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Kidney transplant recipients carrying the CYP3A5*1 allele have lower tacrolimus troughs, and higher dose requirements compared to those with the CYP3A5*3/*3 genotype. However, data on the effect of CYP3A5 alleles on post-transplant tacrolimus management are lacking. The effect of CYP3A5 metabolism phenotypes on the number of tacrolimus dose adjustments and troughs in the first 6 months post-transplant was evaluated in 78 recipients (64% Caucasians). Time to first therapeutic concentration, percentage of time in therapeutic range (TTR), and estimated glomerular filtration rate (eGFR) were also evaluated. Fifty-five kidney transplant recipients were CYP3A5 poor metabolizers (PM), 17 were intermediate metabolizers (IM), and 6 were extensive metabolizers (EM). Compared to PMs, EMs/IMs had significantly more dose adjustments (6.1 vs. 8.1, p = .015). Overall, 33.82% of trough measurements resulted in a dose change. There was no difference in the number of tacrolimus trough measurements between PMs and EM/IMs. The total daily tacrolimus dose requirements were higher in EMs and IMs compared to PMs (

WHAT IS KNOWN AND OBJECTIVE: Pharmacogenomic biomarkers are now used in many clinical care settings and represent one of the successes of precision medicine. Genetic variants are associated with pharmacokinetic and pharmacodynamic changes leading to medication adverse effects and changes in clinical response. Actionable pharmacogenomic variants are common in transplant recipients and have implications for medications used in transplant, but yet are not broadly incorporated into practice. METHODS: From the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines, and PharmGKB databases, 12 pharmacogenomic genes with 30 variants were selected and used to create diplotypes and actionable pharmacogenomic phenotypes. A total of 853 kidney allograft recipients who had genomic information available from a genome-wide association study were included. RESULTS: Each recipient had at least one actionable pharmacogenomic diplotype/phenotype, whereas the majority (58%) had three or four actionable diplotypes/phenotypes and 17.4% had five or more among the 12 genes. The participants carried actionable diplotypes/phenotypes for multiple medications, including tacrolimus, azathioprine, clopidogrel, warfarin, simvastatin, voriconazole, antidepressants and proton-pump inhibitors. WHAT IS NEW AND CONCLUSION: Pharmacogenomic variants are common in transplant recipients, and transplant recipients receive medications that have actionable variants. CLINICAL TRIAL: Genomics of Transplantation, clinicaltrials.gov (NCT01714440).

Tacrolimus trough and dose requirements vary dramatically between individuals of European and African American ancestry. These differences are less well described in other populations. We conducted an observational, prospective, multicenter study from which 2595 kidney transplant recipients of European, African, Native American, and Asian ancestry were studied for tacrolimus trough, doses, and genetic determinants of metabolism. We studied the well-known variants and conducted a CYP3A4/5 gene-wide analysis to identify new variants. Daily doses, and dose-normalized troughs were significantly different between the four groups (P

BACKGROUND: Identifying kidney allograft recipients who are predisposed to acute rejection (AR) could allow for optimization of clinical treatment to avoid rejection and prolong graft survival. It has been hypothesized that a part of this predisposition is caused by the inheritance of specific genetic variants. There are many publications reporting a statistically significant association between a genetic variant, usually in the form of a single-nucleotide polymorphism (SNP), and AR. However, there are additional publications reporting a lack of this association when a different cohort of recipients is analyzed for the same single-nucleotide polymorphism. METHODS: In this report, we attempted to validate 75 common genetic variants, which have been previously reported to be associated with AR, using a large kidney allograft recipient cohort of 2390 European Americans and 482 African Americans. RESULTS: Of those variants tested, only 1 variant, rs2910164, which alters the expression of the microRNA MIR146A, was found to exhibit a significant association within the African American cohort. Suggestive variants were found in the genes CTLA and TLR4. CONCLUSIONS: Our results show that most variants previously reported to be associated with AR were not validated in our cohort. This shows the importance of validation when reporting the associations with complex clinical outcomes such as AR. Additional work will need to be done to understand the role of MIR146A in the risk of AR in kidney allograft recipients.

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.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 engineering of the CYP3A5 *3 locus (rs776746) in human liver cell line HuH-7 (CYP3A5 *3/*3) has led to three CYP3A5 *1 cell lines by deletion of the exon 3B splice junction or point mutation. Cell lines CYP3A5 *1/*3 sd (single deletion), CYP3A5 *1/*1 dd (double deletion), or CYP3A5 *1/*3 pm (point mutation) expressed the CYP3A5 *1 mRNA and had elevated CYP3A5 mRNA (P < 0.0005 for all engineered cell lines) and protein expression compared with HuH-7. In metabolism assays, HuH-7 had less tacrolimus (all P < 0.05) or midazolam (MDZ) (all P < 0.005) disappearance than all engineered cell lines. HuH-7 had less 1-OH MDZ (all P < 0.0005) or 4-OH (all P < 0.005) production in metabolism assays than all bioengineered cell lines. We confirmed CYP3A5 metabolic activity with the CYP3A4 selective inhibitor CYP3CIDE. This is the first report of genomic CYP3A5 bioengineering in human cell lines with drug metabolism analysis.

