Abstract
Background: Tacrolimus dose optimization remains challenging due to its narrow therapeutic range and multiple influencing variables. This systematic review aimed to identify effective analytical modeling techniques for optimal tacrolimus dose prediction in solid organ transplant recipients. Methods: Two independent researchers conducted a comprehensive review of studies examining analytical models that optimize tacrolimus dosing, searching Medline, Scopus, Embase, Web of Science, and PubMed. Results: In total, 115 studies met the inclusion criteria. Pharmacokinetic models (74 studies), particularly two-compartment with Bayesian forecasting, were most frequently used. Machine learning (ML) approaches, with increasing adoption, have demonstrated promising improved predictive accuracy. Key predictive variables included CYP3A5 genotype, hematocrit levels, post-operative days, and weight; however, the significance of genomic features seemed to diminish progressively as therapeutic drug monitoring calibrates dosing in the months following post-transplant. Only ten studies performed external validation, and none incorporated adherence data or predicted long-term graft outcomes. Conclusions: Clinical deployment of predictive models for tacrolimus dosing remains uncommon. In research, pharmacokinetic models remain prevalent, with ML approaches showing early incremental promise. Limited external validation raises generalizability concerns. Future research should prioritize outcome-based evaluation metrics rather than error metrics.