Methods
Protocol Registration
This review is registered in PROSPERO under ID CRD420251007444 and follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [10] and the Cochrane Handbook for Systematic Reviews [11].
Data Sources and Search Strategy
A comprehensive literature search was independently conducted by two authors (M.R.E. and I.R.) across six databases, including PubMed, Web of Science, Scopus, Cochrane Library, VHL, and EMBASE, from inception to February 2, 2025. The search was performed without restrictions on publication date or language. The systematic search strategy was developed using a combination of free-text keywords and MeSH terms for "Vonoprazan" and "transplantation", as follows: ("Vonoprazan" OR Voquezna OR "TAK-438" OR "TAK 438" OR TAK438 OR "potassium-competitive acid blocker" OR P-CAB) AND ("transplant*" OR "graft*"). The search details are shown in (Supplementary Table 1).
Eligibility Criteria.
We included clinical studies that met the following criteria:
No restrictions on setting, language, or publication date were imposed.
Additionally, we excluded animal studies, in vitro and in vivo studies, reviews, books, theses, and other observational studies (cross-sectional, case series, and case reports). Studies with incomplete data, duplicates, missing values, conference abstracts, protocols, and letters were also excluded.
Study Selection
We used Rayyan, an online screening tool, to manage the review process. First, we removed duplicate records. Then, two independent reviewers (MRE and MSE) screened the remaining studies by title and abstract. Studies that met the inclusion criteria underwent a full-text review based on the predefined criteria. Any disagreements were resolved through discussion.
Missing data
Studies with missing outcome data were included using an available case analysis approach. For studies reporting without corresponding standard deviations, we followed the guidance provided in the Cochrane Handbook for Systematic Reviews of Interventions (version 6.4, Chapter 6). Specifically, when standard deviations (SDs) were not reported, we estimated them using available statistical information such as standard errors, confidence intervals, interquartile ranges, or p-values, where applicable.
The pooled result at the bottom (random effects model) also represents the combined raw mean difference across studies, providing an overall summary estimate for the meta-analysis in its natural units. The choice to use "MRAW" is especially relevant when interpretations in natural (untransformed) units are required.
Data Extraction
Four reviewers (M.R.E., M.W., O.K.E., and M.A.E.) independently extracted data using a pre-designed sheet, which was first tested and refined through a pilot extraction process in Excel (Microsoft, USA). The extracted data were then structured into three main sections: (1) summary (Study ID, study design, country, total sample size, interventions, type of transplantation surgery, follow-up duration, and inclusion criteria). (2) Baseline characteristics of patients (e.g., demographic data, transplantation & treatment characteristics, liver function markers, and kidney function markers). (3) Outcomes data: (mean change in tacrolimus C0 and C0/D, AST, ALT, total bilirubin, eGFR, and creatinine).
Quality assessment
We evaluated the quality of the included studies using the NIH Study Quality Assessment Tool for observational and cross-sectional studies [12]. This tool assesses key factors such as study design, sample size, outcome measures, and potential biases, rating each study as poor, fair, or good. Two independent reviewers (MAE and MSE) conducted the assessments, resolving any disagreements with a third reviewer (MRE).
Statistical analysis.
The study employed R version 4.3, utilizing the “Meta” package for statistical analysis. For the analysis of a single-arm design, we used Mean difference (MD) for all continuous outcomes, summarizing the effect estimates with 95% confidence intervals (CIs). A random-effects model was applied when significant heterogeneity was detected (I² > 50%) using the Chi-square test and I² statistic. In the absence of substantial heterogeneity, a fixed-effect (common-effect) model was used. Heterogeneity was interpreted according to Cochrane recommendations: 0–40% indicating low heterogeneity, 30–60% moderate heterogeneity, 50–90% substantial heterogeneity, and 75–100% considerable heterogeneity. A Chi-square test p-value < 0.1 was considered statistically significant for heterogeneity. To address heterogeneity, sensitivity analyses were conducted using leave-one-out, in which each study was sequentially excluded.
Recommendations for monitoring and dose adjustment of tacrolimus when switching from proton pump inhibitors to vonoprazan
We recommend frequent therapeutic drug monitoring of tacrolimus, starting within 3 to 5 days of initiating vonoprazan, as this period is critical for detecting early changes in drug levels.
Results
Study selection
The search yielded 96 records; after deduplication and screening, four studies met the inclusion criteria for qualitative and quantitative synthesis, all conducted in Japan and enrolling a total of 140 recipients with follow-up of 3–6 months.
Study characteristics and baseline/switch details
All studies evaluated conversion from a PPI (predominantly rabeprazole) to vonoprazan in tacrolimus-treated transplant recipients; summarizes design, population, and follow-up; expands per-study baseline and switching details: tacrolimus daily dose pre/post and titrations; switching protocol and timing relative to trough sampling; duration on prior PPI and specific agent; co-medications influencing CYP3A/P-gp (e.g., azoles, macrolides, diltiazem, corticosteroid dose); stability indicators (time since transplant, recent rejection/infection/diarrhea); and CYP3A5/CYP2C19 genotype where available; differences were noted and considered in interpretation with priority given to C0/D over C0 for before–after comparisons.
