Background: The gut microbiome may contribute to variations in the pharmacokinetics of mycophenolic acid (MPA). Hepatic glucuronidation converts MPA to the inactive metabolite, mycophenolate glucuronide (MPAG), which can be excreted into the gastrointestinal tract via the bile. In the gut, bacterial β-glucuronidase enzymes convert MPAG back into MPA, which is reabsorbed, contributing to a secondary plasma peak. Antimicrobial drugs can alter gut microbiome composition and potentially influence MPA reactivation.

Aims: This project investigated whether different antimicrobial drugs influence ex vivo microbiome-derived MPA reactivation.

Methods: Fecal samples from healthy individuals were inoculated in anaerobic Gifu media with six antimicrobial drugs (50 μM) individually added to separate samples and incubated (24-hr). Protein lysates were prepared from fecal cultures through bead beating, sonication and ultrafiltration, then standardized after protein determination. To assess the reactivation of MPA, MPAG was added to the lysates, followed by 37°C incubation with samples collected at 0, 0.25, 0.5, 1, 1.5 and 2-hr. MPA concentrations were quantified by UHPLC-MS/MS, and fitted to a linear regression function, with the slope representing the reactivation rate of MPA.

Results: Ciprofloxacin and erythromycin showed higher MPA reactivation rates compared to controls, with mean±SD relative difference of 84±22% and 94±56%, respectively. Ampicillin and sulfamethoxazole had lower reactivation rates, with relative difference of -25±21% and -27±4%, respectively. Trimethoprim and vancomycin showed no difference compared to controls.

Conclusions: Antimicrobial drugs appear to influence microbiome-derived MPA reactivation to varying degrees. Microbiome composition changes will be assessed to investigate antibiotic-induced shifts to explain these effects.

Background: In kidney transplantation, recipients with a low trough concentration/dose (C0/D) ratio of tacrolimus (<1.05 ng/ml/mg), identified as fast metabolizers, face higher risks of nephrotoxicity, BK-viremia, and graft loss. These patients require higher doses to maintain therapeutic C0, potentially increasing peak tacrolimus concentrations (Cmax) and total drug exposure (AUC24), which may contribute to poorer outcomes (Van Gelder et al., 2020).

Aim: To assess whether fast metabolizers have higher Cmax and to evaluate the C0-AUC24 relationship compared to intermediate and slow metabolizers.

Methods: This retrospective study at Leiden University Medical Centre (2010-2024) included stable kidney transplant recipients with C0 and AUC24 measurements at 12 months post-transplantation. Recipients were categorized as fast (<1.05 ng/ml/mg), intermediate (1.05-1.54 ng/ml/mg), or slow (≥1.55 ng/ml/mg) metabolizers by C0/D ratio. Kruskal-Wallis and Wilcoxon tests compared Cmax and AUC24 across groups. Linear regression assessed the C0-AUC24 relationship.

Results: Among 359 recipients (Fast: n=87, Intermediate: n=97, Slow: n=175), no differences in Cmax were observed (p=0.418). Observed C0 differed significantly (p<0.0001), with fast metabolizers having lower C0 than both intermediate and slow metabolizers. The observed AUC24 was lower in fast vs. slow metabolizers (p=0.025). Linear regression predicted AUC24 at C0=5 and C0=7 as 182 and 246 (Fast), 166 and 230 (Intermediate), and 154 and 217 ng·h/mL (Slow).

Conclusion: Compared to intermediate and slow metabolizers at the same C0, fast metabolizers have a higher AUC24 (but a similar Cmax). The higher tacrolimus exposure may explain poorer outcomes. The difference in AUC24 supports AUC24-based monitoring to optimize tacrolimus dosing in these patients.

Background: Therapeutic drug monitoring (TDM) plays a crucial role in adjusting tacrolimus (Tac) doses. However, Tac dosing is complicated by various factors that influence its pharmacokinetics. Our previous untargeted metabolomics study indicated that plasma microbial toxins may affect Tac trough concentrations, providing new insights for optimizing Tac dosing.

