Precision Dosing of Tacrolimus in Liver Transplantation: Integrating Donor-Recipient CYP3A5 Pharmacogenomics and Drug Interactions.

Authors

Virunya Komenkul, Prawat Chantharit, Piyawat Komolmit, Bunthoon Nonthasoot, Athaya Vorasittha, Anapat Sanpavat, Sirinporn Suksawatamnuay, Chandramouli Radhakrishnan, Thitima Wattanavijitkul

Published in

CPT: pharmacometrics & systems pharmacology. Volume 15. Issue 9. Pages e70339.

Abstract

Tacrolimus dosing in liver transplantation is complicated by a narrow therapeutic index and high CYP3A5 genetic variability. While saturable Michaelis-Menten kinetics can explain nonlinearities, identifying saturable parameters from routine clinical data remains challenging. This study aimed to determine the optimal structural model and develop a precision dosing algorithm. A population pharmacokinetic analysis was conducted in 114 patients, yielding 1989 observations. CYP3A5 genotypes were determined for both recipients and donors. Using Phoenix NLME, we rigorously compared linear versus Michaelis-Menten elimination structures. Stepwise covariate modeling was conducted to quantify the impact of genetic, physiological, and pharmacological factors, followed by Monte Carlo simulations to optimize dosing. A conventional two-compartment model adequately described the data without requiring a Michaelis-Menten structure. The combined CYP3A5 genotype exhibited a distinct stepwise reduction in apparent clearance from the homozygous expressor to the non-expressor group. Fluconazole emerged as a major inhibitor, reducing clearance by 33%, whereas prednisolone showed modest induction. Hemoglobin displayed a significant inverse relationship with clearance. Crucially, incorporating the daily dose as a covariate on clearance effectively captured the apparent nonlinear disposition. Simulations confirmed that fluconazole-treated patients require substantially lower doses (1.5-3.0 mg every 12 h) compared with fluconazole-free patients (2.5-7.0 mg every 12 h). A dose-dependent two-compartment model offers a basis for model-informed dose selection, addressing reported nonlinearities through physiological covariates. We provide a model-informed dosing algorithm that accounts for combined recipient/donor genetics and drug interactions, which may improve target attainment in liver transplant populations.

PMID:
42687584
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

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