Background: The metabolism of azathioprine, an immunosuppressive drug used in inflammatory bowel disease treatment, is significantly influenced by the gut microbiota. This study aims to identify novel gut microbiota biomarkers for the therapeutic drug monitoring of azathioprine.

Methods: The research combines animal experiments and clinical studies. Germ-free rats were employed to establish a model evaluating the impact of gut microbiota on azathioprine pharmacokinetics. Simultaneously, stable Crohn's disease patients receiving azathioprine for over three months were enrolled to explore the correlations between gut microbiota structure and active metabolite levels (6-thioguanosine nucleotides, 6-TGN). Both 16S RNA sequencing and metagenomic sequencing were utilized to identify potential gut microbiota biomarkers for azathioprine's therapeutic drug monitoring.

Results: Variations in gut microbiota composition correlated with differences in 6-TGN levels. Prevotella emerged as indicative bacteria influencing both exposure (AUC, 3148.8±515.4 hour∙ng∙mL-1 in control group vs. 2078.0±290.5 hour∙ng∙mL-1 in germ-free group) and steady-state concentrations of 6-TGN (6-TGN_L: 151.7±29.7, 6-TGN_M: 290.7±58.2, 6-TGN_H: 529.7±130.4; unit: pmol/8×108RBC). Metagenomic analysis revealed significant enrichments of key metabolic enzymes (guanine-methylation-sensitive endonuclease, hypoxanthine phosphoribosyltransferase, and inosine monophosphate dehydrogenase) in the genera Clostridium and Roseburia, potentially contributing to 6-TGN level variations. Known butyrate-producers, including Clostridium and Roseburia, demonstrated a positive correlation with 6-TGN levels (correlation coefficient: 0.483, P＜0.05), suggesting azathioprine alleviates intestinal inflammation through an intrinsic mechanism involving these genera.

Conclusion: Prevotella, Roseburia, and Clostridium constitute novel biomarkers for azathioprine therapeutic drug monitoring, potentially offering implications for personalized treatment strategies.

Keywords: Azathioprine, Gut microbiota, Biomarker, 6-TGN

Background: Immune checkpoint inhibitors (ICIs) demonstrate superior efficacy in various cancers, but challenges such as low response rates and therapeutic resistance remain. Effective plasma biomarkers for predicting ICI efficacy are needed.

Aims: To investigate the roles of tumor necrosis factors (TNFs) and their receptor (TNFR) superfamily in modulating antitumor immunity and responsiveness to ICIs. Additionally, we focus on evaluating the potential of circulating TNF receptors as predictive biomarkers for ICI efficacy.

Methods: A pan-cancer analysis using TCGA data was performed to evaluate the expression patterns of TNF and TNFR genes across various cancer types. Their prognostic significance was analyzed using Cox regression analysis and Kaplan-Meier survival analysis. CIBERSORT and TIMER algorithms were used to assess associations of these genes with tumor microenvironment characteristics. Plasma samples meeting inclusion criteria from lung adenocarcinoma (LUAD) patients treated with ICIs were collected, and ELISA was used to analyze correlation between TNFRSF6B levels and objective response rate. The independent prognostic value of circulating TNFRSF6B levels for ICI efficacy was assessed using multivariate Cox regression analysis.

Results: Among the genes analyzed, TNFRSF6B expression was significantly upregulated in tumors and correlated with poor prognosis in multiple cancer types, including LUAD, kidney renal clear cell carcinoma, and pancreatic adenocarcinoma. TNFRSF6B expression was associated with an immunosuppressive tumor microenvironment. High plasma TNFRSF6B levels indicated reduced response to PD-1/PD-L1 blockade therapy in LUAD patients.

Conclusions: Tumor and plasma TNFRSF6B levels are significant biomarkers for LUAD prognosis and ICI efficacy, respectively.

Keywords: immune checkpoint inhibitors, tumor necrosis factor, TNFRSF6B, plasma biomarker

Background: Research shows a link between gut microbiota and drug-induced liver injury (DILI). Renal transplant patients on Tacrolimus (Tac) therapy often suffer from DILI, but the exact mechanism is unclear.

