ArticleBritish journal of clinical pharmacology2026
Mechanism-informed machine learning for individualized tacrolimus dose adjustment in the early post-kidney transplant period.
Article in British journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
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Who cites it
2 citing papers in PubMed.
- Mechanism-informed machine learning for individualized tacrolimus dose adjustment in the early post-kidney transplant period.British journal of clinical pharmacology · 2026Article
- Protocol-constrained AI enhances tacrolimus dosing accuracy in kidney transplant care.Frontiers in artificial intelligence · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
aimTacrolimus dosing in the early post-kidney transplant period is challenging due to a narrow therapeutic index and substantial interindividual pharmacokinetic (PK) variability. This study aimed to develop and validate mechanism-informed machine learning (ML) models to support individualized tacrolimus dosing during this critical period.
methodsA total of 4311 tacrolimus trough concentrations (C
resultsThe mechanism-informed GRU model outperformed the Bayesian PopPK model in both internal validation (MSE = 7.81 vs. 9.27 ng
conclusionMechanism-informed ML models offer a robust and interpretable approach for individualized tacrolimus dosing, with the potential to improve therapeutic target attainment by enabling accurate dose adjustments in the early post-transplant period.
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Registered trials
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