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Background: For extended-release drug formulations, effective half-life (t1/2eff) is a relevant pharmacokinetic parameter to inform dosing strategies and time to reach steady state. Tacrolimus, an immunosuppressant commonly used for the prophylaxis of organ rejection in transplant patients, is available as both immediate- and extended-release formulations. To the best of our knowledge, the t1/2eff of tacrolimus from these different formulations has not yet been assessed. The objective of this study was to characterize the t1/2eff and terminal half-life (t1/2z) of an extended-release once-daily tacrolimus formulation (LCPT) and twice-daily immediate-release tacrolimus (IR-Tac)., Methods: A noncompartmental analysis of pharmacokinetic data obtained from a phase 2 study in de novo kidney transplant recipients receiving either LCPT or IR-Tac was conducted. Intensive blood sampling was performed on days 1, 7, and 14, and tacrolimus whole blood concentrations were measured using a validated liquid chromatography with tandem mass spectrometry method. T1/2eff was estimated using within-participant accumulation ratios. T1/2z was estimated by linear regression of the terminal phase of the concentration versus time profile., Results: The median accumulation ratios of LCPT and IR-Tac on day 14 were 3.18 and 2.06, respectively.The median (interquartile range; IQR) t1/2eff for LCPT at day 14 of dosing was 48.4 (37.4-77.9) hours, whereas the t1/2z was 20.3 (17.6-22.9) hours. For IR-Tac, the median (IQR) t1/2eff and t1/2z on day 14 were 12.5 (8.8-23.0) hours and 12.2 (9.2-15.7) hours, respectively., Conclusions: Consistent with its prolonged release of tacrolimus, LCPT demonstrated a higher accumulation ratio and a longer t1/2eff compared with IR-Tac. These findings underscore the pharmacokinetic differences between different drug formulations of the same moiety and may help inform dose adjustments for LCPT in kidney transplantation., Competing Interests: J.D. Momper has received consulting fees from Veloxis Pharmaceuticals, Inc. B.M. Masters and S.J. Patel are employees of Veloxis Pharmaceuticals, Inc. R. Venkataramanan, A.S. Jantz, D.M. Cibrik, and K. Birdwell have no conflicts of interest to declare., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology.)

Background: Studies involving organ transplant recipients (OTRs) are often limited to the variables collected in the national Scientific Registry of Transplant Recipients database. Electronic health records contain additional variables that can augment this data source if OTRs can be identified accurately., Objective: The aim of this study was to develop phenotyping algorithms to identify OTRs from electronic health records., Methods: We used Vanderbilt's deidentified version of its electronic health record database, which contains nearly 3 million subjects, to develop algorithms to identify OTRs. We identified all 19,817 individuals with at least one International Classification of Diseases (ICD) or Current Procedural Terminology (CPT) code for organ transplantation. We performed a chart review on 1350 randomly selected individuals to determine the transplant status. We constructed machine learning models to calculate positive predictive values and sensitivity for combinations of codes by using classification and regression trees, random forest, and extreme gradient boosting algorithms., Results: Of the 1350 reviewed patient charts, 827 were organ transplant recipients while 511 had no record of a transplant, and 12 were equivocal. Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity., Conclusions: Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes., (©Lee Wheless, Laura Baker, LaVar Edwards, Nimay Anand, Kelly Birdwell, Allison Hanlon, Mary-Margaret Chren. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 10.12.2020.)

Leukopenia is a serious, frequent side effect associated with azathioprine use. Currently, we use thiopurine methyltransferase (TPMT) testing to predict leukopenia in patients taking azathioprine. We hypothesized that a risk score incorporating additional clinical and genetic variables would improve the prediction of azathioprine-associated leukopenia. In the discovery phase, we developed four risk score models: (1) age, sex, and TPMT metabolizer status; (2) model 1 plus additional clinical variables; (3) sixty candidate single nucleotide polymorphisms; and (4) model 2 plus model 3. The area under the receiver-operating-characteristic curve (AUC) of the risk scores was 0.59 (95% CI: 0.54-0.64), 0.75 (0.71-0.80), 0.66 (0.61-0.71), and 0.78 (0.74-0.82) for models 1, 2, 3, and 4, respectively. During the replication phase, models 2 and 4 (AUC = 0.64, 95% CI: 0.59-0.70 and AUC = 0.63, 95% CI: 0.58-0.69, respectively) were significant in an independent group. Compared with TPMT testing alone, additional genetic and clinical variables improve the prediction of azathioprine-associated leukopenia.

Aim: We sought to identify potential pleiotropy involving pharmacogenes., Methods: We tested 184 functional variants in 34 pharmacogenes for associations using a custom grouping of International Classification and Disease, Ninth Revision billing codes extracted from deidentified electronic health records of 6892 patients., Results: We replicated several associations including ABCG2 (rs2231142) and gout (p = 1.73 × 10(-7); odds ratio [OR]: 1.73; 95% CI: 1.40-2.12); and SLCO1B1 (rs4149056) and jaundice (p = 2.50 × 10(-4); OR: 1.67; 95% CI: 1.27-2.20)., Conclusion: In this systematic screen for phenotypic associations with functional variants, several novel genotype-phenotype combinations also achieved phenome-wide significance, including SLC15A2 rs1143672 and renal osteodystrophy (p = 2.67 × 10(-) (6); OR: 0.61; 95% CI: 0.49-0.75).

Purpose of Review: Pharmacogenomics is the study of differences in drug response on the basis of individual genetic background. With rapidly advancing genomic technologies and decreased costs of genotyping, the field of pharmacogenomics continues to develop. Application to patients with kidney disease provides growing opportunities for improving drug therapy., Recent Findings: Pharmacogenomics studies are lacking in patients with chronic kidney disease and dialysis, but are abundant in the kidney transplant field. A potentially clinically actionable genetic variant exists in the CYP3A5 gene, with the initial tacrolimus dose selection being optimized based on CYP3A5 genotype. Although many pharmacogenomics studies have focused on transplant immunosuppression pharmacokinetics, an expanding literature on pharmacodynamic outcomes, such as calcineurin inhibitor toxicity and new onset diabetes, is providing new information on patients at risk., Summary: Appropriately powered pharmacogenomics studies with well-defined phenotypes are needed to validate existing studies and unearth new findings in patients with kidney disease, especially the chronic kidney disease and dialysis population.