ArticleClinical pharmacokinetics2025
A Population Pharmacokinetic Model and Dosing Algorithm to Guide the Tacrolimus Starting and Follow-Up Dose in Living and Deceased Donor Kidney Transplant Recipients.
Article in Clinical pharmacokinetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accelerating tacrolimus model-informed precision dosing in kidney transplant recipients: Model evaluation and refinement strategies.British journal of clinical pharmacology · 2026Pooled it
- Genetic and clinical determinants of tacrolimus accumulation in early liver transplantation: evidence for a combined genotype-antibiotic effect.Annals of medicine · 2026Article
- A Review of Population Pharmacokinetic Models and Dosing Algorithms Assessing the Influence of CYP3A5 Genotype and Other Clinical Covariates on Tacrolimus Pharmacokinetics.Clinical pharmacokinetics · 2026Review
- Investigating PXR (NR1I2) genetic polymorphisms as possible determinants of the magnitude of the clindamycin-rifampicin pharmacokinetic interaction.European journal of clinical pharmacology · 2026Article
- Mechanism-informed machine learning for individualized tacrolimus dose adjustment in the early post-kidney transplant period.British journal of clinical pharmacology · 2026Article
- Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Decreasing Tacrolimus Concentrations in Routine Therapeutic Drug Monitoring Data Indicate Adherence to Updated Therapeutic Goals.Biomedicines · 2026Article
- Initial Dosage Optimization of Tacrolimus in Connective Tissue Disease-Associated Interstitial Lung Disease Patients: An Exploratory Study.Drug design, development and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTacrolimus treatment is complicated by its narrow therapeutic range and large inter- and intra-patient variability. This study aimed to develop a population pharmacokinetic model and dosing algorithm to predict an individual's dose requirement following living and deceased donor kidney transplantation.
methodsIn this international, multicenter, retrospective study, data was collected from patients who had received a living or a deceased donor kidney and received tacrolimus twice daily. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling (NONMEM).
resultsThis study included 13,427 tacrolimus concentrations from 1180 kidney transplant recipients. A two-compartment model with first-order absorption best described the data. The mean absorption rate was 6.59/h, apparent clearance 20.7 L/h, central volume of distribution 705 L, and peripheral volume of distribution 7670 L. Higher age, creatinine, and hematocrit, as well as lower height, were associated with lower tacrolimus clearance. Tacrolimus clearance was higher for cytochrome P450 (CYP) 3A5*1 carriers compared with CYP3A5*3/*3 individuals, and lower for CYP3A4*22 carriers compared with CYP3A4*1/*1 patients. Together, these covariates explained 19.3% of the inter-individual variability in clearance. From the full model, a starting dose algorithm was developed with age, height, and the CYP3A4 and CYP3A5 genotypes as covariates. Both the full model and the starting dose algorithm were successfully internally validated.
conclusionsIn this international, multicenter study, age, CYP3A4 and CYP3A5 genotype, creatinine, height, and hematocrit were identified as significant covariates associated with tacrolimus pharmacokinetics, and can be used to predict the optimal individual's dose requirement for both living and deceased donor kidney transplant recipients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.