Aims: To compare gut microbiota differences between renal transplant patients on Tac regimen with DILI and those with stable liver function, explore the DILI-flora relationship, find specific DILI-related microbiota, and provide potential biomarkers for early DILI identification.

Methods: Patients from Sichuan Provincial People’s Hospital's Organ Transplant Center on Tac regimen were included. DILI diagnosis followed the "Chinese Guidelines for the Diagnosis and Treatment of Drug-induced Liver Injury (2023 Edition)". Fecal samples were collected, and 16S rRNA gene sequencing was done to analyze gut microbiota differences and their correlation with Tac-induced DILI.

Results: 32 DILI patients and 40 with normal liver function were included. Alpha diversity analysis showed lower Chao1 (P = 0.038) and ACE (P = 0.043) indices in the DILI group, indicating reduced species richness. Beta diversity analysis showed differences in Unweighted UniFrac (P = 0.041), Bray - Curtis (P = 0.001) and Jaccard (P=0.001) distances. Oscillibacter and Ruminococcus gnavus groups were more abundant in the DILI group, while 10 types including Bacteroidetes were less abundant.

Conclusion: Significant gut microbiota composition differences exist between DILI patients and controls. These changes suggest gut microbiota's important role in Tac-induced DILI and potential as DILI biomarkers for early diagnosis and prevention.

Key Words: Gut Microbiota; DILI; Tacrolimus; Renal Transplant.

Background: Pharmacokinetic (PK) models assessing the bioavailability of tacrolimus oral suspension are scarce.

Aims: This study aimed to develop a population PK model to assess the bioavailability of tacrolimus oral suspension in pediatric patients with hematopoietic stem cell transplant (HSCT).

Methods: Sixteen children receiving continuous IV infusion switched to tacrolimus oral suspension (dose conversion 1:4) were included. 339 whole blood samples were collected and quantified by immunoassay: 260 during IV administration and 79 corresponding to 12-hour time profiles (0, 20 minutes, 1, 3, and 12 hours) during oral suspension administration. Data analysis was performed using a non-linear mixed effects approach considering demographic, clinical, and pharmacological factors as potential covariates influencing tacrolimus PK.

Results: A one-compartment model with linear elimination adequately described tacrolimus PK. Typical estimated values (%RSE) for clearance (CL), volume of distribution (Vd), and bioavailability (F) were 1.5 L/h (9.5%), 42.3 L (17.5%), and 40.0% (14.8%), respectively. The delay in the absorption process was characterized by a tlag parameter, with a typical value of 0.3 h-1 (5.3%). Body weight was a covariate that improved the estimation of CL, Vd, and F, while no other covariates significantly influenced tacrolimus PK parameters.

Conclusions: A population PK model adequately described the PK of tacrolimus during continuous IV infusion and oral suspension administration in pediatric patients with HSCT. The model showed that the typical F of the oral suspension was 40%, suggesting that a conversion factor of 2.5 from IV to oral suspension may be more appropriate.

Keywords: tacrolimus, pharmacokinetics, bioavailability, oral suspension

Background: Drug interactions involving immunosuppressants can compromise transplant outcomes. While repeated ketamine administration is known to induce hepatic enzymes in experimental models, there are no clinical reports of induced clearance of cytochrome P450 (CYP) substrates. Continuous subcutaneous (SC) ketamine infusions are increasingly used in opioid-sparing strategies for acute pain management.

Aims: To describe a case of induced changes in tacrolimus and sirolimus clearance in a heart transplant recipient following administration of a continuous subcutaneous ketamine infusion.

Methods: A 55-year-old male heart transplant recipient had thrombectomy for extensive left lower limb deep vein thrombosis. Post-operatively, the patient received rivaroxaban, clopidogrel, and ketamine infusion (5mg/h SC, increased to 10mg/h) for severe post-operative pain over four days. Immunosuppressant trough concentrations (C0) were monitored before, during, and after ketamine, with sequential dose adjustments made in response to the observed concentrations.

Results: Following ketamine initiation, tacrolimus and sirolimus C0 were noted to be low, however dose escalations failed to achieve proportional responses: 2.3- and 3.5-fold dose increases resulted in 0- and 2-fold C0 increases, respectively. Approximately three weeks after ketamine discontinuation, drug clearances appeared to return to pre-ketamine values, resulting in supratherapeutic C0 and transient renal dysfunction, which resolved with dose decreases. There was no evidence of transplant rejection over the period.

