analyses have comprehensively elucidated the impact of Voriconazole on Tacrolimus concentrations. Our study also found that different cytochrome P450 genotypes have different effects on the interaction between Tacrolimus and Voriconazole These findings can serve as a valuable point of reference for the concurrent administration of these two medications. At same time, this study is based on a limited sample size and specific experimental conditions; thus, further validation is required before broader clinical application.
Data availability
The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.Relevant data are stored on github：https://github.com/doctorcsu/PBPK.git.
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Acknowledgements
We acknowledge Reed Liver Institute for their guidance and assistance about the design of this study. We also acknowledge Hua-Lin Cai, Mou-Ze Liu, Jia-Min Wu, Yi-kun Wang for their sincere help and advice. We also appreciate the effort of other members of the Department of Clinical Pharmacy, the Second Xiangya Hospital of Central South University.
Funding
This study was supported by Health Commission of Hunan Provincial [202113012480], the Teaching Reform Program for Graduate Education at Central South University [Grant No. 2023JGB039], the Hunan Provincial Degree and Graduate Education Reform Project [Grant No. 2023JGYB041], and the Hunan Provincial Health High-Level Talent Scientific Research Project [Grant No. R2023061]. Additional funding was provided by the Research Project established by the Chinese Pharmaceutical Association Hospital Pharmacy Department [Grant No. CPA-Z05-ZC-2024002], as well as the Postgraduate Innovative Project of Central South University [Grant No. 2024XQLH030]. It was also supported by the International Research Center for Precision Medicine, Transformative Technology, and Software Services, Hunan, China.
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All authors contributed to the conception and design of the study. Yi-Chang Zhao performed the experiments and analyzed the data. Yi-Chang Zhao, Wen Gao and Yu-Kun Zhang contributed to the statistical analysis. Yi-Chang Zhao, Yu-Kun Zhang, Wen Gao, Chen-Lin Xiao, Jing-Jing Hou, Jia-Kai Li, Bi-Kui Zhang, Huai-Yuan Liu, Da-Xiong Xiang, and Miao Yan provided critical insights and expertise in the field. Yi-Chang Zhao, Huai-Yuan Liu, Yu-Kun Zhang and Indy Sandaradura drafted the manuscript. All authors reviewed and approved the final version of the manuscript.
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This study uses de-identified liver microsomes obtained from the Reed Institute for Liver Disease. The Ethics Committee of The Second Xiangya Hospital of Central South University did not require the study to be reviewed or approved by an ethics committee because the samples were industrial commodities and de-identified.
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Zhao, YC., Zhang, Yk., Gao, W. et al. A preliminary exploration of liver microsomes and PBPK to uncover the impact of CYP3A4/5 and CYP2C19 on tacrolimus and voriconazole drug-drug interactions. Sci Rep 15, 6389 (2025). https://doi.org/10.1038/s41598-025-91356-7
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DOI: https://doi.org/10.1038/s41598-025-91356-7
