SynthesisBritish journal of clinical pharmacology2019
Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.
Synthesis in British journal of clinical pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Model-Informed Precision Dosing of Tacrolimus: A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools.Clinical pharmacokinetics · 2024Pooled it
- Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review.European journal of clinical pharmacology · 2023Pooled it
- Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review.Clinical pharmacokinetics · 2020Pooled it
- Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.British journal of clinical pharmacology · 2019Pooled it
- Therapeutic Drug Monitoring Strategies for Envarsus in De Novo Kidney Transplant Patients Using Population Modelling and Simulations.Advances in therapy · 2021Trial
- Population Pharmacokinetic/Pharmacodynamic Model of the Impact of Cumulative Tacrolimus Exposure on Kidney Function After Liver Transplant.Therapeutic drug monitoring · 2026Article
- Population pharmacokinetics of tacrolimus and CYP3A5-driven variability: implications for model-informed dose individualization in pediatric renal transplant recipients.BMC pharmacology & toxicology · 2026Article
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Analytical Models to Optimize Tacrolimus Dosing in Solid Organ Transplantation: A Systematic Review.Pharmaceutics · 2026Review
- Model Informed Precision Dosing of Tacrolimus in Children Following Heart Transplant.Journal of clinical pharmacology · 2026Article
- Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation.Frontiers in immunology · 2026Review
- State of Art of Dose Individualization to Support tacrolimus drug monitoring: What's Next?Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective.Pharmaceutics · 2023Review
- Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients.European journal of drug metabolism and pharmacokinetics · 2022Article
- Observational
- The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib.Frontiers in pharmacology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThe objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications.
methodsA systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies.
resultsA total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation.
conclusionA meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.