ArticleTherapeutic drug monitoring2026
Population Pharmacokinetic/Pharmacodynamic Model of the Impact of Cumulative Tacrolimus Exposure on Kidney Function After Liver Transplant.
Article in Therapeutic drug monitoring, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTacrolimus-induced nephrotoxicity is a common adverse effect after liver transplantation. In this study, we aimed to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model to evaluate the relationships among tacrolimus dose, instantaneous and cumulative exposure, and kidney injury.
methodsData from adult liver transplant recipients with a once-daily tacrolimus formulation were retrospectively analyzed. A sequential population PK/PD modeling approach using nonlinear mixed-effects modeling was applied. Exposure metrics included trough concentrations, the area under the concentration-time curve [area under the curve (AUC)] over a 24-hour (AUC 0-24h ) period, and cumulative AUC (cAUC) over 1-6 months. Renal function was defined as the change in estimated glomerular filtration rate (eGFR). Demographics, biochemical parameters, and concomitant medications were evaluated as covariates.
resultsTacrolimus trough concentrations and eGFR measurements were obtained from a total of 44 patients for 3360 and 3504 measurements, respectively. Tacrolimus PK was best described by a one-compartment model with additive interindividual variability. Clearance was influenced by hematocrit (HCT) and postoperative days (PODs) within the first 14 days. Among exposure metrics, only cAUC over 3 months was significantly associated with renal function decline and provided the best model fit. The final PK/PD model was a direct linear model, with baseline eGFR identified as a covariate on the slope, reflecting individual susceptibility to tacrolimus-associated renal changes.
conclusionsCumulative tacrolimus exposure over 3 months is a key determinant of nephrotoxicity and a valuable predictor for dose individualization. Our findings suggest that early PODs, HCT, and baseline renal function should be considered when optimizing tacrolimus therapy.
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