Limited Sampling Strategies Fail to Accurately Predict Mycophenolic Acid Area Under the Curve in Kidney Transplant Recipients and the Impact of Enterohepatic Recirculation.

BACKGROUND: Therapeutic drug monitoring for mycophenolic acid (MPA) is challenging due to difficulties in measuring the area under the curve (AUC). Limited sampling strategies (LSSs) have been developed for MPA therapeutic drug monitoring but come with risk of unacceptable performance. The authors hypothesized that the poor predictive performance of LSSs were due to the variability in MPA enterohepatic recirculation (EHR). This study is the first to evaluate LSSs models performance in the context of EHR. METHODS: Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC0-12hr in our full cohort and separately in individuals with high and low EHR. RESULTS: None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC0-12hr (±15% of observed AUC0-12hr) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%). CONCLUSIONS: The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. Models and strategies that incorporate EHR during development are required to accurately ascertain MPA exposure.

Steroid-tacrolimus drug-drug interaction and the effect of CYP3A genotypes.

AIMS: Tacrolimus, metabolized by CYP3A4 and CYP3A5 enzymes, is susceptible to drug-drug interactions (DDI). Steroids induce CYP3A genes to increase tacrolimus clearance, but the effect is variable. We hypothesized that the extent of the steroid-tacrolimus DDI differs by CYP3A4/5 genotypes. METHODS: Kidney transplant recipients (n = 2462) were classified by the number of loss of function alleles (LOF) (CYP3A5*3, *6 and *7 and CYP3A4*22) and steroid use at each tacrolimus trough in the first 6 months post-transplant. A population pharmacokinetic analysis was performed by nonlinear mixed-effect modelling (NONMEM) and stepwise covariate modelling to define significant covariates affecting tacrolimus clearance. A stochastic simulation was performed and translated into a Shiny application with the mrgsolve and Shiny packages in R. RESULTS: Steroids were associated with modestly higher (3%-11.8%) tacrolimus clearance. Patients with 0-LOF alleles receiving steroids showed the greatest increase (11.8%) in clearance compared to no steroids, whereas those with 2-LOFs had a negligible increase (2.6%) in the presence of steroids. Steroid use increased tacrolimus clearance by 5% and 10.3% in patients with 1-LOF and 3/4-LOFs, respectively. CONCLUSIONS: Steroids increase the clearance of tacrolimus but vary slightly by CYP3A genotype. This is important in individuals of African ancestry who are more likely to carry no LOF alleles, may more commonly receive steroid treatment, and will need higher tacrolimus doses.

Extreme Phenotype Sampling and Next Generation Sequencing to Identify Genetic Variants Associated with Tacrolimus in African American Kidney Transplant Recipients.

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n=58) and low (n=60) TAC troughs (N=515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12mg, respectively. Of 34,542 identified variants across 99 genes, 1,406 variants were suggestively associated with TAC troughs in univariate models (p-value <0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome.

Pharmacomicrobiomics: Immunosuppressive Drugs and Microbiome Interactions in Transplantation.

The human microbiome is associated with human health and disease. Exogenous compounds, including pharmaceutical products, are also known to be affected by the microbiome, and this discovery has led to the field of pharmacomicobiomics. The microbiome can also alter drug pharmacokinetics and pharmacodynamics, possibly resulting in side effects, toxicities, and unanticipated disease response. Microbiome-mediated effects are referred to as drug-microbiome interactions (DMI). Rapid advances in the field of pharmacomicrobiomics have been driven by the availability of efficient bacterial genome sequencing methods and new computational and bioinformatics tools. The success of fecal microbiota transplantation for recurrent Clostridioides difficile has fueled enthusiasm and research in the field. This review focuses on the pharmacomicrobiome in transplantation. Alterations in the microbiome in transplant recipients are well documented, largely because of prophylactic antibiotic use, and the potential for DMI is high. There is evidence that the gut microbiome may alter the pharmacokinetic disposition of tacrolimus and result in microbiome-specific tacrolimus metabolites. The gut microbiome also impacts the enterohepatic recirculation of mycophenolate, resulting in substantial changes in pharmacokinetic disposition and systemic exposure. The mechanisms of these DMI and the specific bacteria or communities of bacteria are under investigation. There are little or no human DMI data for cyclosporine A, corticosteroids, and sirolimus. The available evidence in transplantation is limited and driven by small studies of heterogeneous designs. Larger clinical studies are needed, but the potential for future clinical application of the pharmacomicrobiome in avoiding poor outcomes is high.

