Literature DB >> 31471970

Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

Tom M Nanga1, Thao T P Doan1, Pierre Marquet1, Flora T Musuamba2,3.   

Abstract

AIMS: The 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.
METHODS: A 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.
RESULTS: A 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.
CONCLUSION: A 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.
© 2019 The British Pharmacological Society.

Entities:  

Keywords:  meta-analysis; pharmacodynamics; pharmacokinetics; pharmacometrics; population analysis; statistics; study design; tacrolimus

Mesh:

Substances:

Year:  2019        PMID: 31471970      PMCID: PMC6955410          DOI: 10.1111/bcp.14110

Source DB:  PubMed          Journal:  Br J Clin Pharmacol        ISSN: 0306-5251            Impact factor:   4.335


  107 in total

1.  Tacrolimus pharmacokinetics in the early post-liver transplantation period and clinical applicability via Bayesian prediction.

Authors:  Itziar Oteo; John C Lukas; Nerea Leal; Elena Suarez; Andres Valdivieso; Mikel Gastaca; Jorge Ortiz de Urbina; Rosario Calvo
Journal:  Eur J Clin Pharmacol       Date:  2012-06-03       Impact factor: 2.953

2.  Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients.

Authors:  Nayoung Han; Hwi-yeol Yun; Jin-yi Hong; In-Wha Kim; Eunhee Ji; Su Hyun Hong; Yon Su Kim; Jongwon Ha; Wan Gyoon Shin; Jung Mi Oh
Journal:  Eur J Clin Pharmacol       Date:  2012-06-02       Impact factor: 2.953

3.  Total plasma protein effect on tacrolimus elimination in kidney transplant patients--population pharmacokinetic approach.

Authors:  Bojana Golubović; Katarina Vučićević; Dragana Radivojević; Sandra Vezmar Kovačević; Milica Prostran; Branislava Miljković
Journal:  Eur J Pharm Sci       Date:  2013-10-30       Impact factor: 4.384

4.  Population pharmacokinetics of tacrolimus in full liver transplant patients: modelling of the post-operative clearance.

Authors:  Marie Antignac; Jean Sebastien Hulot; Emmanuel Boleslawski; Laurent Hannoun; Yvan Touitou; Robert Farinotti; Philippe Lechat; Saïk Urien
Journal:  Eur J Clin Pharmacol       Date:  2005-07-01       Impact factor: 2.953

Review 5.  Current progress of tacrolimus dosing in solid organ transplant recipients: Pharmacogenetic considerations.

Authors:  Xiao Zhang; Guigao Lin; Liming Tan; Jinming Li
Journal:  Biomed Pharmacother       Date:  2018-03-22       Impact factor: 6.529

6.  The Effect of Weight and CYP3A5 Genotype on the Population Pharmacokinetics of Tacrolimus in Stable Paediatric Renal Transplant Recipients.

Authors:  Agnieszka A Prytuła; Karlien Cransberg; Antonia H M Bouts; Ron H N van Schaik; Huib de Jong; Saskia N de Wildt; Ron A A Mathôt
Journal:  Clin Pharmacokinet       Date:  2016-09       Impact factor: 6.447

7.  Toward better outcomes with tacrolimus therapy: population pharmacokinetics and individualized dosage prediction in adult liver transplantation.

Authors:  Christine E Staatz; Charlene Willis; Paul J Taylor; Stephen V Lynch; Susan E Tett
Journal:  Liver Transpl       Date:  2003-02       Impact factor: 5.799

Review 8.  Pharmacokinetics and Toxicity of Tacrolimus Early After Heart and Lung Transplantation.

Authors:  M A Sikma; E M van Maarseveen; E A van de Graaf; J H Kirkels; M C Verhaar; D W Donker; J Kesecioglu; J Meulenbelt
Journal:  Am J Transplant       Date:  2015-06-04       Impact factor: 8.086

9.  Tacrolimus Updated Guidelines through popPK Modeling: How to Benefit More from CYP3A Pre-emptive Genotyping Prior to Kidney Transplantation.

Authors:  Jean-Baptiste Woillard; Michel Mourad; Michael Neely; Arnaud Capron; Ron H van Schaik; Teun van Gelder; Nuria Lloberas; Dennis A Hesselink; Pierre Marquet; Vincent Haufroid; Laure Elens
Journal:  Front Pharmacol       Date:  2017-06-08       Impact factor: 5.810

10.  Predicting tacrolimus concentrations in children receiving a heart transplant using a population pharmacokinetic model.

Authors:  Joseph E Rower; Chris Stockmann; Matthew W Linakis; Shaun S Kumar; Xiaoxi Liu; E Kent Korgenski; Catherine M T Sherwin; Kimberly M Molina
Journal:  BMJ Paediatr Open       Date:  2017-11-22
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  5 in total

1.  Therapeutic Drug Monitoring Strategies for Envarsus in De Novo Kidney Transplant Patients Using Population Modelling and Simulations.

Authors:  Emilie Henin; Mirco Govoni; Massimo Cella; Christian Laveille; Giovanni Piotti
Journal:  Adv Ther       Date:  2021-09-12       Impact factor: 3.845

2.  Predictive Performance of Published Tacrolimus Population Pharmacokinetic Models in Thai Kidney Transplant Patients.

Authors:  Janthima Methaneethorn; Manupat Lohitnavy; Kamonwan Onlamai; Nattawut Leelakanok
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2021-11-24       Impact factor: 2.441

3.  Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

Authors:  Tom M Nanga; Thao T P Doan; Pierre Marquet; Flora T Musuamba
Journal:  Br J Clin Pharmacol       Date:  2019-12-17       Impact factor: 4.335

4.  Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients.

Authors:  Luis Quintairos; Helena Colom; Olga Millán; Virginia Fortuna; Cristina Espinosa; Lluis Guirado; Klemens Budde; Claudia Sommerer; Ana Lizana; Yolanda López-Púa; Mercè Brunet
Journal:  PLoS One       Date:  2021-01-22       Impact factor: 3.240

Review 5.  The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib.

Authors:  Thierry Buclin; Yann Thoma; Nicolas Widmer; Pascal André; Monia Guidi; Chantal Csajka; Laurent A Decosterd
Journal:  Front Pharmacol       Date:  2020-03-03       Impact factor: 5.810

  5 in total

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