Bayesian dosage adjustment of tacrolimus in kidney transplantation: a systematic review.

Abstract

Background: Tacrolimus is a first-line immunosuppressant used in renal transplant patients but exhibits a narrow therapeutic index and high inter-individual variability. Although therapeutic drug monitoring (TDM) using trough concentrations (C₀) is common, it may not consistently reflect true drug exposure. The area under the concentration-time curve (AUC) is a more realistic metric, and Bayesian estimation methods often improve the precision of AUC estimations using prior pharmacokinetic knowledge.

Methods: We conducted a systematic literature search of PubMed, Scopus, and Embase from inception to August 2024 for studies utilizing Bayesian approaches for estimating tacrolimus exposure in renal transplant patients. Studies were included if they employed Bayesian techniques for AUC estimation, dose adjustment using population pharmacokinetic (PopPk) models, or limited sampling strategies. Data were extracted, appraised using the Critical Appraisal of Clinical Pharmacokinetic Studies (CACPK) tool, and summarized.

Results: Most studies reported that Bayesian methods, particularly Maximum a Posteriori Bayesian Estimation (MAP-BE), provided superior AUC prediction compared to traditional methods. Tools such as Immunosuppressants Bayesian dose Adjustment (ISBA) and softwares such as NONMEM were frequently used. Most studies support the inclusion of trough and post-dose samples for >2 h for accurate predictions.

Conclusion: Bayesian approaches demonstrate enhanced accuracy in tacrolimus dose adjustment for renal transplant patients, outperforming conventional TDM strategies, and showing potential for broader clinical applications.