Risk of bias
Using the NIH Before–After tool, two studies were judged fair and two poor-to-fair due to uncontrolled pre–post design, small samples, potential confounding from co-medication changes and dose titrations, and unclear timing protocols; overall, risk of bias was serious across key domains.
Certainty of evidence (GRADE)
Across outcomes (C0, C0/D, ALT/AST, creatinine, eGFR, bilirubin), certainty was very low due to serious risk of bias (uncontrolled pre–post designs), very serious inconsistency (I² often high; wide prediction intervals), and serious imprecision (small k, wide CIs); indirectness was present for clinical outcomes given lack of pooled clinical endpoints; a Summary of Findings table details downgrades and effect estimates.
Primary outcomes
Tacrolimus C0/D (k=3): The pooled mean difference was 0.18 (95% CI \documentclass[12pt]{minimal}
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\begin{document}$\tau^2$\end{document}τ2 and 95% prediction interval are reported in (A); leave-one-out reduced inconsistency when excluding a single study, but pooled effects generally did not exceed the prespecified 20% C0/D threshold; select individual studies reported threshold-crossing increases and are detailed in and text. Tacrolimus C0 (k=3): The pooled mean difference was 1.00 ng/mL (95% CI \documentclass[12pt]{minimal}
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\begin{document}$-1.11$\end{document}−1.11 to 3.11), not statistically significant, with considerable heterogeneity (I\documentclass[12pt]{minimal}
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\begin{document}$\tau^2$\end{document}τ2 and 95% prediction interval indicated wide between-study dispersion; pooling was treated as exploratory, and narrative synthesis is emphasized given small k and inconsistency; sensitivity analysis identified one influential study affecting I\documentclass[12pt]{minimal}
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\begin{document}$^2$\end{document}2.
Secondary outcomes
AST (k=3): Mean difference \documentclass[12pt]{minimal}
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\begin{document}$-0.38$\end{document}−0.38 IU/L (95% CI \documentclass[12pt]{minimal}
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\begin{document}$-3.92$\end{document}−3.92 to 3.16), not statistically significant, with substantial heterogeneity; sensitivity excluded one study to resolve inconsistency, but estimates remained imprecise; ALT (k=3): Mean difference 1.39 IU/L (95% CI 0.02 to 2.77), statistically significant with I\documentclass[12pt]{minimal}
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\begin{document}$^2$\end{document}2=0%, but magnitude small and clinical relevance uncertain; creatinine (k=2): Mean difference \documentclass[12pt]{minimal}
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\begin{document}$-0.70$\end{document}−0.70 to 0.16), not statistically significant, with substantial heterogeneity; eGFR (k=2): Mean difference 1.45 mL/min/1.73 m\documentclass[12pt]{minimal}
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\begin{document}$-1.99$\end{document}−1.99 to 4.89), not statistically significant, I\documentclass[12pt]{minimal}
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\begin{document}$^2$\end{document}2=0%; total bilirubin (k=2): Mean difference 0.07 mg/dL (95% CI \documentclass[12pt]{minimal}
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\begin{document}$\tau^2$\end{document}τ2 and prediction intervals are reported for random-effects outcomes in , , ; r-sensitivity analyses (0.5–0.8) were directionally robust but imprecise.
Publication bias
Funnel plots and small-study tests were not performed/interpreted due to k<10 and poor reliability of such diagnostics in small meta-analyses; this limitation is acknowledged in Methods and Discussion in 3.3. Risk of Bias and Certainty of Evidence The risk of bias assessment was conducted using the National Institutes of Health (NIH) quality assessment tool for the included studies. Two studies showed good quality, and two assessed fair quality .
Discussion
Tacrolimus is a widely used immunosuppressant in organ transplant recipients, and its therapeutic efficacy depends heavily on maintaining appropriate blood concentrations. Given its narrow therapeutic index, any alterations in its metabolism due to drug-drug interactions (DDIs) may lead to adverse effects such as nephrotoxicity or increased risk of rejection [13].
Several studies have demonstrated that vonoprazan can significantly increase tacrolimus blood concentrations compared with rabeprazole. The rise in tacrolimus levels can be attributed to the shared cytochrome P450 (CYP) 3A4/5 metabolic pathway. Tacrolimus is predominantly metabolized by CYP3A4, and vonoprazan, unlike rabeprazole, is also extensively metabolized by CYP3A4, leading to a competitive inhibition effect [14] This leads to reduced tacrolimus clearance, thereby increasing its systemic exposure. Vonoprazan influences tacrolimus metabolism, which is crucial for optimizing post-transplant patient management [15].