Aims: To quantitatively examine the correlation between plasma microbial toxin levels and Tac dose-adjusted trough concentrations in adult liver transplant recipients, as well as to explore the in vitro interactions of these toxins with P-glycoprotein (P-gp) and CYP3A4/5 enzymes.

Methods: Plasma samples from 124 liver transplant recipients were analyzed for seven microbial toxin levels using liquid chromatography tandem mass spectrometry. Linear regression was performed to assess the relationship between Tac trough concentrations and microbial toxins. MDCK-MDR1 cells were used to assess Tac uptake, and P-gp expression was measured in HepG2 and Huh-7 cells. The effects of microbial toxins on CYP3A4/5 activity were evaluated using recombinant enzymes.

Results: Regression analysis identified microbial toxin P-cresol sulfate (PCS) (β=-0.192, p=0.036), total bilirubin (β=0.018, p=0.048), and diabetes (β=1.294, p=0.015) as independent factors influencing Tac trough concentrations. In vitro, PCS did not affect Tac uptake in MDCK-MDR1 cells but induced P-gp mRNA and protein expression in a time-dependent manner. PCS showed a minor inhibitory effect on CYP3A4 activity, indicated by decreased 1-hydroxymidazolam metabolites.

Conclusions: Plasma PCS levels negatively correlate with Tac trough concentrations in liver transplant recipients. The interaction between PCS and P-gp may inform Tac dose adjustments.

Key Words: tacrolimus, pharmacokinetics, liver transplantation, P-cresol sulfate

Background: Ulcerative colitis (UC) diagnosis currently relies on clinical symptoms, endoscopic findings, and histopathology, yet lacks a specific gold-standard biomarker, often leading to delayed or uncertain diagnoses. 5-Hydroxymethylcytosine (5hmC), an epigenetic regulator of gene expression, exhibits chemical stability and disease-specific modifications, positioning it as a promising biomarker to address this unmet need.

Aims: To identify genome-wide 5hmC signatures in circulating cell-free DNA (cfDNA) and develop non-invasive diagnostic biomarkers for UC.

Methods: Utilizing 5hmC-Seal technology, we profiled 5hmC in cfDNA from 49 UC patients and 74 healthy controls. The cohorts were stratified into training (33 UC/50 healthy) and validation (16 UC/24 healthy) sets. An elastic-net logistic regression model was trained on differentially hydroxymethylated regions (DhMRs) and validated for diagnostic performance.

Results: UC patients exhibited globally reduced 5hmC levels (p<0.0001) and distinct DhMR patterns compared to controls. A 9-DhMR model attained high accuracy (AUC=0.919, sensitivity=0.812, specificity=1) in validation. Notably, a single DhMR within RSRC1 achieved comparable accuracy (AUC=0.924, sensitivity=0.812, specificity=0.958). Functional analysis linked DhMRs to UC-relevant pathways (e.g., Wnt signaling, platelet activation). RSRC1 5hmC levels inversely correlated with C-reactive protein (p<0.05), supporting its role in inflammation. Digital PCR confirmed the reduced RSRC1 5hmC copies in UC (p<0.0001), allowing for simplified detection.

Conclusions: Genome-wide 5hmC profiling in cfDNA effectively distinguishes UC patients from healthy individuals. The RSRC1-centric model provides a robust, minimally invasive diagnostic tool with high clinical translatability, addressing critical unmet needs in UC management.

Keywords: Ulcerative colitis, cell-free DNA, 5-hydroxymethylcytosine, diagnosis, RSRC1

Background: Tacrolimus monitoring in kidney transplant recipients traditionally relies on whole-blood trough levels, but this approach may inadequately reflect lymphocyte drug exposure, limiting its ability to predict graft outcomes. This study evaluated whether tacrolimus monitoring in peripheral blood mononuclear cells (PBMCs) improves clinical utility by analyzing its correlation with allograft function and de novo donor-specific antibody (dnDSA) status.

Aims: In a single-center prospective observational study, 60 kidney transplant recipients underwent paired tacrolimus measurements in PBMCs and whole blood at six timepoints within the first post-transplant year.