Background: Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications. Methods: We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10−3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs. Results: By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10−8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10−4. Conclusions: Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov. [ABSTRACT FROM AUTHOR]

We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because its not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. DEGs were determined in 32 adult kidney recipients, without clinical acute rejection (AR), treated with antibody induction, calcineurin inhibitor, mycophenolate, with and without steroids. Blood was obtained pre-transplant (baseline), week 1, months 3 and 6 post-transplant. PBMCs were isolated, RNA extracted and gene expression measured using RNAseq. Principal components (PCs) were computed using a surrogate variable approach. DEGs post-transplant were identified by controlling false discovery rate (FDR) at < 0.01 with at least a 2 fold change in expression from pre-transplant. The top 5 DEGs with higher levels of transcripts in blood at week 1 were TOMM40L, TMEM205, OLFM4, MMP8, and OSBPL9 compared to baseline. The top 5 DEGs with lower levels at week 1 post-transplant were IL7R, KLRC3, CD3E, CD3D, and KLRC2 (Striking Image) compared to baseline. The top pathways from genes with lower levels at 1 week post-transplant compared to baseline, were T cell receptor signaling and iCOS-iCOSL signaling while the top pathways from genes with higher levels than baseline were axonal guidance signaling and LXR/RXR activation. Gene expression signatures at month 3 were similar to week 1. DEGs at 6 months post-transplant create a different gene signature than week 1 or month 3 post-transplant. RNAseq analysis identified more DEGs with lower than higher levels in blood compared to baseline at week 1 and month 3. The number of DEGs decreased with time post-transplant. Further investigations to determine the specific lymphocyte(s) responsible for differential gene expression may be important in selecting and personalizing immune suppressant drugs and may lead to targeted therapies.

Introduction Innovative smoking cessation approaches that overcome barriers such as traveling to program site or that require the staff and infrastructure for sustaining are likely needed to improve smoking quit rates among American Indian (AI) peoples in the United States. In this study, qualitative methods identified recommendations from AI peoples to guide alignment of an evidence-based smoking cessation smartphone app (i.e. QuitGuide) to the culture and needs of AI persons. Methods Semi-structured interviews were conducted with AI adults who smoke (n = 40) and with public health professionals (n = 6). Questions included: "The app asks if something triggered you to slip and lists several options. What options were you expecting to see on this list?" as well as how to make the app more engaging such as "What would make the app more helpful for AI peoples, like you, who want to quit smoking?." Constant comparative techniques were used to develop codes and themes. Results Loss, grief, and not accessing traditional tobacco were put forward as smoking triggers to be addressed in the app. Features that help users connect with and learn about AI cultures and promote healing, such as encouraging traditional tobacco use, being in community, embracing Native spirituality, and participating in cultural crafting were recommended. Some noted the need to motivate AI peoples to think about legacy and ability to care for younger generations and Indigenizing the app with Native imagery. Conclusions Themes pointed towards promotion of strengths-based factors, such as healing, cultural connectedness and traditional tobacco use, in the app. Implications Results will be used to culturally align a smartphone app for smoking cessation among AI peoples and may be insightful for other tribal, federal, and state public health efforts aimed at advancing health equity for AI peoples. [ABSTRACT FROM AUTHOR]

Introduction: A potential precision medicine approach to smoking cessation is tailoring pharmacotherapy to a biomarker known as the nicotine metabolite ratio (NMR). Little is known about the potential impact and acceptability of this approach for American Indian (AI) persons.Aims and Methods: Tribal-academic collaboration was formed and during 2019-2020 AI adults who smoke(N = 54) were recruited to (1) examine correlations between NMR, dependence, and smoking exposure; (2) assess the extent to which pharmacotherapy preference aligned with NMR-informed recommendations; (3) explore acceptability of NMR-informed pharmacotherapy selection. Participants provided samples for assessment of salivary NMR and urinary total nicotine equivalents (TNE) and completed a questionnaire that assessed cigarettes per day (CPD), Fagerstrom Test for Cigarette Dependence (FTCD), pharmacotherapy preference, and perceptions of NMR-informed pharmacotherapy selection.Results: Significant positive correlations were observed between NMR and FTCD (r = 0.29;p = .0383) and its abbreviated version Heaviness of Smoking Index (HIS) (r = 0.28;p =.0426). Post-hoc analyses suggest that relationships between dependence and NMR were driven by time to first cigarette. Nonsignificant, but directionally consistent, relationships were observed between NMR and CPD (r = 0.21; p =0.1436) and TNE (r = 0.24;p = .2906). Most participants preferred nicotine replacement therapy (71%) over varenicline (29%) and preference for pharmacotherapy matched NMR-based recommendations in 54% of participants. NMR-informed pharmacotherapy selection was supported by 62% of participants.Conclusion: In a sample of AI adults who smoke, NMR was related to cigarette dependence and about one-half of participants' pharmacotherapy preference matched their NMR-informed recommendation. There was lower acceptability of NMR-informed approach in this sample of AI adults than prior studies among white or black/African American people who smoke.Implications: Relationships between NMR, dependence, and self-preference for pharmacotherapy suggest that NMR-informed pharmacotherapy selection may have potential for enhancing smoking quitting success in this Tribe. Lower acceptability of NMR-informed pharmacotherapy in this Tribe suggests that this approach may not be equitably utilized. Future work could include identifying community-driven solutions to mitigate precision medicine concerns. [ABSTRACT FROM AUTHOR]

The document titled "487 Digital Spatial Profiling of Allograft Loss in Kidney Biopsies with Chronic Allograft Dysfunction" discusses the use of digital spatial profiling to assess molecular and cellular mechanisms of allograft loss in kidney biopsies. The study analyzes kidney biopsy tissue from patients with chronic allograft dysfunction and uses Nanostring GeoMX digital spatial profiling to identify specific cell populations and quantify the distribution of transcripts across the biopsy. The results show differences in cell populations and gene expression between biopsies with graft loss and those without. This data can help understand the mechanisms of kidney allograft loss and potentially improve immune suppression in kidney transplant recipients. [Extracted from the article]

Aims Methods Results Conclusions 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.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.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.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. [ABSTRACT FROM AUTHOR]

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