A study by Suzuki et al. (2021) observed a 65.8% increase in tacrolimus blood concentration and a 41.8% rise in its concentration-to-dose (C/D) ratio after switching from rabeprazole to vonoprazan in kidney transplant recipients[14]. Risk of toxicity if tacrolimus dose adjustments are not made appropriately. Additionally, a study by Mei et al. (2020) demonstrated that tacrolimus C0/D increased significantly after conversion from rabeprazole to vonoprazan, albeit with minor clinical implications. This suggests that while the interaction is evident, its clinical impact may be manageable with appropriate therapeutic drug monitoring (TDM)[17].
In Comparison with Conventional PPIs, such as omeprazole and lansoprazole, which are also metabolized by CYP enzymes, primarily CYP2C19 and CYP3A4. They elevate tacrolimus blood concentrations by inhibiting CYP3A4. However, rabeprazole differs from other PPIs as it is primarily metabolized through a non-enzymatic pathway, resulting in minimal interactions with tacrolimus [18,13].
On the other hand, vonoprazan exhibits stronger and more sustained gastric acid suppression due to its mechanism as a potassium-competitive acid blocker. However, this advantage is accompanied by an increased risk of DDIs with tacrolimus. The shift from rabeprazole to vonoprazan, therefore, requires closer monitoring of tacrolimus levels to prevent toxicity while maintaining immunosuppressive efficacy.
One of the major concerns is its impact on liver and kidney function. Tacrolimus-induced nephrotoxicity there is deterioration in liver function parameters, such as aspartate transaminase (AST) and alanine transaminase (ALT)[19,20,16]. Although Tacrolimus trough levels and C0/D values remained stable in LT recipients after switching from PPIs to vonoprazan, Hidaka et al. (2022)[15] Indicated no significant hepatotoxicity. Additionally, the estimated glomerular filtration rate (eGFR) was stable, supporting the safety of vonoprazan Coadministration in kidney transplant recipients.
Our results suggest that while individual studies may report notable changes, the overall effect remains inconclusive due to outcome variability. Our study indicates that ALT may be affected to a measurable extent, and the clinical significance of these changes depends on their magnitude and impact on patient outcomes. A slight reduction in creatinine (-0.27 mg/dL) and an increase in eGFR (1.45 mL/min/1.73 m²) are relatively small. They may not translate into meaningful clinical improvements in kidney function, especially given their wide confidence intervals and non-significance. However, even minor increases in ALT (1.39 IU/L) and the variability in AST levels could indicate potential liver function alterations, which may be clinically relevant in specific populations, such as those with preexisting liver conditions or receiving hepatotoxic treatments. The heterogeneity in creatinine and AST changes suggests that individual patient factors and study differences must be considered before drawing definitive conclusions.
Overall, while these biochemical changes provide insights into potential variations in organ function, their direct impact on clinical decision-making remains limited without further evidence correlating them with patient outcomes or adverse effects.
Although tacrolimus concentrations increased post-conversion, the magnitude of this increase was not significantly different between CYP3A5 expression and non-expression. This suggests that vonoprazan’s effect on tacrolimus metabolism is primarily mediated through CYP3A4 rather than CYP3A5.
Limitation
First, the sample sizes in all four studies were relatively small, limiting the generalizability of the findings. Larger, multicenter studies are needed to validate these results across diverse patient populations. The inclusion of only four studies, all originating from Japan, raises concerns regarding the generalizability of the results to broader, more diverse populations. The homogeneity in geographic origin may reflect specific healthcare practices, patient demographics, or clinical protocols unique to Japan, which may not be applicable elsewhere. Additionally, this concentration of studies from a single country introduces the potential for regional publication bias, thereby limiting the external validity of the conclusions. The total sample size (n=140) across four small studies may be insufficient for detecting clinically meaningful differences. High heterogeneity between studies limits the reliability of pooled estimates.
Another important limitation of this meta-analysis is that all included studies utilized single-arm before-and-after designs without concurrent control groups, which restricts the ability to establish causality between the observed outcomes and the drug-drug interactions under investigation. In the absence of comparator arms, it is challenging to rule out the influence of temporal trends, such as natural disease progression or regression to the mean. Additionally, clinical decision-making factors, including dose titrations, changes in concomitant medications, or evolving treatment guidelines, may have contributed to the observed changes. Unmeasured confounders, such as fluctuations in disease severity, the emergence of comorbid conditions, or variations in patient adherence, may also have impacted results. These factors collectively limit the strength of causal inference and underscore the need for future well-controlled studies to validate these findings.
Future studies with larger cohorts and longer follow-up periods are necessary to further elucidate the long-term clinical implications of vonoprazan-tacrolimus interactions.
We recommended close monitoring of tacrolimus trough levels when switching from rabeprazole to vonoprazan, along with blood level measurements. TDM results and patient-specific factors, such as renal function should guide the magnitude of dose adjustment. Also, Future research should include randomized controlled trials with adequate sample sizes, diverse ethnic populations, and concurrent control groups to establish definitive evidence of vonoprazan-tacrolimus interactions.
Based on available data, rabeprazole may have fewer drug-drug interactions with tacrolimus compared to vonoprazan, but head-to-head trials with other PPIs are lacking.