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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., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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 12 mg, respectively. Of 34,542 identified variants across 99 genes, 1406 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., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Genetic variation in the CYP3A4 and CYP3A5 (CYP3A4/5) genes, which encode the key enzymes in tacrolimus metabolism, is associated with tacrolimus clearance and dose requirements. Tacrolimus has a narrow therapeutic index with high intra- and intersubject variability, in part because of genetic variation. High tacrolimus clearance and low trough concentration are associated with a greater risk for rejection, whereas high troughs are associated with calcineurin-induced toxicity. The objective of this study was to develop a model of tacrolimus clearance with a dosing equation accounting for genotypes and clinical factors in adult kidney transplant recipients of European ancestry that could preemptively guide dosing. Recipients receiving immediate-release tacrolimus for maintenance immunosuppression from 2 multicenter studies were included. Participants in the GEN03 study were used for tacrolimus model development (n = 608 recipients) and was validated by prediction performance in the DeKAF Genomics study (n = 1361 recipients). Nonlinear mixed-effects modeling was used to develop the apparent oral tacrolimus clearance (CL/F) model. CYP3A4/5 genotypes and clinical covariates were tested for their influence on CL/F. The predictive performance of the model was determined by assessing the bias (median prediction error [ME] and median percentage error [MPE]) and the precision (root median squared error [RMSE]) of the model. CYP3A5*3, CYP3A4*22, corticosteroids, calcium channel blocker and antiviral drug use, age, and diabetes significantly contributed to the interindividual variability of oral tacrolimus apparent clearance. The bias (ME, MPE) and precision (RMSE) of the final model was good, 0.49 ng/mL, 6.5%, and 3.09 ng/mL, respectively. Prospective testing of this equation is warranted., (© 2021, The American College of Clinical Pharmacology.)

An extreme phenotype sampling (EPS) model with targeted next-generation sequencing (NGS) identified genetic variants associated with tacrolimus (Tac) metabolism in subjects from the Deterioration of Kidney Allograft Function (DeKAF) Genomics cohort which included 1,442 European Americans (EA) and 345 African Americans (AA). This study included 48 subjects separated into 4 groups of 12 (AA high, AA low, EA high, EA low). Groups were selected by the extreme phenotype of dose-normalized Tac trough concentrations after adjusting for common genetic variants and clinical factors. NGS spanned > 3 Mb of 28 genes and identified 18,661 genetic variants (3961 previously unknown). A group of 125 deleterious variants, by SIFT analysis, were associated with Tac troughs in EAs (burden test, p = 0.008), CYB5R2 was associated with Tac troughs in AAs (SKAT, p = 0.00079). In CYB5R2, rs61733057 (increased allele frequency in AAs) was predicted to disrupt protein function by SIFT and PolyPhen2 analysis. The variants merit further validation.

Background: The immunosuppressants tacrolimus and mycophenolate are important components to the success of organ transplantation, but are also associated with adverse effects, such as nephrotoxicity, anemia, leukopenia, and new-onset diabetes after transplantation. In this report, we attempted to identify genetic variants which are associated with these adverse outcomes., Methods: We performed a genome-wide association study, using a genotyping array tailored specifically for transplantation outcomes containing 722 147 single nucleotide polymorphisms, and 2 cohorts of kidney allograft recipients-a discovery cohort and a confirmation cohort-to identify and then confirm genetic variants associated with immunosuppressant pharmacokinetics and adverse outcomes., Results: Several genetic variants were found to be associated with tacrolimus trough concentrations. We did not confirm variants associated with the other phenotypes tested although several suggestive variants were identified., Conclusions: These results show that adverse effects associated with tacrolimus and mycophenolate are complex, and recipient risk is not determined by a few genetic variants with large effects with but most likely are due to many variants, each with small effect sizes, and clinical factors.