Reduced Enterohepatic Recirculation of Mycophenolate and Lower Blood Concentrations Are Associated with the Stool Bacterial Microbiome after Hematopoietic Cell Transplantation.

Mycophenolate mofetil (MMF) is an important immunosuppressant used after allogeneic hematopoietic cell transplantation (HCT). MMF has a narrow therapeutic index, and blood concentrations of mycophenolic acid (MPA), the active component of MMF, are highly variable. Low MPA concentrations are associated with the risk of graft-versus-host disease (GVHD), whereas high concentrations are associated with toxicity. Reasons for variability are not well known and may include the presence of ß-glucuronidase-producing bacteria in the gastrointestinal tract, which enhance MPA enterohepatic recirculation (EHR) by transforming MPA metabolites formed in the liver back to MPA. This study was conducted to determine whether individuals with high MPA EHR have a greater abundance of ß-glucuronidase-producing bacteria in their stool and higher MPA concentrations compared with those with low EHR. We conducted a pharmacomicrobiomics study in 20 adult HCT recipients receiving a myeloablative or reduced-intensity preparative regimen. Participants received MMF 1 g i.v. every 8 hours with tacrolimus. Intensive pharmacokinetic sampling of MMF was conducted before hospital discharge; total MPA, MPA glucuronide (MPAG), and acyl-glucuronide metabolite (acylMPAG) were measured. EHR was defined as the ratio of MPA area under the concentration-versus-time curve (AUC)4-8 to MPA AUC0-8. Differences in stool microbiome diversity and composition, determined by shotgun metagenomic sequencing, were compared above and below the median EHR (22%; range, 5% to 44%). The median EHR was 12% in the low EHR group and 29% in the high EHR group. MPA troughs, MPA AUC4-8, and acyl-glucuronide metabolite (acylMPAG) AUC4-8/AUC0-8 ratio were greater in the high EHR group compared with the low EHR group (1.53 µg/mL versus .28 µg/mL [P = .0001], 7.33 hour·µg/mL versus 1.79 hour·µg/mL [P = .0003], and .33 hour·µg/mL versus .24 hour·µg/mL [P = .0007], respectively). MPA AUC0-8 was greater in the high EHR group than in the low EHR group, and the difference trended toward significance (22.8 hour·µg/mL versus 15.3 hour·µg/mL; P = .06). Bacteroides vulgatus, Bacteroides stercoris, and Bacteroides thetaiotaomicron were 1.2- to 2.4-fold more abundant (P = .039, .024, and .046, respectively) in the high EHR group. MPA EHR was positively correlated with B. vulgatus (⍴ = .58; P ≤ .01) and B. thetaiotaomicron (⍴ = .46; P < .05) and negatively correlated with Blautia hydrogenotrophica (⍴ = -.53; P < .05). Therapeutic MPA troughs were achieved in 80% of patients in the high EHR group but in no patients in the low EHR group. There was a trend toward differences in MPA AUC0-8 and MPA concentration at steady-state (µg/mL) between the high EHR group versus the low EHR group (P = .06). MPA EHR was variable. Patients with high MPA EHR had greater abundance of Bacteroides species in stool and higher MPA exposure compared with patients with low MPA EHR. Therefore, Bacteroides may be protective against poor outcomes, such as graft-versus-host disease, in some patients but may increase the risk of MPA adverse effects in others. These data need to be confirmed and studied after oral MMF therapy.