Conclusions: This case provides clinical evidence of ketamine's enzyme-inducing impact on immunosuppressants. Prescribers should anticipate this interaction, particularly in transplant recipients. This phenomenon may extend to other CYP450 substrates where reduced effectiveness might go unrecognized.

Keywords: Ketamine, tacrolimus, sirolimus, drug interaction, pharmacokinetics, transplantation

Objectives: Mycophenolic acid (MPA), a potent immunosuppressant, is widely used in solid organ transplantations to prevent rejection. The aim of this study is i) to establish a population pharmacokinetic (PPK) model and limited sampling strategies (LSSs) to estimate the area under the curve of the dosing interval (AUC0-12h) of MPA and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure and iii) to assist therapeutic drug monitoring(TDM) and dosage optimization of MPA during the perioperative period in Chinese liver transplant recipients.

Methods: Population pharmacokinetic analysis was performed using with data obtained from 48 adult patients at steady states during the perioperative period. Various demographic variables were investigated as potential covariates for PPK modeling. The LSSs were established based on multiple linear regression for estimating the AUC0-12h of MPA.

Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model in PPK modeling. Non-linear mixed-effects models were developed. The covariate analysis identified liver transplantation donor ABCC2 24C>T genotype as an individual factor influencing central volume of distribution of MPA. The LSSs showed the four-point model (C1.5h, C2h, C4h, C12h) (R2=0.897) performed best in predicting MPA AUC0-12h.

Conclusion: In conclusion, this study successfully developed a PPK model and LSSs that could be applied in real clinical settings to assist TDM and dosage optimization of MPA during the perioperative period in Chinese liver transplant recipients.

Keywords: mycophenolic acid, population pharmacokinetics, limited sampling strategy, liver transplant recipient, perioperative period, therapeutic drug monitoring

Background: Mycophenolic acid (MPA) is used in fixed doses of 2-3 grams/day. in renal transplantation This has been shown to result in variable patient outcomes. In Indian patients, optimal MPA exposure remains elusive. The recommended therapeutic range of AUC0-12h is 30-60mg.h/L underscores the need for precise dosing to balance efficacy and safety. This study aimed to explore the extent of MPA exposure in renal transplant recipients prescribed standard MMF doses, and to assess its association with rejection and infection episodes at day 30 post-transplantation.

Methods: We enrolled 120 renal transplant recipients with 94 males and 26 females who were prescribed 1g MMF twice daily post-transplantation. The mean age of the renal transplant recipients was 48.2±6.7 years and mean BMI was 20.13±3.58 kg/m². The MPA exposure quantification was done using HPLC. Patients were monitored for rejection and infection episodes at day-30.

Results: Patients with MPA exposure >60mg.h/L (n=68) exhibited mean AUC of 82.65±19.01 mg.hr/L, while those <30mg.h/L (n=13) showed 23.06±5.25, and within therapeutic range (n=39) displayed 64.96±26.73 AUC. Notably, rejection rates did not significantly differ based on MPA exposure (p=0.44), patients with infections had significantly difference in mean AUC 53.61± 20.15) compared to those without infection (mean AUC 66.19±26.59; p=0.008).

Conclusion: Our findings underscore the importance of therapeutic drug monitoring (TDM) in renal transplant care. While MPA exposure did not significantly impact rejection, it significantly influenced infection rates. TDM-guided dosage adjustments may mitigate infection-related complications post-transplantation, emphasising the necessity of personalized immunosuppressive therapy.

