Evaluating the evidence for genotype‐informed Bayesian dosing of tacrolimus in children undergoing solid organ transplantation: A systematic literature review
Bibliographic record
Abstract
Tacrolimus, a calcineurin inhibitor, is a highly effective immunosuppressant used in solid organ transplantation (SOT). However, it is characterized by a narrow therapeutic range and high inter-patient variability in pharmacokinetics. Standard weight-based dosing followed by empiric dose titration is suboptimal in controlling drug concentrations, increasing risk of rejection or toxicity, particularly in the initial months post transplantation. This review explores the potential of combined pre-transplant genotyping and pharmacokinetic (PK) modelling to improve tacrolimus dosing in paediatric SOT recipients. A systematic search of Medline, Embase and Cochrane databases identified studies published between March 2013 and March 2023 that investigated genotype- and PK model-informed tacrolimus dosing in children post-SOT. The Newcastle-Ottawa Scale assessed study quality. Seven studies encompassing paediatric kidney, heart, liver and lung transplants reported using genotype and model-informed dosing. A combination of clinical and genetic factors significantly impacts tacrolimus clearance and thus initial dose recommendation. Body size, transplant organ and co-medications were consistently important, while either time post-transplant or haematocrit emerged in some studies. Several models were identified, however, with limitations evident in some and with absence of evidence for their effectiveness in optimizing initial and subsequent dosing. This review highlights the development of PK models in paediatric SOT that integrate genotype and clinical covariates to personalize early tacrolimus dosing. While promising, prospective studies are needed to validate and confirm their effectiveness in improving time to therapeutic concentrations and reducing under- or overexposure. This approach has the potential to optimize tacrolimus therapy in paediatric SOT, thereby improving outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".