Methods: In a single-center prospective observational study, 60 kidney transplant recipients underwent paired tacrolimus measurements in PBMCs and whole blood at six timepoints within the first post-transplant year.

Results: PBMC tacrolimus levels averaged 3.6% of whole-blood concentrations (P<0.01) and correlated with whole-blood levels at early and mid-term follow-ups. Notably, PBMC tacrolimus levels demonstrated stronger associations with creatinine clearance and estimated glomerular filtration rate at multiple timepoints compared to whole-blood measurements. Patients with dnDSA exhibited significantly higher intrapatient variability in PBMC tacrolimus levels than dnDSA-negative counterparts (P<0.05), a pattern not observed in whole-blood analyses.

Conclusions: These findings suggest that PBMC-based tacrolimus monitoring better reflects drug bioavailability in target immune cells, offering enhanced insights into graft function and immunological risk. By linking PBMC tacrolimus variability to dnDSA status, this study highlights the potential of lymphocyte-level monitoring to optimize immunosuppression management and improve long-term transplant outcomes.

Key Words: kidney transplantation; tacrolimus; intra-patient variability; peripheral blood mononuclear cell; allograft function; de novo donor-specific antibody

Background: Tacrolimus is used for lifelong treatment in kidney transplant recipients, with vomiting being a common complication due to infections or side effects. Vomiting can reduce tacrolimus absorption, potentially decreasing its efficacy.

Aims: To develop a tool that calculates the required additional tacrolimus dose needed after vomiting episodes, to maintain appropriate drug exposure, ensuring therapeutic efficacy and preventing overexposure.

Methods:A published population pharmacokinetics (PK) model for immediate-release tacrolimus in adult kidney transplant recipients (Woillard et al, 2011) was implemented in R software. Ten thousand PK profiles at steady-state, with various vomiting delays and doses, were simulated. Only profiles with a trough concentration (C0) between 4–12 µg/L (Brunet et al, 2019) were considered. Vomiting was assumed to completely empty the depot compartment, and the AUC and C0 were used to evaluate the impact of vomiting and compensatory doses.

Results:A total of 3253 profiles were selected, with mean AUC0-12h and C0 of 128.9 ± 39. h.µg/L and 7.89 ± 2.30 µg/L. Vomiting at 15, 30, and 45 minutes after IR-tac intake led to a decrease in AUC0-12h by 16.9 ± 9.4, 6.0 ± 4.2, and 2.3 ± 2.1%, respectively. Underexposure was corrected (AUC within ± 20 % of steady-state AUC) by adding a half or a full dose.

Conclusions:This study provides a novel and original solution to managing vomiting episodes in kidney transplant recipients. An app under development will offer individualized compensation based on recent C0 values, estimating personal PK parameters.

Keywords:Tacrolimus, Vomiting, Kidney transplant patients, Population pharmacokinetics, Compensatory dosing, AUC

Introduction: Tacrolimus (TAC) blood concentration is key for dose adjustment post-transplantation. Some patients with standard TAC blood concentrations still had rejection. Peripheral blood mononuclear cells (PBMCs) concentration may better indicate rejection risk as TAC mainly targets lymphocytes.

Aims: This study aims to assess the relationship between TAC blood concentration and PBMCs in transplant patients and their link to rejection.

Methods: Patients from March 2023 to March 2024 were divided into rejection and non-rejection groups based on post-transplant outcomes. TAC blood concentrations and PBMCs were measured via LC-MS/MS at days 1, 3, 5 and 7 after stabilization (or rejection diagnosis). SPSS 26.0 was utilized to analyze differences in TAC concentrations between the two groups and to assess correlations with rejection events.

Results: A total of 42 patients were included, with 21 in each group. No significant difference in TAC blood concentrations. Significant differences in PBMC TAC concentrations on days 1-7 (P < 0.05). No increase in PBMCs TAC in the rejection group with higher dosage. No significant correlation between blood and PBMCs TAC concentrations.

Conclusion: PBMC concentration is significantly associated with rejection. However, no significant correlation between TAC blood and PBMC concentrations in rejecting patients.

Key Words: Organ transplantation, Tacrolimus, PBMCs, Rejection reaction