Objective: To find out the biomarkers associated with the response to methotrexate (MTX) therapy in rheumatoid arthritis (RA), and explain the individual differences of MTX therapy from the perspectives of metabolomics.
Methods: Ultra-performance liquid chromatography- quadrupole/time-of-flight mass spectrometry was adopted to acquire the serum profiles of the effective (n=22) and non-effective (n=17) groups of RA patients receiving MTX treatment. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were conducted to find out the biomarkers and metabolic pathways associated with the response to MTX therapy. Ions with variable importance in the projection (VIP) value exceeding 1.0 in PLS-DA and p value lower than 0.05 were retained for further identification.
Results: There existed obvious difference of the metabolic profiles between the effective and non-effective groups. Based on the criteria of VIP values above 1 and p values below 0.05, a total of thirty-nine differential metabolites were identified. Briefly speaking, 12 differential metabolites, including myristic acid, palmitic acid, tryptophan, and uridine, showed a significant increase, while 22 differential metabolites, including glycine cholic acid, sphingosine 1-phosphate, lysophosphatidylcholine, and lysophosphatidylethanolamine, showed a significant decrease, in the non-effective group when compared with those of the effective group. These differential metabolites participated in phenylalanine, tyrosine and tryptophan biosynthesis, tryptophan metabolism, tyrosine metabolism and so on.
Conclusion: Our study revealed the metabolites associated with the response to MTX therapy in RA, which would provide data support for the individualized therapy of MTX.
Keywords: methotrexate, rheumatoid arthritis, individual differences, metabolomics

Background: Intra-patient variability in immunosuppressive blood drug concentrations is a potential biomarker in managing organ transplant patients. However, the association between the time in therapeutic range (TTR) of tacrolimus blood concentrations and its efficacy in preventing graft-versus-host disease (GVHD) remains unknown.

Aims: This study aimed to analyze the relationship between the TTR of tacrolimus blood concentrations and acute GVHD prophylaxis in allogeneic hematopoietic stem cell transplantation (HSCT) recipients to establish a new concept for managing tacrolimus blood concentrations post-HSCT.

Methods: Eligible patients administered tacrolimus were categorized into two groups based on the grade of acute graft-versus-host disease, and propensity score matching was performed using graft-versus-host disease prophylaxis protocols and days to the disease onset to compare time in therapeutic range.

Results: In patients with tacrolimus blood concentration therapeutic range ≥10 ng/mL, time in therapeutic range during the first 4 weeks post-transplantation was significantly lower in the Grade II–III group than in the Grade 0–I group. Among propensity score matching-extracted patients, the Grade II–III group had significantly lower time in therapeutic range during the first 2 and 4 weeks post-transplantation.

Conclusions: This study showed that maintaining a higher tacrolimus TTR in the early post-transplantation period averts severe acute GVHD development. Our data reiterates the need to control deviations in blood concentrations as much as possible and to control them within the therapeutic range when using tacrolimus for acute GVHD prophylaxis after allogeneic HSCT.

Keywords: tacrolimus, therapeutic drug monitoring, time in therapeutic range, hematopoietic stem cell transplantation, graft-versus-host disease

Background: Immunosuppressant therapeutic drug monitoring is routinely used in clinical practice. While first-generation mass spectrometry assays rely on manual sample preparation, many laboratories have transitioned to automated preparation with liquid handlers. A local quality program comparing patient samples across different laboratories uncovered significant discrepancies. Further investigation revealed that these discrepancies were linked to clotting errors during processing with the liquid handler.

Methods: Immunosuppressant concentrations were quantified using LC-MS/MS. Sample preparation was automated with a liquid handler, where 20µL of whole blood was mixed with 80µL of 0.1M ZnSO₄, followed by protein precipitation using 200µL of acetonitrile. The mixture was filtered and injected into the LC-MS/MS. For patient samples exhibiting clotting errors, they were manually processed following the above protocol. Various manual pipetting techniques from different technologists were assessed.

Results: Reverse pipetting and positive displacement pipetting techniques produced results most consistent with the automated liquid handler, regardless of the operator. In contrast, standard pipetting methods yielded more variable results between technologists. Notably, during initial method validation of this automated sample preparation, prior to clinical implementation, the comparison between manual and automated preparation showed minimal bias.

Conclusions: While automated liquid handlers generally outperform manual preparation, challenges remain with clotting samples. Re-collection of samples is not always feasible due to the time-sensitive nature of sample collection/testing. A standardized workflow should be developed for handling clotting samples. Additionally, this highlights the importance of interlaboratory comparisons using real patient samples, which can complement proficiency testing.

Key Words: Immunosuppressant, LC-MS/MS, Liquid handler, Analytical error, Sample preparation