Analytical Models to Optimize Tacrolimus Dosing in Solid Organ Transplantation: A Systematic Review

Area of Science:

• Pharmacology
• Transplant Medicine
• Biostatistics

Background:

• Tacrolimus dosing is challenging due to its narrow therapeutic index and numerous variables.
• Accurate tacrolimus dose prediction is crucial for solid organ transplant recipients.
• Systematic review to identify effective analytical modeling techniques for tacrolimus dose optimization.

Purpose of the Study:

• To systematically review analytical modeling techniques for tacrolimus dose prediction.
• To assess the effectiveness of different modeling approaches in solid organ transplantation.
• To identify key variables influencing tacrolimus dosing.

Main Methods:

• Comprehensive literature search across major databases (Medline, Scopus, Embase, Web of Science, PubMed).
• Inclusion of 115 studies examining analytical models for tacrolimus dosing.
• Independent review by two researchers to ensure study selection rigor.

Main Results:

• Pharmacokinetic models, particularly two-compartment with Bayesian forecasting, are most common (74 studies).
• Machine learning models show increasing adoption and improved predictive accuracy.
• Key predictors include CYP3A5 genotype, hematocrit, post-operative days, and weight; genomic influence wanes with therapeutic drug monitoring.

Conclusions:

• Clinical use of predictive tacrolimus dosing models is limited.
• Pharmacokinetic models dominate research, with machine learning showing incremental promise.
• Limited external validation and lack of outcome-based metrics hinder generalizability and clinical deployment.